Chapter 4. Increasing Productivity Growth in Small Middle-Income Countries

Lamin Y Leigh

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Author: Mr. Lamin Y Leigh

Language:: English

Keywords:: BOOK; government; reserve; Small Middle-Income Country; TFP determinant; TFP data; credit market; political economy; TFP contribution; Productivity; Labor markets; Foreign direct investment; Sub-Saharan Africa

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Abstract

As indicated in Chapter 1, prudent macroeconomic management and improved institutional settings in many of the small middle-income countries (SMICs) in sub-Saharan Africa resulted in impressive economic performance in the past few decades.1

However, in many of the SMICs, as in middle-income countries in other regions, trend growth rates are slowing, reflecting the reduced contribution of total factor productivity (TFP). Per capita real GDP growth for most of these countries has fallen short of the range of the 3–4 percent average needed to avoid the so-called middle-income trap (Figures 4.1, 4.2, and 4.3).^2,3

The growth moderation in many SMICs in sub-Saharan Africa reflects the slowdown in the contribution of TFP to growth, which has reduced their potential to graduate from middle-income status into high-income status (Figure 4.2, and Aiyar and others 2013).⁴ Thus, it is critical that SMICs in sub-Saharan Africa reinvigorate policies to boost TFP growth.

This chapter focuses on identifying policy reforms that would increase productivity growth—a key driver of long-term growth prospects.⁵ The chapter explores policy options that could boost productivity growth in SMICs in sub-Saharan Africa by analyzing the role of productivity in the growth dynamics of these countries using a cross-country study. The chapter contributes to the existing growth literature in two ways: First, it looks not only at the level of education but at its quality and the gap between skills supply and demand. It does so by introducing the skills-mismatch index as an indicator explaining TFP growth. Second, the chapter looks at the impact on productivity growth of macro-stability-friendly forms of financial inclusion.⁶ The main estimation challenges involve endogeneity, cross-country heterogeneity among the TFP determinants, and limited data availability. As a result, we use several econometric techniques in an attempt to account for the endogeneity and heterogeneity problems as well as to provide robustness to our estimates.

Our analysis suggests that structural reforms are needed to foster TFP growth and to accelerate convergence to higher income levels. In particular, boosting productivity growth would require reforms in the financial sector; reducing regulatory barriers to firms; improving the quality of public spending, most notably on secondary and tertiary education to reduce the skills mismatch; alleviating infrastructure bottlenecks; deepening capital markets; and investing in research and development and new technologies. In addition, we find that the extent to which the government can close the infrastructure gap by borrowing is limited, because after a certain threshold, government debt’s marginal impact on productivity growth becomes negative.

The chapter also touches on political economy considerations in the implementation of structural reforms (Box 4.1). Implementing structural reform is not an easy task in any country. Up-front economic and political costs that are usually incurred in the early stages of implementing difficult reforms—including lower growth, the redistribution of income, frictional unemployment,⁷ and the erosion of oligopoly rents—mean that a strong constituency of stakeholders usually favors the status quo since they stand to lose from reforms.

Box 4.1

Political Economy and Structural Reforms in Small Middle-Income Countries

Structural reform is not an easy task in any country, and its success is often influenced by political economy constraints. Creative solutions must be found to overcome these obstacles. In some cases a competitive social and political bargain in which no major stakeholder in the economy has veto power and each interest group has an incentive to come to the table for mutually beneficial solutions can be a mechanism for generating consensus around a package of mutually reinforcing reforms. Mauritius has been a model of such compromises, where a potentially divisive ethnic mix gave birth to successive coalition governments and generous social benefits for all. This approach has been successful, with the country embarking on successive waves of structural reforms. Why have other SMICs not been able to replicate this social and political bargain mechanism to speed up the implementation of productivity-enhancing reforms? Are the impediments related to capacity constraints? Or does the dualistic nature of these SMIC economies (highly unequal societies with poverty rates reminiscent of large emerging markets—see Figure 4.1.1) make it difficult to generate consensus for implementing such productivity-enhancing structural reforms? In some cases, the desire to promote more public investment clashes with social aspirations that lean toward more public services or income redistribution, especially in highly unequal societies with poverty rates close to those of low-income countries (for example, Lesotho and Swaziland). Chapter 6 of this book provides a fuller treatment of the political economy aspects of reform in SMICs.

The rest of the chapter is organized as follows: First, the chapter provides a literature review of the determinants of TFP growth and subsequently discusses stylized facts. The empirical analysis is then presented. Finally, the chapter discusses the conclusions and the policy implications for SMICs and future areas of research.

Literature Review: Determinants of Total Factor Productivity

This section reviews the main determinants of TFP from the relevant literature. TFP contributes to economic growth by improving resource allocation, innovation, and productivity of each of the factor inputs, providing an opportunity to grow more efficiently and sustainably in the long term. Although the concept of TFP is important for enhancing growth sustainability, measurement of TFP remains subject to debate.⁸ As a result, a growth decomposition and TFP-related information should be analyzed with caution. Conceptually, TFP is the growth residual that cannot be accounted for by observed increases in factor inputs and a formulated production function (Solow 1957). Practically, it could be anything that is unobserved—including not only technological change but also measurement errors and a misspecified production function (Abramovitz 1956; Felipe 1999; Hulten 2001). Nonetheless, most economists agree on the importance of TFP for long-term output growth (see, for example, Krugman 1994; Easterly and Levine 2001).

Based on the existing theoretical and empirical literature, a number of determinants have an impact on TFP’s contribution to growth. These determinants can be categorized into several conceptual variables as follows:

Macroeconomic variables. A number of macroeconomic factors could play a role in determining TFP and its growth because they may influence both input use and allocative efficiency. The two macroeconomic variables often discussed in the literature are inflation and size of government. The relationship between inflation and productivity growth is found to be negative in a number of cross-country empirical studies (see, for example, Fischer 1993; De Gregorio 1993; Ghosh and Phillips 1998; Loko and Diouf 2009; Espinoza 2012; Barro 2013). Although the role of government is potentially an important factor in growth performance, the relationship between these two variables remains ambiguous. Empirical studies adopt various measures of the role of government, ranging from the size of the budget and provision of public services to prudent macroeconomic policies. The size of the public sector can both foster and hinder productivity growth (Ranis 1989). Provision of basic public goods and economic infrastructure would enhance overall productivity (Ghali 1999). However, a number of studies point to the negative effect of government spending on economic growth, owing to government inefficiencies and low quality of public spending (see, for instance, Barro 1991; Sala-i-Martin, Doppelhofer, and Miller 2004; Loko and Diouf 2009; Danquah, Moral-Benito, and Outtara 2013).⁹

Openness and technology creation and transfer. Openness to the world economy is another important factor explaining TFP growth. Trade openness increases international contacts and can be a source of learning because technology is often embodied in goods (Lewis 1979; Grossman and Helpman 1993; Sachs and Warner 1995; Sala-i-Martin, Doppelhofer, and Miller 2004; Dollar and Kraay 2004). Foreign direct investment (FDI) is also a key channel for the transfer of advanced technology and research and development (R&D) knowledge from industrial to developing countries. In addition, Loko and Diouf (2009) emphasize that the level of FDI reflects the macroeconomic environment of a country.

Quality of labor input and efficient allocation. An increase in the human capital base can have a positive impact on TFP growth by facilitating structural change and technological improvement (Romer 1990; Barro 2001). In addition, human capital can help absorb positive externalities from international trade and FDI (Loko and Diouf 2009). The gaps between the supply of and demand for skills could account for a decline in TFP growth, especially in low-income countries that make use of technology developed by advanced economies (Acemoglu and Zilibotti 2001).

Female labor force participation. Higher labor force participation, particularly among women, may increase TFP growth if technological progress and the female labor force are complementary (Weil and Galor 2000; Madsen and Ang 2013). However, some of these studies show that the impact of increased female labor force participation on productivity growth is likely to be concave and decline over time (McGuckin and van Ark 2005).

Sectoral composition and structural change. Many studies address the importance of structural change, captured by sectoral production or sectoral employment, in determining TFP growth. A transition from concentration in less productive to more productive sectors would positively affect aggregate productivity growth (Lewis 1954; Ranis and Fei 1961). Although most of the literature finds a positive relationship between structural change and TFP growth at the cross-country level (see, for example, Poirson 2000; Jaumotte and Spatafora 2007; Loko and Diouf 2009), some specific country studies show ambiguous results owing to the preconditions of market institutions, openness, and labor market mobility (Lu 2002). A less diversified economy could portend risk and vulnerability, which may, in turn, undermine TFP growth.

Monetary and financial development. The positive impact of financial sector development on productivity has been well documented (see, for instance, Roubini and Sala-i-Martin 1992; King and Levine 1993; Aghion, Howitt, and Mayer-Foulkes 2005). The main intuition is that financial markets enhance productivity through efficient capital reallocation and that financial development also brings in technological innovation. Financial sector development could also attract investment, and thus capital deepening, which could, in turn, affect productivity.

Institutional and regulatory factors. Although reverse causation is possible, many recent papers have shown that institutional factors can enhance productivity growth by ensuring resource reallocation efficiency and encouraging a good economic environment for investment (for example, Hall and Jones 1999; Acemoglu, Johnson, and Robinson 2004; Glaeser and others 2004; Acemoglu and Johnson 2003; Easterly 2006). In addition, political instability is often regarded as another institutional factor undermining productivity growth because it inhibits investment in innovation and creates market distortions that are likely to lower productive efficiency (for example, Edwards 1998; Nachega and Fontaine 2006; Aisen and Veiga 2013). Furthermore, business restrictions, such as on size, licensing, and state ownership, could result in misallocation of resources at the firm level, which would, in turn, lower aggregate TFP (Hsieh and Klenow 2009).¹⁰

Table 4.1 presents a summary of the key influential factors from the literature and the variables that are used in the qualitative and quantitative analyses of this chapter.

Table 4.1

Summary of Total Factor Productivity (TFP) Determinants and Variables

Source: Authors.

Table 4.1

Summary of Total Factor Productivity (TFP) Determinants and Variables

TFP Determinants from the Literature	Variables
Macroeconomic conditions	Inflation, government debt, public employment
Openness, and technology creation and transfer	Trade, foreign direct investment, research and development, infrastructure
Quality of labor inputs and efficient allocation	Years of schooling, skills mismatch
Female labor force participation	Female labor force participation rate
Sectoral composition and structural change	Sector shares of output, economic diversification
Monetary and financial development	Credit, market capitalization
Institution and regulatory factors	Labor and business regulation indices, doing business indicators, income inequality

Source: Authors.

Table 4.1

Summary of Total Factor Productivity (TFP) Determinants and Variables

TFP Determinants from the Literature	Variables
Macroeconomic conditions	Inflation, government debt, public employment
Openness, and technology creation and transfer	Trade, foreign direct investment, research and development, infrastructure
Quality of labor inputs and efficient allocation	Years of schooling, skills mismatch
Female labor force participation	Female labor force participation rate
Sectoral composition and structural change	Sector shares of output, economic diversification
Monetary and financial development	Credit, market capitalization
Institution and regulatory factors	Labor and business regulation indices, doing business indicators, income inequality

Source: Authors.

Stylized Facts

TFP has played a prominent role in growth episodes in many SMICs. In earlier decades, in a relatively supportive global economic environment, SMICs generally used capital deepening in the form of infrastructure investment programs and FDI to bolster productivity and thus growth (Table 4.2). However, in more recent years, in the face of a less favorable external environment, the growth momentum in many SMICs waned because structural reforms that might sustain TFP growth—such as a business-friendly environment—were not fully in place.¹¹ This limitation was compounded by weak regulatory systems, which often impinged on their institutional setup and prevented their TFP from rising further, although this issue is being addressed in many SMICs.¹²

Table 4.2

Structural Impediments to Productivity Enhancement in SMICs

Source: IMF staff compilation.Note: MIC = middle-income country; SMIC = small middle-income country.

Table 4.2

Structural Impediments to Productivity Enhancement in SMICs

	Macroeconomic Environment	Labor Market	Financial Sector	Public Sector	Environment
Resource-Rich Middle-Income Countries
Botswana	Lack of economic diversification	High reservation wage and skills mismatch	High concentration of bank loans to households	Effectiveness of tax system; quality of public spending
Namibia	Lack of economic diversification	Lack of skills	High concentration of bank loans to households, in particular mortgages	Quality of public spending (not getting value for money) and large public sector
Non-Resource-Rich and Non-Island Middle-Income Countries
Lesotho	Lack of economic diversification	High and persistent structural unemployment; HIV prevalence among labor force	Small and inefficient, high lending rates; large risk premiums	Large and ineffective public sector distorts labor market incentives and creates unfavorable business environment	Droughts; soil degradation
Swaziland	Lack of economic diversification; depressed capital accumulation; uncompetitive business environment			Large expansion with no clear evidence of high fiscal multiplier
Non-Resource-Rich and Island Middle-Income Countries
Cabo Verde	Lack of economic diversification; reliance on imports for food, fuel, manufacturing items, and capital goods	Low labor market efficiency owing to restrictive labor market regulations and shortage of technical skills	Predominantly bank-based, high concentration (four systematic banks account for more than 80 percent); sizable exposure to the real sector	Large public investment in infrastructure financed by borrowing, leading to deteriorating fiscal balance and high public debt
Mauritius	Although economy is relatively diversified, there is still reliance on European markets for trade and tourism	Structural labor market problems, including youth unemployment			Environmental degradation and weather-related disasters
Seychelles	Lack of economic diversification; reliance on imports for food, fuel, manufacturing items, and capital goods	Structural problems including shortage of adequate skills and expertise; complex procedures for hiring qualified foreign workers	Lack of financial deepening	Inefficient public investment; presence of large parastatals operating in quasi-monopoly environments	Environmental degradation and weather-related disasters

Source: IMF staff compilation.Note: MIC = middle-income country; SMIC = small middle-income country.

Table 4.2

Structural Impediments to Productivity Enhancement in SMICs

	Macroeconomic Environment	Labor Market	Financial Sector	Public Sector	Environment
Resource-Rich Middle-Income Countries
Botswana	Lack of economic diversification	High reservation wage and skills mismatch	High concentration of bank loans to households	Effectiveness of tax system; quality of public spending
Namibia	Lack of economic diversification	Lack of skills	High concentration of bank loans to households, in particular mortgages	Quality of public spending (not getting value for money) and large public sector
Non-Resource-Rich and Non-Island Middle-Income Countries
Lesotho	Lack of economic diversification	High and persistent structural unemployment; HIV prevalence among labor force	Small and inefficient, high lending rates; large risk premiums	Large and ineffective public sector distorts labor market incentives and creates unfavorable business environment	Droughts; soil degradation
Swaziland	Lack of economic diversification; depressed capital accumulation; uncompetitive business environment			Large expansion with no clear evidence of high fiscal multiplier
Non-Resource-Rich and Island Middle-Income Countries
Cabo Verde	Lack of economic diversification; reliance on imports for food, fuel, manufacturing items, and capital goods	Low labor market efficiency owing to restrictive labor market regulations and shortage of technical skills	Predominantly bank-based, high concentration (four systematic banks account for more than 80 percent); sizable exposure to the real sector	Large public investment in infrastructure financed by borrowing, leading to deteriorating fiscal balance and high public debt
Mauritius	Although economy is relatively diversified, there is still reliance on European markets for trade and tourism	Structural labor market problems, including youth unemployment			Environmental degradation and weather-related disasters
Seychelles	Lack of economic diversification; reliance on imports for food, fuel, manufacturing items, and capital goods	Structural problems including shortage of adequate skills and expertise; complex procedures for hiring qualified foreign workers	Lack of financial deepening	Inefficient public investment; presence of large parastatals operating in quasi-monopoly environments	Environmental degradation and weather-related disasters

Source: IMF staff compilation.Note: MIC = middle-income country; SMIC = small middle-income country.

Box 4.2

Structural Transformation in Mauritius and Namibia

The reallocation of economic activity from low- to high-productivity activities lies at the heart of the rapidly growing work on structural transformation. An alternative view is that drivers of growth cause both growth and structural change to move simultaneously. The period 1995–2010 was characterized by high growth in a significant number of countries in sub-Saharan Africa, most of which have experienced some degree of structural transformation, albeit at different speeds. Although the share of agriculture in GDP in small middle-income countries (SMICs) in sub-Saharan Africa has fallen somewhat, employment has not moved from agriculture into industry or services. This contrasts with Asian economies that have registered strong growth over the years (Table 4.2.1). An empirical study by Dabla-Norris and others (2013) suggests that product and labor market reforms, openness to trade, and access to finance are factors that explain the variation in sectoral shares across countries.

Table 4.2.1

Change in Output, Employment Shares, and GDP per Capita, 1990–2011

Sources: Haver Analytics; World Bank, World development Indicators; and IMF staff calculations.Note: … = not available; MICs = middle-income countries; SSA = sub-Saharan Africa.

^¹Purchasing power parity, 2005 constant U.S. dollars.

Table 4.2.1

Change in Output, Employment Shares, and GDP per Capita, 1990–2011

	Output Shares			Employment Shares			Average GDP per Capita¹
	Agriculture	Industry	Services	Agriculture	Industry	Services	Average GDP per Capita¹
Asian Countries
Bangladesh	−0.5	0.5	0.0	−0.3	0.4	−0.1	3.6
Cambodia, 1998–2010	−1.8	0.7	1.0	−1.5	0.8	0.8	5.6
Vietnam	−0.7	0.8	−0.1	−1.2	0.5	0.6	5.5
Indonesia, 1993–2011	−0.2	−0.1	0.3	−0.9	0.3	0.6	3.2
India	−0.7	0.0	0.7	−0.7	0.4	0.3	4.8
SSA MICs, 1995–2010 unless indicated otherwise
Botswana	−0.5	−0.1	0.1	0.9	−0.4	−0.1	3.0
Cabo Verde	−0.4	−0.2	0.1	−0.2	−0.1	0.2	7.2
Lesotho	−0.3	0.4	−0.1	…	…	…	2.7
Mauritius, 2000–10	−0.3	−0.2	0.1	−0.3	−0.3	0.3	3.4
Namibia	−0.4	0.0	0.1	−0.2	0.2	0.1	2.3
Seychelles, 2004–10	−0.4	−0.1	0.0	…	…	…	2.9
Swaziland	−0.1	−0.1	0.2	…	…	…	1.1

Sources: Haver Analytics; World Bank, World development Indicators; and IMF staff calculations.Note: … = not available; MICs = middle-income countries; SSA = sub-Saharan Africa.

^¹Purchasing power parity, 2005 constant U.S. dollars.

Table 4.2.1

Change in Output, Employment Shares, and GDP per Capita, 1990–2011

	Output Shares			Employment Shares			Average GDP per Capita¹
	Agriculture	Industry	Services	Agriculture	Industry	Services	Average GDP per Capita¹
Asian Countries
Bangladesh	−0.5	0.5	0.0	−0.3	0.4	−0.1	3.6
Cambodia, 1998–2010	−1.8	0.7	1.0	−1.5	0.8	0.8	5.6
Vietnam	−0.7	0.8	−0.1	−1.2	0.5	0.6	5.5
Indonesia, 1993–2011	−0.2	−0.1	0.3	−0.9	0.3	0.6	3.2
India	−0.7	0.0	0.7	−0.7	0.4	0.3	4.8
SSA MICs, 1995–2010 unless indicated otherwise
Botswana	−0.5	−0.1	0.1	0.9	−0.4	−0.1	3.0
Cabo Verde	−0.4	−0.2	0.1	−0.2	−0.1	0.2	7.2
Lesotho	−0.3	0.4	−0.1	…	…	…	2.7
Mauritius, 2000–10	−0.3	−0.2	0.1	−0.3	−0.3	0.3	3.4
Namibia	−0.4	0.0	0.1	−0.2	0.2	0.1	2.3
Seychelles, 2004–10	−0.4	−0.1	0.0	…	…	…	2.9
Swaziland	−0.1	−0.1	0.2	…	…	…	1.1

Sources: Haver Analytics; World Bank, World development Indicators; and IMF staff calculations.Note: … = not available; MICs = middle-income countries; SSA = sub-Saharan Africa.

^¹Purchasing power parity, 2005 constant U.S. dollars.

Mauritius. Mauritius is often cited as a successful structural transformation story in the region. Several factors underpin that success, including a diverse and competitive political system supportive of economic reforms, and better sequencing of reforms, particularly investment in appropriate education and training, which enhanced the economy’s absorptive capacity and buttressed the authorities’ resolve to create new sectors to drive growth. In addition, flexibility in acquiring necessary skills in the labor market, attracting foreign direct investment (the double taxation treaty with India), and the coherence of micro and macro policies also contributed to Mauritius’s transformation. However, the country also faces some challenges, including public sector administration and efficiency of public services. The authorities plan to accelerate the transformation of the island into a cyberstate while leveraging various opportunities offered by the vast potential of ocean-based sectors. This transformation hinges on the country’s ability to continuously enhance its competitiveness and improve the efficiency of public services.

Namibia. The relatively young nation of Namibia has been successful in achieving political and macroeconomic stability, which has helped improve the living standards of the population. The country is also actively enhancing the diversification process, and its focus on business-oriented infrastructure and support has contributed to a rise in manufacturing activity. However, Namibia’s exports rely heavily on mining. Namibia embarked on an export diversification strategy through several initiatives, including the creation of export processing zones and the establishment of small and medium enterprise development programs. In addition, the Namibian authorities are also exploring the development of commodity-based value chains to enhance growth and economic diversification. However, Namibia still faces a skills shortage in its economy. Futhermore, difficulties in obtaining working permits are a challenge for the private sector. These factors, combined with the socioeconomic challenges of a dual economy, create difficulties in opening the labor market further to pave the way for the high skills that are needed to boost economy-wide productivity and thus potential growth.

During the years of high historical growth, while FDI inflows and infrastructure investments were crucial for enhancing productivity growth, high regulatory burdens on firms hampered productivity growth (IMF 2015).

Years of significant FDI inflows (including indirect inflows such as import duties) helped most of the SMICs accumulate productive capital and bolster TFP growth. The correlation between FDI and TFP growth has been positive in most SMICs (IMF 2015).
The SMICs managed to address their infrastructure gaps by ensuring sufficient coverage of basic infrastructure services such as electricity, water, and telecommunications networks, which constituted the bedrock of productivity growth (IMF 2015).
However, the confluence of a less favorable external environment and regulatory barriers to private sector development negatively affected productivity growth in many SMICs. The structural constraints include regulatory burdens that inhibit private sector development, restrictive labor market regulations (for example, restrictions on employing foreign labor), and high financial access costs.

Empirical Analysis

Methodology

This section assesses empirically the relationship between macroeconomic, structural, and institutional variables and TFP growth in SMICs. In addition to the standard factors and channels identified in the literature, this chapter also looks at two relatively less explored areas. First, it goes beyond the level of education and looks at its quality, because in many SMICs, although the literacy rates are high and governments spend a significant portion of budgetary resources on education, a lack of relevant skills has contributed to persistently high unemployment. Second, the chapter looks at the relationship between stability-friendly forms of financial inclusion and TFP growth.

We use several panel data techniques to identify determinants of TFP growth and estimate their impact. These techniques can complement each other because each has different practical advantages and limitations (Annex 4.1). The following methods are used:

Dynamic panel estimation. This method allows the lag-dependent variable to affect the dependent variable and controls for endogeneity (Arellano and Bond 1991; Blundell and Bond 1998).¹³ Given that it is conducted at the country and year levels, the dynamic panel estimates are expected to provide short-term cross-country evidence. Although this method has been widely used in recent cross-country empirical analyses, it does not allow for heterogeneous effects of the TFP determinants across countries.
Cointegration for heterogeneous panels. This approach enables us to identify and estimate the long-term relationship between TFP growth and its endogenous determinants.¹⁴ In addition, it allows for heterogeneity among countries in the panel and adjusts for potential endogeneity and cross-sectional dependence. However, the cointegration analysis is relatively more data intensive. This drawback, together with limited data availability for middle-income countries, restricts the number of explanatory variables that can be included in each specification.
Binary response model (panel probit analysis). The panel probit analysis measures the extensive margin effects to provide a broader understanding of the structural policy factors that contribute to positive TFP. That is, the outcome of interest is a binary response variable for TFP (1 if TFP contributes positively to growth, and 0 otherwise). Because the approach is relatively simple, it allows us to explore the roles of other variables, whose data availability is limited, on productivity growth in the medium term.¹⁵ These variables include the regulatory environment in the product and labor markets and the size of the financial sector. In addition, this approach provides estimates of the country-specific predictive probabilities for TFP contribution to growth, based on various model specifications.

Data Issues

Our data set comes from five primary sources: the Penn World Tables (PWT), the IMF’s World Economic Outlook (WEO), the World Bank’s World Development Indicators, the Economic Freedom of the World Project, and the Barro-Lee database (2013). Our data set covers 33 upper-middle-income countries for the period 1980–2010 (Annex 4.2). For TFP, we used the human-capital-augmented TFP data calculated by the latest PWT for 30 of the countries in our panel data. The sample size was driven by the chapter’s focus on MICs and the availability of data across this group. We derived TFP for Cabo Verde, Seychelles, and Swaziland with the latest available country data using a growth-accounting tool from the World Bank’s Economic Policy and Debt Department. TFP derived this way closely matches TFP from the PWT for other countries in our panel data. We constructed a skills-mismatch index, following the methodology of Estevão and Tsounta (2011) (Annex 4.1).¹⁶

For many of the SMICs in our study, data availability and quality of those statistics are issues. For example, at the time this chapter was prepared, Swaziland had not published national accounts data in a number of years, so the data used to calculate TFP came from data published in the World Development Indicators database.

Empirical Results

The results across different methods depict a broadly consistent picture. Our analysis confirms the findings in the literature that traditional variables such as trade openness and macroeconomic stability have a significant and positive impact on productivity growth (Table 4.3). In addition, the skills-mismatch index has a statistically significant negative impact on productivity growth in both the short and long terms. Another important factor is government debt, which has a concave relationship with TFP growth: at low levels, debt has a positive impact on TFP growth, but at high levels its impact on TFP growth becomes negative. Our estimates suggest that in the long term, the threshold of public-debt-to-GDP ratio at which its marginal impact on TFP becomes zero is 32–55 percent,¹⁷ whereas in the short term the threshold is 80 percent.

Table 4.3

Summary of the Empirical Results

Source: IMF staff calculations.Note: FDI = foreign direct investment; SMEs = small and medium enterprises.(+)/(–) indicates ambiguous results; * indicates statistical significance at the 10 percent level.

^¹This probably reflects the high number of countries in our sample that have experienced financial crisis.

^²The impact of female participation becomes positive after controlling for the share of agriculture in GDP.

^³The credit and labor market regulation indices range from 0 to 7. A high value implies a less-restrictive regulatory system and/or a more competitive market.

Table 4.3

Summary of the Empirical Results

	Dynamic Panel (Short term)	Cointegration (Long term)	Panel Probit
Government-debt-to-GDP ratio	(–) or Concave*	(–) or Concave*	(+)/(–) or Concave*
Trade-to-GDP ratio	(+)	(+)*
FDI-to-GDP ratio	(+)	(+)/(−)*
Credit-to-GDP ratio		(+)/(−)¹*	(+)
Share of agricultural sector in GDP		(−)*
Share of manufacturing sector in GDP			(+)*
Female labor force participation	(+)/(−)	(+)/(−)²*
Inflation	(+)/(−)	(–)*	(−)*
Years of education	(+)	(+)*
Skills³ mismatch	(−)*	(−)*	(−)*
Credit market regulation index	(+)		(+)*
Labor market regulation index			(+)*
Goods market efficiency index	(+)/(−)
SMEs with credit line			(+)*

^¹This probably reflects the high number of countries in our sample that have experienced financial crisis.

^²The impact of female participation becomes positive after controlling for the share of agriculture in GDP.

^³The credit and labor market regulation indices range from 0 to 7. A high value implies a less-restrictive regulatory system and/or a more competitive market.

Table 4.3

Summary of the Empirical Results

	Dynamic Panel (Short term)	Cointegration (Long term)	Panel Probit
Government-debt-to-GDP ratio	(–) or Concave*	(–) or Concave*	(+)/(–) or Concave*
Trade-to-GDP ratio	(+)	(+)*
FDI-to-GDP ratio	(+)	(+)/(−)*
Credit-to-GDP ratio		(+)/(−)¹*	(+)
Share of agricultural sector in GDP		(−)*
Share of manufacturing sector in GDP			(+)*
Female labor force participation	(+)/(−)	(+)/(−)²*
Inflation	(+)/(−)	(–)*	(−)*
Years of education	(+)	(+)*
Skills³ mismatch	(−)*	(−)*	(−)*
Credit market regulation index	(+)		(+)*
Labor market regulation index			(+)*
Goods market efficiency index	(+)/(−)
SMEs with credit line			(+)*

^¹This probably reflects the high number of countries in our sample that have experienced financial crisis.

^²The impact of female participation becomes positive after controlling for the share of agriculture in GDP.

^³The credit and labor market regulation indices range from 0 to 7. A high value implies a less-restrictive regulatory system and/or a more competitive market.

However, these results should be interpreted with great caution because there are also some differences compared with the literature. These differences arise not only from different time periods and countries of interest, but also from the different methodologies adopted to deal with endogeneity and reverse-causality problems. The interpretation and estimates of the thresholds for the public-debt-to-GDP ratio in our study are different from those of Reinhart and Rogoff (2010) and Herndon, Ash, and Pollin (2013), who estimate a 90 percent threshold of government debt to GDP. Possible explanations for the different results are as follows: First, our study includes only MICs during the years 1980 through 2010, and controls for other heterogeneous structural characteristics across countries. Second, our interpretation of the estimates is based on the concept of marginal returns to government debt on TFP’s contribution to economic growth, whereas the earlier literature estimates the direct impact of government debt on GDP growth and a country’s vulnerability to a financial crisis. Third, it is important to emphasize that the literature has come to no consensus on a specific public debt threshold that triggers an adverse impact on economic growth. Country heterogeneity plays an important role in explaining variations in empirical results, and hence results from a cross-country study implying a common debt threshold may be misleading (Eberhardt and Presbitero 2015).

We also find some ambiguous results on other coefficient estimates that seem counterintuitive and inconsistent with the findings in other studies. Particularly, in some of our specifications we find a negative relationship between TFP and female participation in the labor force. Other results include a negative sign for the estimated coefficients for the credit-to-GDP ratio and tertiary education. Subsequent sections try to provide some explanation for these findings.

System Generalized Method of Moments (GMM)

The dynamic panel estimation results suggest that skills mismatch and government debt have statistically significant short-term effects on TFP growth.¹⁸ The selected results are presented in Table 4.4. TFP growth declines (in a statistically significant way) as the level of skills mismatches increases. For every 10 point increase in the skills-mismatch index, TFP growth declines by about 0.03 percentage points.¹⁹ The relationship between government debt and TFP appears to be concave.²⁰ This result implies that a higher level of government debt reduces TFP growth, and the negative impact increases with the level of debt. In addition, this relationship suggests that some initial government debt may improve TFP growth. However, its positive impact on TFP growth, when significant, declines with the level of debt. These results are robust even after controlling for variables for infrastructure development.²¹ Based on the results, the level of government debt at which the positive returns to government debt will decline to zero is 80 percent. That is, if the specifications are correct, additional government debt has a positive impact on TFP growth, but this positive effect declines as the debt level increase, and will become negative when the debt is greater than 80 percent of GDP.²²

Table 4.4

Dynamic Panel Result (System GMM)

Note: FDI = foreign direct investment; TFP = total factor productivity. One-step generalized method of moments (GMM) estimation method. Standard errors in parentheses.* p<0.1, ** p<0.05, *** p<0.01

Table 4.4

Dynamic Panel Result (System GMM)

	(1-A)	(1-B)	(2-A)	(2-B)	(3-A)	(3-B)
Lagged TFP		0.131 (0.091)		−0.165 (0.234)		0.139 (0.452)
Government-debt-to-GDP ratio (DBT)	0.031	0.004	0.222*	0.254*	−0.441*	−0.364*
	(0.070)	(0.059)	(0.120)	(0.132)	(0.238)	(0.185)
DBT²/100	−0.041	−0.026	−0.141*	−0.155**	0.320	0.248
	(0.036)	(0.030)	(0.068)	(0.068)	(0.215)	(0.185)
Trade-to-GDP ratio	0.010	0.004	0.097	0.106	−0.018	−0.009
	(0.034)	(0.021)	(0.088)	(0.084)	(0.059)	(0.042)
FDI-to-GDP ratio	0.110	0.033	0.037	0.130	0.201	0.235
	(0.168)	(0.099)	(0.242)	(0.256)	(0.261)	(0.187)
Female labor force participation	−0.390	−0.327	−0.155	−0.391	−0.318	−0.231
	(0.239)	(0.204)	(0.598)	(0.596)	(0.221)	(0.181)
Inflation	0.079	0.047	−0.028	−0.056	0.621	0.506
	(0.059)	(0.047)	(0.060)	(0.065)	(0.421)	(0.319)
Years of education			1.599	0.416	0.384	−0.268
			(2.745)	(2.914)	(1.492)	(0.743)
Skills mismatch			−0.279*	−0.304**	−1.715	−1.079
			(0.152)	(0.135)	(2.567)	(1.649)
Credit market regulation index					2.696 (2.230)	2.390 (2.680)
Goods market efficiency index					−0.993 (4.635)	0.639 (4.569)
Constant	13.686	13.094	−13.495	0.938	−5.572	6.809
	(9.097)	(7.956)	(18.461)	(20.017)	(68.703)	(56.218)
N	434	433	265	265	73	73
Countries	30	30	23	23	19	19

Note: FDI = foreign direct investment; TFP = total factor productivity. One-step generalized method of moments (GMM) estimation method. Standard errors in parentheses.* p<0.1, ** p<0.05, *** p<0.01

Table 4.4

Dynamic Panel Result (System GMM)

	(1-A)	(1-B)	(2-A)	(2-B)	(3-A)	(3-B)
Lagged TFP		0.131 (0.091)		−0.165 (0.234)		0.139 (0.452)
Government-debt-to-GDP ratio (DBT)	0.031	0.004	0.222*	0.254*	−0.441*	−0.364*
	(0.070)	(0.059)	(0.120)	(0.132)	(0.238)	(0.185)
DBT²/100	−0.041	−0.026	−0.141*	−0.155**	0.320	0.248
	(0.036)	(0.030)	(0.068)	(0.068)	(0.215)	(0.185)
Trade-to-GDP ratio	0.010	0.004	0.097	0.106	−0.018	−0.009
	(0.034)	(0.021)	(0.088)	(0.084)	(0.059)	(0.042)
FDI-to-GDP ratio	0.110	0.033	0.037	0.130	0.201	0.235
	(0.168)	(0.099)	(0.242)	(0.256)	(0.261)	(0.187)
Female labor force participation	−0.390	−0.327	−0.155	−0.391	−0.318	−0.231
	(0.239)	(0.204)	(0.598)	(0.596)	(0.221)	(0.181)
Inflation	0.079	0.047	−0.028	−0.056	0.621	0.506
	(0.059)	(0.047)	(0.060)	(0.065)	(0.421)	(0.319)
Years of education			1.599	0.416	0.384	−0.268
			(2.745)	(2.914)	(1.492)	(0.743)
Skills mismatch			−0.279*	−0.304**	−1.715	−1.079
			(0.152)	(0.135)	(2.567)	(1.649)
Credit market regulation index					2.696 (2.230)	2.390 (2.680)
Goods market efficiency index					−0.993 (4.635)	0.639 (4.569)
Constant	13.686	13.094	−13.495	0.938	−5.572	6.809
	(9.097)	(7.956)	(18.461)	(20.017)	(68.703)	(56.218)
N	434	433	265	265	73	73
Countries	30	30	23	23	19	19

Note: FDI = foreign direct investment; TFP = total factor productivity. One-step generalized method of moments (GMM) estimation method. Standard errors in parentheses.* p<0.1, ** p<0.05, *** p<0.01

The impact of the economic freedom and efficiency indicators on TFP growth is ambiguous.²³ Better credit market regulation seems to be positively correlated with TFP growth, but it is not statistically significant (Table 4.4, Columns 3-A and 3-B). The effects of goods market efficiency on TFP growth are ambiguous and are not robust to changes in model specification and the inclusion of a lagged dependent variable. Other variables included in the model specifications have economically plausible signs but are statistically insignificant.

Cointegration Analysis

Cointegration analysis confirms the importance of skills mismatch for productivity growth and the concave relationship between government debt and TFP growth.²⁴

The results suggest that skills mismatch has a statistically significant and negative impact on TFP growth in the long term, which underscores the importance of the quality of education (Table 4.5). An increase in the index by 10 points reduces long-term TFP growth by about 0.2 percentage point. This result is robust to the different model specifications.
The results also suggest that at a lower debt-to-GDP ratio the impact of government debt on TFP growth is positive, possibly reflecting the positive impact of public borrowing to finance capital spending and other public goods. However, when debt exceeds 32 percent of GDP the long-term marginal impact of government debt on TFP growth becomes negative. Also, the debt threshold increases to 55 percent of GDP when we control for market capitalization.²⁵
An alternative interpretation of the concavity could be given through the impact of government debt on the financial sector. At the initial stage, issuance of government debt contributes to the development of the financial market, which positively affects productivity growth. However, a high level of government debt starts to crowd out private investment and pushes up long-term interest rates, which has negative implications for productivity. These results broadly confirm the thrust of the findings of the system GMM analysis. The estimated threshold of the debt-to-GDP ratio obtained in the cointegration analysis is lower than the one obtained in the dynamic GMM analysis because it represents a long-term relationship, whereas dynamic GMM estimates are short-term effects.
Consistent with other studies, we find that macroeconomic stability, a small agricultural sector, and trade openness are conducive to TFP growth. The results suggest that high inflation and a large agricultural sector reduce TFP growth, while high FDI and large foreign trade relative to GDP support TFP growth.²⁶ An increase in the FDI-to-GDP ratio of 1 percentage point increases TFP growth by about 0.1 to 0.6 percentage point in the long term. However, when we include the share of foreign trade in GDP with FDI, the impact of FDI becomes negative.²⁷ Also, reducing the relative size of the agricultural sector would improve long-term TFP growth. The analysis reveals some ambiguous results as well. In particular, we find a negative relationship between female participation in the labor force and TFP growth.²⁸ However, when we control for the share of the agricultural sector in GDP, the impact of female participation turns positive in this specification. This finding suggests that the negative coefficient in the first specification could reflect the fact that in many of the sample countries women are more involved in the low-productivity agricultural sector. Another interesting result is that the impact of the credit-to-GDP ratio is negative in some specifications. Some of the countries in our sample, including Latin American and Asian countries, experienced credit expansions followed by financial crises, which could be driving this result. This finding may also highlight the negative long-term effects of financial crises on TFP growth, which generally make recovery from a financial crisis drawn out and harder. In contrast to the findings in the literature, the coefficient on tertiary education turns out to be negative when we include it together with years of schooling. This result might reflect the quality and efficiency of tertiary education in the countries in the sample.

Table 4.5

Cointegration Analysis Results

Note: FDI = foreign direct investment; MKT = market capitalization; TED = tertiary education. Estimated using the panel group mean fully modified ordinary least squares (FMOLS) method.* p<0.1, ** p<0.05, *** p<0.01

Table 4.5

Cointegration Analysis Results

	(I)	(II)	(III)	(IV)	(V)	(VI)
Skill mismatch	−1.92***	−1.87***	−1.30***	−1.62***	−0.14***
	0.017	0.019	0.015	1.59E-12	1.23E-03
Inflation	−3.32***	−4.23***	−7.99 ***			0.09
	0.247	0.234	0.360			0.074
Female labor force participation	−1.89***	0.64***	−3.08***	−0.84 ***	−0.3	−2.31***
	0.413	0.096	0.178	2.63E-12	0.60	0.278
FDI-to-GDP ratio	0.34***	0.59***	−2.56***	0.11**	−0.15***	−0.02**
	0.073	0.033	0.378	0.044	0.01	0.008
Credit-to-GDP ratio	0.09***	−0.44***		−1.06***		−0.07***
	0.018	0.025		9.30E-13		0.015
Trade-to-GDP ratio			1.44*** 0.066
Debt				1.41*** 1.54E-12	0.24*** 0.02
Debt²/100				−2.17*** 1.62E-12	−0.22*** 0.01
MKT					0.01*** 0.00
Years of education						6.45*** 1.706
TED						−1.01*** 0.094
Share of agricultural sector in GDP		−0.99*** 0.265

Table 4.5

Cointegration Analysis Results

	(I)	(II)	(III)	(IV)	(V)	(VI)
Skill mismatch	−1.92***	−1.87***	−1.30***	−1.62***	−0.14***
	0.017	0.019	0.015	1.59E-12	1.23E-03
Inflation	−3.32***	−4.23***	−7.99 ***			0.09
	0.247	0.234	0.360			0.074
Female labor force participation	−1.89***	0.64***	−3.08***	−0.84 ***	−0.3	−2.31***
	0.413	0.096	0.178	2.63E-12	0.60	0.278
FDI-to-GDP ratio	0.34***	0.59***	−2.56***	0.11**	−0.15***	−0.02**
	0.073	0.033	0.378	0.044	0.01	0.008
Credit-to-GDP ratio	0.09***	−0.44***		−1.06***		−0.07***
	0.018	0.025		9.30E-13		0.015
Trade-to-GDP ratio			1.44*** 0.066
Debt				1.41*** 1.54E-12	0.24*** 0.02
Debt²/100				−2.17*** 1.62E-12	−0.22*** 0.01
MKT					0.01*** 0.00
Years of education						6.45*** 1.706
TED						−1.01*** 0.094
Share of agricultural sector in GDP		−0.99*** 0.265

Panel Probit Analysis

The panel probit results broadly support the findings in the system GMM and panel cointegration long-term analyses. In particular, a higher skills-mismatch index decreases the chance that TFP will increase growth (Table 4.6). In line with the previous empirical analysis, higher inflation decreases the probability that TFP contributes to growth. A 1 percentage point increase in inflation reduces the probability by about 2 percent that TFP will contribute to growth. We also find that the link between the probability of TFP contributing to growth and government debt is concave, although the coefficients are not always statistically significant.

Table 4.6

Panel Probit Analysis, Total Factor Productivity

Note: FDI = foreign direct investment; SMEs = small and medium enterprises. Standard errors in parentheses.* p<0.1, ** p<0.05, *** p<0.01

Table 4.6

Panel Probit Analysis, Total Factor Productivity

	(1)	(2)	(3)	(4)	(5)
Inflation	−0.0183**	−0.0222*	−0.0265***	−0.0315	−0.0300***
	(0.00858)	(0.012)	(0.00746)	(0.154)	(0.0111)
Government-debt-to-GDP ratio (DBT)	−0.00522	−0.00249	0.0630**	0.0820*	0.0151
	(0.00897)	(0.00812)	(0.0293)	(0.0474)	(0.033)
DBT²/100	0.000745	−0.000225	−0.0323	−0.0476	0.00377
	(0.00455)	(0.00417)	(0.0239)	(0.0339)	(0.0293)
Share of manufacturing sector in GDP		0.0381* (0.0199)			0.0133 (0.0364)
Trade-to-GDP ratio		−0.0176 (0.0422)
Credit-to-GDP ratio		0.00016 (0.00435)
Labor market regulation index			0.344* (0.181)
Skills mismatch			−0.0511** (0.021)		−0.0308* (0.017)
Credit market regulation index				0.570*** (0.164)	0.166 (0.237)
SMEs with credit line				0.0332** (0.0162)
Constant	0.729**	0.0668	−2.119	−8.784	−0.815
	(0.349)	(0.457)	(1.418)	(0)	(2.566)
Observations	112	107	49	22	53
Number of countries	33	32	20	22	20

Note: FDI = foreign direct investment; SMEs = small and medium enterprises. Standard errors in parentheses.* p<0.1, ** p<0.05, *** p<0.01

Table 4.6

Panel Probit Analysis, Total Factor Productivity

	(1)	(2)	(3)	(4)	(5)
Inflation	−0.0183**	−0.0222*	−0.0265***	−0.0315	−0.0300***
	(0.00858)	(0.012)	(0.00746)	(0.154)	(0.0111)
Government-debt-to-GDP ratio (DBT)	−0.00522	−0.00249	0.0630**	0.0820*	0.0151
	(0.00897)	(0.00812)	(0.0293)	(0.0474)	(0.033)
DBT²/100	0.000745	−0.000225	−0.0323	−0.0476	0.00377
	(0.00455)	(0.00417)	(0.0239)	(0.0339)	(0.0293)
Share of manufacturing sector in GDP		0.0381* (0.0199)			0.0133 (0.0364)
Trade-to-GDP ratio		−0.0176 (0.0422)
Credit-to-GDP ratio		0.00016 (0.00435)
Labor market regulation index			0.344* (0.181)
Skills mismatch			−0.0511** (0.021)		−0.0308* (0.017)
Credit market regulation index				0.570*** (0.164)	0.166 (0.237)
SMEs with credit line				0.0332** (0.0162)
Constant	0.729**	0.0668	−2.119	−8.784	−0.815
	(0.349)	(0.457)	(1.418)	(0)	(2.566)
Observations	112	107	49	22	53
Number of countries	33	32	20	22	20

Note: FDI = foreign direct investment; SMEs = small and medium enterprises. Standard errors in parentheses.* p<0.1, ** p<0.05, *** p<0.01

The results suggest that less strict regulations in credit and labor markets and higher access to finance by small and medium enterprises (SMEs) increase the likelihood that TFP will add to growth. Less strict labor market regulations boost the chance that TFP will contribute to growth significantly (Table 4.6). Lowering the index of labor regulations by 1 point is associated with a 34 percent increase in the probability of positive TFP contribution to growth. The credit market variables are found to be significant with less strict credit regulations and a higher percentage of SMEs with credit lines increasing the likelihood of TFP increasing growth.

The predictive probabilities for TFP in SMICs suggest that most of the countries have achieved macro-stability, but their structural reforms are lagging. The predictive estimates based on the first model specification, which uses mostly macroeconomic variables, are largely similar for our SMICs, showing higher chances that TFP will make a positive contribution to growth (Table 4.7). This outcome suggests that these countries have generally managed to achieve macro-stability, which has contributed to their good economic performance and historically strong growth. However, the estimated predictive probabilities based on the last three specifications, which include structural variables such as regulatory burden on firms and skills mismatch in the labor market, reduce the probability that TFP will contribute to GDP growth significantly for all the SMICs in sub-Saharan Africa except Mauritius. This outcome highlights the need for structural reforms to unlock productivity growth in many of the SMICs and explains the favorable outcomes in Mauritius relative to some other SMICs in sub-Saharan Africa.

Table 4.7

Predictive Probabilities

(Percent)

Table 4.7

Predictive Probabilities

(Percent)

	(1)	(2)	(3)	(4)	(5)
Botswana	69.1	57.5	11.8	13.8	35.3
Cabo Verde	63.3	62.0
Lesotho	63.9	63.8		93.7
Mauritius	64.5	68.0	71.3	86.6	65.9
Namibia	69.3	63.4	11.3	12.8	34.6
Seychelles	48.2	29.0
Swaziland	69.4	92.0

Table 4.7

Predictive Probabilities

(Percent)

	(1)	(2)	(3)	(4)	(5)
Botswana	69.1	57.5	11.8	13.8	35.3
Cabo Verde	63.3	62.0
Lesotho	63.9	63.8		93.7
Mauritius	64.5	68.0	71.3	86.6	65.9
Namibia	69.3	63.4	11.3	12.8	34.6
Seychelles	48.2	29.0
Swaziland	69.4	92.0

Conclusion

This chapter looks at factors that could boost total factor productivity and thus potential growth in SMICs in sub-Saharan Africa. Estimating the relationship between TFP and its determinants is a complicated exercise because of endogeneity and cross-country heterogeneity among these economic variables. These concerns, together with data availability limitations, provide the motivation for using several econometric techniques that attempt to address endogeneity and heterogeneity problems and provide robustness to our estimates. Our empirical results confirm the existing literature that macroeconomic stability and trade openness are conducive to productivity growth. However, they are not sufficient. SMICs need to rationalize their expenditures to further increase their efficiency, most notably expenditures on education to minimize the skills mismatch in the labor market, and reduce the regulatory burden on firms. Some evidence also suggests that improved access to finance for SMEs might be beneficial for growth.

Our case studies suggest that SMICs have room to make the structural transformations necessary to raise their potential growth. Overall, Mauritius fared better than other SMICs in this area (Annex 4.3). Several factors underpin the success in Mauritius: (1) a diverse and competitive governing system supportive of reforms and (2) better sequencing of reforms, particularly investment in appropriate education and training, which enhanced the economy’s absorptive capacity and buttressed the authorities’ resolve to create new sectors. In addition, policy innovation and the flexibility in acquiring necessary skills in the globally competitive labor market, attracting FDI, and coherence in many aspects of micro and macro policies contributed to the relative success of Mauritius’s economic transformation.

This chapter by no means exhausts the factors that could boost potential growth in SMICs, thus begging the question, what other factors could boost potential growth in SMICs?

How can SMICs overcome political economy constraints to reforms? In many SMICs the speed of implementation of structural reforms has been hampered by various political economy constraints. Chapter 6 provides a fuller treatment of the tools that could be used to overcome political economy constraints, drawing on cross-country experiences of MICs that have graduated to high-income status.
How can SMICs leverage global supply chains to boost their potential growth? Global supply chains offer opportunities for developing countries to benefit from trade integration, and are at the heart of the East Asian success story. This is a possible area of future research that is beyond the scope of this book.

Annex 4.1. Methodologies for Modeling Total Factor Productivity

This chapter uses three methodologies to empirically assess the factors that determine TFP in SMICs and thus their potential growth.

The underlying model for the three estimations is as follows:

y_{i, t} = γ y_{i, t - 1} + x_{i, t} β + θ_{t} + (u_{i} + e_{i, t})

in which y_i,t is TFP obtained from the growth decomposition exercise; x_i,t is a vector of time-varying country-specific characteristics (including structural policy factors and other factors influencing TFP); θ_t is time effects; u_t is time-invariant country fixed effects (both observed and unobserved); and e_i,t is the unobserved error term, which is time varying.

Dynamic Panel Estimation: Dynamic-Panel Generalized Method of Moments (Difference and System GMM)²⁹

Pros:

The dynamic-panel GMM allows the lag-dependent variable (y_i,t–1) to affect the dependent variable.
This estimation method can control for the endogeneity issues arising from both the time-varying and time-invariant unobservables.

Cons and assumptions required:

The coefficient estimates are consistent only when N → ∞, and requires T ≥ 3.
The model requires an additional assumption on the error terms, which depends on the selected instrumental variables.³⁰
The estimation requires the included variables to be stationary.

Note that when data are persistent (which is likely in our case), system GMM has been shown to outperform difference GMM (Blundell and Bond 1998; Bond, Hoeffler, and Temple 2001) because system GMM additionally uses cross-country variations to identify the effects of interest.

Panel Cointegration Analysis

We test for the presence of cointegration using Pedroni’s seven statistics: (1) pooled variance ration statistic (nonparametric), (2) pooled rho-statistic (semi-parametric), (3) pooled t-statistic (semi-parametric), (4) pooled t-statistic (parametric), (5) group mean rho-statistic (semi-parametric), (6) group mean t-statistic (semi-parametric), and (7) group mean t-statistic (parametric).

Cointegration tests use the following specification:

Y_{i, t} = α_{i} + β_{i} X_{i, t} + e_{i, t}

in which y_i,t is TFP obtained from the growth decomposition exercise; X_i,t is a vector of time-varying country-specific characteristics (including structural policy factors and other factors influencing TFP); and e_i,t is the unobserved error term, which is time-varying.

Individual cointegration ordinary least squares (OLS) regressions are estimated for each country, and depending on the type of test, pooled or group mean tests are computed based on the estimated residuals. Then appropriate adjustment terms are used to turn the statistics into standard normal distributions (the adjustment terms differ depending on the type of test and the number of regressors).³¹

Group Mean Fully Modified Ordinary Least Squares (FMOLS)

FMOLS estimates the same regression as OLS for each country individually and uses the estimated residuals to compute country-specific long-term covariance matrices. This long-term covariance matrix is used to compute country-specific adjustment terms to adjust individual FMOLS estimates and t-statistics for country-specific serial correlation dynamics and endogeneity. Group mean FMOLS estimators and t-statistics are calculated based on country-specific adjusted FMOLS estimates and t-statistics (Pedroni 2000).

Pros:

The estimations are more suitable to nonstationary data and allow for unit-root regressor processes.
Allows both heterogeneous dynamics and heterogeneous cointegration vectors.
Allows heterogeneous cointegrating slopes with straightforward interpretation.
FMOLS provides estimates (only) for the long-term dynamics.
Good small sample size and power properties.
Accounts for serial correlation dynamics and endogeneity.

Cons and assumptions required:

FMOLS estimator is computationally complex.
FMOLS estimators depend on the assumption of exact unit roots for all the regressors.
Compared with the dynamic panel method, the cointegration approach does not explicitly address the problems of simultaneity and causality.

Binary or Multinomial Response Model: Probit/Logit Estimation

The following specification is used:

I_{i, t} = f (X_{i, t}, u_{i}, e_{i, t})

in which I_i,t is an index indicating whether the TFP contribution is positive or negative (that is, 1{Y_i,t ≥ 0}), or whether TFP falls within a specific range; X_i,t is a vector of time-varying country-specific characteristics (including structural policy factors and other factors influencing TFP); u_i is time-invariant country fixed effects (both observed and unobserved); and e_i,t is the unobserved error term, which is time-varying.

Pros:

The probit/logit estimations allow for nonlinear effects of the explanatory variables.
An index variable can mitigate the problem of measurement errors in the TFP contribution.

Cons and assumptions required:

The dependent variable contains less information (because we convert a continuous dependent variable into a discrete dependent variable).
The choice of thresholds in the multinomial response model can be arbitrary.
The estimations are more complicated when there is an endogenous regressor or a lagged dependent variable.

In addition to the empirical methodologies, the chapter also looks at sectoral shift analysis (World Bank 2008). This approach uses ideas of structural transformation in economies and a shift to dynamic sectors that often lead to a boost in TFP.

Pros:

The sectoral shift analysis identifies how each sector contributes to total productivity growth.
This is a decomposition exercise and is not subject to any econometric assumptions.

Cons:

The analysis requires information on sectoral productivity and employment.
It may not fully answer our questions of interest. The policies related to this would be labor force reallocation and labor market flexibility (not how public sector size affects the TFP contribution).

Construction of the Skills Mismatch Index (SMI)

As discussed in Leigh and Flores (2013), skills mismatch could be one of the reasons for high structural unemployment in many of the SMICs in sub-Saharan Africa. For our analysis, we constructed a skills mismatch index, following Estevão and Tsounta (2011), to determine whether changes in TFP could be captured by country differences in matching supply and demand for skills. The skills mismatch index is calculated by taking the difference between the skills demand and supply for each country in the sample. Following Estevão and Tsounta (2011), the skills mismatch index for each country i at time t is constructed using the following equation:

{Skills - Mismatch {Index}_{it} = Σ_{j = 1}^{3} (S_{ijt} - M_{ijt})}^{2}

in which j is the skill level; S_ijt is the percentage of the population with skill level j at time t in country i (skill level supply), and M_ijt is the percentage of employees with skill level j at time t in country i (skill level demand).

Skill level supply. World Bank educational attainment data are used as a proxy for skill level supply using primary education as low skilled, secondary education as semiskilled, and college and tertiary education as high skilled.
Skill level demand. Skill level demand is approximated by the percentage of employees in three listed sectors: mining and construction (to proxy low-skilled workers), manufacturing (for semiskilled workers), and government and financial services (for high-skilled workers).³²

Annex 4.2. Countries Included in the Analysis

Annex Table 4.2.1

Countries Included in the Regressions

Annex 4.3. Mauritius: A Case Study of Crowding-In Private Investment³³

In Mauritius, rather than a crowding-out effect as experienced in other SMICs, state intervention has, in general, demonstrated a positive effect for additional private investment and job creation. This outcome occurred mainly because the purpose of state intervention was specific and focused. The government intervened in the provision of basic goods and services that were not profitable for the private sector. Thus, provision of rice and flour was ensured through the creation of the State Trading Corporation. The government also helped fill in the gaps that were especially pronounced in the financial services sector. Thus, the State Bank of Mauritius Ltd, the State Insurance Company of Mauritius Ltd, the Mauritius Housing Company Ltd, and the Development Bank of Mauritius (DBM) Ltd were created; these institutions also ensured financial inclusion. By investing in strategic sectors, notably Air Mauritius Ltd and Mauritius Telecom, the state led the way by generating the base demand and building capacity for others to follow, which, in turn, helped diversify the economy. In the longer term, these interventions helped strengthen regional integration and boost trade. Therefore, instead of crowding out private investment, Mauritius provides a good case study of state intervention that has had a crowding-in effect in a number of key economic sectors, as discussed below.

State Intervention in the Financial Sector

A retrospective view of the financial sector reveals that in the 1960s this sector was very weak. The government created the DBM in the 1960s, with a focus on disbursing loans to the micro, small, and medium enterprises (MSMEs) to advance the industrial, agricultural, and economic development of the country. In the early 1970s, the State Bank and the State Insurance Company opened their doors, providing a further boost to the sector. State intervention, aligned with market-conforming measures, acted as a catalyst to development. These actions not only prevented development of a monopolistic financial market, but also helped create a wide market base that, in turn, attracted more financial institutions, including foreign companies, to Mauritius. Currently, there are 21 banks, including HSBC, Barclays, Deutsch Bank, and Standard Chartered, and the financial sector is one of the most promising sectors with regard to growth potential, contributing 10.3 percent to GDP and directly employing some 13,000. In addition, several local financial institutions have grown stronger and more competitive and have even opened up branches outside Mauritius. For instance, the State Bank has operations in Madagascar and India.

State intervention in the financial sector also filled in the gaps that the private sector found unprofitable and promoted inclusiveness, which, in turn, helped crowd in private investment. Without the DBM, it would have been extremely difficult for MSMEs to seek finance from the private sector. In fact, in 2013, the number of small production units operating in Mauritius and Rodrigues was estimated to be 125,500. These units invested 1.67 billion Mauritian rupees (Rs), and employed about 283,000 people, while value added came to more than Rs 82 billion, representing 62 percent of gross output. Likewise, the Mauritius Housing Company (initially created in 1963) was incorporated as a state-owned public company in 1989 to facilitate access to housing finance to the low- and middle-income groups. With the introduction of new schemes and products during the 2000s, the housing sector experienced a significant boost. According to the housing census, the housing stock grew by 20 percent between 2000 and 2011, and today home ownership in Mauritius is about 90 percent.

One of the main reasons that these state institutions continue crowding in private investment is that they continually reinvented themselves, thus enhancing their own performance and keeping up with increasingly demanding conditions. The DBM, for example, became a public company in 1989, and in the future it intends to take up a strategic partner. This is an important move to remain competitive and continue crowding in investment. The State Insurance Corporation of Mauritius was revamped and incorporated as a public company in 1988. The State Bank of Mauritius was listed on the Stock Exchange of Mauritius in 1995, and today it is the second-largest listed company.

State Intervention in the Tourism Sector in Mauritius

At the time of independence in 1968, the tourism sector was almost nonexistent. One of the major factors that boosted growth within the tourism sector was public investment in the national carrier, Air Mauritius. The company, initially set up in 1967 with a government stake of less than 30 percent, experienced a major overhaul in 1995 when it was listed on the Stock Exchange of Mauritius, with the government holding a nearly 50 percent share. Air Mauritius began flight operations in 1972 with a six-seater aircraft; it has now increased its fleet size to 12 aircraft serving 26 destinations and employing more than 2,000 people. In addition, Air Mauritius has codeshare agreements with Air France, Emirates, Malaysian Airlines, South African Airways, and Virgin Australia. The arrangements have contributed considerably to increasing seat capacity. The tourism sector flourished alongside the aviation company, as shown in Annex Figure 4.3.1, constantly crowding in private investment and creating direct employment in this sector.

Since 2009, total room capacity has consistently risen, along with seat capacity and tourist arrivals, such that in 2013 the tourism sector made up 7.8 percent of GDP. At end-December 2013, this sector directly employed more than 28,000 people, and 107 hotels were operating in Mauritius with total room capacity of 12,376. In addition, a number of private investors have set up luxury hotels in Mauritius, including international groups such as Hilton and Oberoi.

However, the tourism sector in the country is now experiencing excess capacity. Thus, an overhaul is needed in the aviation sector to maintain its momentum. In 2014, Air Mauritius signed a tentative deal to buy four A350-900 planes from Airbus and lease two to battle competition from Middle East rivals.

State Intervention in the Telecommunications Sector

Overseas Telecommunication Services and Mauritius Telecommunication Services were set up in 1985 to provide international and national communication service, respectively. In 1992, these two companies merged to become Mauritius Telecom, which was the primary public provider of voice, mobile, Internet, and data communication services in Mauritius. In 2000, Mauritius Telecom entered into a strategic partnership with Orange (formerly France Telecom). The public sector had a 59 percent ownership stake, with a view to strengthening and securing its market share. It has since enjoyed a phenomenal rate of development and is now one of the top companies in the country, with investment outside Mauritius in Vanuatu and generating revenue of Rs 8.4 billion in 2013.

Mauritius Telecom has played a crucial role in the development of the information and communication technology (ICT) sector as an important pillar of the economy. The ICT sector comprises manufacturing activities, telecommunications services, wholesale and retail trade, and other activities such as call centers, software development, website development and hosting, multimedia, information technology consulting, and disaster recovery. In 2013, the number of large establishments (that is, those employing 10 or more persons) operating in the ICT sector increased to 138. These businesses employed more than 14,000 people, and the share of employment in the ICT sector in total employment stood at 4.6 percent in 2013. In 2013, value added by the ICT sector was greater than Rs 20 billion, contributing 6.3 percent to GDP.

In addition to Mauritius Telecoms several other operators, such as Mahanagar Telephone Mauritius Limited, Emtel, and Bharat Telecom, are present in Mauritius. Each operator uses a different technology to provide Internet access. In 2013, the number of Internet service providers increased to 13.

Conclusion

The public sector has played a large role in crowding in private investment, bringing in FDI and hence creating employment. In the early stages of development, the government intervened to fill in the gaps and to provide a leading role by investing in crucial sectors to kick-start the country’s development. For instance, in the aviation and telecommunications sectors state involvement helped meet the huge initial fixed costs and create the market. As these sectors took off and demand increased, the government acted as a facilitator and a promoter, and in doing so induced more private investment and even attracted FDI rather than crowding out investment. However, to continue crowding in investment, public sector institutions need to keep up with new challenges and review their functions and objectives and revamp them at each turning point to ensure that the goods or services they generate are market driven and export oriented.

Annex 4.4. Case Study of Namibia: Promoting Inclusive Growth and Employment³⁴

Despite the initial success after independence in reducing inequality, Namibia still has one of the highest levels of income inequality in the world. Although growth over the past two decades has been inclusive, it has become less so over time. To increase the inclusiveness of growth, it is recommended that a thorough review of the targeting of public transfers and social programs be conducted to assess the extent to which they are reaching their intended beneficiaries. Given the importance of labor income as the main source of income for the great majority of Namibians, the issue of inclusive growth cannot be analyzed independently of the labor market, where unemployment remains high.

Since independence Namibia has managed to significantly reduce poverty and income inequality. Nevertheless, inequality in Namibia remains among the highest in the world (Annex Figure 4.4.1 ³⁵), and the rate at which inequality has declined has slowed: whereas the Gini coefficient declined from 0.70 in 1993/94 to 0.60 in 2003/04, it has declined only marginally since 2003/04, to 0.59 in 2009/10.

Given that labor income is the main source of household income, the issue of income inequality is related to the other main challenge in Namibia, that is, the high and persistent unemployment rate. According to the latest National Income and Expenditure Survey, which corresponds to 2009 and 2010, labor income constituted, on average, 72.3 percent of total income in Namibia, with 49.2 percent corresponding to wages and salaries and 23.1 percent to income from subsistence farming. According to the same survey, the unemployment rate remains high at 34 percent.

Thus, like many SMICs, one of the key challenges for the Namibian economy is how to sustain high growth while decreasing inequality and unemployment. This case study summarizes part of the work that the IMF country team has done on the related issues of income inequality, unemployment, and inclusive growth. In particular, it focuses on the issue of the incidence of growth in Namibia and how inclusive growth has been over the past two decades. It applies the same methodology used in Chapter 2 of the IMF’s October 2011 Regional Economic Outlook: Sub-Saharan Africa (REO). In particular, the growth incidence curves for real consumption per capita are estimated and compared for the sample periods 1993/94–2003/04 and 2003/04–2009/10.

The results suggest that, compared with other sub-Saharan African countries analyzed in the REO, Namibia has performed relatively well as measured by the inclusiveness of growth. As shown in IMF (2013) Namibia registered positive growth in real consumption per capita for all segments of the population over the 11-year period from 1993 to 2004. Growth was inclusive in both absolute and relative terms during the initial period (1993/04–2003/04), since households in the lower per capita income deciles registered higher growth rates than households in the middle and upper deciles. This finding is consistent with the significant decline in the Gini coefficient registered over this period. This positive outcome likely reflects the impact of post-independence social policies that targeted those segments of the population that had been excluded under the previous regime.

Despite Namibia’s favorable inclusive growth pattern in the early stage following independence, from 2003/04 to 2009/10 growth became much more neutral in its incidence across per capita income deciles. Even when growth benefited all households it did not benefit the poorest as much as in the earlier period. Thus, growth became less inclusive in a relative sense. This is consistent with the fact that the Gini coefficient declined only marginally in Namibia.³⁶ The deceleration in the mean and median growth rates of real consumption per capita is noticeable in the figure, since in the second period the whole growth incidence curve shifts downward.

This change in the incidence of growth has possibly been the result of weaker targeting or leakage of social programs, and of the expansion of expenditure not targeted to the poor, both of which have resulted in government expenditure becoming less progressive. Although testing this hypothesis requires using disaggregated data on income by source (labor earnings, rental income, pensions, social transfers, and the like), the fact that further reductions in inequality are proving to be more difficult should be a source of concern given that the level of inequality in Namibia is still among the highest in the world.

For Namibia to return to the inclusive growth pattern registered during the first decade following independence, a rationalization and reallocation of government expenditures (including tax expenditures) should be undertaken to increase their progressiveness. Namibia’s public expenditures as a share of GDP are among the highest among upper-middle-income countries. It is possible to reallocate public expenditures in a way that keeps total spending constant while increasing its progressiveness: based on a thorough analysis of the incidence of public expenditures, social programs and public transfers that do not reach or benefit their intended beneficiaries should be corrected or eliminated. Potential pockets of inefficiencies should also be identified and eliminated.

The second crucial element for increasing the inclusiveness of growth in Namibia is improving the performance of the labor market. As mentioned before, labor is the main source of income in Namibia, and unemployment has remained very high. The results of the recent work by Leigh and Flores (2013) show that the high level of unemployment in many SMICs, including Namibia, is attributable to structural rather than cyclical factors. In particular, their work shows that structural factors, including rapid wage growth above productivity increases, the existing skills mismatch, and wage policies in the public sector, can account for most of the high level of unemployment in the region. Namibia’s Targeted Intervention Initiative for Employment and Economic Growth needs to be complemented with policies to address these structural factors.

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The following SMICs are included in the analysis in this chapter: Botswana, Cabo Verde, Lesotho, Mauritius, Namibia, Seychelles, and Swaziland.

For more detail, see Felipe (2012), who examines a definition of and conditions for avoiding the middle-income trap. Using cross-country data for 124 countries during the period 1950–2010, the paper finds that a middle-income country has to attain an average annual growth rate of per capita income of at least 3.5–4 percent to transit to the next income status, in at most 28 years for lower-middle-income countries and 14 years for upper-middle-income countries.

As shown in Figure 1.5 of Chapter 1, per capita real GDP growth in the SMICs accelerated to about 5 percent during the mid-1980s before slowing and stabilizing around 3 percent for the past 20 years. Meanwhile, emerging market economies experienced a growth slowdown during the late 1980s, recovered thereafter (except for the period of the Asian financial crisis), and achieved per capita real GDP growth of about 6 percent for the past 10 years.

The growth moderation could also reflect, in part, the ongoing global debate about secular stagnation pointed out by a few studies (see, for example, Summers 2014; Teulings and Baldwin 2014).

Although the chapter does not formally test the role of exchange rate regimes, the stylized facts suggest that exchange rate regimes do not play a discernible role in the evolution of growth, which is broadly consistent with the literature stating that by and large the exchange rate regime by itself does not determine economic outcomes (see, for example, Stotsky and others 2012).

The earlier literature focuses more on theoretical models and empirical tests in low-income countries and advanced economies.

Frictional unemployment occurs when workers leave their jobs to find better ones. It is usually thought of as a voluntary exit, but can also occur as a result of a layoff or termination with cause. The time, effort, and expense it takes to find new jobs is known as friction.

See Mahadevan (2003) for a detailed review of TFP critiques.

Based on similar cross-country literature, Akinlo (2005) investigates the importance of these macroeconomic determinants of TFP, particularly within the sub-Saharan Africa region during 1980–2002. Specifically, Akinlo (2005) finds that external debt and inflation negatively contribute to TFP whereas human capital, trade orientation, and financial development are positively related to TFP.

The inclusion of institutional factors in the empirical analysis here was largely driven by data availability in the form of reliable time series. Both broad and narrow institutions can affect economy-wide productivity in different ways; however, a fuller treatment of this aspect is beyond the scope of this chapter.

Similar to this chapter, many studies use business survey indicators as proxies for business-friendly environments. Therefore, it is important to note that the link between the two variables could be weak under some conditions. For example, no regulation—which leads to higher scores on the indicators—may not always be best for business (Berg and Cazes 2007). In addition, a one unit increase in these indicators is likely to have a different meaning depending on the category in which a country currently falls (that is, there are nonlinear marginal effects).

As an additional boost to TFP, structural transformation and creating an enabling environment to move from low-return sectors to high-return sectors (see Table 4.2.1 in Box 4.2) and consistently experimenting and innovating by implementing new ideas are also important ingredients for raising potential growth in an environment characterized by structural impediments (see Box 4.2).

To avoid the problem of overidentification, the instrument set was restricted by (1) creating one instrument for each variable and lag distance (collapsing instrument set) and (2) restricting the number of components from the principal component analysis on the instrument set. By doing so, all the dynamic panel regression analyses pass the over-identification tests (that is, the Hansen P-values are strictly less than 1).

For more detail see Pedroni (2000, 2004). In the cointegration analysis we used routines that were kindly provided by Peter Pedroni.

Our panel probit estimates should reflect the medium-term effects of the TFP determinants because they are estimated using five-year data averages.

The skills-mismatch index measures the difference between the skills demand and supply for each country. Labor supply is approximated by educational attainment of the labor force, while labor demand is proxied by sectoral employment shares and educational attainment anticipated to be required for working in each of the sectors (see more detail in Annex 4.1).

The threshold increases to 55 percent when we control for market capitalization.

This model includes four SMICs in sub-Saharan Africa: Botswana, Cabo Verde, Mauritius, and Namibia.

See Annex 4.1 for more detail on the skill-mismatch index.

Although the relationship between TFP growth and government debt is negative and concave, the coefficient estimates on government debt are not statistically significant for some specifications (Table 4.4, Columns 1-A and 1-B). In the last two specifications, the coefficient estimates on government debt become negative for the linear term, and insignificant for the square terms. This outcome could be because too many variables are added to the specification, while the number of panel observations (countries) is reduced substantially.

The results of the regressions with telephone lines as an additional explanatory variable are not shown here.

The 90 percent confidence interval of the debt-to-GDP threshold is [40.3, 86.4].

The model includes only three SMICs in sub-Saharan Africa (Botswana, Mauritius, and Namibia).

The presence of cointegration is tested by Pedroni’s seven statistics. The results for all specifications reject the no cointegration hypothesis with four out of seven statistics, including the group mean augmented Dickey-Fuller test. A consistent set of explanatory variables cannot be applied across the three methodologies because of limited data availability (the selected econometric approaches require more data points and we cannot afford to lose more degrees of freedom). In particular, the panel cointegration approach requires more time series relative to probit or system GMM methods.

Market capitalization refers to the total value of listed companies’ outstanding shares.

The large coefficient estimates on inflation potentially reflect the impacts of inflation on additional macroeconomic variables that influence TFP growth. Another possible explanation is that the impact of inflation may not actually be linear as specified in the model.

The FDI coefficient is not robust given that the sign changes in other specifications as well, probably reflecting the change in the sample caused by the availability of data.

It is important to note that, because of data availability limitations, a different set of countries is included in each of the specifications. This may be another source of mixed results.

See Arellano and Bond (1991), and Blundell and Bond (1998).

See Roodman (2009) for the criteria of the instrumental variables used.

For more detail see Pedroni (2004).

Although the Estevão and Tsounta (2011) method of estimating skills supply based on educational attainment is reasonably robust, the measures of skill demand and skill intensity do have some weaknesses, including treating the mining sector as low skilled in the skill-intensity spectrum when most of the mining sector employees in SMICs are at least medium to high skilled.

This annex was prepared by the contributors from Mauritius.

This case study is a summary of “Promoting Inclusive Growth and Employment in Namibia,” prepared by Rodrigo Garcia-Verdu, Antonio David, and Floris Fernanzo Fleermuys, which appeared as Appendix V in the 2012 IMF Staff Report of the Article IV Consultation for Namibia.

The countries included in Figure 4.4.1 have some of the highest levels of income inequality according to the World Bank estimates included in the World Development Indicators 2013. There are no regional averages available for estimates of the Gini coefficient because the income and expenditure surveys on which they are based are typically not collected frequently enough.

It should be noted, though, that the Gini coefficient corresponds to income, while the growth incidence curve corresponds to consumption per capita.

Argentina	Gabon	Panama
Botswana	Hungary	Peru
Brazil	Iran	Romania
Bulgaria	Jamaica	Serbia
Cabo Verde	Jordan	Seychelles
China	Kazakhstan	South Africa
Colombia	Lesotho	Swaziland
Costa Rica	Malaysia	Thailand
Dominican Republic	Mauritius	Tunisia
Ecuador	Mexico	Turkey
Fiji	Namibia	Venezuela

Argentina	Gabon	Panama
Botswana	Hungary	Peru
Brazil	Iran	Romania
Bulgaria	Jamaica	Serbia
Cabo Verde	Jordan	Seychelles
China	Kazakhstan	South Africa
Colombia	Lesotho	Swaziland
Costa Rica	Malaysia	Thailand
Dominican Republic	Mauritius	Tunisia
Ecuador	Mexico	Turkey
Fiji	Namibia	Venezuela

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Abstract

Literature Review: Determinants of Total Factor Productivity

Stylized Facts

Empirical Analysis

Methodology

Data Issues

Empirical Results

System Generalized Method of Moments (GMM)

Cointegration Analysis

Panel Probit Analysis

Conclusion

Annex 4.1. Methodologies for Modeling Total Factor Productivity

Dynamic Panel Estimation: Dynamic-Panel Generalized Method of Moments (Difference and System GMM)29

Panel Cointegration Analysis

Group Mean Fully Modified Ordinary Least Squares (FMOLS)

Binary or Multinomial Response Model: Probit/Logit Estimation

Construction of the Skills Mismatch Index (SMI)

Annex 4.2. Countries Included in the Analysis

Annex 4.3. Mauritius: A Case Study of Crowding-In Private Investment33

State Intervention in the Financial Sector

State Intervention in the Tourism Sector in Mauritius

State Intervention in the Telecommunications Sector

Conclusion

Annex 4.4. Case Study of Namibia: Promoting Inclusive Growth and Employment34

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Dynamic Panel Estimation: Dynamic-Panel Generalized Method of Moments (Difference and System GMM)²⁹

Annex 4.3. Mauritius: A Case Study of Crowding-In Private Investment³³

Annex 4.4. Case Study of Namibia: Promoting Inclusive Growth and Employment³⁴