pathways for adapting the sustainable …macroeconomic policy and financing for development division...

PDD WORKING PAPERS Working Paper Series Macroeconomic Policy and Financing for Development Division

WP/16/04

April 2016

Jaebeum Cho, Alberto Isgut and Yusuke Tateno

PATHWAYS FOR ADAPTING THE SUSTAINABLE DEVELOPMENT GOALS TO THE NATIONAL CONTEXT: THE CASE OF PAKISTAN

PATHWAYS FOR ADAPTING THE SUSTAINABLE DEVELOPMENT GOALS TO THE NATIONAL

CONTEXT: THE CASE OF PAKISTAN

Jaebeum Cho, Alberto Isgut, and Yusuke Tateno

Jaebeum Cho, Cornell University, Alberto Isgut and Yusuke Tateno, the Macroeconomic Policy and Financing for Development Division, UNESCAP

For more information, contact: Macroeconomic Policy and Financing for Development Division (MPFD) Economic and Social Commission for Asia and the Pacific United Nations Building, Rajadamnern Nok Avenue, Bangkok 10200, Thailand Email: [email protected]

Series Editor Dr. Aynul Hasan, Director Macroeconomic Policy and Financing for Development Division

Please cite this working paper as: Cho, Jaebeum, Alberto Isgut, and Yusuke Tateno (2016). Pathways for adapting the Sustainable Development Goals to the national context: the case of Pakistan. MPFD Working Paper WP/16/14. Bangkok: ESCAP. Available from www.unescap.org/publications.

MPFD Working Papers WP/16/04

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Contents I. Introduction ....................................................................................................................... 4 II. A complex system of SDGs ................................................................................................ 6 III. The current state of Pakistan ............................................................................................ 8 IV. The SDG system and Pakistan’s position ......................................................................... 13 V. The SDG implementation capacity of Pakistan ............................................................... 16 VI. Optimal pathways for progress ....................................................................................... 23 VII. Identifying bottlenecks in developing SDG capacities .................................................... 30 VIII. Scenario analysis .............................................................................................................. 33 IX. Conclusions ...................................................................................................................... 34 Reference ................................................................................................................................... 36 Annex ......................................................................................................................................... 37 A. List of indicators used for analysis ............................................................................. 37 B. The Method of Reflections ......................................................................................... 44

Tables and Figures Table 1. Pakistan Vision 2025: enablers, pillars, and corresponding SDGs …………………………… 5 Table 2. Top 10 indicators for Pakistan (higher normalized value corresponds to better

attainment) …………………………………………………………………………………………………………..……….. 11

Table 3. Bottom 10 indicators for Pakistan (higher normalized value corresponds to better attainment) …………………………………………………………………………………………………………..……………… 12

Table 4. Suggested areas of priority for Pakistan ……………………………………………………………………… 28 Figure 1. Attainment across SDGs for Pakistan compared to different groups (standard

scores, world average = 0) …………………………………………………………………………………………………10 Figure 2. The SDG system and Pakistan’s position within the system …………………………………….14 Figure 3. Capacities of lower middle‐income countries (capacities are measured as a score

between 0 and 100) …………………………………………………………………………………………………………… 195

Figure 4. SDG Capacities versus GNI per capita (lower middle‐income countries)……………… 226

Figure 5. SDG Capacities versus the human development index (lower middle‐income countries) …………………………………………………………………………………………………………..…………………. 226

Figure 6. Priority SDGs for the implementation of the 2030 Agenda ……………………………………. 29 Figure 7. Pakistan’s optimal pathway for progress ……………………………………………………………………. 31 Figure 8. Comparison of scenarios………………………………………………………………………………………………….. 34

Pathways for Adapting the Sustainable Development Goals to the national context: The case of Pakistan


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WP/16/04

MPFD Working Papers Macroeconomic Policy and Financing for Development Division

PATHWAYS FOR ADAPTING THE SUSTAINABLE DEVELOPMENT GOALS TO THE NATIONAL CONTEXT:

THE CASE OF PAKISTAN1* by

Jaebeum Cho, Alberto Isgut and Yusuke Tateno

April 2016

Abstract

The 2030 Agenda for Sustainable Development is a laudable attempt to portray a comprehensive global vision towards progress in a plethora of socioeconomic and environmental issues that we face today. However, this holistic view of progress presents countries with substantial difficulties in implementation in that the 17 goals and 169 targets are inherently complex and intertwined. Recognizing these difficulties, we are utilizing cutting-edge methods from complexity science coupled with economic analysis to consider the Sustainable Development Goals (SDGs) as a complex system, in an attempt to identify the pathways Pakistan can take in implementing the broad spectrum of goals and targets. Ultimately, we seek to provide for a guidebook that Pakistan can complement with its national development plan, Vision 2025, in the early stages of implementation of the SDGs, fully regarding their unique situations and development paths.

* This paper has been prepared by Jaebeum Cho, Cornell University, Alberto Isgut and Yusuke Tateno, the Macroeconomic Policy and Financing for Development Division, UN-ESCAP. Part of the paper was written while Jaebeum Cho was with the UN-ESCAP. The view expressed herein are those of the authors and do not necessarily reflect the views of the United Nations. It has been issued without formal editing. The authors would like to thank Shamshad Akhtar, Debapriya Bhattacharya, Sudip Ranjan Basu, Aynul Hasan, Hamza Ali Malik, Oliver Paddison and Heather Taylor for useful comments and suggestions on a previous version of this paper.

The views expressed in this Working Paper are those of the author(s) and should not necessarily be considered as reflecting the views or carrying the endorsement of the United Nations. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate. This publication has been issued without formal editing.


Authors’ e-mail address: [email protected].


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I. Introduction After an extensive process of consultation with parliamentarians, federal ministries, provincial governments, business leaders, international institutions, universities, think tanks, and NGOs that concluded in a national conference on 22 November 2013, the Government of Pakistan compiled its Vision 2025 (Pakistan, 2014). This aspirational document puts together the consensus views of national and international stakeholders regarding the future direction of the country. It provides a conceptual platform for the achievement of sustainable and inclusive growth for the benefit of all the citizens of Pakistan, thus offering a national approach for meeting globally agreed goals and targets, such as the recently adopted Sustainable Development Goals (SDGs). Vision 2025 includes five enablers and seven pillars, with many pillars overlapping with the sustainable development goals contained in the 2030 Agenda for Sustainable Development (see Error! Reference source not found.). As such, this document provides a national roadmap for achieving the SDGs. While the focus of this paper is on the SDGs, our analysis compares the differences and similarities between the two pathways of progress encompassed in Vision 2025 and SDGs. In addition, the paper suggests an optimal pathway for the implementation of the SDGs based on the unique current capacities of Pakistan. The Economic and Social Commission for Asia and the Pacific (ESCAP) is conducting research on the achievement of SDGs across the region. Although this is an ambitious and complex undertaking, it provides flexibility and room of maneuverability to countries for implementing the 2030 Agenda in the manner that is most beneficial for them. ESCAP research is based on both analytical methods and a survey among researchers and practitioners from across the region. The analytical research aims at identifying country-specific paths of progress in the implementation of the 2030 Agenda based on their unique circumstances and capabilities. The survey aims at compiling views from a wide spectrum of countries on the priorities, sequencing, challenges, and institutional arrangements for the implementation of 2030 Agenda. The results of these two strands of research have been published in the 2016 issue of the Asia-Pacific Countries with Special Needs Development Report, a new ESCAP flagship publication launched in 2015 (ESCAP, 2016). Technical details of the analytical framework are available in Cho, Isgut and Tateno (2016). This paper includes an application of the analytical methods ESCAP is developing for the analysis of the implementation of the 2030 Agenda to the case of Pakistan. Its purpose is to evaluate the benefits of various possible pathways that can be pursued for achieving SDGs in Pakistan. Pakistan has taken positive strides to alleviate core developmental challenges, and the 2013 elections led to a successful transition from one civilian government to another - a step forward for democracy. Furthermore, women’s participation in politics and economic activity has also improved due to legal reforms, and a landmark piece of legislation strengthened the protection of women against discrimination. In addition, funding for higher education has doubled over the past two years, and newly planned universities in every district expect to educate 10,000 Ph.D. students over the coming years. Nevertheless, numerous development indicators suggest that Pakistan is lagging behind in such areas as poverty, hunger, health, and resilience to natural disasters. For example, according to the United Nations Development Programme (UNDP), Pakistan is ranked as one of the most vulnerable countries to the impacts of climate change, and the cost of environmental degradation is estimated at nearly 6% of GDP per annum.


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Our analytical approach to SDG implementation is based on the assumption that all goals and associated targets in the 2030 Agenda are, to a greater or lesser extent, interdependent. Such interdependencies need to be taken into account when considering alternative paths for the implementation of SDGs. In addition, the lack of attainment of some goals may create bottlenecks for the attainment of other goals. For instance, constraints in the availability of finance, human capital, and productive capacities may impede progress in the attainment of other goals. Such obstacles also need to be considered in national frameworks for the implementation of SDGs. Table 1. Pakistan Vision 2025: enablers, pillars, and corresponding SDGs Pakistan Vision 2025

Sustainable Development Goals Enablers and pillars Goals Enabler I: Shared national vision

SDG 16: Peace, justice and strong institutions

Enabler II: Political stability and continuity of policies

Enabler III: Peace and security Enabler IV: Rule of Law Enabler V: Social Justice

Pillar I: Putting people first - developing human and social capital

1, 2, 3, 4, 5, 6, 7

SDG 3: Good health and well-being SDG 4: Quality education SDG 5: Gender equality SDG 6: Clean water and sanitation

Pillar II: Achieving sustained, indigenous and inclusive growth

8, 9, 10, 11

SDG 1: No poverty SDG 8: Decent work and economic growth SDG 10: Reduced inequalities SDG 17: Partnerships for the Goals

Pillar III: Governance, institutional reform and modernization of the public sector

12 SDG 16: Peace, justice and strong institutions

Pillar IV: Energy, water and food security

13, 14, 15, 16

SDG 2: Zero hunger SDG 6: Clean water and sanitation SDG 7: Affordable and clean energy

Pillar V: Private sector-led growth 17, 18, 19 SDG 8: Decent work and economic growth

Pillar VI: Developing a competitive knowledge economy through value addition

20, 21, 22, 23

SDG 8: Decent work and economic growth SDG 9: Industry, Innovation and Infrastructure

Pillar VII: Modernizing transportation infrastructure and greater regional connectivity

24, 25 SDG 9: Industry, Innovation and Infrastructure

Source: Authors, based on the information from the Ministry of Planning, Development and Reform, Government of Pakistan, Pakistan Vision 2025: One Nation – One Vision, Executive Summary, and the 2030 Agenda for Sustainable Development.


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II. A complex system of SDGs This paper presents a policy-relevant analysis of possible pathways to implement the 2030 Agenda for Sustainable Development in Pakistan. It starts from the premise that the SDGs comprise a complex system. A complex system is in essence a nexus of diverse, multiple and interconnected elements in which the whole is not necessarily equal to the sum of its parts. Such a view of SDGs – or any other set of relevant global issues for that matter – as a system is not new. At the United Nations Conference on Human Environment, later called the Stockholm Conference, in 1972, Indira Gandhi advocated such a view: “The population explosion, poverty; ignorance and disease, the pollution of our surroundings, the stockpiling of nuclear weapons and biological and chemical agents of destruction are all parts of a vicious circle. Each is important and urgent but dealing with them one by one would be wasted effort” (United Nations, 2015). A similar integrated view provided the basis for the agreement on Agenda 21 and 27 of Rio Principles at the United Nations Conference on Environment and Development, or the Earth Summit, in 1992, and was reconfirmed for the United Nations Conference on Sustainable Development, commonly known as the Rio+2). Sustainable development can be defined as development that meets the needs of the present without compromising the ability of future generations to meet their own needs. Underlying this definition is again an integrated, systems perspective. It refers to the various needs of the society, including equity within and across generations, as well as economic and environmental issues. As such, when assessing how, in what manner, and with what focus the SDGs should be implemented, a systems view of the SDGs is highly relevant. The 2030 Agenda for Sustainable Development, which includes 17 goals and 169 targets, are holistic and crisscross a diverse range of economic, social and environmental issues that our societies face today. These goals and targets are inherently intertwined. For example, eradicating poverty (SDG 1) and promoting healthy lives (SDG 3) are closely linked with each other, and cannot be thought of separately as individual targets. Thus, in an attempt to provide a roadmap for achieving the SDGs, we must consider that achieving one goal will help in achieving other closely linked goals. However, this interdependency also presents the challenge that if one SDG is not met, other goals are likely to be missed as well. In our analysis, underlying the complex system of SDGs is a combination of socioeconomic capacities – such as physical, human, and social capital and institutional capabilities – that allow countries to reach specific levels of attainment of a number of indicators for the 17 SDGs and 169 targets. Such capacities are not directly observable but can be inferred from a country’s levels of attainment in each of these indicators. Comparing the levels of attainment of countries with similar capacities provides a map of potential pathways for the implementation of the SDGs that are feasible and appropriate to the specific capacities of a country. In this paper, we base our analysis of the SDG system on the collective economic, social, environmental, and institutional characteristics of middle-income countries (both lower and upper middle-income), and focus on the unique position of Pakistan within this larger system. The aim is to conceive a measure of the underlying capacities that countries – including Pakistan – possess in achieving the SDGs, given the structure of the SDG system. Once this task is complete, an assessment of preparedness of Pakistan in adapting the SDGs can be made, and pathways in which the SDGs could be implemented, taking into account Pakistan’s capacities, can be suggested.2 The analysis complements through a more data driven, empirical analysis

2 See ESCAP (2016) and Cho, Isgut and Tateno (2016) for an overview of the methods used.


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previous work on the interlinkages between SDGs.3 It ultimately aims to provide answers – however preliminary – to the difficult question of how countries should move forward in implementing the SDGs under their current situations.

Data At the time of writing, the Inter-agency and Expert Group on Sustainable Development Goal Indicators (IAEG-SDGs) is in the process of refining and fine-tuning the indicators that will be used to proxy for each of the 17 SDGs and 169 associated targets, for which data collection will proceed in the future. As such, the analysis conducted in this paper uses indicators collected by ESCAP from a variety of sources that best adhere to the specific goals (see annex for the full list of indicators included in the analysis). The selection of indicators is based on the three subsequent criteria: 1. First, the indicators used for the Millennium Development Goals (MDGs) that

overlap in meaning and scope with the SDGs and related targets are utilized, provided they have reasonable coverage across countries.

2. Second, from the list of indicators discussed at the second meeting of the IAEG-

SDGs (26-28 Oct 2015) that have been categorized as “green” (meaning there is general agreement among group members), indicators that have data readily available from official sources and do not overlap with indicators selected from the first criterion are added, again providing they have reasonable coverage across countries.

3. Third, indicators that come from official data sources, which adhere best to the SDGs,

and have reasonable coverage across countries are chosen for goals that do not possess relevant indicators that meet the first two criteria.

It is important to note that the analysis of the SDGs as a complex system (in particular the interlinkages between countries and indicators) requires as much information of the “system” as is possible to incorporate given data constraints. This includes incorporating as many countries as possible (including non CSN) in the analysis, as well as including a wide variety of indicators relevant to the SDGs. However, there is also a trade-off between including more indicators to provide more information of the system and data availability issues across countries for the indicators that may potentially be included. Recognizing this trade-off, the final analysis is conducted based on 82 indicators, which broadly cover all 17 SDGs (admittedly some targets are not covered due to data constraints) while providing decent data coverage across countries. The analysis uses the most recent data available for countries, in lieu of data for a specific year. This is inevitable because data for some indicators are only collected in particular years, while for some indicators data have been collected only once historically. The finalized dataset uses data spanning from 2006 to 2014, with the majority of data points being for the years 2010 onwards. Even after selection of indicators that have reasonable coverage, many countries (120 out of 209 countries) were missing various amounts of data-points for the 82 selected indicators. This presents a problem in that the methods used in the analysis perform poorly with incomplete datasets. Instead of limiting our analysis to just the 89 countries

3 See, for example, ICSU and ISSC (2015) and Le Blanc (2015).


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for which full data is available, Multiple imputation (Rubin 2004) techniques have been used to assign missing data for countries for which data for less than approximately 25 percent of the 82 indicators (less than 20 indicators) were missing. In essence, the imputation technique used utilizes information regarding the relationships between all 82 indicators (including GDP, GDP per capita and population) to predict missing values. Such imputation methods are advantageous over complete-case analysis in this setting due to two reasons. First, much information about the SDG system is lost if analysis only takes place on for the 89 countries, and given that the methods used in this paper utilize information regarding the linkages between countries, a smaller subset of countries may potentially distort relationships. Second, the subset of 89 countries is in itself a biased sample in that it is most likely that countries for which full data is not available are those that have less institutional capacities to collect statistical information, and thus analyses conducted on this subset is not fully representative. After following this approach, the dataset consisted of 174 countries and 82 indicators, with Pakistan fortunately possessing data for all 82 indicators used in the analysis.

III. The current state of Pakistan Before analyzing the SDG system and Pakistan’s position within it, we first examine the current attainment levels for Pakistan across the SDGs to reveal areas of strength and weaknesses. To do this, we compare the current attainment of Pakistan for each SDG with the world and with countries in different income groups. Since our analysis uses multiple indicators for each SDG, the attainment for each SDG is an average of the relevant indicators for each goal.


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Figure 1 illustrates the current attainment of Pakistan, reported in standardized scores with a mean of zero and a standard deviation of one for all 174 countries in our sample. It can be seen that Pakistan is doing well in sustainable energy (SDG 7), compared to the other middle-income countries and even to the upper middle-income countries average. It is also on par with the upper middle-income countries and doing better than the lower middle-income countries in indicators related to poverty (SDG 1), inequality (SDG 10), cities (SDG 11) and climate change (SDG 13). However, considering that Pakistan is classified as a lower middle-income country, it is lagging far behind other countries in this income category across goals such as hunger (SDG 2), health (SDG 3), education (SDG 4), gender (SDG 5), and sustainable consumption and production (SDG 12).


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Figure 1. Attainment across SDGs for Pakistan compared to different groups (standard scores, world average = 0)

Source: Authors’ calculation. Note: The figure reports the attainment of Pakistan in standardized scores with a mean of zero and a standard deviation of one for all 174 countries in the sample. Tables 2 and 3 present indicators for which Pakistan exhibits the largest deviation from the lower middle-income country mean. Inspection of Table 2, that includes 10 indicators for which Pakistan is faring well, shows that, compared to other lower middle-income countries, Pakistan is relatively strong in some of the indicators related to terrestrial ecosystems, growth and employment, inequality, infrastructure, and implementation. Pakistan’s access to electricity (both total and urban) is higher than the average for the lower middle-income countries, and its infrastructure related to logistics is also above average. Economic indicators such as unemployment, export diversification, and the Gini coefficient are better than the lower and even the upper middle-income countries. However, for growth and employment (SDG 8), while the unemployment rate and export diversification are better than average, labor force participation is very low at 56.4% compared to 65.9% for lower middle-income countries, while Pakistan’s ease of doing business is ranked 128th among the 189 countries surveyed. As such, the overall attainment of Pakistan for SDG 8 – even with its strengths in some indicators – is below the world average and on par with the lower middle-income countries. The same is true for infrastructure, with Pakistan’s advantage in logistics being mitigated by other indicators such as telephone, cellular, and internet use for which Pakistan is below the average for low middle-income countries.


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Table 2. Top 10 indicators for Pakistan (higher normalized value corresponds to better attainment)

Indicator SDG Pakistan

(normalized0-100)

Average for lower middle-

income countries

(normalized 0-100)

Average for upper middle-

income countries

(normalized 0-100)

Source (year)

Access to electricity (% of population)

7: Affordable and clean energy

93.6 (93)

68.7 (67)

93.8 (93)

WORLD BANK (2012)

Unemployment, total (% of total labour force) 8: Decent

work and economic growth

5.1 (84)

8.7 (73)

11.3 (64)

ILO (2013)

Export diversification index (higher is less diversified)

2.99 (69)

3.60 (57)

3.47 (59)

IMF (2010)

Logistics performance index (1=low to 5 = high)

9: Industry, innovation and infrastructure

2.67 (38)

2.38 (27)

2.61 (35)

WORLD BANK (2014)

Gini index 10: Reduced Inequalities

29.6 (82)

40.0 (60)

40.5 (58)

WORLD BANK SWIID (2010)

Access to electricity, urban (% of urban population)

11: Sustainable cities and communities

99.8 (100)

86.2 (84)

97.8 (97)

WORLD BANK (2012)

Percentage of the population living on severely or very severely degraded land.

15: Life on land

4.5 (94)

13.0 (82)

9.9 (86)

FAO (2010)

Plant species (higher), threatened

5 (100)

80 (96)

136 (93)

UNEP (2014)

Foreign direct investment, net inflows (BoP, logarithm, current US$)

17: Partnerships for the Goals

9.25 (60)

8.73 (52)

9.23 (60)

IMF (2014)

Statistical capability 82

(100) 78.6 (95)

79.0 (96)

Based on ESCAP

calculationsSource: Authors’ calculation. Looking at the 10 indicators for which Pakistan is struggling with the most, from Table 3. Bottom 10 indicators for Pakistan (higher normalized value corresponds to better attainment) it can be seen that Pakistan is struggling with many indicators related to gender, hunger, and health. The percentage of females with an account at a financial institution is nearly 50% below the average for the lower middle-income countries at 34.7%, and labor force participation for females is also only around half that of these countries. Concerning health, the infant mortality rate in Pakistan is nearly double that of the lower middle-income countries, while the number of


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children immunized against measles is far lower than that of the same group. In addition, air pollution is more than double the levels of lower middle-income countries, which in turn is likely to affect health indicators such as infant or child mortality as well as life expectancy. Table 3. Bottom 10 indicators for Pakistan (higher normalized value corresponds to better attainment)

Indicator SDG Pakistan

(normalized0-100)

Average for lower middle-

income countries

(normalized 0-100)

Average for upper middle-

income countries

(normalized 0-100)

Source (year)

Livestock production index (2004-2006 = 100) 2: Zero

hunger

81.2 (11)

126.5 (26)

122.2 (25)

FAO (2013)

Food production index (2004-2006 = 100)

93.8 (30)

127.2 (50)

122.5 (47)

FAO (2013)

Children 1 year old immunized against measles, percentage 3: Good

health and well-being

61 (49)

85 (81)

92 (91)

UNICEF (2013)

Infant mortality rate (0-1 year) per 1,000 live births

69 (36)

36 (67)

18 (84)

UNICEF (2013)

Account at a financial institution, female (% age 15+)

5: Gender equality

34.7 (9)

84.2 (61)

91.3 (69)

WORLD BANK (2014)

Female to male ratio of HDI

0.75 (34)

0.90 (69)

0.95 (78)

UNDP (2013)

Labor force participation rate, female (% of female population ages 15-64)

25.6 (15)

52.4 (50)

50.6 (48)

ILO (2013)

Gender Parity Index in primary level enrolment

0.87 (46)

0.98 (67)

0.97 (66)

UNSD (2013)

PM2.5 air pollution, mean annual exposure (micrograms per cubic meter)

12: Responsibleconsumption

and production

46.2 (36)

21.7 (74)

16.5 (82)

Brauer and others (2013)

Satisfaction with government

16: Peace, justice and

strong institutions

19.3 (9)

45.8 (43)

43.0 (39)

UNDP (2013)

Source: Authors’ calculation.


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In sum, when compared with countries in the lower middle-income category, Pakistan is faring relatively well in some of the indicators related to energy (SDG 7) and a certain type of infrastructure while seriously needing to improve on areas related to hunger, health, education, and gender equality (SDGs 2– 5). As Pakistan continues on its development path, a key issue pertains to devising a pathway for progress that does not relinquish its advantages while simultaneously attaining goals that are currently in need of effort. IV. The SDG system and Pakistan’s position In this section, we construct a graphical representation of the SDG system as a network consisting of the 82 SDG indicators. This is done by calculating proximity scores that capture how related one indicator is with another in terms of the levels of attainment. More specifically, we compute, for all pairs of SDG indicators and for the entire sample of 174 countries, a conditional probability for a country that has a higher level of attainment in one indicator relative to the reference country, Pakistan in this case, to have a higher level of attainment in the other indicator. We use this probability measure as a proxy to the proximity, or the relatedness of one indicator to another, under the assumption that Pakistan is striving to move forward in achieving better attainment across the SDG indicators. A higher probability suggests that the two indicators move closely together, and thus are considered to have higher proximity within the SDG system. Once the proximity scores are calculated for all pairs of SDG indicators, the network is constructed utilizing the concept of a “maximum spanning tree,” which in essence connects all the nodes in the network together but presents only the highest probability link for each of the SDG indicators.4 Figure 2 shows the network of SDG indicators or equivalently the SDG system. The green nodes stand for the indicators in which Pakistan is doing better than the average of lower middle-income countries, while the grey nodes are for those in which Pakistan is doing poorly relatively to the average. The thickness of the lines connecting two nodes signifies the proximity of those two indicators, and the size of the nodes represents the number of shortest paths from all nodes to all others that pass through that node (called a “betweenness centrality” in technical terms). It can be seen clearly that the SDG system facing Pakistan is divided into two different areas. The top portion of the network is densely connected, meaning that indicators in the group are highly related to each other. This area of the network mainly contains indicators related to gender, health, hunger, and education. The bottom area of the network mainly includes indicators related to the environment, infrastructure, and the economy. It can be also seen from the colors of the nodes that Pakistan has a unique position within the system, mainly populating the nodes on the bottom left which relate to specific infrastructure (particularly electricity access, water, sanitation, and trade infrastructure), the environment, and poverty. On the other hand, Pakistan is absent in the densely connected upper area of the SDG network, which includes most of the indicators related to Pakistan’s areas of weakness identified in the previous section.

4 Afterwards, links that represent probabilities greater than 0.85 are added to the tree. For more details, see Hidalgo and others (2007).


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Figure 2. The SDG system and Pakistan’s position within the system

Source: Authors’ calculation. Notes: (a) The red nodes are the indicators in which Pakistan is doing better than the lower middle-income country average; and (b) The size of nodes represent their importance as gatekeepers (i.e. how important they are as middle links for Pakistan to progress towards better attainment in other indicators).


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The large nodes that are located at the center of the SDG system are considered as gatekeepers or bottlenecks for Pakistan to move towards a different position within the system. Given the structure of the SDG network and Pakistan’s position, the main bottlenecks Pakistan faces in achieving the goals related to gender, education, hunger, and health (the top portion of the network) are the indicators related to telecommunications (telephones and internet), child mortality, and the gender index5. Thus, our results suggest that these four key indicators should be of focus in order for Pakistan to achieve better attainment across its areas of weakness. When considering that higher levels of attainment in these indicators represent higher levels of unobserved capacities that underlie attainment levels in these indicators, the results make intuitive sense. Since Pakistan has a relative advantage over other similar countries in particular aspects of infrastructure such as electricity access, trade infrastructure, and sanitation, it could readily utilize this advantage in creating better infrastructure for telecommunications. Better telecommunication networks would in turn provide the population with better information and education opportunities, and these new opportunities would improve Pakistan’s gender gap, especially in terms of empowerment, employment, and reproductive health. This would again influence child mortality, which is highly related to women’s empowerment and education. Reduced child mortality in turn would provide a pathway to achieving better attainment in the other indicators included in the dense upper portion of the network, which relate to hunger, health, and gender equality. V. The SDG implementation capacity of Pakistan In the previous section, we have highlighted the key indicators for which Pakistan must improve upon in order to achieve broader attainment across the SDGs. However, Pakistan – as is any other country in the world – is limited in its resources and current socioeconomic “capacities” that it utilize to achieve these goals. Thus, a measure of the capacities of Pakistan is needed in order to provide a relevant roadmap in progressing towards achievement of the SDGs. This is because ultimately the issue we are attempting to address relates to identifying Pakistan’s specific pathway of progress given its current state. The capacities of countries can be thought of as building blocks or a Lego piece, with a specific SDG being equivalent to a Lego model, and a country being equivalent to a bucket of Legos (Hidalgo and Hausmann, 2009). Countries are able to achieve higher attainment in a particular SDG indicator (a more complex Lego model) only if the relevant capacities (Lego blocks) needed to increase attainment in an indicator are available within the country’s set of capacities (the Lego bucket). However, these capacities – which include all aspects within the spectrum of socioeconomic capacities relevant in achieving progress – are difficult if not impossible to observe directly due to the complexity of the construct itself, as well as the lack of data that fully capture the construct’s different dimensions. We can however, indirectly measure the unobservable capacities that Pakistan possesses using the information of all countries and their attainment across all indicators. This is done by analyzing the relative attainment of Pakistan across SDG indicators, compared to all countries

5 The gender index – more specifically the gender inequality index – is a composite measure developed by the UNDP reflecting inequality in achievement between women and men in three dimensions: reproductive health, empowerment, and the labour market.


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used in our sample. If Pakistan is achieving higher attainment in a particular indicator relative to the other countries, then Pakistan is considered to have the capacities to build that more complex “Lego model.” If Pakistan is struggling in a particular indicator, this suggests that it does not yet have the required capacities needed to progress towards better attainment in that indicator. In essence, our capacity measure – calculated using the “Method of Reflections”6 – awards a higher capacity value if a country is doing well in indicators that other countries are struggling with, since this is suggestive of the country possessing unique capacities that others do not have. The capacities that a country possesses are to some degree correlated with both income levels, measured as GNI per capita, and the human development index (HDI), as shown in

6 See annex for brief overview of the Method of Reflections. For a more in-depth description of the method used in our analysis, see Hidalgo and Hausmann (2009) and Cho, Isgut and Tateno (2016).


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Figure 4 and Figure 5. However, our measure of capacity for Pakistan is more correlated with HDI than income, which suggests that capacity levels represent a diverse set of socioeconomic capacities. These figures show that Pakistan’s level of capacity is slightly higher than the predicted value, given its level of income and HDI. This is both encouraging and discouraging. Given Pakistan’s level of income and HDI, its capacities are higher than expected, which suggests that Pakistan has more room to progress towards attaining the SDGs relative to countries with similar levels of income or human development. However, this also suggests that Pakistan may be able to achieve higher levels of income or human development given its level of capacity, which implies that Pakistan’s capacities are currently under-utilized.


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Figure 3 compares the calculated capacity values for Pakistan with the group of lower middle-income countries. On a scale of 0 to 100, Pakistan’s capacity is around 30, which is slightly below but roughly on par with the lower middle-income country average. Countries in proximity to Pakistan such as India, Tajikistan, and Bhutan are seen to possess roughly the same level of capacities as Pakistan. Compared to the rest of the world, Pakistan’s capacity level is around two third that of the world average. The capacities that a country possesses are to some degree correlated with both income levels, measured as GNI per capita, and the human development index (HDI), as shown in


20

Figure 4 and Figure 5. However, our measure of capacity for Pakistan is more correlated with HDI than income, which suggests that capacity levels represent a diverse set of socioeconomic capacities. These figures show that Pakistan’s level of capacity is slightly higher than the predicted value, given its level of income and HDI. This is both encouraging and discouraging. Given Pakistan’s level of income and HDI, its capacities are higher than expected, which suggests that Pakistan has more room to progress towards attaining the SDGs relative to countries with similar levels of income or human development. However, this also suggests that Pakistan may be able to achieve higher levels of income or human development given its level of capacity, which implies that Pakistan’s capacities are currently under-utilized.


21

Figure 3. Capacities of lower middle-income countries (capacities are measured as a score between 0 and 100)



22

Figure 4. SDG Capacities versus GNI per capita (lower middle-income countries)

Source: Authors’ calculation. Figure 5. SDG Capacities versus the human development index (lower middle-income countries)


Mean: 2,296 (2014, current US dollars)

Mean: 31.7

Mean: 31.7

Mean: 0.591


23

VI. Optimal pathways for progress When it comes to devising an optimal pathway for progress specific to Pakistan, what is required is well-targeted policy measures tailored to the current state and the specific need of the country as well as consideration of administrative and institutional capacities. Any national development framework or related policy implementation is not viable until such factors are incorporated. Given this background, we set up an optimization problem to identify the optimal pathway for Pakistan to progress towards achieving the Goals and targets set by the 2030 Agenda for Sustainable Development. We first compare Pakistan’s level of attainment across indicators with that of its “peers,” which includes countries that have similar capacity levels to that of Pakistan as well as some countries that are classified within the upper middle-income group.7 Pakistan’s peers selected for this analysis include: Algeria, Bangladesh, Bhutan, Bolivia, Botswana, Cape Verde, Comoros, Equatorial Guinea, Gabon, Gambia, Ghana, Guatemala, Guyana, Honduras, India, Indonesia, Iraq, Lesotho, Mongolia, Namibia, Nicaragua, Paraguay, Sao Tome, Solomon Islands, Sri Lanka, Swaziland, Tajikistan, Turkmenistan, Vanuatu and Yemen. Our optimization problem uses the SDG implementation capacities estimated in the previous section as a planning tool to guide Pakistan on the prioritization and sequencing of the attainment of indicators over time. For that purpose, the value of the capacities measure could be calculated for a small increase in the value of a number of indicators, one at a time, selecting the indicator that yields the largest increase in SDG capacities. Iterating this calculation many times can produce an “optimal” pathway for progress towards the achievement of the SDGs. However, we assume that countries are limited in the indicators that they can improve upon. The set of indicators eligible for improvement is identified by the SDG system and the countries’ position within the SDG system, based on: (a) the degree of complexity of indicators; (b) current attainment level; and (c) synergies and trade-offs across indicators. The first assumption considers the case that not all indicators are equally easy or difficult to improve upon. Some may require substantial resources, and other can come at relatively low costs. In order to capture these different levels of difficulty, the set of indicators that countries can choose for improvement is restricted by levels of complexity for each indicator.8 The rationale behind this setting is that the likelihood of making progress in some areas should be discounted if the country has to face difficulties in improving on the corresponding indicators. The second assumption introduces diminishing returns into the system. Under this assumption, the areas for which Pakistan is lagging far behind its peers are the ones that are likely easier to improve upon, since they are the “low hanging fruits.” This may seem to be a strong assumption at a first glance because the low level of attainment could be attributed to some structural or idiosyncratic factors. One of the fundamental lessons of development theories and practices is that development outcomes can only be understood within their particular historical, political and institutional context. In order to address this concern, the probability for improvement is set to zero for indicators in which Pakistan is the worst performer among the peer group. We also 7 We include some of the upper middle-income countries given that Pakistan seeks to establish itself as an upper middle-income country through Vision 2025 (Pillar II, Goal 8). 8 The complexity levels for each indicator are a by-product in the calculation of the capacity levels for countries using the Method of Reflections.


24

argue that, since our capacity measure is designed to reveal the underlying potential that a country possesses in progressing towards the achievement of the SDGs, countries with similar levels of capacity have a potential to achieve similar levels of attainment across the indicators. The third point is critical in viewing the set of SDGs and their associated targets as interlinked. Because the SDGs – and thus the relevant indicators, are interlinked, it should be more efficient for a country to improve upon indicators that have synergies with each other, and more costly when there are trade-offs.9 In the first phase (2016-2020), the optimal pathway emphasises improvements in gender equality as the top priority area, with 16.4% of the improvements directed towards lowering the gender inequality index. This consistent with the fact that Bangladesh is lagging far behind in SDG 5 relative to its peers (see

9 See Cho, Isgut and Tateno (2016) for a more in-depth description of the analytical framework.


25

Figure 1). Additional priority areas in the first phase include three infrastructure indicators, representing a total of 31% of the improvement, and two indicators for food supply, accounting for 16.8% of the improvements. Again, these indicators are ones in which Pakistan is underperforming as shown in figure 1. The three infrastructure indicators that receive a priority are Internet users (16.4%), fixed-telephone users (12.6%) and air transportation (8%). In the second phase (2021-2025), the top priority indicator for Pakistan is the human inequality index (27.6%), closely followed by education, with 21.2% of the improvements for increasing the percentage of population with secondary education. The importance of education and inequality indicators suggest that Pakistan needs to improve upon overall levels of human capital in early stages of development. The indicators related to infrastructure (19.2%), food supply (9.8%) and gender equality (4.9%) that are highly prioritized in the first phase receive a lower, but still important, priority in the second phase, further highlighting the urgency for Pakistan to invest heavily in these areas early on. In the third phase (2026-2030), equality education becomes the top priority, with two indicators (years of schooling and the percentage of the population with secondary education) representing 48.8% of the improvements. This result is consistent with the low current attainment level of Pakistan on SDG 4 (see figure 1) as well as a particular emphasis given to this area by Vision 2025.


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Table 4 lays out the suggested priority areas for Pakistan based on the objective of maximizing SDG capacities. The results are aggregated into three five-year phases: 2016-2020, 2021-2025 and 2026-2030. The priority levels for each indicator are calculated as the percentage of steps in each phase for which the indicator is chosen as a priority, relative to the total number of steps in each phase. In the first phase (2016-2020), the optimal pathway emphasises improvements in gender equality as the top priority area, with 16.4% of the improvements directed towards lowering the gender inequality index. This consistent with the fact that Bangladesh is lagging far behind in SDG 5 relative to its peers (see


27

Figure 1). Additional priority areas in the first phase include three infrastructure indicators, representing a total of 31% of the improvement, and two indicators for food supply, accounting for 16.8% of the improvements. Again, these indicators are ones in which Pakistan is underperforming as shown in figure 1. The three infrastructure indicators that receive a priority are Internet users (16.4%), fixed-telephone users (12.6%) and air transportation (8%). In the second phase (2021-2025), the top priority indicator for Pakistan is the human inequality index (27.6%),10 closely followed by education, with 21.2% of the improvements for increasing the percentage of population with secondary education. The importance of education and inequality indicators suggest that Pakistan needs to improve upon overall levels of human capital in early stages of development. The indicators related to infrastructure (19.2%), food supply (9.8%) and gender equality (4.9%) that are highly prioritized in the first phase receive a lower, but still important, priority in the second phase, further highlighting the urgency for Pakistan to invest heavily in these areas early on. In the third phase (2026-2030), equality education becomes the top priority, with two indicators (years of schooling and the percentage of the population with secondary education) representing 48.8% of the improvements. This result is consistent with the low current attainment level of Pakistan on SDG 4 (see figure 1) as well as a particular emphasis given to this area by Vision 2025.

10 The human inequality index is a composite measure that reflects inequality in health, education and income.


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Table 4. Suggested areas of priority for Pakistan

First phase (2016-2020)

SDG Indicator Priority level

(%)

5. Gender equality Gender inequality (health, empowerment and labour)

16.4

9. Industry, innovation and infrastructure Internet users 16.4

9. Industry, innovation and infrastructure Fixed-telephone users 12.6

2. Zero hunger Food supply 11.3

8. Decent work and economic growth Commercial banking 11.3

9. Industry, innovation and infrastructure Air transportation 8.0

2. Zero hunger Agriculture value added 5.5

Other 18.4

Second phase (2021-2025)


(%)

10. Reduced inequalities Human inequality (health, education and income)

27.6

4. Quality education Secondary education 21.2




9. Industry, innovation and infrastructure Air transportation 4.9


4.9


9. Industry, innovation and infrastructure Trade and transport-related infrastructure

4.4

Other 12.3

Third phase (2026-2030)


(%) 4. Quality education Education index (years of schooling) 39.7

4. Quality education Secondary education 9.1


7.2



9. Industry, innovation and infrastructure Trade and transport-related infrastructure

4.8



8. Decent work and economic growth Ease of doing business index (regulations)

3.8

Other 16.8 Source: ESCAP. Note: Priority levels for the indicators are calculated as the percentage of steps in each phase for which the indicator is chosen as a priority relative to the total number of steps in each phase.


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Several characteristics can be drawn from these results regarding the optimal pathways for the implementation of the 2030 Agenda in Pakistan. The first characteristic is a large concentration in a relatively small number of indicators: education, gender and human equality, infrastructure and hunger. Although the top indicators and phases shown in the table represent only 13% of the total number of indicators in the data, this small number of indicators concentrates more than 80% of the steps taken by Pakistan in each phase. This suggests a strategic approach for the achievement of the Goals, with a heavy policy focus on selected areas of great importance to Pakistan. A second characteristic is that the results are dependent on Pakistan’s position in the SDG system, tending to emphasize “low hanging fruits” or indicators in which Pakistan underperforms compared with other countries with similar levels of SDG capacities. A third characteristic of the optimal pathways is sequencing, in the sense that the priorities vary from phase to phase. Error! Not a valid bookmark self-reference. illustrates the relative importance of each SDG during subsequent phases of development for Pakistan. SDG 9 (industry, innovation and infrastructure), and to less extent, SDG 2 (zero hunger) and SDG 5 (gender equality) are important early on, but its importance gradually diminishes in later phases, giving way to other SDGs such as SDG 4 (quality education) and SDG 10 (reduced inequalities). Figure 6. Priority SDGs for the implementation of the 2030 Agenda

Source: ESCAP. Arguably, the most striking result here is that the indicators of the top priority areas, namely,


30

education, gender equality, infrastructure – telecommunications in particular – and hunger, coincide with the core gatekeeper nodes of the preceding network analysis (Figure 2). This reconfirms the critical importance of these four factors for Pakistan in progressing towards the achievement of SDGs. These findings suggest that, given the current level of capacity, Pakistan has the potential to improve on various SDG indicators, even in areas where Pakistan has been lagging behind relative to its peers, such as hunger, education and gender. In addition, it would be most efficient and cost-effective if Pakistan places its priorities in these areas. With improved levels of attainment of the SDG indicators, the latent capacity level should also increase, which, in turn, further accelerates progress towards the achievement of the SDGs. VII. Identifying bottlenecks in developing SDG capacities The optimal pathway, illustrated in the previous section, is built so that they focus on improving the indicators in the most effective manner. This implies a preference for indicators in which the country is lagging behind compared with other countries with similar SDG capacities, for instance to take advantage of agglomeration economies, as well as for indicators that are relatively less complex and thus easier to make progress faster on them. This discussion in the previous section provided some examples of these choices. The present section complements the previous discussion by showing graphically the progress of Pakistan in the implementation of the 2030 Agenda in the indicators network shown in Figure 2. Error! Reference source not found. shows the projection of the model results onto the original indicators network. The red nodes represent indicators in which Pakistan is performing better than other countries with similar levels of SDG capacity at the present as already described in Figure 2. The green nodes represent indicators in which Pakistan is progressing from below the mean for the peer group at present to above the mean in 2030. These indicators are prioritized in the optimal pathway, indicating that improving their attainment is effective for Pakistan. Finally, the white nodes represent indicators that are still below the average for Pakistan’s peers by 2030. Figure 6 shows that Pakistan is slowly migrating from the nodes that are scattered at the lower portion of the network towards the upper portion of the system where indicators are densely connected. The optimal pathway projected on this figure includes improvements in indicators such as the gender inequality index, Internet usage and telecommunication that are previous considered as bottlenecks for Pakistan to move towards a different position within the system. This suggests that Pakistan may be able to overcome these bottlenecks if it follows the optimal pathway. However, the figure also illustrates that most of the progress by 2030 is expected to take place only in the lower portion of the network. The top portion of the network contains a cluster of indicators, represented by white nodes, in which Pakistan will not be able to outperform its peers even by the year 2030. These nodes are densely connected and related to gender, health, hunger and education, most of which are related to Pakistan’s areas of weakness as identified in sessions 3 and 4. Another concern is related to the child mortality rate, which appears to be a key bottleneck for Pakistan’s progress towards achieving the SDGs as it is represented by a large white node and located at the very centre of the system. At present, Pakistan has very low levels of attainment for indicators on SDG 3 (good health and well-being), which suggests that there are conditions specific to Pakistan that are impeding it from progressing in these areas. The


31

analysis implies that these impediments may take more time or require higher SDG capacity to improve upon. Addressing this important bottleneck will be necessary for Pakistan to make progress in the upper portion of the network. Figure 7. Pakistan’s optimal pathway for progress

Source: Authors’ calculation. Notes: (a) The size of nodes represent their importance as gatekeepers (i.e. how important they are as middle links for Pakistan to progress towards better attainment in other indicators); and (b) Indicators are coloured based on the level of attainment of Pakistan compared with lower middle-income countries. Red indicators are those in which Pakistan exhibits higher attainment levels compared with lower middle-income countries at the present, while green indicators are those in which Pakistan is predicted to exhibit higher attainment levels relative to lower middle-income countries in 2030 if it follows the optimal pathway.

Initial high attainment (2015)

Predicted high attainment (2030)

Low attainment (2015 – 2030)


32


33

VIII. Scenario analysis The optimization exercise described above is further complemented by comparative analysis of several alternative scenarios, the results of which were compared in terms of the predicted levels of the human development index against the model-proposed optimal pathway. In particular, we compare our optimal pathway with that given by Vision 2025 by constructing a scenario in which only the SDGs that are substantially covered by Vision 2025 are included for enhancement. In this scenario, we exclude some of the goals corresponding to gender (SDG 5)11, cities (SDG 11), sustainable consumption and production (SDG 12), climate change (SDG 13), oceans (SDG 14) and terrestrial ecosystems (SDG 15). Another scenario we consider is a randomized pathway in which progress is made in arbitrary order. This scenario is extreme and unrealistic, but it tries to mimic the situation where there is absolutely no focused area or policy coordination among various government institutions. Specifically, the following three scenarios are analyzed:

1. the model-proposed optimal pathway; 2. the optimal pathway for progress within the focus of Vision 2025; and 3. a randomized pathway for progress that does not give precedence to any indicator

over another. We forecast HDI under the different scenarios to compare Pakistan’s position in the future under different development strategies. This is done by first calculating the new capacity level for Pakistan under the different scenarios, and predicting HDI using this new capacity level. The results of the three scenarios are shown in Figure 8. For comparison purposes, the figure shows the historical trends in the human development index for Pakistan. Figure 8 shows that the optimal pathway results in the highest levels of HDI, while the pathway defined by Vision 2025 follow almost identical courses up to 2026, after which the optimal pathway results in a slightly faster growth in HDI. This suggests that Vision 2025 is a good match for the priorities of Pakistan with regard to implementing the 2030 Agenda. The predicted trajectories in HDI associated with both the optimal and the pathway defined by Vision 2025 exceed the historical trend of the HDI. The random pathway would be unable to guarantee that Pakistan could keep up with or exceed past trends in annual increases. Since HDI is a composite measure that takes into account many different socioeconomic aspects, it is understandable that the optimal and the pathway defined by Vision 2025 do better than the random pathway, for they include improvements in social areas such as education and health as well as improvements in the environment. Overall, our results suggest the following:

1. Planning and prioritization are essential for progress towards sustainable development since the expected outcome from randomized policies are strictly inferior, justifying the need for policy coordination across different state agencies as well as across different levels of governments.

11 While gender issues are present in Pillar 1, only 2 specific indicators (primary and secondary education parity and workforce participation) are included, and thus gender is considered not to be a core area of improvement.


34

2. Vision 2025 is expected to contribute significantly to the progress towards inclusive and sustainable development if the priorities are placed on hunger, education and infrastructure.

3. The lack of progress expected in addressing issues related to health requires careful consideration by national policymakers of Pakistan and development partners.

4. For the areas that are not extensively covered by Vision 2025, especially those related to gender equality and empowerment of women and girls, an appropriate framework as well as various support measures are crucial to ensure that no one will be left behind, which is the key theme of Vision 2025.

Figure 8. Comparison of scenarios

Source: Authors’ calculation. IX. Conclusions The Sustainable Development Goals included in the 2030 Agenda for Sustainable Development are a set of ambitious and holistic goals that encompass many different aspects of the economic, social and environmental development of countries. In this paper, we have attempted to provide a preliminary analysis of how the SDGs should be implemented, given the current situation of Pakistan. The main aim has been to provide a roadmap for which Pakistan may follow in order to meet the SDGs while fostering economic growth and social development. Using the available data, it can be seen that, in comparison with other lower middle-income countries, Pakistan is faring relatively well in areas related to energy and certain types of infrastructure, yet lagging far behind in social aspects, especially hunger, health, education and gender equity. Our analysis of the SDG system as a network of interconnected indicators suggests that social aspects, especially gender, and telecommunication infrastructure are acting as bottlenecks in Pakistan’s progress towards the broad attainment of all SDGs. Improvement in

2013 Lower middle-income average (0.591)


35

these indicators should take place, for the underlying capacities that these indicators encompass are seen to readily spillover onto other areas for which Pakistan is currently struggling with. The optimal pathway for progress given Pakistan’s current position also supports this observation. The optimal pathway suggests that areas related to hunger, education, gender equality, and specific infrastructure should be prioritized first in order for Pakistan to progress efficiently towards attainment of SDGs. The lack of progress expected in addressing issues related to health requires careful consideration by national policymakers of Pakistan and development partners. This optimal pathway is seen to allow Pakistan to progress further towards higher income levels and human development, even compared to the pathway suggested by Vision 2025. Vision 2025 and the set of SDGs are in many ways analogous, yet Vision 2025 leaves out certain areas that are covered in the SDGs, without which Pakistan’s progress is predicted to be sub-optimal. Overall, our results confirm that planning and prioritization are essential in implementing the SDGs as well as Vision 2025, and that state agencies as well as policymakers should coordinate their efforts to provide for a clearly defined pathway for progress. It is expected that if Pakistan follows the optimal pathway for progress given by our optimization exercise, it will be able to reach upper middle-income country status in the near future and have much higher levels of human development.


36

Reference

Brauer, Michael, and others (2015). Ambient air pollution exposure estimation for the global

burden of disease 2013. Environmental Science & Technology, 23 November.

Cho, Jaebeum, Alberto Isgut and Yusuke Tateno (2016). A analytical framework for identifying optimal pathways towards sustainable development. MPFD Working Papers, WP/16/03. Bangkok: ESCAP.

Hidalgo, César A., and Ricardo Hausmann (2009). The building blocks of economic complexity. Proceedings of the National Academy of Sciences, vol. 106, No. 26, pp. 10570-10575.

Hidalgo, César A., and others (2007). The product space conditions the development of nations. Science, vol. 317, No. 5837, pp. 482-487.

International Council for Science, and International Social Science Council (2015). Review of Targets for the Sustainable Development Goals: The Science Perspective. Paris: International Council for Science (ICSU).

Le Blanc, David (2015). Towards integration at last? The sustainable development goals as a network of targets. Working Paper, No. 141. New York: DESA.

United Nations (2015). Global Sustainable Development Report. Available from https://sustainable development.un.org/content/documents/1758GSDR%202015%20Advance%20Unedited%20Version.pdf

United Nations, Economic and Social Commission for Asia and the Pacific (2016). Asia-Pacific Countries with Special Needs Development Report 2016: Adapting the 2030 Agenda for Sustainable Development at the National Level. Sales No. E.16.II.F.11.

Pakistan, Ministry Planning, Development and Reform (2014). Pakistan Vision 2025. Available from http://www.pc.gov.pk/wp-content/uploads/2015/05/Pakistan-Vision-2025.pdf.

Rubin, Donald B. (2004). Multiple Imputation for Nonresponse in Surveys. Hoboken, New Jersey: John Wiley & Sons.


37

Annex

A. List of indicators used for analysis

Goal Indicator Source Notes

1

Population below $1.25 per day (purchasing power parity, percentage)

World BankHigh income countries with missing values are assumed to have a value of 0. Poverty gap ratio at $1.25 a

day (purchasing power parity, percentage)

World Bank

2

Population undernourished (percentage)

Food and Agricultural Organization

(FAO)

Percentage of the population whose food intake is insufficient to meet dietary energy requirements.

Arable land (hectares per person)

FAO

Includes land defined by the FAO as land under temporary crops, temporary meadows for mowing or for pasture, land under market or kitchen gardens, and land temporarily fallow.

Crop production index (2004-2006 = 100)

FAO Crop production index shows agricultural production for each year relative to the base period 2004-2006.

Food production index (2004-2006 = 100)

FAO Food production index covers food crops that are considered edible and that contain nutrients.

Livestock production index (2004-2006 = 100)

FAO

Livestock production index includes meat and milk from all sources, dairy products such as cheese, and eggs, honey, raw silk, wool, and hides and skins.

Food supply (kcal/capita/day)

FAO

Agriculture value added per worker (constant 2005 US$)

FAO/World Bank

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.

3

Health index

United Nations

Development Programme

(UNDP)

Life expectancy at birth expressed as an index using a minimum value of 35 years and a maximum value of 85 years

Tuberculosis detection rate under DOTS (percentage)

World Health

Organization (WHO)

Percentage of estimated new infectious tuberculosis cases detected under the internationally recommended tuberculosis control strategy directly observed treatment short course (DOTS).


38


Tuberculosis incidence rate WHO Estimated number of new tuberculosis cases arising in one year per 100,000.

Tuberculosis prevalence rate

WHO Estimated number of tuberculosis cases in a given point per 100,000.

Tuberculosis death rate WHO Estimated number of tuberculosis deaths per100,000.

Children immunized against measles (percentage)

United Nations

Children's Emergency

Fund (UNICEF)

Children refer to those that are 1 year old

Health expenditure, total (% of GDP)

WHO Recurrent and capital spending from government budgets, external borrowings and grants, and social health insurance funds.

Maternal mortality ratio UNICEF per 100,000 live births Children under five mortality rate

UNICEF per 1,000 live births

Infant mortality rate UNICEF per 1,000 live births

4

Education index UNDP

Calculated using Mean Years of Schooling and Expected Years of Schooling. For technical notes, see http://hdr.undp.org/sites/default/files/hdr14_technical_notes.pdf.

Government expenditure on education, total (% of GDP)

United Nations

Educational, Scientific

and Cultural Organization (UNESCO)

Population with at least some secondary education (percentage)

UNDP Percentage of the population ages 25 and older that reached at least a secondary level of education.

5

Seats held by women in national parliament (percentage)

Inter-Parliamentar

y Union

Inter-Parliamentary Union data used by the United National Statistics Department.

Gender Parity Index in primary level enrolment

United National Statistics

Department (UNSD)

Ratio of the number of female students enrolled at primary, secondary and tertiary levels of education to the number of male students in each level.

Labour force participation rate, female (percentage)

International Labour

Organization (ILO)

Modelled ILO estimate (% of female population ages 15-64).


39


Gender inequality index UNDP

A composite measure reflecting inequality in achievement between women and men in three dimensions: reproductive health, empowerment and the labour market.

Female to male ratio of Human Development Index

UNDP Ratio of female to male Human Development Index (HDI) value .

Account at a financial institution, female (percentage age 15+)

World BankDenotes the percentage of respondents who report having an account at a bank or another type of financial institution

6

Proportion of the population using improved drinking water sources

UNICEF/WHO

Percentage of the population who use any of the following types of water supply for drinking: piped water into dwelling, plot or yard; public tap/standpipe; borehole/tube well; protected dug well; protected spring; rainwater collection and bottled water.

Proportion of the population using improved sanitation facilities

UNICEF/WHO

Percentage of the population with access to facilities that hygienically separate human excreta from human contact.

Water productivity FAO/World

Bank Constant 2005 (United States dollar) GDP per cubic meter of total freshwater withdrawal

7

Renewable electricity output

International Energy Agency (IEA)

Renewable electricity is the share of electricity generated by renewable power plants in total electricity generated by all types of plants.

Renewable energy consumption

IEA percent of total final energy consumption

Energy intensity level of primary energy (MJ/$2011 purchasing power parity, GDP)

IEA

Ratio between energy supply and gross domestic product measured at purchasing power parity. Energy intensity is an indication of how much energy is used to produce one unit of economic output.

Access to electricity (percentage of population)

World Bank

8

Labour force participation rate

ILO Modelled ILO estimate (% of total population ages 15-64)

Unemployment rate ILO Modelled ILO estimate (% of total labour force)

Ease of doing business index

World Bank

Ease of doing business ranks economies with first place being the best. A high ranking (a low numerical rank) means that the regulatory environment is conducive to business operation.

GDP per capita, logarithm (current United States dollar)

World Bank

GDP per capita, PPP, World Bank


40


logarithm (constant 2011 international dollar) Number of commercial bank branches per 100,000 adults

International Monetary

Fund

Calculated as: (number of commercial banks + number of commercial bank branches) X 100,000 / adult population

GDP growth (annual %) World Bank Export diversification index

UNCTAD Higher values indicate lower diversification

9

Fixed-telephone subscriptions per 100 inhabitants

UNSD

Mobile-cellular subscriptions per 100 inhabitants

UNSD

Internet users per 100 inhabitants

UNSD

Air transport, registered carrier departures worldwide per capita

International Civil

Aviation Organization

(ICAO)

Civil Aviation Statistics of the World and ICAO staff estimates.

Air transport, passengers carried per capita

ICAO Civil Aviation Statistics of the World and ICAO staff estimates.

Logistics performance index: Quality of trade and transport-related infrastructure

World Bank

1=low to 5=high. Evaluates the quality of trade and transport related infrastructure (e.g. ports, railroads, roads, information technology).

Average area covered by a permanent post office (km²)

Universal Postal Union

High-technology exports (percentage of manufactured exports)

United Nations

Commodity Trade

Statistics (Comtrade) Database

High-technology exports are products with high research and development intensity, such as in aerospace, computers, pharmaceuticals, scientific instruments, and electrical machinery.

Scientific and technical journal articles

National Science

Foundation of the United

States

Number of scientific and engineering articles published in the following fields: physics, biology, chemistry, mathematics, clinical medicine, biomedical research, engineering and technology, and earth and space sciences.

Manufacturing, value added (percentage of GDP)

World Bank/ Organisation

for Economic

Co-operation

Manufacturing refers to industries belonging to International Standard Industrial Classification divisions 15-37. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs.


41


and Development

10

GINI index

World Bank/United Nations

University - World

Institute for Development Economics Research (UNU-

WIDER)

World Bank PovcalNet data is supplemented by UNU-WIDER and the Standardized World Income Inequality Database (SWIID). Documentation for UNU-WIDER data can be found at https://www.wider.unu.edu/project/wiid-world-income-inequality-database. Documentation for SWIID can be found at http://myweb.uiowa.edu/fsolt/swiid/swiid.html.

Coefficient of human inequality

UNDP Average inequality in three basic dimensions of human development. See Technical note 2 at http://hdr.undp.org/en.

Proportion of the population using improved drinking water sources, urban

WHO/UNICEF

11

Proportion of the population using improved sanitation facilities, urban

WHO/UNICEF

Access to electricity, urban (percentage of urban population)

World BankWorld Bank Sustainable Energy for All (SE4ALL) data

12

PM2.5 air pollution, mean annual exposure (micrograms per cubic meter)


Institute for Health Metrics and Evaluation, University of Washington, Seattle. Brauer and others, 2015. "Ambient Air Pollution Exposure Estimation for the Global Burden of Disease 2013." Data taken from World Bank database.

PM2.5 air pollution, population exposed to levels exceeding WHO guideline value (percentage)


Natural resource depletion UNDP Monetary expression of energy, mineral and forest depletion, expressed as a percentage of total gross national income (GNI).

13

Carbon dioxide (CO2) emissions (metric tons of CO2 per capita)

UNSD Uses Carbon Dioxide Information Analysis Center and United Nations Framework Convention on Climate Change data

Carbon dioxide (CO2) emissions (kg CO2 per $1 GDP, purchasing power perity)

UNSD

Population affected by natural disasters (per million)

Emergency Events

Database,

People requiring immediate assistance during a period of emergency as a result of a natural disaster, including displaced, evacuated,


42


Centre for Research on

the Epidemiolog

y of Disasters

homeless and injured people, expressed per million people.

Emissions of methane and nitrous oxide produced from agricultural activities

FAO

Contains all the emissions produced in the different agricultural emissions sub-domains, providing a picture of the contribution to the total amount of greenhouse gas emissions from agriculture.

14

Renewable internal freshwater resources per capita

FAO

Renewable internal freshwater resources flows refer to internal renewable resources (internal river flows and groundwater from rainfall) in the country.

Fertilizer consumption (kilograms per hectare of arable land)

FAO

Fertilizer consumption measures the quantity of plant nutrients used per unit of arable land. Fertilizer products cover nitrogenous, potash, and phosphate fertilizers.

Fish species, threatened Fish Base database

Fish species are based on Froese, R. and Pauly, D. (eds). 2008. Threatened species are the number of species classified by the IUCN as endangered, vulnerable, rare, indeterminate, out of danger, or insufficiently known.

15

Terrestrial and marine areas protected to total territorial area (percentage)

United Nations

Environment Programme

(UNEP)

Terrestrial protected areas are protected areas of at least 1,000 hectares designated by national authorities as scientific reserves with limited public access, national parks, natural monuments, nature reserves or wildlife sanctuaries, protected landscapes, and areas managed mainly for sustainable use. Marine protected areas are areas of intertidal or subtidal terrain that have been reserved by law or other effective means to protect part or all of the enclosed environment.

Mammal species, threatened

UNEP

Mammal species are mammals excluding whales and porpoises. 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.

Plant species (higher), threatened

UNEP

Higher plants are native vascular plant species. Threatened species are the number of species classified by the IUCN as endangered, vulnerable, rare, indeterminate, out of danger, or insufficiently known.


43


Percent change in forest area (1990-2011)

FAO

Percentage of the population living on severely or very severely degraded land.

FAO Land degradation estimates consider biomass, soil health, water quantity and biodiversity.

16

Refugee population by country or territory of origin per capita

UNHCR

Refugees are people who are recognized as refugees under the 1951 Convention Relating to the Status of Refugees. Country of origin generally refers to the nationality or country of citizenship of a claimant.

Homicide rate UNODC Number of unlawful deaths purposefully inflicted on a person by another person, expressed per 100,000 people.

Overall life satisfaction index

UNDP Average response to the Gallup World Poll question relating to the indicator.

Satisfaction with local labour market

UNDP

Average responses to related Gallup World Poll questions. These three indicators are averaged into a single indicator relating to communities and society.

Trust in other people

Satisfaction with community

Satisfaction with efforts to deal with the poor

UNDP

Average responses to related Gallup World Poll questions. These three indicators are averaged into a single indicator relating to government.

Satisfaction with actions to preserve the environment Trust in national government

17

Foreign direct investment, net inflows (Balance of payment, logarithm, current United States dollar)

IMF

Foreign direct investment refers to direct investment equity flows in the reporting economy. It is the sum of equity capital, reinvestment of earnings, and other capital.

Tax revenue (percentage of GDP)

IMF Tax revenue refers to compulsory transfers to the central government for public purposes.

Time to prepare and pay taxes (hours)

World Bank

Statistical capability ESCAP

calculations

The total number of indicators out of the 81 used for analysis that are available for each country.


44

B. The Method of Reflections

The Method of Reflections (Hidalgo and Hausmann, 2009) uses the information of the constructed network of 1) countries and 2) indicators to calculate measures of capacity and complexity. Taking countries as a starting point, each country’s attainment across all 82 indicators is summed up to produce a first order measure of a country’s capacity. The same can be done for the indicators, where the sum of the links for any indicator represents the overall attainment of the indicator given the set of countries. However, this in itself is not very enlightening in that the measure is the simple sum of attainment. The Method of Reflections allows for iteration by using the information collected at the first order measure to calculate a second order measure, and so forth until higher order measures are calculated. For example, the second order measure for countries would not simply sum the links, but would weight these links based on the values for the indicators calculated in the first order. Higher reflections for countries represent generalized measures of “unobserved capacities” in that the difficulty in achieving a certain level in a particular indicator is taken into consideration, rather than simply summing up the raw attainment levels. The same applies for indicators, where the higher order reflections generate generalized measures of “complexity” in that the unobserved capacities of countries are taken into consideration. For this analysis, we further disaggregate the 82 indicators into 100 different categories, each resulting in a total of 8,200 indicators. The indicators are disaggregated by dividing the attainment of countries in any indicator into 100 groups, and dichotomizing the attainment. For example, a country that is in the bottom 1% with regards to the poverty indicator will score a 1 in the first of the 100 poverty indicator categories, and a 0 for all subsequent categories. A country that is in the top 1% will record a score of 1 for all 100 of the poverty indicator categories. The reasoning behind this disaggregation is that for the bottom categories, many countries will have a score of 1, which will result in that bottom category having a low “complexity” score, while the top category will have very few countries having a score of 1, resulting in a high “complexity” score. Thus disaggregation allows for the differentiation of countries’ attainment into separate “complexity” categories for each indicator.

Recent MPDD Working Papers

WP/09/01 Towards a New Model of PPPs: Can Public Private Partnerships Deliver Basic Services to the Poor? by Miguel Pérez-Ludeña

WP/09/02 Filling Gaps in Human Development Index: Findings for Asia and the Pacific by David A. Hastings

WP/09/03 From Human Development to Human Security: A Prototype Human Security Index by David A. Hastings

WP/09/04 Cross-Border Investment and the Global Financial Crisis in the Asia-Pacific Region by Sayuri Shirai

WP/09/05 South-South and Triangular Cooperation in Asia-Pacific: Towards a New Paradigm in Development Cooperation by Nagesh Kumar

WP/09/06 Crises, Private Capital Flows and Financial Instability in Emerging Asia by Ramkishen S. Rajan

WP/10/07 Towards Inclusive Financial Development for Achieving the MDGs in Asia and the Pacific by Kunal Sen

WP/10/08 G-20 Agenda and Reform of the International Financial Architecture: an Asia-Pacific Perspective by Y. Venugopal Reddy

WP/10/09

The Real Exchange Rate, Sectoral Allocation and Development in China and East Asia: A Simple Exposition by Ramkishen S. Rajan and Javier Beverinotti

WP/10/10 Approaches to Combat Hunger in Asia and the Pacific by Shiladitya Chatterjee, Amitava Mukherjee, and Raghbendra Jha

WP/10/11 Capital Flows and Development: Lessons from South Asian Experiences by Nagesh Kumar

WP/10/12 Global Partnership for Strong, Sustainable and Balanced Growth: An Agenda for the G20 Summit by Nagesh Kumar, Shuvojit Banerjee, Alberto Isgut and Daniel Lee

WP/10/13 Economic Cooperation and Connectivity in the Asia-Pacific Region by Haruhiko Kuroda

WP/11/14 Inflationary pressures in South Asia by Ashima Goyal

WP/11/15 Social and Economic Impact of Disasters: Estimating the Threshold between Low and High Levels of Risk by Clovis Freire

WP/11/16 Financial Crisis and Regional Economic Cooperation in Asia-Pacific: Relevance, Trends and Potential by Nagesh Kumar

WP/11/17 Productive Capacities in Asia and the Pacific by Clovis Freire

WP/11/18 High Food and Oil Prices and Their Impact on the Achievement of MDG 1 in Asia and the Pacific by Clovis Freire and Alberto Isgut

WP/11/19 Impact of Health Expenditure on Achieving the Health-related MDGs by Clovis Freire and Nobuko Kajiura

WP/12/01 High Food Prices in Asia-Pacific: Policy Initiatives in view of Supply Uncertainty and Price Volatility by Clovis Freire, Aynul Hasan and M. Hussain Malik

WP/12/02 Building Productive Capacities: Challenges and Opportunities for Least Developed Countries by Clovis Freire

WP/12/03 Euro zone debt crisis: Scenario analysis and implications for developing Asia-Pacific by Sudip Ranjan Basu, Clovis Freire, Pisit Puapan, Vatcharin Sirimaneetham, and Yusuke Tateno

WP/12/04 Strategies for Structural Transformation in Countries in South and South-West Asia by Clovis Freire

WP/12/05 In which industries to invest? Aligning market and development incentives in Myanmar by Steven Ayres and Clovis Freire

WP/13/01 Policies for structural transformation: An analysis of the Asia-Pacific experience by C.P. Chandrasekhar and Jayati Ghosh

WP/14/01 G20 agenda for the World Economy: Asia-Pacific perspectives By Sudip Ranjan Basu, Alberto Isgut and Daniel Jeongdae Lee

WP/15/01 Infrastructure Financing, Public-Private Partnerships, and Development in the Asia-Pacific Region by Gilberto Llanto, Adoracion Navarro, Ma. Kristina Ortiz

WP/15/02 Financing for Development: Infrastructure Development in the Pacific Islands by ESCAP Pacific Office

WP15/03 Capital Market Development and Emergence of Institutional Investors in the Asia-Pacific Region by Hans Genberg

WP/15/04 Trade Finance for Sustainable Development in Asia and the Pacific by Sailendra Narain

WP/15/05 Financing Small and Medium Sized Enterprises for Sustainable Development: A View from the Asia-Pacific Region by Nick Freeman

WP/15/06 Financing the Social Sector: Regional Challenges and Opportunities by Social Development Division, ESCAP

WP/15/07 Inclusive Finance in the Asia�Pacific Region: Trends and Approaches by Md. Ezazul Islam

WP/15/08 Climate finance in the Asia-Pacific: Trends and Innovative Approaches by Ilaria Carrozza

WP/15/09

Financing Disaster Risk Reduction for sustainable development in Asia and the Pacific by Disaster Risk Reduction Section, ICT and Disaster Risk Reduction Division, ESCAP

WP/15/10 Financing Statistics Development in Asia and the Pacific by Statistics Division, ESCAP

WP/15/11 Financing Sustainable Development – What Can We Learn from The Australian Experience of Reform? by Wayne Swan

WP/15/12 Financing Development Gaps in the Countries with Special Needs in the Asia-Pacific Region by Mustafa K. Mujeri

WP/15/13 Polarizing world: GDP, development and beyond by Michael Shashoua and Sudip Ranjan Basu

WP/16/01

Strengthening the capacities of Asia and the Pacific to protect workers against unemployment By John Carter

WP/16/02 Asia-Pacific’s experience with national systems of TVET By Jenny Grainger, Liz Bowen-Clewley and Sarah Maclean

WP/16/03 An analytical framework for identifying optimal pathways towards sustainable development By Jaebeum Cho, Alberto Isgut, and Yusuke Tateno

WP/16/04 Pathways for adapting the Sustainable Development Goals to the national context: the case of Pakistan By Jaebeum Cho, Alberto Isgut, and Yusuke Tateno

MPDD Working Papers WP/16/04

47

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About Economic and Social Commission for Asia and the Pacific (ESCAP) ESCAP is the regional development arm of the United Nations and serves as the main economic and social development centre for the United Nations in Asia and the Pacific. Its mandate is to foster cooperation between its 53 members and 9 associate members. ESCAP provides the strategic link between global and country-level programmes and issues. It supports Government of countries in the region in consolidation regional positions and advocates regional approaches to meeting the region’s unique socio-economic challenges in a globalizing world. The ESCAP office is located in Bangkok, Thailand. WWW.UNESCAP.ORG TWITTER.COM/UNESCAP FACEBOOK.COM/UNESCAP YOUTUBE.COM/UNESCAP

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