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Institutions, economic freedom and structural transformation in 11 sub-Saharan African countries

FAO AGRICULTURAL DEVELOPMENT ECONOMICS WORKING PAPER 18-01

January 2018

Institutions, economic freedom and structural

transformation in 11 sub-Saharan African

countries

Alessandro Carraro and Panagiotis Karfakis

Food and Agriculture Organization of the United Nations

Rome, 2018

Recommended citation

Carraro, A. & Karfakis, P. 2018. Institutions, economic freedom and structural transformation in 11 sub-Saharan African countries. FAO Agricultural Development Economics Working Paper 18-01. Rome, FAO.

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iii

Contents

Abstract ................................................................................................................................ v

Acknowledgements ............................................................................................................. vi

1 Introduction................................................................................................................... 1

2 Institutions, economic freedom and role in inter-sectoral labour reallocation ................ 3

3 Data ............................................................................................................................. 5

4 Measurement, trends and contribution of structural change in SSA .............................. 7

5 Empirical framework ................................................................................................... 12

6 Empirical findings ....................................................................................................... 14

6.1 Institutional framework and structural change ...................................................... 14

6.2 SYS-GMM estimates ........................................................................................... 17

6.3 Economic freedom effects on structural change .................................................. 18

7 Sensitivity checks ....................................................................................................... 22

7.1 Alternative measures of political and economic institutions quality ....................... 22

7.2 Robustness to additional explanatory variables ................................................... 23

7.3 Allowing for external instruments ......................................................................... 24

7.4 Impact on employment levels in each sector ........................................................ 24

8 Conclusions ................................................................................................................ 27

References ........................................................................................................................ 28

Appendix A ........................................................................................................................ 32

Appendix B ........................................................................................................................ 33

iv

Tables

Table 1 Structural transformation effects, signs and interpretation ............................. 8 Table 2 Descriptive statistics .................................................................................... 13 Table 3 FE Regression of the political institution index on within component ........... 15 Table 4 FE Regression of the political institution index on between static structural

transformation component ........................................................................... 16 Table 5 FE Regression of the political institution index on Joint structural

transformation component ........................................................................... 17 Table 6 SYS-GMM Regression of the political institution index on the three labour

productivity growth components .................................................................. 18 Table 7 Correlation table for economic freedom dimensions .................................... 20 Table 8 SYS-GMM Regression of the economic freedom dimensions on the three

labour productivity growth components ....................................................... 20 Table 9 SYS-GMM Regression of democratic accountability and economic

institutional quality on the three labour productivity growth components ...... 22 Table 10 SYS-GMM Regression of on the three labour productivity growth

components ................................................................................................. 23 Table 11 SYS-GMM Regression of economic freedom on the three labour productivity

growth components, external instruments ................................................... 24 Table 12 SYS-GMM Regression of economic freedom and political institutions

employment levels for each sector .............................................................. 26

Figures

Figure 1 Sample coverage .......................................................................................... 5 Figure 2 Structural change patterns in SSA countries (1990–2011),

within effects ............................................................................................... 10 Figure 3 Structural change patterns in SSA countries (1990–2011),

between static effects .................................................................................. 10 Figure 4 Structural change patterns in SSA countries (1990–2011), joint effects ....... 11 Figure 5 Employment vs Labour productivity, by country and economic activity

(1990–2010) ................................................................................................ 11 Figure 6 Economic Freedom Index and its five dimensions, country average

(1990–2011) ................................................................................................ 21

v

Institutions, economic freedom and structural transformation in 11 sub-Saharan African countries

Alessandro Carraro and Panagiotis Karfakis1

1 Food and Agriculture Organization of the United Nations, Agricultural Development Economics Division (ESA), viale delle Terme di Caracalla, 00153 Rome, Italy

Abstract

Good institutions are a fundamental pre-requisite to successfully achieve structural transformation in growing developing countries (UNECA, 2016). Sub-Saharan Africa is experiencing a rapid growth but a weak and slow structural transformation process, which is mainly characterized by the reallocation of labour from agriculture to low skilled services. The focus of this paper is to explore how political and economic institutions affect structural transformation in a panel of 11 sub-Saharan African countries. Our empirical analysis reveals a positive and statistically significant effect of quality of institutions and economic freedom measures on structural transformation between sectors, which translates into movement out of agriculture. Better institutions appear to not improve productivity within sectors, however results highlight the important role played by institutions in facilitating reallocation or resources across sectors. Our findings suggest that improving the legal system, providing a stable macroeconomic environment, and improving freedom to exchange across borders will facilitate structural transformation processes in sub-Saharan African countries. We finally recommend that measures undertaken by governments should be included in a set of targeted policies designed according to countries’ characteristics.

Keywords: economic freedom, political institutions, structural transformation, sub-Saharan Africa, GMM.

JEL codes: O14, O43, O55.

vi

Acknowledgements

Research for this paper was partially funded by the “Global and Cross country studies” project funded by MAFAP program. We gratefully acknowledge Prof. Marco Bellandi and Erica Santini for useful comments on earlier drafts. This paper reflects the opinions of the authors and not the institution which they represent or with which they are affiliated. We are solely responsible for any errors. Corresponding author: alessandro.carraro@fao.org

1

1 Introduction

The movements of sectorial output, productivity and labour out of agriculture in manufacturing and services sectors are among the key features of countries’ economic development. This process - called interchangeably either structural transformation or structural change - also signified variations in production and trade that brought about a range of social changes and improvements in welfare aspects and changes in patterns of consumption.

Structural transformation has experienced a resurgence of interest in the last decades. Since Fisher (1939) and Kuznets (1966), who included structural transformation as one of the six most relevant stylized facts of development, a vast macroeconomic literature stressed this topic in several ways. A useful review of this literature is provided by Herrendorf et al. (2013) who highlighted the importance of multi-sector models to control for the complexities and the two-way causality relationship between economic growth and structural change. Recent literature examined the relationship between structural transformation and several other issues such as productivity gaps (Caselli, 2005; Duarte and Restuccia, 2010), urbanization (Michaels, 2012; Gollin et al. 2013, Christiansen et al., 2013), demographic transitions (Beegle et al., 2011; de Brauw et al., 2014), land institutions (Deininger et al., 2014), farming systems (Jayne et al., 2014) and environmental externalities (Antoci et al. 2009, 2012).

In sub-Saharan African (SSA) countries around 80 percent of the workforce is allocated in the agricultural sector, which accounts for a large part of the GDP. Some studies show that African countries are going through a path of premature “de-industrialization”, with notably high shares of employment in the services sector and informal activities but without the creation of a major industry or manufacturing sector (Rodrik, 2016). Despite this huge potential for structural change, literature on structural transformation in sub-Saharan Africa is still scant (McMillan and Headey, 2014). Recent contributions on this topic are mostly limited to a rich description of its patterns. Badiane et al (2012) argued that structural change in developing economies was generally productivity reducing, with labour moving out of an “outperforming agricultural sector to an oversized low-productive services sector”. Later, McMillan et al. (2014) and de Vries et al. (2015) confirmed these findings by comparing the extents of changes in the composition of output and variation in the nature and location of employment in Asia, Africa and Latin America. By using Fabricant’s (1942) productivity growth decomposition’s method on different time spans, they found that in recent decades, structural change had a negative effect on aggregate productivity growth, with labour crowding into petty services instead of manufacturing. These analyses, which primarily focus on sub-Saharan African countries’ development paths, tend to underestimate the causal relationships between structural change and a bulk of social, economic and political determinants. Examining the role of governance in the SSA is fundamental since a sustainable and successful structural transformation process aiming at promoting economic growth (in the model of Asian countries) requires their robust and efficient presence. A recent report of UNECA (2016) that focuses on the importance of implementing and putting in practice the principles of good governance, highlight how “effective economic governance institutions are essential for structural transformation and inclusive development in Africa”. Tenets of the conventional wisdom on good institutions imply market-oriented reforms, effective checks and balances, adequate regulatory/legal frameworks and strong enforcement mechanisms, all factors that according to the mainstream policy thinking are critical to ensure efficient resources allocation and economic growth. However, in the last two decades these assumptions have been largely reconsidered, with authors such as Stiglitz (2002, 2008, 2014), arguing that for economic liberalization to succeed, it is essential that reforms should be implemented at the right speed and in the right sequence, or Rodrik (2007),

2

who called for the need to recognise the weak nature of the link between market liberalisation, economic growth and poverty alleviation in Africa (Rodrik 2007).

Without claiming to be exhaustive, we investigate how quality of political institutions and different dimensions of economic freedom enter within the structural transformation process and whether they help foster productivity growth and labour reshuffling within and across sectors in sub-Saharan Africa. To do this, we employ two different indicators for the political and economic institutions’ quality. One that is informative on the concomitant qualities of democratic and autocratic authorities in governing institutions (i.e. a polity scale, Marshall, 2009), and another that captures the degree to which policies are supportive of the personal choice to enter markets and engage in voluntary transactions (i.e. a degree of economic freedom by the Fraser Institute). We employ the system GMM methodology, which allows to deal with endogeneity bias while estimating our empirical models on a panel of 11 sub-Saharan African countries from the Africa Sector Database developed by de Vries et al. (2015). Before proceeding with the econometric analysis, we disentangle the productivity growth in three components applying the decomposition method along the same lines as de Vries et al. (2015) and we provide some preliminary evidence on structural transformation patterns.

The remainder of the paper is organized as follows. In Section 2, we shortly explain the concept and importance of political institutions and economic freedom for structural transformation. Section 3 introduces the data set. Section 4 deals with measurement, trends and contribution of structural change in SSA. Section 5 explains the empirical methodology while section 6 reports the main findings. In section 7 we run sensitivity checks. Section 8 draws final remarks from our results and reviews their implications.

3

2 Institutions, economic freedom and role in inter-sectoral labour reallocation

This section briefly outlines the background and motivation of this research. It discusses the role of institutions and economic freedom in labour reallocation and the possible links between the democratic processes, economic freedom and structural transformation in sub-Saharan Africa.

The concept of institutions is discussed in different fields. The most common and widely used definition can be attributed to the pioneering work of Douglas North (1990) who defines institutions as “the rules of the game in a society, the humanly devised constraints that shape human interaction”. They regulate contracts implementation, procedures to resolve disputes, government bureaucracies, property rights and rules of law and may have heterogeneous impacts on labour reallocation in each sector of the economy. In this sense, the role of political regimes, either democracy or authoritarianism, has been for a long time studied in the growth and development literature (Acemoglu et al. 2001) without converging to a common view and leaving space for controversial and ambiguous conclusions. Some authors identified democracy either as an instrument that fosters economic growth, or as a process that embeds fundamental civil liberties, reduces corruption, social conflicts and protects property rights (Persson, 2005, Acemoglu et al. 2014). On the other side, authors like Blanchard and Shleifer (2000) found that premature democracy would result in economic disorder and political instability, with interest group politics bringing stagnation (Olson, 1982).

While there is a broad agreement among development economists about the key role played by structural transformation for sustained economic development, there is still little consensus on which role political and economic institutions should have to facilitate stable structural transformation paths in SSA. One of the long-standing questions is on whether governments should intervene in markets to facilitate structural transformation. This debate was fuelled in particular after the failure of the ‘Washington Consensus’ policies and the success of the East Asian experiences. The neoclassical school asserts that the best solution should be to avoid government intervention and to allow markets allocating productive resources independently. This view bases its assumptions on markets efficiency and effectiveness and that markets can provide better solutions than governments (Aryeteey and Moyo, 2012). A critic to this position (Stiglitz, 1988, 2008) argues that governments should rather be the ones responsible for the correction of market failures through economic measures and locally dimensioned industrial policies, with interventions that would ensure, among others, the efficient allocation of labour force across sectors. This position is rooted on the idea that markets in developing countries are neither efficient nor perfect and may not lead by themselves to an appropriate allocation of labour resources.

A good institution encourages physical and human capital accumulation through technology improvement, investments and lowering transaction costs, while a weak institution discourages local/foreign research and development implementation, contributes to lock resources in low productivity sectors and struggles to manage FDIs (Aron, 2000). In this sense, economic freedom can be intended as a measure of the extent to which governments intervene to protect the freedom of individuals in making their own economic decisions, by promoting their attitude and values. This is the position we are testing in this paper. Measures such as the protection of property rights, labour/credit/business market regulations, freedom of exchange across borders and other economic measures may lift countries to move resources in sectors where they have a comparative advantage and positively influence sector productivity by favouring exports or enhancing import competition. In this way, sector’s average productivity increases

4

as a result of a decline in the survival rate of less productive firms (see Geroski, 1995). This type of government interventions can push countries to concentrate less on one sector and to release factors of production in a second one, triggering this way the process of structural transformation. In particular, for countries experiencing large productivity gaps, labour reallocation becomes critical for the overall economic productivity. This is the case of SSA countries, where employment expansion largely occurs in informal sectors (e.g. street vendors, hawkers, petty traders and other activities with little skills and few entry barriers), which are often characterized by lower productivity levels.

Our paper empirically explores the links between political institutions, economic freedom and structural transformation, analyzing the extent to which the various areas of economic freedom provided by the Fraser Institute are related to the different components of productivity growth. We do not seek to provide a general recipe for SSA countries, but we believe that market forces require a strong, selective and proactive role of the government so that institutional quality, alongside with targeted economic and industrial policies may generate an environment conducive to prosperity. To the best of our knowledge, this is the first contribution in the literature studying these relationships in SSA by using a dynamic panel data analysis.

5

3 Data

We now turn to a description of the datasets used in this paper and trace briefly the patterns of structural transformation in sub-Saharan African countries. Our empirical approach draws on an annual panel covering the period between 1990 and 2010, consisting of 11 sub-Saharan African selected countries, namely Botswana, Ethiopia, Ghana, Kenya, Malawi, Mauritius, Nigeria, Senegal, South Africa, Tanzania and Zambia (see Figure 1), and covering 47 percent of sub-Saharan population and 63 percent of total sub-Saharan GDP. The core of the dataset is based on observations on employment, value added, and labour productivity (both expressed in 2000 PPP US dollars). Our main source of data is the new Africa Sector Database (de Vries et al., 2015) which is an extension of the Groningen Growth and Development Center (GGDC) 10-Sector productivity database originally developed by Timmer and de Vries (2007, 2009). de Vries et al. complement this dataset using data coming from several population censuses as well as labour and household surveys published by NBS and central banks.1

The institutional quality indicator is gathered from the PolityIV database (Marshall et al., 2009) and it is represented by the Polity indicator “polity2”.2 It is a proxy of the degree of democracy and it is constructed using factors such as the freedom of suffrage, operational constraints and balances on executives, respect for political rights and civil liberties. The polity2 is also called the combined polity score, and it is calculated as democracy score minus autocracy score. Moving to measures of economic freedom, we use changes in the economic freedom index as calculated by the Fraser Institute following the recommendation of recent studies (e.g., Belke et al., 2005; Heckelman and Knack, 2008) and because is “preferable on methodological grounds and it is more transparent to the reader” (Cummings, 2000, p.63). The data is available in five-year intervals over the period 1970–2000 and yearly over the period 2000-2014.3 It provides five comprehensive areas of institutions and policies: Size of Government (GOV), Legal Structure and Security of Property Rights (LSPR), Access to Sound Money (SM), Freedom to Trade (FTI), and Regulation (REG). Each index varies within an interval between 0 and 10. The summary index (EFSI) is constructed by averaging the five area ratings.

1 The covered sectors include Agriculture, Hunting, Forestry and Fishing; Mining, and Quarrying; Manufacturing; Public Utilities (Electricity, Gas, and Water); Construction; Wholesale and Retail Trade, Hotels, and Restaurants; Transport, Storage, and Communications; Finance, Insurance, Real Estate, and Business Services; Community, Social, Personal, and Government Services. 2 The polity2 score is computed by subtracting the AUTOC score from the DEMOC score and the resulting unified polity scale ranges from +10 (strongly democratic) to -10 (strongly autocratic). DEMOC: Democracy index, 0 = least democratic, 10 = most democratic. AUTOC: Autocracy index, 0 = least autocratic, 10 = most autocratic. 3 We use a linear imputation for the data between 1990–2000.

Figure 1 Sample coverage

Source: Author’s elaboration.

NIG GHA

SEN

MUS

ZAF

BWA

ZMB

KEN

TZA

MWI

ETH

6

Data for per capita gross domestic product, population, domestic credit to private sector by bank (as percent of GDP), trade openness and percentage of rural population over the total, are taken from the World Bank’s World Development Indicators (2016). Information on real effective exchange rate is taken from Darvas (2012).4 The result is a balanced panel consisting in 241 observations.

4 We complement the dataset with observation from Ghana taken from WDI and rebased to 2007. The missing years are imputed as the average values of the year before and after.

7

Within-effect Between-static effect

Joint (between dynamic) effect

4 Measurement, trends and contribution of structural change in SSA

Structural transformation entails a rising share of manufacture and services in employment and output and a fall in the agricultural sector. In order to analyse the role of trade liberalisation and political authorities on labour reallocation across the different sectors of the economy we rely on the decomposition (or shift-share) method originally developed by Fabricant (1942).5 Although often criticized in the past (Verdoorn, 1949) some variants of this conventional shift-share analysis have been recently used by many researchers to measure the contribution of labour shifts to aggregate productivity growth in SSA, Latin America and Asian countries (van Ark and Timmer 2003; Timmer and de Vries, 2008; McMillan and Rodrik, 2014; de Vries et al. 2012, 2015; McCaig et al. 2015). In the shift-share analysis, aggregate productivity growth is decomposed into effects that capture the productivity growth within sector (due to capital accumulation or technological progress) and between sectors (due to labour shift across sectors with different productivity and productivity growth levels). In what follows we briefly reproduce the model.

Let’s consider a multi-sector model composed by 𝑛𝑛-sectors,6 two periods 𝑡𝑡 − 1 and 𝑡𝑡, within the same country. Let 𝑌𝑌𝑡𝑡 be the value added in sector 𝑖𝑖 at time 𝑡𝑡 and 𝐸𝐸 the employment in sector 𝑖𝑖 at time 𝑡𝑡. The aggregate labour productivity level at time 𝑡𝑡 for the whole economy (𝐿𝐿𝑃𝑃𝑡𝑡) can be written as the sum of each sector’s labour productivity (𝑙𝑙𝑝𝑝𝑖𝑖𝑡𝑡) weighted by the share (𝛿𝛿) of sector 𝑖𝑖 employment in total employment at time 𝑡𝑡:

𝐿𝐿𝑃𝑃𝑡𝑡 =𝑌𝑌𝑡𝑡𝐸𝐸𝑡𝑡

=∑ 𝑌𝑌𝑡𝑡𝑛𝑛𝑖𝑖=1𝐸𝐸𝑡𝑡

= �𝑌𝑌𝑖𝑖,𝑡𝑡𝐸𝐸𝑖𝑖,𝑡𝑡

𝐸𝐸𝑖𝑖,𝑡𝑡𝐸𝐸𝑡𝑡𝑖𝑖=𝑛𝑛

= �𝑙𝑙𝑝𝑝𝑖𝑖,𝑡𝑡𝛿𝛿𝑖𝑖,𝑡𝑡𝑖𝑖=𝑛𝑛

(1)

The aggregate productivity growth for the whole economy between 𝑡𝑡 and 𝑡𝑡 − 1 can be written in first-differences in the following reduced form:

𝛥𝛥𝐿𝐿𝑃𝑃𝑡𝑡 = �𝛥𝛥𝑙𝑙𝑝𝑝𝑖𝑖,𝑡𝑡𝛿𝛿𝑖𝑖,𝑡𝑡−1

𝑛𝑛

𝑖𝑖=1

+�𝛥𝛥𝛿𝛿𝑖𝑖,𝑡𝑡𝑙𝑙𝑝𝑝𝑖𝑖,𝑡𝑡−1

𝑛𝑛

𝑖𝑖=1

+ �𝛥𝛥𝑙𝑙𝑝𝑝𝑖𝑖,𝑡𝑡𝛥𝛥𝛿𝛿𝑖𝑖,𝑡𝑡

𝑛𝑛

𝑖𝑖=1

(2)

Similarly to de Vries et al. (2015) we decompose the change in aggregate productivity level into three terms to take into account both differences in labour productivity and in marginal labour productivity of additional workers. The first component represents the within (W) sector productivity change between 𝑡𝑡 and 𝑡𝑡 − 1; the second term (between static effect, BS) captures the effects of changes in the sectoral labour reallocation (whether workers move to sector with above average productivity) and finally the dynamic reallocation or joint effect (J) embodies the marginal changes of sector employment with respect to productivity levels (see Table 1 for

5 Examples of Fabricant’s decomposition method’s application include the contributions of e.g. Chenery et al. (1986), Fagerberg (1999), Broadberry and Crafts (2003), Field (2006). 6 We are aware that in SSA countries structural transformation is often translated into large and sometimes growing unemployment / underemployment. However, the lack of data and the nature of the model itself prevented us to explore the patterns of unemployment / underemployment, which we leave for further research.

8

a summary). Both BS and J represent two “structural change” dimensions. The three terms are reported as the sum of all the sectors of the economy whilst Table 1 summarizes each term’s signs interpretation. Differently from de Vries et al. (2012, 2015) and McMillan and Rodrik (2011) we will obtain a yearly estimate of each of three indicators by rolling overtime our estimates of W, BS and J. Moreover, using first-differences instead of levels permits to both mitigate the distortions associated with the original construction of the dataset and to clear out the trend component.

Table 1 Structural transformation effects, signs and interpretation

Positive Negative W 𝑙𝑙𝑝𝑝 growth 𝑙𝑙𝑝𝑝 decline BS Labour shift from 𝑙𝑙𝑝𝑝𝑡𝑡𝑙𝑙𝑙𝑙𝑙𝑙 to 𝑙𝑙𝑝𝑝𝑡𝑡

ℎ𝑖𝑖𝑖𝑖ℎ sector Labour shift from 𝑙𝑙𝑝𝑝𝑡𝑡ℎ𝑖𝑖𝑖𝑖ℎ to 𝑙𝑙𝑝𝑝𝑡𝑡𝑙𝑙𝑙𝑙𝑙𝑙 sector

JOINT(BDE) Labour shift from 𝛥𝛥𝑙𝑙𝑝𝑝𝑡𝑡𝑙𝑙𝑙𝑙𝑙𝑙 to 𝛥𝛥𝑙𝑙𝑝𝑝𝑡𝑡

ℎ𝑖𝑖𝑖𝑖ℎsector Labour shift from 𝛥𝛥𝑙𝑙𝑝𝑝𝑡𝑡

ℎ𝑖𝑖𝑖𝑖ℎ to 𝛥𝛥𝑙𝑙𝑝𝑝𝑡𝑡𝑙𝑙𝑙𝑙𝑙𝑙sector

We briefly analyse the trends resulting from each of the three measures using the last two decades. Similarly to McMillan et al (2014) and de Vries et al (2015) we do not account for the unemployment levels. Differently from them we try to give a description of the structural transformation evolution overtime rather than providing a difference from begin-to-end periods. Dynamics are shown in Figure 2 to Figure 4. Figure 2 depicts the evolution of the within sector productivity over time, while Figure 3 and Figure 4 present the changes of between static and dynamic components. First of all, we notice a strong heterogeneity across countries.

The within component (Figure 2) is almost always positive, in particular for Ghana, Mauritius, South Africa and Zambia where, on average, overall labour productivity grows overtime. In Botswana, Ethiopia, Tanzania, Malawi and Senegal the labour productivity within sector exhibits a larger variability, alternating periods of growth to periods of decline. A clearer trend is evident in Kenya and Nigeria, where positive figures follow negative ones for the first, and vice versa for the latter.

The between static component (Figure 3), which accounts for the reallocation of labourers from low to high productivity sectors, has the advantage of embedding the gains from technological improvement and increased competition. Bearing in mind that the time span considered is represented by the last two decades, we find that structural change is playing an important role in labour reallocation: workers mostly move from low to high productivity sectors. This is what de Vries et al. (2015) call “static gains”. However, the story changes overtime and not all countries experience the same process. During the early 90s in 4 out of 11 countries (Zambia, Nigeria, Tanzania, Ethiopia) workers’ relocation went from high to low productive sectors, with the BS component having a negative incidence on the overall productivity growth. The evolution of labour movements is clearly country-specific, with large differences across countries in terms of both variance and trend. Overall, the BS components of South Africa, Mauritius and Botswana, which represent the three countries with the highest GDP per capita (constant 2005 USD) in our sample, are changing overtime with a downward sloping trend. Kenya and Senegal behave similarly: the contribution of structural change remains positive overtime but is gradually converging to zero. The remaining countries exhibit a positive trend,

9

evidencing that more recently Africa is starting to benefit from the economic reforms and quality of governance and that relocation of labour in productive sectors is increasing (Figure 5).7

Differently, the dynamic reallocation effect (Figure 4) captures the difference in employment change and labour productivity growth across sectors. For instance, the absorption of labourers in services pulls down their relative sector productivity, while the release from agriculture sector pushes it up. In other words, as the denominator of the ratio “value added/employment” increases, the value added per worker of the sector declines. This is to say that the difference in labour productivity is not only due to the value added (which embeds the quality and typology of jobs), but also to labour shifts. The sign of joint effect component can thus be either negative or positive. It is negative (positive) if the summation of all the sectors’ joint terms is negative (positive). The term is (i) negative either when productivity grows as a result of employment shrink (i.e. agriculture in Tanzania, Ethiopia, Ghana among others, see Figure 4 in Appendix), or when productivity variation is negative as a result of positive labour relocation (as wholesale and retail trade services in Zambia). On the other hand, the term turns (ii) positive when a productivity reduction follows an employment reduction (i.e. the sector shrinks and there is no technological innovation to compensate the labour reallocation through sectors), or vice versa a positive movement of labourers complements the positive change in productivity (i.e. research and development associated to an expanding sector). In our time interval, which broadly coincides with the period where market-oriented reforms and trade liberalization led to the so-called “services revolution”, the J effect contributes negatively to the aggregate productivity growth in all the countries under analysis8 with the only exception of Zambia where after 2004 it turns slightly positive. Given that in most cases J < 0, what we observe in our sample is point (i). However, the negative sign should not be taken as an indication of negative development, but rather as a new phase of development (de Vries et al., 2015). Looking more closely at Figure 4, we see a mixed picture. In Zambia, Kenya and Nigeria the J coefficient exhibits an upward trend, meaning that labour reallocation is slowly going towards sectors experiencing an increasingly higher productivity growth. In Malawi, Senegal, Mauritius and Tanzania labour movement is moving overtime towards sectors with an increasingly lower productivity, while the remaining countries show a U-shaped curve.

To sum up, we find a movement of labour towards sectors with above average productivity levels (positive between-static effect), but the dynamic decomposition term shows that, on average, productivity of labour-absorbing sectors grows less than other sectors in the economy (negative between-dynamic effect). Paraphrasing, workers basically quit agriculture and enter other sectors whose productivity level is above the agricultural one. Let’s take for example one of the most targeted sectors, i.e. wholesale/retail trade services, WRT. Then, the continuous flow of workers towards WRT undermines its absorption capacity and dampens its marginal productivity growth, which becomes lower than other sectors. Therefore, people moving to WRT end up doing informal and low productivity jobs. Paths largely vary across countries, and in some of them there seems to be a movement towards a more sustainable structural transformation.

7 Details on the correlation between change in employment and end-of-period estimates of sectoral labour productivity share are reported in figure A1 in Appendix A. 8 Reported as dynamic losses from de Vries et al. (2015).

10

Figure 2 Structural change patterns in SSA countries (1990–2011), within effects

Source: Author’s elaboration on new Africa Sector Database.

Figure 3 Structural change patterns in SSA countries (1990–2011), between static effects

Source: Author’s elaboration on new Africa Sector Database.

-5

0

5

10

19901995

20002005

2010

Year

BWA

-.2

-.1

0

.1

.2

19901995

20002005

2010

Year

ETH

0

.02

.04

.06

.08

19901995

20002005

2010

Year

GHA

-10

-5

0

5

19901995

20002005

2010

Year

KEN

-6-4-2024

19901995

20002005

2010

Year

MWI

-10

0

10

20

19901995

20002005

2010

Year

MUS

-20

0

20

40

60

19901995

20002005

2010

Year

NGA

-60

-40

-20

0

20

19901995

20002005

2010

Year

SEN

-20

-10

0

10

20

19901995

20002005

2010

Year

TZA

-5

0

5

19901995

20002005

2010

Year

ZAF

-200

0

200

400

600

19901995

20002005

2010

Year

ZMB

Structural change patterns in SSA (1990-2011), by country

95% CI PredictedWithin component

-4

-2

0

2

19901995

20002005

2010

Year

BWA

-.05

0

.05

.1

.15

19901995

20002005

2010

Year

ETH

-.02

0

.02

.04

.06

19901995

20002005

2010

Year

GHA

-2

0

2

4

6

19901995

20002005

2010

Year

KEN

-2

0

2

4

6

19901995

20002005

2010

Year

MWI

-4

-2

0

2

4

19901995

20002005

2010

Year

MUS

-60-40-20

02040

19901995

20002005

2010

Year

NGA

-10

0

10

20

30

19901995

20002005

2010

Year

SEN

-20

0

20

40

19901995

20002005

2010

Year

TZA

-4

-2

0

2

4

19901995

20002005

2010

Year

ZAF

-400

-200

0

200

400

19901995

20002005

2010

Year

ZMB

Structural change patterns in SSA (1990-2011), by country

95% CI PredictedBetween-static component

11

Figure 4 Structural change patterns in SSA countries (1990–2011), joint effects

Source: Author’s elaboration on new Africa Sector Database. Figure 5 Employment vs Labour productivity, by country and economic activity

(1990–2010)

Source: Author’s elaboration on new Africa Sector Database.

-1

-.5

0

.5

1

19901995

20002005

2010

Year

BWA

-.008-.006-.004-.002

0.002

19901995

20002005

2010

Year

ETH

-.0020

.002

.004

.006

.008

19901995

20002005

2010

Year

GHA

-.6

-.4

-.2

0

.2

19901995

20002005

2010

Year

KEN

-.3

-.2

-.1

0

.1

19901995

20002005

2010

Year

MWI

-4

-2

0

2

19901995

20002005

2010

Year

MUS

-8

-6

-4

-2

0

19901995

20002005

2010

Year

NGA

-2-1.5

-1-.5

0.5

19901995

20002005

2010

Year

SEN

-10

-5

0

5

19901995

20002005

2010

Year

TZA

-.6

-.4

-.2

0

.2

19901995

20002005

2010

Year

ZAF

-40

-20

0

20

40

19901995

20002005

2010

Year

ZMB

Structural change patterns in SSA (1990-2011), by country

95% CI PredictedJoint component

12

5 Empirical framework

We now explore how the quality of institutions is associated with the structural transformation process, and investigate how sub-Saharan African countries behave when we control for a set of economic performance indicators. To identify the correlates of structural transformation, we begin by considering well-known common factors recognised in the literature (credit, gross domestic product) as affecting workforce reallocation, but extend the variables selection by incorporating other potential regressors that we hypothesize being relevant for sub-Saharan African countries. We include in our model the Log GDP per capita, Real Effective Exchange Rate, Domestic Credit to private sector by banks (percent GDP), share of people living in rural areas (as a proxy for urbanization) and trade openness to the regressions.

Table 2 presents the main summary statistics. Depending on the variables used to perform the econometric analysis the sample varies from 11 to 10 countries. The panel is strongly balanced, that helps to ensure that outliers with only few observations are not confounding our results. The models for the effect of political institutions, which use the variables from Polity IV dataset, maximize the data availability, while the analysis related to economic freedom excludes Ethiopia.

To begin with, the following empirical specification was estimated with panel fixed effects:

𝑆𝑆𝑆𝑆𝑖𝑖𝑡𝑡 = 𝛽𝛽0 + 𝛽𝛽1𝜞𝜞𝒊𝒊𝒊𝒊 + 𝛽𝛽2𝞟𝞟𝒊𝒊𝒊𝒊 + 𝛽𝛽3𝐼𝐼𝐼𝐼𝑆𝑆𝑆𝑆𝑖𝑖𝑡𝑡 + 𝜂𝜂𝑡𝑡 + µ𝑖𝑖𝑡𝑡 + 𝜀𝜀𝑖𝑖𝑡𝑡 (1)

where 𝑆𝑆𝑆𝑆𝑖𝑖𝑡𝑡 represents the structural change component, 𝜞𝜞𝒊𝒊𝒊𝒊 represents a vector of economic indicators (Log GDPpc, REER, Domestic Credit to private sector by banks (percent GDP)), 𝞟𝞟𝒊𝒊𝒊𝒊 embeds the share of people living in rural areas (as a proxy for urbanization) and the trade openness variable, while 𝛽𝛽3𝐼𝐼𝐼𝐼𝑆𝑆𝑆𝑆𝑖𝑖𝑡𝑡 refers to the institutional-related proxies that embed the Polity2 score and its two components, democracy and autocracy (section 6a) and Economic Freedom related variables (section 6b). 𝜂𝜂𝑡𝑡 are the year fixed effects, µ𝑖𝑖𝑡𝑡 is a vector representing the country fixed effects, and 𝜀𝜀𝑖𝑖𝑡𝑡 is the error term which captures all unobserved variables and measurement errors, with 𝐸𝐸(𝜀𝜀𝑖𝑖𝑡𝑡) = 0 for all 𝑖𝑖. Since the error term within a country may be correlated due to omitted common factors or shocks we cluster the standard errors of each equation at the country level. We use population analytic weights to account for the dimensions of each country. The extent of how the institution quality affects structural transformation is summarized by the 𝛽𝛽3 coefficient. Also, since the structural transformation process could be affected by its dynamics (Herrendorf et al, 2013), we also provide a specification of the model with each dependent variable’s lagged term. However, the solely panel fixed effects (FE) estimates would not provide satisfactory estimates, because the Polity2 indicators are likely to be endogenous in the structural transformation equations, even in spite of controlling for country and year fixed effects. These factors, together with the possible measurement errors affecting both the Polity2 variable as well as the other macroeconomic indicators, represent an important source of bias as they do not provide reliable estimates of the causal relationship between Polity2 and SST. Thus, endogeneity is corrected for by complementing our set of estimates with a system Generalized Method of Moments (sys-GMM) estimator (Blundell and Bond, 1998), where the exogenous source of variation in the institutional quality is identified using internal instruments. The sys-GMM combines moment conditions for the model in first differences with moment conditions for the model in levels in a system of equations.

13

Considering the lack of purely exogenous instruments that vary over each panel dimension, the sys-GMM is quite helpful since permits to draw a large number of valid instruments from within the data set by instrumenting endogenous variables with their own lagged values. This permits to correct for endogeneity and reverse causality issues. We used a one-step estimator with robust standard errors. A two-step estimator is more efficient, but the standard error might be downwardly biased for small samples (Arellano and Bond, 1991), as is the case here. The sys-GMM requires the absence of first order autocorrelation, the presence of second order autocorrelation and the instrument validity. The former are checked with AR(1) and AR(2) tests. The latter is tested with the Hansen J test, that evaluates the entire set of overidentifying restrictions/instruments. Given that the number of moment conditions increases with the time periods and the vector of endogenous regressors, the excessive number of instruments can over-fit the model and then generate a number of biases in the estimates. We therefore address the instruments proliferation using the principal components analysis (PCA) of the instruments matrix as a way to shrink the available instruments into a set of linear combinations of the original variables (Kapetanios and Marcellino 2010). We then employ the test Kaiser-Meyer-Olkin (Kaiser, 1970) to measure the sampling adequacy.

Table 2 Descriptive statistics

Variable Mean Std Dev N Polity2 IV 3.55 5.24 241 Economic freedom summary index 6.07 1.03 219 Government size 6.12 1.05 219 Legal system & property rights 5.27 1.06 219 Sound money 6.59 2.18 219 Freedom to trade index 6.10 1.40 219 Regulation 6.29 0.98 219 Rural population (% of total population) 65.33 14.33 241 Trade openness 70.17 23.83 220 REER 107.35 33.10 241 Ln GDPpc 23.30 1.26 241 Domestic credit to private sector 28.79 34.70 241 Within 14.43 69.05 238 Between static 11.36 61.11 238 Joint -0.94 4.70 238

14

6 Empirical findings

We now investigate how the institutional framework and economic freedom measures affect the total labour productivity components, and explore how our selection of SSA countries behave when we control for the external economic environment. This section is divided in two parts: first we will assess the relationship between the institutional framework and structural change by controlling gradually how well the different model specifications enable us to understand the causal effect. Secondly, we will explore the linkages between economic freedom and structural change, controlling first for the aggregated measure and then for its different sub-components. We apply Fixed Effects and System GMM specifications.

6.1 Institutional framework and structural change Fixed Effects estimates, despite their several limitations, provide anyway a useful picture of the linkage between structural change and our set of controls. Table 3 to Table 5 present the fixed-effects panel regression results for our sample of countries based on nine variations of Eqn 1. The underlying hypothesis is that countries with a better institutions’ quality experience an increase in the rate of structural transformation and thus improvement in the institutions’ quality may result in a greater economic development.

In Table 3 we present results for within specification, in Table 4 we incorporate the findings for the between static and finally in Table 5 we report the results for the joint term. In the first column of each table we report a naïve specification of the model, with a simple fixed effects regression of institutional quality on the three components of structural change, with year and country fixed effects but without additional controls. Including additional controls slightly improves the model’s fit. As reported in Column I, Polity2 is positively associated only with the joint structural transformation variable, the naïve specification generates a positive and statistically significant elasticity of about 0.1. Columns II to V report further regression specifications including one-by-one the other economic indicators to gradually investigate the robustness in the behaviour of the Polity2 score. The degree of urbanization expressed as percent of rural population over total is the only variable being stable across the four new specifications, all showing a positive effect on the three dimensions. This result is counterintuitive, a possible interpretation could be that a higher demographic pressure in rural areas may induce a division of labour, with people specializing in off-farm agricultural related jobs. REER is robust over the different specifications and shows a positive relationship with respect to the within and joint component. In all the three cases, the Polity2 score is similar with the naïve specification, but confirming a positive and statistically significant coefficient for both within and joint component, with an elasticity between 0.28 and 2.9 suggesting that other things being equal, one additional unit in the indicator assessing democratic experience in the time span considered is associated with a 0.28 or 2.9 increase in the joint structural transformation component. However, our measure of institutions quality only considers a scale from most autocratic to most democratic regime in country 𝑖𝑖 at time 𝑡𝑡. To deepen our understanding of the differences between democratic and autocratic regimes, we disentangle the score in two parts, isolating autocracy from democracy and checking their relative impact on structural change. In Column VI we replace the Polity2 score with the democracy and autocracy variable. The variation of joint structural change with respect to democratic (autocratic) regimes is 0.19 (-0.17), suggesting a positive (negative) and statistically significant relationship between the two.

15

Furthermore, since none of these three variables capture either the newness of the political regimes or their consolidated nature we build a fourth variable to better proxy the political systems stability. Following Rodrik and Wacziarg (2005) we define a major political regime change, the Political Instability index (PI), when there is a shift of at least three points in a country’s score within the Polity2 variable over three years (PI=1). in Columns VII and VIII we include the PI, with and w/o the Polity2 score.

When the PI is brought in with the Polity2 score, the domestic credit turns significant (Table 3) while the rest of the coefficients remain stable. As Columns VII and VIII report, PI is negative and statistically significant at 5 percent, indicating that political instability hinders structural transformation and labour productivity within sectors. Because some may be sceptical regarding the static dimension of the panel considering the persistency of the structural transformation process, we introduce the lagged dependent variable within the model (column IX). The Polity2 and PI coefficients conserve magnitude, sign and significance, however when including the lagged dependent variable within a static panel with a small time dimension the estimates suffer of the dynamic panel bias, known also as Nickell bias (Nickell, 1981). As it is well known, the inclusion of a lagged dependent variable in a panel model with fixed effects makes standard least square estimators biased (Arellano and Bond, 1991; Caselli et al. 1996). Therefore, we re-estimate the model using the sys-GMM estimator discussed in Arellano and Bover (1995) and Blundell and Bond (1998).

Table 3 FE Regression of the political institution index on within component

I II III IV V VI VII VIII IX Polity index (corrected)

-0.20 1.18* 0.69 1.30 1.62 2.90** 2.30*

[0.59] [0.68] [0.92] [1.00] [1.06] [1.38] [1.31] Rural population (% of total population)

6.15*** 9.86** 9.79** 10.47** 10.73** 9.43** 10.85*** 7.00 [1.91] [4.19] [4.10] [4.23] [4.18] [4.30] [4.07] [4.33]

Trade openness 0.33 0.44* 0.40* 0.41* 0.41* 0.38* 0.24 [0.21] [0.23] [0.22] [0.22] [0.22] [0.21] [0.18] Ln GDP (constant 2005 USD)

55.00 67.90 78.35 84.41* 62.57 75.76 52.27

[44.66] [46.72] [49.10] [49.51] [49.50] [48.27] [45.78] REER 0.19*** 0.20*** 0.27*** 0.17** 0.22*** 0.19*** [0.07] [0.07] [0.10] [0.07] [0.08] [0.07] Domestic credit to private sector by banks (% GDP)

0.30 0.23 0.06 0.40* 0.26 [0.19] [0.20] [0.18] [0.21] [0.19]

Democracy 2.20* [1.19] Autocracy -1.92* [1.15] Political instability index

-14.21* -22.54** -21.21**

[7.30] [9.50] [9.47] L.Within 0.27 [0.20] Country fixed effects yes yes yes yes yes yes yes yes yes Year fixed effects yes yes yes yes yes yes yes yes yes Observations 238 238 216 216 216 216 216 216 206 R2 0.04 0.10 0.14 0.17 0.17 0.19 0.18 0.20 0.24 F 1.25 3.40 2.58 2.88 2.86 4.04 3.46 3.91 4.57

*, **, *** indicates significant effects at the 1 percent, 5 percent, and 10 percent level, respectively. Robust standard errors in brackets. Population analytic weights applied.

16

Table 4 FE Regression of the political institution index on between static structural transformation component

I II III IV V VI VII VIII IX

Polity index (corrected)

0.51 1.14 -0.34 -0.43 -0.27 -0.62 -0.78

[0.64] [0.72] [0.83] [0.84] [0.91] [1.06] [1.04] Rural population (% of total population)

2.78 12.65*** 12.67*** 13.01*** 12.86*** 13.21*** 12.91*** 8.04** [2.14] [4.10] [4.10] [4.28] [4.31] [4.31] [4.33] [3.42]

Trade openness 0.34 0.32 0.30 0.29 0.29 0.30 0.30 [0.24] [0.25] [0.24] [0.25] [0.24] [0.24] [0.21] Ln GDP (constant 2005 USD)

180.56 ***

178.57 ***

183.91 ***

181.22 ***

187.44 ***

184.62 ***

119.40 ***

[46.25] [48.13] [51.21] [51.91] [50.89] [51.24] [42.60] REER -0.03 -0.02 -0.05 -0.02 -0.03 -0.02 [0.06] [0.07] [0.09] [0.06] [0.07] [0.08] Domestic credit to private sector by banks (% GDP)

0.15 0.16 0.20 0.12 -0.05 [0.22] [0.20] [0.20] [0.22] [0.20]

Democracy -0.58 [0.94] Autocracy 0.48 [0.92] Political instability index

4.41 6.19 9.22

[6.77] [7.86] [7.37] L.Within 0.38 [0.24] Country fixed effects

yes yes yes yes yes yes yes yes yes

Year fixed effects yes yes yes yes yes yes yes yes yes Observations 238 238 216 216 216 216 216 216 206 R2 0.15 0.17 0.33 0.33 0.33 0.33 0.33 0.33 0.41 F 3.45 2.80 4.33 4.37 4.15 3.84 4.06 3.88 5.39

*, **, *** indicates significant effects at the 1 percent, 5 percent, and 10 percent level, respectively. Robust standard errors in brackets. Population analytic weights applied.

17

Table 5 FE Regression of the political institution index on Joint structural transformation component

I II III IV V VI VII VIII IX

Polity index (corrected)

0.10** 0.16*** 0.09 0.13* 0.18** 0.28*** 0.23**

[0.05] [0.05] [0.07] [0.07] [0.08] [0.09] [0.11] Rural population (% of total population)

0.29* 0.96*** 0.96*** 1.07*** 1.07*** 0.96*** 1.10*** 0.88* [0.18] [0.36] [0.36] [0.37] [0.37] [0.37] [0.37] [0.47]

Trade openness 0.04* 0.04** 0.04* 0.04* 0.04* 0.03* 0.02 [0.02] [0.02] [0.02] [0.02] [0.02] [0.02] [0.02] Ln GDP (constant 2005 USD)

13.59*** 14.40*** 16.17*** 16.39*** 14.67*** 15.96*** 12.14**

[4.17] [4.33] [4.46] [4.48] [4.32] [4.35] [5.39] REER 0.01* 0.01** 0.02** 0.01* 0.02** 0.01 [0.01] [0.01] [0.01] [0.01] [0.01] [0.01] Domestic credit to private sector by banks (% GDP)

0.05** 0.04* 0.03 0.06*** 0.03 [0.02] [0.02] [0.02] [0.02] [0.02]

Democracy 0.19** [0.09] Autocracy -0.17** [0.08] Political instability index

-1.00 -1.82** -1.53*

[0.66] [0.74] [0.87] L.Within 0.31* [0.18] Country fixed effects yes yes yes yes yes yes yes yes yes Year fixed effects yes yes yes yes yes yes yes yes yes Observations 238 238 216 216 216 216 216 216 206 R2 0.11 0.13 0.23 0.24 0.25 0.26 0.25 0.28 0.34 F 2.23 1.82 2.36 2.53 2.86 2.66 2.60 3.15 3.99

*, **, *** indicates significant effects at the 1 percent, 5 percent, and 10 percent level, respectively. Robust standard errors in brackets. Population analytic weights applied.

6.2 SYS-GMM estimates Table 6 reports the sys-GMM results for the estimations of Eqn. 1 for the three components of labour productivity growth, we replicate the estimates reported in columns IX in the tables above. The correction for the dynamic panel bias yields significant results for both between and joint specifications (column II and III). In specification I the two variables conserve the sign but lose their significance, further research is needed to identify the reasons behind this lack of significance. Conversely, the Polity2 and PI indexes have the expected ex-ante signs for both the two structural change components. In column II the polity index turns statistically significant and positive, while in column III we confirm that better institutions foster structural change and that political instability hinders productivity of labour absorbing sectors, meaning that with a higher level of political instability, people moving to new sectors are basically absorbed in low skilled jobs. In all specifications we do not reject the instrument validity null hypothesis yield by the Hansen-J test. None of the specifications show evidence of second order autocorrelation, the between specification shows a borderline evidence of weak first order autocorrelation.

18

Table 6 SYS-GMM Regression of the political institution index on the three labour productivity growth components

SYS-GMM SYS-GMM SYS-GMM

Within Between Static Joint I II III Polity Index (corrected) 6.91 1.74* 0.57** [0.86] [1.73] [2.35] Political Instability Index -18.65 -2.68 -14.69* [-1.09] [-0.61] [-1.81] 𝜞𝜞𝒊𝒊𝒊𝒊 yes yes yes 𝞟𝞟𝒊𝒊𝒊𝒊 yes yes yes Observations 204 204 204 Kaiser-Meyer-Olkin 0.45 0.66 0.78 AR(1) (p) 0.10 0.12 0.08 AR(2) (p) 0.49 0.33 0.33 Hansen-J (p) 0.96 0.97 0.93 Instruments 11 14 16

*, **, *** indicates significant effects at the 1 percent, 5 percent, and 10 percent level, respectively. The dependent variables are the three dimensions of total labour productivity growth in every year from 1990 to 2011. We use the xtabond2 package provided by Roodman (2009). The coefficients are based on the one-step GMM estimation with all the control variables treated as potentially endogenous in the sys-GMM. The collapsed explanatory variables are taken as instruments for the differenced equation whereas both levels and first differences of the control variables are taken as instruments for the level equation. The number of instruments used in the estimation is reported in the table, its count is entirely based on the number of “collapsed” instruments. Hansen test of overidentification checks the validity of the instruments under the null hypothesis of absence of correlation with the residuals. The AR(1) and AR(2) tests check for the first-order or second-order serial correlation of the residuals under the null hypothesis of no serial correlation. Robust standard errors reported in brackets. Population analytic weights applied. Full estimates are available in the appendix.

6.3 Economic freedom effects on structural change We now explore how the Economic Freedom (𝐸𝐸𝐸𝐸𝐼𝐼) drives the structural transformation process replicating model (1) by replacing the INST variable with EFI (Eqn 2). Results reveal some interesting relationships with the structural transformation components. We estimate both FE and sys-GMM starting from the base-specification reported below, providing comments only on the sys-GMM specifications.

𝑌𝑌𝑖𝑖𝑡𝑡 = 𝛽𝛽0 + 𝛽𝛽1𝜞𝜞𝒊𝒊𝒊𝒊 + 𝛽𝛽2𝞟𝞟𝒊𝒊𝒊𝒊 + 𝛽𝛽3𝐸𝐸𝐸𝐸𝐼𝐼𝑖𝑖𝑡𝑡 + 𝜂𝜂𝑡𝑡 + µ𝑖𝑖𝑡𝑡 + 𝜀𝜀𝑖𝑖𝑡𝑡 (2)

Column I of Table 8 reports the coefficients for the sys-GMM specification for each of the three productivity growth dimensions.9 The underlying hypothesis is that economic freedom measures may facilitate labour reallocation across sectors. The coefficient of the aggregate EFI is positive and statistically significant in both between static and joint specifications, demonstrating that greater economic freedom not only promotes labour shifts from low to high productive sectors, but also improves the marginal absorption of new labourers in targeted sectors. Finding this positive relationship is the most important achievement, however considering that the index is the combination of five components it is relevant to understand also the individual effect of each dimension. Given that they are strongly correlated between

9 FE results are reported in the Appendix.

19

each other (see Table 7) adding them all in the same specification would have returned biased results. Therefore we break down the index and add all the sub-indices one-to-one in individual regressions. We report the results in columns (II-VI). Interesting insights emerge from the between static and joint specifications, where economic freedom sub-indices are positive and statistically significant. Recalling the Fraser Institute methodology, higher ratings of the indices correspond to more economic freedom, lower ratings to heavy handed government interventions in markets. Hence, the presence of a positive (negative) and statistically significant impact indicates that more economic freedom fosters (hinders) structural transformation. For example, in countries where rent-seeking is not an issue the GOV index is higher, meaning that “countries rely less on personal choice and markets rather than government budgets and political decision-making” (Gwartner et al., 2016). Here, coefficients for government size show a positive effect, even though only for joint specification, indicating that in countries where private sector relies less on government subsidies and where marginal tax rates are lower, less labourers are trapped in informal sector jobs. Looking at the effect of Legal System and Property Rights (LSPR) we show that this is a positive and strongly significant contributor for the joint structural transformation. A better legal system (as in Botswana or Mauritius, see Figure 6) would help improving contracts enforcement, reducing in this way the incidence of the informal labour system. For example, securing property rights is crucial when it comes to land ownerships. With better land administration and documentation of tenure rights the adoption of new labour-saving agricultural technologies tends to be pushed up, leading to a reduction in the dependence on household labour, that exits from agriculture and enters in industries or services sectors. For what concerns Sound Money, the positive and statistically significant effect (at 10 percent) of low and less volatile inflation rates shows that higher inflation rates are not a favourable option to shift from a sector to another. Unsound monetary policies de-stabilize the economic environment by distorting market prices, which is a factor that may influence small and medium enterprises’ decisions to invest in research and innovation. This hinders productivity, that ends up in a gradually lower contribution within the process of economic development. The size of inflation and the continued food price instability have already been argued to slow down the path of structural transformation (Timmer, 2017). Moving to the Freedom to Trade Index, not surprisingly we find a positive relationship with both the structural transformation dimensions. In this context, eliminating quotas, reducing tariff, non-tariff barriers and controls on physical and human capital movements can accelerate SSA countries’ structural transformation process, both in static and dynamic terms. Policies aimed at removing obstacles to international trade are likely to be beneficial for structural transformation and for the economy as a whole, while closing down to the world economy with protectionist measures would not bring in foreign investments and technology, thus reducing the competitiveness of sectors and slowing down the movement of labour across sectors. In summary, our results demonstrate that the intervention of the institutions, if aimed at improving the freedom to exchange across borders, innovate and protect local producers from price volatility can have positive effects on the movement of the workforce in sectors with higher productivity.

20

Table 7 Correlation table for economic freedom dimensions

EFSI GOV LSPR SM FTI REG W Between static

Joint

EFSI 1

GOV 0.57 1

LSPR 0.53 -0.04 1

SM 0.89 0.41 0.36 1

FTI 0.88 0.46 0.42 0.70 1

REG 0.79 0.38 0.35 0.63 0.68 1

Within 0.03 0.15 0.06 -0.05 0.06 -0.03 1

Between static 0.25 0.22 0.08 0.22 0.28 0.06 0.30 1

Joint 0.29 0.17 0.10 0.35 0.19 0.19 0.33 0.19 1

Table 8 SYS-GMM Regression of the economic freedom dimensions on the three labour productivity growth components

Dependent variable Within Index K=EFSI K=GOV K=LSPR K=SM K=FTI K=REG

I II III IV V VI K -3.84 2.10 4.54 -3.16 -3.48 -4.31

[-0.53] [0.20] [0.54] [-1.55] [-0.85] [-0.62] Observations 206 206 206 206 206 206 Hansen-J (p) 0.29 0.29 0.24 0.27 0.27 0.28 AR(2) (p) 0.85 0.72 0.72 0.79 0.83 0.83 Dependent variable Between static Index K=EFSI K=GOV K=LSPR K=SM K=FTI K=REG

I II III IV V VI K 16.47* 12.23 9.49 7.47** 11.85** 10.81

[1.92] [1.58] [1.37] [2.16] [2.12] [1.56] Observations 206 206 206 206 206 206 Hansen-J (p) 0.79 0.50 0.52 0.74 0.38 0.48 AR(2) (p) 0.34 0.35 0.37 0.34 0.32 0.35 Dependent variable Joint Index K=EFSI K=GOV K=LSPR K=SM K=FTI K=REG

I II III IV V VI K 1.55** 1.60*** 0.67*** 0.70* 0.75*** 1.39

[2.33] [3.15] [2.58] [1.71] [2.89] [1.55] Observations 206 206 206 206 206 206 Hansen-J (p) 1.00 0.99 1.00 1.00 1.00 1.00 AR(2) (p) 0.59 0.54 0.40 0.27 0.26 0.61

*, **, *** indicates significant effects at the 1 percent, 5 percent, and 10 percent level, respectively. The dependent variables are the three dimensions of total labour productivity growth in every year from 1990 to 2011. We use the xtabond2 package provided by Roodman (2009). The coefficients are based on the one-step GMM estimation with all the control variables treated as potentially endogenous in the sys-GMM. The collapsed explanatory variables are taken as instruments for the differenced equation whereas both levels and first differences of the control variables are taken as instruments for the level equation. The number of instruments used in the estimation is reported in the table, its count is entirely based on the number of “collapsed” instruments. Hansen test of overidentification checks the validity of the instruments under the null hypothesis of absence of correlation with the residuals. In order to correct for serial correlation we increased the number of instruments to 21 in the Joint specification, this has led to instrument proliferation generating an increase in the Hansen J test. We report only AR(2) test check for the second-order serial correlation of the residuals under the null hypothesis of no serial correlation. Robust standard errors reported in brackets. 𝜞𝜞𝒊𝒊𝒊𝒊 and 𝞟𝞟𝒊𝒊𝒊𝒊 included. Population analytic weights applied. Full specifications are available in the appendix.

21

Figure 6 Economic Freedom Index and its five dimensions, country average (1990–2011)

Source: Author’s elaboration.

22

7 Sensitivity checks

Taking into account the concern that rests on the fact that both political and economic institutions may be sensitive to factors like the use of alternative measures of institutional quality and economic freedom, the inclusion of additional explanatory variables, external instruments and alternative measures for the dependent variable, we strengthen our analysis by undertaking a number of robustness checks. This section considers the robustness checks against our GMM-sys baseline results. Our findings are reported below.

7.1 Alternative measures of political and economic institutions quality The first robustness check involves the use of new indexes for political and economic institutions quality. In addition to the Polity2 and EFSI index, we employ the Democratic Accountability (DA) measure from the International Country Risk guide (ICRG) database provided by the Political Risk Services (PRS) Group and the Economic Institutional Quality index (EIQ) taken from Kuncic (2014). ICRG dataset embeds 12 risk indicators addressing both political risk but also various components of political institutions. Democratic accountability of the government is one of them and it is related to the degree of responsiveness of the government towards its citizens as well as the rate of implementation of political rights and civil liberties. The higher the index the better the institutions. Several papers have extensively used the ICRG as a proxy for the institutions quality, to cite few: Boschini et al. (2013) and Farhadi et al. (2016) for natural resources and Busse and Hefeker (2012) for linkages with FDI inflows. Kuncic index of EQI is constructed using a factor analysis model and information about a set of indicators including economic freedom, regulatory quality, credit, labour and business among others. We run the regressions using DA and the EIQ as explanatory variables. Some new results appear, some of the previous ones disappear, but the main result, i.e. the political and economic institutional quality is mostly significantly positive, remains unaltered. The findings reported in Table 9 confirm those obtained in Table 6, implying that better political and economic institutions have a non-significant effect on within change and that enhance static and dynamic structural change. Table 9 SYS-GMM Regression of democratic accountability and economic

institutional quality on the three labour productivity growth components

SYS-GMM SYS-GMM SYS-GMM SYS-GMM SYS-GMM SYS-GMM Within Between

Static Joint Within Between

Static Joint

I II III IV V VI Democratic Accountability

2.05 11.04* 1.19*

[0.37] [1.83] [1.89] Ln Economic Institutional Quality

44.25 66.96** 3.46*

[0.71] [2.41] [1.94] Observations 186 186 186 200 200 200 AR(2) (p) 0.28 0.36 0.39 0.16 0.31 0.23 Hansen-J (p) 0.37 0.94 0.62 0.29 0.87 1.00 Instruments 11 17 13 9 15 22

*, **, *** indicates significant effects at the 1 percent, 5 percent, and 10 percent level, respectively. See notes to Table 6.

23

7.2 Robustness to additional explanatory variables The third robustness check looks at the sensitivity of the baseline estimates by sequentially adding to specification I of Table 8 a set of additional explanatory variables that we believe are relevant correlates of structural transformation. Results are reported in Table 10. Structural transformation may be influenced by education, foreign direct investments and R&D. For what concerns education and FDI we use two measures coming from the World Development Indicators by the World Bank: the youth literacy rate for the population 15-24 years old (expressed as percentage of the total) and the foreign direct investments (net inflows, percent GDP). Data on R&D is gathered from the Agricultural Science and Technology indicators (ASTI) powered by IFPRI and the World Intellectual Property Organization (WIPO). We include the number of agricultural researchers (tot full-time employment over 100,000 farmers) and total patents applications (direct and Patent Cooperation Treaty national phase entries). Even though the number of observations is reduced, the size and significance of results for EFSI coefficient is robust across the different specifications, suggesting again that countries with relatively higher levels of economic freedom experience higher levels of between static and joint structural transformation.

Table 10 SYS-GMM Regression of on the three labour productivity growth components

Dependent variable Within Index Z = FDI Z= Researchers Z=Literacy Z=Patents I II III IV EFSI 3.43 1.84 -3.29 1.94

[0.59] [0.40] [-1.02] [0.32] Z 4.29 -0.02 0.12 0.02

[0.76] [-0.23] [0.40] [0.55] Observations 206 197 177 206 Hansen-J (p) 0.26 0.30 0.31 0.31 AR(2) (p) 0.19 0.16 0.33 0.09 Dependent variable Between static Z = FDI Z= Researchers Z=Literacy Z=Patents I II III IV EFSI 16.72** 17.02** 17.47* 17.36* [2.08] [2.00] [1.89] [1.91] Z -0.01 -0.26 3.43 0.03 [-0.93] [-0.79] [0.88] [0.97] Observations 206 177 206 206 Hansen-J (p) 0.25 0.35 0.55 0.91 AR(2) (p) 0.34 0.36 0.34 0.35 Dependent variable Joint Z = FDI Z= Researchers Z=Literacy Z=Patents I II III IV EFSI 1.96*** 1.62** 1.66** 1.55** [2.68] [2.20] [2.51] [2.34] Z -0.12 0.12 -0.02** -0.00 [-1.59] [0.79] [-2.34] [-1.03] Observations 177 206 197 206 Hansen-J (p) 0.98 0.99 0.99 1.00 AR(2) (p) 0.51 0.77 0.63 0.67

*, **, *** indicates significant effects at the 1 percent, 5 percent, and 10 percent level, respectively. FDI (Foreign Direct Investments: percent GDP), Researchers (tot FTE over 100000 farmers), Patents (Total patents applications, direct and PCT national phase entries), Literacy (Youth Literacy Rate 15-24 yrs ( percent)). See notes to Table 6. We report only AR(2) test check for the second-order serial correlation of the residuals under the null hypothesis of no serial correlation. Robust standard errors reported in brackets. 𝜞𝜞𝒊𝒊𝒊𝒊 and 𝞟𝞟𝒊𝒊𝒊𝒊 included. Population analytic weights applied. Full specifications are available in the appendix.

24

7.3 Allowing for external instruments Another advantage of sys-GMM is that it allows for additional time invariant instruments to be included in the regression (see Farhadi, et al., 2015 and Hildebrandt and Moder, 2015). We follow Mauro (1995) and La Porta et al. (1999) in instrumenting the institutional quality with ethnic fractionalization and latitude. The first instrument captures the extent of political divergence in society between social, ethnic, class or other interests. The second one proxies for the residing areas of Europeans during the colonial period. Given their lack of immunity to tropical diseases they were more likely to reside in more temperate latitudes which are therefore linked with the creation of economic institutions. Overall, our empirical findings are qualitatively close to baseline results obtained in Table 7 and Table 8. Table 11 SYS-GMM Regression of economic freedom on the three labour

productivity growth components, external instruments Dependent variable Within Index K=EFSI K=GOV K=LSPR K=SM K=FTI K=REG

I II III IV V VI K -0.77 2.25 4.21 -0.70 -1.05 -1.14

[-0.17] [0.26] [0.51] [-0.71] [-0.44] [-0.26] Observations 206 206 206 206 206 206 Hansen-J (p) 0.35 0.41 0.45 0.35 0.35 0.39 AR(2) (p) 0.15 0.21 0.49 0.20 0.16 0.13 Dependent variable Between static Index K=EFSI K=GOV K=LSPR K=SM K=FTI K=REG

I II III IV V VI K 17.97* 13.19 9.87 8.30** 12.78** 12.79

[1.86] [1.57] [1.33] [2.14] [1.96] [1.59] Observations 206 206 206 206 206 206 Hansen-J (p) 0.36 0.53 0.63 0.61 0.31 0.51 AR(2) (p) 0.35 0.36 0.38 0.34 0.33 0.36 Dependent variable Joint Index K=EFSI K=GOV K=LSPR K=SM K=FTI K=REG

I II III IV V VI K 1.35*** 1.45*** 0.65** 0.59* 0.73*** 1.43

[2.60] [2.89] [2.20] [1.94] [3.67] [1.60] Observations 206 206 206 206 206 206 Hansen-J (p) 1.00 1.00 1.00 1.00 1.00 1.00 AR(2) (p) 0.10 0.83 0.86 0.10 0.20 0.74

*, **, *** indicates significant effects at the 1 percent, 5 percent, and 10 percent level, respectively. See notes to Table 6. Tests for AR(1) are always rejected except from AGR specification. Full specifications are available on request.

7.4 Impact on employment levels in each sector Despite the reasonableness of our previous results, in order to address the problem differently (and not necessarily more successfully) we dig one layer deeper to check the consistency of our results on employment levels. We replicate the same estimates for each of the GGDC covered sectors. Estimation results appear in table x. Now that we allow for employment levels for each of the employment sectors as dependent variables, the structural change advantage of market oriented policies adoption is still visible. If we look in particular at AGR and MAN not only we find that our proxies for economic freedom and political institutions are almost always statistically significant, but interestingly enough we notice that they exhibit opposite signs. This

25

paves the way on further considerations about the role of institutional freedom. According to our findings it frees up resources from the agricultural sector and positively influence employment in manufacturing sector, that is often recognized as the main channel through which economic growth took place in advanced countries and East Asia (Rodrik, 2013 and 2014). As for services, whose ability to generate labour for unemployed in the formal sector is constrained by skills requirements, results are quite intriguing. Low-productive services employment (WRT, TRA) is negatively related with economic freedom indicators and quality of institutions, while positive and statistically significant signs emerge for relatively high-productive services. In summary, we are inclined to confirm that proper market oriented policies not only promote structural transformation, but they do it in a wealthier and more sustainable way reducing the rate of labour reallocation in low-productive and non-tradable sectors and supporting technology dynamic sectors.

26

Tabl

e 12

SY

S-G

MM

Reg

ress

ion

of e

cono

mic

free

dom

and

pol

itica

l ins

titut

ions

em

ploy

men

t lev

els

for e

ach

sect

or

AG

R

M

AN

K

=PO

L K

=EFS

I K

=GO

V K

=LSP

R

K=S

M

K=F

TI

K=R

EG

K=P

OL

K=E

FSI

K=G

OV

K=L

SPR

K

=SM

K

=FTI

K

=RE

G

I

II III

IV

V

VI

VII

I

II III

IV

V

VI

VII

K

-0.0

0**

-0.0

1***

-0

.01*

* -0

.00

-0.0

1***

-0

.01*

* -0

.01

0.

00

0.02

**

0.02

**

-0.0

1 0.

01**

* 0.

01

0.04

**

[-2

.57]

[-2

.60]

[-2

.54]

[-0

.64]

[-2

.80]

[-2

.25]

[-1

.63]

[0.5

9]

[2.0

4]

[2.0

8]

[-1.6

2]

[4.0

8]

[1.2

2]

[2.3

2]

Obs

erva

tions

20

7 20

6 20

6 20

6 20

6 20

6 20

6

207

206

206

206

206

206

206

Han

sen-

J (p

) 0.

44

0.84

0.

71

0.90

0.

82

0.42

0.

72

0.

98

0.99

0.

99

0.97

0.

99

1.00

0.

99

AR(2

) (p)

0.

31

0.31

0.

31

0.32

0.

31

0.31

0.

31

0.

33

0.29

0.

33

0.39

0.

32

0.31

0.

24

Inst

rum

ents

11

11

11

11

11

11

11

21

21

21

21

21

21

21

W

RT

PU

K=P

OL

K=E

FSI

K=G

OV

K=L

SPR

K

=SM

K

=FTI

K

=RE

G

K

=PO

L K

=EFS

I K

=GO

V K

=LSP

R

K=S

M

K=F

TI

K=R

EG

I II

III

IV

V V

I V

II

I II

III

IV

V V

I V

II K

-0

.00*

* -0

.02

-0.0

0 -0

.03*

**

-0.0

1 -0

.02*

**

0.01

-0.0

1*

-0.0

2 -0

.01*

-0

.02

-0.0

1 -0

.01

-0.0

0

[-2.3

5]

[-1.6

2]

[-0.1

7]

[-4.5

3]

[-1.1

2]

[-4.4

0]

[0.5

9]

[-1

.78]

[-1

.23]

[-1

.76]

[-1

.38]

[-0

.99]

[-0

.69]

[-0

.14]

O

bser

vatio

ns

207

206

206

206

206

206

206

20

7 20

6 20

6 20

6 20

6 20

6 20

6 H

anse

n-J

(p)

0.99

0.

99

1.00

1.

00

0.99

1.

00

0.99

0.96

1.

00

1.00

1.

00

1.00

1.

00

1.00

AR

(2) (

p)

0.69

0.

97

0.69

0.

51

0.57

0.

45

0.54

0.19

0.

30

0.15

0.

21

0.27

0.

22

0.18

In

stru

men

ts

20

20

20

20

20

20

20

21

21

21

21

21

21

21

TRA

CO

N

K

=PO

L K

=EFS

I K

=GO

V K

=LSP

R

K=S

M

K=F

TI

K=R

EG

K=P

OL

K=E

FSI

K=G

OV

K=L

SPR

K

=SM

K

=FTI

K

=RE

G

I

II III

IV

V

VI

VII

I

II III

IV

V

VI

VII

K

-0.0

1 -0

.04*

* 0.

00

-0.0

2 -0

.02

-0.0

3**

0.04

0.00

0.

03**

* 0.

02

0.01

0.

02**

0.

03**

* 0.

04*

[-1

.53]

[-2

.26]

[0

.13]

[-0

.92]

[-1

.14]

[-2

.56]

[0

.89]

[1.0

9]

[2.6

1]

[1.3

0]

[0.4

1]

[2.0

6]

[4.3

2]

[1.6

9]

Obs

erva

tions

20

7 20

6 20

6 20

6 20

6 20

6 20

6

207

206

206

206

206

206

206

Han

sen-

J (p

) 0.

25

0.69

0.

37

0.52

0.

97

0.59

0.

39

0.

99

1.00

1.

00

0.99

1.

00

1.00

1.

00

AR(2

) (p)

0.

40

0.35

0.

33

0.30

0.

32

0.34

0.

37

0.

65

0.46

0.

53

0.56

0.

48

0.48

0.

49

Inst

rum

ents

13

16

13

12

13

14

13

21

21

21

21

21

21

21

FI

RE

G

VT

K

=PO

L K

=EFS

I K

=GO

V K

=LSP

R

K=S

M

K=F

TI

K=R

EG

K=P

OL

K=E

FSI

K=G

OV

K=L

SPR

K

=SM

K

=FTI

K

=RE

G

I

II III

IV

V

VI

VII

I

II III

IV

V

VI

VII

K

0.01

0.

06

-0.0

0 0.

03

0.03

0.

04

-0.0

5

0.00

0.

02**

0.

01

-0.0

0 0.

01**

0.

01**

* 0.

02**

*

[0.8

8]

[1.2

6]

[-0.0

3]

[1.0

9]

[1.0

4]

[1.5

3]

[-0.4

9]

[1

.16]

[2

.12]

[1

.12]

[-0

.50]

[1

.98]

[2

.71]

[2

.89]

O

bser

vatio

ns

207

206

206

206

206

206

206

18

7 18

6 18

6 18

6 18

6 18

6 18

6 H

anse

n-J

(p)

0.84

0.

95

0.89

0.

88

0.89

0.

72

0.64

1.00

1.

00

1.00

1.

00

1.00

1.

00

1.00

AR

(2) (

p)

0.27

0.

28

0.23

0.

39

0.31

0.

29

0.34

0.14

0.

19

0.16

0.

33

0.21

0.

22

0.23

In

stru

men

ts

11

11

11

11

11

11

11

21

21

21

21

21

21

21

*, *

*, *

** in

dica

tes

sign

ifica

nt e

ffect

s at

the

1 pe

rcen

t, 5

perc

ent,

and

10 p

erce

nt le

vel,

resp

ectiv

ely.

See

not

es to

Tab

le T

able

6. W

e re

port

only

AR

(2) t

est c

heck

for t

he s

econ

d-or

der

seria

l cor

rela

tion

of th

e re

sidu

als

unde

r th

e nu

ll hy

poth

esis

of n

o se

rial c

orre

latio

n. R

obus

t sta

ndar

d er

rors

rep

orte

d in

bra

cket

s. 𝜞𝜞

𝒊𝒊𝒊𝒊 a

nd 𝞟𝞟

𝒊𝒊𝒊𝒊 in

clud

ed. P

opul

atio

n an

alyt

ic

wei

ghts

app

lied.

Tes

ts fo

r AR

(1) a

re a

lway

s re

ject

ed e

xcep

t fro

m A

GR

spe

cific

atio

n. F

ull s

peci

ficat

ions

are

ava

ilabl

e on

requ

est.

27

8 Conclusions

Structural transformation of sub-Saharan African economies has been slower than that experienced in Latin America and East Asia (ECA and African Union, 2014). This is largely due to the inability of African markets to attract the labour force to sectors with the highest productivity. In this respect, action taken by governments are crucial, given their central role in setting up the conditions for economic environment to promote growth. We investigate the effect of quality of institutions and economic freedom on structural transformation on a panel of 11 sub-Saharan African countries in the last twenty years. We contribute to the understanding on whether a higher level of political institutions quality and economic freedom enhances labour reallocation across sectors. Robust support is found for the hypothesis that political stability as well as more market-oriented reforms are essential for sub-Saharan countries to achieve productive employment. Good institutions ensure that labour markets create opportunities for high-productivity employment and that labourers are increasingly absorbed by formal sectors. Inflation control, better property rights management, reducing barriers to trade can help increase the competitiveness of firms, avoiding people to be trapped in vulnerable employment or being forced out of the labour force in formal sectors. Almost no support is found for the hypothesis that our indicators improve productivity within sectors. Furthermore, given the positive and statistically significant relationship with employment levels in manufacturing and in partly-high productive services it is also highlighted how wealthier institutions may facilitate a catching up with the East Asian countries developing models. In particular, manufacturing sector should not be neglected, given the great benefits that could potentially originate from the interaction with services sectors. Efficient services provision improves the competitiveness of manufacturing while a strong manufacturing sector creates greater demand for services.

The findings in this paper have direct policy implications, but since we are aware that one-size does not fit all we do not seek to provide a common recipe for promoting structural transformation in SSA. We rather believe that each SSA government should identify goals and limits about its structural transformation paths (imperfect labour markets, poor financial markets, lack of skills or credit constraints among others) and then apply case-by-case targeted structural reforms in combination with industrial policies, by taking for example advantage of the learning instruments recently advocated by Stiglitz (2016). Developing a framework of interlinking and complementary policies and coordinating private with public sector should be at the heart of these strategies. Governments that aim at encouraging a shift towards higher value-added and employment-generating activities need to ensure an expansionary economic environment in which more productive and technology dynamic sectors can flourish. Hence, strengthening the non-traded sector, increasing the levels of supply-side investments (building infrastructures, improving the education system, ensuring credit to SME), implementing demand side policies and a low volatile exchange rate at a level that does not threaten the competitiveness of domestic manufacturers should be among the listed practices of their policy agendas. In doing the above, countries need also to commit themselves to change the current structures prevailing in African economies, which was one of their greatest limits in the past (Aryeteey and Moyo, 2012).

28

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32

Appendix A

Figure A1 Scatter plot matrix between economic freedom index/subindices and within, between static and joint

Source: Authors’ elaboration.

33

Appendix B

Table B1 Country names and relative ISO-3 codes

Country Name ISO3 code Country Name ISO3 code Botswana BWA Nigeria NGA Ethiopia ETH Senegal SEN Ghana GHA South Africa ZAF Kenya KEN United Republic of Tanzania TZA Malawi MWI Zambia ZMB Mauritius MUS

Table B2 Full SYS-GMM Regression of the political institution index on the three labour

productivity growth components

SYS-GMM SYS-GMM SYS-GMM Within Between Static Joint I II III Polity index (corrected) 6.91 1.74* 0.57** [0.86] [1.73] [2.35] Rural population (% of total population) 0.29 -0.75 0.22 [0.39] [-0.82] [1.03] Trade openness -0.38 -0.43 0.05 [-0.82] [-1.28] [0.51] Ln GDP (constant 2005 USD) -2.50 -7.50 0.90 [-0.32] [-1.09] [0.27] REER 0.22 -0.08 0.02* [1.50] [-1.37] [1.68] Domestic credit to private sector by banks (% GDP)

-0.47 -0.25* 0.01

[-0.85] [-1.75] [0.18] Political instability index -18.65 -2.68 -14.69* [-1.09] [-0.61] [-1.81] L.Dependent 0.09 0.15** -0.25 [0.26] [2.21] [-1.39] Observations 204 204 204 Kaiser-Meyer-Olkin 0.45 0.66 0.78 AR(1) (p) 0.10 0.12 0.08 AR(2) (p) 0.49 0.33 0.33 Hansen (p) 0.96 0.97 0.93 Instruments 11 14 16

*, **, *** indicates significant effects at the 1 percent, 5 percent, and 10 percent level, respectively. The dependent variables are the three dimensions of total labour productivity growth in every year from 1990 to 2011. We use the xtabond2 package provided by Roodman (2009). The coefficients are based on the one-step GMM estimation with all the control variables treated as potentially endogenous in the sys-GMM. The collapsed explanatory variables are taken as instruments for the differenced equation whereas both levels and first differences of the control variables are taken as instruments for the level equation. The number of instruments used in the estimation is reported in the table, its count is entirely based on the number of “collapsed” instruments. Hansen test of overidentification checks the validity of the instruments under the null hypothesis of absence of correlation with the residuals. The AR(1) and AR(2) tests check for the first-order or second-order serial correlation of the residuals under the null hypothesis of no serial correlation. Robust standard errors reported in brackets. Population analytic weights applied.

34

Tabl

e B

3 FE

Reg

ress

ion

of th

e ec

onom

ic fr

eedo

m d

imen

sion

s on

the

thre

e la

bour

pro

duct

ivity

gro

wth

com

pone

nts

With

in

Inde

x K

=EFS

I K

=EFS

I K

=GO

V

K=G

OV

K

=LS

PR

K

=LS

PR

K

=SM

K

=SM

K

=FTI

K

=FTI

K

=RE

G

K=R

EG

I.a

I.bII.

aII.

bIII

.aIII

.bIV

.aIV

.bV

.aV

.bV

I.aV

I.bK

-5

.89

-9.3

8-2

.92

-1.7

26.

39**

4.

45

4.64

2.

02

-9.9

5**

-10.

33**

-17.

83**

-15.

92**

[1

0.83

][1

2.70

][3

.02]

[3.1

8][2

.57]

[2

.72]

[5

.77]

[6

.81]

[4

.37]

[5.0

4][7

.08]

[7.1

9]O

bs.

216

206

216

206

216

206

216

206

216

206

216

206

R2

0.17

0.21

0.17

0.21

0.18

0.

21

0.18

0.

21

0.20

0.23

0.19

0.22

F 2.

732.

782.

953.

112.

97

3.14

2.

94

3.17

2.

602.

932.

622.

72B

etw

een

stat

ic

Inde

x K

=EFS

I K

=EFS

I K

=GO

V

K=G

OV

K

=LS

PR

K

=LS

PR

K

=SM

K

=SM

K

=FTI

K

=FTI

K

=RE

G

K=R

EG

I.a

I.bII.

aII.

bIII

.aIII

.bIV

.aIV

.bV

.aV

.bV

I.aV

I.bK

36

.76*

**

27.9

1***

3.

50

2.36

-4

.66

-5.1

919

.90*

**

16.0

2***

12

.84*

**

9.81

* 6.

85

4.63

[8.0

0]

[10.

12]

[3.0

1]

[3.1

6]

[3.3

3][3

.33]

[4.4

4]

[4.5

3]

[3.7

8]

[5.2

6]

[7.3

9]

[7.3

4]

Obs

. 21

6 20

6 21

6 20

6 21

620

621

6 20

6 21

6 20

6 21

6 20

6 R

2 0.

40

0.44

0.

34

0.41

0.

340.

410.

45

0.47

0.

37

0.43

0.

34

0.41

F

5.19

6.

33

4.05

5.

88

4.12

6.11

5.46

6.

14

5.99

6.

81

3.84

5.

60

Join

t In

dex

K=E

FSI

K=E

FSI

K=G

OV

K

=GO

V

K=L

SP

R

K=L

SP

R

K=S

M

K=S

M

K=F

TI

K=F

TI

K=R

EG

K

=RE

G

I.aI.b

II.a

II.b

III.a

III.b

IV.a

IV.b

V.a

V.b

VI.a

VI.b

K

2.65

***

2.36

**

0.95

***

1.15

***

0.17

0.

06

1.51

***

1.22

0.

25

0.15

-1

.37*

*-1

.05

[0

.84]

[1

.20]

[0

.36]

[0

.33]

[0

.34]

[0

.35]

[0

.44]

[0

.76]

[0

.35]

[0

.40]

[0

.62]

[0.7

4]O

bs.

216

206

216

206

216

206

216

206

216

206

216

206

R2

0.30

0.

35

0.28

0.

35

0.25

0.

32

0.34

0.

36

0.26

0.

32

0.27

0.33

F 4.

63

5.04

2.

87

3.54

2.

88

3.55

3.

89

4.26

3.

45

4.04

3.

163.

92

*, *

*, *

** in

dica

tes

sign

ifica

nt e

ffect

s at

the

1 pe

rcen

t, 5

perc

ent,

and

10 p

erce

nt le

vel,

resp

ectiv

ely.

R

obus

t sta

ndar

d er

rors

in b

rack

ets.

Pop

ulat

ion

anal

ytic

wei

ghts

app

lied.

#.b

mod

els

cont

ain

lagg

ed d

epen

dent

var

iabl

e.

Full

estim

ates

ava

ilabl

e on

requ

est.

T abl

e B

4 Fu

ll SY

S-G

MM

Reg

ress

ion

of th

e ec

onom

ic fr

eedo

m in

dex

on th

e th

ree

labo

ur p

rodu

ctiv

ity g

row

th c

ompo

nent

s W

ithin

B

etw

een

Stat

ic

Join

t

K=E

FSI

K=G

OV

K

=LS

PR

K

=SM

K

=FTI

K

=RE

G

K=E

FSI

K=G

OV

K

=LS

PR

K

=SM

K

=FTI

K

=RE

G

K=E

FSI

K=G

OV

K

=LS

PR

K

=SM

K

=FTI

K

=RE

G

I II

III

IV

V

VI

I II

III

IV

V

VI

I II

III

IV

V

VI

K

-3.8

42.

10

4.54

-3

.16

-3.4

8-4

.31

16.4

7*

12.2

3 9.

49

7.47

**

11.8

5**

10.8

1 1.

55**

1.

60**

* 0.

67**

* 0.

70*

0.75

***

1.39

[-0.5

3][0

.20]

[0

.54]

[-1

.55]

[-0.8

5][-0

.62]

[1.9

2]

[1.5

8]

[1.3

7]

[2.1

6]

[2.1

2]

[1.5

6]

[2.3

3]

[3.1

5]

[2.5

8]

[1.7

1]

[2.8

9]

[1.5

5]

Rur

al p

opul

atio

n (%

of t

otal

pop

ulat

ion)

-1.

43-1

.36

-1.2

5-1

.39

-1.4

7-1

.39

-0.3

6-0

.13

-0.4

3-0

.30

-0.3

00.

04

0.02

0.

05**

* 0.

02

0.02

0.

03**

0.

06**

*

[-0.9

2]

[-0.8

3][-0

.79]

[-0.8

9][-0

.93]

[-0.8

5][-0

.93]

[-0.4

2][-1

.13]

[-0.8

3][-0

.77]

[0.1

3]

[1.2

7]

[2.7

2]

[0.4

3]

[0.9

3]

[2.2

2]

[3.3

0]

Trad

e op

enne

ss

-0.2

2-0

.20

-0.0

9-0

.26

-0.2

1-0

.16

-0.0

6-0

.04

0.02

0.07

-0.1

5-0

.02

0.01

0.

01

0.01

0.

01

-0.0

00.

00

[-0.8

0][-0

.57]

[-0.3

7][-1

.01]

[-0.6

7][-0

.46]

[-0.2

3][-0

.17]

[0.0

8][0

.28]

[-0.6

8][-0

.10]

[0.4

4]

[0.7

5]

[0.5

2]

[0.5

8]

[-0.1

4][0

.19]

REE

R

0.01

0.03

0.02

-0.0

1-0

.01

0.02

-0.0

4**

-0.1

2***

-0

.15*

**

-0.0

2*0.

01-0

.08*

**0.

01

0.00

-0

.00

0.01

**

0.01

***

0.00

*

[0.2

1][0

.36]

[0.1

3][-0

.13]

[-0.1

3][0

.24]

[-2.2

9]

[-3.1

1]

[-2.6

6]

[-1.9

5][0

.13]

[-3.4

2][1

.12]

[0

.07]

[-0

.31]

[1.9

7]

[2.7

8]

[1.8

7]

Ln G

DP

per c

apita

-1

0.61

-10.

18-6

.94

-10.

78-1

1.17

-8.6

0-2

.06

-1.8

60.

53

-0.5

7-0

.96

-2.1

2-0

.56*

**

-0.7

3***

-0

.29*

**

-0.3

8***

-0

.38*

**

-0.6

6**

[-0.8

4][-0

.71]

[-0.6

2][-0

.86]

[-0.8

7][-0

.60]

[-1.2

9][-0

.86]

[1.4

4]

[-0.4

9][-0

.84]

[-0.8

4][-3

.43]

[-4

.86]

[-3

.42]

[-3

.06]

[-5

.17]

[-2

.36]

Dom

estic

cre

dit

-0.2

3-0

.26

-0.3

4-0

.20

-0.2

1-0

.25

-0.2

9-0

.07

-0.2

8-0

.25

-0.3

1-0

.13

0.02

***

0.05

***

0.02

**

0.02

***

0.02

***

0.03

***

[-0.7

7][-1

.18]

[-0.9

7][-0

.77]

[-0.7

6][-0

.97]

[-1.5

8][-0

.60]

[-1.3

5][-1

.61]

[-1.6

4][-1

.04]

[4.4

1]

[6.6

4]

[1.9

8]

[3.0

6]

[4.0

0]

[4.5

5]

L.D

epen

dent

var

iabl

e -0

.30*

-0.3

2**

-0.3

4*-0

.26

-0.2

9*-0

.31

0.13

0.12

0.16

0.14

*0.

080.

15-0

.05

-0.2

7*-0

.02

0.01

-0

.00

-0.0

6

[-1.8

2]

[-2.0

5]

[-1.6

8][-1

.40]

[-1.9

3][-1

.57]

[1.4

0][1

.30]

[1.1

7][1

.73]

[0.7

9][1

.26]

[-0.2

2][-1

.73]

[-0.1

1][0

.03]

[-0

.02]

[-0.2

3]

Obs

erva

tions

20

6 20

6 20

6 20

6 20

6 20

6 20

6 20

6 20

6 20

6 20

6 20

6 20

6 20

6 20

6 20

6 20

6 20

6

Han

sen-

J (p

) 0.

29

0.29

0.

24

0.27

0.

27

0.28

0.

79

0.50

0.

52

0.74

0.

38

0.48

1.

00

0.99

1.

00

1.00

1.

00

1.00

N In

stru

men

ts

10.0

0 10

.00

10.0

0 10

.00

10.0

0 10

.00

11.0

0 11

.00

11.0

0 11

.00

11.0

0 11

.00

21.0

0 19

.00

21.0

0 21

.00

21.0

0 21

.00

AR(1

) (p)

0.

09

0.13

0.

26

0.10

0.

07

0.15

0.

08

0.09

0.

15

0.10

0.

06

0.10

0.

09

0.09

0.

07

0.09

0.

09

0.07

AR(2

) (p)

0.

85

0.72

0.

72

0.79

0.

83

0.83

0.

34

0.35

0.

37

0.34

0.

32

0.35

0.

59

0.54

0.

40

0.27

0.

26

0.61

KM

O

0.38

0.

38

0.38

0.

38

0.38

0.

38

0.66

0.

66

0.66

0.

66

0.66

0.

66

0.50

0.

50

0.50

0.

50

0.50

0.

50

Com

pone

nts

3.00

3.

00

3.00

3.

00

3.00

3.

00

5.00

5.

00

5.00

5.

00

5.00

5.

00

15.0

0 13

.00

15.0

0 15

.00

15.0

0 15

.00

*, *

*, *

** in

dica

tes

sign

ifica

nt e

ffect

s at

the

1 pe

rcen

t, 5

perc

ent,

and

10 p

erce

nt le

vel,

resp

ectiv

ely.

The

dep

ende

nt v

aria

bles

are

the

thre

e di

men

sion

s of

tota

l lab

our p

rodu

ctiv

ity

grow

th in

eve

ry y

ear

from

199

0 to

201

1. W

e us

e th

e xt

abon

d2 p

acka

ge p

rovi

ded

by R

oodm

an (

2009

). Th

e co

effic

ient

s ar

e ba

sed

on th

e on

e-st

ep G

MM

est

imat

ion

with

all

the

cont

rol v

aria

bles

trea

ted

as p

oten

tially

end

ogen

ous

in th

e sy

s-G

MM

. The

col

laps

ed e

xpla

nato

ry v

aria

bles

are

take

n as

inst

rum

ents

for t

he d

iffer

ence

d eq

uatio

n w

here

as b

oth

leve

ls

and

first

diff

eren

ces

of th

e co

ntro

l var

iabl

es a

re ta

ken

as in

stru

men

ts fo

r the

leve

l equ

atio

n. T

he n

umbe

r of i

nstru

men

ts u

sed

in th

e es

timat

ion

is re

porte

d in

the

tabl

e, it

s co

unt i

s en

tirel

y ba

sed

on th

e nu

mbe

r of “

colla

psed

” ins

trum

ents

. Han

sen

test

of o

verid

entif

icat

ion

chec

ks th

e va

lidity

of t

he in

stru

men

ts u

nder

the

null

hypo

thes

is o

f abs

ence

of c

orre

latio

n w

ith th

e re

sidu

als.

In o

rder

to c

orre

ct fo

r ser

ial c

orre

latio

n w

e in

crea

sed

the

num

ber o

f ins

trum

ents

to 2

1 in

the

Join

t spe

cific

atio

n, th

is h

as le

d to

inst

rum

ent p

rolif

erat

ion

gene

ratin

g an

incr

ease

in th

e H

anse

n J

test

. The

AR

(1)

and

AR

(2)

test

s ch

eck

for

the

first

-ord

er o

r se

cond

-ord

er s

eria

l cor

rela

tion

of th

e re

sidu

als

unde

r th

e nu

ll hy

poth

esis

of n

o se

rial

corr

elat

ion.

Rob

ust s

tand

ard

erro

rs re

porte

d in

bra

cket

s. P

opul

atio

n an

alyt

ic w

eigh

ts a

pplie

d. F

ull s

peci

ficat

ions

are

ava

ilabl

e in

the

appe

ndix

.

35

Tabl

e B

5 Fu

ll SY

S-G

MM

Reg

ress

ion

of d

emoc

ratic

acc

ount

abili

ty a

nd e

cono

mic

inst

itutio

nal q

ualit

y on

the

thre

e la

bour

pro

duct

ivity

gr

owth

com

pone

nts

SYS-

GM

M

SYS-

GM

M

SYS-

GM

M

SYS-

GM

M

SYS-

GM

M

SYS-

GM

M

With

in

Bet

wee

n st

atic

Jo

int

With

in

Bet

wee

n st

atic

Jo

int

I II

III

IV

V

VI

Dem

ocra

tic a

ccou

ntab

ility

2.

05

11.0

4*

1.19

* [0

.37]

[1

.83]

[1

.89]

Ln

eco

nom

ic in

stitu

tiona

l qua

lity

44.2

5 66

.96*

* 3.

46*

[0.7

1]

[2.4

1]

[1.9

4]

Rur

al p

opul

atio

n (%

of t

otal

pop

ulat

ion)

0.

55

-0.6

90.

02

2.40

-0

.99

-0.0

1

[0.7

0]

[-0.8

8]

[0.3

2]

[1.1

5]

[-0.9

9]

[-0.4

4]

Trad

e op

enne

ss

-0.1

3-0

.12

0.03

-0

.21

-0.5

6*-0

.01

[-0.8

7][-0

.33]

[1.0

8]

[-0.2

9][-1

.91]

[-0.5

9]Ln

GD

P (c

onst

ant 2

005

USD

) 4.

16-4

.06

-1.0

0**

15.4

8-8

.95

-0.7

4***

[0.6

7][-0

.68]

[-2.3

4][0

.89]

[-1.3

4][-4

.09]

REE

R

0.02

-0.0

40.

020.

00-0

.09*

**0.

00[0

.76]

[-0.6

3][1

.42]

[0.0

2][-7

.42]

[0.8

4]D

omes

tic c

redi

t to

priv

ate

sect

or

by b

anks

(% G

DP)

0.

11-0

.28*

0.04

***

0.45

-0.3

5*0.

02**

[0.6

4]

[-1.7

7]

[3.4

1]

[1.2

6]

[-1.7

2]

[2.4

9]

Polit

ical

inst

abili

ty in

dex

-17.

83**

4.87

-1

.95

[-2.2

7][1

.53]

[-1

.36]

L.

Dep

ende

nt2.

14**

*0.

12

-0.6

1***

2.40

-0

.99

-0.0

1[8

.34]

[0.8

7]

[-5.2

5]

[1.1

5]

[-0.9

9][-0

.44]

Obs

erva

tions

18

6 18

6 18

6 20

0 20

0 20

0

Kai

ser-

Mey

er-O

lkin

0.

50

0.41

0.

36

0.50

0.

42

0.50

A

R(1

) (p)

0.

25

0.11

0.

08

0.12

0.

07

0.09

A

R(2

) (p)

0.

28

0.36

0.

39

0.16

0.

31

0.23

H

anse

n (p

) 0.

37

0.94

0.

62

0.29

0.

87

1.00

In

stru

men

ts

11

17

13

9 15

22

*,

**,

***

indi

cate

s si

gnifi

cant

effe

cts

at th

e 1

perc

ent,

5 pe

rcen

t, an

d 10

per

cent

leve

l, re

spec

tivel

y. T

he d

epen

dent

var

iabl

es a

re th

e th

ree

dim

ensi

ons

of to

tal l

abou

r pro

duct

ivity

gr

owth

in e

very

yea

r fro

m 1

990

to 2

011.

We

use

the

xtab

ond2

pac

kage

pro

vide

d by

Roo

dman

(20

09).

The

coef

ficie

nts

are

base

d on

the

one-

step

GM

M e

stim

atio

n w

ith a

ll th

e co

ntro

l var

iabl

es tr

eate

d as

pot

entia

lly e

ndog

enou

s in

the

sys-

GM

M. T

he c

olla

psed

exp

lana

tory

var

iabl

es a

re ta

ken

as in

stru

men

ts fo

r the

diff

eren

ced

equa

tion

whe

reas

bot

h le

vels

an

d fir

st d

iffer

ence

s of

the

cont

rol v

aria

bles

are

take

n as

inst

rum

ents

for t

he le

vel e

quat

ion.

The

num

ber o

f ins

trum

ents

use

d in

the

estim

atio

n is

repo

rted

in th

e ta

ble,

its

coun

t is

entir

ely

base

d on

the

num

ber o

f “co

llaps

ed” i

nstru

men

ts. H

anse

n te

st o

f ove

riden

tific

atio

n ch

ecks

the

valid

ity o

f the

inst

rum

ents

und

er th

e nu

ll hy

poth

esis

of a

bsen

ce o

f cor

rela

tion

with

the

resi

dual

s. T

he A

R(1

) and

AR

(2) t

ests

che

ck fo

r the

firs

t-ord

er o

r sec

ond-

orde

r ser

ial c

orre

latio

n of

the

resi

dual

s un

der t

he n

ull h

ypot

hesi

s of

no

seria

l cor

rela

tion.

Rob

ust

stan

dard

err

ors

repo

rted

in b

rack

ets.

Pop

ulat

ion

anal

ytic

wei

ghts

app

lied.

36

Tabl

e B

6 Fu

ll SY

S-G

MM

on

the

thre

e la

bour

pro

duct

ivity

gro

wth

com

pone

nts,

add

ition

al c

ontr

ols

SYS-

GM

M

SYS-

GM

M

SYS-

GM

M

With

in

Bet

wee

n st

atic

Jo

int

Inde

x Z

= FD

I Z=

Res

earc

h Z=

Lite

racy

Z=

Pat

ents

Z

= FD

I Z=

Res

earc

h Z=

Lite

racy

Z=

Pat

ents

Z

= FD

I Z=

Res

earc

h Z=

Lite

racy

Z=

Pat

ents

I

II IV

V

VII

IX

XI

XII

XIII

XV

XVII

XVIII

EF

SI

3.43

1.

84

-3.2

91.

94

16.7

2**

17.0

2**

17.4

7*

17.3

6*

1.96

***

1.62

**

1.66

**

1.55

**

[0.5

9]

[0.4

0]

[-1.0

2][0

.32]

[2

.08]

[2

.00]

[1

.89]

[1

.91]

[2

.68]

[2

.20]

[2

.51]

[2

.34]

R

ural

pop

ulat

ion

(% o

f tot

al p

opul

atio

n)

0.01

-0

.18

-0.0

9-0

.21

-0.7

8-0

.12

-0.1

8-0

.34

0.07

0.

02**

-0

.00

0.02

[0

.08]

[-0

.47]

[-0.5

3][-0

.48]

[-1.0

5][-0

.30]

[-0.7

6][-0

.96]

[1.4

5]

[2.2

1]

[-0.1

3][1

.11]

Tr

ade

open

ness

0.

05

0.04

0.11

0.11

-0.2

7-0

.14

-0.0

5-0

.03

0.00

0.

01

0.01

0.01

[0

.21]

[0

.18]

[0.8

3][0

.35]

[-0.9

3]

[-0.5

8]

[-0.2

2]

[-0.1

4]

[0.0

1]

[0.4

6]

[0.7

0][0

.37]

R

EER

0.

03

0.03

*0.

000.

02-0

.06*

**-0

.07*

*-0

.04*

*-0

.05*

**0.

01*

0.01

0.

010.

01

[1.4

6]

[1.7

6][0

.02]

[0.7

6][-9

.66]

[-2

.20]

[-2

.23]

[-2

.99]

[1

.65]

[1

.13]

[1

.12]

[1.2

3]

Ln G

DP

per c

apita

-1

.36

0.19

0.74

0.34

0.42

-1

.92

-3.3

2-2

.10

-0.5

2***

-0.6

2***

-0.5

5***

-0.5

7***

[-0.6

5][0

.33]

[1.0

5][0

.57]

[0.2

1]

[-1.5

7][-1

.29]

[-1.3

4][-3

.86]

[-2.8

0][-3

.15]

[-3.5

7]D

omes

tic c

redi

t -0

.00

-0.1

1-0

.08

-0.4

2-0

.45

-0.1

8-0

.18*

**-0

.65

0.05

**0.

02**

*0.

02**

*0.

04*

[-0.0

9][-0

.62]

[-1.0

5][-0

.56]

[-1.3

6][-0

.83]

[-2.8

8][-1

.22]

[1.9

6][4

.03]

[4.1

9][1

.83]

Z 4.

29-0

.02

0.12

0.02

-0.0

1-0

.26

3.43

0.03

-0.1

20.

12-0

.02*

*-0

.00

[0.7

6][-0

.23]

[0.4

0][0

.55]

[-0.9

3][-0

.79]

[0.8

8][0

.97]

[-1.5

9][0

.79]

[-2.3

4][-1

.03]

L.D

epen

dent

var

iabl

e 0.

410.

460.

24**

*0.

270.

100.

170.

160.

11-0

.09

-0.0

7-0

.06

-0.0

7[0

.60]

[0

.77]

[17.

83]

[0.3

8][1

.16]

[1.0

3][1

.63]

[1.0

7][-0

.42]

[-0.2

7][-0

.28]

[-0.3

1]

Obs

erva

tions

20

6 19

7 17

7 20

6 20

6 17

7 20

6 20

6 17

7 20

6 19

7 20

6 H

anse

n-J

(p)

0.26

0.

30

0.31

0.

31

0.25

0.

35

0.55

0.

91

1.00

1.

00

1.00

1.

00

AR(1

) (p)

0.

19

0.16

0.

29

0.10

0.

08

0.10

0.

10

0.07

0.

09

0.08

0.

08

0.08

AR

(2) (

p)

0.19

0.

16

0.33

0.

09

0.34

0.

36

0.34

0.

35

0.51

0.

77

0.63

0.

67

KM

O

0.38

0.

38

0.32

0.

38

0.66

0.

61

0.66

0.

66

0.50

0.

50

0.50

0.

50

N In

stru

men

ts

9 9

9 9

12

12

12

12

22

22

22

22

*, *

*, *

** in

dica

tes

sign

ifica

nt e

ffect

s at

the

1 pe

rcen

t, 5

perc

ent,

and

10 p

erce

nt le

vel,

resp

ectiv

ely.

See

tabl

e ab

ove

for

note

s. F

DI (

Fore

ign

Dire

ct In

vest

men

ts: p

erce

nt G

DP

), R

esea

rche

rs (t

ot F

TE o

ver 1

0000

0 fa

rmer

s), P

aten

ts (T

otal

pat

ents

app

licat

ions

, dire

ct a

nd P

CT

natio

nal p

hase

ent

ries)

, Lite

racy

(You

th L

itera

cy R

ate

15-2

4 ye

ars

[per

cent

]).

37

FAO AGRICULTURAL DEVELOPMENT ECONOMICS WORKING PAPERSThis series is produced by the Agricultural Development Economics Division (ESA) of the Food and Agriculture Organization of the United Nations (FAO) since 2001 to share findings from research produced by the Division and elicit feedback for the authors.

It covers different thematic areas, such as food security and nutrition global trends and governance; food security and resilience; sustainable markets, agribusinesses and rural transformations; and climate-smart agriculture.

The analysis and conclusions are those of the authors and do not indicate concurrence by FAO.

The complete series is available at: www.fao.org/economic/esa/working-papers

The Agricultural Development Economics Division (ESA) is the focal point for FAO’s research and policy analysis on agricultural and economic development. The Division produces evidence-based policy analysis and strengthens the capacity of member countries to improve decision-making on food security and nutrition, resilience, climate-smart agriculture, sustainable markets, agribusinesses and rural transformations.

CONTACTS

Agricultural Development Economics Division (ESA) Food and Agriculture Organization of the United Nations (FAO)

www.fao.org/economic/agricultural-development-economicswww.fao.org/economic/esaESA-Director@fao.org

I8422EN/1/01.18

ISBN 978-92-5-130155-5

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