small states economic review and basic statistics volume 17

Small States: Economic Review and Basic Statistics, Volume 17

Commonwealth SecretariatMarlborough HousePall MallLondon SW1Y 5HXUnited Kingdom

© Commonwealth Secretariat 2014

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Foreword

This is the seventeenth issue of this annual publication, which was previously published under the title Basic Statistical Data on Selected Countries (with populations of less than 5 million). This issue focuses on data and statistical challenges and is intended as an essential reference for key stakeholders and policy-makers in small states and for their development partners. It also provides a useful resource for analysts from research and academic institutions in the broad areas of social, economic and environment policies.

As in the previous editions, the publication is in two parts. Part I provides analysis on the latest economic trends in small states and includes a research article on small states. The analysis focuses on the economic environment, the external performance and the key development issues facing policy-makers in small states. The main article, by Ryan Straughn, focuses on data and statistical challenges in small states. It investigates and documents the dynamics of data and statistical challenges in small states and identifies ways and options for addressing these challenges.

Part II of this publication contains statistical tables covering all small states, as well as a selection of countries that at the end of 2011 had populations of five million or fewer. The inclusion of countries with populations above the small states’ population threshold of 1.5 million is for comparison purposes only. Tables in this volume have been edited and reformatted to ensure consistency in the variables. These tables contain indicators on social, economic, environmental and demographic indicators.

The countries are grouped into three categories on the basis of their 2012 per capita gross national income (GNI). The three categories are: a) low-income countries with per capita income of US$1,025 and below; b) middle-income countries with per capita income of between US$1,026 and $12,475; and c) high-income countries with per capita income of US$12,476 or more.

The publication has been prepared by Economic Adviser, Denny Lewis-Bynoe, Economic Officer, Wonderful Hope Khonje, and Research Officers Heather Cover-Kus, Aimé Sindayigaya and Best Ojighoro, all from the Economic Affairs Division of the Commonwealth Secretariat. Reviews were conducted at various stages by Travis Mitchell and Joel Burman. Janet Strachan, Adviser and Head of Section, provided the overall supervision and guidance.

Cyrus RustomjeeDirector, Economic Affairs DivisionCommonwealth Secretariat

iii

What Are Small States?

The Commonwealth defines small states as sovereign states with a population size of 1.5 million people or fewer.1 Using this standard, 44 countries are small. These countries are:

Country 2012 Population (’000)

Population rank 1 = smallest

Antigua and Barbudaa 89 10Bahamas, Thea 372 24Bahrain 1,318 42Barbadosa 283 20Belizea 324 22Bhutan 742 34Brunei Darussalama 412 25Cape Verde 494 27Comoros 718 32Cook Islands 11 4Cyprusa b 1,129 38Djibouti 860 36Dominicaa 72 8Equatorial Guinea 736 33Estonia 1,339 44Fijia 875 37Grenadaa 105 13Guyanaa 795 35Icelandb 320 21Kiribatia 101 11Luxembourgb 531 28Maldivesa 338 23Maltaa b 418 26Marshall Islands 53 6Mauritiusa 1,291 41Micronesia, Federated States of 103 12Montenegro 621 31Naurua 9 2Niue 1 1Palau 21 5Samoaa 189 18São Tomé and Principe 188 17Seychellesa 88 9Solomon Islandsa 550 30St Kitts and Nevisa 54 7

(continued)

iv

In addition, the Commonwealth includes the larger member countries of Botswana, Jamaica, Lesotho, Namibia and Papua New Guinea, because these countries share many of the characteristics of small states.

Therefore, the Commonwealth currently designates 31 of its member countries as small states.

The following characteristics2 have important implications for development and are shared by many small states:

• Remoteness and insularity: Of the 43 small states, 34 are islands, a number of which are located far from major markets, as in the case of the Pacific islands and Mauritius. Some are widely dispersed multi-island micro-states.

• Susceptibility to natural disasters: Most small states are in regions frequently affected by adverse climatic and other natural events which, typically, affect the entire population and economy. They may also be susceptible to severe environmental and ecological threats.

• Limited institutional capacity: Sovereignty necessitates certain fixed costs of providing public services, including policy formulation, regulatory activities, education and social services, justice, security and foreign affairs. Indivisibilities in the provision of these public goods mean that small states face higher costs per person unless ways can be found to pool such costs, for example on a regional basis.

• Limited diversification: Because of their narrow resource base and small domestic markets, many small states are necessarily relatively undiversified in their production and exports, so capacity in the private sector is also limited, posing difficulties when faced with a need to respond to changing external circumstances.

Country 2012 Population (’000)

Population rank 1 = smallest

St Luciaa 181 16St Vincent and the Grenadinesa 109 15Suriname 535 29Swazilanda 1,231 40Timor-Leste 1,210 39Tongaa 105 14Trinidad and Tobagoa 1,337 43Tuvalua 10 3Vanuatua 247 19

Source: World Bank data website, www.devdata.worldbank.org; CIA Factbook www.cia.gov/library/publications/the-world-factbook/ (accessed 4 February 2014).

Notes: aCommonwealth small states (27)bAdvanced economies

(continued)

What Are Small States? v

• Openness: Small economies tend to rely heavily on external trade and foreign investment to overcome their inherent scale and resource limitations. While this can prove beneficial in exposing them to outside competition and ideas, it leaves them vulnerable to external economic and environmental shocks, especially where the domestic economy is undiversified.

• Access to external capital: Access to global capital markets is important for small states, and is one way to compensate for adverse shocks and income volatility, but the evidence is that private markets tend to see small states as more risky than larger states, so that spreads are higher and market access more difficult.

• Poverty: There is some evidence that poverty levels are higher and income distribution more uneven in smaller than in larger states.

These characteristics result in small states being extremely vulnerable. This vulnerability is manifested by high levels of fluctuations in gross domestic product (GDP), input costs and export earnings.

The main text of this publication, Small States Economic Review and Basic Statistics, covers recent economic developments in the 31 small states of the Commonwealth.

Where data are available, the coverage of the 65 statistical tables includes development indicators for Commonwealth and other small states, as well as for developed and developing countries with populations of less than five million.

Notes 1 Charles, E (1997), A Future for Small States: Overcoming Vulnerability, Commonwealth Secretariat,

London. 2 Small States: Meeting Challenges in the Global Economy, report of the Commonwealth Secretariat/

World Bank Joint Task Force on Small States 2000.

vi Small States: Economic Review and Basic Statistics

Contents

Foreword iii

What Are Small States? iv

Abbreviations and acronyms xi

Part I. Recent Economic Trends in Commonwealth Small States 1

1. Recent Economic Trends in Commonwealth Small States 11.1 Introduction 11.2 Small states economic review 21.3 Prospects for small states’ GDP growth in 2014 and beyond 41.4 Inflation and the consumer prices index 41.5 Unemployment 41.6 International trade 51.7 Development aid 61.8 Remittances 71.9 International reserves 71.10 Foreign direct investment 71.11 Ease of doing business 81.12 Small states’ global competitiveness 81.13 Human development indicators 91.14 Regional and country analyses 10

1.14.1 African small state economies: a snapshot 101.14.2 Summary of developments, outlook and policy priorities in

African small states 101.14.3 Asia-Pacific small state economies: a snapshot 121.14.4 Summary of developments, outlook and policy priorities

for Asian-Pacific small states 131.14.5 Caribbean small state economies: a snapshot 161.14.6 Summary of developments, outlook and policy priorities

for Caribbean small states 161.14.7 European small state economies: a snapshot 191.14.8 Summary of developments, outlook and policy priorities

for European small states 191.15 Conclusion 21References 22

vii

2. The Role of Data and Statistics for Policy-making in Small States 23Ryan Straughn 2.1 Summary 232.2 Introduction 242.3 Literature review 262.4 The dynamics of data challenges in small states 27

2.4.1 Basic data and statistical needs 282.4.2 Millennium Development Goals data gaps 292.4.3 Survey results 312.4.4 Challenges 402.4.5 Priority areas 42

2.5 Addressing data challenges in small states 432.5.1 Adapting NSDS approach to small countries 442.5.2 Improving data and statistical capacity in priority areas 452.5.3 Operational IT 46

2.6 Conclusion 47Acknowledgements 47References 48Appendix 2.1 Data gaps from the survey results 49Appendix 2.2 World Development Indicators data gaps 54

Part II. Social and Economic Data on Small States 66Basic statistics 66Technical notes for tables 66

Economic indicators Table 1. Gross national income (GNI) at market prices 76Table 2. Gross domestic product (GDP) 79Table 3. Sectoral distribution of gross domestic product (% total of GDP) 82Table 4. Growth of production (annual average %) 84Table 5. GDP components (% of total) 86Table 6. Prices (% change) 89Table 7. Exports, imports and trade balance 91Table 8. Destination of merchandise exports (% of total exports) 93Table 9. Destination of merchandise exports to selected regions

(% share of world exports) 95Table 10. Main sources of merchandise imports (% of total imports) 97Table 11. Composition of merchandise exports (% of total exports) 99Table 12. Merchandise imports by selected regions (% share of world imports) 101Table 13. Merchandise trade with Commonwealth countries 103Table 14. Export characteristics 105Table 15. Selected indicators of openness and instability 107Table 16. Migration and remittances 109Table 17. Fish production 111Table 18. Energy production, consumption and trade 113

viii Small States: Economic Review and Basic Statistics

Table 19. Energy consumption and carbon emissions 115Table 20. Tourist arrivals and earnings 118Table 21. International reserves 120Table 22. External debt: selected categories 122Table 23. Total net transfers on external debt 124Table 24. Principal indicators of debt 126Table 25. Composition of debt 127Table 26. Foreign direct investment (FDI) inflows 128Table 27. Total net financial flows from all sources (US$ millions) 130Table 28. Net financial flows by major categories (US$ million) 131Table 29. Official Development Assistance commitments and disbursements 133Table 30. Aid dependency 136Table 31. Average exchange rates 140Table 32. Money supply and nominal interest rates 142Table 33. Doing business 144Table 34. Selected private sector indicators 148

Social and demographic indicators Table 35. Selected size indicators 150Table 36. Population indicators 153Table 37. Distribution of labour force (% of total employment) 155Table 38. Labour force participation 156Table 39. Urban and rural population (%) 160Table 40. Land use 162Table 41. Population distribution by age, actual and projected (%) 164Table 42. Selected demographic indicators 166Table 43. Life expectancy at birth (years) 168Table 44. Adult literacy rates (%) 170Table 45. Primary education level enrolment ratio (%) 172Table 46. Secondary education enrolment level ratio (% gross) 174Table 47. Tertiary education level enrolment ratio (%) 176Table 48. Primary education gender ratio 178Table 49. Selected characteristics of female population 180Table 50. Total government expenditure by main components (%) 182

Other development indicators Table 51. Access to improved water sources (% of population) 184Table 52. Access to improved sanitation (% of population) 186Table 53. Human Development Index (HDI) 188Table 54. Selected characteristics of gender equality 190Table 55. Selected poverty indicators 192Table 56. Births delivered by skilled health personnel and maternal mortality 195Table 57. Universal access to reproductive health 197Table 58. Children’s health 199Table 59. Under-five mortality rate per 1,000 births 201Table 60. Summary statistics on HIV/AIDS 203

Contents ix

Table 61. Summary statistics on tuberculosis (TB) 205Table 62. Environment 207Table 63. Main indicators of internet communications 210Table 64. Main indicators of telephone communications 212Table 65. Transport 214

x Small States: Economic Review and Basic Statistics

Abbreviations and acronyms

ABSD Antigua and Barbuda Statistical DivisionADB Asian Development BankBDS Bahamas Department of StatisticsBSS Barbados Statistical ServiceCARTAC Caribbean Technical Assistance CentreCPA country programmable aidCPI consumer prices indexCSO central statistical officeCSS Commonwealth small statesCYSTAT Statistical Service of CyprusDAC Development Assistance Committee (of the OECD)ECCU Eastern Caribbean Currency UnionEU European UnionFDI foreign direct investmentG20 Group of TwentyGCF gross capital formationGDP gross domestic productGNI gross national incomeHDI Human Development IndexHIV Human Immunodeficiency VirusIMF International Monetary FundICT information and communications technologyIDA International Development AssociationILO International Labour OrganizationIT information technologyIUCN International Union for Conservation of NatureMDG Millennium Development GoalNBS Nauru Bureau of StatisticsNGO non-governmental organisationNSDS national strategy for the development of statisticsNSA Namibia Statistics AgencyNSB National Statistics Bureau (Seychelles)NSO national statistics officeNSS national statistical systemODA official development assistanceOECD Organisation for Economic Co-operation and DevelopmentOECS Organization of Eastern Caribbean StatesPIOJ Planning Institute of Jamaica

xi

PPP purchasing power parityRSDS regional strategy for the development of statisticsRTAC Regional Technical Assistance CentreSACU South African Customs UnionSBS Samoa Bureau of StatisticsSIDS small island developing statesSITC Standard International Trade ClassificationUN United NationsUNESCO United Nations Educational, Scientific and Cultural OrganizationUNICEF United Nations Children’s FundUNSD United Nations Statistical DepartmentWDI World Development IndicatorWHO World Health OrganizationWTO World Trade Organization

xii Small States: Economic Review and Basic Statistics

Part I. recent economIc trends In commonwealth small states

Chapter 1

Recent Economic Trends in Commonwealth Small States

1.1 Introduction

Although recovery began to sprout in 2010, the growth trajectory for the world economy in 2013–2014 remains cautious, with an expected average of 3.75 per cent (IMF 2013a). This conservative forecast arises partly because of the differing growth prospects for advanced economies and emerging and developing economies. Despite the fact that emerging and developing economies are faring relatively well, with a forecast of 5.3 and 5.7 per cent in 2013 and 2014 respectively (ibid.), the sluggish growth projected for advanced economies is a drag on world economic growth. The US is forecast to grow at a modest 1.9 per cent in 2013 and 3 per cent in 2014, while the EU faces a dampened growth prospect of −0.3 per cent in 2013 and 1.1 per cent in 2014. The emergence of new political and financial risks, coupled with the continuing inability of banks to lend, have further constrained the availability of credit to households and companies in advanced economies. Poor profitability and low capital flows for banks, as well as a lack of demand by households, could signal a bleaker and less steady recovery. However, if policy-makers deliver on

their economic policy commitments, real GDP growth in advanced economies might reach, on average, 2 per cent in 2013 and 2.25 per cent in 2014, up from the current projection of 1.25 per cent for this year (ibid). Real gross domestic product (GDP) growth for 2011 to 2014 is shown in Figure 1.1.

Against this backdrop of world economic performance, this chapter examines how small states have fared. It analyses the growth (Section 1.3), inflation (Section 1.4) and unemployment (Section 1.5) outlooks for small states in the context of the prospects for the world economy. It then reviews the international trade prospects (Section 1.6) for small states in a world economy facing uncertainties and weak recovery, and what this means for their abilities to garner development aid (Section 1.7) and effective foreign direct investments (Section 1.10) in the difficult economic environment in advanced economies. The persistence of these uncertain conditions in advanced economies might have serious economic implications for small states’ remittances (Section 1.8) and international reserves (Section 1.9). Small states’ 2013 rankings for ‘ease of doing business’ (Section 1.11),

Figure 1.1 Real GDP growth: world, advanced economies, and emerging and developing economies (2001–2014)

-6

-4

-2

0

2

4

6

8

10

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014Rea

l GD

P gr

owth

(%)

Year

World Advanced economies Emerging market and developing economies

Source: IMF (2013b)

1

competitiveness (Section 1.12) and human development index (Section 1.13) are also analysed in relation to their overall economic performance. The report concludes with an analysis of regional and individual country performance.

1.2 Small states economic review

The average real GDP growth rates in small states approximated 2.9 per cent from 2001 to 2012, underscored by peaks in GDP growth of 4.7 per cent in 2003 and 5.8 per cent in 2006, which were offset by a significant fall in output to 1.2 per cent in 2005 and −1.2 per cent in 2009 due to the global recession, as shown in Figure 1.2.1

This global crisis, which started in 2007 in advanced economies, dampened the growth progress made by a

number of small states. Prior to 2007, the annual average growth rate of the small states group registered 5.8 per cent – more than twice the post-crisis 2012 figure of 2.4 per cent. The new growth trajectory is expected to continue into 2013–2014, well below the high rates accomplished in 2006–2008. This moderate forecast is projected for each of the small state regions (see Figure 1.3).

Asian small states experienced the highest average growth rates before the recession, with 12 per cent growth in 2006. However, they were also hit hard by the global economic crisis, with these economies shrinking by an average of 2.1 per cent in 2009. After rebounding strongly in 2010, Asian small states have continued to grow, but at a slower pace. Small states in the Pacific maintained a relatively stable growth rate of 2 to 3 per cent between 2001 and 2008, followed

Figure 1.2 Average GDP growth (%) for small states annually and average over time (2001–2012)

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Ave

rage

GD

P gr

owth

(%)

Small state average per year 2001–2012 average

Figure 1.3 Average real GDP growth for small states (%) per region (2001–2012)

-6.0%

-4.0%

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012Ave

rage

GD

P gr

owth

(%)

Africa Asia Caribbean Pacific LDCs SIDS

Source: World Bank (2013a)

2 Small States: Economic Review and Basic Statistics

by a decline to 0.4 per cent in 2009. Similarly, small African states experienced relatively steady growth between 2001 and 2007, moving from 2.7 per cent to 6 per cent respectively, before falling to 0.7 per cent in 2009. Following their relatively strong recovery in 2010, African small states have managed to achieve growth rates of around 3 to 4 per cent in 2011 and 2012, somewhat below the peak in the pre-crisis period. Caribbean states have had the most difficulty. The average growth rate of this region was 6.3 per cent in 2006, and fell to −3.5 per cent in 2009; it has not moved above 1 per cent in the subsequent years.

The mediocre performance of small economies is largely the result of their economic structure. Among small states economic production is heavily concentrated on a few activities (tourism, agriculture, fisheries and off-shore financial services), of which tourism is the main industry. As a consequence, these economies are heavily exposed to shocks affecting these sectors. Falling demand for their main exports arising from the economic recession in key trading partners’ economies

has therefore been the main transmission mechanism through which they have underperformed.

Additionally, the consequent fall in both household consumption spending and government final expenditure has also negatively impacted GDP growth. Growth in consumption expenditure fell from 11.2 per cent in 2007 to 2 per cent in 2009. After rising to 4.6 per cent in 2010, household spending again dipped to 1.8 per cent in both 2011 and 2012 (see Figure 1.4).2

Disaggregating household expenditure by regions, the Caribbean was the hardest hit (see Figure 1.5). In the Caribbean, a previously robust consumption expenditure growth of 25 per cent in 2007 fell below zero in 2009 and has registered a rate of −1 per cent each year since. Conversely, African and Asian small states both held steadily to their pre-recession levels of consumption expenditure growth and in 2012, these countries’ growth in household expenditure were at approximately 2 per cent and 5 per cent respectively. These regional differences in spending

Figure 1.4 Annual growth in household consumption in small states (2000–2012)

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Ann

ual %

gro

wth

in c

onsu

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expe

ndit

ure

Small state average per year 2000–2012 average

Figure 1.5 Annual growth in household consumption in small states by region (2000–2012)

-15.0%

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-5.0%

0.0%

5.0%

10.0%

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2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Ann

ual %

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expe

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Africa Asia Caribbean

Recent Economic Trends in Commonwealth Small States 3

patterns reflect the real GDP performance levels, as described above.

In addition to consumption spending, government expenditure growth in small states dipped in 2009 and 2011 (see Figure 1.6).3 The performance in consumption spending and government final expenditures seen in the African and Asian small states was reflected in the robust real GDP growth performance. In contrast, the Caribbean small states registered notably low consumption spending and government expenditure and weak GDP growth.

1.3 Prospects for small states' GDP growth in 2014 and beyond

With the world economy recovering, growth is expected in small economies. Small states’ overall GDP is expected to reach a height of 5.1 per cent in 2014 and 5.7 per cent in 2015. These projections are significantly above the world forecast. However, this aggregate picture hides considerable regional disparities, with African and Asian small states fuelling the improved outlook. These small states are at risk of overheating (World Bank 2013b). At the same time, Caribbean small states are below pre-crisis growth while the Pacific small states are making a steady recovery.

1.4 Inflation and the consumer prices index

Global price inflation fell from 3.75 per cent in 2012 to 3.25 per cent in 2013 (IMF 2013a), a trend that is expected to persist into 2014 due to a projected fall in food and fuel prices (Beidas-Strom et al. 2013).

Risks to the inflation forecasts include quantitative easing in Japan, asset price bubbles and excess leveraging

in fast-growing East Asian economies, and the potential increases in commodity prices fuelled by increased demand. In advanced economies, inflation is expected to decline from 2 per cent to 1.75 per cent during 2013 to 2015. In emerging and developing economies, the forecast is for a deceleration in inflation rates spurred on by falling food and fuel prices. There are, however, threats to this prospect from oil-exporting transitioning Middle Eastern and North African (MENA) economies. Although developing economies have experienced increased oil prices due to unexpected shocks, overall decelerating inflation is expected.

The same trend of low inflation is expected in small states economies, following the trend shown in Figures 1.7 and 1.8.4

Across all small states, inflation, as measured by average consumer prices index (CPI), has decelerated from its 2008 peak of 10 per cent to 4 per cent in 2012–2013. This downward trend is expected to persist into 2015, commensurate with global inflation trends (see Figures 1.7 and 1.8). It is evident from Figure 1.8 that African small states benefited more from a sharp deceleration of inflation moving from double digits in 2008–2009 to single digits in 2010–2012. Caribbean, Asian and Pacific small states exhibited similar patterns to their African counterparts, as inflation fell between 2008 and 2012. This trend, while expected to continue for Asian, Caribbean and Pacific small states, is likely to be different for states in Africa, where it is expected to be sticky upwards as increased demand puts upward pressure on commodities prices.

1.5 Unemployment

Unemployment in small states remained in double digits in 2011. The improved growth prospects post-2009 seemed not to have translated into a reduction in

Figure 1.6 Government expenditure growth rate by region and aggregate average for small states (2000–2012)

-2%

0%

2%

4%

6%

8%

10%

12%

-10%

-5%

0%

5%

10%

15%

20%

25%

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

regi

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ave

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(%)

Gov

ernm

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fina

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endi

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gro

wth

(%

)

YEAR

Africa Asia Caribbean LDCs Annual regional average 13 year regional average


unemployment rates. In 2011, the unemployment rate was 11.5 per cent,5 2.4 percentage points higher than the 2007 level of 9.1 per cent (see the green line on Figure 1.9). High unemployment and a migrating skilled workforce are major concerns for small states seeking to accelerate growth, requiring the necessary skills to facilitate research and development among their fledgling industries. Developing a skilled workforce and reducing unemployment are key policy priorities (OECD 2013a).

1.6 International trade

Since 1980, international trade has been growing by an average of 7 per cent per year, and at the end of 2011 its value stood at US$18 trillion. Reductions in tariffs and trade barriers have led to a doubling of world trade, with developing countries accounting for the bulk of this increase. Over the last decade, developing economies have increased their share of world exports. Furthermore, the ‘South–South’ share in world trade has risen substantially. While there has been a modest increase in the North–South share of world trade, the ‘North–North’ share has fallen since 1990.

In 2012, developing economies’ exports grew by 3.5 per cent, while imports increased by a higher 4.3 per cent. Africa represented the fastest growing region, with a 6.1 per cent growth in exports volumes. Africa also led in real imports expansion, with double-digit growth of 11.3 per cent. Asia’s imports grew 3.7 per cent. Slower growth in world trade was projected for 2012 and a sluggish performance was expected in 2013. This slower growth rate is a result of the continued and renewed uncertainty in the euro, falling trade within the EU, significant downside risk in the EU and divergent prospects for US and EU trade. These factors have led to reduced export volumes from the EU, which add significant weight to world trade volume totals, therefore causing uncertainty in forecast projections in world trade volumes (OECD 2013a).

Among small states, Belize recorded the highest relative increase in trade (both imports and exports) between 2009 and 2011.6 Over the same period, exports fell slightly in Solomon Islands, Antigua and Barbuda, and St Vincent and the Grenadines. Imports also dropped in the latter two countries.7

Figure 1.7 Aggregate consumer price inflation annual change for small states (1999–2012)

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1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

CPI

infl

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)

Figure 1.8 Inflation, consumer prices index (CPI) change in small states by region (1999–2012)

-4.0%

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1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012Ann

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Africa Asia Caribbean Pacific


1.7 Development aid

The Organisation of Economic Co-operation and Development (OECD) recently completed its sixth comprehensive Development Assistance Committee (DAC) survey on donors’ spending plans. This information tracks future aid receipts by developing countries using country programmable aid (CPA) as an overall predictor of future official development assistance (ODA)8 (although some discrepancies may arise from multi-year planning and unforeseen delays in aid programme implementation). The survey forecasts an overall decline in development assistance in 2012–13. The CPA estimate for 2012–13 was US$92.9 billion, a 1 per cent decline from its 2011 level. However, this decline is expected to be offset by the 36 per cent increase in non-DAC donations totalling US$1 billion. These non-DAC donors are a diverse group of non-traditional donors.9 Development aid will be further enhanced by soft loans from the International Development Association (IDA) and the International Fund for Agricultural Development (IFAD).

As Figure 1.11 indicates, aid disbursements to small states have on average increased from US$55 million

in 2006 to US$115 million in 2011. This figure is in line with the increase forecast for 2013 and 2014. Similarly, average aid commitments10 have mirrored the upward trend in aid disbursement, which rose from an average of US$76 million in 2006 to US$133 million in 2012. Promised increases from larger bilateral donors have put disbursement on an upward trajectory (OECD 2013b).

However, global CPA is projected to stall in the years 2014–2016 as a result of the uncertainties in the world economy (ibid). However, the OECD’s Outlook on Aid Survey expects a slight increase in development aid for countries pursuing the Millennium Development Goal (MDG) gaps and those with high poverty levels (ibid). This expected increase shows the international community’s commitment in transitioning to the post-2015 era by directing resources to areas where they are most needed (United Nations 2013).

Further statistics on ODA disbursements and commitments and on aid dependency in individual small states can be found in Tables 29 and 30 in Part II of this publication.

Figure 1.9 Unemployment in small states (2000–2011)

0

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2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011

Une

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(%)

Figure 1.10 Annual % change in world trade volumes (exports and imports) across regions (2008–2013)

40 20

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–302008 2009

Developed economies Developing economies and CIS EU

China

Africa

AsiaJapan

Average 2008–2013

US

World merchandise trade volumes

2010 2011 2012 2013 2008 2009 2010 2011 2012 2013


1.8 Remittances

In 2013, remittances to developing countries were expected to reach US$414 billion, an increase of about 6.3 per cent, and to continue this upward trend into 2017 (World Bank 2013c).

Small states are expected to benefit from this increase in remittances (see Figure 1.12 and Table 16 in Part II). Overall, average remittances to small states have risen from US$328 million in 2002 to US$525 million in 2012, and are projected to rise further in the following years. In nominal terms, Caribbean small states have received more than their African, Asian and Pacific counterparts. In the Caribbean, average remittances increased from US$155 million in 2002 to US$276 million in 2012. In African small states, the figure rose from US$126 million in 2002 to US$176 million in 2012. Over the same period, remittances to Pacific small states grew from US$26 million to US$66 million (see Figure 1.12).

However, remittances as a share of GDP are, on average, smaller in Caribbean and African small states than they are in Asia-Pacific states (see Figure 1.13). Furthermore, the remittances share of GDP has been falling for Caribbean small states over the past five

years. In addition, while the value of remittances to Asia-Pacific countries is relatively small, it represents more than 6 per cent of GDP, but has been on a downward trend since 2009. With regard to African small states, their flow of remittances relative to GDP rose significantly in 2012.

1.9 International reserves

The total international reserves of small states fell by 0.4 per cent in 2012, following growth of 9.2 per cent in 2009. The decline in reserves growth is attributed to the recession, as some small states faced declining demand for their main exports (Dominguez 2012). These figures are forecast to increase between 2013 and 2016 in line with the continued pick-up in global economic activity. See Figure 1.14.

1.10 Foreign direct investment

The challenges in the global economy also affected the level of foreign direct investment (FDI) in some small states (see Figure 1.15). FDI inflows to Caribbean small states declined steadily during the economic crisis, falling by about 65 per cent from a 2008 peak value

Figure 1.12 Remittances to small states by region (2002–2012)

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2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

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Africa Asia Caribbean Pacific

Figure 1.11 ODA disbursements and commitments in small states (2006–2011)

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of US$558 million to US$198 million in 2011. Pacific small states also recorded poor FDI inflows during the same period, falling from US$72 million in 2008 to just US$18 million in 2011. The upward trend in FDI inflows into Pacific small states, which appeared before the recession, has been replaced by a downward trend. However, the trend in FDI flows has been different for African and Asian small states, which maintained an upward trend – reaching post-crisis levels of US$256 million and US$745 million, respectively.

Across small states the average ratio of FDI as a percentage of gross capital formation fell during the recession. However, an upward trend is now emerging. FDI was 35 per cent of gross capital formation pre-crisis and now stands at 21 per cent.11 Table 26 in Part II of this publication provides information on FDI inflows at a country level.

1.11 Ease of doing business

Some small states showed improvements in their business environments, according to the 2013 ‘ease of doing business’ ranking.12 Cyprus, Brunei Darussalam,

Fiji, Jamaica and Mauritius all gained increases in the ranking, with Mauritius maintaining the top rank among Commonwealth small states at 19, up from its previous rank at 23. However, other small states faltered. Most notably, Antigua and Barbuda, Solomon Islands and Grenada have all fallen in the business ranking. Despite having the lowest rank among Commonwealth small states at 136, Lesotho has shown improvement against its 2012 rank of 143.

A closer look at the ten indicators which make up the ‘ease of doing business’ index revealed that small states tend to rank lower in the areas of access to credit and contract enforcement, but higher in construction permit issuance and access to electricity (see Table 33 in Part II of this publication).

1.12 Small states' global competitivenessSome developing economies and small states have recorded increases in their rankings in the World Economic Forum’s Report on Global Competitiveness

Figure 1.14 Total reserves in small states (2005–2012)

937

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1,301 1,338

1,467 1,462

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Figure 1.13 Remittances as a share of GDP in small states by region (2009–2012)

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2013–2014 (World Economic Forum 2013). Thirteen small states were included in the survey, and nine of these were ranked below 100 for the overall index. Economies in the index are classified into three stages. Stage 1 represents factor-driven economies. Here, countries are trying to forge a competitive edge by improving the health and education sectors, developing their key institutions and infrastructure, and achieving macroeconomic stability. Lesotho falls into this category. In stage 2, countries are efficiency driven and depend on the key factors of higher education and training, goods and labour markets efficiency, financial market sophistication, technological readiness and market size to improve competitiveness. Mauritius, Namibia and Swaziland are in this category. Innovating economies fall under stage 3. Among Commonwealth small states, Cyprus and Malta are classified under this stage, while Trinidad and Tobago and Barbados are thought to be transitioning to this stage. Countries in stage 3 rely on innovation and business sophistication to forge a competitive edge for their economies. Among small states, Brunei Darussalam remains the most competitive improving and Swaziland has the lowest

ranking. Improvements were recorded by Barbados, Mauritius, Namibia and Seychelles (see Table 1.1) (World Economic Forum 2013).

1.13 Human development indicators

The Human Development Index (HDI) is a composite statistic of life expectancy, education and income indices used to rank the 187 countries listed in the report as having very high, high, medium or low human development according to their quartile in the HDI distribution. As Table 1.2 shows, five small states are ranked in the ‘very high’ category – with Seychelles moving from high to very high. Ten small states are now in the ‘high’ category, while eleven small states are in the ‘medium’ category (as Tonga and Belize have regressed). The ‘low’ category remained unaltered.

Despite the generally good HDI scores, small states are still highly vulnerable economically and socially. The evidence of small states’ success in capitalising on their positives often belies the extent of effort required to achieve their development goals and the ease with which their development gains can be eroded. Indeed, small states have been slow to recover from the global recession

Figure 1.15 FDI inflows to small states by region and FDI inflows as a percentage of gross capital formation (2003–2011)

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Table 1.1 Components of the global competitiveness index (2013–2014)

Country name Overall index ranking

Basic requirements ranking

Efficiency enhancers ranking

Innovation factors ranking

Botswana 74 66 93 106Guyana 102 107 103 56Jamaica 94 111 79 75Lesotho 123 119 132 135Mauritius 45 42 61 57Namibia 90 85 99 102Seychelles 80 52 95 62Swaziland 124 114 123 110Barbados 47 35 43 48Brunei Darussalam 26 18 65 54Cyprus 58 51 49 50Malta 51 40 47 49Trinidad and Tobago 92 60 82 92


of 2008 and still face relatively high unemployment rates. High per capita income in small states is often taken as evidence of their progress, but masks the lack of progress in many other areas crucial for the achievement of resilience and sustainable development. The recent Commonwealth-commissioned study, The Big Divide: A Ten Year Report of Small Island Developing States and Millennium Development Goals (Roberts and Ibitoye 2012), illustrates small states’ limited achievement of the Millennium Development Goals (MDGs) to date and highlights aspects of the MDG framework, and the institutional and governance frameworks which underpin it, that are not well attuned to the interests of small states.

1.14 Regional and country analyses

1.14.1 African small state economies: a snapshot

See Figures 1.16 and 1.17 for a snapshot of African small state economies.

1.14.2 Summary of developments, outlook and policy priorities in African small states

The robust projection of real GDP growth among African small states will be spurred on mainly by strong demand, private consumption and investment growth, and increased export activities. Average real GDP growth was 3.1 per cent in 2011 and 3.7 per cent in 2012. The brief interruption to growth in 2009 was mainly due to civil conflicts in some countries and a reduction in oil supply and exports. These economic impediments are expected to give way in 2014–2015, to make for a more nuanced increment in real GDP growth in this region. Thus it was projected that growth would reach 5.5 per cent in 2013. Furthermore, improved investment in infrastructural projects and the strengthening of productive capacity, coupled with a robust showing in consumption, will put upward pressure on real GDP growth. Consequently, real GDP

growth is projected to reach 6 per cent in 2014. Both low- and middle-income countries in the region will see improvements in economic activities. There is expected to be some deterioration in the current account of some low-income countries, but investments will boost demand in the medium term to make possible improvements to the current accounts position.13 A projected improvement is also in the cards for inflation, which was 10 per cent in 2011. It was expected to fall to 8 per cent in 2012 and the downward trend was forecast to continue in the following years. This improvement in regional inflation will be helped by the fall in food and fuel prices in the eastern region as monetary policy tightens. Although temporary ‘headwinds’ from regions embarking on energy subsidy reforms will see some upwardly sticky prices, inflation was expected to reach 7 per cent by the end of 2013. The main risks to the economies in this region are those stemming from the external environment, more so political and domestic security risks. The IMF reckons policy-makers should strive to make growth more inclusive by promoting reform in economic diversification, employment, financial sectors and infrastructural gaps (IMF 2013a). Countries undertaking price and fuel regulations should also take note of the budgetary consequences, as shocks to these commodities are vivid possibilities (ibid).

Botswana

In Botswana recovery from the recession had gathered pace since 2010, but has since slowed due to the fall in global diamond demand. As a result, mining activities declined and the growth of real GDP slowed to 6.1 per cent in 2011 and to 3.7 per cent in 2012/2013 (Part II, Table 2). This deceleration in real activities was expected to continue into the first quarter of 2014. Inflation is expected to decline as a result of this slowdown in real GDP, albeit real GDP will remain at 4 per cent until 2014 quarter one. Inflation is within the Bank of Botswana’s (BOB) target range of 3–6 per cent, falling from 8.5 per cent in 2011 to 3 per cent in May 2013. This movement is

Table 1.2 Human development trends in small states (2012–2013)

Very high HDI High HDI Medium HDI Low HDI

Brunei Darussalam (30) The Bahamas (49) Tonga (95) Solomon Islands (143)Cyprus (31) Grenada (63) Belize (96) Papua New Guinea (156)Malta (32) Antigua and Barbuda (67) Samoa (96) Lesotho (158)Barbados (38) Trinidad and Tobago (67) Fiji (96)Seychelles (46) Dominica (72) Maldives (104)

St Kitts and Nevis (72) Guyana (118)Mauritius (80) Botswana (119)St Vincent and the Grenadines (83) Kiribati (121)Jamaica (85) Vanuatu (124)St Lucia (88) Namibia (128)

Swaziland (141)


consistent with the medium-term objective of the BOB. The IMF reckons that the ‘prudent macroeconomic management’ of the Botswana government has strengthened its fiscal position. For the first time since 2008/2009, the budget was in a strong balanced position as at fiscal year 2012/2013, helped in part by an accommodative monetary policy. In an attempt to support expectations of robust non-mining growth in the first quarter of 2014, the government has designed a ‘pro-growth’ budget geared towards ‘consolidating a weak economic environment by reining in spending, committing to growth-promoting capital projects and rebuilding capital buffers by improving the quality of spending’. The government has also committed to simplifying the tax system, which should lead to more cost-effective tax administration and improved tax compliance in Botswana. With regard to the banking system, it is well capitalised and profitable. There is a low number of non-performing loans. This follows from the government’s commitment to financial stability, which is centred on the moderation of household unsecured borrowing. These commitments, together with the implementation of the 10th National Development Plan (NDP10), are expected to create a well-diversified private sector and lead to economic productivity as well as enhanced competitiveness. Botswana recognises that for this development agenda to succeed, the necessary institutions must be put in place to develop and improve

the skills of the general labour force and to expand the knowledge-base sector. The liberalisation of the services sector and reducing the regulatory burden are regarded as requirements to help foster growth measures.

Lesotho

Output growth in Lesotho showed moderately decreased progress in 2011 and 2012, after a major rebound in 2010. Nonetheless, there is optimism that growth in Lesotho will reach an average of 4 per cent in the years 2013 to 2015. This growth is expected to be driven by a robust recovery in agricultural production and increased activities in diamond mining. There was a dip in inflation to 4.6 per cent in June 2013, in line with the fall in international commodity prices. On account of the anticipated increase in export demand, Lesotho’s international reserves and fiscal positions are expected to improve – augmented especially by the boost to revenue collection from the South African Custom Union (SACU). International reserves were projected to reach a gross of US$1 billion in 2013, which is equivalent to four and a quarter months of imports and represents an increase on its 2012 level of three and a half months of imports. There are, however, a number of downside risks to these economic projections. The revenue from SACU depends largely on the shape of the global economy, the global external demand for and the international market price of key exports like diamonds and textiles.

Figure 1.17 CPI inflation in African small states (2003–2012)

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Figure 1.16 Real GDP growth (%): African small states (2003–2012)

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Additionally, textile exports face downside risks from the expiration of the Africa Growth and Opportunity Act (AGOA) in 2015. It is hoped that non-AGOA exports will increase to counter the proposal. Overall, the economy is expected to benefit from the implementation of policy measures currently fostered under the umbrella of the IMF Extended Credit Facility (ECF). These policies are expected to complement domestic fiscal policy geared towards ameliorating exogenous shocks, enhance the visions set out by the National Strategic Development Plan (NSDP) and bring improvements in fiscal and public financial management.

Mauritius

Similar to Lesotho, Mauritius did not perform as well in 2011 and 2012, but the IMF projected 3.5 per cent growth in 2013 due to prudent economic management. This progress is likely to be driven by increased activities in fishing, financial services and information and communications technology (ICT), fostered by a rise in public investment projects. Inflation fell to 4 per cent in 2013, but there continues to be upward pressure on prices linked to the demand to increase public sector wages and an upward trend in administrative price adjustments. If realised, inflation in 2013 was expected to peak at 5.7 per cent and then decline thereafter. Mauritius’ banking sector has brought a welcome security to the financial system, though price and rental growth in the real estate sector should be closely monitored to ensure that the progress made by the banking sector is not jeopardised. The Economic Intelligence Unit (EIU) reckons that as economic growth in Europe picks up, Mauritius’s GDP growth will quicken to 3.7 per cent in 2014. The pace of expansion will then accelerate to an average of 4.3 per cent in 2015–2018 as global growth continues its upward trend.

Namibia

The Namibian economy was lauded for its prudent economic management before the recession, which enabled it to amass a sufficient fiscal surplus. The advent of the recession and its subsequent prolonged and anaemic recovery prompted the government to embark on an expansionary stimulus policy to cushion the impact of the prolonged downturn. Thus, government finances are in worse health than the pre-crisis level. The fiscal deficit was 9 per cent of GDP in 2012 and is projected to fall to 4.4 per cent of GDP in 2013/2014. However, the government is committed to fiscal expansion via its Medium-Term Expenditure Framework (MTEF), which will further strengthen the recovering economy by channelling government spending to foster economic expansion and economic growth. Public debt is projected to reach 27.8 per cent in 2015 and 30.7 per cent in 2016. On a positive note, these figures are

sustainable at just below the 35 per cent debt-to-GDP threshold. A moderation of real GDP growth from 5.0 per cent in 2012 (Part II, Table 2) to 4.2 per cent in 2013 is expected as diamond prices further deteriorate. Other major downside risks are due to elevated uncertainties emanating from the global economy, a possible fall in revenues from the South African Customs Union (SACU) and weather-related shocks.

Seychelles

The tourism sector has performed remarkably well and, since the global recession, it is currently the main engine of growth for the Seychelles economy. Growth held at 2.9 per cent in 2012 as the country successfully attracted non-European arrivals to mitigate the slump in the European market. It is expected that growth will hold steadily at this level until 2014/2015 – some way short of the 5 per cent projected in 2011. The fall in FDI and the impact of rising food and fuel prices affected macroeconomic fundamentals and detracted from the expected growth forecast. The current account deficit is expected to improve slightly in 2014–2015 to an average of 0.8 per cent of GDP, supported by growth in tourism and lower import prices. Tourism grew by 8 per cent in 2012 and is expected to improve as further non-traditional markets are explored. The inflation level peaked at 7.1 per cent in 2012, fuelled mainly by domestic and international developments due to exchange-rate instability that prompted a depreciation in the rupee. Inflation, however, has now abated due to fiscal tightening. The government has committed to reducing debt to 50 per cent of GDP by 2018.

Swaziland

Despite increased windfall revenue from the South African Custom Union (SACU) in 2012 and a 0.2 per cent projected growth for 2012, Swaziland’s economic performance was relatively moderate. The 2012 official estimate indicates contraction in real GDP of −1.5 per cent (Part II, Table 2), attributed to the delayed impact of the fiscal crisis, structural bottlenecks and subdued global recovery. Real GDP growth was forecast to reach 0.7 per cent in 2013, then 1.8 per cent in 2014/2015. These forecasts are, however, constrained by weak competitiveness in Swaziland’s manufacturing sector. Inflation remained in single digits, despite upward pressure on food and fuel prices, notching up 6.1 per cent in 2011, then to 9.4 per cent in 2012 (Part II, Table 6) and 6 per cent in 2013. Inflation in 2014 is expected to fall to 5.3 per cent.

1.14.3 Asia-Pacific small state economies: a snapshot

See Figures 1.18, 1.19 and 1.20 for a snapshot of Asia-Pacific small state economies. There are improved economic prospects in Asian small states, as the risk in


Western economies dissipates and growth is sprouting in some advanced economies. Growth in Asia was expected to average 5.75 per cent at the end of 2013. This increase is expected to be supported by the easing of financial conditions, especially the rapid credit growth in China and the general rebound of credit in the region. An increase in demand from China and a stimulus package in Japan will further boost growth in the Asia-Pacific area. Stability in global food and commodity prices is expected to keep inflation stable at its 2012 level or within central banks’ target zones (IMF 2013c). However, the Asian Development Bank (ADB) argues that the Asia-Pacific area growth rate has lost steam, owing to the fall to 6 per cent in 2013 from a projected 6.6 per cent in 2012. ADB now forecasts that GDP growth within the Asia-Pacific area will increase by 6.2 per cent, 0.5 per cent below that originally predicted.

1.14.4 Summary of developments, outlook and policy priorities for Asia-Pacific small states

Brunei Darussalam

Two-thirds of nominal GDP in the Brunei Darussalam economy is made up of oil and gas production. These products represent 98 per cent of exports and 93 per cent of government revenue. Thus, the government has been able to fund welfare, social benefits and public

sector jobs for citizens. The government is a key player in the economy. It employs 56 per cent of the workforce, while contracts to private enterprises in the non-energy sector are also dominated by government. The lack of major contracts to refurbish key infrastructure in the oil and gas industry led to a decline in real GDP growth of 2.2 per cent in 2012 (Part II, Table 2) as energy sector output fell by 3.2 per cent. This fall was, however, mitigated by 5.1 per cent growth in the non-energy sector. Though there are major risks from volatility in petroleum revenues, the surplus account should enable the government to mitigate any fall in revenues due to oil price shocks and maintain sustainable growth and employment levels. The long-term strategy remains to diversify the economy away from the energy sector and boost non-energy output and employment.

Fiji

The Fijian economy benefited from strong consump-tion and investment, as tourism and public infrastructure investments registered impressive figures despite the adverse effects of three natural disasters in 2012. Investment was forecast at 25 per cent of GDP in 2013, up from its 2012 figure of 18 per cent of GDP. The growth of real GDP was 2.2 per cent in 2012 (Part II, Table 2) and was expected to be 3 per cent in 2013. In 2014, real GDP growth is forecast at 2.3 per cent on account of strong consumption spending linked

Figure 1.18 Real GDP growth (%): Asia-Pacific small states (2004–2012)

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Tonga Tuvalu Vanuatu Fiji Asia-Pacific average 9 year average

Figure 1.19 CPI inflation in Asia-Pacific small states (1999–2012)

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to a reduction in the income tax rate and a boost in infrastructural spending of about $225 million, which will be centred on building new transport infrastructure to further enhance the 2015/2016 growth prospects. The inflation rate was expected to be 4.3 per cent in 2013 and then 4 per cent in 2014/2015. Given the muted inflationary expectations, the IMF advises that the current accommodative monetary stance is the appropriate policy measure, and the government’s anticipated move towards a more flexible exchange rate regime should better withstand shocks. There have been accelerated structural reforms in the sugarcane sector, the civil service, pensions and public enterprises; the major policy issues are fostering sustainable growth, reducing poverty and increasing resilience to shocks.

Kiribati

Real GDP growth in Kiribati was 2.5 per cent in 2012 compared to 1.8 per cent and 1.4 per cent in 2011 and 2010 respectively (Part II, Table 2). Real output is projected to be 2.9 per cent in 2014, and 2.4 per cent in 2015. The predicted improvements are due to increased investment activities in construction, particularly the airport and seaport construction projects. The above-average increase in licensing revenue in 2012 is expected to decline in 2014–2015, in conjunction with the fall in remittances from seamen as a result of the decline in global shipping activities. The current account deficit was 31 per cent of GDP in 2012 and was expected to be 41 per cent of GDP in 2013 and 36.1 per cent of GDP in 2014. The end of year inflation rate, which was −2.9 per cent in 2012 owing to lower rice and staples prices, was projected to rise to 2.5 per cent in both 2013 and 2014. The fiscal deficit was expected to be 18 per cent of GDP in 2013 and 20 per cent of GDP in 2014. Ongoing reforms will be centred on the tax system, public financial management, state-owned enterprises and the private sector.

Maldives

In 2012, there was a major deceleration in tourist activities – the main engine of growth in the Maldivian economy, accounting for around 30 per cent of GDP and about 60 per cent of foreign currency earnings. Tourism activities slumped to 2.6 per cent in 2012 from a high of 17.6 per cent in 2011. This decline was attributed to a weakening of the European tourism market share to 54 per cent from a previous five-year average share of 70 per cent. As a result, real GDP growth slowed from 7 per cent in 2011 to just 3.4 per cent in 2012 (Part II, Table 2). Other sectors of the economy – wholesale trade, transportation and communications – also performed poorly. Real GDP growth was projected to reach 4.3 per cent in 2013 and then rise to 5.5 per cent in 2014/2015. This increase in the economic outlook is tied to an expected doubling of the Maldives’ share of the Asian market to 38 per cent. Inflation is forecast to be high, but expected to stay within single digits at 9.3 per cent in 2013 and at 8.5 per cent in 2014. The current account balance was projected to be −27.8 per cent of GDP in 2013 and −22 per cent GDP in 2014/2015. One major policy concern is the strengthening of government finances, as debt is set to reach 80 per cent of GDP in 2013/2014. Adopting realistic and prudent budget management will be central in this regard.

Nauru

Although increased activities in phosphate production and exports, which peaked in 2012 at a high of 519,000 tonnes, helped GDP reach a growth rate of 4.9 per cent in 2012, the main impetus of growth in Nauru’s economy will be infrastructural development. The Regional Processing Centre (RPC) for asylum seekers reopening in 2012 was projected to boost economic growth by more than 8 per cent in 2013 and 2014 through its impact on construction, hotels

Figure 1.20 Current account balance in Asia-Pacific small states (2005–2012)

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and accommodation, restaurants, retail trade and government finances. The RPC expansion is likely to boost employment for Nauruan workers – employment was expected to increase from 200 in 2011 to 10,086 in 2012/2013. This increase in employment should in turn boost consumption expenditure and hence growth. GDP growth is expected to reach a high of 8 per cent by the end of 2014. Inflation was forecast to be 0.5 per cent in 2013 and 2.5 per cent in 2014.

Papua New Guinea

Papua New Guinea, with a real GDP growth of 8 per cent in 2012 (Part II, Table 2), remains the biggest and fastest developing economy in the Pacific region. The key driver of growth since 2011/2012 has been construction projects related to liquefied natural gas, which contributed about 4.5 per cent of total GDP growth for 2012. Two per cent was added by transport, finance and trade due to the increased demand spurred by increases in activities generated by liquefied natural gas construction projects. As construction projects slowed down in 2013, real GDP growth was expected to be 5.5 per cent and to average 6 per cent in 2014. Output growth is expected to be hampered further by declining activities in the fisheries, forestry and agricultural sectors. It was predicted that inflation would be 6.5 per cent in 2013 and 7.5 per cent in 2014/2015. Future growth is expected to be led by increased activities in the mineral sector, which is projected to expand by 13 per cent at the end of 2015 as various production bottlenecks clear in the gold and copper mines. Reductions in petroleum production are to be offset by growth in mineral outputs of about 60 per cent. According to the Asian Development Bank (ADB), the current account balance would be −15.1 per cent of GDP in 2013 and then −8.4 per cent of GDP in 2014.

Samoa

The cyclone disaster that occurred in December 2012 in Samoa was expected to slow GDP growth to 1 per cent by the end of 2013. In 2014, growth is projected to recover and reach about 3 per cent, stabilising at around 2.5 per cent in the ensuing period. As a result of drought conditions, inflation rose to 11.4 per cent in 2011, but stabilised to an average of 2 per cent in 2012 (Part II, Table 6). This normalisation is largely the result of stabilisation in food prices. The effect of drought and forecast decline in commodity prices is expected to lead to inflation at 1.5 per cent in 2013/2014. The government has put its fiscal consolidation on hold to set a supplementary budget for post-cyclone recovery and reconstruction projects, which have taken expenditure up to an additional 0.8 per cent of GDP. Further rehabilitation work using donor funds is expected to cost in the neighbourhood of 2.4 per cent of GDP.

Solomon Islands

GDP growth in Solomon Islands dropped to 3.9 per cent in 2012, subsequent to more remarkable performances of 7 per cent and 9 per cent in 2010 and 2011 respectively (Part II, Table 2). The 2012 growth rate was helped in part by increased mining activities and from the Festival of Pacific Art. Growth in 2013 was expected to moderate to between 3.5 per cent and 4 per cent as uncertainties in forestry adversely feed into depletions of logging stocks. Economic growth in 2014 and beyond is anticipated to be driven by the mineral and service sectors. The growth moderation that started in 2012 has helped to temper inflation, which stood at 2.6 per cent (Part II, Table 6) at the end of this period. The inflation rate is forecast to rise to 6 per cent in the ‘near-term’ and to stabilise at 4.5–5 per cent in the medium term. The lack of a well-diversified economy means that the potential impact of external shocks – from natural disasters, increases in food and oil prices, and a slowdown in trading activities with major partners – remain a considerable risk. Gross international reserves are at an all-time high at US$500 million, with government debt at 17.5 per cent of GDP. According to the IMF, the key policy initiative in the medium to long term is for a sustained effort committed to a broader-based and inclusive growth initiative to counter and mitigate the effects of shocks.

Tonga

Economic growth in Tonga is expected to recover in 2013–2014 and peak at about 1.5 per cent after the slowdown of 0.8 per cent 2011/2012. This projected improvement is underpinned by an increase in tourism revenues and remittances. There was inflation of 6.3 per cent in 2011, but price growth decelerated to 1.2 per cent in 2012 (Part II, Table 6) and then further to 0.5 per cent in 2013, on grounds of lower food and fuel prices. Inflation is projected to strengthen to 5.5 per cent in 2014/2015. The overall 2013 budget initiative aimed to eliminate the budget deficit, which declined to within 2.5 per cent of GDP in 2012/2013 from a high of 7.6 per cent of GDP in 2011/2012. The current policy mix of fiscal consolidation and monetary accommodation appears to be reaping success for deficit reduction. The main policy issue in Tonga is deregulation of the judiciary, and policy co-ordination is expected to help with the planned boost in investors’ confidence and strengthen growth prospects. Tonga also recognises the need for reform of business licensing to encourage foreign investment.

Tuvalu

Real GDP was projected to grow at a rate of about 1.3 per cent by the end of 2013 and to reach 1.5 per cent


in 2014. The growth forecast is expected to be spurred by the Tuvalu airport construction project on the one hand and by increased activities in the retail sector on the other. There are, however, risks to these expectations. Remittances, which provide a substantial source of income for families in Tuvalu, have fallen dramatically from a height of US$1.2 million in 2001 to just US$0.3 million in 2012, and are projected to decline even further. Moreover, shipping contracts, another important source of revenue, also declined. Inflation was expected to stabilise at 2 per cent at the end of 2013. The ADB estimated that the current account, as a percentage of GDP, would level off at −3.3 per cent in 2013.

Vanuatu

There was increased public investment in the tourism sector, the key driver of growth in 2012, as the number of tourist arrivals rose above expectations in Vanuatu. GDP growth increased to 2.3 per cent in 2012, up from the previous 1.4 per cent registered in 2011 (Part II, Table 2). Higher tourist arrivals, coupled with public investment and the commencement of delayed construction projects – to be further boosted by a projected increase in agricultural activities – were noted as key factors for the 2013 growth rate. Vanuatu expected GDP growth of 3.2 per cent in 2013 and 3.4 per cent in 2014/2015. Both inflation and the current account balance as a percentage of GDP are likely to stabilise at 2.5 per cent and −10 per cent respectively between 2013 and 2014. There is real effort on the part of government to control spending, which means that the fiscal deficit could fall to 1.5 per cent of GDP in 2012/2013, 1.7 per cent in 2013/2014 and 1.8 per cent in 2014/2015. Public debt was expected to be at 21.6 per cent of GDP in 2012/2013 and 22.4 per cent of GDP in 2013/2014. The current budget target is for zero net domestic financing and net repayment of external debt. In light of this, the general view is for the balance of payments to remain in surplus. The government’s push for structural reform could boost private investment and growth, despite the risk emanating from an unrealised increase in tourism arrivals and receipts.

1.14.5 Caribbean small state economies: a snapshot

See Figures 1.21, 1.22 and 1.23 for a snapshot of Caribbean small state economies.

1.14.6 Summary of developments, outlook and policy priorities for Caribbean small states14

Although economic recovery has begun in the majority of the tourism-dependent Caribbean small states, real

GDP growth has remained low and subdued. This lacklustre growth is mainly due to the slowing of economic activities in advanced economies. The overall sluggishness in Caribbean small states economies – characteristically reflective of large output gaps and weakened demand – has helped to slow inflation in the region. However, the current account deficits of most tourism-dependent Caribbean small states have widened. The economic outlook for these countries is a modest, yet sluggish, return to growth. Real output was projected to reach 1.25 per cent in 2013, stifled in part by weak competitiveness and high debt. Key policy issues are centred on fiscal restructuring to reduce high debt levels and external account imbalances, coupled with civil service reforms. Deteriorating asset quality, inadequate provisions, low profitability, contagion and associated fiscal costs risk the viability of the financial system in the Eastern Caribbean Currency Union (ECCU). On the other hand, real GDP growth is projected to reach 3.5–4 per cent for commodity-dependent small states in 2013–2014.

Antigua and Barbuda

There are marked improvements in tourism and construction, key sectors of the Antiguan economy. These improvements, coupled with a shrewd fiscal and debt-restructuring programme, have enabled the fiscal deficit to drop to 1 per cent of GDP from 18 per cent of GDP in 2009. The debt-to-GDP ratio is also on a downward trend, moving from a high value of 102.5 per cent of GDP in 2009 to 89 per cent of GDP in 2012/2013. The steady pace of real GDP recovery, which began during 2012–2013, is expected to gather pace and hinges heavily on the performance of the external economy. If expectations are reasonable, GDP growth should return to its historical trend of between 3 per cent and 3.5 per cent. The current account balance was expected to be −11.4 per cent of GDP in 2012, but is projected to reach –16.2 per cent of GDP in 2017.

The Bahamas

The Bahamian economy continues to improve, with impetus from increased tourist arrivals and the commencement of investment projects that were energised by a US$3.5 billion Baha Mar private project. These developments, in conjunction with an increase in public funding for airport and roadwork modernisation, supported GDP growth of 1.8 per cent in 2012 (Part II, Table 2). Growth was forecast to rise to 2.7 per cent in 2013 and to 2.8 per cent in 2014. The recent rise in oil and food prices, which resulted in inflation of 3.5 per cent in 2011/2012, was expected to abate to 2.3 per cent by the end of 2013. The fiscal deficit is projected to


average 2.5 per cent by 2015, and thus debt is likely to reach approximately 76 per cent of GDP by 2015/2016. In the medium to long term, bringing forward key tourism projects and energy, transportation and communication infrastructure upgrades, as well as modernisation projects, are expected to boost growth. The key policy commitment for The Bahamas going forward into 2015/16 is to place debt on a ‘downward trajectory’, with the aim of reducing debt to its 2012 levels. Moreover, the government has committed to saving a further 3 per cent of GDP by a combination of consumption taxes, increased savings and reductions in transfers to public corporations through using

well-devised public–private partnership arrangements. The weakened fiscal position, rising debt-service costs, lacklustre employment growth and rising crime represent the government’s main policy challenges.

Barbados

Real GDP growth in Barbados was a modest 0.5 per cent in 2013, following a dip to 0 per cent in 2012 from the positive 0.3 per cent output reached in 2010 (Part II, Table 2). The economy is expected to grow to 1 per cent in 2014, and average around 3.1 per cent between 2015 and 2017. This projection is underpinned by key private and public sector projects: 619 million Barbados

Figure 1.21 Real GDP growth (%): Caribbean small states (2005–2012)

0 1 2 3 4 5 6 7 8 9

-15.0

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Antigua and Barbuda The Bahamas Barbados Belize

Dominica Grenada Guyana Jamaica

St Kitts and Nevis St Lucia St Vincent andthe Grenadines

Trinidad and Tobago

Caribbean average

8 year average

Figure 1.23 Current account balance in Caribbean small states (2005–2012)

-40

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Dominica Grenada Guyana Jamaica

St Kitts and Nevis St Lucia St Vincent and the Grenadines Trinidad and Tobago

Small states average

Figure 1.22 CPI inflation in Caribbean small states (2004–2012)

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Belize

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dollars (Bds$) has been earmarked for public projects like the cruise pier and land reclamation project, pier-head marina, the constitutional river project and the church village projects. These will be further supported by a Bds$1,153 million budgeted for the Four Seasons, Port Ferdinand, Platinum Bay and the Sandridge Beach projects. Weakening of demand, large output gaps and falling food and fuel prices are expected to keep inflation low; the projection was for deflation at 0.3 per cent at year-end 2013, rising to 0.7 per cent in 2014 (Part II, Table 6).

Belize

A fall in commodity prices is expected to see average inflation (measured by the GDP deflator) of 1 per cent from the period 2011 (Part II, Table 6) to 2013/14. The inflation rate is expected to remain low, despite the high level of liquidity in the banking sector, as a result of subdued pressure on commodity prices. Belize’s external current account deficit widened to about 1.7 per cent of GDP, up from 1.1 per cent of GDP in 2011, due to a steep drop in oil exports and higher imports of fuel and electricity. Notwithstanding this deterioration, international reserve coverage is estimated at 3.4 months of imports (up from 3 months in 2011), thanks in part to strong FDI inflows from the sugar sector. Despite the significant progress made with ‘cash-flow relief ’, the debt-to-GDP level is expected to remain high and could pose a major threat to growth forecasts; this is a major concern, as debt resulting from ‘mid-valuation’ of the two public utilities could cause financial shortfall in 2016 as a consequence of the compensations paid to shareholders. This in turn could take debt to above 6.5 per cent of GDP in 2016/17 and then 7 per cent thereafter. The key policy issue in Belize will be the amortisation process of the 2038 bond amid the projected 1 per cent GDP rise in debt servicing – this holds significant refinancing challenges for the government.

Dominica

A slowdown in construction activity and a 15 per cent reduction in banana production due to banana leaf disease, as well as a projected slowdown in tourist activities, was projected to slow real GDP growth in Dominica from −1.5 per cent in 2012 to 1.3 per cent by year-end 2013 (Part II, Table 2). The inflation rate averaged 3.25 per cent in 2012, but as food and fuel prices weaken, inflation is expected to remain at a moderate rate of 1.2 to 2.5 per cent over the medium to long term. The current account deficit of 13 per cent of GDP is predicted to be 13 per cent to 15 per cent in 2014–2016; however, FDI and grants should help to finance external borrowing. The positive feedback

from the possibility of geothermal exploration means this avenue should be keenly explored.

Grenada

The Grenadian economy experienced a 6.7 per cent decline in GDP in 2009, followed by a 0.4 per cent output retraction in 2010. The growth situation improved in 2011 and 2012 to 1 per cent and 1.18 per cent respectively. Real GDP was expected to continue on this positive and upward trend in 2013 and afterwards. These forecasts are buoyed by expectations of further recovery in the tourism sector and an improvement in the agriculture sector. Revenue shortfalls caused by an extension of tax-break and exemption policies by the government are expected to widen the current account deficit to 25 per cent of GDP in the period 2013–2014. The IMF argues that the economy could benefit substantially from the ongoing financial sector reform to address weakening credit portfolios, low profitability and the rising levels of non-performing loans.

Jamaica

There was a negative real GDP growth of 0.7 per cent in 2012/2013, as a result of the more than expected weakening in the agriculture, mining and construction sectors. Tourist arrivals – a key driver of growth in Jamaica – improved, but there was a 5 per cent decline in tourism revenue. The fiscal consolidation programme, together with an expected increase in the tourism, construction, agriculture and mining sectors, should allow for an increase in 2013/2014 GDP growth of about 0.8 per cent. Growth is expected to reach 1.8 per cent in 2014/2015 and average 2.5 per cent thereafter. However, there has been a continuous weakening of aggregate demand, while the inflation rate is expected to increase to about 10.5 per cent in 2013/2014, spurred mainly by the depreciation of the exchange rate and increases in prices of utilities and public transport fees. The medium- to long-term inflation projection is 8.8 per cent. High inflation and weak employment growth are likely to dampen private sector consumption as disposable income falls – the official unemployment rate, which was 14.4 per cent in 2012, rose to 16.6 per cent in April 2013. An increase in non-fuel imports, despite larger than expected non-traditional exports, led to a current account deficit of −12.4 per cent of GDP in 2012/2013, which is expected to decrease to −10.8 per cent of GDP in 2013/2014. The key policy priorities for Jamaica are tackling weak domestic demand, a heavy debt burden (125 per cent of GDP), a large current account deficit (12.8 per cent of GDP in 2012) and a high unemployment rate, which could limit growth in 2014–2015 to less than 2 per cent annually.


St Kitts and Nevis

Economic activities in St Kitts and Nevis, as represented by real GDP, declined by 1.3 per cent in the fiscal year 2012, a result mainly attributed to the decline in real activities in the tourism and construction sectors. Driven by further increases in tourism and construction, real GDP growth was expected to recover to 2.4 per cent by the end of 2013 and to further increase thereafter. Inflation’s downward trend suggested that the 2013 output level would be below the 2012 level of about 1.4 per cent. The major policy focus for St Kitts and Nevis was for fiscal consolidation of about 2.5 per cent of GDP to be achieved by the year-end 2013 – this was expected to be bolstered by an increased uptake in tax revenue.15

St Lucia

St Lucia’s economy avoided contraction for most of the world’s economic recession, and as such it is now the best performing economy in the Eastern Caribbean Currency Union (ECCU). This is despite its experiencing domestic supply shocks, weakened demand for tourism, natural disasters and a major outbreak of banana leaf disease, which led to a slowdown of 0.4 per cent in GDP in 2012 (from a previous 1.4 per cent in 2011). Real GDP growth was expected to regain lost momentum by the end of 2013 and rise to about 1.1 per cent. The inflation rate of 2.8 per cent in 2011 jumped to 4.2 per cent in 2012 (Part II, Table 6) due to the introduction of a value added tax. The government’s overall fiscal deficit widened to 12 per cent of GDP, and public debt reached 78 per cent of GDP at the end of 2013. Though the financial system was able to withstand adverse shocks from the financial crisis, the build-up of the pre-crisis credit boom, the largest in the ECCU, has left financial institutions with asset quality problems. The IMF reckons that this is due to the doubling of non-performing loans, as banks in St Lucia are finding it difficult to foreclose on collaterals as a consequence of tedious procedures. The key policy issues to tackle are the building up of vulnerabilities in the fiscal, financial and external sectors in face of a weak global environment and potential external shocks.16

St Vincent and the Grenadines

Real GDP growth in the St Vincent and the Grenadines economy was relatively moderate due to a decline and lack of momentum in the tourism and manufacturing sectors. The real GDP growth rate was hence 1.5 per cent in 2011/2012 (Part II, Table 2). The expected decline in food and fuel prices over the medium to long term should keep inflation pressures low and the balance of payments stable. Though tax-uptakes were lower than expected, planned reductions in

government capital spending are expected to improve the government’s fiscal position. Structural reforms aimed at promoting growth will centre on a vigorous implementation of easing credit, enhancing labour productivities and skills, reducing energy costs and infrastructural improvements. This will be coupled with increased financial regulation to safeguard the financial sector and to boost growth.17

Trinidad and Tobago

Maintenance problems in the energy sector, coupled with crippling industrial disputes in the non-energy sector, contributed to a slower expansion of real GDP to 1.2 per cent in 2012. With both the energy and non-energy sectors expected to recover from these drawbacks, a growth rate of 1.6 per cent was predicted by the year-end 2013 in Trinidad and Tobago. Inflation, excluding food prices, remained moderate at 3.1 per cent in 2012 and fell to 2.2 per cent in March 2013, while unemployment remained at 5 per cent. The decline in energy revenue of about 1.7 per cent of GDP led to a worsening of the current account deficit (of −1.1 per cent of GDP), which was projected to be −2.5 per cent of GDP in 2013. The main policy initiative in Trinidad and Tobago is for tax reform to raise revenue to 3.2 per cent of GDP over the next five years. The IMF recommends that tax reform should include the rationalisation and simplification of VAT exemptions without any increase in rate, phased-in property taxes, and the modernisation of personal and corporate income taxes by revision and simplification.

1.14.7 European small state economies: a snapshot

See Figures 1.24, 1.25 and 1.26 for a snapshot of European small state economies.

1.14.8 Summary of developments, outlook and policy priorities for European small states

The Eurozone economy is tentatively edging towards recovery. However, the high cost of private sector borrowing – especially for small-scale enterprises – the dearth of credit flow as a result of ailing banks, high debt and resounding uncertainties in economic activities have all contributed to a postponement in household spending. This lack of spending is stifling potential economic growth. Real GDP growth in the Euro-area was expected to contract by 0.6 per cent during 2013, only to modestly increase by 0.9 per cent in 2014. Consequently, unemployment, especially youth unemployment, is at record high and inflation is expected to remain relatively subdued (IMF 2013d). The limited room for policy manoeuvre means that debt ratios will


remain elevated. There is also the high possibility that external shocks could severely harm growth, as there is peripheral risk of stagnation. The general view is that to improve growth in the Eurozone countries should move towards encouraging cross-border competition, instilling labour market reforms which will ease rigidities and increase participation, and establishing flexible bargaining arrangements.

Cyprus

Overall GDP declined by 2.4 per cent at the end of 2012 in Cyprus, and was projected to fall further by 6.2 per cent in 2013 and 3.2 per cent in 2014. At 17.3 per cent, the unemployment rate in Cyprus, which was 11.7 per cent in 2012, is the highest in the Eurozone. Unemployment is projected to be 19.5 per cent by 2014. In the short run output is expected to contract, spurred by an expected contraction in domestic demand and a sharp decline in the financial services and construction sectors. The current account deficit was projected to be 2 per cent and 0.6 per cent of GDP in 2013 and 2014 respectively. Debt

is predicted to rise to a peak of 126 per cent of GDP in 2015. According to the IMF, building Cyprus’ resilience to external shocks and recapitalisation of the banking and credit sectors are policy priorities. The main issue for the government will be tackling the social and economic consequences of the economic crisis that erupted in March 2013, when the conditions of the bailout from the EU and the IMF triggered a dramatic two-week bank closure and the imposition of capital controls.

Malta

The Maltese economy is one of few in the Euro-area that suffered only a small decline in both GDP growth and unemployment. Additionally, none of its banks were insolvent nor did they seek liquidity assistance. Hence this economy occupies the enviable position of having stable real GDP growth of 1 per cent (Part II, Table 2), which is better than the Eurozone average. The Maltese economy was expected to grow by 1.5 per cent by the end of 2013 and by 1.8 per cent in 2014 – the highest rise forecast for the Eurozone. The inflation rate dipped

Figure 1.24 Real GDP growth (%): European small states (2002–2012)

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Figure 1.25 CPI inflation in European small states (2002–2012)

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by 0.8 per cent to 2.4 per cent in 2013 and is estimated to average 2 per cent in 2014. Though imports will increase, the current account is expected to be in surplus. The projection for year-end 2013 was 0.5 per cent of GDP, and in 2014/2015 the current account is expected to average 0.8 per cent of GDP. Debt widened to 72 per cent of GDP, but negotiations are ongoing to reopen the Excessive Deficit Procedure (EDP) and achieve durable correction to the debt target for 2014. The projection is for debt to average 73 per cent of GDP in the run up to 2015. The IMF estimates that in the medium to long term there will be robust export service growth, with improved private sector consumption and confidence – which in turn will boost growth and keep the current account positive, despite an expected rise in imports. The key policy objective is to shore up the resilience of Maltese banks and ensure financial stability.

1.15 Conclusion

There is expected to be a furcation in world economic growth in 2013/2014, as advanced economies (the EU and US) face the dichotomy of moderate growth in the US and floundering growth in the EU. The EU’s contribution to the world’s growth figure is therefore expected to be a drag on the entire growth rate in the world economy. Both the EU and US are expected to grow below the world average rate, with the EU struggling to register positive growth. On the other hand, small states’ average growth is expected to be close to the projected world average, albeit slightly lower, as developing economies are expected to lead the growth recovery. This is especially true for the African region (and African small states), which currently runs the risk of overheating with more than expected growth forecast. The Caribbean region – and indeed other regions dependent on tourism – will grow less than expected as lower tourist arrivals from the EU dampen demand and tourist revenues, and hence slacken growth in these

small states. The choppiness in the world growth rate, exacerbated by the growth bifurcation in advanced economies – in the face of dampened demand, falling oil and food prices, and the slowdown in both private investment and public consumption – will lead inflation to abate in most regions.

The pattern of growth, in the world and regional economies, mirrors the trend in international trade. The diminishing share in exports and imports of advanced economies has given way to a healthy increase in the ‘South–South’ share of trade. The African region is turning out to be the star billing, with a much more robust improvement in macroeconomic statistics than the others. The improved performance of small states, subsumed under the progress of developing economies, is further strengthened by ensuring the fruitful culmination of the Millennium Development Goals. These improvements indicate the pre-eminence given to the push for increases in ODA disbursements and commitments to developing economies and small states in particular – an upward trend that is expected to continue.

The major worry for small states, economies is the prevalent high unemployment rate and the lack of statistical wherewithal to fully comprehend and analyse the extent of its debilitating effects. Statistics need to be improved significantly. Small states’ economies would benefit even more from the welcome forecast that remittances will slightly pass the epic half a trillion dollar mark by the turn of 2017 if their full benefits could be harnessed and incorporated into viable economic policies. The automatic stabilising effect of remittances could bring a welcome degree of calm to the high volatility of small states’ persistent business cycles – open economies buffeted by external shocks, unstable exchange rates regimes, ailing deficits and uninspiring welfare conditions could benefit from the cleansing that accompanies remittances. These could set the stage for achieving loftier goals in small state economies

Figure 1.26 Current account balance (% of GDP): European small states (2005–2012)

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– to move even further up the ‘ease of doing business’ table, attract more FDI inflows and further improve the human development and life expectancy indicators for their citizens in a twenty-first century world that’s getting more complex, riskier and more turbulent in all its economic, social and political ramifications.

Notes 1 See Table 2 in Part II for GDP growth at a country level. 2 See also Table 5 in Part II. 3 See also Table 5 in Part II for country-level data. 4 See also Table 6 in Part II. 5 The 2011 figure is a prediction, as there is generally not

enough data across small states on unemployment to give a comprehensive picture of the long-term forecast.

6 See Table 7 in Part II for statistics on exports, imports and trade balances.

7 See Tables 8–14 in Part II for country-level data on trade statistics. 8 This is defined as money that has left the donor countries; the

placement of resources at the disposal of the recipient country or agency – especially as indicated on the Development Assistance Committee (DAC) database.

9 This group includes countries that have previously been recipients of aid (such as Poland) and those that still are (such as Nigeria); countries that respond to disasters domestically; those that host a growing number of refugees; as well as countries that have been contributing to and supporting international development programmes and systems for a number of decades (Smith 2011).

10 A firm written obligation, backed by an appropriation, to provide resources of a specified amount, under specified conditions for a specified purpose for the benefit of the recipient country or a multilateral organisation – the whole value is recorded on the OECD Creditor Reporting System (CRS).

11 See Table 33 in Part II of this publication.12 This is a ranking of economies from 1 to 189 according to

the conduciveness of their regulatory business environment; a high ranking in the ease of doing business table implies that the regulatory environment is more conducive for the starting up and operation of local firms. Also referred to as the World Bank’s Doing Business report.

13 IMF 2013g, 20; also refer to the graphs in the economic snapshot for the Africa region above.

14 IMF 2013f. See graphs on economic snap shot of the Caribbean, above; also refer to the analyses in sections 1.1–1.12.

15 IMF Country Report: Press Release No 13/176.16 IMF Country Report: Article IV consultation press release

information No 13/27.17 IMF Country Report: Article IV consultation press release

information No 12/41.

References

Beidas-Strom, S, M Rousset and S Streifel (2013), Commodity and Market Review from World Economic Outlook, April 2013, IMF, Washington, DC.

Charles, E (1997), A Future for Small States: Overcoming Vulnerability, Commonwealth Secretariat, London.

Dominguez, KM (2012), ‘Foreign reserve management during the global financial crisis’, Journal of

International Money and Finance, Vol 31 No 8, 2017–2037.

International Monetary Fund (IMF) (2013a), World Economic Outlook: Hopes, Realities and Risks, April 2013, IMF, Washington, DC.

IMF (2013b), World Economic Outlook Database, available at: www.imf.org/external/pubs/ft/weo/ 2013/02/weodata/index.aspx (accessed 30 October 2013).

IMF (2013c), Regional Outlook 2013, IMF, Washington, DC.

IMF (2013d), Euro Area Policy Report No. 13/275, IMF, Washington, DC.

Organisation for Economic Co-operation and Development (OECD) (2013a), Africa Economic Outlook: Structural Transformation and Natural Resources, OECD, Paris, available at: www.o e c d - i l i b r a r y. o r g / d e v e l o p m e nt / afr ican-economic-outlook-2013/structural-transformation-and-natural-resources-in-africa_aeo-2013-8-en (accessed 20 March 2014).

OECD (2013b), Outlook on Aid, Survey on Donors Forward Spending Plans, OECD, Paris.

Roberts, John L and Ibukunoluwa Ibitoye (2012), The Big Divide: A Ten Year Report of Small Island Developing States and Millennium Development Goals, Commonwealth Secretariat, London.

Smith, K (2011), Non-DAC Donors and Humanitarian Aid; Shifting structures, changing trends, Global Humanitarian Assistance, Somerset, UK, July.

United Nations (2013), MDG Gap Task Force Report: The Challenge we Face, United Nations, New York.

World Bank (2013a), World Development Indicators, available at http://data.worldbank.org/data-catalog/world-development-indicators (accessed October 2013).

World Bank (2013b), Doing Business 2013, World Bank, Washington, DC, available at: www.doingbusiness.org (accessed 20 March 2014).

World Bank (2013c), Countries remittances, available at: www.worldbank.org/en/news/press- release/2013/10/02/developing-countries-remittances-2013-world-bank (accessed 20 March 2014).

World Economic Forum (2013), The Global Competitiveness Report 2013–2014: Full Data Edition, World Economic Forum, Geneva, available at: www3.weforum.org/docs/WEF_GlobalCompetitivenessReport_2013-14.pdf (accessed 20 March 2014).

World Trade Organization (2013), World Trade Report 2013 – Factors Shaping the Future of World Trade, WTO, Geneva.


Chapter 2

The Role of Data and Statistics for Policy-making in Small States

Ryan Straughn

2.1 Summary

There is great demand for better, more diversified and detailed statistical data and information, now more than ever before. This has been triggered in part by the need to monitor progress towards the MDGs. It has also been triggered by the new emphasis on monitoring implementation and evaluating results. Statistical data and information are needed to demonstrate progress against targets, and also to plan and monitor the implementation and success of policies developed to achieve the targets. Indeed, it is fully accepted that the effectiveness of policies and the efficiency of operations of governments and businesses depend on access to timely and reliable data and information.

The Commonwealth Secretariat hosted the Second Global Biennial Conference on Small States at Marlborough House, London in September 2012. The agenda for this meeting focused on practical policy options related to growth and resilience in small states, including: green growth approaches in small states; tourism development and local economy linkages; migration and development; and enhancing regional integration. In the ensuing discussions, it was widely acknowledged that the lack of data was a critical constraint to the advancement of policies in small states on growth and development. It hinders the efforts of these countries and the international community to formulate and implement sound and informed policies for sustainable growth and development. The meeting learned that these data gaps require urgent action and attention by the international community if meaningful progress is to be achieved in planning and policy formulation.

It was in response to this development that the World Bank and the Commonwealth Secretariat organised a scoping meeting of main actors in two pilot areas (tourism and remittances) in Barbados in December 2012. This meeting examined options for strengthening data support for inclusive growth policies in the Caribbean region. In 2012, the Commonwealth also

launched a publication entitled: The Big Divide: A Ten Year Report of Small Island Developing States and the Millennium Development Goals (Roberts and Ibitoye 2012). This publication further illuminated the depth and breadth of data gaps in small states in comparison with other developing and developed countries in relation to the MDGs.

It was against this background that the present study was commissioned. It seeks to: 1) investigate and document the dynamics of data and statistical challenges in small states; and 2) identify some of the ways of addressing data and statistical shortages in small states.

A review of the available strategic plans for data and statistical development in small states was undertaken in order to assess where countries are in relation to improving their statistical systems, and to get a better understanding of some of the challenges they face in doing so. The review found a number of issues that are quite common across the small states, including outdated legal frameworks, inadequate institutional capacity, lack of statistical advocacy and co-ordination, and generally the low public profile of statistics. The methodology used to assess the existence and magnitude of data gaps was a combined evaluation of: 1) the United Nations Millennium Development Goals Country Snapshots; 2) World Development Indicators (WDIs) published by the World Bank; and 3) a survey of statistical offices across the Commonwealth small states (CSS).

The analysis indicates that small states are faced with significant data gaps and challenges, in particular with respect to social and environmental indicators. Gaps do exist in all countries assessed, with the Asia-Pacific group having the largest estimated data gaps of the four regions (Africa, Asia-Pacific, Caribbean and other regions). The recommendations include the early adoption of rapidly evolving IT tools, while better organisation of resources across the national statistical system (NSS) – if properly implemented – should significantly reduce reported and estimated data gaps.

23

Other issues related to institutional strengthening, technical skills and training are also addressed.

2.2 Introduction

The Commonwealth defines small states as those with a population of less than 1.5 million people that are located in Africa, the Caribbean, Asia-Pacific and other regions.1 Table 2.1 shows the Commonwealth small states by region. They have varying geographic characteristics, with some being completely landlocked (Lesotho), while most are islands. Some are geographically distant from major regional centres of development (Kiribati), while others are closely connected (The Bahamas). Some have valuable natural resources such as oil (Trinidad and Tobago) and diamonds (Botswana), while others have few such resources (Barbados).

Commonwealth and other small states face a unique set of development challenges posed by their small size and commensurate narrow production and export bases, as well as their susceptibility to climate change impacts. These characteristics shape their sustainable development concerns, which focus on building resilience to adverse economic and climate-related shocks.

The characteristics include the following:

Limited diversification possibilities and narrow range of products: The small size of the country (including population) limits the capability of small states to fully exploit economies of scale and price-bargaining power, rendering them powerless to alter some of their key economic outcomes. Among small states economic production is heavily concentrated on a few activities, and as a consequence their economies are heavily exposed to developments within these sectors or affecting these sectors.

Dependence on strategic imports: This concentrated production structure usually implies that small

states tend to be very open – in order to purchase items not produced locally. Given the size of these economies, there is often limited capacity to harness their natural resources; as a result manufacturing industries tend to be limited. Therefore, there is greater dependence on strategic imports, particularly for energy and food, in small states when compared to other groups of countries. As a result, small economies are highly dependent on conditions in the rest of the world. These economies are also heavily exposed to commodity price volatility, in particular energy prices, which directly impacts on their economic performance.

Limited institutional capacity: Sovereignty necessitates certain fixed costs of providing public services, including policy formulation, regulatory activities, education and social services, justice, security and foreign affairs. Indivisibilities in the provision of these public goods mean that small states face higher costs per person. In addition, exposure to natural disasters has a disproportionate impact on small states’ administrative costs.

Remoteness and isolation: Small states are heavily dependent on trade for economic development and social progress. Their remoteness from major markets, in terms of both imports and exports, implies that for many small states, particularly small island developing states (SIDS), high transport costs is a barrier when it comes to trade.

Low competitiveness: Not surprisingly given the aforementioned, small states are often uncompetitive. Their small domestic markets, limited domestic natural and human resources, and remoteness constrain the growth of competitiveness and their attractiveness as hosts for foreign direct investment (FDI).

Susceptibility to natural disasters and environmental change: The unique natural beauty and geographical location on which many small states hinge their

Table 2.1 Commonwealth small states

Africa Asia-Pacific Caribbean Mediterranean

Botswana Brunei Darussalam Antigua and Barbuda CyprusLesotho Fiji The Bahamas MaltaMauritius Kiribati BarbadosNamibia Maldives BelizeSeychelles Nauru DominicaSwaziland Papua New Guinea Grenada

Samoa GuyanaSolomon Islands JamaicaTonga St Kitts and NevisTuvalu St LuciaVanuatu St Vincent and the Grenadines

Trinidad and Tobago


growth and development prospects also present peculiar challenges. These regions are often highly susceptible to natural disasters, such as volcanic eruptions, hurricanes and tsunamis. Furthermore, small states, especially SIDS, face heightened threats posed by global climate change and, although not directly responsible, share in the consequences. While proneness to natural disasters is a characteristic of larger territories as well, the impact is more devastating on small states. A greater proportion of land and activity in small states can be affected by climate change – in particular a rise in sea level – than in larger territories. Limited assimilative and carrying capacity lead to problems associated with waste management, water storage and other factors affected by small territorial size.

High debt burdens: High debt burdens are an added complication for small states. While self-inflicted weaknesses such as lack of fiscal discipline and inadequacies in debt management practices have been singled out as contributors to the debt problem, exposure to external economic shocks and natural disasters have also been identified as significant contributors. The high debt burden of small states is hampering growth prospects; it exacerbates vulnerability to external shocks and negatively affects domestic policy efforts, as well as the international community’s attempts to help build resilience. Additionally, economic output has been further negatively affected by fiscal restraint, threatening the achievement of national development objectives, including improvements in human development.

Limited access to financial resources: Many small states have been ‘graduated’ from concessional financing because, on the measurement of per capita income, they are rated as middle-income countries. At the same time access to global capital markets, while more important for small states, is now more difficult. There is evidence that private markets tend to see small states as more risky than larger states, so that spreads are higher and market access more difficult. The result of this has been extremely high debt and debt-financing costs. Indeed, adequate levels of non-concessional resources have been limited given their small quotas in multilateral organisations, also a function of the small size of these states.

Eroded social gains: A highly educated and skilled workforce is a key element in advancing socioeconomic development, as it can adapt easily to changing technological demands. In recognition of this, most small states have allocated a significant proportion of their national budget towards social expenditure and have consequently made some

strides in social progress, evidenced in their improved position in the Human Development Index, although not mirrored in their achievement of the MDGs. The aforementioned challenges make it difficult for these countries to sustain the high investment in social development.

At the same time, frequent and prolonged exposure to adverse events is eroding social gains. There is a lack of technical skills and institutional arrangements to manage such shocks. This limited capacity not only constrains internal development, but also the ability of small states to meaningfully and effectively track, participate in and engage with the international community on many of the key issues that impact them. Consequently, many of the international rules, regulations and mechanisms that they are required to adhere to are not designed to take account of their peculiar characteristics and often exacerbate these challenges.

Improving data and intelligence to better inform decision-making and implement sound and informed policies for sustainable growth and development is therefore critical to small states and the international community. The shortage of institutional and human capacity is a key constraint and efforts are needed to explore practical ways to enable small states to fill these gaps.

This review shows that national statistical organisations are increasingly undertaking major reforms aimed at meeting the growing demands from statistical users at the national and international levels, while often operating under severe budgetary constraints. Globally, statistical reform has commonly lagged behind other public sector reforms, yet the demand for timely, good-quality statistical data has never been greater. It is against this backdrop that the Commonwealth Secretariat has embarked on this initiative to assess the current data challenges plaguing its smallest member states, and to explore areas requiring urgent attention.

To be effective, any statistical modernisation effort must first critically examine the NSS to identify potential areas for enhancement. To this end, the present study reviews the key challenges facing the broader statistical system across the CSS.

The rest of the chapter is organised as follows: Section 2.3 provides a brief review of the existing body of knowledge on statistics and data challenges in small states; Section 2.4 examines the dynamics of data challenges in small states, drawing on the results of a survey of central statistical offices (CSOs) and key institutions in CSS; Section 2.5 outlines some ways

The Role of Data and Statistics for Policy-making in Small States 25

employed by CSS and other small states to address the data challenges of small states; and Section 2.6 summarises the main arguments of the research.

2.3 Literature review

Much of the literature on evidence-based policy-making is derived mainly from developed countries. Further, when one examines the issue of data challenges in particular, there is literature associated with storing large amounts of existing data and in more recent times the integration of large databases in order to create ‘big data’2 analytics for what would effectively be policy-making and policy-evaluation almost in real time. However, there is a dearth of literature on evidence-based policy-making for developing countries, and in particular for small states where resource scarcity is far more acute. Nevertheless, there is much in the literature that developing countries, including small states, can glean from the experiences of their more developed counterparts. Additional insights can be garnered from a review of available strategic plans for CSS.

The experiences of more developed countries are similar to CSS, with reported incidents of relatively high turnover of trained personnel and a shortage of suitably qualified staff. These challenges are more acute in small states, where the pool of candidates is smaller and the competing demands greater. Banks (2009), in reviewing some of the key issues and challenges in pursuing an evidence-based approach to public policy in Australia, highlights the importance of capability and expertise. Banks notes the decline in the numbers of persons skilled in quantitative methods and other analysis skills within the public service at the very time when it has been called upon to provide an evidence-based approach that relies on such skills. He attributes this budgetary measure over a long period of time with research emerging as a more dispensable function when governments and bureaucracies are cut back. He further acknowledges the increased ‘poaching’ of research staff within the public sector, resulting in a relatively high turnover of trained personnel.

In reviewing some of the general data challenges facing the Caribbean, Busby (2003) points to some of the obstacles to data availability that include the following: lack of financial resources, lack of qualified personnel, lack of institutional capacity, lack of co-ordination between departments and low priority on the political agenda. Most of the obstacles derive from a lack of integration in looking at the data requirements and finding solutions to their provision. The realisation that data collection is expensive

should lead to a common concern across ministries for the most efficient and effective means of mapping data collection to data needs. He further points out the data poverty problem cannot be corrected by a series of successive marginal changes in peripheral policies. SIDS should first confront their attributes of smallness and limited trained human resources and derive paradigms to change the variables that can be changed.

Busby also highlights the emergence of central banks as major producers of statistics in the Caribbean. He cites the resource endowment of the region’s central banks as the reason for the creation and maintenance of vibrant statistical and research units that analyse the methodologies used in the compilation of the key economic indicators and ensure that the best quality indicators are used (Busby 2002). The imperative to produce key statistics juxtaposed to the weak nature of many of the statistical offices has forced the central banks to engage in activities that, under other circumstances, would have been done by the statistical office. In some countries, the Balance of Payments account is produced by the central bank, as is the case in Barbados and countries in the Organisation of Eastern Caribbean States (OECS).

The key recommendations emerging from these reviews are the need to improve processes and institutions within governments and training and retraining of staff.

Examination of the available strategic plans for statistical development among the CSS shows in general that data-producing institutions are weak and under-resourced. In their present form, they are unable to meet user requirements for statistical data and information.

The following are often identified as some of the main challenges facing official statistics and CSOs in CSS:

Low profile of statistics in government: Statistics has consistently had a low status in governments across CSS; this challenge is highlighted in the following examples taken from the various regions. In Swaziland, the strategic plan states that in spite of its acknowledged importance to national development, statistics has consistently had a low status in government, as evidenced by the low status of the director of the CSO in the civil service. In general, the status of the director of the statistics office is low relative to other heads of department in government (Central Statistical Office Swaziland 2003). A recently concluded diagnostic of the statistical system in Barbados also cites the very low profile of the Barbados Statistical Service, both within and outside the Government of Barbados, together with a lack of awareness of the work


it does and the output it provides (Barbados Statistical Service 2013). The newly formed Namibia Statistics Agency (NSA) and the national statistics system (NSS) faces a similar challenge and cites ‘the NSS is not widely known’ as a threat in a SWOT analysis to execution of the strategic plan. (Namibia Statistics Agency 2012) It also goes on to state, ‘Statistics are perceived to be of low value’. The above are indicative of the situation across most CSS, signalling the need for greater advocacy/outreach and resources.

Lack of a vision and mission: A clear vision and mission are essential for efficient and effectual management of the CSO in particular and the NSS in general, and hence for improved provision of official statistics. The absence of a clear vision and mission for many CSOs in CSS has engendered a lack of unity of direction and purpose, as well as a lack of synergy in statistical production.

Inadequate statistical advocacy: There has been inadequate statistical advocacy to: 1) promote ‘statistical thinking’ in society and to put statistics on the national agenda; 2) demonstrate to government, politicians and legislators, and other users the importance of statistics; 3) promote a culture of evidence-based policy formulation and decision-making; and 4) mobilise and utilise national and international resources for statistical capacity development in the countries. A review of the strategic plans points to the fact that CSOs have not done enough to advocate for statistics, attributable in part to lack of capacity on the part of CSO staff to carry out advocacy, as well as lack of good advocacy tools and materials. For example, in Samoa the Statistics Advisory Board is generally supposed to assist with providing statistical advocacy, but had not been functioning for some time.

Inadequate consultation between data users and producers: Inadequate consultation has made it difficult for data producers to continuously assess emerging data needs and to revise data collection instruments, such as survey questionnaires, in order to meet those needs. Many of the strategic plans reviewed have sought to address how to reposition users of official statistics away from the periphery of the data production process to the centre of the process, so that they can play proactive upstream roles in the development of national statistics.

Outdated legal framework: Statistical operations in most countries are underpinned by outdated legislation from the 1960s–1980s, which no longer provides a legal basis for the provision of official statistics to underpin policy, administrative and economic changes that have taken place in the countries since their passage. In particular, several

of the existing acts do not adequately provide for the professional independence of the CSO, or for equal and simultaneous access to statistical data and information. Twenty out of 32 (62.5%) of CSS currently have legislation that was promulgated prior to 1993 and has not been updated. These acts need to be reviewed and updated to ensure that they are appropriate for the current status of the statistics bureau, for available technology, and that they enable the co-ordination, co-operation, and communication between statistical producers necessary for the operation of a comprehensive statistics system.

Inadequate institutional capacity: An explanation that is consistently given for the poor performance of CSOs, line ministries and other data producers is that there is inadequate human capacity (understaffing and/or limited technical skills and competences) and inadequate material and financial resources for data collection, processing and analysis. These inadequacies make it difficult to meet user requirements, and compromise data quality and timeliness. In some cases, understaffing is a result of budgetary cuts as governments face tight fiscal conditions as cited in The Caribbean Technical Assistance Centre (CARTAC) report (PrudHomme 2013).

The internal organisation of CSOs and other staffing issues have also been identified. The organisation structure for some CSOs was found to be inappropriate, with limited capacity for developing an effective management style based on accountable decision-making, priority and timeframe setting, as well as leadership. These factors contribute to staff frustration and high staff turnover.

Inadequate co-ordination: In most CSS, statistical production is largely fragmented and unco-ordinated. Without good co-ordination, data production cannot achieve synergy or cost-effectiveness, which are particularly important given small states’ significant resource constraints. A review of the strategic plans points to a lack of co-ordination on standards, classifications, statistical techniques and methodologies used by the producers of data, resulting in some duplication and inconsistencies arising among datasets. For most CSS, there is no formal system of co-ordination or co-operation and a strong reliance on informal networks for co-ordination, which has both positive and negative impacts.

2.4 The dynamics of data challenges in small states

The increased demand for statistics has exposed the weaknesses in the NSS, a dearth of data for policy as well as social and economic indicators, and the


inadequacy of existing data. In some African countries, for example, the state of statistics is typical of the ‘vicious cycle’ of statistical under-development. In these small countries, inadequate resources constrain the quantity and undermine the quality of statistics, while the poor quality of statistics leads to lower demand and hence fewer resources and an inability to build and maintain capacity.

In examining the challenges facing CSS, similar issues were encountered and identified by stakeholders within the various NSSs, with some variation across the four distinct geographic regions under consideration. In some instances, there is no formal arrangement between the CSO and other statistical producers that would constitute an NSS, which in itself poses a challenge for the effective deployment of resources to ensure data and information challenges are minimised.

In general, member countries have all been well-served by their respective statistical offices in producing good economic indicators, but there are some areas where they are struggling – namely, social, environment and political indicators. Apart from the challenges of valuation impacts and disentangling the effects of simultaneous influences, countries are often faced with more basic data deficiencies. These are typical for social and environmental data, where there is heavy reliance on administrative collections – or indeed, as in quite a number of cases, there may be no collections at all. However, data deficiencies exist for some economic data.

In all of the member countries, statistics resources are generally deployed to cover broad economic aggregates ranging from national accounts, including gross domestic product and gross capital formation, to labour market statistics that include the size of the labour force and unemployment data. Many of the CSS do not have a published calendar of statistics, and so it is difficult to ascertain the availability of data series within each broad economic aggregate. Further, most statistical offices have websites where statistical information is available, but they are not all well organised and data results are not easily accessed. In some instances a painstaking effort was required, involving browsing several PDF reports, in order to compile data series. However, this lack of accessibility to the public does not necessarily mean the data and information are not available to policy-makers. In some cases data are available but unpublished for reasons that are not stated.

A ‘data gap’ is any missing data that impairs a researcher’s ability to meet his/her project goals.

The types of data gaps are informational, temporal, spatial and data quality. In science, this is the inability to obtain certain information crucial to a scientific study or experiment. Data gaps can be created in a number of ways. The data might not exist, might not be accessible, might not be complete or might not have been evaluated and studied adequately. In small states, there are myriad issues that influence the ‘public availability’ of data. In the context of identification of data gaps and their influence on policy-making, a credible assessment cannot be solely based on published information. To that end, the engagement of statistical professionals is critical in order to truly know where the gaps lie.

2.4.1 Basic data and statistical needs

This section provides a summary of the basic data and statistics needs that would be an ideal minimum for effective planning and policy formulation. While not exhaustive by any means, it is intended to provide a sense of the range of data and information requirements to adequately cover activities under the broad aggregates: economic, socio-demographic (health, education, etc.) and environmental.

Broad economic aggregates should include the production of statistics in the following areas: output measures and national accounts; price collection and measurement; labour forces statistics; household income surveys; private enterprise data; public finance; monetary and financial statistics; trade and balance of payments (Powers 1992). Each of these activities often require skills not always available in CSS since the level of detail involved in the production and analytical process in each of these areas is highly specialised and intense. The data derived from these activity are used to monitor and evaluate the effectiveness of implemented policies and reforms as well as the creation of new policy initiatives.

Socio-demographic aggregates include education, health, nutrition, etc. However, here the focus is primarily on education and health since these sectors receive a significant proportion of the national budgets. Education management information systems (EMIS) routinely collect information on schools as part of their regular operations. Such data include location of schools, conditions of school facilities, number of grades offered, number of students by sex and age, number of repeaters, number of teachers by sex and qualification. More sophisticated systems collect data on retention and completion rates, measures of achievement, and the number of children out of school; and examine statistics in terms of gender, ethnicity and income. EMIS are designed to collect and analyse data on the educational


systems to improve planning, resource allocation, monitoring, policy formulation and decision-making (UNESCO 2010).

Effective health management information systems require data from difference sources which are used for multiple purposes at different levels of the health care system. Individual-level data about the patient’s profile, health care needs and treatment serve as the basis for clinical decision-making. Health facility-level data, both from aggregated facility-level records and from administrative sources such as drug procurement records, enable health care managers to determine resource needs, guide purchasing decisions for drugs, equipment and supplies, and develop community outreach. Population-level data are essential for public health decision-making and generate information not only about those who use the services but also, crucially, about those who do not use them. Public health surveillance brings together information from both facilities and communities with a focus mainly on defining problems and providing a timely basis for action (World Health Organization 2008).

Environment data along with economic and socio-demographic data are the three major branches in most national statistical systems. The environment is the newest of the three subjects, but it has quickly become a sprawling field of loosely related topics and no single publication can effectively address all aspects. Environmental aggregates should include the production of statistics in the following components: flora, fauna, atmosphere, water, land/soil, and human settlements.3 The social and economic activity under each component would be monitored to determine the environmental impact and to formulate appropriate responses.

Flora data, for example, should cover agriculture and livestock production (including land clearing, irrigation, grazing, harvesting, use of fertilizers and pesticides), forestry and logging, competing land use (settlements, agriculture, forestry, mining, recreation, etc.), and emissions hazardous to flora. These data would be used to track the proliferation, depletion, extinction of species, depletion/growth of forest and woodlands, impact of pollution on vegetation cover (e.g. acidic precipitation), impact on land/soil (desertification and erosion due to removal of vegetation cover, biochemical in soils) and changes in water regime from deforestation and removal of vegetation (Asian Development Bank 2002).

The challenges posed by environment statistics are generally greater than for most other types of statistics

since the CSO must rely heavily on other agencies to collect and supply the bulk of primary data. Such a high degree of interdependence between different government bodies demands close co-operation and collaboration, some thing that already proves problematic for more established data.

The basic statistics needs outlined above (which are not exhaustive) underscore the major challenge facing small states, since the level of specialisation and expertise are not available in the quantum required to deliver on all aspects of even one of the areas indicated. Thus, national statistical offices operating under less than well-defined arrangements with other data producers struggle to keep pace with the increasing demand for timely and high quality data and statistics.

2.4.2 Millennium Development Goals data gaps

This section presents the data gaps that appear in the MDG Country Progress Snapshot, updated in December 2012 by the UN Statistical Department (UNSD). The MDG Country Progress Snapshot provides an overview of the progress towards the Millennium Development Goals achieved at the country level since 1990. The snapshot is intended mainly to provide the international community with easy access to the information, and is not meant to replace in any way the country profiles produced at the national level in several countries. It is also meant to reflect the contribution of country-level progress to the global and regional trends on progress towards the MDGs. The data used in the snapshot are from the MDGs global database.4 Discrepancies between global and national figures are due to, among others, different methodology and definitions or different choice of data sources. At the global level, the monitoring of the progress aims to ensure better comparability of data among countries.

Table 2.2 tracks the progress of the Commonwealth small states in achieving a selection of MDGs. It also shows where data gaps exist in assessing these key indicators. For example, only Lesotho and Namibia in Africa, as well as Belize and Jamaica in the Caribbean, had data available for all 17 indicators in the snapshot, while no country in the Asia-Pacific or Mediterranean regions had data for all the indicators. This is a very low data representation, since only one in every eight countries’ progress towards achieving the MDGs could be fully assessed. Lesotho and Namibia both were early achievers for net enrolment ratio in primary education (enrolees per 100 children), while Lesotho was also an early achiever


for the share of women in waged employment in the non-agricultural sector (%) and Namibia for HIV incidence rate (number of new HIV infections per year per 100 people aged 15–49).

Swaziland in Africa and Guyana in the Caribbean both had the smallest number of data gaps, with only one indicator missing from the review. In contrast, Brunei Darussalam in the Asia-Pacific group of countries exhibited the largest data gap with nine missing indicators (out of 17), followed by Nauru also in the Asia-Pacific and St Vincent and the Grenadines in

the Caribbean with eight missing indicators. Seychelles in Africa, Tonga in Asia-Pacific and St Kitts and Nevis in the Caribbean all had seven indicators with missing data.

As a grouping, the African small states in the Commonwealth have the smallest relative data gap5 when compared to the rest of the regions, with only approximately 13 per cent of its data missing. The Asia-Pacific has the highest relative data gap of 32 per cent, followed by the Mediterranean (29%) and the Caribbean (24%). The Bahamas and Barbados

Table 2.2 Commonwealth small states on and off track for the MDGs


are ‘early achievers’ for eight indicators, while Belize has the highest number (eight) of ‘on track’ markers. Lesotho is the country exhibiting the slowest progress, with eleven ‘slow’ markers while Guyana and Jamaica both have six markers for ‘regressing/no progress’.

The indicator with the highest data gaps is the HIV incidence rate (number of new HIV infections per year per 100 people aged 15–49), with 25 of the 32 countries (78%) not having this data available. This is followed closely by the proportion of the population living below $1.25 (purchasing power parity [PPP]) per day (%) with 23 countries (72%) missing data. The indicator of the proportion of urban population living in slums (%) also had 23 countries with missing data.

In contrast, there were five (5) indicators that had no data gaps: 1) net enrolment ratio in primary education (enrolees per 100 children), 2) under-five mortality rate (deaths of children per 1,000 births), 3) incidence rate and death rate associated with tuberculosis, 4) proportion of land area covered by forest (%), and 5) internet users per 100 inhabitants.

All of the countries – with the exception of Papua New Guinea which showed slow progress – were early achievers for the indicator net enrolment ratio in primary education (enrolees per 100 children) – shown as ‘Gender primary’ in Table 2.2. Twenty countries were early achievers for the proportion of population using an improved drinking water source (%) shown as ‘Safe drinking water’. Under-five mortality rate (deaths of children per 1,000 births) shows the most progress, with the highest number of ‘On track’ ratings, while slow progress is most evident in the ‘Internet users’ and proportion of seats held by women in national parliament (single or lower house only, %) shown as ‘Women in Parliament’. Proportion of land area covered by forest (%) has the most markers for ‘regressing/no progress’ with 21 countries reporting little or no progress in reversing the loss of forests. It is worth noting that most of these countries reported having approximately the same size of land area covered by forest in 2010 as in 1990.

The 2012 MDG snapshot represents only a small number of indicators used to monitor each country’s progress towards achieving the MDGs by 2015.6 A simple examination of the total number of indicators with data reveals that there are significant data gaps across the Commonwealth small states in general. The last column of Table 2.2 shows the percentage of MDG indicators with data present for the year 2010. The country with the most data available is Swaziland (58%), which is 100 out of 171 indicators, while

Nauru has the least data available, only 21 per cent or 36 indicators. Commonwealth Africa had four of in the top 10 countries with data available (Mauritius #4 with 50 per cent [86],7 Lesotho #7 with 47 per cent [80] and Namibia #10 with 44 per cent [75]). Malta and Cyprus in the Mediterranean were both in the top 10 countries ranked #5 with 49 per cent [84] and #9 with 44 per cent [76] respectively. Data availability for countries in the Caribbean was mixed, as the group had three top 10 countries (Jamaica ranked #3 with 52 per cent [89], Guyana ranked #5 with 49 per cent [84] and Belize ranked #8 with 46 per cent [78]). The Caribbean also had four countries in the bottom 10, as St Kitts and Nevis and St Vincent and the Grenadines both ranked #24 and Dominica and Grenada ranked #26 and #28 respectively. Lastly, the Asia-Pacific region had the largest data gaps in the UN MDG database, with five countries in the bottom 10. The highest ranked country was Papua New Guinea at #12, with 40 per cent or 69 indicators with data available.

As a comparator the proportion of indicators in the MDG database for countries in the EU, G20 and Latin America are provided (see Table 2.3). On average these groupings have 53 per cent [91], 50 per cent [85] and 50 per cent [85] respectively of the indicators present in the database compared to 38 per cent [65] of the indicators on average for the CSS. Jamaica and Swaziland from the CSS ranked favourably with larger countries from within these groupings.

In summary, though the data gaps were relatively large across all Commonwealth regions for 2010, the countries in the Asia-Pacific and the Caribbean in particular need to focus their efforts in order to significantly close some of these gaps. While there are some indicators that are not relevant to some countries, the existence of data gaps across a wide range in indicators makes it difficult to assess the level of progress CSS have made in achieving the MDGs.

2.4.3 Survey results

This section reports summary results from statistical agencies that responded to a survey assessing data gaps and challenges. Respondents were asked to indicate the relative proportion of data that are currently missing from some broad economic and social aggregates, and to provide an assessment of the three priority areas where statistical intervention is most needed in order to enhance the policy-making framework. The full survey instrument can be found in Appendix 2.2.


Of the 31 countries surveyed, 15 statistical offices responded – giving a nearly 50 per cent response rate. Seven of the countries were from the Caribbean (58% response rate), four were from Asia-Pacific (36%), two from Africa (29%) and the other two from the Mediterranean (100% response). The gap analysis is reported first by country and then a summary of the broad aggregates is presented. Broad economic aggregates include data for national accounts, prices, trade, public finances, balance of payments, monetary statistics and the labour market, while broad social and other aggregates include data for health, education, population and demography, crime, environment, information and communications technology (ICT), nutrition and gender.

Figure 2.1 shows the estimated data gaps across the broad economic and social aggregates as reported by the Antigua and Barbuda Statistical Division (ABSD). Data gaps have been reported for all aggregates and are relatively high, particularly for the social and other aggregates. The broad economic aggregates have a lower data gap on average (39%), despite the labour market aggregate having the highest data gap at 80 per cent compared to the broad social and other aggregates, which have an estimated data gap of 56 per cent. Since the ABSD has not historically conducted labour force surveys, this represents a structural data gap. In fact, the

development of labour statistics was identified as the top priority area for future statistics development on the island. National accounts and balance of payments aggregates were reported as having the lowest data gaps at 20 per cent. Under the broad social and other aggregates, crime and nutrition statistics were reported as having the largest data gaps at 70 per cent.

The ABSD identified the following as the top three priority areas for where data gaps exist in Antigua and Barbuda: 1) labour statistics, 2) poverty, inequality and living standards, and 3) trade in services. Some of the

Table 2.3 MDG indicators for EU, G20 and Latin America (LATAM) countries (%)

European Uniona G20 Latin America

Austria 54 Argentina 42 Argentina 42Belgium 57 Australia 53 Bolivia 42Bulgaria 54 Brazil 39 Brazil 39Croatia 51 Canada 50 Chile 51Czech Republic 49 China 39 Colombia 63Denmark 56 France 52 Costa Rica 56Estonia 50 Germany 53 Cuba 46Finland 57 India 50 Dominican Republic 58France 52 Indonesia 51 Ecuador 53Germany 53 Italy 53 El Salvador 53Greece 53 Japan 54 Guatemala 43Hungary 47 Korea 56 Haiti 42Ireland 53 Mexico 64 Honduras 53Italy 53 Russia 48 Mexico 64Latvia 53 Saudi Arabia 35 Nicaragua 46Lithuania 51 South Africa 50 Panama 61Luxembourg 53 Turkey 61 Paraguay 54Netherlands 52 United Kingdom 53 Peru 56Poland 49 United States 57 Puerto Rico 30Portugal 52 Uruguay 51Romania 53 Venezuela 52Slovakia 49Slovenia 50Spain 59Sweden 56United Kingdom 53

aEU excludes Cyprus and Malta.

Figure 2.1 Antigua and Barbuda – survey data gap results

0%10%20%30%40%50%60%70%80%90%

National accounts

Prices

Trade

Public finances

Balance of paym

ents

Monetary statistics

Labour mark

et

Health

Education

Population and demogra

phy

Crime

Environm

entIC

T

Nutrition

Gender


challenges identified in sharing information with other departments of government were: 1) lack of openness to sharing information, 2) no proper organisational guidelines on sharing, 3) the bureaucratic procedures involved in sharing information/knowledge, and 4) no proper IT platform to share.

The ABSD also identified: 1) the recruitment of suitably qualified persons, 2) depth of analysis, and 3) speed of analysis and processing, as the top three most significant challenges facing the agency when it comes to managing large amounts of data. It also indicated a weak data culture on the island, as evidenced by the low response rates to business surveys and other statistical activities, as another challenge that needed to be overcome. Further, at the moment the ABSD is only capturing data; there are no strategic decisions being made with the data nor is it measuring the volume of data being collected. The ABSD is not discussing ‘big data’, but indicated that it could help the agency with: 1) identifying issues within the delivery infrastructure and assisting future project needs, 2) speeding up the time it takes to make data analysis available to decision-makers, and 3) improving the speed and accuracy of decisions.

The ABSD identified the need to be adequately resourced, with trained statistical staff to perform its legal mandate. More specifically, it needs to develop capacity in demography, economic statistics (national accounts and balance of payments), data processing, data validation and data consistency to achieve its objectives. It also identified the need for training in statistical software (such as SPSS, Stata, Teleform), along with the development of data systems that are compatible with data providers and data sources.

As shown in Figure 2.2, The Bahamas Department of Statistics (BDS) reported data gaps for five of the broad aggregates of which the environment, information and communications technology, and nutrition each register at 50 per cent, while the economic aggregates

of national income and prices lie at 20 per cent. In general, The Bahamas has comparatively low reported data gaps, with only an approximate data gap of 6 per cent in the broad economic aggregates and 19 per cent in the broad social and other aggregates.

The BDS identified its top three priority areas for closure of data gaps to be: 1) household expenditures, 2) business transactions, and 3) environment. One of the challenges it encounters when sharing information with other departments is that there are no proper organisational guidelines on sharing, while the bureaucratic procedure involved in sharing information/knowledge is onerous. The Bahamas indicated that it is learning about ‘big data’. It is presently training IT professionals to manage and analyse ‘big data’, and estimates that its agency will be in a position to take full advantage of ‘big data’ frameworks in 3–5 years.

Figure 2.3 shows the data gaps as reported by the Barbados Statistical Service (BSS). Statistics on criminal behaviour have the highest estimated gap at 40 per cent, which is then followed by health, environment, nutrition and gender, all at an estimated 20 per cent. In general, Barbados has the lowest reported data gaps, with 4 per cent for broad economics aggregates and 18 per cent for broad social and other aggregates. The BSS further identified: 1) environmental statistics, e.g. biodiversity of plants and animals, 2) social statistics, e.g. income status of the population, and 3) international business statistics, e.g. revenue generated by international business companies, as the top three priority areas where data and information gaps exist in the country. The capture of large amounts of data, storage capacity and search/retrieval were also highlighted as some of the most significant challenges facing the Barbados agency in relation to managing large amounts of data. The BSS is now learning about ‘big data’, which it indicated would help in the following areas: 1) improving the speed and accuracy of decisions, 2) better understanding of interactions

Figure 2.3 Barbados – survey data gap results

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Figure 2.2 The Bahamas – survey data gap results

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and the meaning of data trends, and 3) speeding up data timeliness for decision-makers.

The BSS identified the need for more technically trained staff, which would allow the agency to fully execute its mandate. More specifically, the agency requires IT staff to manage and maintain the IT infrastructure and human resources (HR) staff to co-ordinate training and HR allocations. It would also benefit from the establishment of a data warehouse, with the appropriate IT security for the NSS to connect with other statistics-producing units for sharing data.

For Cyprus (Figure 2.4), the Statistical Service of Cyprus (CYSTAT) reported data gaps in 12 of the 15 broad aggregates – ranging in size from a 10 per cent data gap in environmental statistics to 50 per cent in nutrition. In general, Cyprus has relatively low reported data gaps on average, with approximately 7 per cent and 24 per cent for the broad economic and social aggregates respectively. Its top three priority areas for statistical improvement were reported as: 1) administrative data inadequacies, 2) migration movements, and 3) supply and use tables. Data capture, distribution and sharing, and speed of analysis/processing were reported as the most significant challenges facing the agency when it comes to managing large amounts of data. Other challenges that contribute to the existence of data gaps in Cyprus – particularly in sharing information with other departments – are an environment that lacks openness to sharing, a high number of bureaucratic procedures being involved in sharing information/knowledge, and the lack of an IT platform for sharing. Poor information systems were highlighted as the biggest barrier to storing information received by CYSTAT more efficiently and effectively.

CYSTAT indicated that ‘big data’ could help the agency in the following ways: 1) identifying issues within the delivery infrastructure and assisting in projecting future needs, 2) better understanding of interactions and attributing meaning to data trends, and 3) improving overall agency

efficiency. However, it also indicated that the agency was not discussing the use of a ‘big data’ framework at this time. Lastly, CYSTAT cited the significant shortage of statisticians and IT specialists, which currently hampers it from fully executing its mandate.

The CSO in Grenada reported having data gaps in all but two of the broad social and economic aggregates, as shown in Figure 2.5. The reported data gaps range from 5 per cent in education statistics and population and demography to 90 per cent gaps in environmental and nutrition statistics, followed closely by the 75 per cent gaps in labour market statistics. In general, Grenada has relatively high data gaps among the CSS, with an approximate 24 per cent and 41 per cent data gap reported for the broad economic and social aggregates respectively. The top three priority areas for statistical improvement were: 1) household survey data for poverty and labour market indicators, 2) environmental statistics, and 3) literacy and numeracy statistics. The main challenges in sharing information with other departments were identified as the absence of proper organisational guidelines on sharing, the bureaucratic procedures involved in sharing information/knowledge and the absence of a proper IT platform to share the information.

The CSO indicated that it would need the following additional resources to fully execute its mandate: 1) an adequate human resource framework with the right competencies – for example, skills in sampling and estimation, demography, national accounting, geographic information system (GIS) mapping, data processing, IT and computer programming; 2) a larger work space; 3) computer hardware and software, including servers, desktop computers, tablets for administering mobile surveys, statistical and database software, anti-virus and firewall software, a website for dissemination of data, and external storage infrastructure; and 4) more financial resources allocated to conduct frequent household surveys.

The Planning Institute of Jamaica (PIOJ) reported significant data gaps in 10 of the 15 broad aggregates

Figure 2.4 Cyprus – survey data gap results

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Figure 2.5 Grenada – survey data gap results

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ranging from 10 per cent in trade, the labour market, health, and population and demography, to 40 per cent in ICT and gender statistics. From Figure 2.6 it is clear that the broad economic aggregates have a lower reported data gap (3% on average) than the broad social and other aggregates, which are estimated at 26 per cent on average. The PIOJ identified: 1) crime, 2) ICT, and 3) education statistics as the top three priority areas where data and information gaps exist in Jamaica.

The PIOJ also identified the recruitment of talent, speed of analysis and processing, and search and retrieval as the most significant challenges facing the agency with regard to managing large amounts of data. Other challenges highlighted as contributing to the existence of data gaps in Jamaica were the absence of an information sharing culture, absence of proper organisational guidelines on sharing, a lack of awareness of clients’ knowledge needs, and the perception of a lack of urgency in sharing information. Organisational policy and directives were identified as being the biggest barriers to storing information received more efficiently and effectively.

The PIOJ indicated that the use of ‘big data’ would help the agency in the following areas: 1) greater understanding of citizen needs and how to meet them, 2) improving the speed and accuracy of decisions, and 3) defining clearer goals and improved resource spending. It is currently designing a ‘big data’ plan or proof of concept, and has already taken steps to: 1) educate senior management on the benefits, 2) improve the security of stored data, 3) and train IT professionals to manage and analyse ‘big data’. With these in place, PIOJ is poised to take full advantage of ‘big data’ analytics within 1–2 years.

The NSO in Kiribati (Figure 2.7) reported very high data gaps, particularly in the broad social and economic aggregates – with no coverage in environmental, ICT or nutrition statistics. Kiribati uses the Australian dollar as its currency and as such has no official central bank

to conduct monetary surveys. The education aggregate has the lowest reported data gaps at 40 per cent. As a result, the average reported data gap for the broad social and other aggregates is approximately 67 per cent, the highest for the CSS in this category, compared to an estimated 21 per cent data gap for the broad economic aggregates.

The NSO identified: 1) environmental statistics, 2) data from private sector and non-governmental agencies, and 3) business statistics as the top three priority areas where data and information gaps exist in Kiribati. Storage capacity, distribution and sharing, search and retrieval, speed of analysis/processing, recruitment of talent and data capture were all cited as the most significant challenges facing the agency in managing large amounts of data. Some of the challenges with sharing information with other departments include: a lack of openness to sharing information, lack of trust, absence of proper organisational guidelines on sharing, and the absence of a proper IT platform to share.

The NSO is at the moment not discussing ‘big data’, but stated that its adoption would help the agency in the following ways: 1) enhanced understanding and analysis of data trends, 2) reducing the time it takes to make data analysis available to decision-makers, and 3) improving the speed and accuracy of decisions.

The NSO in Malta reported having current data gaps for some of the broad social and other aggregates, ranging from 10 per cent in health and education statistics to 100 per cent for nutrition statistics – as shown in Figure 2.8. Its reported estimate of 0 per cent for the broad economic aggregates was the best for the CSS, along with Trinidad and Tobago similarly reporting no gap. Malta’s reported data gap for the broad social and other aggregates was estimated to be 26 per cent on average. Though no gap was reported for the broad economic aggregates, the development of service price indices and tourism satellite accounts were identified as the top two data priorities, with the development of

Figure 2.7 Kiribati – survey data gap results

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Figure 2.6 Jamaica – survey data gap results

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a system of health accounts rounding out the top three priority areas where data and information gaps exist.

The Malta NSO also identified the divergence between data provided and NSO requirements, storage capacity, and speed of analysis/processing as the most significant challenges facing the agency when it comes to managing large amounts of data. Other challenges that contribute to the existence of data gaps in Malta, particularly in sharing information with people from other departments, are the bureaucratic procedures involved in sharing information/knowledge and the absence of a proper IT platform to share. Poor information systems and processes were highlighted as the biggest barrier to storing information received more efficiently and effectively.

The NSO is learning about ‘big data’ and is currently training IT professionals to manage and analyse within this framework. It indicated that it expects to take full advantage of the benefits of the ‘big data’ framework within 3–5 years. However, it cited the need for an additional six ‘full-time-equivalent’ statisticians and financial resources amounting to €500,000 to fully execute its mandate.

Figure 2.9 shows the data gaps reported by the Namibia Statistics Agency (NSA). The agency reported having no significant coverage of environmental statistics, followed by ICT and nutrition with an estimated 80 per cent data gap. This meant that, on average, broad social and other aggregates have data gaps of approximately 55 per cent. For economic aggregates, a 10 per cent data gap for monetary statistics was the only aggregate reported. The NSA indicated that the top three priority areas for statistical improvements were: 1) vital statistics, 2) education, and 3) environmental statistics.

Figure 2.10 shows the reported data gaps by the Nauru Bureau of Statistics (NBS). All of the aggregates have data gaps, with the exception of public finances. Reported gaps range from 10 per cent for education and population and demography to 100 per cent for

monetary statistics. The average reported data gaps for broad economic aggregates is an estimated 41 per cent, which is the largest among CSS respondents, while the average data gap for social and other aggregates is estimated at 38 per cent. This means that Nauru has the worst reported data gaps across all aggregates reported in the survey.

The NBS also reported the following as the top three priority areas for statistical development: 1) national trade statistics data (International Merchandise Trade Statistics [IMTS]); 2) migration statistics; and 3) monetary statistics. The most significant challenges the agency faces in managing large volumes of data are capture, visualisation and data anonymisation, security and metadata documentation. Some of the challenges in sharing information with people from other departments are: the absence of proper organisational guidelines on sharing, the bureaucratic procedures involved in sharing information/knowledge, lack of awareness of the clients’ knowledge needs, and the absence of a proper IT platform to share information. Poor information systems were highlighted as the largest barrier to storing received information efficiently and effectively.

The CSO in St Lucia reported a data gap for every aggregate, as shown in Figure 2.11. Gaps range from 20 per cent for prices to 50 per cent for health and

Figure 2.8 Malta – survey data gap results

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Figure 2.9 Namibia – survey data gap results

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Figure 2.10 Nauru – survey data gap results

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education, for example. St Lucia has comparatively high data gaps across all aggregates, with an average data gap of 36 per cent for broad economic aggregates and 49 per cent for broad social and other aggregates. Only Kiribati and Nauru has higher average data gaps across the broad aggregates.

The CSO highlighted the following as the top three priority areas for statistical development in St Lucia: 1) quarterly GDP/update supply and use tables, 2) data on poverty and living conditions, and 3) environmental accounting and environment statistics. The CSO also identified search/retrieval, the recruitment of talent and analysis as the most significant challenges facing the agency in managing large amounts of data. Other challenges that contribute to the existence of data gaps in St Lucia are the lack of openness in sharing information, lack of trust and an absence of a proper IT platform for sharing information with other departments. Poor tools and technology are the biggest barriers to storing information that is received more efficiently and effectively.

The CSO in St Lucia is learning about ‘big data’ analytics, and indicated that it would: help the agency define clearer goals and improve resource spending; allow better understanding of interactions and attribute meaning to data trends; improve the speed and accuracy of decisions; and speed up the time it takes to make data analysis available to decision-makers. To improve the agency’s ability to manage and make decisions with ‘big data’, the CSO is currently educating senior management on its benefits, improving the security of stored data and investing in IT systems/solutions to improve data processing. It estimates that it can take full advantage of ‘big data’ in 3–5 years.

The Samoa Bureau of Statistics (SBS) reported significant data gaps for all aggregates surveyed. In Figure 2.12 the lowest reported data gap is 20 per cent for population and demography followed by 25 per cent for labour market statistics, while the highest is reported for nutrition statistics (60%). In general, Samoa has approximately

the same average reported data gaps for economic (36%) and social aggregates (39%).

Against this backdrop of high data gaps across all aggregates, the following were identified as priority areas for statistical development: 1) balance of payments, specifically foreign investment data, which is currently very limited and the activity of offshore companies; 2) monetary statistics – limited coverage of non-bank financial institutions; 3) agricultural/food security; and 4) health.

The SBS also identified analysis, capture, and distribution and sharing as the most significant challenges facing the agency when it comes to managing large amounts of data.

Figure 2.13 shows the reported data gaps for Seychelles, which range from 100 per cent in environmental and nutrition to 5 per cent in prices and trade. The large data gaps reported for environment and nutrition skews the average data gaps for the broad social and other aggregates to approximately 31 per cent, which is significantly higher than the reported 7 per cent for broad economic aggregates.

The National Statistics Bureau (NSB) identified: 1) environmental statistics, 2) investment and capital formation, and 3) offshore activities as the top three

Figure 2.12 Samoa – survey data gap results

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Figure 2.13 Seychelles – survey data gap results

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Figure 2.11 St Lucia – survey data gap results

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priority areas where data and information gaps exist in Seychelles. The NSB also highlighted analysis, search and retrieval, and data capture as the most significant challenges facing the agency when it comes to managing large amounts of data. It indicated the lack of proper organisational guidelines on sharing as the primary challenge when sharing information with other departments. Poor information systems and processes are the biggest barrier to storing information that is received more efficiently and effectively.

With respect to the adoption of a ‘big data’ framework, the NSB indicated that it would help the agency with a better understanding of interactions and the meaning of data trends, improving overall agency efficiency, and the speed and accuracy of decisions.

In order to fully execute its mandate, the NSB cited the following to be necessary: 1) in-house IT staff for programming, hardware and networking services; 2) more high-level technical staff to enhance the organisation’s ability to deliver on ever-increasing data demands, and also to provide support to other partners in the national statistical system; 3) enhanced office space; and 4) specialised training in analytical software packages.

Figure 2.14 shows the reported data gaps for Trinidad and Tobago. The CSO reported data gaps for only the broad social and other aggregates – ranging from a 20 per cent estimated gap for crime statistics to 100 per cent for nutrition and gender. As a result, the average reported data gap for broad social and other aggregates was 48 per cent compared to the reported 0 per cent data gap across the broad economic aggregates. The CSO further identified health, nutrition and gender statistics as the top three priority areas where data and information gaps exist in Trinidad and Tobago.

The CSO also identified data capture, distribution and sharing, and search and retrieval as the most significant challenges facing the agency when it comes

to managing large amounts of data. Other challenges that contribute to the existence of data gaps in Trinidad and Tobago, particularly in sharing information with other departments, are the perception of lack of urgent need for data and the bureaucratic procedures involved in sharing information and knowledge. Poor tools and technology were identified as the largest barriers to the agency’s ability to store information received more efficiently and effectively.

With respect to the adoption of a ‘big data’ framework, the CSO is learning about it and indicated that its adoption would help the agency in the following ways: 1) defining clearer goals and improving resource spending, 2) allowing greater understanding of citizen needs and how to meet them, and 3) improving the speed and accuracy of decisions. In order to improve its ability to manage and make decisions with ‘big data’, the CSO is currently investing in IT infrastructure to improve data storage.

Figure 2.15 shows the reported data gaps for Vanuatu – ranging from 4 per cent for population and demographic data to 60 per cent for ICT, with crime and environment both reported at 50 per cent. The average reported data gaps for the broad economic aggregates was 16 per cent, compared to an estimated 33 per cent data gaps for the broad social and other aggregates. The NSO identified: 1) health, 2) ICT, and 3) labour force statistics as the top three priority areas where data and information gaps exist in Vanuatu.

The NSO also identified analysis, search and retrieval, data capture, speed of analysis and processing, and distribution and sharing as the most significant challenges facing the agency. Other challenges cited when sharing information with other departments are: the lack of openness to sharing, lack of awareness about client’s knowledge needs, absence of organisational guidelines on sharing, and absence of an IT platform to share the information.

Figure 2.14 Trinidad and Tobago – survey data gap results

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Figure 2.15 Vanuatu – survey data gap results

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With respect to ‘big data’, the NSO cited that it would help the agency in the following ways: 1) greater understanding of citizen needs and how to meet them, 2) identifying issues within the delivery infrastructure and projecting future needs, and 3) improving overall agency efficiency. It expects to take full advantage of ‘big data’ analytics within 3–5 years, and is currently working to improve its ability to manage and make decisions with ‘big data’ by educating senior management on the benefits of ‘big data’, improving the security of stored data and investing in IT infrastructure to improve data storage.

Tables 2.4 and 2.5 summarise the estimated data gaps across the broad economic and social aggregates. The ‘traffic light’ colour-coding system is used to demonstrate the size of the data gap that exists across countries and broad aggregates. Red indicates a relatively high data gap, while green represents a relatively low data gap and amber shows the relative medium. In general, the average reported data gaps reflect the higher availability of data across the broad economic aggregates, which is not surprising given the relatively high resource allocation for the compilation of economic statistics compared to that for social and other statistics. As shown in Table 2.4, public finances have the lowest average reported data gaps at 12 per cent, but balances of payments and prices, national accounts and trade are only slightly higher. Labour market statistics have the highest reported gaps on average at 25 per cent because, as previously mentioned, some countries in the Organization of Eastern Caribbean States (OECS)

historically have not compiled continuous household labour force surveys.

Table 2.5 shows the results for broad social and other aggregates. The nutrition aggregate has the highest estimated data gap according to the respondents, with 12 of the countries reporting at least a 50 per cent data gap. Kiribati, Malta, Seychelles, and Trinidad and Tobago all indicated having no compiled data and statistics related to nutrition of the respective nation’s population. Nutrition as a broad aggregate has an average estimated data gap of approximately 67 per cent. Environmental data are the next highest aggregate, with an estimated value of 54 per cent, as Kiribati, Namibia and Seychelles all show a 100 per cent data gap and Grenada follows closely with 90 per cent.

Population and demographic data has the lowest estimates gap on average, which reflects the ten-year frequency with which surveys are conducted. It is, however, not surprising that education and health also exhibit the lowest estimated data gaps after population and demography, since many countries receive funding and technical assistance from international agencies like the World Health Organization (WHO) and Pan-American Health Organization (PAHO) in the area of health and the UN Educational, Scientific and Cultural Organization (UNESCO) and the UN Children’s Fund (UNICEF) in the area of education. These agencies are driven by evidence-based initiatives, which require the submission of data and information as part of their routine monitoring and evaluation procedures.

Table 2.4 Gap analysis for broad economic data aggregates (%)

Country Broad economic aggregates

National accounts

Prices Trade Public finances

Balance of payments

Monetary statistics

Labour market

Antigua & Barbuda 20 35 50 40 20 30 80Bahamas, The 20 20 0 0 0 0 0Barbados 10 0 0 10 10 0 0Cyprus 0 10 0 0 10 10 20Grenada 40 0 20 20 15 0 75Jamaica 0 0 10 0 0 0 10Kiribati 0 0 0 0 0 100 50Malta 0 0 0 0 0 0 0Namibia 0 0 0 0 0 10 0Nauru 30 30 60 0 40 100 30St Lucia 40 20 30 40 40 40 40Samoa 45 35 40 40 35 30 25Seychelles 10 5 5 5 10 5 10Trinidad & Tobago 0 0 0 0 0 0 0Vanuatu 0 40 10 20 10 5 30Average data gap 14 13 15 12 13 22 25


In general, social and other aggregates have significantly higher average estimated data gaps when compared to economic aggregates. Across the broad economic aggregates of the countries that responded to the survey, Malta and Trinidad and Tobago have the least reported data gaps, while Antigua and Barbuda and Nauru have the highest data gaps. For social and other aggregates, Barbados has the lowest estimated data gap on average of approximately 18 per cent, while Kiribati exhibited the highest estimated gap of 67 per cent. More information is provided in the charts in Appendix 2.1.

2.4.4 Challenges

There are numerous challenges facing small states in the collection of data and filling data gaps. These include human resources, information technology, as well as geographic and demographic issues to name a few; these are highlighted in the review of key documents on data in small states and captured in the survey responses. Equally worrisome are the important areas in which data gaps exist, namely social and environmental, and the heightened potential for ineffective policy formulation in the absence of such data. The persistence of these data gaps over time merits a deeper analysis of the key challenges. This is done in the following section, drawing on dialogue with statistical offices and key agencies assisting small states, including the IMF technical assistance centres.

Funding

Underfunding of national statistical agencies is a major hurdle for the majority of the small states, which must be overcome in order for the current level of statistical capacity to expand. This has been a perennial problem for most small states, as competing pressures from other social and economic projects have been given greater consideration from all governments. This situation is unlikely to change significantly in the foreseeable future, as countries have experienced low rates of economic growth in recent years which have severely impacted tax revenues. Further, most public finance systems across the CSS are undergoing some form of austerity, so unless external funding support is accessed to facilitate the development of statistical capacity, these agencies will continue to operate under extremely tight budgets which will further impair their capability.

Human resource constraints

According to the survey results and the statistical strategic plans reviewed, in many countries there is a lack of professionals with high competencies and/or relevant experiences to undertake statistical tasks. This causes two main problems in small states: it limits the ability to perform specialised tasks and increases reliance on a few qualified members of staff, which ultimately delays data analysis and production. Most countries and agencies surveyed mentioned the need

Table 2.5 Gap analysis for broad social and other aggregates (%)

Country Broad social and other aggregates

Health Education Population and demography

Crime Environment Information and communications technology

Nutrition Gender

Antigua & Barbuda

40 40 50 70 60 55 70 60

Bahamas, The 0 0 0 0 50 50 50 0Barbados 20 10 0 40 20 10 20 20Cyprus 30 20 20 25 10 20 50 20Grenada 20 5 5 10 90 60 90 50Jamaica 10 30 10 30 25 40 20 40Kiribati 90 40 0 50 100 100 100 55Malta 10 10 20 25 40 0 100 0Namibia 50 50 0 30 100 80 80 50Nauru 20 10 10 60 30 60 80 30St Lucia 50 50 50 50 50 50 50 40Samoa 35 30 20 40 40 50 60 40Seychelles 5 0 0 10 100 10 100 20Trinidad &

Tobago50 30 0 20 40 40 100 100

Vanuatu 40 10 4 50 50 60 40 10Average data

gap31 22 13 34 54 46 67 36


for ‘more high-level technically trained staff ’, and cited lack of key technical capabilities as a hindrance to filling data gaps. This begs the question of where does the problem lie? Is it that there are not sufficient suitably qualified persons residing in these countries?

The results of the survey suggest that the problem is two-fold, based on availability and affordability. First, there is a small pool of suitably qualified persons, attributed in part to the small number of persons opting for a career in statistics and to the relatively high personnel turnover in statistic offices due to low remuneration. The workload and exposure to specialised training also make these individuals prime targets for more lucrative work in the private sector and/or international agencies. As a result, most statistical offices have a large number of statistically untrained or temporary personnel. There is need for better remuneration packages to retain and attract well-qualified staff.

This raises the affordability aspect of the problem, which hinges on a lack of financial resources to recruit and retain the best staff and is compounded by the need for continuous training and re-training to shore up the knowledge base.

Consequently, a relatively small number of people are responsible for dealing with a diverse set of statistical functions such as consumer prices, designing sample surveys and compiling environmental statistics. In almost all countries, there was the absence of a strategy for developing statistical skills among the staff working for the different institutions that form the NSS. Training plans for individuals are rare, and few statistical producers have training plans for the units. Opportunities for external training appear from time to time, but are sometimes missed due to current systems of approval for attendance. Barbados cited the need for dedicated human resources (HR) staff to co-ordinate training and allocation of personnel, while Seychelles indicated the need for a strong HR presence – this also to assist with the training needs of the NSB.

Geographic and demographic characteristics and confidentiality

Another of the challenges identified, linked also to financial resources, is the relatively large samples required in relation to a smaller population size to obtain valid results in statistical surveys, as well as the higher per capita cost of data acquisition for large territories with a relatively small population or an unevenly and sparsely distributed population. Kiribati, for example, has islands 3,000 km apart and people have to take an international flight between these islands. Lack of anonymity of statistical units

in the population requires specialised treatment of aggregated data and public use samples. Issues of diversity between populations or sub-populations also lead to higher costs of implementing harmonised standards, classifications and coding systems.

Strict adherence to the confidentiality principle is observed in small states because of the relative ease with which a specific entity’s records of data may be identified, even when aggregation rules are employed to ensure non-disclosure. However, this fear is ever-present among various segments of the business community, who are of the view that submitted information will be shared with tax authorities. It is vitally important that the public at large is educated as to the flow of information across agencies operating within national statistical systems, so that issues and unfounded fears related to confidentiality are restricted to an absolute minimum.

IT issues

The challenges related to information technology are significant across Commonwealth small states. Some of the ‘pros’ of small statistical systems include a smaller amount of software customisation, due to the relatively narrow level of diversity of data elements and easier adoption of standardised coding and classification systems across statistical units when there is commitment. Some of the ‘cons’ are slow responses of central IT services for hardware and software support, and a lack of statistical data confidentiality from other agencies of government.

Most countries’ response to the survey cited the need for more IT resources in order to fully execute their mandate. Specifically these include the absence of a proper IT platform to share information, resulting in issues of search and retrieval function, processing times and storage. ICT strategic plans can be found under the profiles of some of the small states in the Commonwealth on the UNSD website, but these are generally related to the use of ICT at the national level as opposed to the specific needs of statistical production units within the NSS. A 2013 diagnostic of the Barbados Statistical Service revealed an absence of ICT strategy for a modern statistical information system, with some of the problems identified including a lack of standardisation, data integration and sharing, and security practices that are either not sufficiently designed and/or properly implemented.

Regional and external support

Given the lack of capacity and sometimes resource availability, most of the statistical offices in the CSS rely heavily on external and externally funded technical


expertise. For example, CARTAC is one of eight IMF Regional Technical Assistance Centres (RTACs) located around the world in the Pacific, the Caribbean, in Africa, the Middle East and Central America. CARTAC provides technical assistance in economic and financial management to 20 countries and territories in the Caribbean. The Government of Canada (through the Canadian International Development Institute [CIDA]) has provided the largest share of the centre’s funding, which is complemented by contributions from the IMF and other international donors. While clearly needed, this heavy reliance on external sources of funding means that the agenda for reform of these statistical systems is often externally determined and driven.

2.4.5 Priority areas

Given the challenges that small states face, in particular the financial resource constraints, it is not surprising that data gaps exist in key areas. It is unlikely that these will be resolved soon given ongoing resource constraints, and so efforts must focus on prioritising. The following priorities have been identified based on the information compiled by statistics professionals in the various member countries, and from a number of published strategic plans for the development of NSSs. It is by no means exhaustive.

Economic data priorities

Though economic data receives the largest share of statistics resources and attention across all CSS, there are still some stubborn areas where data gaps exist. Closure of these gaps would significantly improve existing economic policy-making frameworks:

• Development of quarterly national accounts

• Establishment of tourism satellite accounts

• Supply and use tables

• Enhanced labour markets indicators (including skills gap analyses)

• Improvements in agricultural statistics coverage and food security

• Enhanced centralised common business registers (including international business registers).

Most of the small states compile their national accounts on an annual basis using mainly the production and expenditure approaches. Transitioning to a quarterly basis would be a Herculean task given available resources, but the benefits accruing from more effective policy formulation are likely to exceed the investment. A quarterly national accounting framework provides the avenue for continual evaluation. Adopting quarterly national accounts would also facilitate the adoption of tourism satellite accounts and full supply and use tables.

In order to adequately address these areas, specialised training of personnel will be required within a well-defined executable work plan tailored specifically to each country’s needs and supported by donors and other development partners.

Social data priorities

As demonstrated in the analyses previously presented, there is a dearth of social data available for effective policy-making in most CSS. In particular, the provision of social integrated data are critically important. Though public expenditure on these areas accounts for a significant proportion of the government’s fiscal budget, not enough attention is paid to the collection of data and statistics to evaluate the effectiveness of such expenditures. Enhanced policy formation in this area could potentially result in significant improvements in resource allocations and efficiency.

Education information systems (EIS): In general, across the CSS there is no mechanism to track the performance of individuals as they progress through the various segments of the school system. This is especially critical for small states, as the rate of return on public investment should be monitored to assess the effectiveness of resource allocations. Timely strategic intervention could help close the achievement gaps that currently exist. These systems would also allow for better management and deployment of resources, since they would be driven by the country’s needs rather than from special interests. Identification of specific skill sets would make it easier for businesses and governments to plan more effectively to satisfy the needs of the current and future labour markets. Jamaica and Namibia identified education as one of their top three priority areas for statistical development.

Health information systems (HIS): These systems are not dissimilar to EIS, other than the focus on the delivery of better health care and management of the health system. The digitisation of health records helps access by medical officials at the right time, saves lives and reduces the overall cost of health care delivery. Improvements in national health surveillance would improve significantly and, with appropriate intervention, could also contribute to a reduction in long-term costs. Research opportunities for the purpose of enhancing preventative care would be a significant benefit. Malta identified the establishment of a system of health accounts as one of its top three priority areas where data and information gaps exist, as did Trinidad and Tobago and Vanuatu.

Environmental data priorities

The reality of climate change is just one of many serious challenges that confront SIDS, in particular, and therefore environmental indicators are essential to summarise the


results of any monitoring programme. Air and water pollution, water scarcity, desertification, and the depletion of natural resources cause frequent disasters such as floods and landslides, and are thus having an adverse impact on almost all forms of economic activity and generally diminishing the quality of life in small states.

To address these problems, a broad-based programme of environmental policies and regulations is needed. Such programmes require that countries collect and compile authentic environment data for use by government officials and other decision-makers. Data relating to existing environmental conditions are crucial for environmental planning and decision-making. CSS will therefore need to collect and collate environment statistics on an urgent basis. Table 2.6 below outlines the priority areas for environmental data collection, particularly for SIDS.

Environmental management that is conducive to the development of renewable energy, energy efficiency, and waste management will be the catalyst for reducing the impacts of economic shocks, as good management improves the room for manoeuvre when a country is faced by such shocks. Environmental management is particularly important for adaptation to climate change impacts, which could potentially usher in catastrophic situations, associated with, among other things, sea-level rise, earthquakes, floods, tsunamis, health hazards, and increased frequency of extreme events. Data are the lynch-pin of any good environmental management framework.

2.5 Addressing data challenges in small states

This section focuses on recommendations to address the data and statistical shortages in small states.

Table 2.6 Environment data priorities

Environmental component Social and economic activity

1. Flora Agricultural and livestock production (including land clearing, irrigation, grazing, harvesting, use of fertilisers and pesticides)

Forestry and loggingCompeting land use (settlements, agriculture, forestry, mining, recreation, etc.)Emissions hazardous to flora

2. Fauna Livestock productionHunting, trapping and game propagationCompeting land use (agriculture, ranching, settlements, etc.)Emission hazardous to fauna

3. Atmosphere Land use affecting climate (deforestation, desertification, drainage, irrigation, urban sprawl, infrastructure)

Emission of air pollutants from stationary and mobile sources (industry, agriculture, household, transportation)

4. Water a. Freshwater Water withdrawal (surface water, ground waters other)

Water use (industrial, domestic and municipal, agriculture)In-stream water use (hydropower generation, transportation, fishing)Waste water and discharge (includes sedimentation)

b. Marine water Non-consumptive water use (tidal energy generation, transportation, fishing, etc.)Waste withdrawal and use (desalination, consumption)Competing coastal land use (infrastructure, tourism, recreation)Seabed mining (including offshore oil drilling)Emissions from coasts and rivers, sea dumping, oil spills

5. Land/soil a. Surface Land use (agriculture and livestock, forestry and logging, mining and quarry, human

settlement, transportation, etc.)Waste and waste water discharge

b. Sub-surface Mining and treatment of metallic and non-metallic mineralsExtraction of energy resources (fossil fuels, geothermal and nuclear)Discharges (dusts and air pollutants, acid drainings, tailings, liquid wastes, radioactive

waste disposal)6. Human settlements Population growth and migration

Construction (residential, non-residential)Utilities (energy and water supply)Transportation (public, private)Land use in settlements (residential, industrial commercial, transportation and other

infrastructure)


A large proportion of this work is already underway, led by international agencies such the IMF through its RTACs. In the Asia-Pacific region, the United Nations Statistical Institute for Asia and the Pacific (UNSIAP) provides technical assistance to the CSS in the region, together with the Secretariat of the Pacific Community (SPC) and the Pacific Financial Technical Assistance Centre (PFTAC). In the Caribbean, the Caribbean Community (CARICOM) provides statistical technical assistance and support to the member countries, along with CARTAC. For the African countries, the African Regional Technical Assistance Centre (AFRITAC) South provides technical support to the southern African countries while the Statistical Office of the European Commission (EUROSTAT) provides additional support to the Mediterranean countries. The only drawback is that most of this work takes place across broad economic aggregates. UN agencies like UNESCO and UNICEF, and other international agencies like the WHO and the International Labour Organization (ILO), provide technical support in their areas of expertise, but generally it is not in the area of statistical development. Any technical assistance rendered is often not as sustained as with economic technical assistance.

2.5.1 Adapting NSDS approach to small countries

Partnership in Statistics for Development in the 21st Century (PARIS21) focuses its efforts on assisting and encouraging all low-income and lower-middle-income countries to design, implement and monitor national strategies for the development of statistics (NSDSs) and to have nationally owned and produced data for all MDG indicators. An NSDS is expected to provide a country with a strategy for developing statistical capacity across the entire national statistical system (NSS). The NSDS will provide a 5–10 year vision and will set milestones for getting there. It will present a comprehensive and unified framework for continual assessment of the evolving user needs and priorities for statistics, and for building the capacity needed to meet these needs in a co-ordinated, synergistic and efficient manner (PARIS21 2004). The framework will also include strategies for mobilising, harnessing and leveraging resources (both national and international) and a basis for effective and results-oriented strategic management of the NSS. Some of the considerations for adapting the NSDS to small countries are discussed hereafter.

Engendering political and legal commitment: In formulating NSDSs in small states, it is important to place special emphasis on support at the highest political level. Structural and systemic change to institutions producing statistical information is required in the

form of legislation that ensures that the statistical law of the country permits access to datasets for statistical purposes, such as national insurance, tax agencies etc. Specific methods to address this issue must be included in the NSDS endorsing and visioning process.

IT standardisation: It is important for each country to determine specific policies on the adoption of standardised software for specific applications, to reduce the cost of maintenance and ongoing ownership associated with acquiring software updates, i.e. trade processing or consumer prices etc. This approach could be linked to regional support mechanisms to lower the cost of ownership of software and hardware.

Statistical standards: NSDSs should put emphasis on: (a) ensuring compliance with international standards, International Classification of Diseases (ICD10), International Standard Classification of Occupations 2008 (ISCO-08), International Species Information System (ISIS), Harmonized System (HS), and System of National Accounts (SNA) 2008, which promote harmonisation and comparability of data; and (b) utilising strong statistical standards including data and metadata standards through the use of tools such as International Household Survey Network (IHSN) (Data Documentation Initiative [DDI], National Data Archive [NADA] catalogues), Development Information Database System (DEVInfo) (Statistical Data and Metadata Exchange [SDMX], DDI, Dublin Core Metadata Initiative [DCMI]), REtrieval of DATa for small Areas by Microcomputer (REDATAM) and Census and Survey Processing System (CSPRO). The application of these standards and data tools will allow wider availability and accessibility of data without additional technical overhead which is of limited availability in small states.

Integration of NSDS into regional strategies for the development of statistics (RSDS): For small states, the NSDS should be a buttress to a wider range of institutional supports composed of regional governing political, international, bureaucratic and specifically statistical entities. A mechanism for the co-ordination of RSDSs with NSDSs needs to be established. This mechanism should enable testing statistical activities in a pilot country, and replication of the exercise based on lessons learned to other countries within respective regions.

Within the framework of NSDSs and RSDSs, it is important to establish a set of protocols in order to deal with external agencies, as well as to co-ordinate funding and technical support received from these agencies. A few principles are relevant in this context:

• Recognition of small states’ limitations is important, especially in regard to expertise and specialisation. These challenges can be taken up through


consistent, persistent international/regional expert support and the creation of technical/administrative support centres.

• The link between RSDSs and NSDSs must be made for the purpose of enhancing the availability of timely, relevant and accessible statistical evidence for policy-making support and for maintaining the national ownership in the process. For this, the national benefits of any strategy must be clearly articulated and must enjoy wide intrinsic support among national actors. In addition, NSDSs and RSDSs linkages should be extended to the funding of regional/national support mechanisms, particularly in the implementation phase of NSDSs. The linkage between the NSDS and national development strategies is vital to enable statistics to be considered as national priorities for policy measurement and monitoring.

• For staff in NSOs, the regular participation in regional training programmes is highly recommended to build in-country expertise. The option of twinning countries – bilateral collaboration between less competent countries and countries with strong statistics – should be considered as an ongoing arrangement to provide statistical leadership and mentoring, as well as technical support in the long term.

Importance of users: More collaboration between data producers and data users needs to become the foundation of the NSDS approach. A particular characteristic of small states is the strong adherence to non-disclosure. However, this tradition must be balanced by recognising the need of users for reliable samples/datasets of anonymised data, inclusive of online dynamic use of encrypted databases. The adoption of the NSDS process will require support from user–producer workshops and could be enhanced by the use of social media, such as Twitter, Facebook and LinkedIn, in order to announce major data releases on a pre-announced calendar and answer and/or interact with users on questions pertinent to the specific dataset, where corresponding explanations are not deemed to be adequate on methodological grounds. The recognition of the valuable data validation and consistency review that users can bring to the process in terms of adding metadata to series, adding to coherence with related series, adding explanations to series volatility, as a consistency check for unusual results, must be incorporated into the collaborative relationships built.

Addressing human capacity issues: It is important to address human capacity issues if small states are to address data gaps. This will be challenging, but small states are making some effort to tackle this problem. For example, in the Pacific, the University of the South Pacific (USP) operates in all independent states in the Pacific

except Papua New Guinea, which has its own university. Since 2005 it has provided a modular degree programme in official statistics, part-funded by the EU, which allows non-graduate staff working in official statistics to study for qualifications and which will lead to professional status in their organisation. Students must pay their own fees or get their employer to pay, as there are no scholarships available. USP also runs relevant modules in economics and population studies. This results in better and more staff training and development, which are high on the Pacific countries’ list of necessary improvements.

A good example of the reform process at work is the case of Namibia. In order to address some of the challenges faced in Namibia, the NSA is currently undertaking initiatives to raise the profile of the newly created agency with a view to closing many of the data gaps that exist. The NSA is marketing and promoting the NSS’s statistical products and services, using the NSA’s website to promote access, issuing advance release calendars to promote confidence in the statistics being produced, and assisting users in making better use of statistics. The NSA website has enhanced the accessibility of statistics, with an interactive database from which users can download data quickly. These formats have to be standardised so that future outputs, whether printed or digital, will easily be recognised as emanating from the NSA. Accessibility has been improved by offering users a full range of publication options, including print version, website downloads, press releases and email messages, depending on user preferences. The NSA has taken statistics to schools and to the regions, and is currently collaborating with universities and other research institutions.

2.5.2 Improving data and statistical capacity in priority areas

Given the current level of resources available to most statistical offices, the primary objective would be to streamline as much as possible the workload of existing staff and improve organisational resource management. This requires much better planning on the part of senior management than currently exists in most countries, with the main focus of eliminating redundant activities.

At the moment, there is not a well-established relationship between agencies in the NSS and local universities and community colleges in many countries. The primary recommendation would be to create statistical courses at these educational institutions, which require students across the various specialisations to participate in focused research projects and/or data gathering exercises for which credit will be given. In the prevailing economic climate, this is the most cost-efficient way to tackle the problem and, once properly co-ordinated, could significantly


improve the availability of data and statistics in the areas previously identified. Programmes such as these would also prove useful in raising awareness of the importance of having data collected and produced on a timely basis. For example, teachers, doctors and nurses, lawyers in training etc. would each participate in a specific project whose output would actually be filling a specific data gap. Relationships would be established between various statistical units and these frontline staff, who in the future would be providing statistics to the agencies. This kind of forward planning is especially important in small states, where specialised resources are not always available and which therefore need a system that has a sufficiently large and varied set of non-statistics professionals capable of producing statistics at some minimum level.

The second recommendation in this area would be to also establish a working relationship between statistical agencies and other stakeholders such as NGOs. NGOs – though facing similar challenges – are a valuable source of data, information and sometimes resources, and one that remains highly underutilised across most of the CSS. As with the previous recommendation, the management of these relationships will prove critical to the success of such programmes. In Namibia, for example, in order to overcome issues related to low statistical advocacy and awareness, the NSA proposes to carry out the following between 2013 and 2017: 1) market and promote statistical products and services; 2) issue advance release calendars to promote confidence; 3) disseminate statistics in a user-friendly manner; 4) use press releases, press conferences and workshops for publicity; 5) take statistics to schools and to the regions; 6) collaborate with universities and other research institutions; and 7) collaborate with local and regional governments.

It is important for national statistics offices to foster and encourage cohesion and integration, sharing among international, regional and subregional partners mandated to assist with statistical development. This could take place as part of the overall monitoring and evaluation mechanism of the NSDS, with a view to providing adequate developmental support during the implementation phase of the NSDS. It will also help to enhance the confidence of the political directorate in the professionalism and the adequacy of quality standards in the statistical system.

2.5.3 Operational IT

Greater use of technology, in particular mobile technology, would substantially improve the coverage of statistics across the statistical systems. This requires funding to acquire the right hardware and software that is fit for the purpose; this is currently beyond the scope of many operational budgets in CSS. Naturally,

the significant reduction in time for data processing would allow more in-depth analysis to be completed as close to real time as possible. Moreover, truly integrated national IT platforms would also allow the delivery of ‘big data’ analytics to policy-makers and other users.

Leveraging the use of standardised software would play a key role in reducing training requirements of staff to make available to users data on a timely basis through the use of content management websites. Census and Statistics Dissemination’s (CELADE) REDATAM, for instance, helps build an interactive and dynamic website for the dissemination of census and survey data after a two-week training programme. The NSS has to designate a content manager, who will be in charge of updating content on the website based on a specific user publication security protocol and engaging in social interactions with users via Facebook, Twitter and LinkedIn. This will ensure quick and ready publication of recently released data to the general public, and will leverage the considerable ability of the user community to assist with metadata explanations of fluctuations in the data series, among other benefits. In general, statistical offices also need to exploit more open-source software to reduce the burden on their operational budgets.

Data storage is always a problem for national statistical systems, and for small states seeking to close gaps and expand coverage this will continue to be a challenge. Borrowing from the private sector, during the economic crisis organisations started looking at innovative ways to optimise their storage with the intention of maximising utilisation and optimising cost efficiency. Storage optimisation and efficiencies such as de-duplication,8 compression, thin provisioning, space-efficient snapshots, caching and tiering contribute to the objectives of maximising storage utilisation and minimising costs. Primary data de-duplication is proving to have the most dramatic impact on data footprint reduction and should be seriously considered.

Storage and server virtualisation allows a number of workloads with different characteristics to run on a single storage system to better allocate resources. Storage virtualisation is also being adopted to enable efficient usage of capacity, hence reducing the need to purchase additional storage systems.

Many of the above-mentioned efficiencies can be implemented on primary datasets. Efficiencies, such as primary de-duplication, bridge the gap between a lower cost reduction rate and higher data growth within an organisation. The ability to reasonably reduce the rate of data growth at the source (primary dataset) using primary de-duplication gives any IT organisation a better handle on capacity and costs.


2.6 Conclusion

Effective monitoring of development policies and programmes requires a stream of comprehensive socioeconomic and environment data. Such data provide baseline values for monitoring indicators. There is a dearth of data on important developmental issues, including the profile of rural populations, household food security and nutrition, poverty profiles of the population, the state of the environment, gender issues etc. This is bound to constrain effective monitoring, especially of poverty reduction programmes and attainment of the MDGs.

Existing data have quality problems caused by a number of factors – such as outdated sampling frames, inadequate survey instruments, definitional problems, very low response rates in business surveys, inadequate or lack of training for and supervision of data collectors, inadequate data scrutiny and handling, especially in line ministries, using outdated weights, insufficient data validation etc. Lack of data quality creates problems in setting some development targets, and there is also the danger that such data may lead to policy-makers drawing incorrect conclusions from movements in certain indicators. These observations are supported by the assessment of the MDGs, WDIs and the results of the present survey.

Many data series lack timeliness, which affects their usability – especially for decision-making. This is influenced to a large degree by the limited human resource capacity across the various statistical systems. Consequently, CSOs do not fully meet users’ needs for statistics in terms of timeliness, coverage, relevance and transparency, as well as harmonisation with other local data producers.

To date a number of countries have already embarked on redevelopment of their national statistical systems, with considerable support from international agencies and experts. A core aspect to the successful implementation of NSDSs is the leadership shown, especially within the statistical system in terms of openness to change and improvement that would enhance the timeliness, accuracy, relevance and accessibility of statistical data. It is therefore very critical that leaders exert a clearly articulated vision and mission for the NSDS process.

There is still a considerable amount of work to be done in order for small states to move towards a fully functioning, evidence-based, policy-making framework with the significant closure of exposed data gaps. In most cases, this will require the absolute buy-in of all stakeholders involved to provide the necessary leadership required to move the process along. International donor agencies, too, can play a role in facilitating this move by using their leverage and good relations to achieve better resource allocations through enhanced decision-making.

For small states in particular, the skill set that resides in statistical offices is among the highest in the country. Yet because of low public profiles, these skills are not being fully utilised in the policy-making framework other than for providing data and reports. It is the present author’s considered view that it is especially important for small states to fully engage all available knowledgeable resources in the policy-making framework.

This review has also revealed the strong need for agencies to improve their level of organisational efficiency as a prerequisite for an effective policy-making framework. The introduction of IT (hardware and software) must be matched with a constant drive to improve upon existing methodologies along with the relatively early adoption of the latest tools. Statistics professionals can be much more effective in small states if this kind of approach is deployed and accompanied by the right kind of management ethos. This would help to foster better collaboration within the NSS to the benefit of the respective country.

Acknowledgements

Gratitude and thanks are extended to the following persons who provided valuable assistance:

1. Statchel Edwards, Chief Statistician, Statistics Division (Antigua and Barbuda)

2. Victor Browne, Director (Ag.), Barbados Statistical Service

3. Karras Lui, Manager of Economics Department, Central Bank of Samoa

4. Laupua Fiti, CEO (Ag.) Economic Statistics, Samoa Bureau of Statistics

5. Marc PrudHomme, Real Sector Specialist, CARTAC

6. Laura Ahtime, Chief Executive Officer, National Bureau of Statistics (Seychelles)

7. Sterling Chadee, Assistant Director (Ag.), CSO (Trinidad and Tobago)

8. Benuel Linge, Senior Statistician, Vanuatu National Statistics Office

9. Costas Diamantides, Senior Statistics Officer, Statistical Service of Cyprus

10. Halim Brizan, Director, CSO (Grenada)

11. Ryan Als, Technical Officer, National Conservation Commission (Barbados)

12. Frederick Gordon, Manager, JAMSTATS, Planning Institute Of Jamaica

13. Joseph Bonello, Director – Economic Statistics, National Statistics Office (Malta)


14. Jackie Noabeb, Director: IT and Data Processing, Namibia Statistics Agency

15. Ipia Gadabu, Director, Nauru Bureau of Statistics

16. Edwin St Catherine, Director of Statistics, CSO St Lucia

17. Leona Wilson, Deputy Director, Bahamas Depart-ment of Statistics

18. Taiaopo Faumuina, Principal Statistician-Census and Survey, Samoa Bureau of Statistics

19. Aritita Tekaieti, Senior Statistician (Economic Statistics), National Statistics Office (Kiribati)

Notes 1 The definition of a small state as a country with 1.5 million

people or less was agreed by the Commonwealth Advisory Group in its report, A Future for Small States: Overcoming Vulnerability (Charles 1997). Jamaica, Lesotho, Namibia, Botswana and Papua New Guinea are included in the Commonwealth group of small states because they share similar characteristics to those of small states.

2 ‘Big data’ refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage and analyse. As technology advances over time, the size of datasets that qualify as big data will also increase. Also note that the definition can vary by sector, depending on what kinds of software tools are commonly available and what sizes of datasets are common in a particular industry. With those caveats, big data in many sectors today will range from a few dozen terabytes to multiple petabytes (thousands of terabytes) (Manyika et al. 2011).

3 More details can be found in ADB 2002. 4 See: http://mdgs.un.org/unsd/mdg/Data.aspx. The metadata

and responsible agencies can be found on http://mdgs.un.org/unsd/mdg/Metadata.aspx (accessed October 2013).

5 The relative data gap is calculated as the number of missing data divided by the total number of data points by region.

6 The full MDG database is available for download at http://mdgs.un.org/unsd/mdg/Default.aspx (accessed September 2013) and each country has 171 indicators ranging from 1990 up until the present.

7 Figures in square brackets [] are the number of indicators in the MDG database with data available for 2010.

8 Data de-duplication (often called ‘intelligent compression’ or ‘single-instance storage’) is a method of reducing storage needs by eliminating redundant data. Only one unique instance of the data is actually retained on storage media, such as disk or tape. Redundant data is replaced with a pointer to the unique data copy. See: http://SearchStorage.techtarget.com (accessed March 2014).

9 Although Papua New Guinea has a population of about 7 million, it faces many of the same challenges that other Commonwealth small states face and has thus been included in this review.

10 Dudley, N (ed.) (2008), Guidelines for Appling Protected Areas Management Categories, IUCN: Gland, Switzerland, 8–9.

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Appendix 2.1 Data gaps from the survey results

The figures below give a graphical representation of the reported data gaps by economic and social aggregates from the survey results.

Figure A2.1.1 Survey data gap results: national accounts

0%

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Antigua & Barb

uda

The Bahamas

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Jamaica

Kiribati

Malta

Namibia

Nauru

St Lucia

Samoa

Seychelles

Trinidad &

Tobago

Vanuatu

Figure A2.1.2 Survey data gap results: prices

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The Bahamas

Barbados

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Jamaica

Kiribati

Malta

Namibia

Nauru

St Lucia

Samoa

Seychelles

Trinidad &

Tobago

Vanuatu

Figure A2.1.3 Survey data gap results: public finances

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Antigua & Barb

uda

The Bahamas

Barbados

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Jamaica

Kiribati

Malta

Namibia

Nauru

St Lucia

Samoa

Seychelles

Trinidad &

Tobago

Vanuatu

49

Figure A2.1.4 Survey data gap results: monetary statistics

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Antigua & Barb

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The Bahamas

Barbados

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Grenada

Jamaica

Kiribati

Malta

Namibia

Nauru

St Lucia

Samoa

Seychelles

Trinidad &

Tobago

Vanuatu

Figure A2.1.5 Survey data gap results: trade

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The Bahamas

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Kiribati

Malta

Namibia

Nauru

St Lucia

Samoa

Seychelles

Trinidad &

Tobago

Vanuatu

Figure A2.1.6 Survey data gap results: balance of payments

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Antigua & Barb

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The Bahamas

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Nauru

St Lucia

Samoa

Seychelles

Trinidad &

Tobago

Vanuatu


Figure A2.1.7 Survey data gap results: labour market

0%

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Antigua & Barb

uda

The Bahamas

Barbados

Cyprus

Grenada

Jamaica

Kiribati

Malta

Namibia

Nauru

St Lucia

Samoa

Seychelles

Trinidad &

Tobago

Vanuatu

Figure A2.1.8 Survey data gap results: population and demography

0%

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Antigua & Barb

uda

The Bahamas

Barbados

Cyprus

Grenada

Jamaica

Kiribati

Malta

Namibia

Nauru

St Lucia

Samoa

Seychelles

Trinidad &

Tobago

Vanuatu

Figure A2.1.9 Survey data gap results: health

0%

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Antigua & Barb

uda

The Bahamas

Barbados

Cyprus

Grenada

Jamaica

Kiribati

Malta

Namibia

Nauru

St Lucia

Samoa

Seychelles

Trinidad &

Tobago

Vanuatu


Figure A2.1.10 Survey data gap results: education

0%

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Antigua & Barb

uda

The Bahamas

Barbados

Cyprus

Grenada

Jamaica

Kiribati

Malta

Namibia

Nauru

St Lucia

Samoa

Seychelles

Trinidad &

Tobago

Vanuatu

Figure A2.1.11 Survey data gap results: information and communications technology

0%

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Antigua & Barb

uda

The Bahamas

Barbados

Cyprus

Grenada

Jamaica

Kiribati

Malta

Namibia

Nauru

St Lucia

Samoa

Seychelles

Trinidad &

Tobago

Vanuatu

Figure A2.1.12 Survey data gap results: crime

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Antigua & Barb

uda

The Bahamas

Barbados

Cyprus

Grenada

Jamaica

Kiribati

Malta

Namibia

Nauru

St Lucia

Samoa

Seychelles

Trinidad &

Tobago

Vanuatu


Figure A2.1.13 Survey data gap results: environment

0%

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Antigua & Barb

uda

The Bahamas

Barbados

Cyprus

Grenada

Jamaica

Kiribati

Malta

Namibia

Nauru

St Lucia

Samoa

Seychelles

Trinidad &

Tobago

Vanuatu

Figure A2.1.14 Survey data gap results: nutrition

0%

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Antigua & Barb

uda

The Bahamas

Barbados

Cyprus

Grenada

Jamaica

Kiribati

Malta

Namibia

Nauru

St Lucia

Samoa

Seychelles

Trinidad &

Tobago

Vanuatu

Figure A2.1.15 Survey data gap results: gender

0%

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40%

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80%

90%

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Antigua & Barb

uda

The Bahamas

Barbados

Cyprus

Grenada

Jamaica

Kiribati

Malta

Namibia

Nauru

St Lucia

Samoa

Seychelles

Trinidad &

Tobago

Vanuatu


Appendix 2.2 World Development Indicators data gaps

The World Development Indicators (WDI) are the World Bank’s primary indicators for development, compiled from officially recognised international sources. They present the most current and accurate global development data available, and include national, regional and global estimates. A review of some of the data published on small states on the WDI database shows data gaps across many of the broad economic and social indicators. In general there are areas for which data are available at the top level, but are not available at lower levels that would allow for more in-depth analysis. In the presentation of figures below, each quadrant will show data for Commonwealth small states in Africa and the Mediterranean together, Asia-Pacific, Caribbean and then the variations of small states aggregates within the WDI database. The reader should note that Nauru does not appear in the list of countries in the WDI, and so no data are registered for any of the variables presented below. All data are for the year 2010 unless otherwise indicated.

Figure A2.2.1 shows GDP at purchasers’ prices, which is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. It is calculated without making deductions for depreciation of fabricated assets or for depletion and degradation of natural resources. Data are in current US dollars.

Dollar figures for GDP are converted from domestic currencies using single year official exchange rates. Data for Tuvalu have been appropriately scaled to be visible on the chart in the Asia-Pacific quadrant.

This indicator has no data gaps for the period in question, with the exception of Nauru as mentioned previously, since this variable is estimated and produced in all countries at least once every quarter in most instances. However, an examination of gross capital formation (GCF), a subcomponent of GDP, quickly reveals the data gaps.

Figure A2.2.2 shows GCF (formerly gross domestic investment), which consists of outlays on additions to the fixed assets of the economy plus net changes in the level of inventories. Fixed assets include land improvements (fences, ditches, drains and so on); plant, machinery and equipment purchases; and the construction of roads, railways and the like, including schools, offices, hospitals, private residential dwellings, and commercial and industrial buildings. Inventories are stocks of goods held by firms to meet temporary or unexpected fluctuations in production or sales, and ‘work in progress’. According to the 2008 System of National Accounts (SNA), net acquisitions of valuables are also considered capital formation. The data gaps for this indicator are most acute in the Asia-Pacific region.

Figure A2.2.1 GDP (current US$ million)


Figure A2.2.3 shows public health expenditure (as a percentage of total health expenditure), which consists of recurrent and capital spending from government (central and local) budgets, external borrowings and grants (including donations from international agencies and non-governmental organisations) and

social (or compulsory) health insurance funds. Total health expenditure is the sum of public and private health expenditure. It covers the provision of health services (preventive and curative), family planning activities, nutrition activities and emergency aid designated for health, but does not include provision

Figure A2.2.2 Gross capital formation (% of GDP)

Figure A2.2.3 Public health expenditure (% of total health expenditure)


of water and sanitation. This indicator has almost complete coverage across the CSS in the WDI database.

Figure A2.2.4 shows the fish species threatened across the CSS. Threatened species are the number of species classified by the International Union for the Conservation of Nature (IUCN) as endangered, vulnerable, rare, indeterminate, out of danger or insufficiently known. All countries, except Nauru, had data available on the WDI database in 2012.

Figure A2.2.5 shows carbon dioxide emissions stemming from the burning of fossil fuels and the manufacture of cement. CO2 emissions include carbon dioxide produced during consumption of solid, liquid and gas fuels and gas flaring. Data for Antigua and Barbuda, Belize, Dominica, Grenada, Lesotho, St Kitts and Nevis, St Lucia, and St Vincent and the Grenadines were scaled to appear on the chart. Trinidad and Tobago accounts for approximately half of the total emissions for all small states due to exploitation of its oil and gas resources.

Figure A2.2.6 shows the data available on the WDI database for marine protected areas. These are areas of intertidal or sub-tidal terrain – and overlying water and associated flora and fauna and historical and cultural features – that have been reserved by law or other effective means to protect part or the entire enclosed environment. No data were present in the database for Maldives in 2010, whereas Guyana was the only country in the Caribbean with no marine protected areas. For Africa, Botswana, Lesotho and Swaziland

are all landlocked countries and thus have no marine areas as defined for the indicator. The data values for Barbados, Dominica, Fiji, Grenada and St Lucia were scaled to appear on their respective charts.

Figure A2.2.7 shows the percentage of the total population living in areas where the elevation is 5 metres or less above sea level for the year 2000. The landlocked countries of Botswana, Lesotho and Swaziland in Africa are highly elevated, and therefore no one lives below 5 m.

Figure A2.2.8 shows public expenditure on education as a percentage of total government expenditure, which is the total public education expenditure (current and capital) expressed as a percentage of total government expenditure for all sectors in a given financial year. Public education expenditure includes government spending on educational institutions (both public and private), education administration, and subsidies for private entities (students/households and other private entities). Unlike public health expenditure, public education expenditure data are sketchy across all regions for CSS. Data gaps are more acute in the Asia-Pacific region, followed by Africa.

Figure A2.2.9 shows the primary completion rate, which is the total number of new entrants in the last grade of primary education, regardless of age, expressed as percentage of the total population of the theoretical entrance age to the last grade of primary. This indicator is also known as the ‘gross intake rate

Figure A2.2.4 Fish species threatened, 2012


to the last grade of primary’. The ratio can exceed 100 per cent due to over-aged and under-aged children who enter primary school late/early and/or repeat grades. The Caribbean and Mediterranean countries have full data coverage for 2010, while Africa and Asia lag behind. This indicator is better than the public education expenditure shown in Figure A2.2.8,

particularly as all the regional aggregates for small states have sufficient data to register.

Figure A2.2.10 shows the net enrolment rate for primary school. This is the ratio of children of the official primary school age who are enrolled in primary school to the total population of the official primary school age. Only three

Figure A2.2.5 CO2 emissions (kt)

Figure A2.2.6 Marine protected areas (% of territorial waters)


Asia-Pacific countries (Maldives, Samoa and Solomon Islands) had data available in 2010 on the WDI database. In Africa, only two countries (Lesotho and Namibia) had data available, while in the Caribbean, Barbados, Dominica and Grenada did not have data available on the database. Despite the gaps indicated, all the small states aggregate measures had data available.

Figure A2.2.11 shows public expenditure, which is cash payments for operating activities of the government in providing goods and services. It includes compensation of employees (such as wages and salaries), interest and subsidies, grants, social benefits and other expenses such as rent and dividends. There are significant data gaps for the Asia-Pacific region as only Maldives has

Figure A2.2.7 Population living below 5 metres above sea level (% of total population), 2000

Figure A2.2.8 Public expenditure on education (% total government expenditure)


data available. Half of the Caribbean countries have data missing, along with three African countries.

Figure A2.2.12 shows the size of the labour force in the respective countries. The total labour force comprises people aged 15 and older who meet the ILO definition of the economically active population: all people who

supply labour for the production of goods and services during a specified period. It includes both the employed and the unemployed. While national practices vary in the treatment of such groups as the armed forces and seasonal or part-time workers, in general the labour force includes the armed forces, the unemployed and first-time job seekers, but excludes homemakers

Figure A2.2.9 Primary completion rate, total (% of relevant age group)

Figure A2.2.10 School enrolment, primary (% net)


and other unpaid caregivers and workers in the informal sector. The data gaps are most significant in the Caribbean region, particularly because some of the OECS countries, namely Antigua and Barbuda, Dominica, Grenada, and St Kitts and Nevis, historically have not conducted labour force surveys. Similarly, none of countries in the Asia-Pacific region (Kiribati,

Nauru and Tuvalu) or Seychelles in Africa conduct frequent labour force surveys. These are structural data gaps that will require sustained intervention.

Figure A2.2.13 shows the unemployment rate, which refers to the share of the labour force that is without work but available for and seeking employment.

Figure A2.2.11 Public expenditure (% of GDP)

Figure A2.2.12 Labour force


Definitions of ‘labour force’ and ‘unemployment’ differ by country. A significant number of countries across all three regions of Africa, Asia-Pacific and the Caribbean have missing data, which makes it impossible for any of the small states aggregates to be accurately generated. Only the Mediterranean shows on the aggregated small states chart.

Figure A2.2.14 shows the current account of the balance of payments, which is the sum of net exports of goods and services, net primary income and net secondary income. Data are in current US dollars. This variable has full coverage in three regions of Africa, the Caribbean and the Mediterranean. The Asia-Pacific has five countries (Brunei Darussalam, Kiribati, Nauru, Tonga and Tuvalu)

Figure A2.2.13 Unemployment, total (% of total labour force)

Figure A2.2.14 Current account of the balance of payments (US$ million)


with missing data for 2010. Of special note is that there is no data present on the database for Kiribati and Tuvalu. Additionally, none of the small states aggregates has any data present on the WDI database.

Figure A2.2.15 and Figure A2.2.16 show exports and imports of goods and services as a percentage of GDP.

Exports/imports of goods and services represent the value of all goods and other market services provided to/received from the rest of the world. They include the value of merchandise, freight, insurance, transport, travel, royalties, licence fees and other services, such as communication, construction, financial, information, business, personal and

Figure A2.2.15 Exports of goods and services (% of GDP)

Figure A2.2.16 Imports of goods and services (% of GDP)


government services. They exclude compensation of employees and investment income (formerly called ‘factor services’) and transfer payments. In both charts, Guyana is the only Caribbean country with missing data, while in Asia-Pacific, Kiribati, Nauru, Papua New Guinea and Tuvalu all have missing data.

Africa and the Mediterranean have full coverage, along with the small states aggregates.

Figure A2.2.17 shows trade in services as a percentage of GDP, which is the sum of service exports and imports divided by the value of GDP, all in current US dollars.

Figure A2.2.17 Trade in services (% of GDP)

Figure A2.2.18 Domestic credit to private sector (% of GDP)


Five Asia-Pacific countries now register data gaps, with all other regions having no such gaps.

Figure A2.2.18 shows domestic credit to private sector, which refers to financial resources provided to the private sector, such as through loans, purchases of non-equity securities, and trade credits and other accounts receivable that establish a claim for

repayment. For some countries, these claims include credit to public enterprises. Barbados is the only Caribbean country with no data for this variable on the WDI database for 2010. The countries in the Asia-Pacific region (Kiribati, Nauru and Tuvalu) with no data use the Australian dollar as their currency and have no monetary authority to monitor these variables.

Figure A2.2.19 New businesses registered (number)

Figure A2.2.20 Passenger cars (per 1,000 people)


Figure A2.2.19 shows the number of new businesses (limited liability corporations) registered in a calendar year. This variable clearly has a very large data gap, as only nine countries have data available and no region has complete coverage on the database. Figure A2.2.20 shows the number of passenger cars, which are road motor vehicles, other than two-wheelers, intended for the carriage of passengers and designed to seat no more than nine people (including the driver). This variable also shows that a significant data gap exists across the CSS with respect to some private sector variables.

In summary, a review of randomly selected WDI indicators is one of the simplest ways of obtaining data for this unique grouping of countries. The WDI database is available free of charge on the internet, and provides high ease of accessibility to data for multiple countries across multiple indicators simultaneously. Its coverage of indicators includes agricultural and rural development, aid effectiveness, climate change, gender, health and education, to name a few. Like the MDGs, there are areas where small states simply do not have the data and information available to accurately assess or measure their developmental progress.


Part II. SocIal and EconomIc Data on Small States

Basic statistics

The set of statistical tables in this volume covers all small states (with populations of 1.5 million or less), as well as other countries with populations of up to 5 million. The 65 tables that follow set out basic data on country size, together with the latest information on selected economic, social development and environmental indicators, as well as characteristics of the major sectors of small economies. It is hoped that the statistical data presented will form a useful supplement to those available from other sources on larger countries. In order to indicate the scope of the data presented in the tables and to outline the concepts and methodology used in their computation, a series of technical notes is presented. The notes review and explain the variables presented in the tables. The sources cited at the foot of the tables provide more comprehensive explanations of the concepts used. Apart from international sources, a large number of national sources have been used in the compilation wherever the definitions are comparable.

Technical notes for tables

The criterion used in selecting the 66 countries covered in the tables is that their 2011 populations were approximately 5 million or less.28 Although more than 66 countries fall within this classification, some were excluded from the tabulation due to lack of reliable statistical data.

The countries are grouped into two categories on the basis of their 2011 per capita GNI or by dominant characteristic. The two categories are: (a) middle-income countries with per capita income of US$1,036 to US$12,615; and (b) high-income countries with per capita income of US$$12,615 or more. None of the Commonwealth small states is classified as low-income based on per capita GNI of 2011.

Although the data are drawn from authoritative and reliable sources, they may not always be comparable because of the lack of standardised definitions used by different countries in collecting primary data. For comparative purposes, they should therefore be used with caution.

Economic indicators

Tables 1–4. Selected economic indicators

Gross national income (GNI) is the market value of goods and services produced within a country

and abroad in a given period. GNI per capita is the country’s GNI divided by its population and reflects the average income of a country’s citizen in a given period. Gross domestic product (GDP) is the market value of goods and services produced within a country in a given period. GDP at market prices, on the other hand, is the sum of the gross value added of all resident producers at market prices, plus taxes less subsidies on imports. PPP stands for ‘purchasing power parity’ and is the theory that the exchange rate will adjust so as to offset differences in countries’ inflation rates, with the result that the same quantity of international traded goods can be bought at home as abroad with a given amount of the domestic currency. GNI per capita PPP is gross national income converted to international dollars using purchasing power parity rates.

The sectoral distribution of GDP gives the percentage contribution of the three main sectors of the economy, which are agriculture, industry and services, to total GDP within a country in a given period. The distribution will total less than 100 per cent if the data are incomplete. The agricultural sector covers agriculture, fishing, hunting and forestry. The industrial sector comprises mining, manufacturing, construction, electricity, gas and water. All other economic activities are classified as services. Growth of production in agriculture, industry, manufacturing and services is mainly derived from the sum of gross output minus the value of intermediate inputs used in production in each respective field. All growth rates have been computed using the least square method.

Tables 5. GDP components

Gross capital formation is measured by the total value of the gross fixed capital formation, changes in inventories and acquisitions, less disposals of valuables for a unit or sector. It consists of outlays on additions to the fixed assets of the economy, plus net changes in the level of inventories. Fixed assets include land improvements (fences, ditches, drains and so on); plant, machinery and equipment purchases; and the construction of roads, railways and the like, including schools, offices, hospitals, private residential dwellings and commercial and industrial buildings. Public consumption (or government consumption) includes all current expenditure for purchase of goods and services by all levels of government. Capital expenditure on national defence and security is regarded as consumption expenditure. Private consumption is

66

the market value of all goods and services purchased or received as income in kind by households and non-profit organisations within an economy. It includes imputed rent for owner-occupied dwellings. Gross savings are calculated as gross national income less total consumption, plus net transfers.

Table 6. Percentage change in the Consumer Prices Index

Percentage change in the Consumer Prices Index (CPI) has frequently been used to measure or indicate the rate of inflation in a specified period. Inflation as measured by the annual growth rate of the GDP implicit deflator shows the rate of price change in the economy as a whole. The GDP implicit deflator is the ratio of GDP in current local currency to GDP in constant local currency.

Tables 7–13. Trade statistics

Exports are the market value (free on board [FOB]) of movable goods, including non-monetary gold whose ownership changes from a resident to a foreigner. Imports are the market value (cost, insurance and freight [CIF]) of movable goods, including non-monetary gold whose ownership changes from a foreigner to a resident. The growth rates of merchandise exports and imports are in nominal terms. Trade balance is the difference between the monetary value of exports (FOB) and imports (CIF) of output in an economy over a specified period. A positive balance is referred to as a trade surplus, and it consists of more exports than imports; a negative balance is referred to as a trade deficit or a trade gap, and consists of more imports than exports. The destination of merchandise exports is the markets to which a country’s goods are exported, while the source of merchandise imports is the markets from which goods are imported.

With regard to the composition of merchandise exports, Food refers to the commodities in Standard International Trade Classification (SITC) sections 0 (food and live animals), 1 (beverages and tobacco), 4 (animal and vegetable oils and fats) and SITC division 22 (oil seeds, oil nuts and oil kernels). Fuels comprise SITC section 3 (mineral fuels). Manufactures refer to commodities in SITC sections 5 (chemicals), 6 (basic manufactures), 7 (machinery and transport equipment) and 8 (miscellaneous manufactured goods), excluding division 68 (non-ferrous metals). High-technology exports are products with high research and development (R&D) intensity, such as in aerospace, computers, pharmaceuticals, scientific instruments and electrical machinery. Agricultural raw materials comprise SITC section 2 (crude materials, except fuels) excluding divisions 22, 27 (crude fertilisers and minerals, excluding coal, petroleum and precious

stones) and 28 (metalliferous ores and scrap). Ores and metals comprise the commodities in SITC sections 27 (crude fertiliser, minerals), 28 (metalliferous ores, scrap) and 68 (non-ferrous metals).

Note: Merchandise export shares may not sum to 100 per cent because of unclassified trade.

Table 14. Export characteristics

The export characteristics comprise the concentration and diversification indices. The indices range between 0 and 1, with 1 representing the most extreme concentration and diversification of a country export.

Table 15. Selected indicators of openness and instability

Current account balance is the sum of net exports of goods and services, net primary income, and net secondary income. Export instability indices provide an indication of the relative magnitude of fluctuations in total merchandise export values, and have been calculated in the same way as coefficients of variation. This was done by dividing the mean of the total export in goods and services with the total export’s standard deviation. The indices range between 0 and 100, with 100 representing the most extreme volatility.

Table 16. Migration and remittances

Migrants’ remittances are current transfers by migrants who are employed or intend to remain employed for more than a year in another economy in which they are considered residents. Some developing countries classify workers’ remittances as a factor income receipt (and thus as a component of GNI). The World Bank adheres to international guidelines in defining GNI, and its classification of workers’ remittances may therefore differ from national practices. This item shows receipts by the reporting country. Data are in current US dollars.

Net migration refers to the number of immigrants minus the number of emigrants over a given period, expressed in thousands. It is calculated using the medium variant as an assumption.

Table 17. Fish catches

The data relate to nominal catches of freshwater, brackish water and marine species of fish, crustaceans, molluscs and other aquatic animals and plants killed, caught, trapped, collected, bred or cultivated for all commercial, industrial, recreational and subsistence purposes. The concept ‘nominal catches’ refers to the landings, and in most cases the quantity caught is converted to a live weight basis.

Social and Economic Data on Small States 67

Table 18. Energy production, consumption and trade

Data on energy refer to commercial forms of primary energy: coal and lignite, crude petroleum, natural gas and natural gas liquids, and hydro and nuclear electricity, converted to the coal equivalent. Botswana, Lesotho and Swaziland have no separate data, as they are incorporated in the South Africa Customs Union.

Table 19. Energy consumption and carbon emissions

Energy use per capita refers to use of primary energy before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport, divided by the population total.

Energy use intensity is the kilogram of oil equivalent of energy use per constant PPP GDP. PPP GDP is gross domestic product converted to 2005 constant international dollars using purchasing power parity rates. An international dollar has the same purchasing power over GDP as a US dollar has in the United States. For instance, the table indicates it would have cost $1,000 to purchase 86 kg of oil in Botswana in 2009, while the same amount would have purchased 120 kg of oil in Nauru.

Electric power consumption measures the production of power plants and combined heat and power plants less transmission, distribution and transformation losses and own-use by heat and power plants.

Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid and gas fuels and gas flaring.

Table 20. Tourist arrivals and earnings

Unless otherwise stated, the number of tourist arrivals refers to persons staying at least 24 hours or making at least one overnight stay in a country. Tourist earnings are defined as the receipts of a country from the payment of goods and services made by foreign tourists. International fare receipts are excluded. Total export earnings are defined as the total value of goods and services, including tourism.

Table 21. International reserves

International reserves consist of a country’s holdings of monetary gold, Special Drawing Rights (SDRs) and foreign exchange, as well as its reserve position in the

IMF. These data are also presented in terms of months of merchandise imports that the reserves could finance at current import levels.

Table 22. External debt: selected categories

External debt refers to debt owed to non-residents, repayable in convertible currency, goods or services, which have an original or extended maturity of over one year. Debt outstanding (disbursed only) is total outstanding debt drawn by the borrower at the end of the year. Short-term debt is debt with an original maturity of less than one year. Concessional debt must contain at least a 25 per cent grant element.

Table 23. Total net transfers on external debt

Net transfers on external debt are net flows minus interest payments during the year; negative transfers show net transfers made by the borrower to the creditor during the year. Data are in current US dollars. The total net transfers on external debt are aggregated as a percentage of gross domestic product.

Table 24. Principal indicators of debt

The principal indicators of debt comprise the external debt which is that part of the total debt in a country that is owed to creditors outside the country. The external debt is calculated in relation to the exports of goods and services (XGS). The XGS are the total value of all goods and services (including workers’ remittances) sold to the rest of the world.

Table 25. Composition of debt

The composition of debt comprises official and commercial debt. Official debt refers to loans from international organisations (multilateral loans), governments (bilateral loans) and their agencies, and autonomous public bodies. Commercial debt comprises loans from suppliers (manufacturers, exporters or other suppliers of goods), financial markets (private banks, other private financial institutions and publicly issued and privately placed bonds) and others (external liabilities on account of nationalised properties and unclassified debts).

Table 26. Foreign direct investment inflows

Foreign direct investment (FDI) is defined as a long-term investment by a foreign investor in an enterprise resident in an economy other than that in which the foreign direct investor is based. This table represents inward FDI flows. The foreign direct investment inflows have been aggregated as a percentage of gross domestic product.


Tables 27–28. Financial flows

Financial flow is defined as any and all of the transaction in the financial account of the balance of payments, most importantly international borrowing and lending and acquisition across borders of financial and real assets. All reported flows are in net terms. This refers to gross disbursements of grants and loans, minus repayments of previous loans. The net flows are separately reported from all sources and by major categories.

Table 29. Official development assistance commitments and disbursements

Official development assistance (ODA) is defined as those concessional flows to developing countries and multilateral institutions provided by official agencies, including state and local governments, or by their executive agencies. Use of IMF credit indicates any transactions in the IMF’s general account that take place outside its gold tranche.

Table 30. Aid dependency

Net bilateral aid flows from DAC donors are the net disbursements of official development assistance (ODA) or official aid from the members of the OECD’s Development Assistance Committee (DAC). Net official development assistance is disbursement flows (net of repayment of principal) that meet the DAC definition of ODA and are made to countries and territories on the DAC list of aid recipients. Net official development assistance per capita is calculated by dividing net ODA received by the mid-year population estimate.

The IDA Resource Allocation Index is based on the results of the World Bank’s annual Country Policy and Institutional Assessment (CPIA) exercise that covers the IDA-eligible countries. The CPIA rates countries against a set of 16 criteria grouped in four clusters: (a) economic management; (b) structural policies; (c) policies for social inclusion and equity; and (d) public sector management and institutions. The index is obtained by calculating the average score for each cluster and then by averaging those scores. For each of 16 criteria, countries are rated on a scale of 1 (low) to 6 (high).

GNI stands for gross national income and GCF stands for gross capital formation.

Table 31. Average exchange rates

Data refer to the period average of market exchange rates and official exchange rates for countries quoting rates in units of national currency per US dollar.

Table 32. Money supply and average national interest rates

Money supply equals the sum of currency outside banks and demand deposits other than those of the central government. In most cases the IMF’s category ‘money’ (or ‘quasi money’) is used, but where this is not available the definition M1 is used.

The interest rates refer to the rate at which the monetary authorities lend for the short- and medium-term financing needs of the private sector. These rates are normally differentiated according to creditworthiness of borrowers and objectives of financing.

Table 33. Ease of doing business

Ease of doing business ranks 189 economies, with first place being the best. A high ranking (a low numerical rank) means that the regulatory environment is more conducive to the starting and operation of a local firm. The index averages the country’s centile rankings on 10 topics covered in the World Bank’s Doing Business report. The ranking on each topic is the simple average of the centile rankings on its component indicators.

The data on Table 33 show the overall ranking and then the distance of each economy to the ‘frontier’ for each category of the index. The frontier represents the highest performance observed for each of the indicators across all economies measured in Doing Business (DB) since the inclusion of the indicator. An economy’s distance to frontier is reflected on a scale from 0 to 100, where 0 represents the lowest performance and 100 represents the frontier. For example, a score of 75 in DB 2013 means an economy was 25 percentage points away from the frontier constructed from the best performances across all economies and across time. A score of 80 in DB 2014 would then indicate the economy is improving. In this way the distance to frontier measure complements the annual ‘ease of doing business’ ranking, which compares economies with one another at a point in time. The table illustrates the performance scale on each topic that determines how easy it is to do business in any economy. A list of the subject topics is as follows:

• Starting a business: Measures the number of procedures, time and cost for a small or medium-sized limited liability company to start up and formally operate.

• Dealing with construction permits: This topic tracks the procedures, time and costs to build a warehouse – including obtaining necessary licences and permits, completing required notifications and inspections, and obtaining utility connections.


• Getting electricity: This topic tracks the procedures, time and cost required for a business to obtain a permanent electricity connection for a newly constructed warehouse.

• Registering property: This topic examines the steps, time and cost involved in registering property, assuming a standardised case of an entrepreneur who wants to purchase land and a building that is already registered and free of title dispute.

• Getting credit: This topic explores two sets of issues – the strength of credit reporting systems and the effectiveness of collateral and bankruptcy laws in facilitating lending.

• Protecting investors: This topic measures the strength of minority shareholder protections against misuse of corporate assets by directors for their personal gain.

• Paying taxes: This topic addresses the taxes and mandatory contributions that a medium-sized company must pay or withhold in a given year, and measures the administrative burden in paying taxes.

• Trading across borders: Measures the time and cost (excluding tariffs) associated with exporting and importing a standardised cargo of goods by sea transport. The time and cost necessary to complete every official procedure for exporting and importing the goods are recorded; however, the time and cost for sea transport are not included. All documents needed by the trader to export or import the goods across the border are also recorded.

• Enforcing contracts: The topic assesses the efficiency of the judicial system by following the evolution of a commercial sale dispute over the quality of goods and tracking the time, cost and number of procedures involved from the moment the plaintiff files the lawsuit until payment is received.

• Resolving insolvency: This topic identifies weaknesses in existing bankruptcy law and the main procedural and administrative bottlenecks in the bankruptcy process

Table 34. Selected private sector indicators

Domestic credit to private sector refers to financial resources provided to the private sector, such as through loans, purchases of non-equity securities and trade credits, and other accounts receivable that establish a claim for repayment. For some countries, these claims include credit to public enterprises.

Interest rate spread is the interest rate charged by banks on loans to prime customers, minus the interest rate paid by commercial or similar banks for demand, time or savings deposits.

Total tax rate is the total amount of taxes payable by businesses (except for labour taxes) after accounting for deductions and exemptions as a percentage of profit. Tax revenue refers to compulsory transfers to the central government for public purposes. Certain compulsory transfers such as fines, penalties and most social security contributions are excluded. Refunds and corrections of erroneously collected tax revenue are treated as negative revenue.

Social and demographic indicators

Tables 35–36. Size and population data

Surface area is a country’s total area, including areas under inland bodies of water and some coastal waterways. Arable and permanently cropped land refers to the land being used by agricultural undertakings and farms, i.e. excluding state land reserves and areas belonging to non-agricultural undertakings. Population density is defined as the number of individuals per given unit of land (usually per square kilometre) and is derived by dividing the total population of a given area (usually a country) by its surface area. Population estimates are based on national population censuses, which vary in frequency and quality.

Table 37. Distribution of labour force

The table shows the distribution of the labour force in three different sectors, namely the agriculture, industry and service sectors. Agricultural labour force refers to those engaged in farming, forestry, hunting and fishing as a percentage of total labour force.

Table 38. Labour force participation

Total labour force comprises people aged 15 and older who meet the International Labour Organization definition of the economically active population, i.e. all people who supply labour for the production of goods and services during a specified period. Labour force participation rate is the proportion of the population aged 15 and older that is economically active. Female labour force as a percentage of the total shows the extent to which women are active in the labour force.

Employment to population ratio is the proportion of a country’s population that is employed. People aged 15–24 are generally considered the youth population. People aged 15 and older are generally considered the


working-age population. Vulnerable employment is unpaid family workers and own-account workers as a percentage of total employment. GDP per person employed is gross domestic product (GDP) divided by total employment in the economy. Purchasing power parity (PPP) GDP is GDP converted to 1990 constant. Unemployment refers to the share of the labour force that is without work but available for and seeking employment.

Table 39. Urban and rural populations

Urban population refers to the percentage of mid-year population classified as living in urban areas. These are usually defined as populated centres with certain characteristics, such as certain public and municipal services. They typically refer to centres of 2,000 people or more. Rural population is calculated as the difference between the total population and the urban population.

Table 40. Land use

Food production index shows the relative level of the aggregate volume of agricultural production for each year in comparison with the base period 2004–2006. It covers food crops that are considered edible and that contain nutrients. Coffee and tea are excluded because, although edible, they have no nutritive value.

Agriculture value added per worker is a measure of agricultural productivity. Value added in agriculture measures the output of the agricultural sector (ISIC divisions 1–5) less the value of intermediate inputs. Agriculture comprises value added from forestry, hunting and fishing, as well as cultivation of crops and livestock production. Data are in constant 2005 US dollars.

Cereal yield, measured as kilograms per hectare of harvested land, includes wheat, rice, maize, barley, oats, rye, millet, sorghum, buckwheat and mixed grains. Production data on cereals relate to crops harvested for dry grain only. Cereal crops harvested for hay or harvested green for food, feed or silage and those used for grazing are excluded. The indicator is as percentage of the country land area.

Forest area is land under natural or planted stands of trees of at least 5 metres in situ, whether productive or not, and excludes tree stands in agricultural production systems (for example, in fruit plantations and agroforestry systems) and trees in urban parks and gardens. The indicator is as percentage of the country land area.

Arable land includes land defined by the Food and Agriculture Organization (FAO) as land under

temporary crops (double-cropped areas are counted once), temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow. Land abandoned as a result of shifting cultivation is excluded.

Table 41. Population distribution by age

Population data have been classified into four age groups: 0–4; 5–14; 15–64; and 65+. The projections are based on assumptions of the medium variant.

Table 42. Selected demographic indicators

Crude birth rate refers to annual live births per thousand of mid-year population. Crude death rate refers to annual deaths per thousand of mid-year population. Infant mortality rate gives annual deaths of infants under one year of age per thousand live births.

Table 43. Life expectancy at birth

Life expectancy refers to average number of years of life remaining at birth.

Table 44. Adult literacy rate

Adult literacy rate refers to literate adults, able to read and write, as a percentage of total adult population aged 15 years and over. People aged 15–24 are generally considered the youth population.

Tables 45–47. Education statistics

Primary school enrolment refers to enrolment of all ages at the primary level as percentages of respective primary school age population. It includes children aged 6–11 years. For countries with universal education, enrolment may exceed 100 per cent, since some pupils are outside the age limits. Secondary education requires at least four years of approved education and provides general vocational or teacher training instructions for pupils usually of 12 to 17 years of age. For the tertiary level, the population used is the five-year age group following on from the secondary school leaving age.

Table 48. Primary education gender ratio

Ratio of girls to boys in primary and secondary education is the percentage of girls to boys enrolled at primary and secondary levels in public and private schools. Primary completion rate is the percentage of students completing the last year of primary school. It is calculated by taking the total number of students in the last grade of primary school, minus the number of


repeaters in that grade, divided by the total number of children of official graduation age. The rate of primary completion could exceed 100, because it also includes pupils who completed primary school below the official graduation age.

Table 49. Selected characteristics of female population

The female population is expressed as a percentage share of the total population. Women in total labour force is expressed as the number of females in the labour force as a percentage share of the total labour force. The total fertility rate represents the number of children who would be born per woman, if the woman were to live to the end of her child-bearing years and bear children at each age in accordance with the prevailing age-specific fertility rates.

Table 50. Percentage of total government expenditure by main components

Total government expenditure covers all agencies performing central government functions. It excludes government-owned and/or controlled agencies that sell goods and services to the public, as well as government financial institutions. The table indicates the percentages of central government expenditure (or budgetary expenditure) with regard to defence and the social sectors.

Other development indicators

Table 51. Access to improved water sources

Access to an improved water source refers to the percentage of the population with reasonable access to an adequate amount of water from an improved source, such as a household connection, public standpipe, borehole, protected well or spring, or rainwater collection. Reasonable access is defined as the availability of at least 20 litres per person per day from a source within 1 kilometre of the dwelling.

Table 52. Access to improved sanitation

Access to improved sanitation facilities refers to the percentage of the population with at least adequate access to excreta disposal facilities that can effectively prevent human, animal and insect contact with excreta. Improved facilities range from simple but protected pit latrines to flush toilets with a sewerage connection.

Table 53. Human Development Index

The Human Development Index (HDI) is a composite index that measures the average achievement in a country in three basic dimensions of human

development: a long and healthy life measured by life expectancy at birth; knowledge, measured by adult literacy rates and enrolment in primary, secondary and tertiary levels; and standard of living measured by GDP per capita in PPP. A score of 1–0.8 represents ‘very high’ human development, 0.79–0.71 is ‘high’, 0.70–0.536 is ‘medium’ and scores below 0.536 represent ‘low’ human development.

Table 54. Selected characteristics of gender equality

The Gender Inequality Index (GII) reflects women’s disadvantage in three dimensions – reproductive health, empowerment and the labour market. The index shows the loss in human development due to inequality between female and male achievements in these dimensions. It ranges from 0, which indicates that women and men fare equally, to 1, which indicates that women fare as poorly as possible in all measured dimensions.

The share of women in wage employment in the non-agricultural sector is the share of women workers in wage employment in the non-agricultural sector expressed as a percentage of total wage employment in that same sector. Women in parliament is the percentage of parliamentary seats in a single or lower chamber held by women.

Table 55. Selected poverty index

The Inequality-adjusted Income Index is the Human Development Index adjusted for inequality in income distribution based on data from household surveys.

The Multi-dimensional Poverty Index (MPI) uses 10 indicators to measure three critical dimensions of poverty at the household level: education, health and living standards in 104 developing countries. These directly measure deprivations in health and educational outcomes, as well as key services such as water, sanitation and electricity. The measure reveals not only how many people are poor, but also the composition of their poverty. The MPI also reflects the intensity of poverty – the sum of weighted deprivations that each household faces at the same time. A person who is deprived in 70 per cent of the indicators is clearly worse off than someone who is deprived in 40 per cent of the indicators. Zero per cent indicates no deprivation in that indicator, while 100 per cent indicates deprivation in that indicator.

The poorest quintile’s percentage share of national income or consumption is the share that accrues to the bottom fifth (quintile) of the population. Inequality in the distribution of income is reflected in the percentage


shares of income or consumption accruing to portions of the population ranked by income or consumption levels.

The population living on less than US$1.25 a day at 2005 international prices shows the percentage of the population living below the poverty line. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.

The poverty gap is the mean shortfall from the poverty line (counting the non-poor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence. Note: Data showing as 0.5 signifies a poverty gap of less than 0.5 per cent. A theoretical value of zero implies that all the extremely poor people are exactly at the poverty line. A theoretical value of 100 per cent implies all the extremely poor people have zero income.

The proportion of the population below the minimum level of dietary energy consumption, referred to as the ‘prevalence of undernourishment’, is the percentage of the population that is undernourished or food deprived. The undernourished or food deprived are those individuals whose food intake falls below the minimum level of dietary energy requirements.

Table 56. Births delivered by skilled health personnel and maternal mortality

Percentage of births attended by skilled health personnel includes births attended by appropriately trained doctors, nurses, midwives or auxiliary nurses. Complications during pregnancy and childbirth are a leading cause of death and disability among women of reproductive age in developing countries. The maternal mortality ratio represents the risk associated with each pregnancy, i.e. the obstetric risk. This is also an MDG indicator.

Table 57. Universal access to reproductive health

Contraceptive prevalence rate is the percentage of women who are practising, or whose sexual partners are practising, any form of contraception. It is usually measured for married women aged 15–49 only. The adolescent birth rate measures the annual number of births to women 15 to 19 years of age per 1,000 women in that age group. It represents the risk of childbearing among adolescent women 15 to 19 years of age. It is also referred to as the ‘age-specific fertility rate’ for women aged 15–19.

Antenatal care coverage (at least one visit) is the percentage of women aged 15–49 with a live birth in a given time period that received antenatal care

provided by skilled health personnel (doctors, nurses or midwives) at least once during pregnancy, as a percentage of women age 15–49 years with a live birth in a given time period.

Women with unmet need for family planning are those who are fecund and sexually active but are not using any method of contraception, and report not wanting any more children or wanting to delay the next child. The concept of ‘unmet need’ points to the gap between women’s reproductive intentions and their contraceptive behaviour.

Table 58. Prevalence of underweight children

Prevalence of underweight children under 5 years is the proportion of those falling below −2 standard deviations (moderate underweight) and −3 deviations (severe underweight) from the median weight for age of the reference population.

Child immunisation measures the percentage of children aged 12–23 months who received vaccinations before 12 months or at any time before the survey. A child is considered adequately immunised against diphtheria, pertussis (or whooping cough) and tetanus (DPT) after receiving three doses of vaccine.

Table 59. Under-five mortality rate

Under-five mortality rate is the probability (expressed as a rate per 1,000 live births) of a child born in a specified year dying before reaching the age of five, if subject to current age-specific mortality rates.

Table 60. Summary statistics on HIV/AIDS

These estimates include all people with HIV infection, whether or not they have developed symptoms of AIDS. Estimated prevalence refers to the percentage of the adult population carrying the virus. Death rates refers to the estimate of the total number of people who have died from AIDS or related illness during the last year.

Condom use is the percentage of young men and women aged 15–24 reporting the use of a condom during sexual intercourse with a non-cohabiting, non-marital sexual partner in the last 12 months. The indicator is calculated by dividing the number of respondents ages 15–24 reporting use of a condom during sexual intercourse with a non-marital, non-cohabiting sexual partner in the last 12 months by the number of respondents ages 15–24 reporting sexual relations with a non-cohabitating, non-marital sexual partner in the last 12 months.

Percentage of population aged 15–24 years with comprehensive, correct knowledge of HIV/AIDS


(UNICEF-WHO) is the percentage of population aged 15–24 who correctly identify the two major ways of preventing the sexual transmission of HIV (using condoms and limiting sex to one faithful, uninfected partner), who reject the two most common local misconceptions about HIV transmission, and who know that a healthy-looking person can transmit HIV. The UNICEF, Joint UN Programme on HIV/AIDS (UNAIDS) and WHO proxy for this indicator is: (a) percentage of women and men 15–24 who know that a person can protect herself from HIV infection by ‘consistent use of condom’; (b) percentage of women and men 15–24 who know a healthy-looking person can transmit HIV.

The ratio of school attendance of orphans to school attendance of non-orphans aged 10–14 measures the impact of the AIDS epidemic on orphans by looking at the ratio of the current school attendance rate of children aged 10–14 both of whose biological parents have died, to the current school attendance rate of children aged 10–14 both of whose parents are still alive and who currently live with at least one biological parent.

Proportion of population with advanced HIV infection with access to retroviral drugs is the percentage of adults and children with advanced HIV infection currently receiving antiretroviral therapy according to nationally approved treatment protocols (or WHO/Joint UN Programme on HIV/AIDS standards) among the estimated number of people with advanced HIV infection.

Table 61. Summary statistics on tuberculosis

Estimated TB deaths is an estimate of the total number of deaths related to TB within a given country. Incidence of tuberculosis refers to the estimated number of TB cases arising in a given time period (expressed here as rate per 100,000 population/year). All forms of TB are included, including cases in people with HIV.

Tuberculosis treatment success rate under directly observed treatment short course (DOTS) is the proportion of new smear-positive TB cases registered under DOTS in a given year that successfully completed treatment, whether with bacteriologic evidence of success (‘cured’) or without (‘treatment completed’). At the end of treatment, each patient is assigned one of the following six mutually exclusive treatment outcomes: cured; completed; died; failed; defaulted; and transferred out with outcome unknown. The proportions of cases assigned to these outcomes, plus any additional cases registered for treatment but not assigned to an outcome, add up to 100 per cent of cases registered.

Table 62. Environment

Carbon dioxide emissions are those stemming from the burning of fossil fuels and the manufacture of cement. They account for the largest share of greenhouse gases, which are associated with global warming. Forest change rate relates to the number of hectares of forest lost or gained and the percentage change. Threatened species are the number of species classified by the International Union for Conservation of Nature (IUCN) as endangered, vulnerable, rare, indeterminate, out of danger or insufficiently known.

Consumption of ozone depleting substances is used to monitor the reduction in the usage of ozone depleting substances (ODSs) as a result of the Montreal Protocol. Therefore the indicator covers only ODSs controlled under the Montreal Protocol. Reducing consumption ultimately leads to reductions in emissions, since most uses of ODSs finally lead to the substances being emitted into the atmosphere. The units of measurement are metric tonnes of ODSs weighted by their ozone depletion potential (ODP), otherwise referred to as ‘ODP tonnes’.

Proportion of total renewable water resources used is the total volume of groundwater and surface water withdrawn from their sources for human use (in the agricultural, domestic and industrial sectors), expressed as a percentage of the total volume of water available annually through the hydrological cycle (total actual renewable water resources). The terms ‘water resources’ and ‘water withdrawal’ are understood to be freshwater resources and freshwater withdrawal.

Proportion of terrestrial and marine areas protected to territorial area. The units of measure in this indicator are terrestrial protected areas, as well as marine protected areas in territorial waters (up to 12 nautical miles from the coast). The International Union for Conservation of Nature (IUCN) defines a protected area as ‘a clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values’29.

Table 63. Main indicators of internet communications

Internet users is based on nationally reported data. In some cases, surveys have been carried out that give a more precise figure for the number of internet users. The reported figure for internet users – which may refer only to users above a certain age – is divided by the total population to obtain users per 1,000 inhabitants. Secure servers are servers using encryption technology


in internet transactions. Fixed broadband internet subscribers are the number of broadband subscribers with a digital subscriber line, cable modem or other high-speed technology.

Table 64. Main indicators of telephone communications

Mobile cellular subscriptions refers to users of portable telephones subscribing to an automatic public mobile telephone service using cellular technology that provides access to the public switched telephone network (PSTN). Per 100 inhabitants is obtained by dividing the number of cellular subscribers by the population and multiplying by 100.

Population covered by mobile cellular network measures the percentage of inhabitants that are within range of a mobile cellular signal, irrespective of whether or not they are subscribers. This is calculated by dividing the number of inhabitants within range of a mobile cellular signal by the total population. Note that this is not the same as the mobile subscription density or penetration.

Mobile cellular tariff is based on the Organisation for Economic Co-operation and Development’s low-user definition, which includes the cost of monthly mobile use for 25 outgoing calls per month, spread over the same mobile network, other mobile networks and mobile to fixed-line calls and during peak, off-peak and weekend times, as well as 30 text messages per month.

Telephone lines per 100 population refers to a fixed telephone line which connects the subscriber’s terminal equipment to the public switched network and has a dedicated port in the telephone exchange equipment.

Table 65. Transport

Total road network includes motorways, highways, and main or national roads, secondary or regional roads, and all other roads in a country. A motorway is a road designed and built for motor traffic that separates the traffic flowing in opposite directions. Registered carrier departures worldwide are domestic take-offs and take-offs abroad for air carriers registered in the country.

The Liner Shipping Connectivity Index captures how well countries are connected to global shipping networks. It is computed by the United Nations Conference on Trade and Development (UNCTAD) based on five components of the maritime transport sector: number of ships; their container-carrying capacity; maximum vessel size; number of services; and number of companies that deploy container ships in a country’s ports. For each component, a country’s value is divided by the maximum value of each component in 2004, the five components are averaged for each country, and the average is divided by the maximum average for 2004 and multiplied by 100. The index generates a value of 100 for the country with the highest average index in 2004. The underlying data come from Containerisation International Online.


Tabl

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2011

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2011

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2012

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1,24

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6,70

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2,87

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3,90

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3,72

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017

1,15

41,

106

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7.6

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ldo

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269

3,57

74,

244

4,51

41,

632

1,97

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ngo

lia3,

247

3,45

44,

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4,55

72,

286

3,18

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673

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2,73

52,

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6,63

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253

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a21

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23,1

5625

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28,3

677,

355

8,37

39,

534

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São

To

mé

and

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cipe

139

145

152

158

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355

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54.

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rinam

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2,18

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289

2,39

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319

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569

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398

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as, T

he7,

501

7,57

57,

701

7,84

221

,881

21,4

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1.0

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1.9

Bar

bado

s4,

023

4,03

34,

064

4,06

414

,656

13,0

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0.3

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8–

Bru

nei D

arus

sala

m9,

600

9,85

010

,067

10,2

8430

,880

40,2

4441

,127

2.6

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Cyp

rus

18,9

6119

,207

19,3

0318

,840

27,8

8929

,372

26,3

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Mal

ta6,

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6,65

36,

773

6,84

019

,625

21,9

6420

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2.7

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St K

itts

and

Nev

is57

457

558

557

913

,667

14,1

2213

,969

0.2

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Trin

idad

and

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bago

18,9

4918

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18,4

9918

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15,5

7317

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17,9

340.

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27.

013

.21.

7

80

Tabl

e 2.

Gro

ss d

omes

tic

prod

uct

(GD

P) (c

onti

nued

)

Gro

up/c

ount

ryG

DP

(con

stan

t U

S$

mill

ion)

GD

P pe

r cap

ita

(cur

rent

US

$)G

DP

grow

th

(ann

ual %

)G

DP

per c

apit

a gr

owth

(a

nnua

l %)

2009

2010

2011

2012

2010

2011

2012

2010

2011

2012

2010

2011

2012

Oth

er c

ount

ries

Bah

rain

17,0

5317

,792

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6618

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32.

13.

413

.1–

–C

roat

ia46

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46,2

7946

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45,3

4813

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14,4

3513

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Equa

toria

l Gui

nea

9,88

79,

719

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92.

515

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toni

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14,0

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5414

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16,5

3416

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Icel

and

17,0

8816

,388

16,8

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91.

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911

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land

208,

558

206,

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209,

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17,0

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Lith

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a26

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27,3

9029

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30,0

6211

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14,1

4814

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1.5

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Luxe

mbo

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40,0

9041

,259

41,9

4242

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103,

574

114,

211

107,

476

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No

rway

314,

294

315,

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319,

643

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veni

a38

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38,9

8839

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38,2

6522

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24,4

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81

Table 3. Sectoral distribution of gross domestic product (% total of GDP)

Group/country Agriculture Industry Services

2008 2009 2010 2011 2008 2009 2010 2011 2008 2009 2010 2011

Middle-incomeCommonwealth countriesBelize 12 – – – 23 – – – 65 – – –Botswana 2 3 2 3 53 40 45 46 45 57 52 52Dominica 14 14 13 14 16 14 15 15 69 72 72 71Fiji 14 13 12 13 18 18 19 19 68 69 69 68Grenada 4 5 5 5 20 17 17 17 75 78 78 78Guyana 26 24 21 21 31 31 33 34 43 45 46 45Jamaica 6 6 6 7 23 21 21 22 72 73 73 72Kiribati 26 26 25 – 9 8 8 – 66 66 67 –Lesotho 8 8 9 8 37 33 32 34 55 59 60 59Maldives 6 6 6 6 17 12 12 13 77 82 82 82Mauritius 4 4 4 4 28 28 27 26 68 68 69 70Namibia 8 8 8 8 38 33 30 31 54 59 62 61Nauru – – – – – – – – – – – –Papua New Guinea 34 36 36 36 48 45 45 45 18 20 19 20St Lucia 5 4 3 3 18 18 17 16 77 78 80 81St Vincent and the

Grenadines7 7 7 6 20 20 19 20 74 73 73 74

Samoa 12 12 10 10 29 26 28 27 60 62 62 63Seychelles 3 2 2 2 16 14 14 14 82 84 84 84Solomon Islands 41 39 – – 6 6 – – 53 55 – –Swaziland 8 8 7 7 46 46 47 46 46 46 46 47Tonga 18 18 19 20 19 19 20 22 63 63 61 58Tuvalu 21 22 22 23 13 11 11 12 66 66 67 65Vanuatu 20 20 – – 9 10 – – 71 70 – –Other countriesAlbania 20 19 19 19 19 18 16 16 61 63 65 66Armenia 18 19 20 21 44 36 36 37 38 45 44 42Bhutan 19 19 17 16 44 43 45 44 37 38 38 40Bosnia and Herzegovina 8 8 8 9 30 28 28 26 62 64 64 65Cape Verde 6 9 10 10 18 18 18 18 75 73 72 72Congo, Republic of 4 5 4 3 77 71 75 77 19 24 21 20Costa Rica 7 7 7 6 29 27 26 26 64 65 67 68Djibouti – – – – – – – – – – – –Gabon 4 5 4 4 64 53 60 64 32 41 36 32Georgia 9 9 8 9 22 22 22 23 69 69 69 67Lao PDR 35 35 33 31 29 27 32 35 37 38 35 35Lebanon 7 6 6 6 22 23 23 21 71 70 71 74Macedonia, FYR 12 11 11 11 30 27 28 28 59 61 61 61Mauritania 19 20 17 13 41 35 44 50 41 45 39 37Moldova 11 10 14 15 14 13 13 14 75 77 72 71Mongolia 21 20 16 14 34 33 38 36 44 47 46 49Montenegro 9 10 9 9 21 20 21 19 70 70 70 71Panama 5 5 5 4 18 17 17 17 77 78 79 79São Tomé and Principe – – – – – – – – – – – –Suriname 10 11 10 10 43 38 38 38 47 52 52 52Timor-Leste – – – – – – – – – – – –High-incomeCommonwealth countriesAntigua and Barbuda 2 2 2 2 21 22 21 18 78 76 77 79Bahamas, The 2 2 2 2 15 14 15 16 83 84 83 82Barbados 3 3 – – 23 23 73 74 – –Brunei Darussalam 1 1 1 1 74 65 67 72 25 34 32 28Cyprus 2 – – – 20 – – – 78 – – –Malta 2 2 2 37 33 33 61 65 65 –

(continued)

82

Table 3. Sectoral distribution of gross domestic product (% total of GDP) (continued)


2008 2009 2010 2011 2008 2009 2010 2011 2008 2009 2010 2011

St Kitts and Nevis 1 1 2 2 27 25 24 23 71 74 74 75Trinidad and Tobago 0 1 1 1 66 57 59 60 33 43 40 40Other countriesBahrain – – – – – – – – – – – –Croatia 5 5 5 5 28 27 27 26 67 67 68 68Equatorial Guinea 2 – – – 96 – – – 2 – – –Estonia 3 3 4 – 29 27 29 – 68 71 68 –Iceland 6 7 – – 27 25 – – 66 68 – –Ireland 1 1 – – 31 32 – – 67 67 – –Kuwait – – – – – – – – – – – –Latvia 3 3 4 – 23 21 22 – 74 76 74 –Lithuania 4 3 4 – 32 27 28 – 65 70 68 –Luxembourg 0 0 0 0 15 13 13 13 85 87 87 86Norway 1 1 2 – 45 39 40 54 60 58 –Oman – – – – – – – – – – – –Qatar – – – – – – – – – – – –Slovenia 3 2 2 – 34 31 32 64 66 66 –Uruguay 11 10 9 10 26 25 26 25 63 65 64 65

Note: – = not availableSource: World Bank, World Development Indicators 2013, available at: http://databank.worldbank.org (accessed May 2013)

83

Table 4. Growth of production (annual average %)

Group/country GDP Agriculture Industry Manufacturing Services

1999–2005

2006–2012

1999–2005

2006–2012

1999–2005

2006–2012

1999–2005

2006–2012

1999–2005

2006–2012

Middle-incomeCommonwealth countriesBelize 7.0 2.4 10.6 −5.7 5.6 8.3 6.4 8.1 6.5 2.6Botswana 5.3 4.6 −2.1 −1.0 5.6 7.0 1.7 3.3 – –Dominica 0.4 3.3 −1.3 0.6 −0.6 4.5 −3.7 −4.0 1.0 4.6Fiji 2.8 0.7 1.9 −0.8 2.3 0.2 1.6 0.6 3.3 0.8Grenada 5.3 −0.6 −3.7 8.7 10.9 −9.0 1.6 −0.9 4.1 1.5Guyana 0.7 3.1 0.2 −0.5 −1.2 5.7 0.7 15.3 2.3 6.6Jamaica 1.1 −0.6 −3.1 5.0 1.0 −2.7 −1.9 −1.3 2.0 0.5Kiribati 1.9 0.4 −0.2 1.4 −1.6 −0.9 4.0 −1.0 2.6 −0.3Lesotho 2.9 4.8 −1.2 1.5 7.6 5.9 11.5 2.6 2.0 4.4Maldives 6.0 8.0 6.0 −1.2 15.6 7.0 8.0 4.1 4.3 9.1Mauritius 3.9 4.7 0.5 0.6 2.0 2.8 1.0 1.9 6.0 6.1Namibia 4.6 4.4 4.1 −1.8 4.9 3.9 6.3 5.2 4.5 5.1Nauru – – – – – – – – – –Papua New Guinea 1.1 6.7 2.7 3.2 1.6 5.0 −1.0 3.8 −1.5 5.0St Lucia 1.4 2.0 −10.5 1.0 −0.7 4.2 2.6 2.6 2.9 3.0St Vincent and the

Grenadines3.7 1.1 −0.7 −1.2 2.6 0.0 −0.3 −0.2 4.2 0.8

Samoa 4.9 0.9 −1.7 −2.4 7.3 0.1 5.9 −5.6 5.7 2.3Seychelles 0.8 4.6 2.1 −3.2 2.7 3.0 0.9 −0.4 0.2 5.6Solomon Islands −1.2 6.2 4.0 5.9 −7.0 5.5 −7.1 1.6 −1.4 8.0Swaziland 2.2 1.6 2.0 0.8 1.6 −0.6 1.4 −0.6 0.4 4.4Tonga 2.8 1.0 1.2 −1.8 2.8 4.6 1.9 −1.0 3.4 0.6Tuvalu −0.2 1.9 −0.2 1.7 4.0 15.4 4.1 12.8 −0.5 0.4Vanuatu 1.6 4.1 1.7 3.0 −0.5 9.1 −3.2 12.4 2.0 4.1Other countriesAlbania 6.3 4.2 1.6 4.2 9.8 4.2 5.8 – 8.4 –Armenia 10.0 4.8 6.6 1.8 14.8 3.7 9.2 3.3 11.1 5.4Bhutan 7.8 9.4 2.9 1.6 9.6 12.5 5.7 13.7 9.6 9.0Bosnia and Herzegovina 5.7 2.4 −1.4 1.6 2.0 3.6 2.4 2.9 11.1 2.9Cape Verde 6.6 6.2 5.9 12.5 4.7 7.3 – – 7.2 6.3Congo, Republic of 3.6 4.8 4.3 5.1 2.0 5.3 10.6 7.9 5.8 4.5Costa Rica 4.4 4.7 2.2 3.1 5.2 3.6 5.6 3.2 4.8 5.4Djibouti 2.5 4.8 2.8 4.3 4.4 3.7 1.9 2.3 2.2 –Gabon −0.3 3.5 1.4 3.7 −1.9 −0.2 2.2 1.9 1.1 6.3Georgia 5.9 5.6 2.3 −0.6 8.2 5.1 7.5 5.0 6.9 8.1Lebanon 2.8 5.3 1.8 1.3 1.1 6.5 2.6 1.0 2.6 5.9Macedonia, FYR 2.4 3.0 0.0 2.8 2.8 3.4 3.3 0.1 2.6 3.3Mauritania 4.4 5.6 2.0 5.2 3.2 7.8 0.2 7.4 7.2 4.3Moldova 4.9 3.2 2.0 0.8 3.7 −1.1 7.5 −1.2 6.6 8.3Mongolia 5.3 8.9 −1.6 4.8 6.4 5.1 6.6 8.2 7.8 11.0Montenegro 1.1 3.8 −4.5 4.2 3.2 5.6 3.2 −1.1 4.1 3.5Panama 4.1 9.1 4.9 −2.1 2.0 8.4 −1.7 2.7 4.7 17.9São Tomé and Príncipe 3.7 4.8 – – – – – – – –Suriname 4.0 4.2 2.5 5.6 8.8 2.9 11.7 2.0 1.3 2.1Timor-Leste 2.4 9.2 – – – – – – – –High-incomeCommonwealth countriesAntigua and Barbuda 3.9 0.4 3.5 2.5 6.0 1.6 2.5 −1.6 3.4 −0.4Bahamas, The 2.8 0.3 1.6 −3.2 2.5 3.7 4.8 1.3 3.2 2.5

(continued)

84

Table 4. Growth of production (annual average %) (continued)

Group/country GDP Agriculture Industry Manufacturing Services

1999–2005

2006–2012

1999–2005

2006–2012

1999–2005

2006–2012

1999–2005

2006–2012

1999–2005

2006–2012

Barbados 0.7 −0.3 0.0 – 0.8 – −4.4 – 2.6 –Brunei Darussalam 2.3 1.1 7.6 0.8 1.9 −1.4 1.8 −0.7 3.0 4.7Cyprus 3.7 1.5 0.1 −5.0 2.3 3.2 0.5 0.0 4.2 4.6Malta 2.3 2.0 0.6 −3.3 −1.2 −0.1 −1.3 −0.6 4.4 4.6St Kitts and Nevis 3.5 1.2 4.1 −5.3 3.8 −2.1 6.8 −1.5 3.5 1.5Trinidad and Tobago 7.2 2.3 −3.3 7.2 11.4 6.5 8.6 22.8 4.9 −4.3Other countriesBahrain 5.7 5.8 – – – – – – – –Croatia 3.6 0.2 0.9 0.5 4.4 −1.5 3.7 −0.8 3.4 1.0Equatorial Guinea 28.3 4.2 4.2 – 31.6 – 137.9 . 27.7 –Estonia 6.5 2.0 0.4 3.4 8.2 0.4 10.3 1.5 6.2 0.5Iceland 4.3 0.8 0.1 −0.2 4.0 0.4 1.5 5.3 4.7 1.7Ireland 5.0 0.7 – – 6.7 – – – – –Kuwait 6.4 3.5 10.9 – 1.4 – 0.2 – 8.2 –Latvia 7.5 1.5 4.2 1.9 6.5 −1.6 5.5 −0.7 8.0 1.0Lithuania 5.9 2.4 0.6 1.2 6.6 0.4 8.6 0.2 6.1 2.1Luxembourg 5.0 1.7 −3.8 −2.3 3.8 −1.4 2.8 −2.6 5.1 2.4Norway 2.3 1.2 1.7 3.0 1.0 −2.4 1.7 1.0 3.2 2.5Oman 3.3 6.0 2.0 – 1.2 – 14.0 – 4.9 –Qatar 8.5 17.0 – – – – – – – –Slovenia 4.0 1.0 0.0 −1.4 4.6 1.1 5.2 1.3 4.1 2.7Uruguay −0.3 5.6 0.9 1.6 −0.3 3.4 0.8 3.6 −0.1 6.3

Note: – = not availableSource: World Bank, World Development Indicators 2013, available at: http://databank.worldbank.org (accessed June 2013)

85

Tabl

e 5.

GD

P co

mpo

nent

s (%

of t

otal

)

Gro

up/c

ount

ryG

ross

cap

ital

fo

rmat

ion

Hou

seho

ld

cons

umpt

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Gov

ernm

ent

cons

umpt

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Exte

rnal

bal

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on

good

s an

d se

rvic

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ross

sav

ings

2002

2007

2011

2002

2007

2011

2002

2007

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2007

2011

Mid

dle-

inco

me

Com

mon

wea

lth c

ount

ries

Bel

ize

2420

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67–

1415

–−1

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–7

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–B

ots

wan

a27

3031

4134

5421

2220

1114

−530

48.9

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om

inic

a13

2022

8185

8017

1617

−10

−21

−19

–−1

.25

Fiji

2021

–67

77–

1617

–−2

−15

−11

248.

9–

Gre

nada

2735

2179

8488

1213

16−1

8−3

3−2

5–

0.8

−6G

uyan

a21

2524

6688

9126

1515

−13

––

610

.9–

Jam

aica

2727

2174

8086

1515

16−1

6−2

2−2

316

15.5

8K

iriba

ti–

––

––

––

––

––

––

––

Leso

tho

3224

3510

810

699

3736

33−7

8−6

6−6

643

28.3

15M

aldi

ves

––

––

––

––

––

11–

––

–M

aurit

ius

2227

2561

6973

1413

133

−9−1

227

22.1

14N

amib

ia19

2420

6257

6321

2125

−2−1

−826

31.8

19N

auru

––

––

––

––

––

––

––

–P

apua

New

Gui

nea

21–

1661

–73

15–

83

–3

17–

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Luc

ia22

2834

6984

7120

1316

−11

−26

−21

–−2

.312

St V

ince

nt a

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e G

rena

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s23

2725

6985

8716

1616

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8−2

9–

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––

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7−4

8−5

3–

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Solo

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––

88–

–22

––

−10

−20

−24

−3–

–Sw

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129

7075

8416

1415

−5−1

−822

18.7

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nga

2522

3898

104

8814

1817

−37

−43

−43

2217

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Tuva

lu–

––

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Van

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1932

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58–

2117

–−1

4−7

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24.5

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ther

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3025

9287

8910

98

−26

−26

−22

1519

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Arm

enia

2238

3186

7281

1010

12−1

7−2

0−2

515

31.1

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huta

n60

3759

3946

3821

1922

−20

−2–

–41

.2–

Bo

snia

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vina

1931

2110

484

8025

2222

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−37

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115

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Cap

e V

erde

3647

3779

6273

2226

21−3

6−3

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024

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22

(con

tinue

d)

86

Tabl

e 5.

GD

P co

mpo

nent

s (%

of t

otal

) (co

ntin

ued)

Gro

up/c

ount

ryG

ross

cap

ital

fo

rmat

ion

Hou

seho

ld

cons

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ent

cons

umpt

ion

Exte

rnal

bal

ance

on

good

s an

d se

rvic

esG

ross

sav

ings

2002

2007

2011

2002

2007

2011

2002

2007

2011

2002

2007

2011

2002

2007

2011

Co

ngo,

Rep

ublic

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88

Table 6. Prices (% change)

Group/country Consumer Prices Index (CPI) Inflation, GDP deflator (annual %)

2009 2010 2011 2012 2009 2010 2011 2012

Middle-incomeCommonwealth countriesBelize −1.1 5.6 −3.7 1.3 −1.1 0.7 1.5 –Botswana 8.0 6.9 8.5 7.5 3.6 20.6 7.6 −5.7Dominica 0.0 3.2 2.4 1.4 7.9 −2.7 −0.7 2.1Fiji 3.7 5.5 8.7 4.3 −0.2 8.2 10.2 −0.7Grenada −0.3 3.4 3.0 2.4 0.0 0.1 0.4 2.0Guyana 2.9 2.1 5.0 2.4 2.1 6.7 8.4 5.7Jamaica 9.6 12.6 7.5 6.9 10.9 9.7 6.3 6.7Kiribati – – – – 0.9 −1.3 2.8 2.5Lesotho 7.4 3.6 5.0 6.1 4.2 2.8 9.5 5.4Maldives 4.0 4.7 14.8 11.3 8.9 0.4 7.5 5.0Mauritius 2.5 2.9 6.5 3.9 −0.1 −1.6 3.9 3.4Namibia 8.8 4.5 5.0 6.5 4.0 1.5 6.9 10.5Nauru – – – – – – – –Papua New Guinea 6.9 6.0 8.4 2.2 −4.3 9.3 4.6 2.8St Lucia −1.7 3.3 2.8 4.2 −0.2 2.6 −0.4 1.0St Vincent and the

Grenadines0.4 1.5 3.2 2.6 −0.8 4.5 2.2 1.6

Samoa 6.3 0.8 5.2 2.0 3.0 1.4 2.2 1.7Seychelles 31.8 −2.4 2.6 7.1 26.3 −4.9 6.4 4.8Solomon Islands 7.1 1.1 7.3 2.6 −2.0 5.6 11.0 7.8Swaziland 7.4 4.5 6.1 9.4 6.0 −1.0 6.3 8.3Tonga 1.4 3.6 6.3 1.2 −2.1 4.3 5.2 1.0Tuvalu – – – – −2.1 3.3 −1.2 1.4Vanuatu 4.3 2.8 0.9 1.4 2.3 2.6 2.1 1.4Other countriesAlbania 2.3 3.6 3.5 2.0 2.4 3.5 3.0 3.0Armenia 3.4 8.2 7.7 2.6 2.6 7.9 4.2 −1.6Bhutan 4.4 7.0 8.8 10.9 4.8 6.0 8.8 1.5Bosnia and Herzegovina −0.4 2.2 3.7 2.0 0.1 2.3 2.3 1.8Cape Verde 1.0 2.1 4.5 2.5 4.2 3.3 3.9 3.5Congo, Republic of 5.3 5.0 1.3 3.9 −20.6 20.7 10.7 −1.2Costa Rica 7.8 5.7 4.9 4.5 8.4 8.0 4.1 4.0Gabon 1.9 1.5 1.3 2.7 −16.6 18.4 15.1 1.3Georgia 1.7 7.1 8.5 −0.9 −2.0 8.5 9.7 1.3Lebanon 1.2 4.0 – – 6.2 – 4.9 5.6Macedonia, FYR −0.7 1.6 3.9 3.3 0.7 2.7 3.4 –Mauritania 2.2 6.3 5.6 4.9 −10.9 21.4 14.1 −3.6Moldova −0.1 7.4 7.7 4.7 2.2 11.1 7.7 7.5Mongolia 6.3 10.1 9.5 15.0 1.8 20.0 12.1 12.0Montenegro 3.5 0.7 3.2 – 2.4 1.6 1.0 2.3Panama 2.4 3.5 5.9 5.7 1.1 4.2 4.4 4.6Suriname −0.1 6.9 17.7 5.0 6.5 8.2 12.1 6.4Timor-Leste 0.7 6.8 13.5 11.8 5.1 1.4 12.5 9.1High-incomeCommonwealth countriesAntigua and Barbuda −0.6 3.4 3.5 3.4 1.8 1.4 1.9 2.2Bahamas, The 2.1 1.3 3.2 2.0 −1.0 −0.1 −1.8 1.6Barbados 3.6 5.8 9.4 4.5 3.4 – – –Brunei Darussalam 1.0 0.4 2.0 0.5 −22.1 5.3 19.4 0.8Cyprus 0.4 2.4 3.3 2.4 −0.3 1.7 2.4 1.9Malta 2.1 1.5 2.7 2.4 2.6 2.9 4.9 2.3St Kitts and Nevis 2.0 0.5 7.1 1.4 2.5 0.6 2.8 1.1Trinidad and Tobago 7.0 10.5 5.1 9.3 −27.6 8.0 17.6 0.9

(continued)

89

Table 6. Prices (% change) (continued)

Group/country Consumer Prices Index (CPI) Inflation, GDP deflator (annual %)

2009 2010 2011 2012 2009 2010 2011 2012

Other countriesBahrain 2.8 2.0 −0.4 2.8 −14.4 13.7 – –Croatia 2.4 1.0 2.3 3.4 2.9 −0.1 2.0 2.1Equatorial Guinea 4.7 7.8 6.9 6.1 −30.0 26.3 24.4 11.1Estonia −0.1 3.0 5.0 3.9 −1.4 0.7 2.9 3.2Iceland 12.0 5.4 4.0 5.2 8.3 6.9 3.3 3.0Ireland −4.5 −0.9 2.6 1.7 −4.6 −2.2 0.2 1.9Kuwait 4.0 4.0 4.7 2.9 −18.9 13.1 26.4 –Latvia 3.5 −1.1 4.4 2.3 −1.5 −2.3 6.2 3.0Lithuania 4.4 1.3 4.1 3.1 −3.7 2.0 6.2 2.9Luxembourg 0.4 2.3 3.4 2.7 0.5 7.6 5.1 3.9Norway 2.2 2.4 1.3 0.7 −5.4 6.3 6.8 2.5Oman 3.9 3.2 4.1 2.9 −23.5 18.7 17.6 –Qatar −4.9 −2.4 1.9 1.9 −24.2 11.9 14.4 –Slovenia 0.9 1.8 1.8 2.6 3.0 −1.1 2.3 –Uruguay 7.1 6.7 8.1 8.1 4.9 4.8 8.0 8.8


90

Table 7. Exports, imports and trade balance

Group/country Exports (US$ million) Imports (US$ million) Trade balance (US$ million)

2009 2010 2011 2009 2010 2011 2009 2010 2011

Middle-incomeCommonwealth countriesBelize 213 816 949 237 810 945 −25 7 4Botswana 3,745 4,917 6,782 4,934 5,956 7,682 −1,189 −1,040 −899Dominica 157 170 166 265 262 261 −108 −93 −94Fiji 1,313 1,709 1,814 1,702 2,005 2,219 −389 −296 −404Grenada 174 168 184 361 378 392 −187 −210 −208Guyana – – – – – – – – –Jamaica 4,151 4,134 4,459 6,280 6,647 7,768 −2,129 −2,513 −3,309Kiribati – – – – – – – – –Lesotho 783 971 1,181 1,978 2,482 2,732 −1,195 −1,511 −1,551Maldives 1,712 2,007 2,351 1,482 1,693 2,265 230 315 86Mauritius 4,326 5,101 6,011 5,152 6,196 7,475 −826 −1,095 −1,463Namibia 4,191 5,255 5,636 4,970 6,067 6,557 −779 −812 −920Nauru – – – – – – – – –Papua New Guinea 4,572 5,295 6,596 4,509 5,032 6,227 64 263 369St Lucia 544 629 559 648 786 823 −104 −157 −263St Vincent and the

Grenadines192 184 186 388 389 386 −196 −206 −200

Samoa 166 199 201 291 364 373 −124 −165 −172Seychelles 392 394 483 793 985 1,050 −401 −591 −567Solomon Islands 220 211 217 308 418 415 −88 −207 −199Swaziland 1,860 2,063 2,642 2,384 2,633 2,965 −524 −570 −322Tonga 45 49 77 203 214 263 −159 −165 −186Tuvalu – – – – – – – – –Vanuatu 300 327 351 292 375 396 7 −48 −45Other countriesAlbania 3,443 3,845 4,380 6,485 6,389 7,257 −3,042 −2,544 −2,877Armenia 1,338 1,929 2,406 3,719 4,197 4,928 −2,381 −2,268 −2,522Bhutan 578 631 634 786 926 967 −208 −294 −332Bosnia and

Herzegovina5,410 5,956 7,647 9,293 9,409 11,727 −3,883 −3,453 −4,079

Cape Verde 570 640 803 1,087 1,113 1,379 −517 −473 −576Congo, Republic of 6,756 10,221 12,591 4,816 6,568 5,021 1,939 3,653 7,570Costa Rica 12,423 13,784 15,279 12,258 14,607 17,020 165 −823 −1,741Djibouti – – – – – – – – –Gabon – – – 4,228 4,783 6,103 – – –Georgia 3,202 4,068 5,237 5,269 6,141 7,919 −2,067 −2,073 −2,683Lebanon 7,146 8,232 9,520 16,760 18,641 20,220 −9,614 −10,409 −10,700Macedonia, FYR 3,649 4,349 5,694 5,685 6,095 7,735 −2,036 −1,746 −2,040Mauritania 1,362 2,067 2,764 1,389 1,927 2,493 −27 140 272Moldova 2,006 2,280 3,151 3,996 4,566 6,037 −1,990 −2,286 −2,885Mongolia 2,305 3,392 5,462 2,637 3,866 7,608 −333 −474 −2,146Montenegro 1,334 1,428 1,809 2,707 2,594 2,975 −1,374 −1,166 −1,166Panama 19,625 20,606 21,636 15,379 18,608 22,516 4,246 1,998 −880São Tomé and

Príncipe20 24 28 103 129 142 −83 −105 −114

Suriname – – – – – – – – –Timor-Leste – – – – – – – – –High-incomeCommonwealth countriesAntigua and Barbuda 562 524 537 706 680 644 −145 −156 −107Bahamas, The 3,117 3,223 3,431 3,728 3,895 4,401 −611 −671 −970Barbados 2,179 1,946 – 2,322 2,153 – −143 −208 –

(continued)

91

Table 7. Exports, imports and trade balance (continued)

Group/country Exports (US$ million) Imports (US$ million) Trade balance (US$ million)

2009 2010 2011 2009 2010 2011 2009 2010 2011

Brunei Darussalam 7,811 10,074 13,297 3,841 4,066 4,765 3,970 6,007 8,531Cyprus 9,480 9,279 – 10,726 10,770 – −1,245 −1,490 –Malta 6,380 7,199 8,707 6,399 6,920 8,269 −19 279 438St Kitts and Nevis 170 199 228 347 335 315 −178 −136 −87Trinidad and Tobago 9,986 12,113 – 7,363 6,890 – 2,623 5,223 –Other countriesBahrain 15,705 – – 11,354 – – 4,351 – –Croatia 22,781 23,399 26,111 24,969 23,709 26,170 −2,188 −310 −59Equatorial Guinea 8,531 10,136 14,129 7,418 7,696 9,382 1,112 2,440 4,746Estonia 12,437 14,969 20,274 11,338 13,701 19,400 1,099 1,268 874Iceland 6,401 7,081 8,320 5,359 5,813 7,122 1,042 1,268 1,197Ireland 203,335 207,645 231,654 167,192 168,850 183,159 36,143 38,795 48,495Kuwait 62,985 74,702 116,409 31,133 32,687 37,191 31,852 42,015 79,218Latvia 11,356 12,920 16,758 11,739 13,262 17,849 −383 −342 −1,091Lithuania 20,129 24,898 33,231 20,653 25,273 33,868 −524 −375 −637Luxembourg 81,848 90,505 104,484 65,744 73,650 86,001 16,104 16,855 18,483Norway 151,689 170,525 203,736 105,021 120,129 137,455 46,668 50,395 66,280Oman 24,671 – – 19,443 – – 5,228 – –Qatar 45,958 – – 30,692 – – 15,266 – –Slovenia 28,644 30,690 35,815 27,939 30,425 35,298 704 265 518Uruguay 8,544 10,562 12,645 8,305 10,254 12,771 239 307 −126

Note: – = not availableSource: IMF, World Economic Outlook Database, available at: www.imf.org/external/pubs/ft/weo/2013/02/weodata/index.aspx

(accessed October 2013)

92

Tabl

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Tabl

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96

Table 10. Main sources of merchandise imports (% of total imports)

Group/country High income economies Developing economies

2007 2008 2009 2010 2011 2007 2008 2009 2010 2011

Middle-incomeCommonwealth countriesBelize 55.1 57.8 57.8 56.7 59.5 44.9 42.2 42.2 43.3 40.4Botswana – – – – – – – – – –Dominica 64.4 78.2 80.5 78.5 78.0 35.0 21.3 18.9 21.0 21.3Fiji 82.1 78.2 76.0 78.0 79.4 16.2 20.2 22.1 19.9 18.8Grenada 81.8 78.0 76.1 75.4 76.4 9.2 12.9 12.4 14.1 13.5Guyana 72.4 73.9 73.7 70.9 64.1 27.6 26.1 26.3 29.1 35.8Jamaica 72.3 71.5 66.2 65.3 60.4 26.3 27.7 31.8 32.2 37.3Kiribati 45.6 47.7 49.8 53.8 62.3 – – – – –Lesotho – – – – – – – – – –Maldives 63.0 66.2 61.2 62.4 62.9 36.8 33.6 38.6 37.4 37.0Mauritius 45.4 39.3 42.5 39.8 38.6 54.5 60.7 57.4 60.2 61.4Namibia – – – – – – – – – –Nauru – – – – – – – – – –Papua New Guinea 79.5 75.1 73.2 77.9 73.3 18.8 23.2 25.5 20.8 25.7St Lucia 30.4 12.7 13.4 19.6 16.6 69.6 87.3 86.6 80.4 83.3St Vincent and the

Grenadines82.7 82.4 71.6 80.9 79.0 17.3 17.5 28.3 19.1 21.0

Samoa 66.0 61.3 61.7 58.7 66.1 31.4 34.5 35.7 39.0 30.9Seychelles 68.7 69.2 61.1 63.1 63.8 21.2 22.0 27.5 23.3 22.9Solomon Islands 65.2 58.4 62.9 62.8 65.2 17.1 20.4 22.9 25.3 24.3Swaziland – – – – – – – – – –Tonga 51.0 48.5 48.5 53.7 47.5 44.0 47.6 47.0 42.4 51.1Tuvalu 39.3 33.7 71.7 53.7 41.8 0.0 0.0 0.0 0.0 0.0Vanuatu 73.7 75.5 74.9 89.8 57.7 22.1 20.8 21.6 8.5 38.5Other countriesAlbania 66.8 66.0 69.6 69.7 71.4 31.0 33.4 29.6 29.4 27.7Armenia 42.9 39.7 37.5 34.3 35.4 57.0 60.2 62.4 65.6 64.5Bhutan – – – – – – – – – –Bosnia and

Herzegovina87.1 86.1 89.0 86.4 83.6 11.8 12.4 9.8 12.6 15.3

Cape Verde 85.4 84.2 83.8 83.7 85.5 13.7 14.9 15.1 15.3 13.7Congo, Republic of 65.8 56.6 62.6 59.7 51.1 32.9 41.9 35.7 38.4 47.3Costa Rica 65.5 65.7 63.4 65.5 65.1 34.2 33.9 32.1 32.9 33.2Djibouti 45.9 44.5 44.1 37.8 36.3 1.3 2.9 2.2 2.3 2.6Gabon 77.1 73.5 71.2 68.3 70.4 21.3 24.8 26.8 29.7 28.0Georgia 36.3 39.8 36.7 35.3 34.4 63.7 60.1 63.3 64.7 65.5Lebanon 63.2 63.7 63.9 62.1 59.7 35.3 35.3 35.2 37.3 39.7Macedonia, FYR 51.8 52.6 55.4 54.0 64.9 48.2 47.4 44.4 45.6 34.6Mauritania 54.1 55.0 49.3 52.2 52.0 34.8 35.1 41.2 37.9 39.8Moldova 37.0 36.4 37.2 37.5 38.2 62.9 63.4 62.6 62.4 61.7Mongolia 29.0 27.7 31.9 27.0 28.4 71.0 72.3 68.1 73.0 71.6Montenegro – – – – – – – – – –Panama 56.7 46.6 44.1 42.9 40.0 26.0 27.8 29.0 28.7 29.0São Tomé and

Príncipe85.5 86.2 77.0 71.7 84.2 – – – – –

Suriname 79.7 78.7 80.0 60.1 72.9 20.3 21.1 19.8 39.9 27.1Timor-Leste – – – – – – – – – –High-incomeCommonwealth countriesAntigua and Barbuda 66.7 64.8 63.0 50.0 55.5 4.2 4.6 4.5 4.3 5.0Bahamas, The 74.3 72.7 66.6 63.2 65.5 23.8 25.5 31.5 34.7 32.2Barbados 87.4 74.8 77.4 64.0 58.3 12.6 25.2 22.6 35.9 41.7Brunei Darussalam 65.9 66.1 64.8 62.7 57.9 33.9 33.7 35.0 37.2 42.0Cyprus 83.7 83.7 85.8 83.3 85.6 16.1 16.0 14.0 14.9 13.5

(continued)

97

Table 10. Main sources of merchandise imports (% of total imports) (continued)

Group/country High income economies Developing economies

2007 2008 2009 2010 2011 2007 2008 2009 2010 2011

Malta 90.9 90.4 90.1 88.8 85.1 9.1 9.0 9.5 9.9 10.7St Kitts and Nevis 86.4 84.4 86.9 85.1 32.0 13.0 14.9 12.2 14.1 67.6Trinidad and Tobago 55.0 53.3 54.5 59.6 48.5 44.9 46.6 45.3 40.2 51.4Other countriesBahrain 82.6 78.5 80.4 73.0 73.4 16.7 21.0 19.0 26.4 26.0Croatia 70.9 70.6 70.3 67.0 67.8 29.0 29.4 29.7 32.9 32.1Equatorial Guinea 70.4 60.6 61.8 57.6 68.4 – – – – –Estonia 68.4 65.7 63.1 68.4 68.0 31.6 34.3 36.9 31.6 32.0Iceland 87.5 85.3 82.7 76.0 78.9 12.5 14.6 17.3 23.2 20.4Ireland 90.3 90.5 89.0 88.5 88.6 7.7 7.5 9.0 9.5 9.5Kuwait 76.6 70.8 70.5 66.7 66.5 23.4 29.1 29.5 33.3 33.4Latvia 68.5 63.7 63.9 64.2 64.0 31.5 36.3 36.1 35.8 36.0Lithuania 67.3 55.9 55.3 52.4 52.6 32.7 44.0 44.7 47.6 47.4Luxembourg 80.6 80.2 79.5 89.0 90.3 18.9 19.5 19.0 10.6 9.7Norway 83.3 82.2 80.9 79.2 79.2 16.7 17.8 19.1 20.8 20.8Oman 80.0 78.7 77.6 80.6 77.4 20.0 21.3 22.3 19.3 22.6Qatar 81.1 76.7 76.4 83.0 80.2 18.9 23.3 22.7 15.8 18.6Slovenia 83.9 82.1 81.1 78.4 78.9 15.8 16.7 17.9 20.1 20.2Uruguay 29.5 27.7 26.5 28.0 29.3 70.2 72.1 73.3 71.8 70.5


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15.2

12.7

0.3

0.6

0.4

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Trin

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and

To

bago

11.3

10.3

7.7

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38.1

40.5

34.3

39.1

4.0

5.2

3.7

4.2

Oth

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Bah

rain

28.3

17.6

17.3

17.2

8.4

12.5

11.1

10.1

5.7

7.6

7.6

9.5

Cro

atia

62.8

60.2

61.8

62.4

3.2

2.8

2.6

2.7

6.8

7.2

7.1

7.1

Equi

toria

l Gui

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––

––

––

––

––

––

Esto

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80.5

84.2

84.1

83.9

1.5

1.1

1.5

0.8

2.5

3.8

4.6

4.4

Icel

and

51.8

0.2

0.1

0.1

8.9

10.0

12.1

11.4

5.0

6.0

6.3

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Irela

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.865

.968

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.014

.714

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.13.

84.

13.

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8K

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.921

.111

.814

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.112

.59.

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––

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––

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Lith

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.157

.157

.357

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51.

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.363

.362

.864

.38.

48.

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68.

67.

88.

59.

19.

3O

man

17.1

17.8

15.6

14.5

6.9

6.2

6.6

6.7

4.8

4.8

4.3

6.3

Qat

ar32

.331

.427

.026

.513

.215

.913

.414

.97.

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25.

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.712

.714

.010

.711

.812

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.49.

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102

Table 13. Merchandise trade with Commonwealth countries

Group/country Total exports (% of world) Total imports (% of world)

2008 2009 2010 2011 2012 2008 2009 2010 2011 2012

Middle-incomeCommonwealth countriesBelize 34.6 39.3 36.2 32.4 35.3 8.9 7.7 9.9 12.0 13.7Botswana – – – – – – – – – –Dominica 37.0 50.8 45.3 39.5 38.5 17.5 27.2 22.0 32.4 29.8Fiji 46.8 46.9 42.9 45.6 41.5 74.3 69.9 72.6 70.6 69.3Grenada 78.5 74.1 81.5 80.4 74.7 54.0 54.9 58.7 59.2 59.5Guyana 52.2 55.5 48.0 49.6 49.5 33.4 34.8 34.7 38.0 33.4Jamaica 17.3 26.8 29.4 30.8 19.8 0.1 0.0 24.3 21.3 19.8Kiribati – – – – – – – – – –Maldives 29.1 36.9 49.6 34.0 29.1 54.5 54.5 51.6 48.6 47.7Mauritius 42.8 36.8 33.3 33.6 35.1 44.9 40.9 43.9 43.9 42.5Namibia – – – – – – – – – –Nauru – – – – – – – – – –Papua New Guinea 34.1 34.2 32.3 36.9 34.2 67.2 60.6 64.5 64.9 63.2St Lucia – – – – – – – – – –St Vincent and the

Grenadines31.3 30.6 42.0 55.9 71.7 48.6 38.7 51.6 55.1 63.2

Samoa 43.6 30.4 36.3 29.8 30.3 65.2 66.8 60.6 66.3 64.4Seychelles 32.5 33.7 30.6 28.4 25.4 31.9 24.7 26.7 22.5 20.0Solomon Islands 7.3 7.2 6.4 18.7 26.9 72.0 67.8 70.1 71.9 69.8Tonga 31.1 26.8 30.8 29.2 41.4 269.7 154.4 68.4 72.9 71.9Tuvalu – – – – – – – – – –Vanuatu 4.3 6.1 6.5 7.8 7.5 47.0 44.4 23.8 52.9 42.4Other countriesAlbania 0.2 0.2 1.0 2.8 2.9 2.1 2.3 3.0 3.2 2.4Armenia 6.1 5.7 4.0 6.5 8.7 4.3 4.1 4.5 5.1 4.5Bosnia and Herzegovina 1.5 1.0 0.9 1.7 1.4 0.7 0.7 0.8 0.7 0.6Cape Verde 11.9 0.6 0.7 5.4 4.8 2.6 2.2 3.6 1.4 1.6Congo, Republic of 5.7 9.3 12.0 12.1 13.4 15.7 15.8 17.2 18.7 14.8Costa Rica 6.0 6.7 7.5 7.8 17.0 3.8 3.9 4.7 4.1 3.7Djibouti 2.1 1.6 1.2 1.3 1.9 26.4 25.8 27.9 26.9 19.5Gabon 9.9 17.2 25.5 23.3 25.6 13.9 13.9 13.9 12.8 12.8Georgia 12.7 11.8 9.5 7.6 10.2 3.4 4.2 3.7 3.2 4.0Lao PDR 4.1 4.7 4.8 5.6 8.6 3.3 3.0 2.4 2.6 2.2Lebanon 7.7 7.9 15.7 25.0 23.3 8.2 7.7 6.9 6.6 7.2Macedonia, FYR 2.0 3.1 2.1 1.7 2.2 2.8 3.1 7.1 8.7 10.0Mauritania 4.3 5.0 4.8 3.9 4.7 10.9 9.0 11.2 10.5 7.5Moldova 3.6 5.0 5.6 2.8 3.8 2.6 3.1 2.8 1.8 1.8Mongolia 13.5 14.7 9.9 7.2 4.7 4.3 4.7 5.3 4.9 3.9Montenegro 1.6 1.9 2.5 1.8 3.5 2.5 3.3 2.9 2.9 3.4Nicaragua 0.0 0.0 7.3 8.8 11.3 0.0 0.0 2.7 2.7 2.7Panama 7.3 4.5 13.8 20.4 21.0 2.4 1.7 1.7 1.8 1.6São Tomé and Príncipe 17.0 8.3 10.3 14.6 14.7 3.3 9.3 19.9 5.0 4.4Suriname 43.6 38.2 44.3 30.7 25.3 22.9 27.2 32.6 13.2 12.2Timor-Leste – – – – – – – – – –High-incomeCommonwealth countriesAntigua and Barbuda – – – – – – – – – –Bahamas, The 26.1 22.3 18.8 31.6 33.7 9.6 12.2 24.7 30.8 34.8Barbados 68.0 65.7 55.8 62.7 69.9 37.2 42.7 38.1 35.7 50.1Brunei Darussalam 18.7 23.0 21.3 22.6 24.6 62.0 63.0 58.3 56.4 61.5

(continued)

103

Table 13. Merchandise trade with Commonwealth countries (continued)

Group/country Total exports (% of world) Total imports (% of world)

2008 2009 2010 2011 2012 2008 2009 2010 2011 2012

Cyprus 13.8 13.2 14.2 15.3 15.4 12.9 12.5 11.6 11.0 9.5Malta 21.0 20.4 13.1 9.1 8.4 20.9 19.8 20.0 12.7 12.6St Kitts and Nevis – – – – – – – – – –Trinidad and Tobago 18.6 22.9 22.7 18.6 16.6 15.2 15.6 17.2 13.7 14.4Other countriesBahrain 11.5 8.1 7.1 7.6 8.5 12.0 12.9 13.8 13.4 12.3Equatorial Guinea 1.3 6.1 9.2 8.8 9.8 8.0 5.5 7.5 6.6 5.8Estonia 4.7 7.6 3.5 3.9 4.2 3.7 3.1 3.0 4.9 4.7Iceland 13.7 15.6 13.0 12.0 13.7 11.6 11.0 10.0 8.6 9.0Ireland 22.6 20.1 19.7 19.5 20.9 41.2 39.5 41.1 43.4 42.9Kuwait 29.3 28.2 25.2 27.0 26.5 14.6 14.7 17.1 16.5 13.0Latvia 5.2 4.9 5.3 4.6 4.9 2.5 2.8 3.3 4.0 3.3Lithuania 8.7 7.2 7.8 6.3 7.3 2.5 2.3 2.1 2.6 2.9Norway 31.3 27.8 31.3 31.5 28.2 12.0 11.0 12.6 13.0 12.1Oman 7.6 15.2 15.4 16.7 12.3 14.3 15.9 12.8 13.0 16.4Qatar 19.8 22.7 22.4 25.8 23.7 12.6 13.0 13.1 15.5 15.2Uruguay 5.5 6.3 5.5 4.8 5.3 11.0 8.8 9.7 8.2 7.2

Note: – = not availableSource: IMF, Direction of Trade Statistics Yearbook 2013, available at: http://esds80.mcc.ac.uk (accessed June 2013)

104

Tabl

e 14

. Ex

port

cha

ract

eris

tics

Gro

up/c

ount

ryN

umbe

r of p

rodu

cts

expo

rted

Div

ersi

ficat

ion

inde

xC

once

ntra

tion

inde

x

2008

2009

2010

2011

2008

2009

2010

2011

2008

2009

2010

2011

Mid

dle-

inco

me

Com

mon

wea

lth c

ount

ries

Bel

ize

145

227

189

870.

574

0.50

10.

535

0.73

60.

460

0.16

40.

382

0.36

0B

ots

wan

a25

425

418

615

80.

883

0.86

00.

800

0.89

10.

642

0.49

50.

595

0.78

3D

om

inic

a14

1624

200.

734

0.73

20.

743

0.71

00.

424

0.41

40.

407

0.37

9Fi

ji24

124

325

325

60.

734

0.73

40.

724

0.72

60.

224

0.21

10.

174

0.18

8G

rena

da16

232

2527

0.63

40.

656

0.64

00.

680

0.27

70.

224

0.23

50.

190

Guy

ana

220

7797

840.

822

0.82

90.

828

0.86

50.

322

0.37

70.

395

0.44

0Ja

mai

ca12

312

922

114

50.

781

0.76

10.

757

0.70

40.

556

0.37

10.

425

0.41

6K

iriba

ti97

1195

990.

701

0.74

30.

718

0.73

40.

665

0.71

10.

760

0.79

4Le

soth

o20

2123

630.

876

0.88

00.

881

0.83

40.

449

0.42

80.

437

0.32

6M

aldi

ves

164

4138

154

0.76

10.

818

0.82

30.

727

0.75

10.

746

0.75

20.

671

Mau

ritiu

s23

117

022

923

60.

725

0.70

70.

714

0.70

90.

249

0.26

00.

245

0.24

6N

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721

321

922

30.

792

0.75

60.

764

0.76

40.

271

0.22

50.

223

0.22

3N

auru

––

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0.76

80.

662

–0.

722

0.95

70.

839

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Pap

ua N

ew G

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a13

612

913

213

80.

808

0.82

40.

815

0.81

20.

342

0.35

70.

371

0.34

5St

Luc

ia19

610

010

092

0.58

30.

622

0.62

10.

617

0.26

80.

299

0.30

40.

316

St V

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e G

rena

dine

s36

3425

240.

671

0.84

60.

782

0.82

30.

770

0.67

30.

529

0.63

1S

amo

a12

313

913

147

0.68

20.

729

0.77

80.

731

0.78

60.

672

0.74

40.

579

Seyc

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s20

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7667

0.74

30.

838

0.81

50.

838

0.45

20.

521

0.45

80.

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Solo

mo

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land

s23

2126

330.

849

0.85

40.

862

0.84

20.

646

0.68

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728

0.61

5Sw

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916

619

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00.

733

0.71

50.

729

0.75

00.

214

0.23

10.

242

0.24

3To

nga

88

151

152

0.55

60.

654

0.69

00.

657

0.27

70.

296

0.26

30.

232

Tuva

lu78

––

10.

550

0.54

00.

589

0.65

20.

303

0.17

20.

350

0.68

2V

anua

tu27

2816

160.

761

0.82

90.

822

0.82

60.

629

0.66

10.

795

0.63

2O

ther

cou

ntrie

sA

lban

ia24

724

624

218

80.

651

0.59

60.

698

0.65

80.

159

0.15

60.

191

0.20

1A

rmen

ia23

122

423

124

00.

719

0.74

40.

744

0.75

80.

256

0.21

80.

228

0.20

6B

huta

n48

5072

870.

777

0.64

30.

786

0.80

70.

447

0.44

80.

269

0.32

2B

osn

ia24

925

325

125

30.

585

0.61

10.

578

0.62

40.

115

0.10

60.

108

0.10

7C

ape

Ver

de21

136

1632

0.67

50.

699

0.73

50.

717

0.33

30.

372

0.48

40.

469

Co

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Rep

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of

9410

712

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60.

774

0.78

40.

792

0.80

60.

806

0.79

40.

762

0.78

5C

ost

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ica

246

246

245

246

0.68

20.

713

0.68

30.

748

0.26

70.

309

0.36

20.

471

Djib

out

i21

417

520

621

40.

670

0.64

80.

622

0.60

20.

419

0.41

20.

300

0.24

0G

abo

n10

794

137

150

0.83

60.

850

0.84

10.

821

0.73

60.

724

0.73

50.

750

Geo

rgia

152

223

160

190

0.74

30.

738

0.70

50.

689

0.21

70.

180

0.21

50.

185

Leba

non

254

249

250

226

0.61

90.

629

0.61

90.

640

0.10

40.

106

0.09

80.

123

(con

tinue

d)

105

Tabl

e 14

. Ex

port

cha

ract

eris

tics

(con

tinu

ed)

Gro

up/c

ount

ryN

umbe

r of p

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expo

rted

Div

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ficat

ion

inde

xC

once

ntra

tion

inde

x

2008

2009

2010

2011

2008

2009

2010

2011

2008

2009

2010

2011

Mac

edo

nia,

FYR

197

241

217

198

0.68

20.

628

0.65

20.

623

0.23

10.

253

0.18

60.

168

Mau

ritan

ia16

216

818

918

70.

760

0.78

70.

805

0.82

00.

469

0.45

10.

484

0.48

2M

old

ova

227

175

178

187

0.67

50.

682

0.67

10.

640

0.17

30.

145

0.13

60.

133

Mo

ngo

lia13

511

412

713

10.

836

0.83

60.

845

0.84

20.

408

0.42

40.

489

0.52

2M

ont

eneg

ro20

911

711

711

60.

701

0.70

20.

670

0.75

60.

418

0.38

50.

371

0.38

9P

anam

a25

225

424

825

00.

615

0.60

50.

586

0.57

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167

0.19

60.

132

0.16

1S

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om

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rínci

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810

415

0.59

90.

692

0.63

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611

0.56

50.

392

0.43

70.

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Surin

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207

122

203

131

0.76

90.

839

0.83

50.

780

0.44

60.

518

0.45

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484

Tim

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Lest

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48

187

0.83

90.

779

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746

0.72

70.

755

0.45

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Hig

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676

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519

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526

0.43

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287

0.14

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121

Bah

amas

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210

201

8220

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775

0.79

20.

799

0.78

70.

396

0.44

90.

419

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2B

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241

243

231

218

0.56

80.

504

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660

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143

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321

Bru

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812

913

512

50.

824

0.84

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846

0.83

60.

677

0.66

50.

674

0.67

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124

024

824

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514

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80.

503

0.52

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172

0.14

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0.23

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212

204

177

174

0.61

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631

0.61

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659

0.38

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385

0.28

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St K

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818

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119

90.

738

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60.

367

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360

0.35

5O

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sB

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124

525

324

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716

0.68

80.

705

0.70

90.

394

0.33

90.

341

0.34

2Eq

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123

3013

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761

0.77

70.

737

0.74

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769

0.73

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819

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5Es

toni

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225

525

325

50.

436

0.44

30.

462

0.48

80.

100

0.11

80.

108

0.15

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221

622

021

90.

758

0.77

90.

827

0.77

60.

422

0.40

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456

0.45

2Ire

land

251

252

252

250

0.60

90.

658

0.67

30.

690

0.22

50.

249

0.26

00.

260

Kuw

ait

247

246

227

234

0.78

90.

799

0.80

70.

788

0.70

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689

0.72

30.

738

Latv

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125

225

225

20.

464

0.46

30.

475

0.45

10.

079

0.07

70.

081

0.09

2Lu

thia

nia

254

252

253

253

0.49

10.

483

0.48

70.

479

0.20

20.

169

0.18

80.

209

Luxe

mbo

urg

258

257

257

257

0.55

20.

521

0.54

50.

554

0.16

40.

122

0.12

30.

131

No

rway

253

253

256

253

0.60

90.

616

0.62

80.

618

0.41

70.

379

0.38

80.

403

Om

an24

324

421

823

70.

718

0.70

20.

683

0.67

10.

624

0.54

90.

458

0.59

3Q

atar

246

240

226

215

0.79

90.

775

0.79

60.

787

0.54

60.

516

0.48

60.

537

Uru

guay

188

181

203

209

0.66

10.

659

0.68

20.

674

0.19

70.

189

0.18

10.

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29 J

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106

Tabl

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indi

cato

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f ope

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stab

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110

Table 17. Fish production

Group/country Catches (’000 metric tonnes)

2002 2003 2004 2005 2006 2007 2008 2009 2010

Middle-incomeCommonwealth countriesBelize 54.7 19.1 16.8 41.3 26.0 30.6 43.9 14.6 114.0Botswana 0.1 0.1 0.2 0.1 0.1 0.1 0.1 0.1 0.0Dominica 1.2 1.0 0.7 0.6 0.7 0.7 0.7 0.8 1.0Fiji 40.3 37.2 50.2 45.0 50.4 48.1 45.7 41.5 41.0Grenada 2.2 2.5 2.0 2.1 2.2 2.4 2.4 2.6 2.0Guyana 48.0 60.3 57.3 54.0 54.4 48.1 42.5 44.1 45.0Jamaica 7.8 15.5 18.0 18.8 25.9 22.2 19.1 19.0 15.0Kiribati 36.7 33.7 35.0 35.6 34.5 35.2 29.1 42.0 45.0Lesotho – – – – – – – – –Maldives 163.4 155.4 158.5 186.2 184.3 144.4 133.3 116.9 95.0Mauritius 10.7 11.0 10.3 10.3 9.1 8.3 6.9 8.1 8.0Namibia 626.2 637.8 571.9 554.2 509.8 413.7 373.4 370.1 370.0Nauru 0.0 0.0 0.3 0.3 0.3 0.3 0.2 0.2 0.0Papua New Guinea 174.6 215.9 243.2 254.7 253.7 247.9 222.8 230.1 225.0St Lucia 1.6 4.8 8.8 1.8 4.8 5.2 3.9 4.0 2.0St Vincent and the

Grenadines44.5 4.8 8.6 1.7 4.7 5.3 3.8 – 66.0

Samoa 7.1 4.5 9.9 9.8 12.4 14.1 13.9 13.3 3.0Seychelles 63.6 87.1 101.8 109.5 93.4 65.9 69.5 81.5 87.0Solomon Islands 25.9 35.9 35.2 30.4 39.7 31.4 26.4 28.1 35.0Swaziland 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.0Tonga 5.3 4.8 3.4 2.7 3.1 3.2 2.6 2.1 2.0Tuvalu 0.6 1.5 2.5 2.6 2.6 2.6 2.6 4.2 11.0Vanuatu 45.0 58.4 210.7 227.4 224.1 211.2 171.5 144.7 98.0Other countriesAlbania 4.5 4.3 6.1 6.5 7.7 7.5 7.4 8.1 6.0Armenia 1.5 1.6 1.0 1.0 1.4 4.7 5.7 5.9 1.0Bhutan 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.0Bosnia and Herzegovina 2.0 2.0 8.4 9.1 9.6 9.6 9.6 9.6 2.0Cape Verde 8.2 8.1 10.4 21.6 24.6 18.3 23.8 16.8 20.0Congo, Republic of 51.9 53.9 57.1 54.7 59.1 59.2 54.2 61.3 65.0Costa Rica 33.0 49.9 45.6 46.4 42.0 47.5 48.8 46.5 22.0Djibouti 0.3 1.1 1.2 1.6 1.3 1.2 1.2 1.1 1.0Gabon 41.6 45.6 46.2 43.9 41.6 38.6 30.1 30.1 32.0Georgia 1.8 3.3 12.1 10.0 9.8 18.4 26.7 25.3 31.0Lebanon 4.0 3.9 4.7 4.6 4.6 4.6 4.6 4.6 4.0Macedonia, FYR 0.2 0.2 1.2 1.1 0.7 1.2 1.5 1.8 0.0Mauritania 156.1 199.7 270.7 304.9 165.3 223.2 195.3 178.5 276.0Moldova 0.6 0.4 5.0 5.0 5.6 5.9 6.1 6.3 2.0Mongolia 0.3 0.2 0.3 0.4 0.3 0.2 0.1 0.1 0.0Montenegro – – – – 3.0 3.0 3.0 3.0 1.0Panama 229.7 233.9 215.9 252.8 266.8 230.4 233.5 228.5 163.0São Tomé and Príncipe 3.7 4.0 4.1 4.2 4.2 4.3 4.3 4.3 5.0Suriname 27.2 32.7 30.7 27.7 30.8 29.7 23.8 25.9 34.0Timor-Leste 0.8 1.3 1.7 2.2 2.6 3.3 4.2 4.7 3.0High-incomeCommonwealth countriesAntigua and Barbuda 2.4 2.6 2.5 3.0 3.1 3.1 3.5 2.5 –Bahamas, The 12.3 12.7 11.4 11.1 10.7 8.4 9.2 9.1 12.0Barbados 2.5 2.8 2.1 2.2 2.0 2.2 3.6 3.5 3.0Brunei Darussalam 2.1 2.2 3.1 2.9 2.5 2.9 2.8 2.8 2.0Cyprus 2.0 1.8 3.7 4.3 4.8 5.0 5.4 4.8 1.0Malta 1.1 1.1 2.0 2.1 2.4 3.8 3.0 4.1 2.0St Kitts and Nevis 0.4 1.5 1.5 1.4 1.5 1.6 1.7 1.9 21.0Trinidad and Tobago 18.8 14.6 14.7 18.1 13.1 13.1 13.8 13.9 14.0

(continued)

111

Table 17. Fish production (continued)

Group/country Catches (’000 metric tonnes)

2002 2003 2004 2005 2006 2007 2008 2009 2010

Other countriesBahrain 13.6 14.5 14.5 11.9 15.6 15.0 14.2 16.4 13.0Croatia 21.2 20.0 40.5 45.8 51.4 53.1 61.1 69.2 53.0Equatorial Guinea 3.5 3.6 3.5 3.8 4.0 4.5 5.4 7.7 7.0Estonia 102.6 80.2 88.9 100.1 87.6 100.2 103.3 99.1 95.0Iceland 2149.5 2008.7 1764.0 1694.0 1356.3 1425.9 1311.7 1169.6 1 061Ireland 318.7 301.2 368.6 357.2 294.4 301.5 279.7 345.8 319.0Kuwait 5.4 4.1 5.2 5.2 6.2 4.7 4.7 4.7 4.0Latvia 113.7 114.5 125.9 151.2 141.0 156.0 158.5 163.7 165.0Lithuania 150.2 157.2 164.7 141.8 156.8 190.9 185.8 176.1 150.0Luxembourg – – – – – – – – –Norway 2922.9 2702.2 3309.5 3208.4 3114.2 3356.7 3279.7 3486.3 2 675Oman 142.7 138.5 165.5 157.5 147.8 151.8 152.0 158.7 164.0Qatar 7.2 11.3 11.1 13.9 16.4 15.2 17.7 14.1 14.0Slovenia 1.7 1.3 2.6 2.6 2.5 2.5 2.2 2.3 1.0Uruguay 108.8 117.3 123.0 125.9 134.0 108.8 108.8 81.5 74.0

Note: – = not availableSource: Food and Agriculture Organisation, Yearbook of Fishery Statistics, available at: www.fao.org (accessed June 2013)

112

Table 18. Energy production, consumption and trade

Group/country Primary energy production (thousand

tonnes of oil equivalent)

Commercial energy consumption (thousand tonnes of oil equivalent)

Net energy imports (% of total energy

consumption)

2009 2010 2011 2009 2010 2011 2009 2010 2011

Middle-incomeCommonwealth countriesBelize – – – – – – – – –Botswana 936 1,096 979 2,023 2,263 2,215 54 52 56Dominica – – – – – – – – –Fiji – – – – – – – – –Grenada – – – – – – – – –Guyana – – – – – – – – –Jamaica 534 464 549 3,022 2,830 3,066 82 84 82Kiribati – – – – – – – – –Lesotho – – – – – – – – –Maldives – – – – – – – – –Mauritius – – – – – – – – –Namibia 327 317 334 1,497 1,552 1,589 78 80 79Nauru – – – – – – – – –Papua New Guinea – – – – – – – – –St Lucia – – – – – – – – –St Vincent and the Grenadines – – – – – – – – –Samoa – – – – – – – – –Seychelles – – – – – – – – –Solomon Islands – – – – – – – – –Swaziland – – – – – – – – –Tonga – – – – – – – – –Tuvalu – – – – – – – – –Vanuatu – – – – – – – – –Other countriesAlbania 1,252 1,622 1,486 2,068 2,059 2,173 39 21 32Armenia 832 878 887 2,610 2,483 2,716 68 65 67Bhutan – – – – – – – – –Bosnia and Herzegovina 4,426 4,374 4,621 6,161 6,451 7,095 28 32 35Cape Verde – – – – – – – – –Congo, Republic of 15,300 17,371 16,672 1,416 1,511 1,659 −981 −1,050 −905Costa Rica 2,367 2,436 2,412 4,561 4,646 4,655 48 48 48Djibouti – – – – – – – – –Gabon 13,587 14,322 14,273 1,925 1,984 1,997 −606 −622 −615Georgia 1,185 1,312 1,117 3,096 3,122 3,543 62 58 68Lebanon 187 207 206 6,652 6,382 6,349 97 97 97Macedonia, FYR 1,607 1,616 1,784 2,811 2,883 3,122 43 44 43Mauritania – – – – – – – – –Moldova 130 130 123 3,171 3,426 3,331 96 96 96Mongolia 7,833 14,686 19,310 3,252 3,454 3,607 −141 −325 −435Montenegro 614 891 791 994 1,174 1,179 38 24 33Panama 843 843 818 3,367 3,710 4,058 75 77 80São Tomé and Príncipe – – – – – – – – –Suriname – – – – – – – – –Timor-Leste – – – – – – – – –High-incomeCommonwealth countriesAntigua and Barbuda – – – – – – – – –Bahamas, The – – – – – – – – –Barbados – – – – – – – – –Brunei Darussalam 18,939 18,573 18,695 3,043 3,240 3,832 −522 −473 −388Cyprus 83 89 96 2,525 2,443 2,368 97 96 96

(continued)

113

Table 18. Energy production, consumption and trade (continued)

Group/country Primary energy production (thousand

tonnes of oil equivalent)

Commercial energy consumption (thousand tonnes of oil equivalent)

Net energy imports (% of total energy

consumption)

2009 2010 2011 2009 2010 2011 2009 2010 2011

Malta 2 3 48 777 848 857 100 100 94St Kitts and Nevis – – – – – – – – –Trinidad and Tobago 43,254 44,171 42,163 20,277 21,370 20,918 −113 −107 −102Other countriesBahrain 17,548 17,725 18,081 9,340 9,472 9,506 −88 −87 −90Croatia 4,068 4,219 3,786 8,722 8,564 8,439 53 51 55Equatorial Guinea – – – – – – – – –Estonia 4,158 4,930 5,038 4,749 5,568 5,603 12 11 10Iceland 4,402 4,429 4,804 5,384 5,369 5,731 18 18 16Ireland 1,521 1,932 1,788 14,364 14,219 13,216 89 86 86Kuwait 130,568 134,653 154,345 30,815 32,586 32,523 −324 −313 −375Latvia 2,097 2,114 2,074 4,403 4,644 4,371 52 54 53Lithuania 4,392 1,519 1,534 8,766 7,052 7,287 50 78 79Luxembourg 114 123 116 3,956 4,220 4,171 97 97 97Norway 214,677 203,457 195,353 29,775 32,338 28,137 −621 −529 −594Oman 67,198 72,140 73,508 18,279 23,158 25,276 −268 −212 −191Qatar 142,464 178,345 211,229 24,942 28,973 33,285 −471 −516 −535Slovenia 3,665 3,729 3,764 7,097 7,230 7,249 48 48 48Uruguay 1,520 2,054 1,865 4,134 4,174 4,430 63 51 58


114

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117

Table 20. Tourist arrivals and earnings

Group/countryNumber of tourist arrivals

(’000)Tourist receipts

(US$ million)

Tourist receipts as a proportion of total export

earnings (%)

2009 2010 2011 2009 2010 2011 2009 2010 2011

Middle-incomeCommonwealth countriesBelize 232 242 250 256 264 253 35.2 31.8 26.8Botswana 2,103 2,145 – 230 222 5.9 4.4 –Dominica 75 77 76 86 96 98 54.9 56.5 58.9Fiji 542 632 675 607 713 599 46.2 42.5 –Grenada 114 110 118 99 97 105 56.8 57.7 57.0Guyana 141 152 157 35 80 – 3.7 7.1 –Jamaica 1,831 1,922 1,952 2,070 2,095 2,060 51.3 52.3 48.1Kiribati 4 5 5 3 – – – – –Lesotho 320 414 397 30 25 26 3.9 2.7 2.1Maldives 656 792 931 1,366 1,713 1,868 85.1 85.3 80.3Mauritius 871 935 965 1,390 1,585 1,813 33.3 32.0 30.5Namibia 980 984 – 511 560 645 13.4 11.4 12.1Nauru – – – – – – – – –Papua New Guinea 126 146 165 2 3 4 0.0 0.0 –St Lucia 278 306 312 296 329 317 54.4 52.3 56.7St Vincent and the

Grenadines75 72 74 88 86 90 45.8 46.8 48.3

Samoa 122 122 121 115 124 135 66.4 63.6 68.2Seychelles 158 175 194 349 352 378 33.8 35.5 34.6Solomon Islands 18 21 23 50 65 – 21.3 19.8 –Swaziland 908 868 879 40 51 – 2.3 2.5 –Tonga 51 45 46 17 – – 40.1 – –Tuvalu 2 2 1 – – – – – –Vanuatu 99 97 94 214 242 252 70.5 73.8 71.2Other countriesAlbania 1,856 2,417 2,932 2,014 1,780 1,833 65.8 53.5 48.5Armenia 575 684 758 374 456 485 28.0 23.5 20.4Bhutan 23 41 66 51 64 76 8.9 10.5 10.2Bosnia and

Herzegovina311 365 392 753 662 719 17.5 13.5 12.6

Cape Verde 287 336 428 349 387 438 61.6 60.8 55.9Congo, Republic of 94 101 – – – – – – –Costa Rica 1,923 2,100 2,192 2,001 2,179 2,374 20.7 20.6 20.5Djibouti – – – 16 18 19 4.1 4.4 4.8Gabon – – – – – – – – –Georgia 1,500 2,032 2,822 537 737 1,059 16.9 18.3 20.2Lebanon 1,844 2,168 1,655 7,157 8,184 7,070 33.9 39.3 28.1Macedonia, FYR 259 262 327 232 209 250 7.7 5.8 5.2Mauritania – – – – – – – – –Moldova 7 8 11 240 232 262 14.0 11.7 9.6Mongolia 411 457 – 253 288 258 11.0 8.5 4.7Montenegro 1,044 1,088 1,201 705 713 826 52.4 49.1 45.1Panama 1,200 1,324 1,473 2,280 2,552 2,925 13.0 13.5 12.1São Tomé and Príncipe 15 8 – 8 11 16 42.3 45.7 54.2Suriname 151 205 220 70 69 69 4.1 3.0 2.6Timor-Leste 44 40 51 16 26 21 24.2 26.4 21.1High-incomeCommonwealth countriesAntigua and Barbuda 234 230 241 305 298 312 54.3 56.9 58.1Bahamas, The 1,327 1,370 1,346 2,025 2,159 2,269 66.1 67.6 66.0Barbados 519 532 568 1,122 1,074 – 59.6 51.9 –

(continued)

118

Table 20. Tourist arrivals and earnings (continued)

Group/countryNumber of tourist arrivals

(’000)Tourist receipts

(US$ million)

Tourist receipts as a proportion of total export

earnings (%)

2009 2010 2011 2009 2010 2011 2009 2010 2011

Brunei Darussalam 157 214 242 254 – – 3.1 – –Cyprus 2,141 2,173 2,392 2,474 2,371 2,724 26.2 24.9 25.5Malta 1,182 1,336 1,412 1,117 1,257 1,480 16.3 15.6 16.0St Kitts and Nevis 93 92 – 83 90 93 49.0 45.2 40.8Trinidad and Tobago 419 386 – 548 630 – 5.5 5.2 –Other countriesBahrain 8,861 11,952 6,732 1,873 2,163 1,766 11.9 12.1 7.7Croatia 8,694 9,111 9,927 9,308 8,255 9,614 40.7 35.5 36.6Equatorial Guinea – – – – – – – – –Estonia 2,059 2,372 2,665 1,445 1,412 1,683 11.7 9.5 8.3Iceland 494 489 566 547 560 750 8.6 7.9 9.0Ireland 7,189 7,134 7,630 8,458 8,187 9,629 4.2 3.9 4.2Kuwait 297 207 269 660 527 525 1.0 0.7 0.5Latvia 1,323 1,373 1,493 1,013 963 1,098 9.2 7.6 6.8Lithuania 1,341 1,507 1,775 1,102 1,097 1,417 5.5 4.4 4.3Luxembourg 849 793 871 4,148 4,100 4,807 5.7 5.1 5.3Norway 4,346 4,767 4,963 4,957 5,814 6,399 3.3 3.4 3.2Oman 1,280 1,048 1,092 1,246 1,612 3.7 3.2 3.3Qatar 1,659 1,519 2,527 – – 4,463 – – 3.7Slovenia 1,824 1,869 2,037 2,735 2,721 2,920 9.5 8.8 8.1Uruguay 2,055 2,353 2,857 1,451 1,649 2,375 16.8 15.5 18.7


119

Table 21. International reserves

Group/country Total international reserves (US$ million)

Months of imports that reserves could finance

2008 2009 2010 2011 2012 2008 2009 2010 2011 2012

Middle-incomeCommonwealth countriesBelize 166 214 218 237 289 1.8 2.9 2.7 2.7 3.0Botswana 9,119 8,704 7,885 8,082 7,628 16.5 18.8 14.5 12.2 10.1Dominica 55 75 76 81 95 2.1 3.2 3.3 3.5 4.2Fiji 322 570 721 833 920 1.4 3.8 3.9 – –Grenada 105 129 119 121 119 2.5 3.8 3.3 3.3 3.3Jamaica 1,773 2,076 2,501 2,282 1,981 1.9 3.4 4.2 3.2 2.7Kiribati – – – – – – – – – –Lesotho – – – – – – – – – –Maldives 244 276 364 349 318 1.2 1.9 2.2 1.6 1.4Mauritius 1,796 2,316 2,619 2,775 2,837 3.1 5.0 2.8 3.9 4.1Namibia 1,293 2,051 1,696 1,787 1,746 3.2 4.5 3.2 3.0 –Nauru – – – – – – – – – –Papua New Guinea 2,008 2,629 3,121 4,353 3,930 4.2 5.9 5.4 –St Lucia 143 175 206 213 232 1.9 2.9 2.9 2.9 3.4St Vincent and the

Grenadines84 88 113 90 111 2.2 2.5 3.2 2.6 3.1

Samoa 87 166 209 167 169 2.8 6.3 6.3 4.6 –Seychelles 64 191 236 290 319 0.6 2.0 2.3 2.6 –Solomon Islands 89 146 266 412 469 2.0 3.6 4.6 6.4 –Swaziland 752 959 756 601 741 3.6 4.3 3.0 – –Tonga 70 96 105 143 152 3.8 6.0 – –Tuvalu – – – – – – – – – –Vanuatu 115 149 161 174 – 2.8 5.2 4.1 4.0 –Other countriesAlbania 2,364 2,369 2,541 2,471 2,516 4.0 4.3 4.9 4.2 5.0Armenia 1,407 2,004 1,866 1,932 1,799 3.2 5.7 4.6 4.2 3.9Bhutan 765 891 1,002 790 955 11.0 14.5 12.0 6.8 –Bosnia and Herzegovina 4,480 4,575 4,411 4,248 4,283 4.6 6.3 6.0 4.8 5.4Cape Verde 361 398 382 339 376 3.4 4.2 3.8 2.8 3.6Congo, Republic of 3,881 3,806 4,447 5,641 5,550 – – – – –Costa Rica 3,801 4,068 4,630 4,758 6,857 3.2 4.5 4.5 3.9 5.1Djibouti 175 242 249 244 249 3.0 5.0 6.1 4.3 –Gabon 1,935 1,993 1,736 2,157 2,352 – – – – –Lebanon 28,265 39,132 44,476 47,859 37,186 10.8 14.8 16.4 16.8 –Macedonia, FYR 2,110 2,288 2,277 2,667 2,528 3.5 5.1 4.7 4.3 4.9Mauritania 199 238 288 502 949 – – – – –Moldova 1,672 1,480 1,718 1,965 2,511 3.5 4.5 4.6 4.0 5.1Mongolia 657 1,327 2,288 2,448 3,930 2.0 5.6 6.1 3.5 5.5Montenegro 436 573 556 393 459 1.2 2.4 2.4 1.5 1.9Panama 2,424 3,028 2,714 2,304 2,466 1.4 1.9 1.4 0.9 0.9São Tomé and Príncipe 61 67 49 51 52 6.4 7.6 4.8 4.1 4.3Suriname 603 657 691 817 885 3.9 4.6 4.6 3.9 4.7Timor-Leste 210 250 406 462 884 3.1 2.5 3.6 3.0 –High-incomeCommonwealth countriesAntigua and Barbuda 138 128 137 148 162 1.6 2.0 2.3 2.6 2.9Bahamas, The 568 1,010 1,044 1,070 847 1.4 3.1 3.1 2.8 1.9Barbados 739 871 834 813 840 3.0 4.1 3.9 – –Brunei Darussalam 751 1,357 1,563 2,584 3,315 2.0 4.1 – – –Cyprus 1,005 1,281 1,142 1,187 449 0.6 1.0 0.9 1.0 1.0Malta 373 539 540 515 688 0.4 0.7 0.6 0.5 0.7St Kitts and Nevis 110 136 169 244 262 3.0 4.1 5.3 8.3 8.4Trinidad and Tobago 9,496 9,245 9,692 10,501 9,794 10.0 12.8 14.1 – –

(continued)

120

Table 21. International reserves (continued)

Group/country Total international reserves (US$ million)

Months of imports that reserves could finance

2008 2009 2010 2011 2012 2008 2009 2010 2011 2012

Other countriesBahrain 4,051 4,008 5,299 4,774 5,205 2.0 3.1 3.8 2.3 –Croatia 12,957 14,895 14,133 14,484 14,807 4.0 6.3 6.3 5.9 6.5Equatorial Guinea 4,431 3,252 2,346 3,054 4,397 – – – – –Estonia 3,972 3,981 2,567 207 287 2.3 3.7 2.0 0.1 0.2Iceland 3,571 3,883 5,789 8,548 4,085 3.1 5.5 8.2 9.9 5.4Ireland 1,039 2,151 2,114 1,695 1,386 0.0 0.1 0.1 0.1 0.1Kuwait 19,321 23,028 24,805 29,682 28,886 5.5 8.3 8.2 8.9 9.1Latvia 5,244 6,902 7,606 6,378 7,111 3.0 7.6 6.6 4.1 4.4Lithuania 6,443 6,623 6,598 8,202 8,218 2.1 3.8 2.9 2.8 2.7Luxembourg 398 809 848 1,011 871 0.0 0.0 0.0 0.0 0.0Norway 50,950 48,859 52,798 49,397 51,856 3.4 4.5 4.2 3.5 3.6Oman 11,582 12,204 13,025 14,366 14,400 4.6 5.8 5.6 5.3 4.4Qatar 9,997 18,804 31,182 16,809 32,521 – – – 3.2 5.4Slovenia 958 1,078 1,071 987 782 0.3 0.4 0.4 0.3 0.3Uruguay 6,360 8,038 7,656 10,302 13,591 6.4 9.9 7.6 8.4 10.0


121

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Tabl

e 23

. To

tal n

et t

rans

fers

on

exte

rnal

deb

t

Gro

up/c

ount

ryTo

tal n

et t

rans

fers

on

exte

rnal

deb

t (U

S$

mill

ion)

Gro

ss d

omes

tic

prod

uct

(c

urre

nt U

S$

mill

ions

)To

tal n

et t

rans

fers

on

exte

rnal

deb

t (%

of G

DP)

2008

2009

2010

2011

2008

2009

2010

2011

2008

2009

2010

2011

Mid

dle-

inco

me

Com

mon

wea

lth c

ount

ries

Bel

ize

−38

−34

−88

−77

1,36

41,

349

1,39

91,

448

−2.8

−2.5

−6.3

−5.3

Bo

tsw

ana

61,

145

7057

611

,113

10,1

0713

,747

15,2

920.

111

.30.

53.

8D

om

inic

a9

−53

25−4

452

482

475

476

2.0

−11.

05.

3−0

.8Fi

ji−1

440

127

43,

591

2,88

23,

173

3,81

8−0

.41.

40.

07.

2G

rena

da30

−12

2−2

582

677

177

078

03.

6−1

.50.

2−3

.2G

uyan

a11

218

329

933

71,

923

2,02

62,

259

2,57

75.

89.

013

.213

.1Ja

mai

ca−7

20−4

112,

299

−477

13,6

8112

,069

13,2

0314

,426

−5.3

−3.4

17.4

−3.3

Kiri

bati

131

120

142

167

0.0

0.0

0.0

0.0

Leso

tho

8−1

189

1,63

11,

717

2,20

42,

525

0.5

−0.1

0.8

0.4

Mal

dive

s73

4212

838

1,89

21,

985

2,13

42,

154

3.9

2.1

6.0

1.8

Mau

ritiu

s−5

714

325

317

79,

641

8,83

59,

718

11,2

42−0

.61.

62.

61.

6N

amib

ia–

––

–8,

830

8,85

911

,066

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auru

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Pap

ua N

ew G

uine

a−1

0595

2,60

36,

703

8,01

07,

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9,48

012

,394

−1.3

1.2

27.5

54.1

St L

ucia

295

−439

128

−144

1,16

51,

167

1,20

01,

211

25.3

−37.

710

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1.9

St V

ince

nt a

nd th

e G

rena

dine

s−6

−545

169

567

468

169

1−0

.9−0

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60.

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amo

a17

2668

3557

450

157

263

43.

05.

111

.95.

5Se

yche

lles

4275

111

278

967

847

973

1,06

04.

38.

911

.526

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lom

on

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nds

−14

525

2464

660

167

986

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62.

8Sw

azila

nd29

−66

158

−75

3,02

03,

161

3,69

13,

969

1.0

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4.3

−1.9

Tong

a3

1336

3134

731

836

942

90.

94.

29.

77.

1Tu

valu

––

––

3027

3236

––

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Van

uatu

211

1223

608

610

701

786

3.5

0.2

1.7

2.9

Oth

er c

ount

ries

Alb

ania

1,05

742

029

169

112

,969

12,1

1911

,858

12,9

608.

23.

52.

55.

3A

rmen

ia41

11,

358

1,05

092

611

,662

8,64

89,

260

10,1

383.

515

.711

.39.

1B

huta

n−6

013

9518

11,

257

1,26

51,

585

1,83

4−4

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16.

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9B

osn

ia a

nd H

erze

govi

na−8

4592

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918

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8316

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4−4

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ape

Ver

de43

6917

113

91,

562

1,60

11,

659

1,90

12.

84.

310

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3C

ong

o, R

epub

lic o

f−1

19−2

2644

188

11,6

7511

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13,1

0815

,654

−1.0

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3.4

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Co

sta

Ric

a34

7−1

,702

594

198

29,8

3829

,431

36,3

4641

,072

1.2

−5.8

1.6

0.5

(con

tinue

d)

124

Tabl

e 23

. To

tal n

et t

rans

fers

on

exte

rnal

deb

t (c

onti

nued

)

Gro

up/c

ount

ryTo

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et t

rans

fers

on

exte

rnal

deb

t (U

S$

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ion)

Gro

ss d

omes

tic

prod

uct

(cur

rent

U

S$

mill

ions

)To

tal n

et t

rans

fers

on

exte

rnal

deb

t (%

of G

DP)

2008

2009

2010

2011

2008

2009

2010

2011

2008

2009

2010

2011

Djib

out

i18

6−3

−4–

––

––

––

–G

abo

n−8

18−1

6110

022

315

,732

12,0

7614

,539

18,7

91−5

.2−1

.30.

71.

2G

eorg

ia81

357

783

968

512

,799

10,7

6711

,638

14,4

356.

35.

47.

24.

7Le

bano

n−2

,658

−1,3

21−1

,623

−1,3

2530

,080

34,6

5137

,124

40,0

94−8

.8−3

.8−4

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.3M

aced

oni

a, F

YR38

568

965

169,

834

9,31

49,

339

10,3

953.

97.

40.

70.

2M

aurit

ania

274

7743

911

53,

790

3,02

73,

671

4,27

37.

22.

512

.02.

7M

old

ova

224

739

955

06,

055

5,43

95,

812

7,01

53.

70.

16.

97.

8M

ong

olia

7326

124

618

5,62

34,

584

6,20

08,

761

1.3

5.7

4.0

0.2

Mo

nten

egro

190

785

−733

449

4,52

04,

158

4,11

54,

502

4.2

18.9

−17.

810

.0P

anam

a−6

011,

015

−601

431

23,0

0224

,163

26,5

9031

,316

−2.6

4.2

−2.3

1.4

São

To

mé

and

Prín

cipe

318

2252

183

196

201

248

1.8

9.3

10.8

21.0

Surin

ame

––

––

3,53

33,

875

4,36

74,

304

–.

––

Not

e: –

= n

ot a

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125

Table 24. Principal indicators of debt

Group/countryExternal debt (EDT)

(% of GNI)

External debt (% of exports of goods

and services)

Total debt service (% of total exports of goods and services)

2008 2009 2010 2011 2008 2009 2010 2011 2008 2009 2010 2011

Middle-incomeCommonwealth countriesBelize 106.4 104.4 105.2 96.0 146.1 176.8 156.7 134.3 16.9 17.2 15.3 13.9Botswana 3.4 14.9 12.2 13.8 7.2 39.9 33.0 – 1.1 1.1 1.4 –Dominica 68.5 58.2 61.1 59.9 178.0 166.8 160.7 164.4 11.2 12.7 8.9 9.8Fiji 11.2 18.7 18.0 23.6 20.4 39.0 31.6 . 1.3 1.9 1.3 –Grenada 70.5 80.6 79.0 73.8 278.9 313.3 330.5 296.4 10.9 12.8 15.3 13.3Guyana 44.6 58.3 65.4 – 82.1 122.9 128.5 – 2.6 2.1 2.7 –Jamaica 81.2 94.1 106.4 98.8 173.0 260.3 334.1 318.0 19.4 34.8 28.0 36.5Lesotho 32.2 33.5 28.7 27.1 41.0 48.8 43.7 – 2.2 2.5 1.9 –Maldives 50.8 52.8 50.8 50.2 86.3 117.7 105.7 – 9.1 11.2 8.9 –Mauritius 6.9 11.1 12.5 12.5 11.6 21.1 12.3 12.0 3.0 3.1 1.3 1.4Namibia – – – – – – – – – – – –Nauru – – – – – – – – – – – –Papua New Guinea 18.1 22.8 64.4 101.2 22.9 38.6 97.8 168.2 15.6 11.7 13.3 15.8St Lucia 78.8 41.0 50.1 37.6 153.0 77.3 89.7 77.6 10.6 7.7 7.1 7.8St Vincent and the Grenadines 30.3 33.7 41.6 42.1 92.9 107.7 139.3 141.9 13.4 15.1 16.0 15.2Samoa 38.4 49.9 56.2 58.5 107.2 139.5 161.6 180.8 4.2 4.7 5.3 5.8Seychelles 168.0 214.9 166.0 184.4 143.2 164.8 148.8 161.9 8.8 5.9 4.8 3.2Solomon Islands 30.3 36.5 41.6 37.9 57.6 69.7 65.8 33.7 5.2 4.0 5.9 2.0Swaziland 14.5 17.5 19.9 15.5 21.4 23.9 30.3 27.1 2.7 2.2 1.9 1.9Tonga 27.8 35.4 42.5 43.5 165.5 221.4 271.0 286.4 9.3 7.4 8.9 8.8Tuvalu – – – – – – – – – – – –Vanuatu 25.7 27.3 25.9 25.4 48.1 46.9 47.8 51.4 1.5 1.7 1.6 1.6Other countriesAlbania 30.0 38.8 41.5 46.0 105.0 135.5 131.7 144.9 4.2 6.3 8.3 9.3Armenia 29.2 57.0 64.3 68.3 128.7 244.7 215.5 202.9 13.3 20.6 33.4 25.4Bhutan 64.7 66.5 63.9 65.0 101.2 128.7 145.2 136.1 11.9 12.8 13.5 11.1Bosnia and Herzegovina 51.1 64.2 64.6 58.6 122.1 178.6 160.5 134.8 6.7 8.3 11.8 10.9Cape Verde 41.3 46.7 56.2 55.5 84.8 123.3 137.2 128.6 4.2 5.7 5.5 5.0Congo, Republic of 68.3 74.9 29.0 23.1 – – – – – – – –Costa Rica 31.8 28.6 24.2 25.7 85.2 82.5 78.9 87.3 14.6 13.0 9.0 13.5Djibouti 78.6 79.8 – – 211.7 211.5 173.3 – 8.4 8.5 8.1 –Gabon 17.3 24.0 22.4 19.7 – – – – – – – –Georgia 60.2 80.1 83.4 79.1 179.8 233.0 207.4 186.0 16.7 21.0 17.5 26.9Lebanon 79.7 71.5 63.9 61.7 98.0 107.2 110.5 92.6 17.8 19.9 18.7 19.9Macedonia, FYR 48.3 61.7 66.4 62.8 103.4 178.8 157.2 125.2 10.4 17.3 17.1 18.9Mauritania 55.3 69.6 72.1 70.8 97.3 130.7 110.8 87.3 3.1 4.7 4.7 3.6Moldova 54.9 65.6 76.4 72.0 119.7 163.4 175.8 149.4 16.5 16.9 14.5 12.8Mongolia 35.0 50.5 44.4 32.7 62.7 95.4 73.2 46.8 2.7 4.6 5.0 2.1Montenegro 32.7 56.8 39.1 45.6 75.2 149.8 95.4 99.7 3.5 4.4 5.9 10.2Panama 43.3 49.7 45.7 43.7 51.6 59.2 56.2 48.4 7.3 4.6 5.2 3.6São Tomé and Príncipe 71.2 80.8 89.0 92.2 671.5 745.0 698.1 747.5 11.9 9.8 6.4 5.4


126

Table 25. Composition of debt

Group/countryOfficial debt as a

percentage of total debt outstanding (disbursed)

Commercial debt as a percentage of total debt outstanding (disbursed)

Debt at variable interest rate as a percentage of total debt outstanding

(disbursed)

2008 2009 2010 2011 2008 2009 2010 2011 2008 2009 2010 2011

Middle-incomeCommonwealth countriesBelize 80.0 80.4 78.2 79.6 20.0 19.6 21.8 20.4 32.0 32.4 34.9 34.4Botswana 88.7 81.3 75.2 79.2 11.3 18.7 24.8 20.8 11.1 61.3 58.8 68.5Dominica 67.4 74.3 77.8 80.7 32.6 25.7 22.2 19.3 3.7 3.9 4.3 5.4Fiji 92.2 67.2 70.3 63.3 7.8 32.8 29.7 36.7 23.0 16.9 18.9 15.5Grenada 86.5 86.6 85.3 85.9 13.5 13.4 14.7 14.1 40.5 39.0 38.4 38.5Guyana 79.4 66.5 59.6 57.1 20.6 33.5 40.4 42.9 2.4 2.0 2.0 2.2Jamaica 66.5 60.4 53.6 54.1 33.5 39.6 46.4 45.9 32.0 40.9 45.6 46.4Lesotho 94.9 90.0 89.9 89.9 5.1 10.0 10.1 10.1 1.3 1.1 1.2 1.4Maldives 53.3 56.9 63.6 69.6 46.7 43.1 36.4 30.4 45.3 41.5 30.1 21.3Mauritius 89.0 75.4 79.4 81.3 11.0 24.6 20.6 18.7 42.1 44.8 52.8 56.2Namibia – – – – – – – – – – – –Nauru – – – – – – – – – – – –Papua New Guinea 74.4 58.5 17.5 8.5 25.6 41.5 82.5 91.5 43.7 39.0 77.5 91.2St Lucia 38.0 72.7 55.4 69.2 62.0 27.3 44.6 30.8 8.0 15.2 11.2 13.0St Vincent and the Grenadines

99.6 91.5 93.4 91.9 0.4 8.5 6.6 8.1 14.1 17.3 17.5 17.1

Samoa 99.2 89.5 92.0 93.0 0.8 10.5 8.0 7.0 0.0 0.0 0.0 0.0Seychelles 43.3 41.4 31.2 25.8 56.7 58.6 68.8 74.2 6.9 6.3 6.3 5.3Solomon Islands 82.1 77.1 54.3 46.1 17.9 22.9 45.7 53.9 15.4 12.5 33.2 36.2Swaziland 81.3 79.2 55.8 56.9 18.7 20.8 44.2 43.1 17.2 17.4 12.7 11.1Tonga 89.8 91.0 93.4 94.7 10.2 9.0 6.6 5.3 – – – –Tuvalu – – – – – – – – – – – –Vanuatu 59.5 63.6 57.3 49.0 40.5 36.4 42.7 51.0 – – – –Other countriesAlbania 56.3 60.0 64.4 53.9 43.7 40.0 35.6 46.1 34.8 44.5 42.9 46.2Armenia 40.8 47.3 41.0 37.1 59.2 52.7 59.0 62.9 38.9 38.9 44.9 47.2Bhutan 97.5 98.1 98.3 98.6 2.5 1.9 1.7 1.4 0.0 1.1 3.2 4.3Bosnia and Herzegovina 30.6 31.6 34.1 36.1 69.4 68.4 65.9 63.9 69.8 62.0 63.6 59.8Cape Verde 97.6 96.3 97.3 97.9 2.4 3.7 2.7 2.1 11.3 14.6 14.3 13.7Congo, Republic of 88.2 83.2 74.4 72.3 11.8 16.8 25.6 27.7 23.4 21.7 19.4 21.5Costa Rica 34.9 39.4 44.6 38.1 65.1 60.6 55.4 61.9 30.1 40.8 41.4 52.0Djibouti 80.4 82.2 80.3 83.7 19.6 17.8 19.7 16.3 0.8 1.7 2.6 4.3Gabon 93.6 85.7 84.3 85.6 6.4 14.3 15.7 14.4 11.5 13.0 17.4 24.7Georgia 39.0 41.3 43.5 39.0 61.0 58.7 56.5 61.0 41.3 39.7 37.0 41.0Lebanon 84.7 83.1 82.2 83.2 15.3 16.9 17.8 16.8 6.0 4.1 3.5 3.4Macedonia, FYR 33.5 32.9 31.4 33.5 66.5 67.1 68.6 66.5 42.9 42.8 43.4 42.5Mauritania 83.4 85.8 85.0 87.9 16.6 14.2 15.0 12.1 7.8 7.6 5.7 4.9Moldova 22.0 21.5 17.4 15.7 78.0 78.5 82.6 84.3 40.6 38.2 43.9 41.0Mongolia 86.8 82.0 71.1 71.2 13.2 18.0 28.9 28.8 2.5 3.1 8.8 9.0Montenegro 56.7 46.4 84.7 73.8 43.3 53.6 15.3 26.2 24.0 17.4 28.1 29.7Panama 87.6 87.1 88.6 86.5 12.4 12.9 11.4 13.5 26.1 25.1 25.5 28.2São Tomé and Príncipe 82.6 78.1 80.2 81.9 17.4 21.9 19.8 18.1 2.3 5.0 4.4 3.5


127

Tabl

e 26

. Fo

reig

n di

rect

inve

stm

ent

(FD

I) in

flow

s

Gro

up/c

ount

ryIn

war

d flo

ws

Gro

ss d

omes

tic

prod

uct

(Cur

rent

US

$ m

illio

n)FD

I (%

of G

DP)

2008

2009

2010

2011

2012

2008

2009

2010

2011

2012

2008

2009

2010

2011

2012

Mid

dle-

inco

me

Com

mon

wea

lth c

ount

ries

Bel

ize

170

109

9695

–1,

364

1,34

91,

399

1,44

8–

12.4

8.1

6.9

6.6

–B

ots

wan

a52

112

9−6

414

293

11,1

1310

,107

13,7

4715

,292

14,4

114.

71.

30.

02.

72.

0D

om

inic

a57

4124

34–

452

482

475

476

480

12.5

8.6

5.1

7.2

–Fi

ji35

013

519

620

4–

3,59

12,

882

3,17

33,

818

3,88

29.

84.

76.

25.

4–

Gre

nada

142

103

6041

–82

677

177

078

079

017

.213

.37.

85.

3–

Guy

ana

168

208

270

165

–1,

923

2,02

62,

259

2,57

72,

851

8.7

10.3

11.9

6.4

–Ja

mai

ca1,

377

485

186

173

–13

,681

12,0

6913

,203

14,4

2614

,840

––

––

–K

iriba

ti3

34

4–

131

120

142

167

176

2.0

2.6

2.6

2.3

–Le

soth

o11

210

011

413

2–

1,63

11,

717

2,20

42,

525

2,44

86.

95.

85.

25.

2–

Mal

dive

s17

515

421

628

2–

1,89

21,

985

2,13

42,

154

2,22

29.

37.

810

.113

.1–

Mau

ritiu

s37

825

743

027

3–

9,64

18,

835

9,71

811

,242

10,4

923.

92.

94.

42.

4–

Nam

ibia

750

550

686

969

–8,

830

8,85

911

,066

12,6

2313

,072

8.5

6.2

6.2

7.7

–N

auru

––

––

––

––

––

––

––

–P

apua

New

Gui

nea

−31

419

29−3

09–

8,01

07,

915

9,48

012

,394

15,6

54−0

.45.

30.

3−2

.5–

St L

ucia

161

146

110

81–

1,16

51,

167

1,20

01,

211

1,18

613

.812

.59.

26.

7–

St V

ince

nt a

nd th

e G

rena

dine

s15

910

410

311

0–

695

674

681

691

713

22.9

15.5

15.1

15.9

–

Sam

oa

463

515

–57

450

157

263

467

78.

00.

60.

92.

3–

Seyc

helle

s11

911

515

613

9–

967

847

973

1,06

01,

032

12.3

13.6

16.0

13.1

–So

lom

on

Isla

nds

9111

512

214

6–

646

601

679

867

1,00

814

.019

.217

.916

.9–

Swaz

iland

106

6613

695

–3,

020

3,16

13,

691

3,96

93,

747

3.5

2.1

3.7

2.4

–To

nga

4–

910

–34

731

836

942

947

21.

2–

2.3

2.4

–Tu

valu

22

22

–30

2732

3637

5.5

8.3

4.7

5.0

–V

anua

tu38

3242

58–

608

610

701

786

785

6.2

5.3

5.9

7.4

–O

ther

cou

ntrie

sA

lban

ia1,

241

1,34

31,

089

1,37

01,

265

12,9

6912

,119

11,8

5812

,960

13,1

199.

611

.19.

210

.69.

6A

rmen

ia93

577

757

066

348

911

,662

8,64

89,

260

10,1

389,

910

8.0

9.0

6.2

6.5

4.9

Bhu

tan

37

1916

–1,

257

1,26

51,

585

1,83

41,

780

0.2

0.5

1.2

0.9

–B

osn

ia a

nd H

erze

govi

na1,

005

139

329

380

633

18,5

4317

,083

16,7

7518

,242

17,0

485.

40.

82.

02.

13.

7C

ape

Ver

de21

112

711

610

553

1,56

21,

601

1,65

91,

901

1,89

713

.57.

97.

05.

52.

8C

ong

o, R

epub

lic o

f2,

526

1,86

22,

209

2,93

1–

11,6

7511

,204

13,1

0815

,654

17,8

7021

.616

.616

.918

.7–

Co

sta

Ric

a2,

078

1,34

71,

466

2,15

7–

29,8

3829

,431

36,3

4641

,072

45,1

537.

04.

64.

05.

3–

Gab

on

209

3353

172

8–

15,7

3212

,076

14,5

3918

,791

18,6

611.

30.

33.

73.

9–

Geo

rgia

1,59

165

386

91,

154

–12

,799

10,7

6711

,638

14,4

3515

,829

12.4

6.1

7.5

8.0

–Le

bano

n4,

333

4,80

44,

280

3,47

6–

30,0

8034

,651

37,1

2440

,094

42,9

4514

.413

.911

.58.

7–

(con

tinue

d)

128

Tabl

e 26

. Fo

reig

n di

rect

inve

stm

ent

(FD

I) in

flow

s (c

onti

nued

)

Gro

up/c

ount

ryIn

war

d flo

ws

Gro

ss d

omes

tic

prod

uct

(Cur

rent

US

$ m

illio

n)FD

I (%

of G

DP)

2008

2009

2010

2011

2012

2008

2009

2010

2011

2012

2008

2009

2010

2011

2012

Mac

edo

nia,

FYR

612

260

301

495

325

9,83

49,

314

9,33

910

,395

9,61

76.

22.

83.

24.

83.

4M

aurit

ania

343

−313

145

–3,

790

3,02

73,

671

4,27

34,

199

9.0

−0.1

3.6

1.1

–M

old

ova

727

135

202

294

169

6,05

55,

439

5,81

27,

015

7,25

412

.02.

53.

54.

22.

3M

ong

olia

845

624

1,69

14,

715

–5,

623

4,58

46,

200

8,76

110

,271

15.0

13.6

27.3

53.8

–M

ont

eneg

ro96

01,

527

760

558

–4,

520

4,15

84,

115

4,50

24,

231

21.2

36.7

18.5

12.4

–P

anam

a2,

534

1,08

62,

195

3,22

333

8323

,002

24,1

6326

,590

31,3

1636

,253

11.0

4.5

8.3

10.3

9.3

São

To

mé

and

Prín

cipe

7916

5135

2218

319

620

124

826

443

.17.

925

.214

.18.

5Su

rinam

e−2

31−9

3−2

4814

5–

3,53

33,

875

4,36

74,

304

4,73

8−6

.5−2

.4−5

.73.

4–

Tim

or-

Lest

e40

5029

47–

563

635

695

770

1,29

37.

17.

94.

16.

1–

Hig

h-in

com

eC

omm

onw

ealth

cou

ntrie

sA

ntig

ua a

nd B

arbu

da15

981

9758

–1,

240

1,09

11,

013

984

1,17

612

.87.

49.

55.

9–

Bah

amas

, The

860

664

872

595

–7,

828

7,50

17,

575

7,70

18,

149

11.0

8.9

11.5

7.7

–B

arba

dos

462

458

669

334

–4,

197

4,02

34,

033

4,06

44,

225

11.0

11.4

16.6

8.2

–B

rune

i Dar

ussa

lam

222

326

626

1,20

8–

9,77

39,

600

9,85

010

,067

16,9

542.

33.

46.

412

.0–

Cyp

rus

1,01

52,

180

711,

080

–19

,282

18,9

6119

,207

19,3

0322

,981

5.3

11.5

0.4

5.6

–M

alta

917

880

1,11

746

741

26,

654

6,47

76,

653

6,77

38,

722

13.8

13.6

16.8

6.9

4.7

St K

itts

and

Nev

is17

813

113

411

4–

610

574

575

585

748

29.1

22.8

23.3

19.5

–Tr

inid

ad a

nd T

oba

go2,

801

709

549

574

–19

,820

18,9

4918

,989

18,4

9923

,986

14.1

3.7

2.9

3.1

–O

ther

cou

ntrie

sB

ahra

in1,

794

257

156

781

–16

,540

17,0

5317

,792

18,1

66–

10.8

1.5

0.9

4.3

–C

roat

ia6,

057

3,42

884

81,

265

1,27

550

,443

46,9

3946

,279

46,2

7356

,442

12.0

7.3

1.8

2.7

2.3

Equa

toria

l Gui

nea

−794

1,63

61,

369

737

–9,

805

9,88

79,

719

10,1

9917

,697

−8.1

16.6

14.1

7.2

–Es

toni

a1,

873

1,86

72,

052

436

1,61

715

,774

13,5

5414

,005

15,1

6521

,854

11.9

13.8

14.6

2.9

7.4

Icel

and

1,20

864

258

1,10

751

218

,289

17,0

8816

,388

16,8

6113

,657

6.6

0.4

1.6

6.6

3.8

Irela

nd23

,259

53,9

3537

,764

11,5

0632

,933

220,

595

208,

558

206,

960

209,

921

210,

331

10.5

25.9

18.2

5.5

15.7

Kuw

ait

−61,

114

319

400

–93

,126

88,3

3086

,236

91,6

72–

0.0

1.3

0.4

0.4

–La

tvia

1,43

5−4

443

31,

502

916

18,9

6015

,556

15,3

0716

,138

28,3

247.

6−0

.32.

89.

33.

2Li

thua

nia

1,90

889

702

1,44

367

331

,654

26,9

8827

,390

29,0

2342

,087

6.0

0.3

2.6

5.0

1.6

Luxe

mbo

urg

49,1

57−3

1,73

6−8

4,66

318

,366

–41

,794

40,0

9041

,259

41,9

4257

,117

117.

6−7

9.2

−205

.243

.8–

No

rway

23,9

027,

482

20,3

997,

281

–31

9,51

831

4,29

431

5,79

731

9,64

349

9,66

77.

52.

46.

52.

3–

Om

an2,

952

1,50

91,

142

788

–39

,279

39,7

1141

,930

42,0

52–

7.5

3.8

2.7

1.9

–Q

atar

3,77

98,

125

4,67

0−8

732

770

,895

79,4

0392

,688

104,

702

–5.

310

.25.

0−0

.1–

Slo

veni

a1,

823

−349

633

818

−10

41,8

5538

,503

38,9

8839

,264

45,2

804.

4−0

.91.

62.

10.

0U

rugu

ay2,

142

1,60

32,

191

2,17

7–

20,6

3921

,102

22,9

9024

,492

49,0

6010

.47.

69.

58.

9–

Not

e: –

= n

ot a

vaila

ble

Sou

rce:

Wo

rld B

ank,

Wor

ld D

evel

opm

ent I

ndic

ator

s 20

13, a

vaila

ble

at: h

ttp:

//da

taba

nk.w

orld

bank

.org

(acc

esse

d Ju

ne 2

013)

129

Table 27. Total net financial flows from all sources (US$ millions)

Group/country 2005 2006 2007 2008 2009 2010 2011

Middle-incomeCommonwealth countriesBelize 24 −92 210 −124 41 11.7 30.3Botswana 82 96 128 643 1,249 239.3 796.2Dominica 7 349 38 1 35 29.0 27.0Fiji 118 −2 37 150 65 88.4 148.3Grenada 95 35 24 11 47 29.4 5.4Guyana 483 −24 112 214 143 170.8 176.4Jamaica −1,735 256 −61 239 121 958.9 603.4Kiribati 29 26 19 28 29 24.0 68.0Lesotho 50 60 110 134 118 254.0 291.0Maldives 127 142 121 73 64 114 −48Mauritius 147 749 12,572 985 1,819 4101 −1640Namibia 170 498 234 528 643 −102 664Nauru 12 18 26 33 26 28 37Papua New Guinea 496 429 514 325 2,224 5360.1 2040.3St Lucia 34 33 46 21 232 −38 131St Vincent and the Grenadines −12 16 500 215 56 2.3 −24Samoa 73 50 66 81 90 112 70Seychelles 21 −32 135 47 70 156 28Solomon Islands 170 216 256 263 215 403.1 350.0Swaziland 51 38 54 54 40 25 52Tonga 34 24 23 36 46 49 65Tuvalu 9 19 11 18 – 13 29Vanuatu 51 79 47 156 173 141 69Other countriesAlbania 481 503 673 839 799 887 801Armenia 213 364 560 481 657 584 511Bhutan 95 100 89 91 188 173 152Bosnia and Herzegovina 724 1,149 1,158 1,298 438 772 552Cape Verde 260 193 111 270 272 317 286Congo, Republic of 1,301 609 803 405 338 306 −868Costa Rica 651 1,331 764 806 −107 305 411Djibouti 96 182 133 155 328 106 188Gabon −68 166 582 −359 −277 509 533Georgia 346 765 580 1,447 1,295 867 1054Lebanon 385 532 819 1,115 – 839 132Macedonia, FYR 382 553 228 556 308 540 597Mauritania 189 182 316 314 300 390 478Moldova 157 376 433 462 233 658 405Mongolia 198 299 423 344 381 486 444Montenegro – 142 173 586 313 113 −43Panama 5,950 5,707 5,277 4,276 7,246 4693 1165São Tomé and Príncipe 32 27 13 42 34 46 64Suriname 19 38 71 118 – 134 305Timor-Leste 186 146 279 281 – 293 271

Note: – = not availableSource: OECD (2013), Geographical Distribution of Financial Flows to Developing Countries, 82–83

130

Tabl

e 28

. N

et fi

nanc

ial fl

ows

by m

ajor

cat

egor

ies

(US

$ m

illio

n)

Gro

up/c

ount

ryN

et b

ilate

ral fl

ows

from

DA

C

coun

trie

sM

ulti

late

ral fl

ows

Priv

ate

flow

s

Net

bila

tera

l flo

ws

from

EU

co

untr

ies

2008

2009

2010

2011

2008

2009

2010

2011

2008

2009

2010

2011

2008

2009

Mid

dle-

inco

me

Com

mon

wea

lth c

ount

ries

Bel

ize

−142

.26.

40

4.6

1634

12.5

25.8

−144

.81

10.1

0.1

−137

.512

.8B

ots

wan

a58

1.1

225.

419

016

3.7

6410

2349

.562

4.4

−92

95.9

84.2

73.7

379.

638

.2D

om

inic

a−1

8.5

6.7

3.4

7.1

19.9

2823

.920

.5−1

92

−2.3

−1−8

.215

.3Fi

ji11

1.6

44.5

64.5

122.

437

.821

23.3

25.5

76−8

−2.9

51.4

12.7

0.7

Gre

nada

––

––

––

––

––

––

13.7

2.9

Gre

nada

−18

−10.

94.

229

4330

.12.

2−2

1−4

−1.1

−2.2

−12

9.9

Guy

ana

84.3

6.1

70.8

87.6

128.

613

799

.888

.843

−35

6.6

17.2

75.6

16.9

Jam

aica

107.

9−1

8339

.924

4.4

132

304

920.

436

0.9

102

−145

32.9

184.

7−5

.622

8.1

Kiri

bati

20.6

24.6

2263

.16.

74.

71.

54.

8–

2−0

.13.

16.

33.

7Le

soth

o61

.468

90.2

139.

873

.346

161.

114

8.5

−4.5

−3−3

.9−2

.554

.352

.1M

aldi

ves

29.2

8.6

84.3

−56.

113

.957

20.3

2.9

12−7

28.5

−80.

438

.8−3

.6M

aurit

ius

816

1598

.541

01−1

639.

615

6.6

222

346.

615

280

315

2639

72.5

−175

3.1

765.

214

32N

amib

ia46

7.8

536.

7−1

02.4

663.

957

105

143.

346

.731

729

0−5

9.9

430.

539

2.2

499.

7N

auru

31.4

24.8

27.2

37.5

0.5

10.

9−0

.22

2–

−0.3

3.7

2.7

Pap

ua N

ew G

uine

a26

6.1

2074

5393

.620

07.7

−54.

459

.966

.532

.5−2

214

2741

03.6

−171

255

30.4

St L

ucia

−919

1.2

−39.

113

130

.741

25.5

20.3

−8.8

185

−40.

412

8.3

1.3

4.5

St V

ince

nt a

nd th

e

G

rena

dine

s19

1.9

17.8

2.3

−23.

524

.239

57.3

1618

213

1.3

−24.

519

5.4

24.8

Sam

oa

63.7

–11

269

.614

.831

57.6

39.7

3812

21.8

717

.816

.4Se

yche

lles

38.6

6015

628

.47

1038

.810

.634

4812

3.7

21.8

37.5

62.7

Solo

mo

n Is

land

s25

5.9

211

324.

132

2.9

147.

279

.527

.53.

740

.13.

58.

310

4Sw

azila

nd19

.514

.824

.652

.135

2644

.845

.71.

9−4

−6.5

−14.

816

7To

nga

26.6

41.3

48.8

658.

55

12.5

26.9

2.9

6−9

.5−2

.74

7.1

Tuva

lu15

14.8

1329

1.3

2.7

0.3

11.1

––

–0.

70.

30.

4V

anua

tu14

4.1

167.

814

0.9

68.6

11.5

50.

41.

753

.968

30.9

−22.

918

.827

.9O

ther

cou

ntrie

sA

lban

ia63

161

6.7

727.

751

0.6

198.

615

714

5.3

281.

836

436

650

1.5

299.

566

0.6

637.

3A

rmen

ia35

9.6

275.

926

5.5

179.

711

9.2

379

294.

932

2.7

130

1739

.919

.420

614

4.6

Bhu

tan

54.2

109.

498

.965

.536

.177

73.7

85.6

5.2

5125

.6−4

.628

.881

.7B

osn

ia a

nd H

erze

govi

na96

9.2

215.

839

0.6

373.

529

8.1

164.

529

1.5

108.

562

8.5

−57.

115

5.4

130.

710

37.2

185.

6C

ape

Ver

de20

7.2

210.

922

2.9

184.

363

6194

.510

2.2

44.5

49−2

7.7

−53.

520

4.8

178.

2C

ong

o, R

epub

lic o

f30

729

221

7.5

−938

9845

87.8

69.7

124

166.

5−3

2.1

−106

5.9

375.

727

5.9

Co

sta

Ric

a91

1.5

−128

.3−2

27.1

259.

8−1

05.7

2153

1.8

150.

283

5−2

41−3

0419

3.1

269.

7−8

.8

(con

tinue

d)

131

Tabl

e 28

. N

et fi

nanc

ial fl

ows

by m

ajor

cat

egor

ies

(US

$ m

illio

n) (

cont

inue

d)

Gro

up/c

ount

ryN

et b

ilate

ral fl

ows

from

DA

C

coun

trie

sM

ulti

late

ral fl

ows

Priv

ate

flow

s

Net

bila

tera

l flo

ws

from

EU

co

untr

ies

2008

2009

2010

2011

2008

2009

2010

2011

2008

2009

2010

2011

2008

2009

Djib

out

i98

.518

3.4

6313

1.9

46.3

134

35.3

49.3

32.6

50.4

−48.

741

.399

.915

0.2

Gab

on

−393

.9−3

29.1

511.

339

4.4

3552

−2.4

136.

5−2

42−2

9445

638

4.7

−18.

6−1

36.3

Geo

rgia

987.

649

035

6.2

574.

143

3.2

763

474

440.

338

341

13.3

135.

544

9.7

327.

1Le

bano

n70

3.2

414

642

17.8

342

109

124.

181

.949

.911

5.2

433.

7−1

72.3

680.

119

8.6

Mac

edo

nia,

FYR

369.

626

4.5

351.

726

1.2

171.

434

.514

7.5

157.

249

.911

5.2

258.

918

9.6

346.

124

0.9

Mau

ritan

ia14

9.3

143.

168

192.

114

113

630

3.9

273.

9−8

.724

−48

52.1

141.

515

9.3

Mo

ldo

va24

410

5.9

227.

811

.719

5.1

116.

339

7.6

362.

513

3.7

1213

1.4

−85.

526

7.3

166.

7M

ong

olia

173.

717

8.1

241

322.

215

8.8

181

232.

111

4.2

−61.

1–

20.3

55.4

103.

621

.7M

ont

eneg

ro51

5.9

247

712.

514

06.1

53.1

61.2

49.4

78.1

412

169

2.2

−141

.929

5.6

237.

7O

man

172.

97.

412

28.6

–−6

1.4

−3.2

−10.

7–

−149

.4−3

52.6

1245

–−1

56−9

7.3

Pan

ama

4103

.669

22.2

4408

.510

64.4

172.

632

228

4.2

99.9

4024

6839

3994

.361

5.5

−120

1018

.6S

ão T

om

é an

d P

rínci

pe20

.922

.729

.527

.220

.511

16.3

36.8

−4.9

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Mid

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Com

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135


Group/country NET ODA received

Net ODA received (current US$ ’000)

Net bilateral aid flows from DAC donors, total (current US$ ’000)

NET ODA received per capita (current US$ ’000)

Gross national income (%)

Gross capital formation (%)

Imports (%)

IDA Resource Allocation Index (1 = low to 6 = high)

2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2012

Commonwealth countriesBelize 27,560 24,760 29,790 17,170 19,250 15,470 92 80 90 2 2 2 – – – 3 3 3 – – – –Botswana 279,160 156,140 126,360 255,680 145,360 105,360 143 79 61 3 1 1 8 4 2 5 2 2 – – – –Dominica 35,950 32,450 24,790 27,710 29,210 23,690 506 456 341 8 7 5 38 32 22 13 12 9 4 4 4 4Fiji 70,990 76,400 75,230 62,510 68,660 71,620 83 89 87 2 2 2 – – – 4 3 – – – – –Grenada 47,810 33,840 12,800 18,090 18,820 12,120 458 323 114 7 5 2 26 20 7 12 8 3 4 4 4 4Guyana 173,350 153,210 159,430 92,800 93,470 98,310 222 195 200 9 7 6 32 27 26 12 9 3 3 3 3Jamaica 149,300 141,230 53,620 112,900 105,110 10,430 55 52 16 1 1 0 6 5 1 2 2 1 – – – –Kiribati 27,140 22,820 63,960 24,410 22,040 62,470 282 233 641 16 11 27 – – – – – – 3 3 3 3Lesotho 122,390 256,230 264,580 86,780 168,410 195,740 62 128 128 6 10 9 26 39 39 5 9 8 4 3 3 3Maldives 33,200 110,750 46,010 22,270 62,190 21,140 104 340 139 2 6 3 – – – 2 5 2 3 3 3 3Mauritius 154,970 125,270 191,540 156,790 126,030 183,860 122 98 142 2 1 2 8 5 6 3 1 2 – – – –Namibia 325,530 256,440 284,590 281,710 224,290 255,510 152 118 124 4 2 2 16 11 11 6 4 4 – – – –Nauru – – – – – – – – – – – – – – – – – – – – – –Papua New Guinea 411,700 511,410 612,320 354,540 490,790 575,750 61 75 87 5 6 5 26 30 30 8 7 3 3 3 3St Lucia 40,970 41,150 36,930 22,840 25,060 22,110 234 232 197 4 4 3 12 10 8 6 5 4 4 4 4 4St Vincent and the

Grenadines30,760 16,850 18,810 18,250 11,440 13,310 282 154 162 5 3 3 19 10 10 7 4 4 4 4 4 4

Samoa 77,310 147,480 101,110 55,510 100,770 81,940 419 793 532 16 27 16 – – – 24 37 23 4 4 4 4Seychelles 22,760 56,040 21,330 23,760 33,880 13,030 261 624 239 3 6 2 – – – 2 5 2 – – – –Solomon Islands 205,850 340,490 333,750 205,920 321,390 305,190 400 647 620 44 61 50 – – – 42 49 43 3 3 3 3Swaziland 56,030 91,450 129,740 33,660 52,550 95,390 48 77 103 2 3 3 17 24 33 2 3 – – – – –Tonga 39,240 70,450 93,730 35,360 59,230 75,820 379 677 896 12 19 21 50 63 58 20 – – 3 3 3 3Tuvalu 17,470 13,330 42,660 15,180 13,230 33,060 1781 1356 4323 36 26 77 – – – – – – – – – 3Vanuatu 103,190 108,310 92,060 100,870 109,560 92,160 447 458 377 18 16 12 43 45 43 30 23 17 3 3 3 3Other countriesAlbania 356,960 340,700 307,270 315,340 302,220 334,570 113 108 111 3 3 3 10 11 11 5 5 5 – – – –Armenia 525,970 342,820 374,280 274,210 239,840 264,450 177 116 128 6 4 4 18 11 12 12 7 7 4 4 4 4Bhutan 125,380 131,000 143,850 58,190 77,890 72,570 178 183 197 10 9 8 24 16 14 17 13 10 4 4 4 4Bosnia and Herzegovina 414,310 510,370 424,620 354,190 352,330 531,410 108 133 162 2 3 3 12 16 17 5 6 6 4 4 4 4Cape Verde 195,600 327,890 245,650 184,440 284,880 249,030 403 672 511 13 21 14 31 52 36 17 27 17 4 4 4 4Congo, Republic of 283,280 1,312,250 253,700 252,320 1,247,570 200,900 71 319 61 4 15 2 13 53 7 3 3 3 3Costa Rica 108,570 95,030 38,460 105,690 94,210 33,150 24 20 8 – – – 2 1 0 1 1 – – – – –Djibouti 166,710 132,240 141,620 107,950 108,850 101,720 203 159 167 – – – – – – 29 27 21 3 3 3 3Gabon 77,210 104,000 75,630 61,760 96,890 62,150 51 67 43 1 1 0 – – – – – – – – – –Georgia 907,160 625,190 549,670 609,190 508,620 498,990 206 140 132 8 5 4 65 25 16 16 9 6 4 4 4 4Lebanon 580,290 447,930 432,280 463,430 317,010 355,130 137 103 108 2 1 1 5 4 4 2 1 1 – – – –Macedonia, FYR 192,470 187,170 164,660 186,870 150,270 175,160 92 89 92 2 2 2 8 8 7 4 3 3 – – – –Mauritania 373,470 374,400 370,230 157,860 130,920 214,140 106 104 103 12 10 9 40 38 29 – – – 3 3 3 3Moldova 243,580 470,370 451,040 205,490 232,400 274,550 68 132 132 4 7 6 19 34 27 6 10 8 4 4 4 4Mongolia 370,980 301,970 339,820 219,400 238,890 256,550 139 111 123 8 5 4 24 12 6 13 7 4 3 3 3 3Montenegro 75,030 80,280 73,690 61,970 56,930 101,210 121 129 200 2 2 3 7 9 14 3 3 4 – – – –Panama 65,010 128,870 97,920 60,810 125,290 104,210 18 35 29 0 0 0 1 2 1 0 1 – – – – –São Tomé and Príncipe 30,510 49,300 75,080 23,270 38,990 42,850 176 277 408 16 25 30 – – – 29 40 50 3 3 3 3Suriname 157,040 103,660 94,580 151,110 97,260 74,770 302 197 179 4 2 2 – – – 9 6 4 – – – –Timor-Leste 216,440 291,500 283,760 193,280 272,740 261,660 195 255 241 8 9 7 – – – 18 21 15 3 3 3 3High-incomeCommonwealth countriesAntigua and Barbuda 5,640 19,070 15,300 3,350 19,270 15,080 65 219 168 – 2 1 1 5 4 1 3 2 – – – –Bahamas, The – – – – – – – – – – – – – – – – – – – – – –Barbados 11,960 16,200 – 10,650 14,800 – 43 58 – – – – 1 3 – – 1 – – – – –Brunei Darussalam – – – – – – – – – – – – – – – – – – – – – –Cyprus – – – – – – – – – – – – – – – – – – – – – –Malta – – – – – – – – – – – – – – – – – – – – – –St Kitts and Nevis 5,130 11,420 16,470 4,130 8,710 18,110 99 218 297 1 2 2 2 5 7 1 3 4 – – – –

(continued)

136








Imports (%)


2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2012

Commonwealth countriesBelize 27,560 24,760 29,790 17,170 19,250 15,470 92 80 90 2 2 2 – – – 3 3 3 – – – –Botswana 279,160 156,140 126,360 255,680 145,360 105,360 143 79 61 3 1 1 8 4 2 5 2 2 – – – –Dominica 35,950 32,450 24,790 27,710 29,210 23,690 506 456 341 8 7 5 38 32 22 13 12 9 4 4 4 4Fiji 70,990 76,400 75,230 62,510 68,660 71,620 83 89 87 2 2 2 – – – 4 3 – – – – –Grenada 47,810 33,840 12,800 18,090 18,820 12,120 458 323 114 7 5 2 26 20 7 12 8 3 4 4 4 4Guyana 173,350 153,210 159,430 92,800 93,470 98,310 222 195 200 9 7 6 32 27 26 12 9 3 3 3 3Jamaica 149,300 141,230 53,620 112,900 105,110 10,430 55 52 16 1 1 0 6 5 1 2 2 1 – – – –Kiribati 27,140 22,820 63,960 24,410 22,040 62,470 282 233 641 16 11 27 – – – – – – 3 3 3 3Lesotho 122,390 256,230 264,580 86,780 168,410 195,740 62 128 128 6 10 9 26 39 39 5 9 8 4 3 3 3Maldives 33,200 110,750 46,010 22,270 62,190 21,140 104 340 139 2 6 3 – – – 2 5 2 3 3 3 3Mauritius 154,970 125,270 191,540 156,790 126,030 183,860 122 98 142 2 1 2 8 5 6 3 1 2 – – – –Namibia 325,530 256,440 284,590 281,710 224,290 255,510 152 118 124 4 2 2 16 11 11 6 4 4 – – – –Nauru – – – – – – – – – – – – – – – – – – – – – –Papua New Guinea 411,700 511,410 612,320 354,540 490,790 575,750 61 75 87 5 6 5 26 30 30 8 7 3 3 3 3St Lucia 40,970 41,150 36,930 22,840 25,060 22,110 234 232 197 4 4 3 12 10 8 6 5 4 4 4 4 4St Vincent and the

Grenadines30,760 16,850 18,810 18,250 11,440 13,310 282 154 162 5 3 3 19 10 10 7 4 4 4 4 4 4

Samoa 77,310 147,480 101,110 55,510 100,770 81,940 419 793 532 16 27 16 – – – 24 37 23 4 4 4 4Seychelles 22,760 56,040 21,330 23,760 33,880 13,030 261 624 239 3 6 2 – – – 2 5 2 – – – –Solomon Islands 205,850 340,490 333,750 205,920 321,390 305,190 400 647 620 44 61 50 – – – 42 49 43 3 3 3 3Swaziland 56,030 91,450 129,740 33,660 52,550 95,390 48 77 103 2 3 3 17 24 33 2 3 – – – – –Tonga 39,240 70,450 93,730 35,360 59,230 75,820 379 677 896 12 19 21 50 63 58 20 – – 3 3 3 3Tuvalu 17,470 13,330 42,660 15,180 13,230 33,060 1781 1356 4323 36 26 77 – – – – – – – – – 3Vanuatu 103,190 108,310 92,060 100,870 109,560 92,160 447 458 377 18 16 12 43 45 43 30 23 17 3 3 3 3Other countriesAlbania 356,960 340,700 307,270 315,340 302,220 334,570 113 108 111 3 3 3 10 11 11 5 5 5 – – – –Armenia 525,970 342,820 374,280 274,210 239,840 264,450 177 116 128 6 4 4 18 11 12 12 7 7 4 4 4 4Bhutan 125,380 131,000 143,850 58,190 77,890 72,570 178 183 197 10 9 8 24 16 14 17 13 10 4 4 4 4Bosnia and Herzegovina 414,310 510,370 424,620 354,190 352,330 531,410 108 133 162 2 3 3 12 16 17 5 6 6 4 4 4 4Cape Verde 195,600 327,890 245,650 184,440 284,880 249,030 403 672 511 13 21 14 31 52 36 17 27 17 4 4 4 4Congo, Republic of 283,280 1,312,250 253,700 252,320 1,247,570 200,900 71 319 61 4 15 2 13 53 7 3 3 3 3Costa Rica 108,570 95,030 38,460 105,690 94,210 33,150 24 20 8 – – – 2 1 0 1 1 – – – – –Djibouti 166,710 132,240 141,620 107,950 108,850 101,720 203 159 167 – – – – – – 29 27 21 3 3 3 3Gabon 77,210 104,000 75,630 61,760 96,890 62,150 51 67 43 1 1 0 – – – – – – – – – –Georgia 907,160 625,190 549,670 609,190 508,620 498,990 206 140 132 8 5 4 65 25 16 16 9 6 4 4 4 4Lebanon 580,290 447,930 432,280 463,430 317,010 355,130 137 103 108 2 1 1 5 4 4 2 1 1 – – – –Macedonia, FYR 192,470 187,170 164,660 186,870 150,270 175,160 92 89 92 2 2 2 8 8 7 4 3 3 – – – –Mauritania 373,470 374,400 370,230 157,860 130,920 214,140 106 104 103 12 10 9 40 38 29 – – – 3 3 3 3Moldova 243,580 470,370 451,040 205,490 232,400 274,550 68 132 132 4 7 6 19 34 27 6 10 8 4 4 4 4Mongolia 370,980 301,970 339,820 219,400 238,890 256,550 139 111 123 8 5 4 24 12 6 13 7 4 3 3 3 3Montenegro 75,030 80,280 73,690 61,970 56,930 101,210 121 129 200 2 2 3 7 9 14 3 3 4 – – – –Panama 65,010 128,870 97,920 60,810 125,290 104,210 18 35 29 0 0 0 1 2 1 0 1 – – – – –São Tomé and Príncipe 30,510 49,300 75,080 23,270 38,990 42,850 176 277 408 16 25 30 – – – 29 40 50 3 3 3 3Suriname 157,040 103,660 94,580 151,110 97,260 74,770 302 197 179 4 2 2 – – – 9 6 4 – – – –Timor-Leste 216,440 291,500 283,760 193,280 272,740 261,660 195 255 241 8 9 7 – – – 18 21 15 3 3 3 3High-incomeCommonwealth countriesAntigua and Barbuda 5,640 19,070 15,300 3,350 19,270 15,080 65 219 168 – 2 1 1 5 4 1 3 2 – – – –Bahamas, The – – – – – – – – – – – – – – – – – – – – – –Barbados 11,960 16,200 – 10,650 14,800 – 43 58 – – – – 1 3 – – 1 – – – – –Brunei Darussalam – – – – – – – – – – – – – – – – – – – – – –Cyprus – – – – – – – – – – – – – – – – – – – – – –Malta – – – – – – – – – – – – – – – – – – – – – –St Kitts and Nevis 5,130 11,420 16,470 4,130 8,710 18,110 99 218 297 1 2 2 2 5 7 1 3 4 – – – –

137

Table 30. Aid dependency (continued)







Imports (%)


2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2012

Trinidad and Tobago 6,840 4,330 6,010 4,070 – 5 3 – – – – – – – – – – – – – –Other countriesBahrain – – – – – – – – – – – – – – – – – – – – – –Croatia 168,820 150,710 161,040 142,660 280 38 34 – – – – 1 1 1 1 – – – – –Equatorial Guinea 31,490 84,710 24,190 28,210 78,940 21,800 47 122 34 – 1 – – 1 – – – – – –Estonia – – – – – – – – – – – – – – – – – – – – – –Kuwait – – – – – – – – – – – – – – – – – – – – – –Latvia – – – – – – – – – – – – – – – – – – – – – –Lithuania – – – – – – – – – – – – – – – – – – – – – –Oman 153,820 −40,320 – 8,390 7,550 – 58 −14 – – – – – – – 1 – – – – – –Qatar – – – – – – – – – – – – – – – – – – – – – –Slovenia – – – – – – – – – – – – – – – – – – – – – –Uruguay 50,030 46,710 16,100 44,230 40,020 11,060 15 14 4 – – – 1 1 – 1 – – – – – –


138

Table 30. Aid dependency (continued)







Imports (%)


2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2012

Trinidad and Tobago 6,840 4,330 6,010 4,070 – 5 3 – – – – – – – – – – – – – –Other countriesBahrain – – – – – – – – – – – – – – – – – – – – – –Croatia 168,820 150,710 161,040 142,660 280 38 34 – – – – 1 1 1 1 – – – – –Equatorial Guinea 31,490 84,710 24,190 28,210 78,940 21,800 47 122 34 – 1 – – 1 – – – – – –Estonia – – – – – – – – – – – – – – – – – – – – – –Kuwait – – – – – – – – – – – – – – – – – – – – – –Latvia – – – – – – – – – – – – – – – – – – – – – –Lithuania – – – – – – – – – – – – – – – – – – – – – –Oman 153,820 −40,320 – 8,390 7,550 – 58 −14 – – – – – – – 1 – – – – – –Qatar – – – – – – – – – – – – – – – – – – – – – –Slovenia – – – – – – – – – – – – – – – – – – – – – –Uruguay 50,030 46,710 16,100 44,230 40,020 11,060 15 14 4 – – – 1 1 – 1 – – – – – –


139

Table 31. Average exchange rates

Group/country National currency per US$

2007 2008 2009 2010 2011 2012

Middle-incomeCommonwealth countriesBelize 2.0 2.0 2.0 2.0 2.0 2.0Botswana 6.1 6.8 7.2 6.8 6.8 7.6Dominica 2.7 2.7 2.7 2.7 2.7 2.7Fiji 1.6 1.6 2.0 1.9 1.8 1.8Grenada 2.7 2.7 2.7 2.7 2.7 2.7Guyana 202.3 203.6 204.0 203.6 204.0 204.4Jamaica 69.2 72.8 87.9 87.2 85.9 88.8Kiribati 1.2 1.2 1.3 1.1 1.0 1.0Lesotho 7.0 8.3 8.5 7.3 7.3 8.2Maldives 12.8 12.8 12.8 12.8 14.6 15.4Mauritius 31.3 28.5 32.0 30.8 28.7 30.1Namibia 7.0 8.3 8.5 7.3 7.3 8.2Nauru – – – – – –Papua New Guinea 3.0 2.7 2.8 2.7 2.4 2.1St Lucia 2.7 2.7 2.7 2.7 2.7 2.7St Vincent and the Grenadines 2.7 2.7 2.7 2.7 2.7 2.7Samoa 2.6 2.6 2.7 2.5 2.3 2.3Seychelles 6.7 9.5 13.6 12.1 12.4 13.7Solomon Islands 7.7 7.7 8.1 8.1 7.6 7.4Swaziland 7.0 8.3 8.5 7.3 7.3 8.2Tonga 2.0 1.9 2.0 1.9 1.7 1.7Tuvalu – – – – – –Vanuatu 102.4 101.3 106.7 96.9 89.5 –Other countriesAlbania 90.4 83.9 95.0 103.9 100.9 108.2Armenia 342.1 306.0 363.3 373.7 372.5 401.8Bhutan 41.3 43.5 48.4 45.7 46.7 53.4Bosnia and Herzegovina 1.4 1.3 1.4 1.5 1.4 1.5Cape Verde 80.6 75.3 79.4 83.3 79.3 85.8Congo, Republic of 479.3 447.8 472.2 495.3 471.9 510.5Costa Rica 516.6 526.2 573.3 525.8 505.7 502.9Djibouti 177.7 177.7 177.7 177.7 177.7 177.7Gabon 479.3 447.8 472.2 495.3 471.9 510.5Georgia 1.7 1.5 1.7 1.8 1.7 1.7Lebanon 1507.5 1507.5 1507.5 1507.5 1507.5 1507.5Macedonia, FYR 44.7 41.9 44.1 46.5 44.2 47.9Mauritania 258.6 238.2 262.4 275.9 281.1 296.6Moldova 12.1 10.4 11.1 12.4 11.7 12.1Mongolia 1170.4 1165.8 1437.8 1357.1 1265.5 1357.6Montenegro 0.7 0.7 0.7 0.8 0.7 0.8Panama 1.0 1.0 1.0 1.0 1.0 1.0São Tomé and Príncipe 13536.8 14695.2 16208.5 18498.6 17622.9 19068.4Suriname 2.7 2.7 2.7 2.7 3.3 3.3Timor-Leste 1.0 1.0 1.0 1.0 1.0 1.0High-incomeCommonwealth countriesAntigua and Barbuda 2.7 2.7 2.7 2.7 2.7 2.7Bahamas, The 1.0 1.0 1.0 1.0 1.0 1.0Barbados 2.0 2.0 2.0 2.0 2.0 2.0Brunei Darussalam 1.5 1.4 1.5 1.4 1.3 1.2Cyprus 0.4 – – – – –Malta 0.3 – – – – –St Kitts and Nevis 2.7 2.7 2.7 2.7 2.7 2.7Trinidad and Tobago 6.3 6.3 6.3 6.4 6.4 6.4

(continued)

140

Table 31. Average exchange rates (continued)

Group/country National currency per US$

2007 2008 2009 2010 2011 2012

Other countriesBahrain 0.4 0.4 0.4 0.4 0.4 0.4Croatia 5.4 4.9 5.3 5.5 5.3 5.9Equatorial Guinea 479.3 447.8 472.2 495.3 471.9 510.5Estonia 11.4 10.7 11.3 11.8 – –Iceland 64.1 87.9 123.6 122.2 116.0 125.1Ireland – – – – – –Kuwait 0.3 0.3 0.3 0.3 0.3 0.3Latvia 0.5 0.5 0.5 0.5 0.5 0.5Lithuania 2.5 2.4 2.5 2.6 2.5 2.7Luxembourg – – – – – –Norway 5.9 5.6 6.3 6.0 5.6 5.8Oman 0.4 0.4 0.4 0.4 0.4 0.4Qatar 3.6 3.6 3.6 3.6 3.6 3.6Slovenia – – – – – –Uruguay 23.5 20.9 22.6 20.1 19.3 20.3


141

Table 32. Money supply and nominal interest rates

Group/country Money supply (millions national currency) Interest rates (% per annum)

2008 2009 2010 2011 2012 2008 2009 2010 2011 2012

Middle-incomeCommonwealth countriesBelize 706 713 708 839 1,103 14.1 14.1 13.9 13.4 12.4Botswana 7,768 7,108 9,264 8,675 10,555 16.5 13.8 11.5 11.0 11.0Dominica 210 231 229 224 272 9.1 10.0 9.5 8.8 9.0Fiji 1,357 1,262 1,411 2,000 2,092 8.0 7.9 7.5 7.5 7.0Grenada 509 480 474 460 446 9.5 11.0 10.6 10.7 9.7Guyana 75,525 79,639 97,300 113,587 132,055 14.6 14.5 14.5 14.5 13.9Jamaica 112,147 120,510 153,851 169,413 159,453 16.8 16.4 20.5 19.5 17.6Kiribati – – – – – – – – – –Lesotho 3,877 4,179 4,989 2,822 3,476 16.2 13.0 11.2 10.4 10.1Maldives 9,812 11,500 12,523 15,028 15,804 13.0 13.0 10.4 10.2 10.5Mauritius 75,590 86,001 85,798 89,733 96,801 11.5 9.3 8.9 8.9 8.7Namibia 17,998 20,547 23,061 26,319 24,949 13.7 11.1 9.7 8.7 8.7Nauru – – – – – – – – – –Papua New Guinea 5,520 6,233 7,644 9,620 11,148 9.2 10.1 10.4 10.8 10.8St Lucia 823 807 810 840 897 10.1 10.6 10.6 10.0 9.5St Vincent and the

Grenadines461 413 420 417 420 9.5 9.2 9.2 9.1 9.4

Samoa 189 224 265 254 246 12.7 12.1 10.7 10.0 9.9Seychelles 4,725 4,784 5,355 6,331 6,217 11.8 15.3 12.7 11.2 12.2Solomon Islands 995 1,128 1,304 1,873 2,396 14.4 15.3 14.4 13.2 11.3Swaziland 1,972 2,337 2,539 2,882 3,439 14.8 11.4 9.8 9.0 8.8Tonga 96 91 107 123 148 12.5 12.5 11.5 11.4 10.9Tuvalu – – – – – – – – – –Vanuatu 20,012 22,448 22,132 22,413 – 5.3 5.5 5.5 – –Other countriesAlbania 312,520 324,929 325,086 325,193 330,730 13.0 12.7 12.8 12.4 10.9Armenia 482,881 500,786 547,279 636,701 694,308 17.0 18.8 19.2 17.8 17.2Bhutan 14,165 22,634 29,074 33,216 33,376 13.8 13.8 14.0 14.0 14.0Bosnia and Herzegovina

6,799 6,625 7,111 7,286 7,196 7.0 7.9 7.9 7.4 6.9

Cape Verde 46,112 44,538 48,122 45,332 47,745 10.0 11.0 11.0 9.8 9.9Congo, Republic of 834,642 871,033 1,165,809 1,627,683 1,941,388 – – – – –Costa Rica 4,019,365 4,244,603 4,760,511 5,277,225 – 15.8 19.7 17.1 16.1 18.2Djibouti 82,236 102,678 127,292 123,096 – 11.6 11.1 10.3 10.6 –Gabon 759,260 764,766 933,103 1,236,573 1,221,798 – – – – –Georgia 2,641 2,787 3,695 4,069 4,262 23.0 24.2 22.5 22.2 22.1Lebanon 4,269,286 4,839,666 5,728,339 6,138,351 7,103,571 10.0 9.6 8.3 7.5 7.2Macedonia, FYR 69,744 72,710 79,845 85,166 88,739 9.7 10.1 9.5 8.9 8.5Mauritania 203,057 232,111 259,969 325,221 363,488 20.3 19.5 17.0 17.0 17.0Moldova 13,560 16,006 18,641 20,823 23,962 21.1 20.5 16.4 14.4 13.4Mongolia 930,460 1,023,239 1,923,758 2,521,915 2,694,515 20.6 21.7 20.1 16.6 18.1Montenegro 577 534 586 579 614 9.2 9.4 9.5 9.7 9.6Panama – – – – – 8.2 8.2 7.7 6.9 6.9São Tomé and Príncipe – – – – – 32.4 31.1 28.9 27.0 26.2Suriname 2,422 2,656 2,985 3,523 4,291 12.2 11.7 11.6 11.8 11.7Timor-Leste 104 157 141 163 206 13.1 11.2 11.0 11.0 12.2High-incomeCommonwealth countriesAntigua and Barbuda 1,238 967 978 950 965 10.4 10.1 11.0 10.9 10.2Bahamas, The 1,255 1,252 1,310 1,408 1,537 5.5 5.5 5.5 5.1 4.8Barbados 3,497 3,592 – – – 10.0 9.2 8.7 8.7 8.7Brunei Darussalam – – – – – 5.5 5.5 5.5 5.5 5.5Cyprus 9,314 10,560 10,926 11,381 11,820 – – – – –Malta 3,861 4,345 4,966 5,373 5,918 5.9 4.5 4.6 4.7 4.7

(continued)

142

Table 32. Money supply and nominal interest rates (continued)

Group/country Money supply (millions national currency) Interest rates (% per annum)

2008 2009 2010 2011 2012 2008 2009 2010 2011 2012

St Kitts and Nevis 726 678 744 857 1,002 8.7 8.8 8.6 9.4 8.7Trinidad and Tobago 19,227 26,655 – – – 12.4 11.9 9.3 8.0 –Other countriesBahrain 1,899 2,158 2,304 2,637 2,611 8.2 7.9 7.2 6.8 6.0Croatia 56,005 48,184 53,695 57,905 59,766 10.1 11.6 10.4 9.7 9.5Equatorial Guinea 509,698 668,395 891,786 960,842 1,480,100 – – – – –Estonia 3,697 3,612 4,646 7,201 8,219 8.5 9.4 7.8 6.1 5.7Iceland 518,118 539,395 524,986 505,957 467,736 – – – – –Ireland 79,601 101,213 98,467 91,392 93,376 – – – – –Kuwait 4,901 5,159 6,128 6,858 8,010 7.6 6.2 4.9 5.2 5.0Latvia 3,344 2,980 3,771 4,357 4,833 11.9 16.2 9.6 6.4 5.5Lithuania 23,322 22,049 27,397 31,318 35,896 8.4 8.4 6.0 – –Luxembourg 70,349 73,066 94,761 94,852 110,228 – – – – –Oman 2,289 2,697 3,298 3,443 3,968 7.1 7.4 6.8 6.2 5.7Qatar 67,306 71,393 87,579 113,318 118,577 6.8 7.0 7.3 5.5 5.4Slovenia 10,097 10,911 12,061 12,388 12,858 6.7 5.9 – – –Uruguay 120,218 121,379 156,219 186,611 203,325 12.4 15.3 10.3 9.8 11.2


143

Table 33. Doing business

Group/country Rank Starting a business Dealing with construction permits Getting electricity Registering property Getting credit

2010 2011 2012 2013 2010 2011 2012 2013 2014 2010 2011 2012 2013 2014 2010 2011 2012 2013 2014 2010 2011 2012 2013 2014 2010 2011 2012 2013 2014Middle-incomeCommonwealth countriesBelize 93 99 104 106 70.74 70.46 70.05 69.97 70.64 90.77 90.69 90.62 90.61 90.72 78.6 78.5 78.41 78.39 78.55 61.89 61.91 61.93 61.9 61.85 43.75 43.75 43.75 43.75 43.75Botswana 50 52 65 56 72.55 72.54 72.57 72.59 72.58 60.81 60.62 61.06 61.61 70.95 70.51 70.4 70.72 70.92 70.79 79.87 79.9 79.82 79.74 79.77 62.5 62.5 62.5 62.5 62.5Dominica 85 88 69 77 87.41 87.48 87.72 88.5 88.34 83.39 83.39 83.39 83.43 83.42 77.33 77.27 77.43 78.37 78.16 58.67 58.69 59.46 59.39 59.35 56.25 56.25 56.25 56.25 56.25Fiji 61 62 58 62 74.52 74.7 72.98 66.49 66.59 75.41 75.44 75.4 75.29 75.31 76.62 76.95 76.54 77.43 77.69 82.39 82.39 82.39 78.91 78.91 68.75 68.75 68.75 68.75 68.75Grenada 98 92 102 107 83.86 84.88 84.89 85.61 85.54 88.73 88.73 88.73 88.79 88.78 74.28 74.19 74.24 74.51 74.52 55.49 58.87 58.86 58.82 58.83 43.75 43.75 43.75 43.75 43.75Guyana 101 100 113 115 79.03 80.33 81.65 81.57 81.75 82.53 82.71 82.76 82.75 82.77 50.29 51.84 51.98 51.89 52.12 71.9 71.9 67.16 67.16 67.16 25 25 25 25 25Jamaica 79 81 91 94 88.8 88.81 88.79 88.84 90.88 84.35 84.42 84.69 84.85 85.75 67.37 67.39 67.55 66.98 67.04 61.04 66.48 66.48 66.48 63.12 50 50 50 50 50Kiribati 91 93 117 122 79.02 78.81 78.92 78.89 78.82 71.74 71.64 71.69 71.68 71.64 50.38 49.31 49.89 49.75 49.4 53.29 53.28 53.28 53.27 53.27 31.25 31.25 31.25 31.25 31.25Lesotho 137 138 139 136 76.82 76.94 77.12 81.5 81.69 55.5 55.39 56.27 57.08 58.34 59.75 59.78 60.54 63.29 64.34 58.4 58.38 58.52 58.63 70.48 37.5 37.5 37.5 37.5 37.5Maldives 96 85 81 95 89.6 89.69 89.76 90.17 90.13 84.58 84.56 84.57 84.62 84.62 66.04 66.21 66.36 65.96 65.9 0 48.23 48.45 49.49 49.4 50 50 50 50 50Mauritius 20 20 20 20 91.16 91.2 91.23 91.26 91.23 66.53 66.57 66.59 66.61 66.62 81.65 81.78 81.85 82.94 82.99 68.97 69 69.46 70.27 70.28 56.25 56.25 56.25 68.75 75Namibia 68 69 94 98 67.17 67.41 67.56 67.4 67.85 82.58 82.85 82.88 82.77 82.85 72.52 72.74 72.93 75.44 75.77 58.12 54.86 55.26 50.78 47.09 68.75 68.75 68.75 68.75 68.75Nauru – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Papua New Guinea 108 103 108 113 77.77 77.92 78.17 77.98 78.04 60.38 60.49 60.68 60.87 60.92 86.26 86.25 86.28 86.31 86.32 73.26 73.27 73.3 73.32 73.33 31.25 50 50 56.25 56.25St. Lucia 45 53 59 64 87.29 87.05 86.75 87.55 87.46 89.81 89.77 89.76 89.9 91.11 84.7 84.63 84.53 84.67 84.64 58.8 58.88 58.91 58.62 58.66 43.75 43.75 43.75 43.75 43.75St. Vincent and the

Grenadines72 75 75 82 84.47 84.66 84.53 85.05 85.14 89.84 89.78 89.77 89.8 89.8 – – – – – 55.56 55.56 55.55 55.59 55.6 43.75 43.75 43.75 43.75 43.75

Samoa 59 61 55 61 91.59 91.61 91.62 91.65 91.63 72.5 72.51 72.52 72.53 72.52 87.65 87.73 87.82 88.06 88.09 67.48 80.96 82.32 82.35 78.74 43.75 43.75 43.75 43.75 43.75Seychelles 92 95 77 80 73.21 73.42 73.6 73.81 74.19 75.49 75.4 75.44 75.5 75.52 60.18 59.86 60.09 60.36 61.53 74.44 74.44 74.44 74.44 74.44 25 25 25 25 25Solomon Islands 106 96 92 97 68.73 67.73 73.67 81.66 81.72 78.88 78.07 78.46 78.76 78.79 66.29 63.99 65.11 65.95 65.69 28.8 51.97 52.11 52.17 52.18 25 56.25 56.25 56.25 56.25Swaziland 120 123 63.26 64.25 64.7 65.33 68.82 82.64 82.68 82.84 83.06 82.91 57.71 57.91 58.54 59.39 58.8 56.45 56.6 58.95 58.93 58.94 68.75 68.75 68.75 68.75 68.75Tonga 66 71 60 57 88.3 88.45 90.02 90.26 90.33 84.77 84.99 85.04 85.14 85.22 81.58 81.66 82.78 82.82 82.85 55.3 55.51 51.64 51.66 51.68 43.75 43.75 56.25 56.25 68.75Tuvalu – – – – – – – – – – – – – – – – – – – – – – – – – – – – –Vanuatu 59 60 78 74 70.92 71.55 74.31 74.29 74.41 95.12 95.39 87.99 84.33 84.42 48.19 48.67 48.77 67.49 67.56 54.78 54.78 64.89 64.86 64.85 56.25 56.25 56.25 56.25 68.75Other countriesAlbania 81 82 82 90 86.01 86.06 86.46 89.3 89.44 47.33 47.38 – – – 55.52 55.51 55.62 55.67 55.78 56.41 54.93 59.28 60.18 60.75 87.5 87.5 87.5 87.5 87.5Armenia 44 48 40 37 89.59 89.54 94.45 94.49 97.32 64.36 64.15 71.25 72.69 72.67 47.87 47.72 47.8 55.02 55.05 92.57 92.14 92.18 92.38 92.41 68.75 68.75 75 75 75Bhutan 140 142 146 141 76.75 76.84 79.02 79.02 80.02 65.2 65.34 65.33 65.43 65.59 72.37 72.76 72.76 74.59 75.09 74.57 74.57 74.57 74.57 74.57 18.75 18.75 43.75 43.75 50Bosnia and Herzegovina 110 110 130 131 62.04 62.87 68.24 70.95 70.95 52.93 52.65 59.3 59.4 59.42 49.4 49.77 49.84 49.85 49.85 61.82 67.42 67.43 68.32 68.32 62.5 62.5 62.5 62.5 62.5Cape Verde 142 132 128 121 76.63 80.89 81.24 81.73 85.59 70.4 69.89 70.81 71.5 71.97 59.33 58.99 59.35 59.86 60.2 62.33 68.54 73.38 73.56 74.69 31.25 31.25 50 50 50Congo, Republic of 177 177 186 185 41.98 37.35 42.56 46.53 50.06 47 42.51 49.52 63.47 67.66 42.54 35.27 44.38 46.04 46.47 57.41 58.96 43.94 43.94 43.94 31.25 31.25 50 50 50Costa Rica 121 125 109 102 66.22 66.25 66.3 66.25 79.44 64.27 64.39 64.46 71.65 78.81 79.21 79.25 79.31 79.47 79.58 75.27 75.39 78.84 78.85 78.86 50 50 50 56.25 56.25Djibouti 157 158 172 160 26.58 32.34 32.89 31.73 59.9 51.63 54.73 50.58 53.39 54.19 37.36 38.24 38.31 42.07 43.14 56.6 56.86 56.86 57.06 57.11 12.5 12.5 12.5 12.5 18.75Gabon 158 156 169 163 71.72 70.92 71.78 72.32 76.02 70.88 70.69 70.88 70.83 77.84 58.59 58.22 58.59 61.47 61.65 61.23 61.23 61.23 53.94 53.94 31.25 31.25 50 50 50Georgia 13 12 9 8 95.44 95.28 97.36 97.43 97.47 91.44 91.43 91.46 91.5 91.52 72.75 72.53 72.93 83.77 83.94 96.4 99.78 99.8 99.81 99.81 75 81.25 87.5 93.75 93.75Lebanon 109 113 105 111 79.08 80.01 81.2 81.22 80.09 59.34 60.14 60.46 59.74 57.5 78 78.14 78.24 78.24 78.25 64.01 64.01 64.04 64.04 63.93 50 50 50 50 50Macedonia, FYR 36 38 36 25 – – – – – – – – – – – – – – – 68.16 68.16 69.85 69.68 71.03 68.75 68.75 81.25 81.25 93.75Mauritania 167 165 171 173 52.38 54.63 59.36 59.79 60.63 53.81 55.26 68.1 50.75 52 45.28 45.5 51.7 50.95 51.36 75.72 75.72 76.57 76.6 76.65 25 25 25 25 25Moldova 87 90 86 78 84.03 83.89 86.16 86.32 88.61 47.79 47.8 47.97 48.08 48.12 45.82 53.32 53.82 54.12 54.25 84.5 84.49 84.51 84.51 84.52 50 50 75 75 87.5Mongolia 63 73 80 76 84.02 83.86 84.43 86.48 90.39 61.8 61.76 61.89 61.99 66.23 46.91 46.76 47.46 47.8 51.85 81.94 81.86 81.98 81.97 82.02 56.25 56.25 62.5 68.75 68.75Montenegro 65 66 50 44 79.32 86.98 88.78 88.81 88.81 57.97 56.42 53.81 57.03 72.14 77.38 77.17 77.2 77.36 77.37 66.59 66.59 66.81 66.83 70.22 87.5 87.5 87.5 93.75 93.75Panama 62 72 61 55 87.32 87.98 88.25 88.59 90.79 75.62 75.71 75.75 76.92 78.64 84.1 84.11 84.11 84.12 84.13 – – – – – – – – – –Suriname 160 161 165 161 39.46 39.12 39.72 40.22 40.6 72.2 72.22 72.25 72.27 74.8 74.59 84.71 85.26 85.3 85.69 37.74 37.75 37.79 37.92 48.02 25 25 25 25 25Timor-Leste 174 174 167 172 29.32 47.13 55.25 60.49 59.99 63.17 63.11 63.14 63.26 63.24 93.45 92.68 89.69 91.43 91.26 – – – – – 12.5 12.5 31.25 31.25 31.25High-incomeCommonwealth countriesAntigua and Barbuda 56 64 66 71 81.87 81.7 81.52 81.71 81.76 84.94 84.91 84.87 84.91 84.92 89.43 89.37 89.31 89.38 89.4 62.06 58.63 58.56 58.64 58.67 43.75 43.75 43.75 43.75 43.75Bahamas, The 71 77 76 84 81.71 82.98 82.99 83.02 83.01 75.38 75.36 75.36 75.38 75.37 79.33 79.3 79.31 79.34 79.35 28.21 45.05 42.32 42.38 45.75 56.25 56.25 56.25 56.25 56.25Barbados – – 84 91 – – 82.77 82.81 82.81 – – 60.78 60.78 60.78 – – 66.43 66.44 66.44 – – 56.66 56.67 56.67 – – 56.25 56.25 56.25Brunei Darussalam 117 112 79 59 48.82 50.56 51.63 51.77 51.87 55.16 55.14 55.15 70.99 71 77.92 77.88 81.09 81.1 81.12 45.63 45.62 45.63 45.63 45.63 43.75 43.75 43.75 43.75 68.75Cyprus 35 37 38 39 88.03 87.92 87.86 87.95 87.96 61.72 61.78 61.75 61.77 61.71 54.36 54.39 54.37 54.4 54.37 60.69 61.5 61.16 62.42 62.76 56.25 68.75 68.75 68.75 68.75Malta 100 103 – – 72.23 72.26 72.26 – – 63.3 63.4 64.41 – – 68.37 68.43 68.43 – – 69.78 69.78 69.78 – – 18.75 18.75 18.75St. Kitts and Nevis 83 87 97 101 83.89 83.89 83.86 84.15 84.31 83.26 83.22 83.22 83.23 83.24 91.8 91.81 91.78 92.07 92.12 51.56 51.56 51.56 51.58 51.59 43.75 43.75 43.75 43.75 43.75Trinidad and Tobago 95 97 63 66 76.35 78.56 78.56 78.57 81.11 68.34 68.33 68.33 68.33 68.33 87.02 87.2 87.19 87.2 87.2 48.57 48.56 48.54 52.7 52.7 81.25 81.25 81.25 81.25 81.25Other countriesBahrain 25 28 47 46 77.74 73.86 74.53 76.02 76.16 88.39 88.34 88.34 88.36 88.36 76.35 76.28 76.29 76.32 76.32 91.76 88.68 88.68 88.68 88.68 37.5 37.5 37.5 37.5 43.75Croatia 89 84 88 89 83.03 87.61 87.72 87.9 88.32 50.59 60.6 60.58 58.37 59.98 78.13 78.1 78.1 78.13 78.13 66.6 66.6 66.71 66.71 66.71 68.75 68.75 75 75 75Equatorial Guinea 161 164 164 166 37.55 36.81 37.37 37.61 37.5 73.86 72.87 73.61 73.94 73.79 71.24 71.24 72.2 72.63 72.43 59.5 59.49 59.5 59.5 59.5 31.25 31.25 50 50 50Estonia 17 17 21 22 90.2 90.06 90.13 90.26 90.35 80.72 79.44 79.45 79.47 79.48 79.52 79.43 79.46 79.54 79.59 90.71 90.62 90.61 90.61 90.66 68.75 75 75 75 75Iceland 14 15 13 13 90.74 91.01 90.97 91.06 91.14 77.95 78.04 78.03 78.04 78.07 92.61 92.61 92.59 92.58 92.59 88.89 88.89 88.88 88.94 88.94 75 75 75 75 75Ireland 8 9 15 15 91.88 91.87 91.87 92.53 92.54 79.84 74.08 73.79 73.69 75.62 60.29 60.24 60.22 60.21 60.23 71.19 71.92 71.49 78.31 78.26 87.5 87.5 87.5 87.5 87.5Kuwait 69 74 101 104 68.01 66.8 69.8 70.56 69.51 64.13 63.7 63.9 64.17 64.14 69.69 69.63 69.68 69.7 69.7 69.7 69.68 70.64 70.64 70.64 43.75 43.75 43.75 43.75 43.75Latvia 27 24 24 24 88.63 88.61 91.06 91.09 91.79 61.36 63.23 63.21 63.24 71.89 60.89 60.73 72.41 72.6 72.83 74.94 75.28 81.3 81.3 81.31 93.75 93.75 93.75 93.75 93.75Lithuania 26 23 25 17 81.89 84.26 84.29 83.47 93.23 77.05 77.03 77.42 78.67 78.7 70.96 71.01 68.24 68.27 68.3 91.71 91.7 91.75 91.76 91.77 68.75 68.75 68.75 68.75 81.25Luxembourg 42 45 56 60 84.74 85.71 85.87 85.89 85.89 72.6 72.56 80.13 80.13 80.13 72.17 72.12 72.14 72.15 72.15 59.53 59.73 59.83 59.84 59.84 25 25 25 25 25Norway 7 8 7 9 90.29 90.23 90.26 90.97 90.99 74.15 74.08 74.08 84.67 84.69 86.48 86.48 86.48 86.48 86.48 95.49 95.49 95.49 95.49 95.5 62.5 62.5 62.5 62.5 62.5Oman 57 57 44 47 76.6 75.73 77.45 79.91 80.58 75.96 75.93 76.97 77.06 77.08 73.52 73.51 73.52 73.56 73.57 89.85 89.85 89.85 89.85 89.85 37.5 37.5 50 56.25 56.25Qatar 39 50 45 48 84.07 78.43 81.22 82.18 82.08 80.99 80.99 80.99 80.99 80.99 83.18 83.17 83.18 83.18 83.18 78.23 78.23 78.23 78.23 78.23 31.25 31.25 43.75 43.75 43.75Slovenia 43 42 31 33 93.09 94.8 94.86 94.85 94.84 76.59 77.72 77.74 77.73 80.43 83.33 83.3 83.31 83.31 83.31 46.55 67.28 71 71.01 70.99 50 50 50 50 50Uruguay 122 124 85 88 63.25 63 88.55 88.63 88.82 56.87 56.89 56.96 57.02 57.05 82.3 82.3 82.3 82.31 82.3 53.97 57.3 57.31 57.31 57.32 62.5 62.5 62.5 62.5 62.5

Table 33. Doing business (continued)

Group/country Protecting investors Paying taxes Trading across borders Enforcing contracts Resolving insolvency

2010 2011 2012 2013 2014 2010 2011 2012 2013 2014 2010 2011 2012 2013 2014 2010 2011 2012 2013 2014 2010 2011 2012 2013 2014Middle-incomeCommonwealth countriesBelize 43.33 43.33 43.33 43.33 43.33 68.76 68.76 74.77 74.77 74.77 69.01 68.91 68.98 70.2 70.79 35.64 35.64 35.64 35.64 35.64 67.15 67.33 67.46 67.74 68.29Botswana 60 60 60 60 60 76.36 75.78 75.78 75.78 75.78 44.14 44.29 47.77 52.24 50.73 60.58 62.28 63.47 63.47 63.47 60.92 64.43 65.24 65.57 65.57Dominica 63.33 63.33 63.33 63.33 63.33 69.66 69.66 69.57 69.57 69.57 70.4 71.02 70.53 70.73 70.91 41.89 41.89 41.89 41.89 41.89 29.83 28.95 30.01 30.19 29.97Fiji 60 60 60 60 60 67.87 68.34 68.89 68.73 69.18 60.01 60.92 61.28 61.71 60.82 62.88 62.88 62.88 62.88 62.88 47.02 47.78 47.88 47.85 48.24Grenada 63.33 63.33 63.33 63.33 63.33 67.63 67.63 67.63 67.63 67.63 73.29 78.02 78.04 77.79 78.02 42.93 42.93 42.93 42.93 42.93 – – – – –Guyana 53.33 53.33 53.33 53.33 53.33 61.51 61.51 64.4 64.4 66.65 70.09 71.02 71.38 71.81 72.08 60.44 60.44 60.44 60.44 60.44 18.59 18.68 18.68 18.68 19.01Jamaica 53.63 53.33 53.33 53.33 53.33 33.27 33.96 36.52 54.01 54 62.38 63.88 65.09 65.3 65.98 52.21 52.21 52.21 52.21 52.21 68.36 68.91 69.17 66.87 68.03Kiribati 60 60 60 60 60 88.86 88.86 88.86 88.86 88.86 72.8 72.85 72.43 72.59 72.73 62.83 62.83 62.83 62.83 62.83 – – – – –Lesotho 36.67 36.67 36.67 50 50 68.09 68.09 68.09 68.09 68.09 42.47 50.79 50.95 54.86 56.03 44.25 44.25 45.44 51.35 51.35 27.1 29.04 29.9 30.02 30.25Maldives 53.33 53.33 53.33 53.33 53.33 100 100 100 79.94 63.92 60.36 59.3 59.35 60.15 60.14 55.35 55.35 55.35 55.35 55.35 51.58 51.58 53.37 53.56 53.35Mauritius 76.67 76.67 76.67 76.67 76.67 89.56 88.97 88.69 88.84 88.86 84.91 85.5 85.42 85.87 85.92 59.87 61.92 61.92 61.92 63.13 35.54 37.22 37.22 43.3 43.47Namibia 53.33 53.33 53.33 53.33 53.33 65.19 65.19 65.19 66.38 66.38 56.93 57.49 56.89 57.82 59.28 62.38 62.38 62.38 62.38 63.48 32.9 35.71 36.37 36.94 37.01Nauru – – – – – – – – – – – – – – – – – – – – – – – – –Papua New Guinea 56.67 56.67 56.67 56.67 56.67 65.8 65.78 65.78 65.16 65.16 60.19 59.83 60.55 59.4 59.62 28.8 28.8 28.8 28.8 28.8 26.2 25.36 25.09 24.91 24.91St. Lucia 63.33 63.33 63.33 63.33 63.33 75.04 75.3 75.04 74.95 74.74 69.42 68.72 68.98 69.15 66.97 42.67 42.67 42.67 42.67 42.67 45.4 43.99 44.21 44.48 45.05St. Vincent and the Grenadines 63.33 63.33 63.33 63.33 63.33 68.22 69.53 69.53 69.53 69.67 78.57 78.52 78.82 78.79 78.89 54.18 54.18 54.18 54.18 54.18 – – – – –Samoa 63.33 63.33 63.33 63.33 63.33 70.69 70.69 70.69 70.69 70.69 74.26 74.37 74.42 74.5 74.56 56.36 56.36 56.36 56.36 56.36 18.37 18.71 19.18 19.27 19.31Seychelles 56.67 56.67 56.67 56.67 56.67 73.01 73.01 83.11 83.25 83.25 76.01 77.88 78.15 78.54 80.03 59.41 59.01 53.68 53.68 53.68 42.04 39.88 41.51 41.98 41.23Solomon Islands 56.67 56.67 60 60 60 79.23 79.23 79.23 79.23 79.23 70.48 70.33 70.69 71.87 72.29 43.22 43.22 43.22 43.22 43.22 25.03 24.77 25.21 25.27 25.7Swaziland 20 43.33 43.33 43.33 43.33 72.84 72.76 72.76 72.76 72.47 52.31 58.74 57.56 61.91 63.66 33.79 33.79 33.79 34.23 34.23 36.97 39.83 40.43 40.56 40.75Tonga 46.67 46.67 46.67 46.67 46.67 76.12 76.85 76.85 77.39 74.56 73.97 73.91 73.08 73.44 73.47 63.64 63.64 63.64 63.64 63.64 26.71 26.75 27.14 27.25 27.59Tuvalu – – – – – – – – – – – – – – – – – – – – – – – – –Vanuatu 53.33 53.33 53.33 53.33 53.33 78.95 78.95 78.95 78.95 78.95 62.9 62.6 65.68 65.89 66.02 60.54 60.54 60.54 60.54 60.54 43.65 45.27 45.27 45.27 44.72Other countriesAlbania 73.33 73.33 73.33 73.33 73.33 53.82 56.4 56.78 57.16 58.48 68.85 68.98 69.01 69.16 69.31 57.19 58.28 58.28 56.64 54.59 41 41.22 42.6 41.89 43.07Armenia 50 50 50 66.67 66.67 39.44 39.42 51.94 69.16 70.8 57.26 65.72 65.7 66.25 66.03 55.3 55.3 47.52 47.52 47.52 38.79 37.68 37.42 38.26 38.59Bhutan 36.67 36.67 36.67 36.67 36.67 69.81 69.81 69.81 69.81 69.81 39.73 33.68 34.71 36.63 36.86 66.18 66.18 66.18 66.18 66.18 – – – – –Bosnia and Herzegovina 46.67 46.67 46.67 46.67 46.67 51.37 51.37 51.37 58.1 60.29 66.2 65.42 65.94 66.13 66.27 56.4 55.68 55.68 55.68 55.68 38.04 36.75 37.03 37.55 38.17Cape Verde 40 40 40 40 40 55.29 69.31 69.13 70.03 70.03 67.95 68.27 68.51 68.79 69.03 64.75 64.75 65.47 65.47 65.47 – – – – –Congo, Republic of 33.33 33.33 33.33 33.33 33.33 16.6 16.37 16.94 17.17 24.57 24.16 6.94 10.03 11.46 13.5 41.34 41.34 41.34 41.34 41.34 18.83 18.83 18.94 18.83 18.94Costa Rica 30 30 30 30 30 49.18 49.58 56.83 62.16 62.16 76.59 77.34 78.01 79.46 79.5 48.6 48.6 48.6 48.6 48.6 26.93 22.46 23.52 23.84 25.97Djibouti 23.33 23.33 23.33 23.33 23.33 79.28 71.58 71.96 71.96 71.96 75.84 75.95 76.54 76.85 77.1 34.89 34.89 34.89 34.89 34.89 16.84 16.53 17.47 17.51 17.76Gabon 33.33 33.33 33.33 33.33 33.33 53.51 54.2 54.2 54.2 54.2 60.11 57.17 59.91 61.87 61.02 41.4 41.4 41.4 41.4 41.4 16.06 16.06 16.06 16.06 16.06Georgia 60 66.67 70 70 70 66.71 66.71 80.37 85.5 85.5 82.07 81.37 80.76 82.7 82.83 66.83 66.83 66.83 70.4 70.4 29.56 39.01 39.4 33.42 35.54Lebanon 50 50 50 50 50 80.37 80.37 80.37 80.37 80.37 67.67 68.19 69.61 69.51 69.96 53.4 53.4 53.4 53.4 53.4 31.71 32.62 33.62 34.08 34.34Macedonia, FYR 63.33 63.33 63.33 63.33 70 72.6 74.08 80.09 80.09 80.09 70.28 70.71 70.96 71.28 71.32 55.6 55.6 55.6 55.6 57.32 43.82 43.56 44.52 44.68 46.36Mauritania 36.67 36.67 36.67 36.37 36.67 14.75 24.11 24.11 24.11 24.11 47.97 46.45 50.41 51.83 52.43 55.03 55.03 55.03 55.03 55.03 – – – – –Moldova 46.67 46.67 46.67 53.33 53.33 61.76 62.08 61.85 62.24 67.91 48.88 49.23 52.52 53.43 54.27 75.05 75.05 72.61 72.1 71.83 30.29 29.87 33.15 33.85 34.72Mongolia 63.33 63.33 63.33 66.67 66.67 70.04 70.04 70.04 70.04 70.04 22.57 22.96 25.52 23.63 24.3 70.54 70.54 70.54 70.54 70.54 23.35 21.15 22.36 24.06 22.86Montenegro 63.33 63.33 63.33 63.33 63.33 48.77 48.85 60.87 70.47 70.47 73.36 77.28 77.43 77.31 77.46 45.77 45.77 45.77 45.77 45.77 46.28 45.99 45.85 51.18 51.26Panama – – – – – 40.06 40.09 40.2 40.02 45.06 90.93 90.72 90.58 90.82 91.01 53.34 53.34 53.34 53.34 53.34 28.23 28.23 29 29.13 29.13Suriname 20 20 20 20 20 75.31 75.31 75.31 75.31 75.31 66.38 66.57 66.76 66.64 67.09 25.87 25.87 25.87 25.87 25.87 8.62 9.18 9.12 9.11 9.12Timor-Leste 46.67 46.67 46.67 46.67 46.67 85.14 85.14 85.14 78.58 78.58 66.38 66.69 66.78 67.45 67.91 – 1.5 1.5 1.5 1.5 – – – – –

High-incomeCommonwealth countriesAntigua and Barbuda 63.33 63.33 63.33 63.33 63.33 52.12 52.12 52.12 52.12 52.12 76.59 76.75 76.28 71 71.19 58.11 58.11 58.11 58.11 58.11 37.6 38.84 37.02 37.26 38.03Bahamas, The 46.67 46.67 46.67 46.67 46.67 78.31 78.31 77.38 77.37 77.36 77.73 76.66 76.58 76.43 75.73 47.83 47.83 47.83 47.83 47.83 65.84 65.84 65.84 66.67 67.27Barbados – – 30 30 30 – – 66.11 66.66 66.65 – – 81.49 81.49 81.49 – – 39.32 39.32 39.32 – – 68.93 68.93 68.93Brunei Darussalam 46.67 46.67 46.67 46.67 46.67 84.18 86.55 82.29 82.29 82.29 76.13 75.29 78.51 79.42 79.29 44.33 44.33 44.33 44.33 44.33 50.03 50.03 50.03 50.03 50.03Cyprus 50 50 63.33 63.33 63.33 78.66 78.66 78.66 78.23 78.23 81.23 81.21 80.96 81.11 81.23 51.09 51.09 51.09 51.09 51.09 74.91 74.51 74.95 74.85 74.64Malta – – 56.67 56.67 56.67 – – 82.7 82.74 82.71 – – 78.72 78.88 79.02 – – 53.88 53.88 53.88 – – 41.47 41.47 41.52St. Kitts and Nevis 63.33 63.33 63.33 63.33 63.33 66.11 66.09 57.23 57.57 57.57 74.99 75.24 75.89 74.75 74.88 50.33 50.33 50.33 50.33 50.33 – – – – –Trinidad and Tobago 66.67 66.67 66.67 66.67 66.67 65.76 65.76 68.59 68.59 68.59 68.58 67.85 68.3 71.81 71.87 29.55 29.55 29.55 29.55 29.55 26.07 26.77 27.96 28.42 28.53Other countriesBahrain 46.67 46.67 46.67 46.67 46.67 92.81 92.81 92.81 92.81 92.81 70.36 70.29 71.17 71.17 71.17 48.49 48.49 48.49 48.49 48.49 66.98 68.04 69.87 70.1 71.35Croatia 33.33 33.33 33.33 33.33 33.33 76.41 75.86 75.86 75.86 81.87 67.47 67.71 67.51 67.67 68.62 62.74 62.74 62.74 62.44 62.44 32.27 30.36 31.5 31.91 32.09Equatorial Guinea 36.67 36.67 36.67 36.67 36.67 42.74 42.74 42.74 42.74 42.74 54.56 49.93 53.56 56.48 57.57 61.01 61.01 61.01 61.01 61.01 – – – – –Estonia 56.67 56.67 56.67 56.67 56.67 81.4 81.14 75.46 70.41 80.68 91.68 91.67 91.71 91.56 91.73 66.95 66.95 66.95 66.95 67.09 39.67 37.61 39.1 40.77 41.2Iceland – – – – – 78.54 78.54 77.12 76.55 78.62 78.16 79.15 79.92 80.69 82.06 81.95 81.51 81.51 81.51 81.51 81.15 83.16 89.46 89.98 89.46Ireland 83.33 83.33 83.33 83.33 83.33 93.08 93.08 93.08 92.89 92.89 93.31 92.97 92.8 92.72 92.78 – – – – – 91.68 92.61 92.06 92.72 92.82Kuwait 50 50 50 50 53.33 90.39 90.39 90.39 90.39 90.39 62.91 60.9 61.92 63.93 63.91 47.7 47.7 47.7 47.7 47.7 29.73 32.66 37.79 33.59 33.87Latvia 56.67 56.67 56.67 56.67 56.67 77.91 77.24 77.7 79.63 79.63 80.42 81.59 81.47 81.77 83.57 79.35 79.35 77.71 74.98 74.98 30.73 33.75 49.15 50.67 51.3Lithuania 50 50 56.67 56.67 56.67 77.21 76.46 77.58 77.62 77.6 83.71 83.72 84.45 85.26 85.65 73.46 73.46 73.46 73.46 73.46 52.29 51.14 51.25 51.42 51.26Luxembourg 43.33 43.33 43.33 43.33 43.33 86.25 86.25 86.25 86.25 86.44 80.47 80.52 81.3 81.79 82.14 85.07 85.07 85.07 85.07 85.07 44.21 46.29 46.05 46.07 46.06Norway 66.67 66.67 66.67 66.67 66.67 87.02 87.02 87.02 87.02 87.21 84.69 84.28 84.73 84.04 84.23 76.6 76.6 76.6 76.6 76.6 94.27 96.23 95.96 96.13 96.72Oman 50 50 50 50 50 91.02 91.02 91.02 91.02 90.73 71.59 73.1 73.95 74.78 74.78 48.75 48.75 48.75 48.75 48.75 37.17 36.96 37.81 38.73 39.53Qatar 43.33 43.33 43.33 43.33 43.33 98.28 98.28 98.28 97.15 97.49 73.47 71.76 71.75 74.65 74.65 50.98 50.98 50.98 53.72 53.72 55.85 56.15 56.19 58.75 58.92Slovenia 66.67 66.67 66.67 73.33 73.33 71.16 72.36 72.77 78.78 79.6 71.3 75.35 76.8 76.86 76.88 50.37 50.37 50.37 50.37 50.91 48.16 53.86 54.12 52.79 53.06Uruguay 50 50 50 50 50 47.63 47.63 47.63 59.81 59.81 66.93 67.41 68.52 69.81 71.07 52.94 52.94 52.94 52.81 52.81 45.54 42.04 45.96 45.85 47.72

Note: – = not availableSource: World Bank, Doing Business, available at: www.doingbusiness.org/custom (accessed December 2013)

Table 34. Selected private sector indicators

Group/country

Domestic credit to private sector

(% of GDP)

Interest rate spread (lending

rate minus deposit rate, %)

Tax revenue collected by

central government (% of GDP)

Taxes payable by businesses (total

tax rate % of profit)

2010 2011 2012 2010 2011 2012 2010 2011 2012 2010 2011 2012

Middle-incomeCommonwealth countries – – – – – – – – – – – –Belize 62.3 60.3 – 6.1 6.9 – – – – 33.2 33.2 33.2Botswana 25.3 27.5 32.0 5.9 5.9 – 20.2 20.8 – 19.5 19.4 25.3Dominica 55.2 58.5 59.9 6.2 5.6 – – – – 37.3 37.5 37.5Fiji 82.6 74.6 78.4 2.1 3.7 – – – – 39.3 38.3 37.6Grenada 84.1 84.7 83.9 7.5 7.6 – 18.4 18.3 – 45.3 45.3 45.3Guyana 37.2 37.9 41.0 12.3 12.5 – – – – 39.1 36.1 36.1Jamaica 26.4 26.8 28.8 14.1 15.6 – 26.5 25.6 – 50.1 45.6 45.6Kiribati – – – – – – – – – 31.8 31.8 31.8Lesotho 13.6 14.7 18.8 7.5 7.7 – – – – 19.7 16.0 16.0Maldives 57.8 51.2 42.8 6.3 6.0 – 11.0 – 9.3 9.3 30.7Mauritius 87.9 91.5 100.7 0.5 1.8 – 18.6 18.4 – 27.2 28.5 28.5Namibia 50.1 48.5 48.4 4.7 4.4 – – 23.3 22.9 22.7Nauru – – – – – – – – – – – –Papua New Guinea 31.7 30.4 31.1 9.1 9.9 – – – – 42.3 42.3 42.2St Lucia 112.4 114.2 125.1 7.3 6.5 – – 34.0 34.4 34.6St Vincent and the Grenadines

51.9 51.8 52.8 6.3 6.1 – 23.1 22.2 – 38.7 38.7 38.7

Samoa 45.8 47.3 47.0 8.0 7.7 – – 18.9 18.9 18.9Seychelles 27.6 26.0 25.3 9.8 9.1 – 27.8 31.7 – 44.1 32.2 25.7Solomon Islands 27.2 23.1 23.2 11.1 11.2 – – – – 26.2 26.2 25.3Swaziland 23.0 27.2 25.0 5.9 6.2 – – – – 36.8 36.8 36.8Tonga 39.7 31.9 29.9 7.5 7.5 – – – – 25.5 25.7 25.7Tuvalu – – – – – – – – – – – –Vanuatu 64.7 67.9 – 3.9 – – – – – 8.4 8.4 8.4Other countriesAlbania 37.7 39.3 38.3 6.4 6.6 – – – – 40.6 38.5 38.7Armenia 28.4 35.4 42.9 10.3 8.5 – 16.9 17.0 – 38.6 38.8 38.8Bhutan 41.4 47.3 47.5 12.0 9.5 – – – – 40.8 40.8 40.8Bosnia and Herzegovina 63.3 62.0 62.3 4.7 4.6 – 20.3 20.9 – 22.7 24.1 24.1Cape Verde 62.1 64.5 59.4 7.9 6.5 – – – – 37.1 37.8 37.2Congo, Republic of 6.5 7.8 9.6 – – – – – – 65.5 65.9 62.9Costa Rica 45.2 47.3 49.1 11.8 12.1 – 13.5 13.8 – 55.0 55.0 55.0Djibouti – – – 9.3 9.1 – – – – 38.7 38.7 38.7Gabon 8.1 9.2 10.4 – – – – – – 43.5 43.5 43.5Georgia 31.8 32.7 34.5 15.0 16.4 – 22.1 23.9 – 15.3 16.5 16.5Lebanon 85.5 89.6 92.2 2.1 1.6 – 18.5 17.0 – 30.2 30.2 30.2Macedonia, FYR 44.5 45.3 47.5 2.4 3.0 – – – – 10.1 9.7 9.4Mauritania 28.0 26.3 28.8 9.0 9.0 – – – – 68.3 68.3 68.2Moldova 33.3 33.6 38.1 8.7 6.9 – 18.2 18.3 – 30.9 31.3 31.2Mongolia 39.6 51.6 52.3 8.2 6.1 – 22.7 21.9 – 24.3 24.6 24.6Montenegro 66.9 55.8 52.7 5.8 6.6 – – – – 26.6 22.3 22.3Panama 91.8 89.9 89.6 4.7 4.6 – – – – 48.7 45.2 42.0Suriname 24.0 24.3 25.4 5.4 5.4 – – – – 27.9 27.9 27.9Timor-Leste 12.5 12.2 12.4 10.2 10.2 – – – – 14.9 15.1 15.1High-incomeCommonwealth countries – – – –Antigua and Barbuda 80.0 77.5 71.8 7.6 7.5 – – – – 41.5 41.5 41.5Bahamas, The 83.4 84.4 81.4 2.1 2.4 – 14.1 16.8 – 46.1 47.7 47.8Barbados – – – 6.0 6.0 – 27.2 – – 45.4 45.4

(continued)

148

Table 34. Selected private sector indicators (continued)

Group/country

Domestic credit to private sector

(% of GDP)

Interest rate spread (lending

rate minus deposit rate, %)

Tax revenue collected by

central government (% of GDP)

Taxes payable by businesses (total

tax rate % of profit)

2010 2011 2012 2010 2011 2012 2010 2011 2012 2010 2011 2012

Brunei Darussalam 40.9 31.8 31.5 5.0 5.1 – – – – 17.3 16.8 16.8Cyprus 283.6 294.8 302.2 – – – 25.9 26.1 – 23.2 23.1 23.0Malta 132.8 129.9 127.9 – – – 27.0 27.7 – 41.6 41.6St Kitts and Nevis 65.5 65.3 65.2 4.0 5.0 – 17.0 19.7 – 52.7 52.7 52.1Trinidad and Tobago 33.2 30.5 30.7 7.8 6.5 – – – – 33.1 29.1 29.1Other countriesBahrain 67.7 68.9 70.0 6.0 5.8 – – – – 13.9 13.9 13.9Croatia 69.3 70.8 68.0 8.6 8.0 – 19.2 18.4 – 33.0 32.9 32.8Equatorial Guinea 8.9 8.9 6.7 – – – – 46.0 46.0 46.0Estonia 98.4 84.7 79.3 6.7 4.8 – 16.1 16.0 – 49.6 58.6 67.3Iceland 108.7 97.0 96.8 – – – 21.9 22.3 – 26.8 31.8 33.0Ireland 214.4 204.3 186.1 – – – 21.4 23.1 – 26.3 26.3 26.4Kuwait 76.3 61.7 – 2.6 3.0 – 0.9 0.7 – 10.7 10.7 10.7Latvia 99.1 82.2 67.7 7.7 5.9 – 12.8 13.3 – 38.5 37.9 36.6Lithuania 63.3 53.5 51.3 4.3 – 13.4 13.4 – 46.0 43.9 43.7Luxembourg 187.1 170.7 165.4 – – – 24.8 24.3 – 20.9 20.8 21.0Norway – – – – – – 27.4 28.4 – 41.6 41.6 41.6Oman 42.2 40.0 41.2 3.5 3.4 – 2.6 2.2 – 21.6 22.0 22.0Qatar 44.7 38.9 36.1 4.4 3.7 – 14.4 – – 11.3 11.3 11.3Slovenia 94.2 90.1 87.4 – – – 17.1 17.9 – 35.4 34.7 34.7Uruguay 23.1 23.8 24.1 6.2 5.2 – 19.3 19.6 – 42.0 42.0 42.0

Note: – = not available

Source: World Bank, World Development Indicators 2013, available at: http://databank.worldbank.org (accessed June 2013)

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152

Table 36. Population indicators

Group/countryTotal population (’000)

Annual population growth (% per year)

2007 2008 2009 2010 2011 2012 2008 2009 2010 2011 2012

Middle-incomeCommonwealth countriesBelize 286 294 301 309 316 324 2.6 2.5 2.5 2.5 2.5Botswana 1,915 1,934 1,952 1,969 1,987 2,004 1.0 0.9 0.9 0.9 0.9Dominica 71 71 71 71 71 72 0.1 0.2 0.2 0.3 0.4Fiji 835 844 852 861 868 875 1.0 1.0 0.9 0.9 0.8Grenada 104 104 104 105 105 105 0.3 0.4 0.4 0.4 0.4Guyana 770 776 781 786 791 795 0.7 0.7 0.6 0.6 0.6Jamaica 2,676 2,687 2,696 2,701 2,707 2,712 0.4 0.3 0.2 0.2 0.2Kiribati 93 95 96 98 99 101 1.5 1.5 1.5 1.5 1.5Lesotho 1,956 1,972 1,990 2,009 2,030 2,052 0.8 0.9 1.0 1.0 1.1Maldives 308 314 320 326 332 338 1.8 1.9 1.9 1.9 2.0Mauritius 1,260 1,269 1,275 1,281 1,286 1,291 0.6 0.5 0.5 0.4 0.4Namibia 2,081 2,111 2,143 2,179 2,218 2,259 1.4 1.5 1.7 1.8 1.9Nauru – – – – – – – – – – –Papua New Guinea 6,398 6,551 6,705 6,859 7,013 7,167 2.4 2.4 2.3 2.2 2.2St Lucia 170 173 175 177 179 181 1.5 1.4 1.3 1.1 0.9St Vincent and the

Grenadines109 109 109 109 109 109 0.1 0.1 0.1 0.0 0.0

Samoa 182 183 185 186 187 189 0.7 0.7 0.7 0.8 0.8Seychelles 85 87 87 90 87 88 2.3 0.4 2.8 −2.6 0.4Solomon Islands 492 504 515 526 538 550 2.3 2.3 2.2 2.2 2.2Swaziland 1,135 1,154 1,174 1,193 1,212 1,231 1.7 1.7 1.7 1.6 1.6Tonga 102 103 104 104 105 105 0.6 0.6 0.5 0.4 0.4Tuvalu 10 10 10 10 10 10 0.2 0.2 0.2 0.2 0.2Vanuatu 220 225 231 236 242 247 2.5 2.4 2.4 2.3 2.3Other countriesAlbania 3,166 3,157 3,151 3,150 3,154 3,162 −0.3 −0.2 0.0 0.1 0.3Armenia 2,990 2,977 2,968 2,963 2,964 2,969 −0.4 −0.3 −0.2 0.0 0.2Bosnia and Herzegovina 3,869 3,861 3,853 3,846 3,839 3,834 −0.2 −0.2 −0.2 −0.2 −0.1Cape Verde 484 485 486 488 491 494 0.2 0.2 0.4 0.6 0.8Congo, Republic of 3,759 3,876 3,995 4,112 4,225 4,337 3.1 3.1 2.9 2.8 2.6Costa Rica 4,463 4,533 4,601 4,670 4,738 4,805 1.6 1.5 1.5 1.5 1.4Djibouti 799 810 822 834 847 860 1.4 1.5 1.5 1.5 1.5Gabon 1,447 1,483 1,519 1,556 1,594 1,633 2.4 2.4 2.4 2.4 2.4Georgia 4,388 4,384 4,411 4,453 4,483 4,512 −0.1 0.6 0.9 0.7 0.6Lebanon 4,140 4,186 4,247 4,341 4,383 4,425 1.1 1.5 2.2 1.0 1.0Macedonia, FYR 2,097 2,099 2,101 2,102 2,104 2,106 0.1 0.1 0.1 0.1 0.1Mauritania 3,330 3,423 3,516 3,609 3,703 3,796 2.8 2.7 2.7 2.6 2.5Moldova 3,577 3,570 3,566 3,562 3,561 3,560 −0.2 −0.1 −0.1 0.0 0.0Mongolia 2,595 2,633 2,672 2,713 2,754 2,796 1.5 1.5 1.5 1.5 1.5Montenegro 618 619 619 620 621 621 0.1 0.1 0.1 0.1 0.1Panama 3,491 3,553 3,616 3,678 3,740 3,802 1.8 1.8 1.7 1.7 1.7São Tomé and Príncipe 163 168 173 178 183 188 3.0 3.0 2.9 2.8 2.7Suriname 510 515 520 525 530 535 1.0 0.9 0.9 0.9 0.9Timor-Leste 1,046 1,078 1,110 1,143 1,176 1,210 3.0 3.0 2.9 2.9 2.9

High-incomeCommonwealth countriesAntigua and Barbuda 84 85 86 87 88 89 1.1 1.1 1.1 1.1 1.0Bahamas, The 342 348 354 360 366 372 1.8 1.8 1.7 1.6 1.5Barbados 276 278 279 280 282 283 0.5 0.5 0.5 0.5 0.5Brunei Darussalam 381 388 394 401 407 412 1.7 1.6 1.6 1.5 1.4Cyprus 409 412 414 416 417 418 0.7 0.5 0.5 0.2 0.4

(continued)

153

Table 36. Population indicators (continued)

Group/countryTotal population (’000)

Annual population growth (% per year)

2007 2008 2009 2010 2011 2012 2008 2009 2010 2011 2012

Malta 1,063 1,077 1,091 1,104 1,117 1,129 1.3 1.3 1.2 1.2 1.1St Kitts and Nevis 50 51 52 52 53 54 1.3 1.2 1.2 1.2 1.2Trinidad and Tobago 1,310 1,316 1,323 1,328 1,333 1,337 0.5 0.5 0.4 0.4 0.3Other countriesBahrain 1,032 1,116 1,192 1,252 1,293 1,318 8.1 6.8 5.0 3.3 1.9Croatia 4,436 4,434 4,429 4,418 4,281 4,267 0.0 −0.1 −0.3 −3.1 −0.3Equatorial Guinea 640 658 677 696 716 736 2.9 2.9 2.9 2.8 2.8Estonia 1,342 1,341 1,340 1,340 1,340 1,339 −0.1 0.0 0.0 0.0 0.0Iceland 312 317 318 318 319 320 1.9 0.3 −0.1 0.3 0.4Ireland 4,357 4,426 4,459 4,474 4,577 4,589 1.6 0.8 0.3 2.3 0.3Kuwait 2,555 2,702 2,850 2,992 3,125 3,250 5.8 5.5 5.0 4.4 4.0Latvia 2,276 2,266 2,255 2,239 2,058 2,025 −0.4 −0.5 −0.7 −8.1 −1.6Lithuania 3,376 3,358 3,339 3,287 3,030 2,986 −0.5 −0.6 −1.6 −7.8 −1.5Luxembourg 480 489 498 507 518 531 1.8 1.9 1.8 2.2 2.5Norway 4,709 4,768 4,829 4,889 4,953 5,019 1.3 1.3 1.3 1.3 1.3Oman 2,570 2,594 2,663 2,803 3,025 3,314 0.9 2.7 5.2 7.9 9.6Qatar 1,152 1,359 1,564 1,750 1,911 2,051 17.9 15.1 11.9 9.2 7.3Slovenia 2,018 2,021 2,040 2,049 2,053 2,058 0.2 0.9 0.4 0.2 0.3Uruguay 3,338 3,349 3,360 3,372 3,383 3,395 0.3 0.3 0.3 0.3 0.3

Note: – = not available

Source: World Bank, World Development Indicators 2013, available at: http://databank.worldbank.org (accessed June 2013)

154

Table 37. Distribution of labour force (% of total employment)


2009 2010 2011 2009 2010 2011 2009 2010 2011

Middle-incomeCommonwealth countriesBelize – – – – – – – – –Botswana – – – – – – – – –Dominica – – – – – – – – –Fiji – – – – – – – – –Grenada – – – – – – – – –Guyana – – 1.4 15.1 – – 68.9Jamaica 20.2 20.2 17.6 16.6 15.9 16.1 63.2 63.8 66.3Lesotho – – – – – – – – –Maldives – – – – – – – – –Mauritius 9 8.7 8.4 29.7 28.2 27.6 61.3 63.1 64Namibia – – – – – –Nauru – – – – – – – – –Papua New Guinea – – – – – – – – –St Lucia – – – – – – – – –St Vincent and the Grenadines – – – – – – – – –Samoa – – – – – – – – –Seychelles – – – – – – – – –Solomon Islands – – – – – – – – –Swaziland – – – – – – – – –Tonga – – – – – – – – –Tuvalu – – – – – – – – –Vanuatu 60.5 – – 7 – – 31.1 – –Other countriesAlbania 44.1 – – 19.9 – – 36 – –Armenia – – – – – – – – –Bhutan 65.4 59.5 60.1 6.4 6.7 9.2 28.2 33.8 30.6Bosnia and Herzegovina 21.2 19.7 19.6 31.4 31 28.9 47.3 49.3 51.5Cape Verde – – – – – – – – –Congo, Republic of – – – – – – – – –Costa Rica 12.3 15 14.1 21.6 19.5 19.8 62.2 64.7 65.8Djibouti – – – – – – – – –Gabon – – – – – – – – –Georgia – – – – – – – – –Lebanon – – – – – – – – –Macedonia, FYR – – – – – – – – –Mauritania – – – – – – – – –Moldova 28.2 27.5 27.5 19.3 18.7 18.7 52.5 53.8 53.7Mongolia 40 – – 14.9 – – 45 – –Montenegro 6.5 6.2 5.6 20.7 20 19 72.8 73.9 75.5Panama 17.9 17.4 17 19.1 18.7 18.6 63 63.9 64.4Suriname – – – – – – – – –Timor-Leste – – – – – – – – –High-incomeCommonwealth countriesAntigua and Barbuda – – – – – – – – –Bahamas, The 2.9 – 3.7 16 – 12.9 80.8 – 83Barbados – 2.8 3.3 – 19.6 20.3 – 77.6 76.4Cyprus 3.9 3.8 3.9 22.2 20.8 21.4 73.9 75.3 74.7Malta 1.4 1.2 1.1 24.7 24.5 24.4 72.8 73 73.2St Kitts and Nevis – – – – – – – – –Trinidad and Tobago – – – – – – – – –Other countriesBahrain – – – – – – – – –Croatia 13.9 14.9 15.4 28.9 27.3 27.5 57 57.6 56.7Equatorial Guinea – – – – – – – – –Estonia 4 4.2 4.4 31.3 30.1 31.9 64.1 65.1 62.9Iceland 4.8 5.5 5.5 18.8 17.9 18.1 74.7 75.2 75.2Ireland 5 4.6 4.6 21.3 19.5 18.9 73.3 75.4 76.2Kuwait – – – – – – – – –Latvia 8.7 8.8 9.5 25 24 23.3 66.3 66.9 66.7Lithuania 9.2 9 8.5 26.8 24.4 24.4 63.6 66.2 66.7Luxembourg 1.3 1 1.2 12.4 12 12.7 83.7 81.1 82.7Norway 2.7 2.5 – 20.2 19.7 – 76.9 77.6 –Oman – – – – – – – – –Qatar 1.6 – 1.4 58.4 – 54.1 40.1 – 44.6Slovenia 9.1 8.8 – 33 32.5 – 57.4 58.3 –Uruguay 11.1 11.5 10.7 20.8 21.3 21.1 68.1 67.2 68.1



Group/country

Total labour force

Labour force (female) (% of labour force)

Participation rate 15 and older (%) Employment population ratio (%) Vulnerable GDP per person Unemployed

Male Female 15 and older Youth 15–24Employment (% of total employment)

Employed (constant 1990 PPP$)

Total (% of total labour force)

2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011

Middle-incomeCommonwealth countriesBelize 124,131 128,871 133,565 37.0 37.3 37.5 81.6 81.8 81.8 47.4 48.0 48.3 58.7 59.3 59.0 44.2 44.7 43.9 – – – – – – – – –Botswana 976,526 991,801 1,005,715 46.9 46.8 46.7 81.3 81.5 81.6 71.4 71.6 71.7 63.1 63.5 63.3 41.1 41.3 41.3 – – – – – – – – –Dominica – – – – – – – – – – – – – – – – – – – – – – – – – – –Fiji 361,057 364,577 368,216 32.3 32.4 32.4 79.6 79.6 79.5 39.3 39.3 39.3 57.0 57.0 56.7 39.9 40.0 39.4 – – – – – – – – –Grenada – – – – – – – – – – – – – – – – – – – – – – – – – – –Guyana 293,237 295,504 299,189 34.4 34.8 35.1 80.1 79.6 79.1 40.8 41.3 41.8 53.4 53.5 53.7 35.1 35.0 34.8 – – – – – – 20.2 21.0 21.0Jamaica 1,228,523 1,222,906 1,233,691 45.0 45.1 45.1 73.1 72.0 71.8 56.7 56.0 56.0 57.3 55.9 55.6 27.3 24.9 25.6 – – – 8,947 8,686 8,668 11.4 12.4 12.7Lesotho 808,367 824,318 839,962 46.3 46.1 46.1 73.2 73.3 73.4 58.6 58.7 58.9 48.0 50.0 48.4 28.9 30.4 29.2 – – – – – – – – –Maldives 144,856 150,069 155,275 41.7 41.9 42.0 76.1 76.4 76.8 54.5 55.1 55.7 57.2 57.1 57.7 43.2 42.7 42.8 – – – – – – – – –Mauritius 583,701 600,893 607,287 37.0 37.5 37.7 75.1 75.7 75.5 42.6 43.9 44.1 54.2 54.9 54.7 32.3 31.4 31.9 15.9 16.0 15.1 – – – 7.3 7.7 7.9Namibia 846,542 869,080 894,912 47.8 47.9 47.9 69.8 69.8 69.9 58.2 58.4 58.6 39.9 40.0 42.9 10.9 11.1 13.6 – – – – – – – – –Nauru – – – – – – – – – – – – – – – – – – – – – – – – – – –Papua New Guinea 2,953,485 3,026,382 3,110,615 48.3 48.3 48.3 74.2 74.1 74.1 70.9 70.6 70.6 70.8 70.6 70.7 55.1 54.5 54.6 – – – – – – – 4.0 –St Lucia 90,741 93,083 95,095 46.7 46.7 46.7 76.7 77.0 77.3 63.5 63.9 64.2 – – – – – – – – – 9,534 9,827 9,915 – – –St Vincent and the

Grenadines53,436 53,907 54,219 40.8 41.0 41.1 78.4 78.5 78.4 55.2 55.5 55.7 – – – – – – – – – – – – – – –

Samoa 69,399 70,044 70,459 34.1 34.2 34.3 78.4 78.2 77.8 42.9 42.9 42.8 – – – – – – – – – – – – – – –Seychelles – – – – – – – – – – – – – – – – – – – – – – – – – – –Solomon Islands 203,298 208,660 214,191 39.6 39.6 39.6 79.8 79.9 79.9 53.1 53.1 53.2 64.1 64.4 64.5 44.7 45.1 45.2 – – – – – – – – –Swaziland 402,211 413,019 424,051 39.6 39.7 39.5 70.5 70.7 70.8 43.3 43.5 43.6 43.5 43.6 43.8 25.8 25.8 25.5 – – – – – – – – –Tonga 41,494 41,786 41,960 42.7 42.7 42.7 75.3 75.2 75.0 53.6 53.6 53.6 – – – – – – – – – – – – – – –Tuvalu – – – – – – – – – – – – – – – – – – – – – – – – – – –Vanuatu 100,164 103,099 106,309 43.5 43.5 43.5 79.7 79.7 79.7 61.3 61.3 61.3 – – – – – – 70.0 – – – – – 4.6 – –Other countriesAlbania 1,454,792 1,468,341 1,481,955 41.3 41.4 41.4 71.3 71.3 71.3 49.7 49.7 49.6 52.1 52.1 52.3 37.8 38.7 38.6 – – – 14,943 15,168 15,324 13.8 – –Armenia 1,363,721 1,370,922 1,384,754 42.4 42.2 42.0 69.1 69.6 70.2 48.7 49.0 49.4 39.9 41.1 40.9 17.8 18.1 18.5 – – – 27,248 27,463 28,502 – –Bhutan 346,415 358,039 369,673 41.6 41.5 41.5 75.4 76.0 76.5 65.2 65.5 65.8 67.9 68.3 69.1 41.8 41.7 42.8 74.2 68.8 70.9 4.0 3.3 3.1Bosnia and

Herzegovina1,444,382 1,470,900 1,477,101 38.5 39.3 39.2 58.5 58.5 58.6 34.0 35.2 35.2 34.6 33.7 33.5 16.5 14.0 13.9 27.2 26.6 25.2 28,251 28,330 28,906 24.1 27.2 27.6

Cape Verde 215,795 220,700 226,236 38.3 38.3 38.5 83.0 83.1 83.3 49.8 50.2 50.8 61.7 62.0 62.4 53.8 53.7 53.6 – – – – – – – – –Congo, Republic of 1,623,475 1,674,739 1,721,621 48.7 48.6 48.7 72.5 72.7 72.9 68.1 68.2 68.4 66.1 66.2 66.3 40.4 40.4 40.2 – – – – – – – – –Costa Rica 2,140,472 2,198,391 2,252,521 36.0 36.2 36.4 78.9 78.9 78.9 45.5 46.0 46.4 59.2 59.8 59.8 42.1 42.5 42.2 20.1 20.4 20.2 17,716 18,116 18,518 7.8 7.3 7.7Djibouti 273,810 281,252 288,356 34.5 34.6 34.9 66.9 67.0 67.2 35.1 35.5 36.0 – – – – – – – – – – – – – – –Gabon 561,040 577,777 594,604 46.3 46.3 46.3 64.8 64.9 65.0 55.8 56.0 56.3 50.2 50.4 50.9 14.6 14.7 15.0 – – – – – – – – –Georgia 2,327,861 2,356,832 2,380,869 47.0 47.0 47.0 73.7 73.8 74.2 55.5 55.6 55.8 53.0 53.5 55.0 20.4 20.0 21.8 – – – 16,136 17,110 18,039 16.9 16.3 15.1Lebanon 1,457,550 1,513,589 1,552,450 23.7 23.9 24.0 70.7 70.8 70.8 22.3 22.5 22.6 41.6 41.7 41.6 22.9 22.7 22.2 – – – – – – – – –Macedonia, FYR 958,204 966,803 972,885 38.2 38.5 38.6 68.7 68.9 68.9 42.3 42.7 42.9 37.6 37.9 38.4 15.7 15.4 15.5 22.9 23.1 22.5 15,327 15,402 15,748 32.2 32.0 31.4Mauritania 1,112,724 1,152,180 1,187,609 26.3 26.4 26.7 79.0 79.1 79.2 28.1 28.4 28.7 35.8 36.0 36.1 18.7 18.9 18.9 – – – – – – – – –Moldova 1,262,344 1,216,982 1,233,244 49.5 49.2 49.3 45.9 44.5 45.1 39.6 37.9 38.4 39.9 38.0 38.7 18.4 18.0 18.8 28.5 28.6 28.7 12,269 13,591 14,655 6.4 7.4 6.7Mongolia 1,146,127 1,175,069 1,203,092 46.0 46.1 46.1 64.4 65.0 65.5 53.4 53.9 54.3 55.4 57.3 57.6 30.4 32.0 32.2 57.5 – – – – – – – –Montenegro – – – – – – – – – – – – – – – – – – – – – – – – 19.1 19.7 19.7Panama 1,679,462 1,715,863 1,755,576 36.9 37.1 37.3 83.0 82.6 82.5 49.0 49.3 49.6 61.7 61.7 62.0 42.9 42.1 42.1 32.2 30.9 29.3 – – – 6.6 6.5 4.5Suriname 199,891 203,422 207,142 36.8 37.1 37.3 68.5 68.7 68.7 39.7 40.1 40.5 46.9 47.3 47.7 19.4 19.4 19.4 – – – – – – – – –Timor-Leste 331,672 341,224 352,853 33.3 33.4 33.6 74.8 74.4 74.1 38.5 38.4 38.4 54.8 54.5 54.3 41.0 40.6 40.5 – 69.9 – – – – – 3.6 –High-incomeCommonwealth countriesAntigua and

Barbuda– – – – – – – – – – – – – – – – – – – – – – – – – – –

Bahamas, The 202,249 207,327 211,650 48.3 48.3 48.3 79.3 79.4 79.3 69.2 69.3 69.3 63.6 64.6 65.0 38.8 39.9 41.1 – – – – – – 14.2 – 13.7Barbados 158,286 159,494 160,420 46.5 46.4 46.4 76.3 76.3 76.2 64.8 64.8 64.8 63.4 62.9 62.5 43.3 42.4 41.8 – – – 19,395 19,375 19,663 10.0 10.8 11.2Cyprus 571,331 584,478 594,546 43.2 43.5 43.5 71.5 71.3 71.5 56.2 57.1 57.2 60.6 60.4 59.5 36.1 34.3 31.2 14.7 13.9 13.6 26,629 27,009 27,360 5.3 6.2 7.7Malta 175,472 179,713 180,321 34.0 34.4 34.6 67.2 67.8 67.4 34.0 35.0 35.2 46.9 47.7 47.8 44.6 45.1 45.0 9.3 9.8 9.3 32,142 32,318 32,664 6.9 6.9 6.4St Kitts and Nevis – – – – – – – – – – – – – – – – – – – – – – – – – – –Trinidad and Tobago 686,124 692,788 698,723 42.1 42.2 42.2 77.7 78.0 78.3 54.3 54.6 54.9 62.1 62.9 62.3 47.3 48.1 46.7 – – – 49,936 49,815 50,599 – – –

(continued)

156


Group/country

Total labour force






2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011

Middle-incomeCommonwealth countriesBelize 124,131 128,871 133,565 37.0 37.3 37.5 81.6 81.8 81.8 47.4 48.0 48.3 58.7 59.3 59.0 44.2 44.7 43.9 – – – – – – – – –Botswana 976,526 991,801 1,005,715 46.9 46.8 46.7 81.3 81.5 81.6 71.4 71.6 71.7 63.1 63.5 63.3 41.1 41.3 41.3 – – – – – – – – –Dominica – – – – – – – – – – – – – – – – – – – – – – – – – – –Fiji 361,057 364,577 368,216 32.3 32.4 32.4 79.6 79.6 79.5 39.3 39.3 39.3 57.0 57.0 56.7 39.9 40.0 39.4 – – – – – – – – –Grenada – – – – – – – – – – – – – – – – – – – – – – – – – – –Guyana 293,237 295,504 299,189 34.4 34.8 35.1 80.1 79.6 79.1 40.8 41.3 41.8 53.4 53.5 53.7 35.1 35.0 34.8 – – – – – – 20.2 21.0 21.0Jamaica 1,228,523 1,222,906 1,233,691 45.0 45.1 45.1 73.1 72.0 71.8 56.7 56.0 56.0 57.3 55.9 55.6 27.3 24.9 25.6 – – – 8,947 8,686 8,668 11.4 12.4 12.7Lesotho 808,367 824,318 839,962 46.3 46.1 46.1 73.2 73.3 73.4 58.6 58.7 58.9 48.0 50.0 48.4 28.9 30.4 29.2 – – – – – – – – –Maldives 144,856 150,069 155,275 41.7 41.9 42.0 76.1 76.4 76.8 54.5 55.1 55.7 57.2 57.1 57.7 43.2 42.7 42.8 – – – – – – – – –Mauritius 583,701 600,893 607,287 37.0 37.5 37.7 75.1 75.7 75.5 42.6 43.9 44.1 54.2 54.9 54.7 32.3 31.4 31.9 15.9 16.0 15.1 – – – 7.3 7.7 7.9Namibia 846,542 869,080 894,912 47.8 47.9 47.9 69.8 69.8 69.9 58.2 58.4 58.6 39.9 40.0 42.9 10.9 11.1 13.6 – – – – – – – – –Nauru – – – – – – – – – – – – – – – – – – – – – – – – – – –Papua New Guinea 2,953,485 3,026,382 3,110,615 48.3 48.3 48.3 74.2 74.1 74.1 70.9 70.6 70.6 70.8 70.6 70.7 55.1 54.5 54.6 – – – – – – – 4.0 –St Lucia 90,741 93,083 95,095 46.7 46.7 46.7 76.7 77.0 77.3 63.5 63.9 64.2 – – – – – – – – – 9,534 9,827 9,915 – – –St Vincent and the

Grenadines53,436 53,907 54,219 40.8 41.0 41.1 78.4 78.5 78.4 55.2 55.5 55.7 – – – – – – – – – – – – – – –

Samoa 69,399 70,044 70,459 34.1 34.2 34.3 78.4 78.2 77.8 42.9 42.9 42.8 – – – – – – – – – – – – – – –Seychelles – – – – – – – – – – – – – – – – – – – – – – – – – – –Solomon Islands 203,298 208,660 214,191 39.6 39.6 39.6 79.8 79.9 79.9 53.1 53.1 53.2 64.1 64.4 64.5 44.7 45.1 45.2 – – – – – – – – –Swaziland 402,211 413,019 424,051 39.6 39.7 39.5 70.5 70.7 70.8 43.3 43.5 43.6 43.5 43.6 43.8 25.8 25.8 25.5 – – – – – – – – –Tonga 41,494 41,786 41,960 42.7 42.7 42.7 75.3 75.2 75.0 53.6 53.6 53.6 – – – – – – – – – – – – – – –Tuvalu – – – – – – – – – – – – – – – – – – – – – – – – – – –Vanuatu 100,164 103,099 106,309 43.5 43.5 43.5 79.7 79.7 79.7 61.3 61.3 61.3 – – – – – – 70.0 – – – – – 4.6 – –Other countriesAlbania 1,454,792 1,468,341 1,481,955 41.3 41.4 41.4 71.3 71.3 71.3 49.7 49.7 49.6 52.1 52.1 52.3 37.8 38.7 38.6 – – – 14,943 15,168 15,324 13.8 – –Armenia 1,363,721 1,370,922 1,384,754 42.4 42.2 42.0 69.1 69.6 70.2 48.7 49.0 49.4 39.9 41.1 40.9 17.8 18.1 18.5 – – – 27,248 27,463 28,502 – –Bhutan 346,415 358,039 369,673 41.6 41.5 41.5 75.4 76.0 76.5 65.2 65.5 65.8 67.9 68.3 69.1 41.8 41.7 42.8 74.2 68.8 70.9 4.0 3.3 3.1Bosnia and

Herzegovina1,444,382 1,470,900 1,477,101 38.5 39.3 39.2 58.5 58.5 58.6 34.0 35.2 35.2 34.6 33.7 33.5 16.5 14.0 13.9 27.2 26.6 25.2 28,251 28,330 28,906 24.1 27.2 27.6

Cape Verde 215,795 220,700 226,236 38.3 38.3 38.5 83.0 83.1 83.3 49.8 50.2 50.8 61.7 62.0 62.4 53.8 53.7 53.6 – – – – – – – – –Congo, Republic of 1,623,475 1,674,739 1,721,621 48.7 48.6 48.7 72.5 72.7 72.9 68.1 68.2 68.4 66.1 66.2 66.3 40.4 40.4 40.2 – – – – – – – – –Costa Rica 2,140,472 2,198,391 2,252,521 36.0 36.2 36.4 78.9 78.9 78.9 45.5 46.0 46.4 59.2 59.8 59.8 42.1 42.5 42.2 20.1 20.4 20.2 17,716 18,116 18,518 7.8 7.3 7.7Djibouti 273,810 281,252 288,356 34.5 34.6 34.9 66.9 67.0 67.2 35.1 35.5 36.0 – – – – – – – – – – – – – – –Gabon 561,040 577,777 594,604 46.3 46.3 46.3 64.8 64.9 65.0 55.8 56.0 56.3 50.2 50.4 50.9 14.6 14.7 15.0 – – – – – – – – –Georgia 2,327,861 2,356,832 2,380,869 47.0 47.0 47.0 73.7 73.8 74.2 55.5 55.6 55.8 53.0 53.5 55.0 20.4 20.0 21.8 – – – 16,136 17,110 18,039 16.9 16.3 15.1Lebanon 1,457,550 1,513,589 1,552,450 23.7 23.9 24.0 70.7 70.8 70.8 22.3 22.5 22.6 41.6 41.7 41.6 22.9 22.7 22.2 – – – – – – – – –Macedonia, FYR 958,204 966,803 972,885 38.2 38.5 38.6 68.7 68.9 68.9 42.3 42.7 42.9 37.6 37.9 38.4 15.7 15.4 15.5 22.9 23.1 22.5 15,327 15,402 15,748 32.2 32.0 31.4Mauritania 1,112,724 1,152,180 1,187,609 26.3 26.4 26.7 79.0 79.1 79.2 28.1 28.4 28.7 35.8 36.0 36.1 18.7 18.9 18.9 – – – – – – – – –Moldova 1,262,344 1,216,982 1,233,244 49.5 49.2 49.3 45.9 44.5 45.1 39.6 37.9 38.4 39.9 38.0 38.7 18.4 18.0 18.8 28.5 28.6 28.7 12,269 13,591 14,655 6.4 7.4 6.7Mongolia 1,146,127 1,175,069 1,203,092 46.0 46.1 46.1 64.4 65.0 65.5 53.4 53.9 54.3 55.4 57.3 57.6 30.4 32.0 32.2 57.5 – – – – – – – –Montenegro – – – – – – – – – – – – – – – – – – – – – – – – 19.1 19.7 19.7Panama 1,679,462 1,715,863 1,755,576 36.9 37.1 37.3 83.0 82.6 82.5 49.0 49.3 49.6 61.7 61.7 62.0 42.9 42.1 42.1 32.2 30.9 29.3 – – – 6.6 6.5 4.5Suriname 199,891 203,422 207,142 36.8 37.1 37.3 68.5 68.7 68.7 39.7 40.1 40.5 46.9 47.3 47.7 19.4 19.4 19.4 – – – – – – – – –Timor-Leste 331,672 341,224 352,853 33.3 33.4 33.6 74.8 74.4 74.1 38.5 38.4 38.4 54.8 54.5 54.3 41.0 40.6 40.5 – 69.9 – – – – – 3.6 –High-incomeCommonwealth countriesAntigua and

Barbuda– – – – – – – – – – – – – – – – – – – – – – – – – – –

Bahamas, The 202,249 207,327 211,650 48.3 48.3 48.3 79.3 79.4 79.3 69.2 69.3 69.3 63.6 64.6 65.0 38.8 39.9 41.1 – – – – – – 14.2 – 13.7Barbados 158,286 159,494 160,420 46.5 46.4 46.4 76.3 76.3 76.2 64.8 64.8 64.8 63.4 62.9 62.5 43.3 42.4 41.8 – – – 19,395 19,375 19,663 10.0 10.8 11.2Cyprus 571,331 584,478 594,546 43.2 43.5 43.5 71.5 71.3 71.5 56.2 57.1 57.2 60.6 60.4 59.5 36.1 34.3 31.2 14.7 13.9 13.6 26,629 27,009 27,360 5.3 6.2 7.7Malta 175,472 179,713 180,321 34.0 34.4 34.6 67.2 67.8 67.4 34.0 35.0 35.2 46.9 47.7 47.8 44.6 45.1 45.0 9.3 9.8 9.3 32,142 32,318 32,664 6.9 6.9 6.4St Kitts and Nevis – – – – – – – – – – – – – – – – – – – – – – – – – – –Trinidad and Tobago 686,124 692,788 698,723 42.1 42.2 42.2 77.7 78.0 78.3 54.3 54.6 54.9 62.1 62.9 62.3 47.3 48.1 46.7 – – – 49,936 49,815 50,599 – – –

157

Table 38. Labour force participation (continued)

Group/country

Total labour force






2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011

Other countriesBahrain 657,579 707,016 732,938 19.4 19.4 19.4 86.2 87.2 87.3 38.5 39.2 39.4 63.6 64.9 65.0 32.2 32.4 30.0 2.0 2.0 – 9,278 9,335 9,175 – – –Croatia 1,985,139 1,964,550 1,906,924 45.9 45.8 45.8 60.0 59.6 59.7 46.5 46.0 46.0 48.2 46.3 45.5 25.5 25.1 23.6 16.6 17.8 18.0 23,293 23,971 25,129 9.1 11.8 13.4Equatorial Guinea 354,494 366,258 377,875 44.7 44.7 44.7 92.3 92.3 92.3 80.5 80.6 80.6 81.1 81.1 80.3 68.2 68.1 67.0 – – – – – – – – –Estonia 699,640 697,270 700,504 49.4 50.1 50.0 68.7 67.7 68.2 55.7 56.5 56.7 53.1 51.1 54.2 30.0 26.5 31.3 4.4 4.9 4.6 41,537 44,605 45,481 13.8 16.9 12.5Iceland 186,491 187,373 188,497 47.0 47.4 47.3 78.5 78.2 78.4 69.9 70.8 70.8 68.9 68.8 69.4 58.0 56.5 58.4 7.7 8.4 8.2 45,538 43,847 44,647 7.2 7.6 7.1Ireland 2,147,374 2,122,113 2,174,578 43.9 44.2 44.2 69.5 68.3 68.5 52.8 52.4 52.6 53.9 52.2 51.8 35.6 31.3 30.7 12.2 11.9 11.8 52,972 55,068 56,749 11.8 13.6 14.4Kuwait 1,430,499 1,514,779 1,585,115 23.9 23.9 23.9 81.6 82.2 82.3 43.1 43.3 43.4 65.8 66.3 66.4 31.1 31.2 30.7 – – – 10,932 11,055 11,440 – – –Latvia 1,185,740 1,151,326 1,068,632 50.0 50.4 50.3 68.3 66.3 67.2 55.4 54.7 55.2 50.7 48.7 51.2 28.5 27.3 30.0 7.7 7.5 7.7 27,037 28,299 28,935 17.1 18.7 15.4Lithuania 1,633,349 1,621,550 1,506,729 50.4 50.7 50.6 63.2 63.4 63.9 53.3 53.9 54.1 49.9 47.9 49.6 21.8 19.6 20.7 9.6 8.8 8.2 27,177 29,055 29,778 13.7 17.8 15.4Luxembourg 234,863 238,396 244,090 43.3 43.4 43.6 66.1 65.4 65.2 48.9 48.9 49.2 54.4 54.6 54.4 27.0 21.5 21.7 5.9 4.9 5.9 52,145 52,585 51,938 5.1 4.4 4.9Norway 2,602,970 2,612,210 2,653,584 47.2 47.0 47.1 70.7 70.2 70.1 62.3 61.5 61.7 64.4 63.5 63.7 53.1 51.6 52.5 5.9 5.6 5.2 50,281 50,494 51,456 3.2 3.6 3.3Oman 1,104,412 1,220,810 1,377,914 18.8 17.9 16.8 78.5 79.9 81.6 27.5 28.0 28.3 53.8 55.3 56.5 31.7 32.2 30.6 – – – 24,376 24,724 25,157 – – –Qatar 1,138,229 1,304,192 1,430,538 12.7 12.3 12.0 94.3 95.2 95.2 51.5 52.1 51.8 84.8 86.0 86.0 64.3 66.5 64.7 0.1 – 0.2 16,005 16,903 18,585 0.3 – 0.6Slovenia 1,038,834 1,039,292 1,038,926 45.9 45.6 45.6 65.4 65.2 65.1 53.5 53.1 53.1 55.8 54.7 54.2 34.7 33.5 33.4 12.8 13.5 13.2 35,071 36,478 37,109 5.9 7.2 8.2Uruguay 1,700,162 1,708,962 1,725,293 44.5 44.5 44.5 76.8 76.6 76.5 55.4 55.4 55.6 60.8 61.0 61.6 43.8 43.6 44.1 23.2 22.2 – 22,064 23,775 25,016 7.3 6.8 6.0


158

Table 38. Labour force participation (continued)

Group/country

Total labour force






2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011 2009 2010 2011

Other countriesBahrain 657,579 707,016 732,938 19.4 19.4 19.4 86.2 87.2 87.3 38.5 39.2 39.4 63.6 64.9 65.0 32.2 32.4 30.0 2.0 2.0 – 9,278 9,335 9,175 – – –Croatia 1,985,139 1,964,550 1,906,924 45.9 45.8 45.8 60.0 59.6 59.7 46.5 46.0 46.0 48.2 46.3 45.5 25.5 25.1 23.6 16.6 17.8 18.0 23,293 23,971 25,129 9.1 11.8 13.4Equatorial Guinea 354,494 366,258 377,875 44.7 44.7 44.7 92.3 92.3 92.3 80.5 80.6 80.6 81.1 81.1 80.3 68.2 68.1 67.0 – – – – – – – – –Estonia 699,640 697,270 700,504 49.4 50.1 50.0 68.7 67.7 68.2 55.7 56.5 56.7 53.1 51.1 54.2 30.0 26.5 31.3 4.4 4.9 4.6 41,537 44,605 45,481 13.8 16.9 12.5Iceland 186,491 187,373 188,497 47.0 47.4 47.3 78.5 78.2 78.4 69.9 70.8 70.8 68.9 68.8 69.4 58.0 56.5 58.4 7.7 8.4 8.2 45,538 43,847 44,647 7.2 7.6 7.1Ireland 2,147,374 2,122,113 2,174,578 43.9 44.2 44.2 69.5 68.3 68.5 52.8 52.4 52.6 53.9 52.2 51.8 35.6 31.3 30.7 12.2 11.9 11.8 52,972 55,068 56,749 11.8 13.6 14.4Kuwait 1,430,499 1,514,779 1,585,115 23.9 23.9 23.9 81.6 82.2 82.3 43.1 43.3 43.4 65.8 66.3 66.4 31.1 31.2 30.7 – – – 10,932 11,055 11,440 – – –Latvia 1,185,740 1,151,326 1,068,632 50.0 50.4 50.3 68.3 66.3 67.2 55.4 54.7 55.2 50.7 48.7 51.2 28.5 27.3 30.0 7.7 7.5 7.7 27,037 28,299 28,935 17.1 18.7 15.4Lithuania 1,633,349 1,621,550 1,506,729 50.4 50.7 50.6 63.2 63.4 63.9 53.3 53.9 54.1 49.9 47.9 49.6 21.8 19.6 20.7 9.6 8.8 8.2 27,177 29,055 29,778 13.7 17.8 15.4Luxembourg 234,863 238,396 244,090 43.3 43.4 43.6 66.1 65.4 65.2 48.9 48.9 49.2 54.4 54.6 54.4 27.0 21.5 21.7 5.9 4.9 5.9 52,145 52,585 51,938 5.1 4.4 4.9Norway 2,602,970 2,612,210 2,653,584 47.2 47.0 47.1 70.7 70.2 70.1 62.3 61.5 61.7 64.4 63.5 63.7 53.1 51.6 52.5 5.9 5.6 5.2 50,281 50,494 51,456 3.2 3.6 3.3Oman 1,104,412 1,220,810 1,377,914 18.8 17.9 16.8 78.5 79.9 81.6 27.5 28.0 28.3 53.8 55.3 56.5 31.7 32.2 30.6 – – – 24,376 24,724 25,157 – – –Qatar 1,138,229 1,304,192 1,430,538 12.7 12.3 12.0 94.3 95.2 95.2 51.5 52.1 51.8 84.8 86.0 86.0 64.3 66.5 64.7 0.1 – 0.2 16,005 16,903 18,585 0.3 – 0.6Slovenia 1,038,834 1,039,292 1,038,926 45.9 45.6 45.6 65.4 65.2 65.1 53.5 53.1 53.1 55.8 54.7 54.2 34.7 33.5 33.4 12.8 13.5 13.2 35,071 36,478 37,109 5.9 7.2 8.2Uruguay 1,700,162 1,708,962 1,725,293 44.5 44.5 44.5 76.8 76.6 76.5 55.4 55.4 55.6 60.8 61.0 61.6 43.8 43.6 44.1 23.2 22.2 – 22,064 23,775 25,016 7.3 6.8 6.0


159

Tabl

e 39

. U

rban

and

rura

l pop

ulat

ion

(%)

Gro

up/c

ount

ryU

rban

Rur

alA

nnua

l urb

an g

row

th ra

te

2009

2010

2011

2012

2009

2010

2011

2012

2009

2010

2011

2012

Mid

dle-

inco

me

Com

mon

wea

lth c

ount

ries

Bel

ize

45.2

45.0

44.8

44.6

54.8

55.0

55.2

55.4

1.9

1.9

2.0

2.0

Bo

tsw

ana

60.2

61.0

61.6

62.3

39.8

39.0

38.4

37.7

2.1

2.1

1.9

1.9

Do

min

ica

67.0

67.1

67.2

67.3

33.0

32.9

32.8

32.7

0.2

0.3

0.5

0.6

Fiji

51.4

51.8

52.2

52.6

48.6

48.2

47.8

47.4

1.8

1.7

1.6

1.5

Gre

nada

38.5

38.8

39.2

39.5

61.5

61.2

60.8

60.5

1.2

1.2

1.3

1.3

Guy

ana

28.3

28.3

28.4

28.5

71.7

71.7

71.6

71.5

0.7

0.6

0.9

0.9

Jam

aica

52.0

52.0

52.1

52.2

48.0

48.0

47.9

47.8

0.3

0.2

0.4

0.4

Kiri

bati

43.8

43.8

43.9

44.1

56.2

56.2

56.1

55.9

1.6

1.6

1.8

1.8

Leso

tho

26.1

26.8

27.6

28.3

73.9

73.2

72.4

71.7

3.7

3.6

3.7

3.7

Mal

dive

s38

.740

.041

.142

.261

.360

.058

.957

.85.

15.

04.

74.

6M

aurit

ius

41.9

41.8

41.8

41.8

58.1

58.2

58.2

58.2

0.3

0.3

0.4

0.5

Nam

ibia

37.3

37.8

38.4

39.0

62.7

62.2

61.6

61.0

3.0

3.1

3.3

3.3

Nau

ru–

––

––

––

––

––

–P

apua

New

Gui

nea

12.5

12.4

12.5

12.6

87.5

87.6

87.5

87.4

2.1

2.1

2.8

2.7

St L

ucia

19.3

18.3

17.6

17.0

80.7

81.7

82.4

83.0

−3.4

−3.8

−2.7

−3.0

St V

ince

nt a

nd th

e G

rena

dine

s48

.548

.949

.349

.751

.551

.150

.750

.30.

90.

80.

80.

8

Sam

oa

20.3

20.1

19.9

19.7

79.7

79.9

80.1

80.3

−0.4

−0.4

−0.2

−0.2

Seyc

helle

s52

.953

.253

.654

.047

.146

.846

.446

.01.

03.

4−1

.91.

1So

lom

on

Isla

nds

19.6

20.0

20.5

20.9

80.4

80.0

79.5

79.1

4.5

4.4

4.4

4.3

Swaz

iland

21.4

21.3

21.3

21.2

78.6

78.7

78.7

78.8

1.1

1.1

1.4

1.4

Tong

a23

.323

.423

.523

.676

.776

.676

.576

.40.

80.

70.

90.

8Tu

valu

49.7

50.1

50.6

51.0

50.3

49.9

49.4

49.0

1.0

1.0

1.0

1.0

Van

uatu

24.3

24.6

24.9

25.2

75.7

75.4

75.1

74.8

3.6

3.6

3.6

3.5

Oth

er c

ount

ries

Alb

ania

51.2

52.3

53.4

54.4

48.8

47.7

46.6

45.6

2.0

2.1

2.1

2.2

Arm

enia

64.1

64.1

64.1

64.2

35.9

35.9

35.9

35.8

−0.4

−0.2

0.1

0.2

Bhu

tan

34.0

34.8

35.6

36.3

66.0

65.2

64.4

63.7

4.0

4.0

3.9

3.8

Bo

snia

and

Her

zego

vina

47.2

47.7

48.3

48.8

52.8

52.3

51.7

51.2

0.9

0.8

1.0

1.0

Cap

e V

erde

61.0

61.8

62.6

63.3

39.0

38.2

37.4

36.7

1.6

1.7

1.8

2.0

Co

ngo,

Rep

ublic

of

62.8

63.2

63.6

64.1

37.2

36.8

36.4

35.9

3.7

3.6

3.4

3.3

Co

sta

Ric

a63

.764

.264

.665

.136

.335

.835

.434

.92.

32.

22.

22.

1D

jibo

uti

77.0

77.0

77.1

77.2

23.0

23.0

22.9

22.8

1.5

1.5

1.6

1.6

Gab

on

85.4

85.8

86.1

86.5

14.6

14.2

13.9

13.5

3.0

3.0

2.8

2.7

Geo

rgia

52.7

52.7

52.9

53.0

47.3

47.3

47.1

47.0

0.7

1.0

0.9

0.9

Jord

an82

.282

.582

.783

.017

.817

.517

.317

.02.

52.

52.

52.

5La

o P

DR

32.0

33.1

34.2

35.3

68.0

66.9

65.8

64.7

5.7

5.5

5.2

5.1

(con

tinue

d)

160

Tabl

e 39

. U

rban

and

rura

l pop

ulat

ion

(%) (

cont

inue

d)

Gro

up/c

ount

ryU

rban

Rur

alA

nnua

l urb

an g

row

th ra

te

2009

2010

2011

2012

2009

2010

2011

2012

2009

2010

2011

2012

Leba

non

87.0

87.1

87.2

87.4

13.0

12.9

12.8

12.6

1.6

2.3

1.1

1.1

Mac

edo

nia,

FYR

59.2

59.2

59.3

59.4

40.8

40.8

40.7

40.6

0.1

0.1

0.3

0.3

Mau

ritan

ia41

.141

.241

.541

.858

.958

.858

.558

.23.

13.

03.

23.

2M

old

ova

46.2

46.9

47.7

48.4

53.8

53.1

52.3

51.6

1.5

1.5

1.5

1.5

Mo

ngo

lia66

.667

.668

.569

.333

.432

.431

.530

.73.

03.

02.

82.

8M

ont

eneg

ro62

.963

.163

.363

.537

.136

.936

.736

.50.

40.

40.

40.

4P

anam

a73

.874

.675

.275

.826

.225

.424

.824

.22.

82.

82.

52.

4P

arag

uay

60.8

61.4

61.9

62.4

39.2

38.6

38.1

37.6

2.7

2.7

2.6

2.6

São

To

mé

and

Prín

cipe

61.2

62.0

62.7

63.3

38.8

38.0

37.4

36.7

4.2

4.1

3.8

3.7

Surin

ame

68.9

69.3

69.7

70.1

31.1

30.7

30.3

29.9

1.6

1.5

1.5

1.5

Tim

or-

Lest

e27

.628

.028

.328

.772

.472

.071

.771

.34.

34.

24.

24.

2H

igh-

inco

me

Com

mon

wea

lth c

ount

ries

Ant

igua

and

Bar

buda

30.0

29.9

29.9

29.9

70.0

70.1

70.1

70.1

0.6

0.6

1.0

1.0

Bah

amas

, The

83.9

84.1

84.3

84.4

16.1

15.9

15.7

15.6

2.0

1.9

1.8

1.8

Bar

bado

s43

.343

.944

.444

.956

.756

.155

.655

.11.

71.

71.

71.

6B

rune

i Dar

ussa

lam

75.2

75.6

76.0

76.3

24.8

24.4

24.0

23.7

2.2

2.1

2.0

1.9

Cyp

rus

70.1

70.3

70.5

70.7

29.9

29.7

29.5

29.3

1.5

1.4

1.4

1.4

Mal

ta94

.594

.794

.895

.05.

55.

35.

25.

00.

70.

70.

30.

6St

Kitt

s an

d N

evis

32.0

31.9

32.0

32.1

68.0

68.1

68.0

67.9

1.1

1.1

1.4

1.4

Trin

idad

and

To

bago

13.2

13.4

13.7

14.0

86.8

86.6

86.3

86.0

2.5

2.4

2.3

2.3

Oth

er c

ount

ries

Bah

rain

88.6

88.6

88.7

88.8

11.4

11.4

11.3

11.2

6.6

5.0

3.3

2.0

Cro

atia

57.3

57.5

57.8

58.1

42.7

42.5

42.2

41.9

0.3

0.1

−2.7

0.2

Equa

toria

l Gui

nea

39.2

39.3

39.5

39.7

60.8

60.7

60.5

60.3

3.1

3.0

3.3

3.2

Esto

nia

69.5

69.5

69.5

69.6

30.5

30.5

30.5

30.4

0.0

0.0

0.1

0.0

Icel

and

93.5

93.6

93.7

93.8

6.5

6.4

6.3

6.2

0.5

0.0

0.4

0.5

Irela

nd61

.661

.962

.262

.538

.438

.137

.837

.51.

20.

82.

80.

8K

uwai

t98

.298

.298

.398

.31.

81.

81.

71.

75.

34.

94.

44.

0La

tvia

67.8

67.7

67.7

67.7

32.2

32.3

32.3

32.3

−0.6

−0.8

−8.4

−1.6

Lith

uani

a66

.967

.067

.167

.233

.133

.032

.932

.8−0

.4−1

.5−8

.0−1

.3Lu

xem

bour

g84

.985

.285

.485

.615

.114

.814

.614

.42.

12.

12.

52.

8N

orw

ay78

.879

.179

.479

.621

.220

.920

.620

.41.

71.

71.

61.

7O

man

72.9

73.2

73.4

73.7

27.1

26.8

26.6

26.3

3.0

5.5

8.0

9.5

Qat

ar98

.498

.798

.898

.91.

61.

31.

21.

114

.311

.58.

97.

2S

love

nia

50.1

50.0

49.9

49.9

49.9

50.0

50.1

50.1

0.7

0.2

0.1

0.2

Uru

guay

92.3

92.5

92.5

92.6

7.7

7.5

7.5

7.4

0.5

0.5

0.4

0.4

Not

e: –

= n

ot a

vaila

ble

Sou

rce:

Wo

rld B

ank,

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opm

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ndic

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13, a

vaila

ble

at: h

ttp:

//da

taba

nk.w

orld

bank

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(acc

esse

d Ju

ne 2

013)

161

Tabl

e 40

. La

nd u

se

Gro

up/c

ount

ryFo

od p

rodu

ctio

n in

dex

(200

4–20

06)=

100

Agr

icul

tura

l Pro

duct

ivit

y (C

onst

ant

US

$ 20

05)

Cer

eal y

ield

(kg

per h

ecta

re)

Fore

st a

rea

(%

of la

nd a

rea)

Ara

ble

land

(% o

f lan

d ar

ea)

2009

2010

2011

2009

2010

2011

2009

2010

2011

2010

2011

2009

2010

2011

Mid

dle-

inco

me

Com

mon

wea

lth c

ount

ries

Bel

ize

88.4

89.9

86.7

3,56

73,

474

3,28

32,

972

2,79

92,

817

61.1

60.6

3.2

3.3

3.3

Bo

tsw

ana

112.

812

3.8

127.

6–

––

359

374

393

20.0

19.8

0.6

0.5

0.5

Do

min

ica

112.

411

3.5

110.

87,

467

6,68

87,

091

1,14

81,

417

2,28

859

.559

.28.

08.

08.

0Fi

ji87

.583

.182

.52,

605

2,47

42,

661

2,81

62,

602

3,07

555

.555

.79.

29.

29.

2G

rena

da83

.687

.691

.53,

737

3,51

23,

660

792

861

1,03

250

.050

.08.

88.

88.

8G

uyan

a10

4.8

105.

911

3.4

4,28

44,

472

4,68

54,

366

4,16

74,

741

77.2

77.2

2.1

2.1

2.1

Jam

aica

98.9

98.6

103.

53,

136

3,17

23,

516

1,25

71,

172

1,27

031

.131

.111

.111

.111

.1K

iriba

ti11

6.3

117.

411

5.7

2,18

1–

––

––

15.0

15.0

2.5

2.5

2.5

Leso

tho

94.0

111.

410

7.4

309

392

368

421

909

664

1.4

1.5

11.0

10.6

10.1

Mal

dive

s77

.783

.587

.32,

928

2,90

33,

067

1,97

32,

264

2,50

83.

03.

010

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aurit

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100.

898

.596

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083

6,91

37,

317

8,00

06,

833

7,44

217

.217

.339

.439

.438

.4N

amib

ia92

.391

.291

.3–

––

365

373

354

8.9

8.8

1.0

1.0

1.0

Nau

ru–

––

––

––

––

––

––

–P

apua

New

Gui

nea

116.

311

4.8

111.

6–

––

4,15

64,

279

4,45

763

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.10.

70.

70.

7St

Luc

ia11

1.5

90.7

95.6

2,17

61,

780

1,66

4–

––

77.0

77.0

4.9

4.9

4.9

St V

ince

nt a

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e G

rena

dine

s96

.510

7.0

117.

93,

478

2,83

22,

425

3,35

03,

281

5,29

768

.568

.712

.812

.812

.8

Sam

oa

107.

210

8.4

103.

82,

854

2,53

62,

493

––

–60

.460

.42.

82.

82.

8Se

yche

lles

83.5

79.1

86.7

831

780

807

––

–88

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.52.

22.

22.

2So

lom

on

Isla

nds

114.

112

0.4

119.

01,

177

––

2,92

33,

500

4,21

179

.178

.90.

60.

60.

6Sw

azila

nd10

1.5

104.

210

5.0

1,28

71,

329

1,37

31,

077

1,22

61,

148

32.7

33.0

10.2

10.2

10.2

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12.0

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63.6

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10.7

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7.3

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6.5

5.7

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10.9

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9.5

8.3

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3.7

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(con

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164

Tabl

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dis

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2020

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2010

2015

2020

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6.3

7.5

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67.9

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70.5

70.3

7.4

7.7

8.8

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Mac

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nia,

FYR

5.4

5.2

4.9

4.7

12.2

11.1

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68.7

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6.8

6.5

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6.2

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47.3

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.272

.369

.169

.269

.369

.574

.875

.075

.275

.3Tu

valu

––

––

––

––

––

––

Van

uatu

70.2

70.5

70.8

71.1

68.4

68.6

68.9

69.2

72.2

72.5

72.9

73.2

Oth

er c

ount

ries

Alb

ania

76.6

76.8

77.0

77.2

73.6

73.8

74.0

74.2

79.9

80.0

80.1

80.3

Arm

enia

73.9

74.1

74.2

74.3

70.7

70.8

70.9

71.1

77.4

77.5

77.7

77.8

Bhu

tan

66.0

66.5

67.0

67.5

65.7

66.2

66.7

67.2

66.2

66.8

67.3

67.8

Bo

snia

and

H

erze

govi

na75

.575

.775

.876

.073

.073

.173

.373

.578

.278

.378

.478

.6

Cap

e V

erde

73.1

73.5

73.9

74.2

69.3

69.7

70.0

70.4

77.2

77.5

77.9

78.2

Co

ngo,

Rep

ublic

of

55.9

56.6

57.2

57.8

54.7

55.3

55.9

56.4

57.2

57.9

58.6

59.2

Co

sta

Ric

a78

.979

.179

.379

.576

.676

.877

.077

.381

.381

.581

.781

.8D

jibo

uti

59.3

59.8

60.3

60.8

57.8

58.3

58.8

59.3

60.8

61.3

61.9

62.4

Gab

on

61.4

61.9

62.3

62.7

60.4

60.8

61.3

61.7

62.4

62.9

63.4

63.8

Geo

rgia

73.4

73.5

73.7

73.8

69.8

70.0

70.1

70.3

77.1

77.3

77.4

77.5

(con

tinue

d)

168

Tabl

e 43

. Li

fe e

xpec

tanc

y at

bir

th (y

ears

) (co

ntin

ued)

Gro

up/c

ount

ryTo

tal

Mal

eFe

mal

e

2008

2009

2010

2011

2008

2009

2010

2011

2008

2009

2010

2011

Leba

non

78.5

78.9

79.3

79.6

76.4

76.8

77.1

77.5

80.8

81.2

81.5

81.8

Mac

edo

nia,

FYR

74.4

74.6

74.7

74.9

72.2

72.4

72.5

72.7

76.8

76.9

77.0

77.2

Mau

ritan

ia60

.760

.961

.061

.259

.259

.459

.659

.762

.262

.462

.662

.7M

old

ova

68.2

68.3

68.5

68.6

64.5

64.6

64.7

64.8

72.2

72.3

72.4

72.6

Mo

ngo

lia66

.466

.666

.967

.162

.562

.863

.063

.270

.470

.771

.071

.2M

ont

eneg

ro74

.174

.374

.474

.571

.972

.072

.272

.376

.576

.676

.876

.9P

anam

a76

.676

.776

.977

.273

.773

.974

.174

.379

.679

.880

.080

.1Sã

o To

mé

and

Prín

cipe

65.5

65.7

65.9

66.0

63.6

63.8

64.0

64.1

67.4

67.6

67.8

68.0

Surin

ame

69.8

70.1

70.3

70.6

66.5

66.8

67.1

67.4

73.2

73.4

73.7

73.9

Tim

or-

Lest

e64

.865

.465

.966

.563

.363

.964

.565

.066

.466

.967

.568

.1H

igh-

inco

me

Com

mon

wea

lth c

ount

ries

Ant

igua

and

Bar

buda

75.0

75.2

75.3

75.5

72.6

72.8

73.0

73.2

77.4

77.6

77.8

78.0

Bah

amas

, The

74.2

74.4

74.6

74.8

71.3

71.5

71.6

71.8

77.4

77.5

77.7

77.9

Bar

bado

s74

.574

.674

.875

.072

.272

.372

.572

.676

.977

.177

.277

.4B

rune

i Dar

ussa

lam

77.6

77.8

78.0

78.2

75.7

75.9

76.1

76.3

79.6

79.8

80.0

80.1

Cyp

rus

79.0

79.2

79.3

79.5

76.9

77.1

77.3

77.5

81.2

81.3

81.4

81.6

Mal

ta79

.479

.980

.982

.076

.777

.678

.980

.282

.382

.383

.183

.9St

Kitt

s an

d N

evis

––

––

––

––

––

––

Trin

idad

and

To

bago

69.4

69.5

69.6

69.7

65.9

66.0

66.1

66.2

73.0

73.1

73.3

73.4

Oth

er c

ount

ries

Bah

rain

76.0

76.1

76.3

76.4

75.2

75.4

75.5

75.6

76.7

76.9

77.0

77.2

Cro

atia

75.9

76.2

76.5

76.9

72.4

72.9

73.5

73.9

79.6

79.6

79.6

80.0

Equa

toria

l Gui

nea

50.4

51.0

51.5

52.1

49.1

49.6

50.2

50.7

51.8

52.4

53.0

53.6

Esto

nia

73.8

74.8

75.4

76.1

68.6

69.8

70.6

71.2

79.2

80.1

80.5

81.3

Icel

and

81.6

81.8

81.9

82.4

80.0

79.8

79.8

80.7

83.3

83.8

84.1

84.1

Irela

nd79

.179

.980

.980

.576

.877

.478

.778

.381

.682

.583

.282

.8K

uwai

t74

.074

.174

.274

.373

.173

.273

.273

.374

.975

.075

.175

.3La

tvia

72.4

73.1

73.5

73.6

67.2

68.3

68.8

68.6

77.9

78.1

78.4

78.8

Lith

uani

a71

.872

.973

.373

.666

.367

.568

.068

.177

.678

.678

.879

.3Lu

xem

bour

g80

.580

.680

.681

.078

.178

.177

.978

.583

.183

.383

.583

.6N

orw

ay80

.680

.881

.081

.378

.378

.678

.979

.183

.083

.183

.283

.6Q

atar

77.8

78.0

78.1

78.3

77.0

77.1

77.3

77.4

78.8

78.9

79.0

79.2

Slo

veni

a78

.879

.079

.480

.075

.475

.876

.376

.882

.382

.382

.783

.3U

rugu

ay76

.376

.576

.676

.872

.873

.073

.273

.479

.980

.180

.280

.3

Not

e: –

= n

ot a

vaila

ble

Sou

rce:

Wo

rld B

ank,

Wor

ld D

evel

opm

ent I

ndic

ator

s 20

13, a

vaila

ble

at: h

ttp:

//da

taba

nk.w

orld

bank

.org

(acc

esse

d Ju

ne 2

013)

169

Tabl

e 44

. A

dult

lite

racy

rate

s (%

)

Gro

up/c

ount

ry20

0920

10

Adu

lts

(15

and

olde

r)Yo

uth

(15–

24 y

ears

old

)A

dult

s (1

5 an

d ol

der)

Yout

h (1

5–24

yea

rs o

ld )

Tota

lM

ale

Fem

ale

Tota

lM

ale

Fem

ale

Tota

lM

ale

Fem

ale

Tota

lM

ale

Fem

ale

Mid

dle-

inco

me

Com

mon

wea

lth c

ount

ries

Bel

ize

––

––

––

––

––

––

Bo

tsw

ana

84.1

83.8

84.5

95.3

––

84.5

84.0

84.9

95.3

93.6

96.9

Do

min

ica

––

––

––

––

––

––

Fiji

––

––

––

––

––

––

Gre

nada

––

––

––

––

––

––

Guy

ana

85.0

82.4

87.3

93.1

92.4

93.7

––

––

––

Jam

aica

86.4

81.2

91.2

95.2

––

86.6

81.6

91.4

95.4

92.5

98.4

Kiri

bati

––

––

––

––

––

––

Leso

tho

75.8

65.5

85.0

83.2

74.2

92.1

89.6

83.3

95.6

91.9

85.8

98.1

Mal

dive

s–

––

––

––

––

––

–M

aurit

ius

87.9

90.6

85.3

96.5

––

88.5

90.9

86.2

96.7

95.7

97.7

Nam

ibia

88.5

88.9

88.1

93.0

––

88.8

89.0

88.5

93.1

91.1

95.1

Nau

ru–

––

––

––

––

––

–P

apua

New

Gui

nea

60.1

63.6

56.5

67.5

––

60.6

63.9

57.3

68.4

65.1

71.9

St L

ucia

––

––

––

––

––

––

St V

ince

nt a

nd th

e G

rena

dine

s–

––

––

––

––

––

–

Sam

oa

98.8

99.0

98.5

99.5

––

98.8

99.0

98.6

99.5

99.4

99.6

Seyc

helle

s91

.891

.492

.399

.1–

–91

.891

.492

.399

.198

.899

.4So

lom

on

Isla

nds

––

––

––

––

––

––

Swaz

iland

––

––

––

87.4

88.1

86.8

93.6

92.1

95.1

Tong

a–

––

––

––

––

––

–Tu

valu

––

––

––

––

––

––

Van

uatu

––

––

––

82.6

84.3

80.8

94.3

94.1

94.4

Oth

er c

ount

ries

Alb

ania

––

––

––

––

––

––

Arm

enia

99.5

99.7

99.4

99.8

––

99.6

99.7

99.4

99.8

99.7

99.8

Bhu

tan

––

––

––

––

––

––

Bo

snia

and

Her

zego

vina

97.8

99.4

96.4

99.7

––

97.9

99.4

96.5

99.7

99.7

99.7

Cap

e V

erde

84.8

90.1

80.2

98.2

––

84.3

89.3

79.4

98.3

97.5

99.1

Co

ngo,

Rep

ublic

of

––

––

––

––

––

––

Co

sta

Ric

a–

––

––

–96

.295

.996

.498

.297

.898

.7D

jibo

uti

––

––

––

––

––

––

Gab

on

87.7

91.4

84.1

97.6

––

88.4

91.9

84.9

97.7

98.7

96.8

Geo

rgia

99.7

99.8

99.7

99.8

––

99.7

99.8

99.7

99.8

99.8

99.9

(con

tinue

d)

170

Tabl

e 44

. A

dult

lite

racy

rate

s (%

) (co

ntin

ued)

Gro

up/c

ount

ry20

0920

10

Adu

lts

(15

and

olde

r)Yo

uth

(15–

24 y

ears

old

)A

dult

s (1

5 an

d ol

der)

Yout

h (1

5–24

yea

rs o

ld )

Tota

lM

ale

Fem

ale

Tota

lM

ale

Fem

ale

Tota

lM

ale

Fem

ale

Tota

lM

ale

Fem

ale

Leba

non

––

––

––

––

––

––

Mac

edo

nia,

FYR

97.1

98.7

95.6

98.7

––

97.3

98.7

95.9

98.7

98.8

98.5

Mau

ritan

ia57

.564

.550

.367

.7–

–58

.064

.951

.268

.371

.365

.3M

old

ova

98.5

99.0

98.0

99.5

––

98.5

99.1

98.1

99.5

99.3

99.6

Mo

ngo

lia–

––

––

–97

.496

.997

.995

.894

.497

.4M

ont

eneg

ro98

.499

.497

.499

.399

.499

.398

.499

.497

.499

.399

.499

.3P

anam

a93

.694

.293

.096

.4–

–94

.194

.793

.597

.697

.997

.3S

ão T

om

é an

d P

rínci

pe88

.893

.784

.095

.3–

–89

.293

.984

.795

.394

.795

.9Su

rinam

e–

––

––

–94

.795

.494

.098

.498

.098

.8T

imo

r-Le

ste

––

––

––

58.3

63.6

53.0

79.5

80.5

78.6

Hig

h-in

com

eC

omm

onw

ealth

cou

ntrie

sA

ntig

ua a

nd B

arbu

da99

.098

.499

.4–

––

99.0

98.4

99.4

––

–B

aham

as, T

he–

––

––

––

––

––

–B

arba

dos

––

––

––

––

––

––

Bru

nei D

arus

sala

m95

.396

.893

.799

.7–

–95

.296

.893

.699

.799

.899

.7C

ypru

s97

.399

.196

.999

.0–

–98

.399

.297

.399

.999

.999

.9M

alta

––

––

––

––

––

––

St K

itts

and

Nev

is–

––

––

––

––

––

–Tr

inid

ad a

nd T

oba

go98

.799

.298

.399

.5–

–98

.899

.298

.499

.699

.599

.6O

ther

cou

ntrie

sC

roat

ia–

––

––

–91

.999

.598

.210

0.0

100.

010

0.0

Equa

toria

l Gui

nea

––

––

––

98.8

97.1

90.6

99.6

99.6

99.7

Esto

nia

––

––

––

99.8

99.8

99.8

98.0

97.7

98.3

Icel

and

––

––

––

99.8

––

99.8

99.7

99.8

Irela

nd–

––

––

––

––

––

–La

tvia

99.8

99.8

99.8

99.7

––

99.8

99.8

99.8

99.7

99.7

99.7

Lith

uani

a99

.799

.799

.799

.8–

–99

.799

.799

.799

.899

.899

.8Lu

xem

bour

g–

––

––

––

––

––

–N

orw

ay–

––

––

––

––

––

–O

man

––

––

––

––

––

––

Qat

ar–

96.5

95.4

––

––

96.5

95.4

––

–S

love

nia

99.7

99.7

99.7

99.8

––

99.7

99.7

99.7

99.8

99.8

99.9

Uru

guay

98.3

97.6

98.6

99.0

––

98.1

97.6

98.5

98.8

98.4

99.2

Not

e: –

= n

ot a

vaila

ble

Sou

rce:

Wo

rld B

ank,

Wor

ld D

evel

opm

ent I

ndic

ator

s 20

13, a

vaila

ble

at: h

ttp:

//da

taba

nk.w

orld

bank

.org

(acc

esse

d Ju

ne 2

013)

171

Tabl

e 45

. Pr

imar

y ed

ucat

ion

leve

l enr

olm

ent

rati

o (%

)

Gro

up/c

ount

ry20

0920

1020

11

Tota

lM

ale

Fem

ale

Tota

lM

ale

Fem

ale

Tota

lM

ale

Fem

ale

Mid

dle-

inco

me

Com

mon

wea

lth c

ount

ries

Bel

ize

120

127

114

121

127

116

121

126

116

Bo

tsw

ana

110

112

108

––

––

––

Do

min

ica

107

109

106

112

113

111

119

119

118

Fiji

105

106

104

––

–10

510

510

5G

rena

da10

510

810

210

310

510

2–

––

Guy

ana

8584

8685

8386

8785

89Ja

mai

ca89

9187

8991

87–

––

Leso

tho

103

103

102

103

104

102

103

105

101

Mal

dive

s11

011

110

810

610

810

410

410

510

3M

aurit

ius

––

––

––

––

–N

amib

ia10

710

810

710

710

810

6–

––

Nau

ru–

––

––

––

––

Pap

ua N

ew G

uine

a–

––

––

––

––

St L

ucia

9697

9494

9692

9394

92St

Vin

cent

and

the

Gre

nadi

nes

––

––

––

––

–

Sam

oa

103.

010

3.2

102.

710

7.5

106.

510

8.6

104.

610

2.8

106.

5So

lom

on

Isla

nds

––

–14

514

614

4–

––

Swaz

iland

111

115

106

116

121

111

115

121

109

Tong

a–

––

––

––

––

Tuva

lu–

––

––

––

––

Van

uatu

110

113

107

117

120

114

––

–O

ther

cou

ntrie

sA

lban

ia–

––

––

––

––

Arm

enia

––

––

––

––

–B

huta

n11

010

911

111

111

011

211

111

111

2B

osn

ia a

nd H

erze

govi

na10

510

410

688

8788

9090

91C

ape

Ver

de11

011

510

611

011

410

510

911

410

5C

ost

a R

ica

112

112

111

110

110

109

107

108

107

Djib

out

i55

5851

––

–59

6256

Geo

rgia

111

110

112

109

107

111

106

105

108

Leba

non

102

103

101

105

106

103

108

109

106

Mac

edo

nia,

FYR

8989

9090

8991

––

–M

aurit

ania

100

9810

310

299

105

101

9810

4M

old

ova

9494

9394

9493

9494

93M

ong

olia

110

111

109

122

123

121

120

121

118

(con

tinue

d)

172

Tabl

e 45

. Pr

imar

y ed

ucat

ion

leve

l enr

olm

ent

rati

o (%

) (co

ntin

ued)

Gro

up/c

ount

ry20

0920

1020

11

Tota

lM

ale

Fem

ale

Tota

lM

ale

Fem

ale

Tota

lM

ale

Fem

ale

Mo

nten

egro

112

113

111

107

107

106

9595

95P

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911

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710

810

910

610

710

910

6S

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pe–

––

––

––

––

Surin

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113

116

111

––

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511

811

2T

imo

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109

112

106

117

119

115

124

126

122

Hig

h-in

com

eC

omm

onw

ealth

cou

ntrie

sA

ntig

ua a

nd B

arbu

da10

110

398

102

106

9799

102

95B

aham

as, T

he11

311

111

411

411

311

5–

––

Bar

bado

s11

411

211

612

011

912

212

612

612

5B

rune

i Dar

ussa

lam

111

111

111

108

107

109

105

104

106

Cyp

rus

105

106

105

102

102

102

––

–M

alta

––

––

––

––

–St

Kitt

s an

d N

evis

9493

9593

9394

9089

91Tr

inid

ad a

nd T

oba

go10

410

610

210

510

710

3–

––

Oth

er c

ount

ries

Bah

rain

––

––

––

––

–Eq

uato

rial G

uine

a85

8784

8788

8587

8886

Esto

nia

9910

098

9999

98–

––

Icel

and

9999

100

9999

99–

––

Irela

nd10

810

710

810

810

810

810

710

810

7K

uwai

t–

––

––

––

––

Latv

ia10

010

198

101

101

100

100

100

100

Lith

uani

a97

9896

9696

9594

9593

Luxe

mbo

urg

––

–97

9698

––

–N

orw

ay99

9999

9999

99–

––

Om

an10

510

710

4–

––

104

105

103

Qat

ar10

610

710

610

310

310

310

510

610

4S

love

nia

9898

9798

9998

9999

99U

rugu

ay11

311

511

111

211

411

0–

––

Not

e: –

= n

ot a

vaila

ble

Sou

rces

: Wo

rld B

ank,

Wor

ld D

evel

opm

ent I

ndic

ator

s 20

13, a

vaila

ble

at: h

ttp:

//da

taba

nk.w

orld

bank

.org

; Uni

ted

Nat

ions

, Mille

nium

Dev

elop

men

t Goa

l Ind

icat

ors,

ava

ilabl

e at

: htt

p://

mdg

sun.

org

/un

sd/m

dg/S

erie

sDet

ail.a

spx?

srid

=591

(acc

esse

d Ju

ne 2

013)

173

Tabl

e 46

. S

econ

dary

edu

cati

on e

nrol

men

t le

vel r

atio

(% g

ross

)

Gro

up/c

ount

ry20

0820

0920

1020

11

Tota

lM

ale

Fem

ale

Tota

lM

ale

Fem

ale

Tota

lM

ale

Fem

ale

Tota

lM

ale

Fem

ale

Mid

dle-

inco

me

––

––

––

––

––

––

Com

mon

wea

lth c

ount

ries

––

––

––

––

––

––

Bel

ize

––

––

––

––

––

––

Bo

tsw

ana

––

––

––

––

––

––

Do

min

ica

102

100

104

102

9610

898

9410

398

9510

2Fi

ji88

8591

8683

91–

––

9087

94G

rena

da11

011

510

510

110

110

110

810

610

9–

––

Guy

ana

9289

9692

8995

9187

9693

8998

Jam

aica

9693

9896

9596

9391

94–

––

Leso

tho

4135

4744

3751

4639

5449

4157

Mal

dive

s–

––

––

––

––

––

–M

aurit

ius

––

––

––

––

––

––

Nam

ibia

––

––

––

––

––

––

Nau

ru–

––

––

––

––

––

–P

apua

New

Gui

nea

––

––

––

––

––

––

St L

ucia

9391

9596

9497

9697

9695

9794

St V

ince

nt a

nd th

e G

rena

dine

s10

810

211

410

910

711

210

710

610

9–

––

Sam

oa

––

–84

7890

8579

9182

7788

Seyc

helle

s11

110

411

911

510

812

211

911

412

512

411

713

1So

lom

on

Isla

nds

––

––

––

4851

45–

––

Swaz

iland

––

–54

5454

5858

5860

6159

Tong

a–

––

––

––

––

––

–Tu

valu

––

––

––

––

––

––

Van

uatu

––

––

––

5554

55–

––

Oth

er c

ount

ries

Alb

ania

––

––

––

––

––

––

Bhu

tan

5557

5461

6161

6665

6670

6971

Bo

snia

and

Her

zego

vina

9291

9393

9294

9088

9189

8891

Cap

e V

erde

8782

9185

7893

8880

9590

8397

Co

sta

Ric

a91

8893

9794

100

100

9710

310

199

104

Djib

out

i29

3424

3136

27–

––

3640

32G

eorg

ia89

9187

86–

––

––

––

–Le

bano

n83

7887

8479

8881

7786

8379

88M

aced

oni

a, F

YR83

8582

8384

8284

8483

––

–M

aurit

ania

2224

2124

2622

2426

2227

2925

Mo

ldo

va88

8790

8988

9088

8789

8887

89M

ong

olia

––

–96

9299

8986

9293

9095

(con

tinue

d)

174

Tabl

e 46

. S

econ

dary

edu

cati

on e

nrol

men

t le

vel r

atio

(% g

ross

) (co

ntin

ued)

Gro

up/c

ount

ry20

0820

0920

1020

11

Tota

lM

ale

Fem

ale

Tota

lM

ale

Fem

ale

Tota

lM

ale

Fem

ale

Tota

lM

ale

Fem

ale

Mo

nten

egro

9690

103

100

100

101

104

103

105

9797

97P

anam

a71

6974

7370

7674

7277

7471

77Su

rinam

e76

6786

7567

83–

––

8574

97T

imo

r-Le

ste

5050

5058

5858

5656

5658

5759

Hig

h-in

com

eC

omm

onw

ealth

cou

ntrie

sA

ntig

ua a

nd B

arbu

da11

111

310

810

810

810

810

510

510

610

510

610

4B

aham

as, T

he94

9395

9493

9596

9398

––

–B

arba

dos

102

9710

810

196

106

101

9610

510

498

110

Bru

nei D

arus

sala

m10

410

210

510

710

610

911

010

811

211

211

111

3C

ypru

s98

9899

9898

9991

9192

––

–M

alta

9697

9410

511

198

101

107

95–

––

St K

itts

and

Nev

is10

299

105

9995

102

9898

9794

9396

Trin

idad

and

To

bago

9087

93–

––

––

––

––

Oth

er c

ount

ries

Bah

rain

––

––

––

––

––

––

Equa

toria

l Gui

nea

––

––

––

––

––

––

Esto

nia

102

101

104

104

103

105

107

107

107

––

–Ic

elan

d10

910

711

110

710

610

910

810

710

9–

––

Irela

nd11

411

011

811

711

412

112

111

812

411

911

712

1K

uwai

t10

198

104

––

––

––

––

–La

tvia

100

9910

194

9494

9596

9496

9894

Lith

uani

a98

9898

9898

9899

100

9899

100

97Lu

xem

bour

g98

9699

––

–10

110

010

3–

––

No

rway

112

113

111

110

111

109

111

112

110

––

–O

man

9696

9610

010

110

0–

––

104

105

103

Qat

ar86

7699

8777

100

9486

104

102

9810

6S

love

nia

9797

9797

9797

9798

9797

9897

Uru

guay

8882

9490

8596

9085

96–

––

Not

e: –

= n

ot a

vaila

ble

Sou

rce:

Wo

rld B

ank,

Wor

ld D

evel

opm

ent I

ndic

ator

s 20

13, a

vaila

ble

at: h

ttp:

//da

taba

nk.w

orld

bank

.org

(acc

esse

d Ju

ly 2

013)

175

Tabl

e 47

. Te

rtia

ry e

duca

tion

leve

l enr

olm

ent

rati

o (%

)

Gro

up/c

ount

ry20

0820

0920

1020

11

Tota

lM

ale

Fem

ale

Tota

lM

ale

Fem

ale

Tota

lM

ale

Fem

ale

Tota

lM

ale

Fem

ale

Mid

dle-

inco

me

Com

mon

wea

lth c

ount

ries

Bel

ize

1914

2322

1627

2117

2621

1626

Bo

tsw

ana

––

––

––

––

––

––

Fiji

––

––

––

––

–62

––

Gre

nada

––

–53

4561

––

––

––

Guy

ana

11 9

1311

715

12 7

1712

717

Jam

aica

2516

3525

1535

26–

––

––

Leso

tho

––

––

––

––

––

––

Mal

dive

s13

1214

––

––

––

––

–M

aurit

ius

2522

2829

2632

3128

3432

2837

Nam

ibia

9 8

10–

––

––

––

––

Nau

ru–

––

––

––

––

––

–P

apua

New

Gui

nea

––

––

––

––

––

––

St L

ucia

15 9

2016

923

11 6

1615

1119

St V

ince

nt a

nd th

e G

rena

dine

s–

––

––

––

––

––

–

Sam

oa

––

––

––

––

––

––

Seyc

helle

s–

––

––

––

––

3 1

4So

lom

on

Isla

nds

––

––

––

––

––

––

Swaz

iland

––

––

––

––

– 6

6 6

Tong

a–

––

––

––

––

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valu

––

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––

––

Van

uatu

––

––

––

––

––

––

Oth

er c

ount

ries

Alb

ania

2925

3330

2534

3934

4544

3850

Arm

enia

4841

5450

4456

5245

5849

4355

Bhu

tan

7 8

5–

––

7 9

5 9

10 7

Bo

snia

and

Her

zego

vina

3531

3936

3141

3631

4138

3343

Cap

e V

erde

1210

1415

1317

1816

2020

1724

Co

sta

Ric

a–

––

––

––

––

4338

49D

jibo

uti

3 3

2 3

4 3

3 4

3 5

6 4

Geo

rgia

3431

3725

2328

2825

3130

2733

Leba

non

5247

5853

4958

5449

5958

5462

Mac

edo

nia,

FYR

4037

4440

3744

3936

42–

––

Mau

ritan

ia 4

5 2

4 5

2 4

6 3

5 7

3M

old

ova

4033

4738

3245

3833

4439

3445

Mo

ngo

lia48

3759

5140

6253

4265

5746

69

(con

tinue

d)

176

Tabl

e 47

. Te

rtia

ry e

duca

tion

leve

l enr

olm

ent

rati

o (%

) (co

ntin

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Gro

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ry20

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0920

1020

11

Tota

lM

ale

Fem

ale

Tota

lM

ale

Fem

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Tota

lM

ale

Fem

ale

Tota

lM

ale

Fem

ale

Mo

nten

egro

3834

4245

3951

4843

53–

––

Pan

ama

4535

5445

3554

4636

56–

––

Surin

ame

––

––

––

––

––

––

Tim

or-

Lest

e–

––

1719

14–

––

––

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igh-

inco

me

Com

mon

wea

lth c

ount

ries

Bah

amas

, The

––

––

––

––

––

––

Bar

bado

s–

––

7243

103

6640

9562

3690

Bru

nei D

arus

sala

m18

1223

1914

2417

1222

2015

25C

ypru

s43

4442

5256

4848

5146

––

–M

alta

3025

3633

2839

3530

41–

––

St K

itts

and

Nev

is18

1225

––

––

––

––

–Tr

inid

ad a

nd T

oba

go–

––

––

––

––

––

–O

ther

cou

ntrie

sB

ahra

in–

––

––

––

––

––

–C

roat

ia49

4455

4943

5554

4662

––

–Es

toni

a63

4779

6347

7964

4980

––

–Ic

elan

d74

5197

7452

9779

5710

1–

––

Irela

nd58

5264

6155

6766

6271

6864

72K

uwai

t–

––

––

––

––

––

–La

tvia

6847

8966

4786

6044

7757

4471

Lith

uani

a76

6093

7762

9374

5990

6956

83Lu

xem

bour

g11

1110

––

–18

1719

––

–N

orw

ay73

5691

7456

9274

5793

––

–O

man

20–

–24

2128

2421

2929

2534

Qat

ar11

627

105

2610

526

125

31S

love

nia

8670

103

8771

103

9074

106

8666

107

Uru

guay

6547

8363

4681

6347

80–

––

Not

e: –

= n

ot a

vaila

ble

Sou

rce:

The

Wo

rld B

ank,

Wor

ld D

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opm

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ndic

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13, d

ata

vaila

ble

at h

ttp:

//da

taba

nk.w

orld

bank

.org

/ (ac

cess

ed J

uly

2013

)

177

Tabl

e 48

. Pr

imar

y ed

ucat

ion

gend

er ra

tio

Gro

up/c

ount

ryR

atio

of g

irls

to b

oys

in p

rim

ary

and

seco

ndar

y ed

ucat

ion

(%)

Prim

ary

com

plet

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rate

(% o

f rel

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oup)

Fem

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Mal

eTo

tal

2009

2010

2011

2009

2010

2011

2009

2010

2011

2009

2010

2011

Mid

dle-

inco

me

Com

mon

wea

lth c

ount

ries

Bel

ize

––

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9810

710

711

311

410

310

511

0B

ots

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a–

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98–

–97

––

97–

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om

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––

––

––

––

–Fi

ji10

410

410

5–

103

105

–10

410

5–

103

Gre

nada

––

––

––

––

––

––

Guy

ana

105

107

107

9485

8791

8284

9283

85Ja

mai

ca98

99–

7473

–75

74–

7573

–K

iriba

ti–

––

––

––

––

––

–Le

soth

o10

810

610

678

7976

5860

6068

7068

Mal

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s–

––

114

105

103

126

122

111

120

114

107

Mau

ritiu

s–

––

––

––

––

––

–N

amib

ia–

––

8885

–80

77–

8481

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––

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––

––

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uine

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––

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––

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Sam

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107

108

109

9810

310

310

310

095

101

101

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Solo

mo

n Is

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9492

7278

7872

7676

7277

77Sw

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Tuva

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8483

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84–

8583

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––

––

–A

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2–

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82–

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Bhu

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101

102

102

9110

198

8389

9287

9595

Bo

snia

and

Her

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vina

102

102

102

–71

76–

6975

–70

76C

ape

Ver

de10

410

410

395

9896

9510

094

9599

95C

ost

a R

ica

102

102

102

9797

101

9595

9796

9699

Djib

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34–

4537

4736

46G

eorg

ia–

––

108

116

112

116

–11

011

6–

Leba

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104

104

103

8789

8983

8585

8587

87

(con

tinue

d)

178

Tabl

e 48

. Pr

imar

y ed

ucat

ion

gend

er ra

tio

(con

tinu

ed)

Gro

up/c

ount

ryR

atio

of g

irls

to b

oys

in p

rim

ary

and

seco

ndar

y ed

ucat

ion

(%)

Prim

ary

com

plet

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rate

(% o

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evan

t ag

e gr

oup)

Fem

ale

Mal

eTo

tal

2009

2010

2011

2009

2010

2011

2009

2010

2011

2009

2010

2011

Mac

edo

nia,

FYR

9910

0–

93–

–92

––

92–

–M

aurit

ania

101

101

101

72–

–68

––

70–

–M

old

ova

101

101

101

9193

9094

9192

9392

91M

ong

olia

103

103

102

–10

911

6–

109

115

–10

911

5M

ont

eneg

ro10

010

010

0–

116

––

114

–11

5P

anam

a10

110

110

210

197

101

101

9710

110

197

101

Surin

ame

105

–11

091

–93

81–

8286

88T

imo

r-Le

ste

9698

9868

6774

6864

7168

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179

Table 49. Selected characteristics of female population

Group/country Percentage share of females in total labour force

Total fertility rate (number of children who would be born per female)

2008 2009 2010 2011 2008 2009 2010 2011

Middle-incomeCommonwealth countriesBelize 37 37 37 37 2.9 2.8 2.8 2.7Botswana 47 47 47 47 2.9 2.8 2.8 2.7Dominica – – – – – – – –Fiji 32 32 32 32 2.7 2.7 2.7 2.6Grenada – – – – 2.3 2.3 2.2 2.2Guyana 34 34 35 35 2.7 2.7 2.7 2.6Jamaica 45 45 45 45 2.4 2.4 2.3 2.3Kiribati – – – – 3.1 3.1 3.0 3.0Lesotho 46 46 46 46 3.3 3.3 3.2 3.1Maldives 42 42 42 42 2.4 2.4 2.3 2.3Mauritius 37 37 37 38 1.6 1.5 1.5 1.5Namibia 48 48 48 48 3.4 3.3 3.2 3.2Nauru – – – – – – – –Papua New Guinea 48 48 48 48 4.1 4.0 4.0 3.9St Lucia 47 47 47 47 2.0 2.0 2.0 2.0St Vincent and the Grenadines 41 41 41 41 2.1 2.1 2.1 2.0Samoa 34 34 34 34 4.4 4.4 4.3 4.3Seychelles – – – – 2.3 2.4 2.1 2.1Solomon Islands 40 40 40 40 4.4 4.3 4.2 4.2Swaziland 40 40 40 40 3.7 3.6 3.6 3.5Tonga 43 43 43 43 4.0 4.0 3.9 3.9Tuvalu – – – – – – – –Vanuatu 44 43 44 43 3.6 3.5 3.5 3.5Other countriesAlbania 41 41 41 41 1.8 1.7 1.7 1.7Armenia 43 42 42 42 1.7 1.7 1.7 1.7Bhutan 41 42 42 42 2.5 2.4 2.4 2.3Bosnia and Herzegovina 38 39 39 39 1.2 1.2 1.2 1.3Cape Verde 38 38 38 38 2.6 2.5 2.4 2.4Congo, Republic of 49 49 49 49 5.1 5.1 5.1 5.0Costa Rica 35 36 36 36 1.9 1.9 1.8 1.8Djibouti 34 34 35 35 3.8 3.7 3.6 3.5Gabon 46 46 46 46 4.3 4.2 4.2 4.2Georgia 47 47 47 47 1.8 1.8 1.8 1.8Lebanon 24 24 24 24 1.6 1.5 1.5 1.5Macedonia, FYR 39 38 38 39 1.5 1.5 1.5 1.4Mauritania 26 26 26 27 4.9 4.9 4.8 4.8Moldova 50 49 49 49 1.5 1.5 1.5 1.5Mongolia 46 46 46 46 2.4 2.4 2.4 2.4Montenegro – – – – 1.7 1.7 1.7 1.7Panama 37 37 37 37 2.6 2.6 2.5 2.5São Tomé and Príncipe 37 37 37 38 4.4 4.4 4.3 4.2Suriname 37 37 37 37 2.4 2.4 2.3 2.3Timor-Leste 33 33 33 34 5.7 5.7 5.6 5.5High-incomeCommonwealth countriesAntigua and Barbuda – – – – 2.2 2.1 2.1 2.1Bahamas, The 48 48 48 48 1.9 1.9 1.9 1.9Barbados 47 46 46 46 1.8 1.8 1.8 1.8Brunei Darussalam 42 42 42 42 2.1 2.1 2.1 2.0Cyprus 43 43 44 43 1.5 1.5 1.5 1.5Malta 34 34 34 35 1.4 1.4 1.4 1.4St Kitts and Nevis – – – – – – – –Trinidad and Tobago 42 42 42 42 1.8 1.8 1.8 1.8

(continued)

180

Table 49. Selected characteristics of female population (continued)

Group/country Percentage share of females in total labour force

Total fertility rate (number of children who would be born per female)

2008 2009 2010 2011 2008 2009 2010 2011

Other countriesBahrain 20 19 19 19 2.2 2.2 2.1 2.1Croatia 45 46 46 46 1.5 1.5 1.5 1.5Equatorial Guinea 45 45 45 45 5.3 5.2 5.1 5.0Estonia 49 49 50 50 1.7 1.6 1.6 1.5Iceland 47 47 47 47 2.2 2.2 2.2 2.0Ireland 43 44 44 44 2.1 2.1 2.1 2.1Kuwait 24 24 24 24 2.7 2.7 2.7 2.7Latvia 49 50 50 50 1.4 1.3 1.2 1.3Lithuania 50 50 51 51 1.5 1.6 1.6 1.8Luxembourg 43 43 43 44 1.6 1.6 1.6 1.5Norway 47 47 47 47 2.0 2.0 2.0 1.9Oman 19 19 18 17 2.9 2.9 2.9 2.9Qatar 13 13 12 12 2.2 2.1 2.1 2.1Slovenia 46 46 46 46 1.5 1.5 1.6 1.6Uruguay 44 44 45 45 2.1 2.1 2.1 2.1

Note: – = not availableSource: World Bank, World Development Indicators 2013, available at: http://databank.worldbank.org (accessed July 2013)

181

Table 50. Total government expenditure by main components (%)

Group/country Defence Education Health

2009 2010 2011 2009 2010 2011 2009 2010 2011

Middle-incomeCommonwealth countriesBelize 5.0 – – – – – 12.9 13.1 13.4Botswana 8.3 8.3 7.9 16.2 – – 7.2 8.7 8.7Dominica – – – – 9.3 8.6 10.5 12.3Fiji – – – 14.7 – 14.4 10.3 10.8 9.1Grenada – – – – – – 9.5 8.2 11.0Guyana – – – 13.2 16.7 13.5 17.3 14.8 16.2Jamaica 2.3 2.2 – – 11.5 – 5.5 6.8 6.6Kiribati – – – – – – 12.0 10.0 10.0Lesotho – – – – – – 11.0 13.8 14.6Maldives – – – 16.0 – 16.6 12.9 9.3 9.3Mauritius – 0.7 0.4 11.6 13.7 – 8.7 10.8 9.7Namibia – – – – – – 6.5 6.5 6.5Nauru – – – – – – – – –Papua New Guinea – – – – – – 10.0 9.8 12.8St Lucia – – – 10.3 10.9 11.9 15.1 13.3 11.1St Vincent and the Grenadines – – – 13.8 10.2 – 13.2 11.6 11.7Samoa – – – – – – 18.3 23.4 25.1Seychelles 3.5 3.0 3.3 – – – 8.8 9.3 9.3Solomon Islands – – – 26.0 34.0 – 21.1 20.3 25.5Swaziland – – – 18.0 16.0 21.0 13.5 13.7 14.9Tonga – – – – – – 11.7 13.0 15.8Tuvalu – – – – – – 13.8 18.1 18.0Vanuatu – – – 23.7 – – 17.7 17.2 15.0Other countriesAlbania – – – – – – 8.5 8.5 9.8Armenia 17.5 18.6 18.1 12.7 11.5 11.7 6.6 6.4 5.8Bhutan – – – 11.0 9.4 11.5 10.7 8.4 7.9Bosnia and Herzegovina 3.5 3.3 3.5 – – – 15.7 16.7 16.2Cape Verde 1.9 – – 15.9 14.4 – 9.9 8.2 7.9Congo, Republic of – – – – – – 4.6 6.5 6.5Costa Rica – – – 23.1 – – 30.6 29.0 28.0Djibouti – – – – – – 14.1 14.1 14.1Gabon – – – – – – 6.6 6.6 6.6Georgia 18.1 14.8 12.1 7.7 – – 6.1 6.9 6.9Lebanon 14.0 15.4 16.0 7.2 7.2 7.1 8.5 5.8 5.8Macedonia, FYR – – – – – – 11.6 11.9 11.7Mauritania – – – – 15.2 14.7 11.0 13.1 10.9Moldova 1.2 0.9 0.9 21.0 22.3 22.0 13.4 13.1 13.3Mongolia 3.1 3.3 3.3 14.6 – 11.9 9.1 8.6 6.8Montenegro – – – – – – 13.6 13.6 13.6Panama – – – – – 14.8 14.7 15.1 12.8São Tomé and Príncipe – – – – – – 5.6 5.6 5.6Suriname – – – – – – 11.9 11.9 11.9Timor-Leste – – – 15.5 11.7 8.1 7.1 5.0 2.9High-incomeCommonwealth countriesAntigua and Barbuda – – – 9.8 – – 9.1 18.7 15.9Bahamas, The – – – – – – 16.8 15.2 14.9Barbados – – – 14.3 13.5 7.8 9.3 10.9Brunei Darussalam – – – – 8.5 13.7 7.5 8.8 8.8Cyprus 4.8 4.9 5.0 17.3 15.8 – 6.8 6.9 6.9Malta 1.7 1.7 1.7 12.6 – – 12.7 13.0 13.3St Kitts and Nevis – – – – – – 5.4 6.8 6.9Trinidad and Tobago – – – – – – 7.6 9.3 8.0

(continued)

182

Table 50. Total government expenditure by main components (%) (continued)

Group/country Defence Education Health

2009 2010 2011 2009 2010 2011 2009 2010 2011

Other countriesBahrain – – – – – – 11.4 9.6 9.2Croatia 4.9 4.6 4.8 – – – 17.7 17.7 17.7Equatorial Guinea – – – – – – 7.0 7.0 7.0Estonia 6.2 5.0 5.2 13.5 14.0 – 11.6 12.3 12.3Iceland 0.2 15.3 – – 15.5 14.5 15.4Ireland 1.4 1.0 1.3 13.3 – – 14.5 13.5 13.5Kuwait 12.0 10.3 9.8 – – – 5.6 6.9 5.9Latvia 4.0 3.0 3.4 12.8 11.3 – 9.3 9.3 9.3Lithuania 3.5 3.0 3.0 12.9 13.2 – 12.6 12.6 12.6Luxembourg – – – – – – 15.5 15.5 15.5Norway 4.5 4.3 4.6 15.7 15.2 – 17.7 17.7 17.7Oman 36.4 35.1 21.2 – – – 5.8 6.2 4.9Qatar – – – – – – 6.4 5.2 5.8Slovenia 3.9 3.9 3.3 11.6 11.4 – 13.7 13.0 13.0Uruguay 7.1 6.6 6.5 – – – 19.2 18.8 20.0


183

Table 51. Access to improved water sources (% of population)

Group/country Urban Rural Population with access (%)

2007 2008 2009 2010 2007 2008 2009 2010 2007 2008 2009 2010

Middle-incomeCommonwealth countriesBelize 96 97 97 98 93 95 97 99 95 96 97 98Botswana 99 99 99 99 91 91 92 92 96 96 96 96Dominica 96 96 96 96 92 – – – 95 – – –Fiji 100 100 100 100 95 95 95 95 98 98 98 98Grenada 97 97 97 97 – – – – – – – –Guyana 96 97 97 98 91 92 92 93 92 93 93 94Jamaica 98 98 98 98 88 88 88 88 93 93 93 93Kiribati – – – – – – – – – – – –Lesotho 92 92 91 91 74 74 73 73 78 79 78 78Maldives 100 100 100 100 96 97 97 97 97 98 98 98Mauritius 100 100 100 100 99 99 99 99 99 99 99 99Namibia 99 99 99 99 86 88 90 90 91 92 93 93Nauru – – – – – – – – – – – –Papua New Guinea 87 87 87 87 33 33 33 33 40 40 40 40St Lucia 98 98 98 98 95 95 95 95 96 96 96 96St Vincent and the

Grenadines– – – – – – – – – – – –

Samoa 96 96 96 96 94 95 95 96 94 95 95 96Seychelles 98 100 100 100Solomon Islands – – – – – – – – – – – –Swaziland 90 90 91 91 58 60 62 65 65 66 68 71Tonga 100 100 100 100 100 100 100 100 100 100 100 100Tuvalu – – – – – – – – – – – –Vanuatu 97 97 97 98 82 83 85 87 86 86 88 90Other countriesAlbania 97 97 96 96 94 94 94 94 95 96 95 95Armenia 99 99 99 99 92 94 96 97 96 97 98 98Bhutan 99 99 100 100 90 92 93 94 93 94 95 96Bosnia and Herzegovina 100 100 100 100 98 98 98 98 99 99 99 99Cape Verde 88 89 89 90 84 84 85 85 86 87 87 88Congo, Republic of 95 95 95 95 34 33 33 32 71 71 71 71Costa Rica 100 100 100 100 90 91 91 91 96 97 97 97Djibouti 98 99 99 99 55 54 54 54 88 88 88 88Gabon 95 95 95 95 41 41 41 41 87 87 87 87Georgia 100 100 100 100 96 96 96 96 98 98 98 98Lebanon 100 100 100 100 100 100 100 100 100 100 100 100Macedonia, FYR 100 100 100 100 99 99 99 99 100 100 100 100Mauritania 51 52 52 52 45 47 48 48 47 49 50 50Moldova 99 99 99 99 92 92 93 93 95 95 96 96Mongolia 99 100 100 100 50 51 53 53 80 81 82 82Montenegro 99 99 99 99 96 96 96 96 98 98 98 98Panama 97 97 97 97 83 83 83 – 93 93 93 –São Tomé and Príncipe 89 89 89 89 85 88 88 88 87 89 89 89Suriname 97 97 97 97 80 81 81 81 92 92 92 92Timor-Leste 84 86 88 91 57 58 59 60 64 66 67 69High-incomeCommonwealth countriesAntigua and Barbuda 95 95 95 95 – – – – – – – –Bahamas, The 98 98 98 98 – – – – – – – –Barbados 100 100 100 100 100 100 100 100 100 100 100 100Brunei Darussalam – – – – – – – – – – – –Cyprus 100 100 100 100 100 100 100 100 100 100 100 100Malta 100 100 100 100 100 100 100 100 100 100 100 100St Kitts and Nevis 99 99 99 99 99 99 99 99 99 99 99 99Trinidad and Tobago 98 98 98 98 93 93 93 93 94 94 94 94

(continued)

184

Table 51. Access to improved water sources (% of population) (continued)

Group/country Urban Rural Population with access (%)

2007 2008 2009 2010 2007 2008 2009 2010 2007 2008 2009 2010

Other countriesBahrain 100 100 100 100 – – – – – – – –Croatia 100 100 100 100 97 97 97 97 99 99 99 99Equatorial Guinea – – – – – – – – – – – –Estonia 99 99 99 99 97 97 97 97 98 98 98 98Iceland 100 100 100 100 100 100 100 100 100 100 100 100Ireland 100 100 100 100 100 100 100 100 100 100 100 100Kuwait 99 99 99 99 99 99 99 99 99 99 99 99Latvia 100 100 100 100 96 96 96 96 99 99 99 99Lithuania 98 98 98 98 81 81 81 – 92 92 92 –Luxembourg 100 100 100 100 100 100 100 100 100 100 100 100Norway 100 100 100 100 100 100 100 100 100 100 100 100Oman 91 92 93 93 77 77 78 78 87 88 89 89Qatar 100 100 100 100 100 100 100 100 100 100 100 100Slovenia 100 100 100 100 99 99 99 99 99 99 99 99Uruguay 100 100 100 100 98 100 100 100 100 100 100 100


185

Table 52. Access to improved sanitation (% of population)

Group/country Urban Rural

2008 2009 2010 2011 2008 2009 2010 2011

Middle-incomeCommonwealth countriesBelize 91 92 93 93 86 87 87 87Botswana 75 75 75 78 40 41 41 42Dominica – – – – – – – –Fiji 94 94 94 92 71 71 71 82Grenada 96 96 96 – 97 97 97 –Guyana 87 87 88 88 81 81 82 82Jamaica 78 78 78 78 82 82 82 82Kiribati 50 50 51 51 29 29 30 30Lesotho 33 33 32 32 24 24 24 24Maldives 98 98 98 98 92 95 97 98Mauritius 91 91 91 92 88 88 88 90Namibia 57 57 57 57 16 17 17 17Nauru – – – – – – – –Papua New Guinea 57.5 27.3 57 57 13.2 13.2 13.3 13St Lucia 71 71 71 70 63 63 63 64St Vincent and the Grenadines – – – – 96 96 96 –Samoa 99 99 98 93 98 98 98 91Seychelles 97 98 98 97 97 97 97 97Solomon Islands 98 98 98 81 15 15 15 15Swaziland 64 64 64 63 54 55 55 55Tonga 98 98 98 99 96 96 96 89Tuvalu – – – – – – – –Vanuatu 62 63 64 65 51 52 54 55Other countriesAlbania 95 95 95 95 90 92 93 93Armenia 95 95 95 96 80 80 80 81Bhutan 71 72 73 74 29 29 29 29Bosnia and Herzegovina 99 99 99 100 92 92 92 92Cape Verde 70 72 73 74 40 42 43 45Congo, Republic of 20 20 20 20 16 15 15 15Costa Rica 95 95 95 95 96 96 96 92Djibouti 63 63 63 73 10 10 10 22Gabon 33 33 33 33 30 30 30 30Georgia 96 96 96 96 93 93 93 91Lebanon 100 100 100 100 – – – –Macedonia, FYR 92 92 92 97 82 82 82 83Mauritania 50 51 51 51 9 9 9 9Moldova 88 89 89 89 80 81 82 83Mongolia 64 64 64 64 29 29 29 29Montenegro 92 92 92 92 87 87 87 87Panama 75 75 77 77 51 51 54 54São Tomé and Príncipe 30 30 30 41 19 19 19 23Suriname 90 90 90 90 66 66 66 66Timor-Leste 69 71 73 68 36 37 37 27High-incomeCommonwealth countriesAntigua and Barbuda 98 98 98 91 – – – 91Bahamas, The 100 100 100 – 100 100 100 –Barbados 100 100 100 – 100 100 100 –Brunei Darussalam – – – – – – – –Cyprus 100 100 100 100 100 100 100 100Malta 100 100 100 100 100 100 100 100St Kitts and Nevis 96 96 96 – 96 96 96 –Trinidad and Tobago 92 92 92 92 92 92 92 92

(continued)

186

Table 52. Access to improved sanitation (% of population) (continued)

Group/country Urban Rural

2008 2009 2010 2011 2008 2009 2010 2011

Other countriesBahrain 100 100 100 99 99 99 99 99Croatia 99 99 99 99 98 98 98 98Equatorial Guinea – – – – – – – –Estonia 96 96 96 100 94 94 94 94Iceland 100 100 100 100 100 100 100 100Ireland 100 100 100 100 98 98 98 98Kuwait 100 100 100 100 100 100 100 100Latvia 82 82 – – 71 71 – –Lithuania 95 95 95 95 69 69 – –Luxembourg 100 100 100 100 100 100 100 100Norway 100 100 100 100 100 100 100 100Oman 100 100 100 97 92 95 95 95Qatar 100 100 100 100 100 100 100 100Slovenia 100 100 100 100 100 100 100 100Uruguay 100 100 100 99 99 99 99 98


187

Table 53. Human Development Index (HDI)

Group/country 2010 2011 2012

Rank 0–1 value Rank 0–1 value Rank 0–1 value

Middle-incomeCommonwealth countriesBelize 78 0.694 93 0.699 96 0.702Botswana 98 0.633 118 0.633 119 0.634Dominica – – 81 0.724 72 0.745Fiji 86 0.699 100 0.688 96 0.702Grenada – – 67 0.748 63 0.770Guyana 104 0.611 117 0.633 118 0.636Jamaica 80 0.688 79 0.727 85 0.730Kiribati – – 122 0.624 121 0.629Lesotho 141 0.427 160 0.45 158 0.461Maldives 107 0.602 109 0.661 104 0.688Mauritius 72 0.701 77 0.728 80 0.737Namibia 105 0.606 120 0.625 128 0.608Nauru – – – – – –Papua New Guinea 137 0.431 153 0.466 156 0.466St Lucia – – 82 0.723 88 0.725St Vincent and the Grenadines – – 85 0.717 83 0.733Samoa – – 99 0.688 96 0.702Seychelles – – 52 0.773 46 0.806Solomon Islands 123 0.494 142 0.51 143 0.530Swaziland 13 0.874 140 0.522 141 0.536Tonga 85 0.677 90 0.704 95 0.710Tuvalu – – – – – –Vanuatu – – 125 0.617 124 0.626Other countriesAlbania 64 0.719 70 0.739 70 0.749Armenia 76 0.695 86 0.716 87 0.729Bhutan – – 141 0.522 140 0.538Bosnia and Herzegovina 68 0.71 74 0.733 81 0.735Cape Verde 118 0.534 133 0.568 132 0.586Congo, Republic of 126 0.489 137 0.533 142 0.534Costa Rica 62 0.725 69 0.744 62 0.773Djibouti 147 0.402 165 0.43 164 0.445Gabon 93 0.648 106 0.674 106 0.683Georgia 74 0.698 75 0.733 72 0.745Lebanon – – 71 0.739 72 0.745Macedonia, FYR – – – – – –Mauritania 136 0.433 159 0.453 155 0.467Moldova – – – – 113 0.660Mongolia 100 0.622 110 0.653 108 0.675Montenegro 49 0.769 54 0.771 52 0.791Panama 54 0.755 58 0.768 59 0.780São Tomé and Príncipe 127 0.488 144 0.509 144 0.525Suriname 94 0.646 104 0.68 105 0.684Timor-Leste 120 0.502 147 0.495 134 0.576High-incomeCommonwealth countriesAntigua and Barbuda – – 60 0.76 67 0.760Bahamas, The 43 0.784 53 0.771 49 0.794Barbados 42 0.788 47 0.838 38 0.825Brunei Darussalam 37 0.805 33 0.854 30 0.855Cyprus 35 0.81 31 0.84 31 0.848Malta 33 0.815 36 0.832 32 0.847St Kitts and Nevis – – 72 0.735 72 0.745Trinidad and Tobago 59 0.736 62 0.76 67 0.760

(continued)

188

Table 53. Human Development Index (HDI) (continued)

Group/country 2010 2011 2012

Rank 0–1 value Rank 0–1 value Rank 0–1 value

Other countriesBahrain 39 0.801 42 0.806 48 0.796Croatia 51 0.767 46 0.796 47 0.805Equatorial Guinea 117 0.538 136 0.537 136 0.554Estonia 34 0.812 34 0.835 33 0.846Iceland 17 0.869 14 0.898 13 0.906Ireland 5 0.895 7 0.908 7 0.916Kuwait 47 0.771 63 0.76 54 0.790Latvia 48 0.769 43 0.805 44 0.814Lithuania 44 0.783 40 0.81 41 0.818Luxembourg 24 0.852 25 0.867 26 0.875Norway 1 0.938 1 0.943 1 0.955Oman – – 89 0.705 84 0.731Qatar 38 0.803 37 0.831 36 0.834Slovenia 29 0.838 21 0.884 21 0.892Uruguay 52 0.765 48 0.783 51 0.792

Note: – = not availableSource: Human Development Report data, available at: http://hdr.undp.org (accessed 25 February 2013)

189

Table 54. Selected characteristics of gender equality

Group/country Gender inequality index 2012

Share of women in wage employment (%)

Seats held by women in parliament (%)

Ranking 0–1 value 2007 2008 2009 2010 2009 2010 2011 2012

Middle-incomeCommonwealth countriesBelize 79 0.435 37.7 – – – – – – 3.1Botswana 102 0.485 43.4 – 41.6 41.4 7.9 7.9 7.9 7.9Dominica – – – – – – 14.3 12.5 12.5 12.5Fiji – – – – – – – – – –Grenada – – – – – – 13.3 13.3 13.3 13.3Guyana 104 0.49 – – – – 30 30 31.3 31.3Jamaica 87 0.458 46.3 48.2 – – 13.3 13.3 12.7 12.7Kiribati – – – – – – 4.3 4.3 8.7 8.7Lesotho 113 0.534 – – – – 24.2 24.2 24.2 26.7Maldives 64 0.357 – – – – 6.5 6.5 6.5 6.5Mauritius 70 0.377 37.2 37 36.9 37.6 17.1 18.8 18.8 18.8Namibia 86 0.455 – – – – 26.9 24.4 24.4 24.4Nauru – – – – – – – – – –Papua New Guinea 134 0.617 – – – – 0.9 0.9 0.9 2.7St Lucia – – – – – – 11.1 11.1 16.7 16.7St Vincent and the Grenadines – – – – – – 21.7 14.3 17.4 17.4Samoa – – – – – – 8.2 8.2 4.1 4.1Seychelles – – – – – – 23.5 23.5 43.8 43.8Solomon Islands – – – – – – – – – 2Swaziland 112 0.525 – – – – 13.6 13.6 13.6 13.6Tonga 90 0.462 – – – – 3.1 0 3.6 3.6Tuvalu – – – – – – – – – –Vanuatu – – 37.8 38.9 – – 3.8 3.8 1.9 –Other countriesAlbania 41 0.251 – – – – 16.4 16.4 15.7 15.7Armenia 59 0.34 41.4 40.4 43.1 – 9.2 9.2 8.4 10.7Bhutan 92 0.464 – – 26.8 – 8.5 8.5 8.5 8.5Bosnia and Herzegovina – – 38.2 39.3 40.5 41 19 16.7 21.4 21.4Cape Verde – – – – – – 18.1 18.1 20.8 20.8Congo, Republic of 132 0.61 – – – – 7.3 7.3 7.3 7.4Costa Rica 62 0.346 41.1 41.5 42.8 43.3 36.8 38.6 38.6 38.6Djibouti – – – – – – 13.8 13.8 13.8 13.8Gabon 105 0.492 – – – 34.5 14.7 14.7 14.2 15.8Georgia 81 0.438 47.8 46.2 46.7 48.5 5.1 6.5 6.6 12Lebanon 78 0.433 – – – – 3.1 3.1 3.1 3.1Macedonia, FYR 30 0.162 43 41.9 42.2 42.2 32.5 32.5 30.9 32.5Mauritania 139 0.643 – – – – 22.1 22.1 22.1 22.1Moldova 49 0.303 54.6 54.1 54.3 55 23.8 18.8 19.8 19.8Mongolia 56 0.328 51.2 51.1 52.6 52.7 3.9 3.9 3.9 14.9Montenegro – – 46.3 11.1 11.1 12.3 17.3Panama 108 0.503 43.1 42.2 42.1 42.9 8.5 8.5 8.5 8.5São Tomé and Príncipe – – – – – – 7.3 18.2 18.2 18.2Suriname 94 0.467 – – – 36.3 25.5 9.8 11.8 11.8Timor-Leste – – – – – – 29.2 29.2 32.3 38.5High-incomeCommonwealth countriesAntigua and Barbuda – – 50.6 50.6 – – 10.5 10.5 10.5 10.5Bahamas, The 53 0.316 48.8 49.2 50.2 – 12.2 12.2 12.2 13.2Barbados 51 0.343 50.4 50.7 – – 10 10 10 10Brunei Darussalam – – – – – – – – – –Cyprus 22 0.134 49.1 48.7 48.3 48.8 12.5 12.5 10.7 10.7Malta 39 0.236 35.7 35.9 36.4 – 8.7 8.7 8.7 8.7St Kitts and Nevis – – – – – – 6.7 6.7 6.7 6.7Trinidad and Tobago 50 0.311 – – – – 26.8 28.6 28.6 28.6

(continued)

190

Table 54. Selected characteristics of gender equality (continued)

Group/country Gender inequality index 2012

Share of women in wage employment (%)

Seats held by women in parliament (%)

Ranking 0–1 value 2007 2008 2009 2010 2009 2010 2011 2012

Other countriesBahrain 45 0.258 9.8 9.6 9.6 10 2.5 2.5 10 10Croatia 33 0.179 45.5 45.8 47.1 47 23.5 23.5 23.8 23.8Equatorial Guinea – – – – – – 10 10 10 10Estonia 29 0.158 52.3 51.8 53.9 54 22.8 22.8 19.8 20.8Iceland 10 0.089 50 49.4 51.1 51.7 42.9 42.9 39.7 39.7Ireland 19 0.121 48.2 49.2 51.8 52.1 13.9 13.9 15.1 15.1Kuwait 47 0.274 – – – – 7.7 7.7 7.7 6.2Latvia 36 0.216 52 52.7 54.2 54.2 22 20 23 23Lithuania 28 0.157 53 – 54.3 54.4 19.1 19.1 19.1 24.5Luxembourg 26 0.149 43.8 43.3 43.7 44 20 20 25 21.7Norway 5 0.065 49.2 49.1 49.6 49.4 39.6 39.6 39.6 39.6Oman 59 0.34 – 21.9 – – – – 1.2 1.2Qatar 117 0.546 12.3 9.8 12.1 – – – –Slovenia 8 0.08 46.8 47.4 47.9 47.8 14.4 14.4 32.2 32.2Uruguay 69 0.367 45.5 – – – 14.1 15.2 12.1 12.1

Note: – = not availableSources: World Bank, World Development Indicators 2013, available at: http://databank.worldbank.org; Gender Inequality Index,

available at: http://undp.org (accessed July 2013)

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194

Table 56. Births delivered by skilled health personnel and maternal mortality

Group/country Birth attended by skilled health personnel (% of total)

Maternal mortality ratio (per 100,000 live births)

2000 2005 2010 2010

Middle-incomeCommonwealth countriesBelize 100 89 94 53Botswana 94 – – 160Dominica 100 99 – –Fiji 99 – 100 26Grenada 100 100 – 24Guyana 86 94 – 280Jamaica 96 97 – 110Kiribati – 63 – –Lesotho 60 – – 620Maldives – – – 60Mauritius – 99 – 60Namibia 76 – – 200Nauru – – – –Papua New Guinea 41 – – 230St Lucia 100 100 – 35St Vincent and the Grenadines 100 100 – 48Samoa – – – 100Seychelles – – – –Solomon Islands – – – 93Swaziland 70 – 82 320Tonga 95 – 98 110Tuvalu – – – –Vanuatu – – – 110Other countriesAlbania 99 100 – 27Armenia 97 98 100 30Bhutan 24 – 65 180Bosnia and Herzegovina 100 100 – 8Cape Verde – 78 – 79Congo, Republic of – 83 – 560Costa Rica – – 99 40Djibouti – – – 200Gabon 86 – – 230Georgia 96 98 – 67Lebanon – – – 25Macedonia, FYR 98 98 – 10Mauritania – – – 510Moldova 98 100 – 41Mongolia 97 99 99 63Montenegro 99 – – 8Panama – 91 – 92São Tomé and Príncipe 79 – – 70Suriname 85 – – 130Timor-Leste – – 29 300High-incomeCommonwealth countriesAntigua and Barbuda 100 100 – –Bahamas, The 99 – 47Barbados 98 100 – 51Brunei Darussalam – 100 – 24Cyprus – – – 10Malta – – – 8St Kitts and Nevis 99 100 – –

(continued)

195

Table 56. Births delivered by skilled health personnel and maternal mortality (continued)

Group/country Birth attended by skilled health personnel (% of total)

Maternal mortality ratio (per 100,000 live births)

2000 2005 2010 2010

Trinidad and Tobago 96 – – 46Other countriesBahrain – 99 – 20Croatia 100 100 100 17Equatorial Guinea 65 – – 240Estonia 100 100 – 2Iceland – – – 5Ireland – – – 6Kuwait – – – 14Latvia 100 100 – 34Lithuania 100 100 – 8Luxembourg – – – 20Norway – – – 7Oman 95 98 – 32Qatar – – – 7Slovenia 100 100 – 12Uruguay – – – 29


196

Table 57. Universal access to reproductive health

Group/country Contraceptive prevalence rate

(%) (female 15–49 years)

Adolescent birth rate per 1,000 women (ages 15–19)

(%)

Antenatal care coverage, at

least one visit (%)

Unmet need for family planning

(% of total) (15–49 years)

Survey year % 2001 2009 2010 2011 2007–2012 2005–2012

Middle-incomeCommonwealth countriesBelize 2006 34.3 94.2 75.5 74.0 72.4 94 –Botswana 2007 52.8 63.6 48.8 47.1 45.5 94 –Dominica – – – – – – 100 –Fiji 2010 31.8 42.4 44.3 43.8 43.3 100 –Grenada 2006 54.0 53.2 39.6 38.2 36.8 100 –Guyana 2009 42.5 80.7 62.6 59.7 56.8 92 29Jamaica 2008 72.3 87.0 74.3 72.8 71.2 99 –Kiribati 2009 22.3 – – – – 88 –Lesotho 2009 47.0 90.5 68.4 65.9 63.3 92 23Maldives 2009 34.7 27.7 11.4 11.0 10.6 99 29Mauritius – – 36.4 34.0 33.3 32.6 – –Namibia 2007 55.1 83.5 66.4 62.4 58.4 95 21Nauru – – – – – – 95 –Papua New Guinea 2006 32.4 71.9 64.9 63.9 63.0 79 –St Lucia – – 63.5 59.3 58.2 57.0 99 –St Vincent and the

Grenadines2006 48.0 66.9 57.0 56.1 55.1 100 –

Samoa 2009 28.7 38.6 27.2 26.6 26.1 93 48Seychelles – – – – – – – –Solomon Islands 2007 34.6 70.3 68.0 66.9 65.7 74 11Swaziland 2010 65.2 103.8 77.5 74.3 71.1 97 13Tonga 2010 31.5 21.5 20.6 19.7 18.9 98 –Tuvalu 2007 30.5 – – – – 97 24Vanuatu 2007 38.4 58.8 52.6 52.0 51.3 84 –Other countriesAlbania 2009 69.3 16.2 16.7 16.1 15.5 97 13Armenia 2010 54.9 40.9 34.7 34.2 33.7 99 19Bhutan 2010 65.6 61.6 48.1 47.0 45.9 97 12.0Bosnia and Herzegovina 2006 35.7 19.9 15.2 14.6 14.0 99 –Cape Verde – – 96.9 76.6 74.1 71.7 98 17Congo, Republic of – – 130.4 116.2 115.0 113.8 89 20Costa Rica 2010 82.1 78.5 64.2 63.4 62.7 90 –Djibouti 2008 22.5 29.3 21.6 20.9 20.2 92 –Gabon – – 111.7 86.4 84.6 82.8 94 –Georgia 2010 53.4 50.9 42.6 41.6 40.5 98 16Lebanon 2009 53.7 22.3 15.9 15.7 15.6 96 –Macedonia, FYR – – 27.7 20.3 19.5 18.6 – –Mauritania 2007 9.3 89.7 76.1 74.5 72.9 75 –Moldova – – 41.6 31.9 31.0 30.1 – –Mongolia 2010 54.9 24.0 20.0 19.6 19.1 – 14Montenegro 2006 39.4 21.4 16.8 16.2 15.5 97 –Panama 2009 52.2 89.8 79.9 78.6 77.2 96 –São Tomé and Príncipe 2009 38.4 83.0 61.8 59.7 57.5 98 48Suriname 2006 45.6 46.0 37.7 36.7 35.8 90 –Timor-Leste 2010 22.3 70.5 60.4 57.7 55.0 84 32High-incomeCommonwealth countriesAntigua and Barbuda – – – – – – 100 –Bahamas, The – – 47.6 30.4 29.7 29.0 98 –Barbados – – 44.9 41.9 41.5 41.2 100 –

(continued)

197

Table 57. Universal access to reproductive health (continued)

Group/country Contraceptive prevalence rate

(%) (female 15–49 years)

Adolescent birth rate per 1,000 women (ages 15–19)

(%)

Antenatal care coverage, at

least one visit (%)

Unmet need for family planning

(% of total) (15–49 years)

Survey year % 2001 2009 2010 2011 2007–2012 2005–2012

Brunei Darussalam – – 26.7 24.1 23.7 23.2 99 –Cyprus – – 8.4 6.1 5.9 5.7 99 –Malta – – 16.3 15.1 14.0 12.9 100 –St Kitts and Nevis 2008 54.0 – – – – 100 –Trinidad and Tobago 2006 42.5 36.7 33.5 32.9 32.2 96 –Other countriesBahrain – – 16.1 14.9 14.9 14.8 100 –Croatia – – 15.0 13.2 13.1 12.9 – –Equatorial Guinea – – 129.8 119.6 117.9 116.3 86 –Estonia – – 23.9 20.5 19.4 18.3 – –Iceland – – 18.5 13.4 12.8 12.2 – –Ireland – – 19.0 14.0 12.3 10.6 – –Kuwait – – 18.3 14.0 14.2 14.3 100 –Latvia – – 17.7 15.9 14.9 13.8 92 –Lithuania – – 23.0 18.3 17.6 16.9 100 –Luxembourg – – 11.4 9.4 9.1 8.8 – –Norway – – 10.2 8.3 8.0 7.7 – –Oman 2008 24.4 23.1 9.3 9.3 9.3 99 –Qatar – – 19.7 15.9 15.8 15.6 100 –Slovenia – – 9.3 4.8 4.7 4.6 100 –Uruguay – – 64.3 60.3 59.9 59.5 96 –

Note: – = not availableSources: World Bank, World Development Indicators 2013, available at: http://databank.worldbank.org; United Nations, data available

at: http://data.un.org/ and http://unstats.un.org/unsd/mdg/ (accessed July 2013)

198

Table 58. Children’s health

Group/country Prevalence of underweight children under 5 years of age Immunisation DPT rate (% of children age

12–23 months)

Survey yearAge

(months)

Underweight

Moderate /severe (%)

Severe (%)

Stunted/ severe (%) 2008 2009 2010 2011

Middle-incomeCommonwealth countriesBelize 2006 0–59 4.3 0.7 21.6 94 97 96 95Botswana 2007 0–59 11.2 3.8 31.4 96 96 96 96Dominica – – – – – 96 99 98 98Fiji – – – – – 99 99 99 99Grenada – – – – – 99 99 97 94Guyana 2009 0–59 10.5 1.6 18.2 93 98 95 93Jamaica 2007 0–59 2 0 3.7 99 99 99 99Kiribati 1999 0–59 0 0 0 82 86 91 99Lesotho 2009 0–59 13.2 2.3 39.2 83 83 83 83Maldives 2009 0–59 17.3 3.3 18.9 98 98 96 96Mauritius 1995 0–59 – – – 99 99 99 98Namibia 2006–2007 0–59 16.6 3.8 29 83 83 83 82Nauru 2007 0–59 4.8 0.8 24 – – – –Papua New Guinea 2005 0–59 18.4 5.3 42.6 52 62 56 61St Lucia – – – – – 96 95 97 97St Vincent and the Grenadines – – – – – 99 99 99 95Samoa – – – – – 46 72 87 91Seychelles – – – – – 99 99 99 99Solomon Islands 2007 0–59 11.8 2.4 32.8 78 81 79 88Swaziland 2010 0–59 5.8 1 30.9 94 94 89 91Tonga – – – – – 99 99 99 99Tuvalu 2007 0–59 1.6 0.3 10 99 88 89 96Vanuatu 2007 0–59 – – – 68 68 68 68Other countriesAlbania 2008–2009 0–59 5.2 1.7 19.3 99 98 99 99Armenia 2010 0–59 4.7 1.2 19.3 89 93 94 95Bhutan 2010 0–59 12.7 3.2 33.5 96 93 91 95Bosnia and Herzegovina 2005 0–59 1.4 0.4 10.4 91 90 89 88Cape Verde 2006 6–59 – – – 99 99 99 90Congo, Republic of 2005 0–59 11.4 2.9 30 89 91 90 90Costa Rica 2008–2009 0–59 1.1 5.6 90 86 91 85Djibouti 2010 6–59 22.9 4.9 30.8 89 89 88 87Gabon 2000 0–59 8.4 2.2 25.1 45 45 45 45Georgia 2009 0–59 1.1 0.5 11.3 92 88 91 94Lebanon 2004 0–59 – – – 81 81 81 81Macedonia, FYR – – – – – 95 96 95 95Mauritania 2010 6–59 20.3 3.5 22.5 74 64 64 75Moldova 2005 0–59 3.2 0.5 10.3 95 85 90 93Mongolia 2010 0–59 4.7 2.4 15.9 96 95 96 99Montenegro 2005 0–59 1.7 0.5 7 95 92 94 95Panama 2008 0–59 3.9 – 19.1 86 85 94 87São Tomé and Príncipe 2008–2009 0–59 13.1 3.1 29.3 99 98 98 96Suriname 2006 0–59 7.2 0.7 10.7 85 87 96 86Timor–Leste 2009–2010 0–59 44.7 15.4 58.1 79 72 72 67High-incomeCommonwealth countriesAntigua and Barbuda – – – – – 99 99 98 99Bahamas, The – – – – – 93 96 99 98Barbados – – – – – 85 93 86 91Brunei Darussalam – – – – – 98 99 95 97

(continued)

199

Table 58. Children’s health (continued)

Group/country Prevalence of underweight children under 5 years of age Immunisation DPT rate (% of children age

12–23 months)

Survey yearAge

(months)

Underweight

Moderate/severe (%)

Severe (%)

Stunted/ severe (%) 2008 2009 2010 2011

Cyprus – – – – – 97 99 99 99Malta – – – – – 72 73 76 96St Kitts and Nevis – – – – – 98 97 95 97Trinidad and Tobago 2000 0–59 – – – 90 90 90 90Other countriesBahrain 1995 0–59 – – – 98 98 99 99Croatia 1995–1996 12–71 – – – 96 96 97 96Equatorial Guinea 2004 0–59 11 − 35 33 33 33 33Estonia – – – – – 95 95 94 93Iceland – – – – – 98 96 96 96Ireland – – – – – 93 94 94 95Kuwait 1996 0–59 – – – 99 99 98 99Latvia – – – – – 97 95 89 94Lithuania – – – – – 96 98 95 92Luxembourg – – – – – 99 99 99 99Norway – – – – – 94 94 93 94Oman 2009 0–59 8.6 1 9.8 99 98 99 99Qatar 1995 – – – – 97 99 97 93Slovakia – – – – – 97 96 96 96Uruguay 2002 0–59 5.4 1.6 14.7 94 95 95 95

Note: – = not availableSources: World Bank, World Development Indicators 2013, available at: http://databank.worldbank.org; UNICEF and WHO data,

available at: http://unstats.un.org/unsd/mdg/Metadata.aspx?IndicatorId=0&SeriesId=762 (accessed July 2013)

200

Table 59. Under-five mortality rate per 1,000 births

Group/country 2007 2008 2009 2010 2011 2012

Middle-incomeCommonwealth countriesBelize 21 20 20 19 19 18Botswana 62 62 61 58 56 53Dominica 14 14 13 13 13 13Fiji 22 23 23 23 23 22Grenada 15 14 14 14 14 14Guyana 40 39 38 37 36 35Jamaica 20 19 18 18 17 17Kiribati 65 65 64 63 61 60Lesotho 120 117 113 108 102 100Maldives 19 16 15 13 12 11Mauritius 16 16 15 15 15 15Namibia 57 53 49 45 41 39Nauru – – – – – –Papua New Guinea 72 70 68 67 65 63St Lucia 19 19 19 18 18 18St Vincent and the Grenadines 24 24 24 24 24 23Samoa 19 19 18 18 18 18Seychelles 14 14 14 14 13 13Solomon Islands 35 34 34 33 32 31Swaziland 117 114 106 92 85 80Tonga 15 15 14 14 13 13Tuvalu 35 34 33 32 31 30Vanuatu 21 20 20 19 19 18Other countriesAlbania 20 20 19 18 17 17Armenia 21 20 19 18 17 16Bhutan 55 52 50 48 46 45Bosnia and Herzegovina 9 8 8 8 7 7Cape Verde 27 26 25 24 23 22Congo, Republic of 110 107 105 102 99 96Costa Rica 10 11 10 10 10 10Djibouti 94 91 89 86 83 81Gabon 75 72 70 67 65 62Georgia 24 23 22 22 21 20Lebanon 12 11 11 10 10 9Macedonia, FYR 12 12 11 10 9 7Mauritania 97 94 92 89 87 84Moldova 21 20 19 19 18 18Mongolia 37 34 32 30 29 28Montenegro 9 8 8 7 6 6Panama 22 21 20 20 19 19São Tomé and Príncipe 64 61 59 57 55 53Suriname 25 24 23 22 21 21Timor-Leste 71 68 65 62 59 57High-incomeCommonwealth countriesAntigua and Barbuda 12 11 11 11 10 10Bahamas, The 18 18 18 18 17 17Barbados 20 20 20 19 19 18Brunei Darussalam 9 8 8 8 8 8Cyprus 4 4 4 4 3 3Malta 7 7 7 7 7 7St Kitts and Nevis 11 11 10 10 10 9Trinidad and Tobago 24 23 23 22 21 21

(continued)

201

Table 59. Under-five mortality rate per 1,000 births (continued)

Group/country 2007 2008 2009 2010 2011 2012

Other countriesBahrain 11 11 10 10 10 10Croatia 6 6 6 5 5 5Equatorial Guinea 118 114 111 107 104 100Estonia 6 5 5 4 4 4Iceland 3 3 3 3 2 2Ireland 5 5 4 4 4 4Kuwait 11 11 11 11 11 11Latvia 11 10 10 9 9 9Lithuania 9 8 7 7 6 5Luxembourg 3 3 3 3 2 2Norway 4 4 3 3 3 3Oman 13 12 12 12 12 12Qatar 9 9 9 8 8 7Slovenia 4 4 4 3 3 3Uruguay 13 12 10 9 8 7


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204

Table 61. Summary statistics on tuberculosis (TB)

Group/country TB death rates, TB mortality (% per 100,000 population)

(upper bound)

Incidence of TB 2011 (per 100,000

population)

TB treatment success rate under DOTS

(2009–2010)

2008 2009 2010 2011 2011 2009 2010

Middle-incomeCommonwealth countriesBelize 4.1 4 4.4 4.6 40 – –Botswana 45 45 42 41 455 79 81Dominica 2.6 3.1 3.6 4.4 13 100 100Fiji 2.7 2.1 2.3 2.2 26 94 67Grenada 1.5 1.4 1.4 1.2 4 50 75Guyana 26 32 34 36 110 70 71Jamaica 0.4 0.4 0.4 0.3 7 70 47Kiribati 10 8.9 7.5 6.2 356 97 93Lesotho 28 28 28 31 632 70 69Maldives 1.9 2.1 2.4 2.9 34 47 82Mauritius 1 1 1 1 21 88 90Namibia 21 20 19 18 723 85 85Nauru 13 11 9 6.2 – – –Papua New Guinea 106 109 108 107 346 72 58St Lucia 2.6 2.5 2.4 2.2 5 57 89St Vincent and the Grenadines 2.3 2.1 1.8 1.6 24 – 0Samoa 2.9 2.8 2.4 2 10 90 100Seychelles 1.3 1.5 1.6 1.9 30 64 100Solomon Islands 37 33 31 29 103 88 87Swaziland 57 63 71 96 1,317 69 73Tonga 5 5 5.2 5.4 16 83 83Tuvalu 98 98 87 84 – – –Vanuatu 21 14 18 16 67 96 80Other countriesAlbania 0.5 0.4 0.4 0.3 13 89 91Armenia 11 12 11 11 55 73 72Bhutan 64 45 22 22 192 92 90Bosnia and Herzegovina 7.2 7.2 7.2 7.2 49 99 –Cape Verde 51 49 47 44 145 – –Congo, Republic of 80 80 79 77 387 78 77Costa Rica 1.2 1 0.9 0.8 12 54 87Djibouti 158 155 144 136 620 79 80Gabon 113 106 92 82 450 55 63Georgia 4.8 4.6 4.4 4.3 125 75 76Lebanon 2.2 2.2 2.6 2.7 15 82 80Macedonia, FYR 2.4 2.1 1.7 1.3 20 90 90Mauritania 143 148 153 159 344 63 69Moldova 19 18 18 18 161 54 57Mongolia 6 5.8 5.6 5.5 223 88 86Montenegro 0.7 0.2 0.2 0.2 17 86 87Panama 6.3 6.4 6.8 6.8 48 80 80São Tomé and Príncipe 20 22 25 28 94 98 78Suriname 2.5 2.7 3.3 3.5 44 66 60Timor-Leste 138 118 112 115 498 – 88High-incomeCommonwealth countriesAntigua and Barbuda 1.4 1.3 0.2 1.1 7 67 33Bahamas, The 0.9 0.8 0.7 0.7 13 81 68Barbados 0.5 0.5 0.1 0.6 – 100 100Brunei Darussalam 3 2.9 2.7 2.7 70 71 81Cyprus 0.2 0.2 0.2 0.1 4 29 –Malta 0.3 0.3 0.3 0.3 9 80 20

(continued)

205

Table 61. Summary statistics on tuberculosis (TB) (continued)

Group/country TB death rates, TB mortality (% per 100,000 population)

(upper bound)

Incidence of TB 2011 (per 100,000

population)

TB treatment success rate under DOTS

(2009–2010)

2008 2009 2010 2011 2011 2009 2010

St Kitts and Nevis 0.3 2.9 2.9 2.9 6 80 100Trinidad and Tobago 2 2.1 2.2 2.2 21 69 76Other countriesBahrain 0.7 0.6 0.6 0.7 18 98 96Croatia 2.5 2.3 2.2 2.1 17 63 75Equatorial Guinea 47 65 70 60 202 66 70Estonia 3.8 3.4 3.1 2.7 25 59 68Iceland 0.3 0.3 0.3 0.3 5 75 88Ireland 0.5 0.5 0.5 0.5 8 67 62Kuwait 0.9 0.9 0.9 0.9 36 85 87Latvia 5.7 5.3 4.7 4.2 42 75 76Lithuania 9.6 9.5 8.9 8.4 59 73 68Luxembourg 0.4 0.3 0.3 0 1 – –Norway 0.1 0.1 0.1 0.2 6 82 –Oman 1.4 1.5 1.3 1.2 14 98 97Qatar 0.2 0.2 0.2 0.2 37 80 67Slovenia 1 1.1 1.1 1.1 9 87 85Uruguay 1.9 1.9 1.7 1.7 21 80 85

Note: – = not availableSources: World Health Organization data available at: www.who.int; Millenium Development Goals Indicators, data available at:

http://mdgs.un.org (accessed July 2013)

206

Tabl

e 62

. En

viro

nmen

t

Gro

up/c

ount

ry

Car

bon

diox

ide

emis

sion

s (t

onne

s pe

r cap

ita)

Fore

st c

hang

e ra

te

Num

ber o

f spe

cies

th

reat

ened

wit

h ex

tinc

tion

(201

2)C

onsu

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of

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e de

plet

ing

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tanc

es (t

onne

s)

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orti

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tal w

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re

sour

ces

used

(% to

tal)

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f te

rres

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l and

m

arin

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prot

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d to

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l are

a (%

tota

l)ha

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dle-

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me

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273

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(con

tinue

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207

(con

tinue

d)

Tabl

e 62

. En

viro

nmen

t (c

onti

nued

)

Gro

up/c

ount

ry

Car

bon

diox

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ita)

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hang

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te

Num

ber o

f spe

cies

th

reat

ened

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tion

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tal w

ater

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tal)

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e ar

eas

prot

ecte

d to

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ant

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2000

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520

05–2

009

Mam

mal

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(hig

her)

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2010

Bo

snia

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erze

govi

na7.

78.

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313

5.8

3.5

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and

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he7.

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208

Tabl

e 62

. En

viro

nmen

t (c

onti

nued

)

Gro

up/c

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ry

Car

bon

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sion

s (t

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hang

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te

Num

ber o

f spe

cies

th

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ened

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h ex

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tion

(201

2)C

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tal w

ater

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tal)

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d to

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rrit

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a (%

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ant

2007

2008

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520

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Mam

mal

Fish

(hig

her)

2009

2010

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2005

2010

2009

2010

Equa

toria

l Gui

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13, a

vaila

ble

at: h

ttp:

//da

taba

nk.w

orld

bank

.org

(acc

esse

d Ju

ly 2

013)

215

small states economic review and basic statistics volume 17

Documents