Policy Research Working Paper 8852 Productivity Growth Patterns and Determinants across the World Young Eun Kim Norman V. Loayza Development Economics Development Research Group May 2019 Policy Research Working Paper 8852 Abstract This is the background paper for the productivity extension growth and the overall determinant index is identified. The of the World Bank’s Long-Term Growth Model (LTGM). variance decomposition results show that the highest con- Based on an extensive literature review, the paper identi- tributor among the determinants to the variance in TFP fies the main determinants of economic productivity as growth is market efficiency for Organisation for Economic innovation, education, market efficiency, infrastructure, Co-operation and Development countries and education and institutions. Based on underlying proxies, the paper for developing countries in the most recent decade. The constructs indexes representing each of the main catego- regression results indicate that, controlling for country- and ries of productivity determinants and, combining them time-specific effects, TFP growth has a positive and sig- through principal component analysis, obtains an overall nificant relationship with the proposed TFP determinant determinant index. This is done for every year in the three index and a negative relationship with initial TFP. This decades spanning 1985–2015 and for more than 100 coun- relationship is then used to provide a set of simulations on tries. In parallel, the paper presents a measure of total factor the potential path of TFP growth if certain improvements productivity (TFP), largely obtained from the Penn World on TFP determinants are achieved. The paper presents and Table, and assesses the pattern of productivity growth across discusses some of these simulations for groups of countries regions and income groups over the same sample. The paper by geographic region and income level. An accompanying then examines the relationship between the measures of TFP Excel-based toolkit, linked to the LTGM, provides a larger and its determinants. The variance of productivity growth set of simulations and scenario analysis at the country level is decomposed into the share explained by each of its main for the next few decades. determinants, and the relationship between productivity This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ykim10@worldbank.org and nloayza@worldbank.org. The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Produced by the Research Support Team Productivity Growth: Patterns and Determinants across the World Young Eun Kim Norman V. Loayza World Bank World Bank Keywords: Productivity, Innovation, Education, Efficiency, Infrastructure, Institutions, Growth JEL: D24, O33, I25, G14, H54, O43, O47 Acknowledgements: For helpful comments, we would like to thank Wen Chen, Steven Michael Pennings, Jorge Luis Guzman Correa, Zixi Liu, Ha Nguyen, Luis Servén, Rongsheng Tang, Yong Wang, and participants in seminars at the World Bank, the Federation of ASEAN Economic Associations, the Shanghai University of Finance and Economics, and Peking University. The views expressed herein are those of the authors and do not necessarily reflect the views of the World Bank, its Executive Directors, or the countries they represent. “It is not by augmenting the capital of the country, but by rendering a greater part of that capital active and productive than would otherwise be so, that the most judicious operations of banking can increase the industry of the country.” Adam Smith, An Inquiry into the Nature and Causes of the Wealth of Nations (page 131). 1. Introduction With the same amount of inputs –including labor, human and physical capital, and materials—some countries, sectors, and firms produce more and others less. This difference depends on how productive they are in allocating and using resources in the production process. One of the most important lessons in economics is that productivity improvement is key to sustained economic growth. (See, among others, Hall and Jones 1999, Easterly and Levine 2001, and Caselli 2005.) Productivity was a main concern of the fathers of modern economics, Adam Smith and David Ricardo, in the eighteenth century, as they considered the advantages of specialization and trade as the basis for the wealth of nations. In the first half of the twentieth century, as advanced economies started to recover from the Great Depression, Hicks (1939) and Schumpeter (1942) emphasized the importance of productivity improvements, linking them to enterprise renewal and “creative destruction.” When economists turned their attention to developing countries, they described productivity growth as crucial in the search for sustained growth and development. For Lewis (1954), Kuznets (1957), and Chenery (1960), economic development required a structural transformation that shifted resources from less to more productive sectors of the economy. More recently, since the productivity slowdown in developed countries in the 1970s, the lackluster growth of developing countries in the 1980s, and the collapse of the communist regimes in Eastern Europe and East Asia in the early 1990s, interest in understanding the sources of growth and productivity has grown exponentially (see Woo, Parker, and Sachs 1997; Ben-David and Papell 1998; Easterly 2001; Jorgenson, Ho, and Stiroh 2008). Placing the study of productivity in the context of economic growth research may bring about important insights. In the 1950s, Solow and Swan developed a growth model in which changes in physical capital, labor, and total factor productivity (TFP) determine the economy’s growth rate (Solow 1956; Swan 1956). It has proven to be a 2 workhorse of growth theory and applications for over 50 years. A drawback of this model, however, is that the path of TFP is assumed to be exogenous. At least since the mid-1980s, theoretical economists have addressed this shortcoming. For example, Romer (1987, 1990), Grossman and Helpman (1991), and Aghion, Philippe, and Howitt (1992) incorporated technological advance through research and development (R&D) as a driver of long- run growth. Lucas (1988) argued that the accumulation of human capital through education creates a positive externality that drives productivity, which in turn explains long-run growth. Rebelo (1991) included both human and physical capital in a composite measure that faces no decreasing returns, suggesting that continuous investment can lead to long-term growth. Barro (1990) and Barro and Sala-I-Martin (1992) incorporated tax- financed public goods and assumed that they complement private capital, so that concurrent investment in both public and private capital could lead to growth in the long run. Engerman and Sokoloff (2000) and Acemoglu, Johnson, and Robinson (2001, 2004) deepen the notion of public goods to argue that the role of political and economic institutions is fundamental to economic growth. It can be said that in all these cases, the proposed mechanisms driving productivity are ways of explaining economic growth in the long run without resorting to exogenous changes. The interest in understanding the microeconomic foundations of aggregate behavior has also led to important insights on productivity. Hopenhayn (1992), Hopenhayn and Rogerson (1993), Caballero and Hammour (1996), and Davis, Haltiwanger, and Schuh (1996) pioneered research on the role of firm dynamics driving productivity and, consequently, economic growth. The conclusion from this extensive body of research is that resource reallocation (including firm entry and exit, innovation and renewal, and structural transformation) explains a substantial share of productivity improvement in the economy. Resource reallocation requires, however, a costly adjustment: the adoption of new technologies, the assimilation of production inputs by expanding firms, and the shedding of labor and capital by declining firms. Differences in the ease of resource reallocation can then explain why some countries are more productive than others. These differences can be related to the level of development of the country (e.g., lack of human capital and functioning justice system; see Caballero and Hammour 1998 and Daron Acemoglu and Zilibotti 2001) and to the quality of government’s regulations and interventions (e.g., 3 excessive labor regulations, subsidies to inefficient sectors, and barriers of firm entry and exit; see Parente and Prescott 2000). Although more refined in the mechanisms, the microfoundations literature points to the same conclusions as the aggregate literature highlighted above, regarding the roles of innovation, education, regulatory environment, and public goods and institutions in driving productivity. The surge in theoretical research on economic growth and productivity has been paralleled by an enormous empirical literature. A selected review is offered in the second section of the paper. In brief, this empirical research attempted to, first, test the validity of recent growth theories in contrast to (or in conjunction with) the neoclassical growth theory, and, second, determine the quantitative importance of various proposed drivers of growth. The first wave of empirical studies on new growth research focused on aggregate, cross-country data. In academic circles, this line of work is best exemplified by Barro's (1991) seminal study. In the World Bank and other policy-oriented organizations, empirical studies such as Easterly and Levine (1997, 2001), Loayza, Fajnzylber, and Calderón (2005), and Loayza and Servén (2010) offered a guide for understanding economic growth and its determinants, including policies and institutions. As micro-level data became more widely available in the 1990s, a second wave of empirical research used data at the industry and firm levels to study firm renewal, resource redistribution, and structural transformation. This led to insights and findings that could not have been obtained using country-level data, as shown in Foster, Haltiwanger, and Krizan (2001), Restuccia and Rogerson (2008), and Hsieh and Klenow (2009). Considering numerous studies on economic growth and productivity published in the past few decades, in this paper we take stock of the main conceptual conclusions surrounding productivity growth and synthesize the quantitative implications through original data collection and analysis. Apart from its independent contribution, this paper serves as background for an extension of the World Bank Long-Term Growth Model (Loayza and Pennings 2018). This extension quantifies how changes in TFP growth are driven by changes in its underlying determinants, and, in turn, how changes in TFP growth lead to different paths for economic growth. 4 The drivers of productivity growth can be grouped into five components (Kim, Loayza, and Meza-cuadra 2016): innovation, to create and adopt new technologies; education, to spread these new technologies throughout the economy and to develop the capacity of the workforce to assimilate them; market efficiency, to promote the effective and flexible allocation of resources across sectors and firms; infrastructure (in transport, telecommunication, energy, and water and sanitation), to support and facilitate the economic activity of households, businesses, and markets; and institutions (in the regulatory, justice, policy, and political systems), to provide social and economic stability, to defend property rights, and to safeguard basic civil rights. These five components are interrelated and can clearly influence one another. In this paper we identify the main determinants of productivity growth, propose proxies to measure them, assess the pattern of TFP growth across regions and over time, and quantify the relationship between the TFP determinants proxies and TFP growth. For this purpose, we first conduct an extensive literature review on productivity that considers not only concepts and theories but also empirical studies. Then, we estimate TFP and construct indexes representing each of the five main determinants of TFP for a large group of countries in the past three decades (from 1985 to 2014). Finally, we measure the relative contribution of each of the main determinants to the variance of TFP growth, and we estimate their overall effect on TFP growth. As mentioned above, these results are used to build a TFP module for the extended Long-Term Growth Model (LGTM). In the rest of the paper, section 2 presents a review of the literature; this is important because it not only frames the context of the paper but also helps to identify and categorize the drivers of TFP growth. Section 3 describes the methodology, including the selection of countries and variables, the estimation of TFP, the construction of indexes to measure each TFP determinant category, and the variance decomposition and regression analysis. Section 4 presents and discusses the main results, from descriptive statistics to regression analysis. Section 5 uses the main results to generate simulations on the path of TFP growth if certain reforms are accomplished. Section 6 concludes. 5 2. Literature review To identify and categorize the main determinants of TFP, we conduct a literature review spanning papers published from 1990 to 2016. We start with the reviews conducted by Isaksson (2007) and Syverson (2011) and expand the search further by using the key terms “total factor productivity,” “economic growth,” and “determinants.” We filter papers based on abstracts and main texts, choosing those that present a quantitative relationship between productivity and its potential determinants, using evidence from developing and developed countries. We select papers that examine time-variant determinants that a country can improve either through market forces or by public policy decision and implementation. (See Appendix A for the full list of the papers.) Based on the literature review, the main determinants of productivity are categorized into five components: innovation, education, market efficiency, infrastructure, and institutions (Kim, Loayza, and Meza-Cuadra 2016). Innovation Innovation, as the generation and adoption of new technologies, leads to the development of higher value-added activities, products, and processes and improves the performance of existing ones. Historically, a small number of countries have created new technologies based on investment in research and development (R&D) by the public and private sectors and an advanced level of human capacity and physical capital (Furman and Hayes 2004; Griffith, Redding, and Reenen 2004). Other countries have then adapted and adopted technological changes, with varying time lags and degrees of intensity (Comin, Hobijn, and Rovito 2008). Using indicators such as investment in R&D, the number of patents, and the number of scientific and technological journal publications, many studies show that the creation or adoption of a new technology is positively associated with TFP growth (see, for example, Nadiri 1993; Chen and Dahlman 2004; Guellec and van Pottelsberghe de la Potterie 2004). For instance, Jorgenson, Ho, and Stiroh (2008) and Oliner, Sichel, and Stiroh (2008) show that Information and Communication Technologies (ICT) played a central role in accelerating productivity in the United States (U.S.) from the mid-1990s to the 2000s after the lackluster pace of productivity growth in the 1970s and 1980s. The comparison of Europe and the U.S. highlights the critical role of new 6 technologies in expanding productivity. Van Ark, O’Mahony, and Timmer (2008) show that the productivity slowdown in Europe during the 1990s and 2000s is attributable to the lower contribution of ICT to growth, the smaller share of technology-producing industries, and slower advances in technology and innovation as compared to the U.S. Not only the development of new technologies but also the adoption of existing ones play a substantial role in enhancing productivity and income growth. Comin and Hobijn (2010) and Comin and Mestieri (2018), using data on the diffusion of more than 15 technologies across a large number of countries over the last two centuries, show that varying patterns of the adoption and diffusion of technologies since 1820 account for at least 25 percent of the income divergence across countries and 75 percent of the income difference between rich and poor countries. Education Education, as the knowledge and skills of the population, is essential to generate new technologies, as well as to disseminate, adapt, and implement them throughout the economy. Education allows workers not only to produce more and better, but also to expand and disseminate the technological frontier. For education to contribute to productivity, it must consist of strong basic foundations and sufficient specialization, rich in both quantity and quality, and spread throughout the population (Barro 2001; Hanushek and Woessmann 2015). Studies suggest that indicators such as the number of schooling years and the completion rate of secondary and tertiary education of the population are associated with output growth through both TFP improvements and the direct contribution of human capital (Benhabib and Spiegel 1994; Griffith, Redding, and Reenen 2004; Bronzini and Piselli 2009; Erosa, Koreshkova, and Restuccia 2010). Having a sufficiently high level of education increases productivity growth in developing countries by enabling them to adopt new technologies from frontier countries. Benhabib and Spiegel (2005), for example, show that a country’s average years of schooling (as a proxy for education) has a positive impact on TFP growth through technology catch-up. Miller and Upadhyay (2000) show that education (also using the years of schooling as a proxy) can affect how developing countries adopt new technologies through trade, with a positive impact on TFP. Barro (2001) shows in a study of around 100 countries that the quantity and quality of education, using the years of schooling and student test scores as respective 7 proxies, are significantly related to economic growth. Wei and Hao (2011) show that education quality, using government expenditure on education and teacher-student ratio as proxies, is significantly associated with TFP growth in China. Market Efficiency Market efficiency, defined as the efficient allocation of resources (e.g., labor, capital, and materials) across firms and sectors, enhances TFP by inducing unproductive firms to exit the market, facilitating productive firms to grow, and allowing new firms to emerge (Foster, Haltiwanger, and Krizan 2001; Hsieh and Klenow 2009; Parente and Prescott 2000; Restuccia and Rogerson 2017). Market efficiency has several components, including the proper functioning of output markets, financial systems, and labor markets. A number of studies find that market efficiency is associated with variation in productivity across firms, sectors, and countries. Jerzmanowski (2007) shows that inefficiency in the allocation of human and physical capital is the main explanation for low income level among around 80 countries from 1960 to 1995. Hsieh and Klenow (2009) estimate that, if capital and labor had been allocated at the relatively efficient level of the U.S., productivity in manufacturing sectors could have been 1.3 times higher for China and 1.6 times higher for India in 2005. Melitz (2003) shows that exposure to trade induces more productive firms to enter the export market and the least productive firms to exit, leading to an increase in aggregate industry productivity growth. The quality of the regulatory framework matters significantly for the ease of resource reallocation, including firm dynamics and structural transformation (Djankov et al. 2002; Loayza and Servén 2010). Drawing the link between shortcomings in technological adoption and burdensome regulations, Bergoeing, Loayza, and Piguillem (2016) argue that regulatory barriers of firm entry and exit account for 26 to 60 percent of the income gap between the United States and 107 developing countries and that not just removing these barriers but removing them jointly is critical. Nicoletti and Scarpetta (2003) and Arnold, Nicoletti, and Scarpetta (2008) show that burdensome market regulations, as well as and the lack of reforms for promoting private corporate governance and competition, caused industries that use or produce ICT to have meager productivity levels in several European countries and deterred firms from catching up to the international technological frontier. 8 Regarding financial systems, Rajan and Zingales (1998) show that financial development facilitates economic growth by reducing the costs of external finance to firms for a large number of countries in the 1980s. Beck, Levine, and Loayza (2000) argue that financial development affects economic growth mainly through its positive effect on TFP. Buera, Kaboski, and Shin (2011) show that financial frictions distort the allocation of capital and entrepreneurial talent across production units, adversely affecting TFP and sectoral relative productivity. With respect to labor markets, studies show that regulations that provide flexibility in the allocation of labor enhance productivity. Haltiwanger, Scarpetta, and Schweiger (2008) and Bartelsman, Gautier, and De Wind (2016) show that employment protection regulations preclude efficient labor reallocation because they curb job flows or discourage firms from adopting risky but highly productive technologies. Barro (2001) shows that the education of female students has an insignificant impact on economic growth unlike that of male students, suggesting that labor market reforms to incorporate female workers has a potential to increase TFP. Infrastructure Public infrastructure –in transport, telecommunication, energy, and water and sanitation – can provide timely and cost-effective access to input and output markets, workplaces, and knowledge and information sources, thus supporting all possible economic activities (Straub 2008; Galiani, Gertler, and Schargrodsky 2005). An appropriate infrastructure network –in terms of quantity, quality, and diversity – can complement private capital and labor, increasing their returns and impact on economic growth. In this way, expanding public infrastructure becomes a source of TFP growth. The evidence that appropriate public infrastructure has a positive impact on productivity and economic growth is convincing. Hulten (1996) shows that 25 percent of the growth difference between East Asia and Africa over 1970−90 is explained by the efficient use of infrastructure. Aschauer (1989) argues that public capital stock, especially core infrastructure such as highways, airports, sewers, and water systems, was critical in determining productivity in the U.S. over 1950s–1980s. Straub (2008) shows in a study of 140 countries over 1989–2007 that the infrastructure stock has a positive external impact on growth, for example, by allowing firms to invest in more productive machineries, decreasing workers’ commuting times, and promoting health and education. Considering 9 also a panel of countries over time, Calderón and Servén (2010, 2012, 2014) argue persuasively that infrastructure can have positive effects on both growth and distributive equity. These beneficial effects, however, require a framework that regulates, organizes, and coordinates the governments and companies that build public infrastructure and provide its services. Moreover, as highlighted by Pritchett (1996) and Devadas and Pennings (2018), the amount of infrastructure spending is not necessarily an indication of effective infrastructure investment. The quality of spending matters, and this seems to be highly related to the strength of public institutions (World Bank 2003, 2017r). Institutions Public Institutions – in the regulatory, justice, policy, and political systems – can promote social and economic stability, provide a safe living and working environment, defend property rights, and safeguard basic civil rights. The environment and policies that public institutions provide have a large, fundamental impact on economic development (North 1990; Acemoglu, Johnson, and Robinson 2004). The evidence that good governance (reflected in political stability, the rule of law, the protection of property rights, bureaucratic quality, transparency and accountability, and the absence of corruption) has a positive effect on productivity and economic growth is large, comprehensive, and convincing. Barro (1991) shows in a study of around 100 countries for 1960–85 that economic growth is positively related to political stability and inversely to government-induced market distortions. Using ethnolinguistic fractionalization as an instrumental variable for measures of government corruption, Mauro (1995) finds that corruption has a statistically significant and economically large negative effect on economic growth. Knack and Keefer (1995) find that property rights, proxied by contract enforceability and risk of expropriation, has a substantial impact on economic growth, even after accounting for capital accumulation. Rodrik, Subramanian, and Trebbi (2004) show that the quality of institutions, measured by a composite indicator of the protection of property rights and the rule of law, has a positive impact on income levels across a large sample of countries. Chanda and Dalgaard (2008) find that the quality of institutions (proxied by a composite index of the rule of law, bureaucratic quality, corruption, the risk of expropriation, and the government repudiation of contracts) is positively related to 10 productivity. Easterly and Levine (2003) show that institutions is a channel for geographical endowments to have an impact on economic development. They also show that when institutional quality is controlled for, macroeconomic policies do not account for development, implying good governance leads to conducive macroeconomic environments. The five categories of TFP determinants presented above span a comprehensive array of factors driving productivity. They are also the channels through which other potential variables affect TFP. Some of them are time-invariant, such as historical origins and geographic conditions. Their effect is captured by our proposed determinants. For example, Rodrik, Subramanian, and Trebbi (2004) show that geography has an impact on incomes by influencing the quality of institutions. Other potential variables account for slow-moving processes, such as social mobility and income inequality. Their effect on TFP growth, however, can be explained by education, market efficiency, and governance. Consider, as illustration, the following papers. Cingano (2014) shows that income inequality has a negative impact on economic growth by impeding skill development among individuals with poorer parental education background. Dabla-Norris and others (2015) show that low-income households and small firms face difficulties in accessing financial services, which decreases economic growth. Hoeller, Joumard, and Koske (2014) argue that the lack of policies that provide more inclusive access to education, financial services, and labor markets leads to income inequality, and eventually lower economic growth. 3. Methods First, we present the sample of countries and years included in the analysis. Second, we report how TFP growth at the country level is estimated. Third, we construct a set of indexes representing each of the main productivity determinants; we then obtain an overall index by grouping the indexes together. Fourth, we analyze the relationship between TFP growth and the proposed indexes of TFP determinants. 11 3.1 Sample We conduct the statistical analysis using a sample of 98 developing and developed countries for the period 1985– 2014. They are selected from the larger sample of countries featured in the Penn World Table (PWT) 9.0 and the World Bank World Development Indicators (WDI) databases. We exclude countries that do not have a minimal set of historical data for statistical analysis, countries that depend heavily on oil production (because the contribution of oil to output could result in a large overestimation of TFP growth),1 and small countries, defined as those with population less than 2 million (in 2016) (World Bank 2017m). For the descriptive analysis of TFP growth across regions and decades (in section 4.1), we add 16 countries for which data on the share of labor in income is missing in PWT 9.0 but available from the Global Trade Analysis Project (GTAP) 9.0 (Aguiar, Narayanan, and McDougall 2016). For the descriptive analysis of TFP determinants (in section 4.2), we additionally include 22 countries, which, though not having information to obtain TFP estimates, do have data for the proposed determinant indicators. For growth projections in the Long-Term Growth Model (LTGM), we add back small countries, heavily oil dependent countries, and those for which we can complete missing data from other sources and additional assumptions; thus, the TFP extension of the LTGM can be applied to about 190 countries for growth projections. We classify high-income countries that have been members of OECD for more than 40 years as the OECD group. The rest of countries are classified by region and income. We use the average of GDP per capita (World Bank 2017e) over 1985–2014 to break the sample into income quintiles. Table B.1 shows the country list by region and income quintile groups, indicating their inclusion in the samples by type of analysis (descriptive and statistical), data source (PWT, GTAP, and WDI), and other characteristics (oil rent and population). 1 Heavy dependence is defined as reliance on oil production for more than 32 percent of GDP on average during 2006-15, which is 90th – 100th percentile among 98 countries with positive oil rents (World Bank 2017j); Angola (45%), Congo, Rep. (46%), Equatorial Guinea (42%), Gabon (32%), Iraq (52%), Kuwait (47%), Libya (54%), Oman (37%), Saudi Arabia (44%), and South Sudan (45%). 12 3.2 Construction of total factor productivity Total factor productivity is commonly measured as a residual, that is, the portion of GDP that remains after accounting for the direct contributions of capital and labor inputs in total GDP (Barro and Sala-i-Martin 2004). The aggregate capital stock is usually computed through the perpetual-inventory method, as the accumulation of gross physical investment (from a given initial capital stock), discounting the depreciation of existing stocks. Labor input can be calculated as the number of employed people, adjusted for human capital. The capital share is the fraction of total GDP used to pay for capital, and the labor share is the fraction of total GDP used to pay for labor. The shares of each factor of production are often assumed to be constant over time. For the level of (relative) TFP, we use the estimate provided in Penn World Table (PWT) 9.0, labeled rtfpna (Feenstra, Inklaar, and Timmer 2015). This series is obtained by setting the TFP level of 2011 equal to 1, and then computing the remaining TFP levels backwards and forwards by applying the TFP growth rates. The TFP growth rates are obtained implicitly through the following equations: / , , where, , 1 . (1) : , ℎ , , , , : : : ℎ : Human capital based on the average years of schooling from Barro and Lee (2013) and an assumed rate for primary, secondary, and tertiary education from Caselli (2005) : ℎ ℎ : , and : 13 For our analysis, we calculate annual TFP growth rates by differencing the log-transformed TFP levels of year t and t-1, . As a robustness check, we calculate TFP using mainly data from the World Development Indicators database (instead of PWT). In Appendix E, we compare the results (on descriptive statistics and econometric analysis) using this alternative TFP measure. 3.3 Construction of main determinant indexes We construct subcomponent indexes that represent each of the five determinants ─innovation, education, market efficiency, infrastructure, and institutions─ and an overall index representing the five determinants all together. First, to construct a subcomponent index, we select relevant indicators and combine them using factor analysis, which captures as much of common variance in the indicators as possible in a single index (figure 1A) (Mulaik 2009). Figure 1. Comparison of factor analysis and principal component analysis Source: SAS 2017. The authors revised the original diagrams. Then, to construct an overall determinant index, we combine the five subcomponent indexes using principal component analysis (PCA), which captures as much of total variance in the five subcomponent indexes as 14 possible in a single index (figure 1B) (Jolliffe 2002). 2 We use PCA for the overall determinant index because it is intended to represent the different features of each of the subcomponent indexes. This is unlike each subcomponent index, which is supposed to represent the common feature of its indicators.3 For each category of TFP determinants, we select indicators based on whether they measure an important characteristic, have been used in the literature, and have data available across countries and over time. In a few cases where most but not all information is available, we impute missing values based on income groups or trends, as explained at the end of this subsection. Innovation. To construct a subcomponent index for innovation (Innov), we choose the following indicators: Public and private expenditure on R&D as a percentage of GDP as an indicator of the effort to create new technologies (World Bank 2017o); and the number of patent applications by residents and nonresidents and the number of scientific and technical journal articles as indicators of the outcome of R&D activities (World Bank 2017k, 2017l, 2017p). Education. To construct a subcomponent index for education (Educ), we choose the following indicators: Government expenditure on education as percentage of GDP as an indicator of public investment in foundational human capital (World Bank 2017f); the shares of population aged 25 and over with completed secondary education and with completed tertiary education (Barro and Lee 2013) as indicators of educational attainment among workers; and a standardized international test score – a single average of scores in math, science, and 2 We select a factor, or a principal component in the case of PCA, with an eigenvalue higher than 1. In our analysis, there is only one factor for each subcomponent index and one principal component for the overall index with an eigenvalue higher than 1. 3 In order for the variables to enter factor/principal component analysis, they must have a sufficiently high degree of commonality. We run the Kaiser-Meyer-Olkin test to examine whether the indicators have enough common variance. A test value below the critical value of 0.5 means that an indicator or a group of indicators are unacceptable. For factor analysis, the test results show that the selection of indicators as a group is acceptable with a value of 0.60 for innovation, 0.69 for education, 0.63 for market efficiency, 0.83 for infrastructure, and 0.92 for institutions. The test results for individual indicators in each category are also above the critical value. For principal component analysis, used to construct the overall index, the test result is 0.88 for the group of the subcomponent indexes, and also above the critical value for each subcomponent index. 15 reading on the Programme for International Student Assessment (PISA) – as an indicator of educational quality (OECD 2016a, 2016b, 2016c). Market Efficiency. To construct a subcomponent index for market efficiency (Effi), we classify markets into output, financial, and labor markets. We select the World Bank Doing Business scores as an indicator of output market efficiency, which measure the regulatory environment in terms of ease for firms to start a business, trade across borders, register property, get credit, and the like (World Bank 2017a). We choose the International Monetary Fund (IMF) Financial Development Index as an indicator of financial market efficiency, which measures the level of financial development by including the size and liquidity of financial markets, ease for individuals and firms to access financial services, and the ability of financial institutions to provide services at low costs with sustainable revenues (Svirydzenka 2016). As indicators of labor market efficiency, we construct an composite index, using factor analysis, consisting of minimum wage (% of value added per worker), severance pay for redundancy dismissals (weeks of salary), and the share of women in wage employment in the nonagricultural sector from World Bank databases (World Bank 2017h, 2017q). Infrastructure. For a subcomponent index for infrastructure (Infra), we select fixed-telephone and mobile subscriptions (per 100 people) (World Bank 2017c, 2017i); the length of paved roads (km per 100 people) (International Road Federation 2017a, 2017b); electricity production (kw per 100 people) (OECD/IEA 2017); and access to an improved water source and improved sanitation facilities (% of population) (WHO/UNICEF 2017b, 2017a). Institutions. To construct a subcomponent index for institutions (Inst), we select the World Bank Worldwide Governance Indicators. These include measures of voice and accountability (citizens’ participation in selecting their government and freedom of expression); control of corruption (the extent to which public power is exercised for personal gain); government effectiveness (the quality of public services and policy formulation and implementation); political stability (the absence of politically motivated conflict); regulatory quality (the ability of 16 government to formulate and implement regulations that promote private sector development); and the rule of law (the extent to which citizens have confidence in and abide by laws) (Kaufmann and Kraay 2017). When necessary, we impute missing values of the selected indicators to balance sample sizes across countries and maximize the number of countries in the sample. We use different methods depending on the number of available data and the characteristics of the indicators. For a country that has data for more than 10 out of 30 years (1985−2014) for an indicator, we project a linear trend over years to impute missing values. For a country that has data for less than 10 years, we replace missing values with a median value corresponding to the country’s income and regional group. We apply a different method for PISA scores because available data are less than for 10 years for all countries. Considering a statistically significant correlation of 0.66 (p-value<0.01) between PISA scores and log-transformed GDP per capita lagged by five years, we regress PISA scores on the lagged log-transformed GDP per capita, controlling for time-effects in a cross-country, time-series pooled data set.4 Then, we replace missing PISA scores with a median score by the country’s income and regional group using scores predicted by the regression model. For minimum wage and severance pay, we apply the oldest available data (2014) to the period before 2014, because available data (2014−2017) are insufficient to evaluate a time trend and their values are difficult to impute based on the country’s income and regional group. 3.4 Relationship between the main determinants of TFP and TFP growth The relative contribution of the main determinants to the variance of total factor productivity growth To help assess the relative contribution of the five main determinants to TFP growth, we decompose the variance of the TFP growth rate (over t-5 to t) to that explained by each subcomponent index (at t-5), controlling for an initial TFP level (at t-5) and time-effects for 98 countries. A review of measures of relative importance based on variance decomposition by Grömping (2007) suggests that the “dominance analysis” approach (Budescu 1993; Azen and Budescu 2003) is a reasonable method, mainly to deal with the presence of covariance across individual 4 , ln , , : 1, … ,76 , : 2003/06/09/12/15 ; 187.1*, 28.7 *** (***: p-value<0.01), 0.444 17 determinants. This approach calculates the contribution of a subcomponent index as the increase in the explained variance when the subcomponent index is added to each subset of other subcomponent indexes. For instance, the contribution of the innovation index ( , is computed by averaging5 the increase in the explained variance of TFP growth rate when , is added to each of the 16 additive subsets of other four subcomponent indexes ( . , , , … , , , , , , , … , , , , , , , , , , …, , , , , , , , , , , , , , .) The relationship between the overall determinant index and total factor productivity growth To quantify the relationship between the overall determinant index and TFP growth, we build a regression model in which TFP growth rate is a function of a time-lagged overall determinant index and a time-lagged TFP level with country- and time-effects (equation 2). We rescale the overall index to be from 1, representing the lowest performance, to 100, the best across countries over the last three decades. For this purpose, we use the following linear transformation, / ℎℎ ∗ 100 1 1. According to preliminary analysis, the relationship between the index and TFP growth declines as the index increases; to allow for this non-linearity, we log-transform the rescaled index. We apply a time lag of five years to reduce the likelihood of endogeneity as reverse causation. This also allows us to smooth the TFP growth series, considering that, at shorter frequencies, it may be driven by business-cycle fluctuations (see Beck, Levine, and Loayza 2000; and Giavazzi and Tabellini 2005). We run different regressions for comparison and robustness check: without country-effects and with random country-effects, and with different time lags of three and seven years. We use (White-Huber) robust standard errors. After fitting the models to the sample, we incorporate the results into the Long-Term Growth Model (Loayza and Pennings 2018) in order to run country and region simulations on the potential path of TFP growth. 5 Two-step average: First, the additional contributions are averaged within a group of the same size of the subset, then the results from the first step are averaged across groups with different sizes of the subset. 18 ℎ , , , , , . (2) ℎ , , : annualized TFP growth over t-5 and t , : overall determinant index, rescaled 1 to 100 , : 2011 1 : country effect : time effect , : residuals 4. Results 4.1 Total factor productivity Figure 2 shows that for 21 OECD countries, the median and (simple) average annual TFP growth rates are positive during 1985–2004 and decrease below zero for 2005–14; whereas for 93 developing countries, they are negative during 1985–94 and increase above zero for 1995–2014. Figure 3 shows median and (simple) average annual TFP growth rates for developing countries by region. For East Asia and Pacific, TFP growth rates are positive for the last three decades between 0.4 percent and 1.3 percent. For Europe and Central Asia, TFP growth rates are negative for 1985–94, increase in the next decade to above 2 percent, and decrease to around 1.2 percent for the last decade. For Latin America and Caribbean, TFP growth rates increase from around -0.4 percent during 1985–2004 to around 0.5 percent for 2005–14. For Middle East and North Africa, TFP growth rates increase from near zero or negative in 1985–94 to around 0.5 percent in the next decade and decrease to below -0.5 percent in the last decade. For South Asia, TFP growth rates are positive for the last three decades, ranging between 0.3 percent and 1.5 percent. For Sub-Saharan Africa, TFP growth rates increase from around -1 in 1985–1994 to +1 in the two decades spanning 1994–2014. Figure 4 shows regional average TFP growth rates weighted by total GDP (World Bank 2017d), the trend of which is similar to that of the unweighted average TFP growth rates in figure 3. 19 Figure 2. Annual TFP growth rate for all, OECD, and developing countries, median and simple average by decade 1.0% Annual TFP growth rate 0.8% 0.6% 0.4% 0.2% 0% -0.2% -0.4% -0.6% -0.8% -1.0% 85-94 94-04 04-14 85-94 94-04 04-14 85-94 94-04 04-14 All OECD Developing Median Average Median Average Median Average Source: Authors' calculation, using PWT 9.0 data complemented, in a few cases, with GTAP data. Note: The OECD group includes high-income countries that have been members of OECD for more than 40 years; the former Soviet Union countries are excluded in the period 1985-94 considering their independence in the early 1990s. Figure 3. Annual TFP growth rate for developing countries, median and simple average by region and decade 3% Annual TFP growth rate 2% 1% 0% -1% -2% 85 94 04 85 94 04 85 94 04 85 94 04 85 94 04 85 94 04 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 East Asia Europe Latin America Middle East South Sub-Saharan and and and and Asia Africa Pacific Central Asia Caribbean North Africa Median Average Source: Authors' calculation, using PWT 9.0 data complemented, in a few cases, with GTAP data. Note: The former Soviet Union countries are excluded in the period 1985-94 considering their independence in the early 1990s. 20 Figure 4. Annual TFP growth rate for developing countries, average weighted by real GDP by region and decade 3% average weighted by GDP Annual TFP growth rate, 2% 1% 0% -1% -2% 85 94 04 85 94 04 85 94 04 85 94 04 85 94 04 85 94 04 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 East Asia Europe Latin America Middle East South Sub-Saharan and and and and Asia Africa Pacific Central Asia Caribbean North Africa Source: Authors' calculation, using PWT 9.0 data complemented, in a few cases, with GTAP data. Note: The former Soviet Union countries are excluded in the period 1985-94 considering their independence in the early 1990s. 4.2 Main determinant indexes Figure 5 shows the median of the subcomponent indexes representing the main categories of TFP determinants, as well as the median of the overall index, for all, 21 OECD, and 115 developing countries by decade. All the median indexes are lower for the developing countries as compared to the OECD countries. A noticeable difference is that the innovation index stays at the lowest level for the developing countries, whereas it increases in the OECD group over time. For both groups, the subcomponent indexes of education, market efficiency, and infrastructure increase over decades, whereas that of institutions stays at the same level. Figure 5. Median of subcomponent and overall determinant indexes for all, OECD, and developing countries by decade World 80 100 60 40 20 1 85 95 05 85 95 05 85 95 05 85 95 05 85 95 05 85 95 05 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 Innovation Education Market Efficiency Infrastructure Institutions Overall 21 OECD 80 100 60 40 20 1 85 95 05 85 95 05 85 95 05 85 95 05 85 95 05 85 95 05 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 Innovation Education Market Efficiency Infrastructure Institutions Overall Developing 80 100 60 40 20 1 85 95 05 85 95 05 85 95 05 85 95 05 85 95 05 85 95 05 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 Innovation Education Market Efficiency Infrastructure Institutions Overall Source: Authors' calculation. Note: The OECD group includes high-income countries that have been members of OECD for more than 40 years; the indexes are rescaled to range from 1, the lowest performance, to 100, the best among all countries over the three decades. For the innovation subcomponent index, the indicators carry similar weights (equation 3). Factor analysis shows that the subcomponent index accounts for 76 percent of the total variance of the indicators, accounting for 90 percent of the variance of R&D expenditure (R&D), 61 percent of that of the number of patents (patent), and 79 percent of that of the number of journal articles (article). , 0.41 ∗ & , 0.34 ∗ , 0.39 ∗ , , (3) where , . For the education subcomponent index, the performance-related indicators have similar weights and the education-expenditure indicator has a lower weight (equation 4). Factor analysis indicates that the subcomponent index accounts for 55 percent of the total variance in the indicators, accounting for 20 percent of the variance of education expenditure (eduexp), 63 percent of that of secondary attainment (secondary), 75 percent of that of tertiary attainment, and 63 percent of that of PISA scores (pisa). The lower weight and the smaller contribution of 22 the education expenditure indicator to the common variance shows that this indicator has a low correlation with the outcome indicators. , 0.20 ∗ , 0.36 ∗ , 0.39 ∗ , 0.36 ∗ , . (4) For the market-efficiency subcomponent index, the indicators are combined with similar weights (in absolute terms) (equation 5). Factor analysis shows that the subcomponent index accounts for 69 percent of the total variance in the three indicators, accounting for 79 percent of the variance of Doing Business scores (business), 78 percent of that of Financial Development Index (financial), and 49 percent of the labor index (labor). In turn, factor analysis shows that the labor index accounts for 48 percent of the total variance of the minimum wage (minwage), 53 percent of that of the severance pay (severance), and 52 percent of that of the share of women employed in the nonagricultural sector (women). , 0.43 ∗ , 0.43 ∗ , 0.34 ∗ , , (5) ℎ , 0.45 ∗ , 0.47 ∗ , 0.47 ∗ , For the infrastructure subcomponent index, all indicators except for mobile subscription have similar weights (equation 6). Factor analysis shows that the subcomponent index accounts for 65 percent of the total variance in its indicators, accounting for 78 percent of the variance of the number of telephone subscription (tele), 28 percent of that of mobile subscription (mobile), 64 percent of that of paved road (road), 67 percent of that of electricity production (elec), 70 percent of that of access to improved water source (water), and 76 percent of that of access to improved sanitation facilities (sanit). , 0.23 ∗ , 0.14 ∗ , 0.21 ∗ , 0.21 ∗ _ , 0.22 ∗ , 0.23 ∗ , . (6) The institutions subcomponent index consists of the six indicators with similar weights (equation 7). The subcomponent index accounts for 87 percent of the total variance in its indicators, accounting for 83 percent of 23 the variance of voice and accountability (va), 90 percent of that of the control of corruption (cc), 93 percent of that of government effectiveness (ge), 71 percent of that of political stability (ps), 89 percent of that of regulatory quality (rq), and 94 percent of that of the rule of law (rl). , 0.18 ∗ , 0.19 ∗ , 0.19 ∗ , 0.16 ∗ , 0.18 ∗ , 0.19 ∗ , . (7) The overall determinant index is a linear combination of the (standardized) five subcomponent indexes with similar weights (equation 8). The overall index, obtained through principal component analysis, represents the innovation index with a correlation of 0.88; the education index, 0.90; the market-efficiency index, 0.94; the infrastructure index, 0.94; and the institutions index, 0.87. , 0.43 ∗ , 0.44 ∗ , 0.46 ∗ , 0.47 ∗ , 0.43 ∗ , . (8) Appendix C shows the average values of the individual indicators, as well as the subcomponent and overall indexes, over 1985–2014 by income and regional group. 4.3 Relationship between the main determinants of TFP and TFP growth The relative contribution of the main determinants to the variance of total factor productivity growth Figure 6 shows the decomposition of the total explained variance of the TFP growth rate corresponding to each of the main TFP determinants by decade for all, OECD, and developing countries (controlling for the five-year- lagged TFP level and time-effects). For the OECD countries, a notable trend is that the contribution of the market- efficiency index increases and accounts for 45 percent of the explained variance of TFP growth in the last decade; whereas that of infrastructure decreases and explains the least. For developing countries, in 1985-94 the TFP determinant with the highest explanatory power of TFP growth variance is institutions; however, its contribution decreases afterward. The contribution of education increases over the two decades and accounts for almost 50 percent of the explained variance of TFP growth in the last decade. 24 The variance decomposition analysis helps understand what drives the differences across countries regarding TFP growth. It does not, however, indicate what the most important or relevant drivers of TFP growth are for specific countries. For this, we would need to know the country-specific gaps in each determinant of TFP. We turn to this issue in section 6, on simulations and scenario analysis. Before, however, we need to obtain a reasonable estimate of the effect of the overall index on TFP growth, which we attempt next. Figure 6. Variance decomposition of TFP growth rate corresponding to the determinant subcomponent indexes by decade for all, OECD, and developing countries, controlling for initial TFP and time effects World 1985-1994 1994-2004 2004-2014 Innovation 0.06 0.05 0.09 Education 0.13 0.45 0.36 Market efficiency 0.14 0.13 0.18 Infrastructure 0.19 0.12 0.17 Institutions 0.48 0.25 0.19 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 OECD 1985-1994 1994-2004 2004-2014 Innovation 0.16 0.09 0.21 Education 0.11 0.20 0.16 Market efficiency 0.31 0.18 0.45 Infrastructure 0.18 0.37 0.08 Institutions 0.24 0.16 0.11 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Developing 1985-1994 1994-2004 2004-2014 Innovation 0.02 0.03 0.07 Education 0.13 0.47 0.49 Market efficiency 0.08 0.17 0.15 Infrastructure 0.20 0.12 0.14 Institutions 0.56 0.22 0.15 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Source: Authors' calculation. Note: The OECD group includes high-income countries that have been members of OECD for more than 40 years. 25 The relationship between the overall determinant index and total factor productivity growth Table 1 shows the regression results for equation 2 in which the TFP growth rate is a function of the lagged overall determinant index and the lagged TFP level, along with country- and time-effects. We do not attempt a regression with the five subcomponent indexes as individual regressors because they are very highly correlated, and their estimated marginal effects would be contaminated by multicollinearity. As Table 1 shows, the lagged overall index and the lagged TFP level are statistically significant in all regressions, with no, random, and fixed country-specific effects, respectively. Based on the Hausman test, which suggests bias estimation if correlated country-specific effects are not considered, we choose to focus on the regression with fixed (correlated, not random) country-specific effects. Table 1. Linear regression results Dependent variable: ℎ , , Number of observations: 477 Number of groups 98 (countries): Country effects: None Random Fixed Regressors (below): Coefficient (SE) Coefficient (SE) Coefficient (SE) , 0.004 (0.0011) *** 0.004 (0.0011) *** 0.050 (0.0183) *** , -0.082 (0.0052) *** -0.082 (0.0052) *** -0.099 (0.0151) *** Year 1999 -0.001 (0.0036) -0.001 (0.0036) -0.006 (0.0034) Year 2004 0.012 (0.0031) *** 0.012 (0.0031) *** 0.004 (0.0034) Year 2009 0.010 (0.0030) *** 0.010 (0.0030) *** -0.001 (0.0045) Year 2014 0.010 (0.0034) *** 0.010 (0.0034) *** -0.004 (0.0063) (Reference year: 1993) Constant -0.021 (0.0055) ** -0.021 (0.0055) *** -0.180 (0.0636) *** : Within Not applicable 0.2784 0.3048 Between Not applicable 0.8573 0.2749 Overall 0.4022 0.4022 0.1586 SE = Standard error; *: significant at 10%; **: significant at 5%; ***: significant at 1% level Note: Hausman test rejects the null hypothesis (Ho: coefficients are consistent under both random and fixed effects) with Chi-square 22.90 and p-value less than 0.01. The in the case of the fixed-effects estimator does not consider the explanatory contribution of the country-specific constants (which is why its overall value is lower than in the other cases). In the regression with no country effects, we use clustered robust (White-Huber) variance estimation, treating countries as clusters. 26 In the fixed effects model, an increase of the lagged overall determinant index by 1 percent is associated with an increase of the annual TFP growth rate by 0.05 percent, after controlling for the lagged TFP level and country and time effects. Suggesting convergence, an increase of lagged TFP by 1 percent is associated with a decrease of annual TFP growth rate by 0.10 percent, holding other variables constant. This implies that countries with a higher level of TFP need to increase the determinant index more than those with a lower level of TFP to achieve the same amount of increase in TFP growth. These results are robust in terms of signs and significance when we use different lags of three and seven years (see appendix D). They are also robust when we use the WDI-based data in the construction of TFP levels and growth rates (see appendix E). 5. Simulations and Scenario Analysis For illustration purposes, in this section we simulate the change in TFP growth rate for 78 low- and middle- income developing countries (that is, countries with GDP per capita in 2014 lower than $12,056, constant USD 2010). We present the simulation results in averages by region or income group. More generally, the Long-Term Growth Model (LTGM) toolkit can be used to generate country-specific projections for TFP growth for a much larger set of countries. This allows of the LTGM users to replace the assumption of an exogeneous path for TFP growth by one that is based on improvements in innovation, education, market efficiency, infrastructure, and institutions, feeding into the overall determinant index. We provide four scenarios below. They present different ways and extents of improving the TFP determinant index, to regional or world benchmarks (or leaders). We use the fixed-effect regression results to relate changes in TFP growth to changes in the overall determinant index. The corresponding increase in TFP growth depends directly on the speed of progress in the country’s TFP determinants and inversely on the extent of previous TFP improvement. Thus, countries with a larger gap on their TFP determinant index with respect to the benchmark could experience a larger increase in TFP growth if they made reforms to approach the leader. In turn, countries with a high increase in TFP growth would slow down their subsequent growth in TFP. The positive impact of improvements in the TFP determinant index and the negative impact of previous TFP growth create an interesting, nonlinear path of projected TFP growth: In most cases, TFP growth follows a convex path that 27 increases at a decreasing rate, reaches a maximum, and then decreases or stabilizes. Since in the simulations the reforms to improve TFP determinants do not occur immediately but gradually over time (in two scenarios, to imitate the actual trajectory of benchmark countries in the last three decades), the projected TFP growth has an additional source of convexity, as the growth rate of the TFP determinant index tends to decline over time. Scenario I: Improving to the highest TFP determinant index in the region Scenario I assumes that a country improves its overall determinant index to the highest index among all developing (non-OECD) countries in its region. We assume a country’s overall determinant index increases at constant increments from the initial value (in 2014) to the current index of its benchmark country, shown in table 2, over 15 years and keeps increasing with the same slope afterward. Table 2. Benchmark countries with the highest overall determinant index as of 2014 by region Region Country with the highest index as of 2014 East Asia and Pacific Korea Europe and Central Asia Czech Republic Latin America and Caribbean Chile Middle East and North Africa United Arab Emirates South Asia India Sub-Saharan Africa South Africa Figure 7 shows the average TFP growth rate under scenario I. For East Asia and Pacific, starting from the highest historical average TFP growth rate over 1985─2014 among all regions, the average TFP growth rate is expected to increase to 2.5 percent over the next 12 years and then gradually decrease. For Sub-Saharan Africa, the average TFP growth is expected to increase to 1.9 percent over the next 15 years, which is the sharpest increase from the corresponding historical TFP growth rate among all regions. For Europe and Central Asia, Latin America and Caribbean, and Middle East and North Africa, the simulated average TFP growth rates are similar in that they increase to almost 1 percent in the next 23 years and decrease gradually. For South Asia, the average TFP growth rate stays in the range from 0.6 to 0.8 percent. Using regional benchmarks limits the possibility of progress in TFP growth because the regional leaders may not be very advanced themselves. Such is the case of India for South Asia. 28 Figure 7. Simulated average TFP growth rate by region with the scenario that a country increases its overall determinant index to the highest index among developing countries in its region over 15 years Scenario II: Following the trajectory of the most improving TFP overall index in the region Scenario II assumes that a country replicates the trajectory, in terms of annual change, in the last three decades of the TFP overall determinant index corresponding to the regional benchmark country. The regional benchmark under scenario II is the country whose overall determinant index increases the most over 1985─2014 among all developing (non-OECD) countries in a given region (see table 3). Table 3. Benchmark countries with the most increase in the overall determinant index during 1985-2014 by region Region Country with the most increase in the overall index during 1985-2014 East Asia and Pacific Korea Europe and Central Asia Czech Republic Latin America and Caribbean Colombia Middle East and North Africa United Arab Emirates South Asia India Sub-Saharan Africa Rwanda 29 We apply the annual change in the index of the benchmark country over 1985─2014 to that of all countries in the same region, starting from the initial index (2014) for the next 30 years and the average change over 2005─2014 for subsequent years. Figure 8 shows the predicted average TFP growth rate under scenario II. For East Asia and Pacific, starting from the highest historical average TFP growth rate over 1985─2014, the average TFP growth rate is expected to increase to 1.7 percent over the next 15 years and then decrease. For Latin America and Caribbean and Sub- Saharan Africa, the simulated average TFP growth rate increases for more than 30 years to 0.9 and 1.2 percent, respectively. For Europe and Central Asia and Middle East and North Africa, the average TFP growth rate is expected to increase to 0.7 and 0.6 percent, respectively, over the next 20 years and decrease gradually. For South Asia, the simulated TFP growth rate stays in the range from 0.6 to 0.9 percent. Figure 8. Simulated average TFP growth rate by region with the scenario that a country replicates the annual index change that its benchmark country has had in the last three decades 30 Scenario III: Improving to the highest TFP determinant index among all developing countries Scenario III assumes that a developing country increases its overall determinant index to the highest index among all developing (non-OECD) countries as of 2014, which is that of the Republic of Korea. We assume a country’s overall determinant index increases linearly from the value in 2014 to the index of Korea over 15 years and keeps increasing with the same slope afterward. Figure 9a shows that Sub-Saharan Africa, which has the largest gap with respect to the benchmark and has a relatively low TFP growth rate, is expected to have the highest increase from its historical average over 1985─2014 (initial value in the graph) and reach the highest average TFP growth rate of 3.4 percent in 11 years and then gradually decline. South Asia is expected to increase its TFP growth rate to 3.2 percent in 11 years and then decrease, similarly to Sub-Saharan Africa. East Asia and Pacific, with the highest historical average, is expected to increase its average TFP growth rate to 2.5 percent in 11 years; this is smallest gain from the historical average among all regions, reflecting its already high TFP growth in the past. Latin America and Caribbean and Middle East and North Africa, with negative historical average TFP growth, are expected to increase the average TFP growth rate to 2.2 and 2.1 percent, respectively, in 15 years. For Europe and Central Asia, with a negative historical growth, the average TFP growth rate increases to 1.7 percent in 16 years and then decreases. Grouping countries by income level reveals interesting patterns. Figure 9b shows that the low-income group is expected to increase its average TFP growth rate the most to 3.3 percent in 11 years, the low-middle income group to 2.6 percent in 12 years, and the upper-middle income group to 1.8 percent in 16 years. In all cases, TFP growth gradually declines after reaching a peak, approaching around 1.5 percent in 35 years. These results confirm the notion obtained from the regional results: a country, region, or group with a larger gap in the TFP determinant index with respect to the benchmark has more to gain and can experience a substantial increase in TFP growth if they conduct the corresponding reforms. For those with already high TFP growth and for those whose TFP growth rises sufficiently, subsequent TFP growth will tend to taper down. 31 Figure 9. Simulated average TFP growth rate by region and income group with the scenario that a country increases its overall determinant index to the highest index among developing countries over 15 years 9a. Average TFP growth rate by region 9b. Average TFP growth rate by income levela a Low: average GDP per capita over 1985-2014 (constant 2010 USD) ≤ $995; Low middle: $995-$3,895; Upper middle: $3,895-$12,055. 32 Scenario IV: Following the trajectory of the most improving TFP overall index among all developing countries Scenario IV assumes that a country replicates a trajectory, in terms of annual change, of the world benchmark country. This is the country that has increased its overall determinant index the most over 1985─2014 among all developing (non-OECD) countries, which is Korea. We apply the annual change in the index of Korea over 1985─2014 to that of a country, starting from the initial index (2014) for the next 30 years and the average change over 2005─2014 for subsequent years. Figure 10a shows that Sub-Saharan Africa, with the largest gap with respect to the benchmark and a relatively low TFP growth rate, has the highest increase from its historical average over 1985─2014 (initial value in the graph) and reaches the highest average TFP growth rate of 2.1 percent in 16 years. South Asia is expected to increase its TFP growth rate to 2.0 percent in 16 years and decrease afterwards. East Asia and Pacific, with the highest historical average TFP growth, has the smallest projected increase in TFP growth, to 1.7 percent in 15 years. Latin America and Caribbean, Middle East and North Africa, and Europe and Central Asia, with negative historical average TFP growth, are expected to increase their TFP growth rates to 1.2 to 1.4 percent in 19─20 years. Figure 10b presents the results for income groups. It shows that the low-income group is expected to increase its average TFP growth rate the most to 2.0 percent in 16 years, the low-middle income group to 1.7 percent in 17 years, and the upper-middle income group to 1.2 percent in 20 years. The results in figure 10 confirm the insight that countries, regions, or groups with a larger gap with respect to the benchmark, such as Sub-Saharan Africa, have more to gain in terms of future TFP growth, and those with higher TFP growth, for example, East Asia and Pacific, have a slower subsequent TFP growth. 33 Figure 10. Simulated average TFP growth rate by region and income group with the scenario that a country replicates the trajectory of the overall index of Korea, which increases the index the most among all developing countries in the last three decades 10a. Average TFP growth rate by region 10b. Average TFP growth rate by income levela a Low: average GDP per capita over 1985-2014 (constant 2010 USD) ≤ $995; Low middle: $995-$3,895; Upper middle: $3,895-$12,055. 34 6. Conclusion This is the background paper for the TFP extension of the World Bank’s Long-Term Growth Model (LTGM). It proposes a way to project the future path of TFP growth for most developing countries around the world if they were to follow a program of reforms that would approach them to regional and global leaders. The paper is accompanied by an Excel-based toolkit, which can be used for scenario analysis on TFP and corresponding income growth (available at the LTGM’s website: https://www.worldbank.org/LTGM). Based on a comprehensive literature review, we select innovation, education, market efficiency, infrastructure, and institutions as the five main categories of TFP determinants. For each of these categories, we construct an index as a linear combination of representative indicators (or proxies) by factor analysis, that is, by accounting for as much of the common variance in the indicators as possible. We then combine the five subcomponent indexes into an overall index by the principal component analysis, which accounts for as much of the total variance in the subcomponent indexes as possible. Using dominance analysis, the variance decomposition of the TFP growth rate into the main subcomponent indexes shows that for OECD countries, market efficiency contributes the most to the variance of TFP growth and infrastructure, the least for the recent decade; and for developing countries, the contribution of education increases continuously and is the largest among the determinants in the recent decade. Although the variance decomposition of TFP into its determinants is not necessarily a guide for policy reform, it illustrates how the observed variation in TFP growth can be explained differentially over time and across development levels. This suggests patterns that countries can use to assess their own progress in the various determinants of productivity. On its part, regression analysis shows that an increase in the overall determinant index is significantly associated with an increase in the TFP growth rate, controlling for the initial TFP level and country- and time-effects. Countries that have a larger room for improvement in the determinants of TFP and make a stronger effort of reform would experience a larger increase in TFP growth, which is expected to rise over time and then taper down. The slowdown of TFP growth in the long run is explained by the increasing difficulty of expanding TFP 35 when its level is higher (given the estimated negative regression coefficient on past TFP) and the deceleration (in proportional terms) in the TFP determinant index itself. Though significant and reasonable by historical standards, the increase in TFP growth is projected to be between 2.5-3 percentage points in the best cases of substantial reform, not enough by itself to support overly ambitious economic growth targets. Alongside productivity improvements, savings, investment, labor participation, and human capital formation should continue to figure prominently in countries’ growth and development agendas. This study has some limitations that should be considered when interpreting the results. One limitation is that the TFP determinants could be endogenous in relation to TFP growth. To mitigate this risk, we use lagged observations of the TFP determinant index in the variance decomposition and regression analyses. This may be a more straightforward and less biased approach than using instrumental variables that could be questionable (see Young 2017). Another limitation is that we do not include all possible determinants of productivity, either as broad categories or specific indicators. For instance, we do not directly include geographic conditions, workforce demographics, income and wealth inequality, or firm-specific entrepreneurship, and managerial ability (Feyrer 2007; Mastromarco and Zago 2012; Kremer, Rao, and Schilbach 2019). We attenuate the potential problem by including country-specific effects, a reasonable strategy to control for productivity determinants that are persistent over time. Also, we include a number of indicators that represent not only their limited definition but also proxy for a wider array of variables not represented in our measurements. A third limitation deals with the well-known drawbacks of measuring productivity as a residual. In a sense, the Solow residual is a “measure of our ignorance” (Abramovitz 1956), capturing not only productivity proper but also a variety of factors, from excess capacity and natural resources to heterogeneous and intangible capital (Hulten 2001; and Corrado, Hulten, and Sichel 2009). Nevertheless, we believe that focusing on average growth rates of TFP over several years (rather than on TFP levels or high-frequency TFP growth) is conducive to reducing mismeasurement and allowing the possibility of explaining TFP growth (Jorgenson and Griliches 1967). A fourth limitation is that the study focuses on global patterns, not taking sufficiently into account country heterogeneity. 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The number of patents and journal publications is statistically + Chen and 92 countries, significant in terms of real GDP growth via their effects on the Dahlman (2004) 1960–2000 TFP growth rate. Innovation-enhancing policies and infrastructure need to be +/- Furman and 29 countries, developed to achieve leadership in innovation, but these are Hayes (2004) 1978–99 insufficient unless coupled with ever-increasing financial and human capital investments in innovation. Griffith, R&D is statistically and economically important in both + 12 OECD Redding, and technological catch up and innovation. Human capital also countries, 1974–90 Reenen (2004) plays a major role in productivity growth. Guellec and van R&D performed by the business sector, the public sector, and + 16 OECD Pottelsberghe de foreign firms is a significant determinant of long-term countries, 1980–98 la Potterie (2004) productivity growth. The results suggest a positive relationship between per capita +/- Ulku and 30 OECD GDP and innovation in both OECD and non-OECD countries. Subramanian countries, 1981–97 However, the effect of the R&D stock on innovation is (2004) significant only in the OECD countries with large markets. Industries that produce or use information technology (IT) Jorgenson, Ho, United States, account for only 30% of U.S. GDP but contributed to half of + and Stiroh (2005) 1990s and 2000s the acceleration in economic growth in the 1990s and 2000s. Long-run elasticity of TFP with respect to the stock of patents + is positive, but small. These results seem to suggest that while Abdih and Joutz United States, research workers benefit greatly from “standing on the (2006) 1948–97 shoulders” of prior researchers, the knowledge that they produce seems to have complex and slowly diffusing impacts on TFP. Information technology was critical to the dramatic + Jorgenson, Ho, United States acceleration of U.S. labor productivity growth in the mid- and Stiroh (2008) 1960–2006 1990s. Authors confirm the central role for IT in the productivity + Oliner, Sichel, United States, revival during 1995–2000 and show that IT played a and Stiroh (2008) 1990s–2000s. significant, though smaller, role after 2000. The slow-down in productivity in Europe can be attributed to + the slower emergence of the knowledge economy in Europe van Ark, United States, compared to the United States. Explanations include lower O’Mahony, and Europe, 1980s– growth contributions from investment in information and Timmer (2008) 2000s communication technology in Europe, the relatively small share of technology-producing industries in Europe, and 49 slower multifactor productivity growth (a proxy for advances in technology and innovation). 2. Education (9 studies) Study Country and year Results Relationship Human capital is not significant in explaining per capita Benhabib and 78 countries, growth rates. However, the growth rate of TFP depends on a + Spiegel (1994) 1965–85 nation's human capital stock level. Human capital generally contributes positively to TFP. In poor Miller and 83 countries, countries, human capital interacts with openness to achieve a +/- Upadhyay (2000) 1960–89 positive effect, on balance. Growth is significantly related to the years of schooling at the secondary and higher levels for males and students’ test scores 100 countries, (a proxy for the quality of education). The insignificant Barro (2001) +/- 1965–95 relation between growth and years of schooling for females implies that women are not well utilized in the labor markets of many countries. Griffith, 12 OECD Human capital (percentage of higher school attained in the Redding, and countries, 1974– total population) affects the rate of convergence of TFP + Reenen (2004) 90 growth. Results support that human capital plays a positive role in the Benhabib and 27 countries, determination of total factor productivity growth rates through + Spiegel (2005) 1960–95 its influence on the rate of technology catch-up. Bronzini and Elasticity of TFP with respect to years of schooling is positive Italy, 1985–2001 + Piselli (2009) and statistically significant (0.379). Authors find evidence that countries where the ease of doing Coe, Helpman, business and the quality of tertiary education systems are 24 countries, and Hoffmaister relatively high tend to benefit more from their own R&D + 1971–2004 (2009) efforts, from international R&D spillovers, and from human capital formation. Erosa, United States, Human capital accumulation strongly amplifies TFP Koreshkova, and + 1990–95 differences across countries. Restuccia (2010) School enrollment has significant and positive effects on the TFP growth of Chinese provinces. When education quality (as measured by the teacher-student ratio and government expenditure on education) is incorporated, TFP growth Wei and Hao appears to be significantly enhanced by quality improvements China, 1985–2004 + (2011) in primary education at the national level. TFP growth is significantly associated with secondary education in the eastern region; with primary and university education in the central region; and with primary education in the western region. 50 3. Market Efficiency (21 studies) Study Country and year Results Relationship Coe, Helpman, 77 developing + Based on data for 77 developing countries, R&D spillovers via and Hoffmaister countries, 1971– trade with 22 industrial countries are substantial. (1997) 90 Borensztein, De FDI contributes to economic growth only when a host +/- 70+ countries, Gregorio, and economy has sufficient capability to absorb advanced 1970–89 Lee (1998) technology. 16 OECD and 17 +/- FDI has a positive relationship with TFP growth in OECD de Mello (1999) non-OECD, 1970– countries, but a negative relationship in non-OECD countries. 90 While structural change on average has not been conducive to productivity growth, countries that have managed to increase 39 countries, Fagerberg (2000) their presence in the technologically most progressive industry +/- 1973–90 (electronics) have experienced higher productivity growth than other countries. The contribution of reallocation of outputs and inputs from Foster, United States, less productive to more productive establishments plays a Haltiwanger, and + 1977–87 significant role in accounting for aggregate productivity Krizan (2001) growth. Productivity growth is boosted by reforms that promote private corporate governance and competition. In manufacturing, the 18 OECD productivity gains from liberalization are greater the further a Nicoletti and countries, 1984– given country is from the technology leader. Strict product + Scarpetta (2003) 98 market regulations – and lack of regulatory reforms – appear to underlie the meagre productivity performance in industries where Europe has accumulated a technology gap. Structural change generates positive as well as negative contributions to aggregate productivity growth. Because many of these effects net out, structural change on average appears 28 OECD to have only a weak impact. Given that certain industries Peneder (2003) countries, 1990– +/- systematically achieve higher rates of productivity growth and 98 expansion of output than others, structural change in favor of specific industries might still be conducive to aggregate growth. Alfaro, Chanda, FDI alone plays an ambiguous role in contributing to Kalemli-Ozcan, 49+ countries, economic growth. However, countries with well-developed + and Sayek (2004) 1975-95 financial markets gain significantly from FDI. ~100 developing + Dollar and Kraay and developed Large increases in trade and significant declines in tariffs lead (2004) countries, 1960s– to faster growth and poverty reduction in poor countries. 90s Inefficiency appears to be the main explanation for low incomes throughout the world; it explains 43% of output 79 developing and variation in 1995, and its importance has increased over time. Jerzmanowski developed Countries with an inadequate mix of inputs are unable to + (2007) countries, 1960– access the most productive technology. The world technology 95 frontier appears to be shifting out faster at input combinations close to that of the R&D leader. 51 Within OECD countries that are not in the G7, technology +/- 16 OECD imports increase the host country’s TFP. The effect is stronger Mendi (2007) countries, 1971– in the initial years of the sampling period. There is no evidence 95 on this positive effect of technology trade on productivity among G7 countries. Tight regulation of services has slowed down growth in Arnold, Nicoletti, sectors that use IT by hindering the allocation of resources OECD countries, and Scarpetta toward the most dynamic and efficient firms. Regulations + 1985−2004 (2008) especially hurt firms that are catching up to the technology frontier and that are close to international best practice. A development accounting analysis suggests that as much as Chanda and 40+ countries, 85% of the international variation in aggregate TFP can be + Dalgaard (2008) 1985 attributed to variation in relative efficiency across sectors. Haltiwanger, 16 industrial and Hiring and firing costs tend to curb job flows, particularly in Scarpetta, and emerging those industries and firm size classes that require more + Schweiger (2008) economies, 1990s frequent labor adjustment. The estimated model implies that more productive firms in each cohort grow faster and consequently crowd out less Lentz and Denmark, 4900 productive firms in steady state. This selection effect accounts + Mortensen (2008) firms, 1992–97 for 53% of aggregate growth in the estimated version of the model. Alfaro, Kalemli- +/- 60+ countries, Countries with well-developed financial markets gain Ozcan, and Sayek 1975–95 significantly from FDI via TFP improvements. (2009) United States, Bridgman, Qi, Government’s enforcement on domestic and import sales sugar and Schmitz quotas significantly distorted sugar production at each factory + manufacturing (2009) and the location of the industry. firms, 1934–74 The growth effects of openness may be significantly improved + Chang, Kaltani, if certain complementary reforms are undertaken in the areas 82 countries, and Loayza of investment in education, financial depth, inflation 1960–2000 (2009) stabilization, public infrastructure, governance, labor market flexibility, ease of firm entry, and ease of firm exit. China (1998– When capital and labor are hypothetically reallocated to 2005) and India Hsieh and equalize marginal products to the extent observed in the (1987–95) vs. + Klenow (2009) United States, manufacturing TFP gains are expected to be United States substantial in China and India. (1977–97) Comparing blue- and white-color labor in terms of the Chile, Petrin and marginal product and cost of an input suggests that the manufacturing + Sivadasan (2011) increase in severance pay is associated with the decrease in firms, 1982–94 allocative efficiency. Countries which have extensive employment protection European Bartelsman, legislation (EPL) benefit less from the arrival of new risky countries, United Gautier, and De technology than countries with limited EPL. The model is + States, 1980s– Wind (2016) consistent with the slowdown in productivity in the European 2000s Union relative to the United States since the mid-1990s. 52 4. Infrastructure (11 studies) Authors Country and year Results Relationship United States, Aschauer (1989) There is a large return to public investment. + 1949–85 On balance, public investment has a positive effect on private Munnell (1992) Not applicable + investment, output, and employment growth. 4 East Asian and 25% of the growth difference between East Asia and Africa is Hulten (1996) 17 African due to inefficient use of infrastructure. This result may partly + countries, 1970–90 proxy for TFP differences. Pritchett presents theory and calculations to show that part of the explanation of slow growth in many poor countries is not ~100 countries, that governments did not spend on investments, but that these Pritchett (1996) thought investments did not create productive capital. A variety of +/- experiment calculations suggest that in a typical developing country, less than 50 cents of capital were created for each public dollar invested. Galiani, Gertler, and Improved water services are associated with significant Argentina, 1990s + Schargrodsky reductions in deaths from infectious and parasitic diseases. (2005) Canning and >40 countries, While infrastructure does tend to cause long-run economic + Pedroni (2008) 1950–92 growth, there is substantial variation across countries. Good infrastructure allows firms to have more productive investments in machinery, reduces time wasted commuting, promotes better health and education, and so on. The analysis 140 countries, Straub (2008) obtains positive effects of infrastructure on growth when it uses +/- 1989–2007 physical indicators of infrastructure. However, the effects are not clear when infrastructure investment flows are used as proxies for infrastructure. Calderón and >100 countries, The estimates illustrate the potential contribution of + Servén (2010) 1960–2005 infrastructure development to growth and equity across Africa. An increase in infrastructure expenditures from 5 to 6 percent Loayza and Egypt, Arab Rep., of gross domestic product would raise the annual per capita + Odawara (2010) 1971–2005 growth rate of GDP by about 0.5 percentage points in a decade’s time and 1 percentage point by the third decade. Poor infrastructure is a key obstacle to economic development. The experience of Latin America shows that there is no question that private participation did deliver some Calderón and Latin America, efficiency and quality gains. But they were held back by weak + Servén (2012) 1981–2005 regulatory and supervisory frameworks, and poorly designed concession and privatization agreements, which led to ubiquitous renegotiations and ended up costing governments enormous sums. Recent theoretical and empirical literature finds positive effects (Calderón and Not applicable of infrastructure development on income growth and, more + Servén 2014) tentatively, on distributive equity. 53 5. Institutions (10 studies) Study Country and year Results Relationship Growth is inversely related to the share of government consumption in GDP, but insignificantly related to the share of 98 countries, Barro (1991) public investment. Growth rates are positively related to +/- 1960–85 measures of political stability and inversely related to a proxy for market distortions. Political institutions do matter for growth, but thinking in terms Przeworski and Review of of regimes, democracy, autocracy, or bureaucracy does not +/- Limongi (1993) previous studies seem to capture the relevant differences. The transmission of malaria, which is strongly affected by 60+ countries, Sachs (2003) ecological conditions, directly affects the level of per capita +/- 1995 income after controlling for the quality of institutions. Output is driven by differences in institutions and government policies, which the authors call “social infrastructure.” The Hall and Jones 100+ countries, authors treat social infrastructure as endogenous, determined + (1999) 1986–95 historically by location and other factors captured in part by language. 10 OECD A big government size causes economic growth with some Ghali (1999) countries, 1970– disparities, through the increase of government spending, +/- 94 investment, or international trade. Total factor productivity on average is weaker in countries Dar and where government size is larger due to policy-induced 19 OECD, 1971– AmirKhalkhali distortions such as burdensome taxation, crowding-out effects +/- 99 (2002) for new capital that embodies new technology, and the lack of market forces that could foster efficient use of resources. Tropics, germs, and crops affect development through institutions. No evidence is found that tropics, germs, and Easterly and 64+ countries, crops affect country incomes directly other than through + Levine (2003) 1995 institutions. Macroeconomic policies on development are not significant once the factor of institutional quality is controlled. Differences in economic institutions, rather than geography or Korea, Rep., Acemoglu, culture, cause differences in per capita colonized Johnson, and incomes. Countries with more secure property rights (that is, + countries by Robinson (2004) with better economic institutions), have European powers higher average incomes. The study estimates the respective contributions of Rodrik, institutions, geography, and trade in determining income levels Subramanian, 79+ countries, around the world, using recently developed instrumental + and Trebbi 1995 variables for institutions and trade. Results indicate that the (2004) quality of institutions "trumps" everything else. The study compiles a Government Anti-Diversionary Policy index (GADP), an average of five indices capturing the quality of government: rule of law, bureaucratic quality, risk of Chanda and 40+ countries, expropriation, government repudiation of contracts, and + Dalgaard (2008) 1985 corruption. The GADP is strongly related to total factor productivity. Introducing geographical variables reduces the impact of GADP considerably. Geographical explanations seem to be as important as institutional explanations. 54 Appendix B. Countries in the sample Table B.1 shows countries we use in the analysis with their region and income quintile by group depending on analysis type (descriptive and statistical), data source (PWT, GTAP, and WDI), and some characteristics (oil rent and population). We calculate income quintiles for non-OECD countries using the average GDP per capita over 1985─2014 (World Bank 2017e). The OECD group includes high-income countries that have been members of the OECD for more than 40 years. Table B.1. Countries with region and income quintile by group 1. Countries from PWT 9.0 for the statistical analysis (98) ID Country Region Income quintile* 1 Argentina Latin America and Caribbean 5 2 Armenia Europe and Central Asia 3 3 Australia OECD Not applicable 4 Austria OECD Not applicable 5 Belgium OECD Not applicable 6 Benin Sub-Saharan Africa 2 7 Bolivia Latin America and Caribbean 3 8 Botswana Sub-Saharan Africa 4 9 Brazil Latin America and Caribbean 5 10 Bulgaria Europe and Central Asia 4 11 Burkina Faso Sub-Saharan Africa 1 12 Burundi Sub-Saharan Africa 1 13 Cameroon Sub-Saharan Africa 2 14 Canada OECD Not applicable 15 Central African Republic Sub-Saharan Africa 1 16 Chile Latin America and Caribbean 5 17 China East Asia and Pacific 3 18 Colombia Latin America and Caribbean 4 19 Costa Rica Latin America and Caribbean 4 20 Côte d'Ivoire Sub-Saharan Africa 2 21 Croatia Europe and Central Asia 5 22 Czech Republic Europe and Central Asia 5 23 Denmark OECD Not applicable 24 Dominican Republic Latin America and Caribbean 4 25 Ecuador Latin America and Caribbean 4 26 Egypt, Arab Rep. Middle East and North Africa 3 27 Finland OECD Not applicable 28 France OECD Not applicable 29 Germany OECD Not applicable 30 Greece OECD Not applicable 31 Guatemala Latin America and Caribbean 3 32 Honduras Latin America and Caribbean 3 33 Hong Kong SAR, China East Asia and Pacific 5 34 Hungary Europe and Central Asia 5 35 India South Asia 2 36 Indonesia East Asia and Pacific 3 37 Iran, Islamic Rep. Middle East and North Africa 4 38 Ireland OECD Not applicable 39 Israel Middle East and North Africa 5 40 Italy OECD Not applicable 41 Jamaica Latin America and Caribbean 4 42 Japan OECD Not applicable 55 1. Countries from PWT 9.0 for the statistical analysis (98) 43 Jordan Middle East and North Africa 3 44 Kazakhstan Europe and Central Asia 4 45 Kenya Sub-Saharan Africa 2 46 Korea, Rep. East Asia and Pacific 5 47 Kyrgyz Republic Europe and Central Asia 2 48 Lao PDR East Asia and Pacific 2 49 Lesotho Sub-Saharan Africa 2 50 Lithuania Europe and Central Asia 5 51 Malaysia East Asia and Pacific 5 52 Mauritania Sub-Saharan Africa 2 53 Mexico Latin America and Caribbean 5 54 Moldova Europe and Central Asia 3 55 Mongolia East Asia and Pacific 3 56 Morocco Middle East and North Africa 3 57 Mozambique Sub-Saharan Africa 1 58 Namibia Sub-Saharan Africa 4 59 Netherlands OECD Not applicable 60 New Zealand OECD Not applicable 61 Nicaragua Latin America and Caribbean 3 62 Niger Sub-Saharan Africa 1 63 Nigeria Sub-Saharan Africa 3 64 Norway OECD Not applicable 65 Panama Latin America and Caribbean 4 66 Paraguay Latin America and Caribbean 3 67 Peru Latin America and Caribbean 4 68 Philippines East Asia and Pacific 3 69 Poland Europe and Central Asia 5 70 Portugal OECD Not applicable 71 Qatar Middle East and North Africa 5 72 Romania Europe and Central Asia 4 73 Russian Federation Europe and Central Asia 5 74 Rwanda Sub-Saharan Africa 1 75 Senegal Sub-Saharan Africa 2 76 Serbia Europe and Central Asia 4 77 Sierra Leone Sub-Saharan Africa 1 78 Singapore East Asia and Pacific 5 79 Slovak Republic Europe and Central Asia 5 80 Slovenia Europe and Central Asia 5 81 South Africa Sub-Saharan Africa 4 82 Spain OECD Not applicable 83 Sri Lanka South Asia 3 84 Sudan Sub-Saharan Africa 2 85 Sweden OECD Not applicable 86 Switzerland OECD Not applicable 87 Tajikistan Europe and Central Asia 2 88 Tanzania Sub-Saharan Africa 1 89 Thailand East Asia and Pacific 4 90 Togo Sub-Saharan Africa 1 91 Tunisia Middle East and North Africa 3 92 Turkey Europe and Central Asia 5 93 Ukraine Europe and Central Asia 3 94 United Kingdom OECD Not applicable 95 United States OECD Not applicable 96 Uruguay Latin America and Caribbean 5 97 Venezuela, RB Latin America and Caribbean 5 98 Zimbabwe Sub-Saharan Africa 2 56 B. Additional countries using GTAP 9.0 (for labor share) for the descriptive analysis of TFP growth (16) ID Country Region Income quintile* 1 Albania Europe and Central Asia 3 2 Bangladesh South Asia 1 3 Cambodia East Asia and Pacific 1 4 Congo, Dem. Rep. Sub-Saharan Africa 1 5 Ethiopia Sub-Saharan Africa 1 6 Ghana Sub-Saharan Africa 2 7 Liberia Sub-Saharan Africa 1 8 Madagascar Sub-Saharan Africa 1 9 Malawi Sub-Saharan Africa 1 10 Mali Sub-Saharan Africa 2 11 Nepal South Asia 1 12 Pakistan South Asia 2 13 Uganda Sub-Saharan Africa 1 14 Vietnam East Asia and Pacific 2 15 Yemen, Rep. Middle East and North Africa 2 16 Zambia Sub-Saharan Africa 2 C. Additional countries for the descriptive analysis of the determinant indexes (22) ID Country Region Income quintile* 1 Afghanistan South Asia 1 2 Algeria Middle East and North Africa 4 3 Azerbaijan Europe and Central Asia 3 4 Belarus Europe and Central Asia 4 5 Bosnia and Herzegovina Europe and Central Asia 4 6 Chad Sub-Saharan Africa 2 7 Cuba Latin America and Caribbean 4 8 El Salvador Latin America and Caribbean 3 9 Eritrea Sub-Saharan Africa 1 10 Gambia, The Sub-Saharan Africa 1 11 Georgia Europe and Central Asia 3 12 Guinea Sub-Saharan Africa 1 13 Haiti Latin America and Caribbean 2 14 Lebanon Middle East and North Africa 4 15 Macedonia, FYR Europe and Central Asia 4 16 Myanmar East Asia and Pacific 1 17 Papua New Guinea East Asia and Pacific 2 18 Puerto Rico Latin America and Caribbean 5 19 Turkmenistan Europe and Central Asia 4 20 United Arab Emirates Middle East and North Africa 5 21 Uzbekistan Europe and Central Asia 2 22 West Bank and Gaza Middle East and North Africa 3 57 D. Additional countries to include in the LTGM toolkit: high oil rent (10) ID Country Region Income quintile* 1 Angola Sub-Saharan Africa 3 2 Congo, Rep. Sub-Saharan Africa 3 3 Equatorial Guinea Sub-Saharan Africa 5 4 Gabon Sub-Saharan Africa 5 5 Iraq Middle East and North Africa 4 6 Kuwait Middle East and North Africa 5 7 Libya Middle East and North Africa 5 8 Oman Middle East and North Africa 5 9 Saudi Arabia Middle East and North Africa 5 10 South Sudan Sub-Saharan Africa 2 E. Additional countries to include in the LTGM toolkit: small population (50) ID Country Region Income quintile* 1 American Samoa East Asia and Pacific 5 2 Andorra Europe and Central Asia 5 3 Antigua and Barbuda Latin America and Caribbean 5 4 Aruba Latin America and Caribbean 5 5 Bahamas, The Latin America and Caribbean 5 6 Bahrain Middle East and North Africa 5 7 Barbados Latin America and Caribbean 5 8 Belize Latin America and Caribbean 4 9 Bhutan South Asia 2 10 Brunei Darussalam East Asia and Pacific 5 11 Cabo Verde Sub-Saharan Africa 3 12 Comoros Sub-Saharan Africa 2 13 Cyprus Europe and Central Asia 5 14 Djibouti Middle East and North Africa 2 15 Dominica Latin America and Caribbean 4 16 Estonia Europe and Central Asia 5 17 Fiji East Asia and Pacific 4 18 Greenland Europe and Central Asia 5 19 Grenada Latin America and Caribbean 4 20 Guam East Asia and Pacific 5 21 Guinea-Bissau Sub-Saharan Africa 1 22 Guyana Latin America and Caribbean 3 23 Iceland Europe and Central Asia 5 24 Kiribati East Asia and Pacific 3 25 Latvia Europe and Central Asia 5 26 Liechtenstein Europe and Central Asia 5 27 Luxembourg Europe and Central Asia 5 28 Macao SAR, China East Asia and Pacific 5 29 Malta Middle East and North Africa 5 30 Marshall Islands East Asia and Pacific 3 31 Mauritius Sub-Saharan Africa 4 32 Micronesia, Fed. Sts. East Asia and Pacific 3 33 Monaco Europe and Central Asia 5 34 Montenegro Europe and Central Asia 4 35 Palau East Asia and Pacific 5 36 Samoa East Asia and Pacific 3 37 São Tomé and Príncipe Sub-Saharan Africa 2 38 Seychelles Sub-Saharan Africa 5 39 Solomon Islands East Asia and Pacific 2 40 St. Kitts and Nevis Latin America and Caribbean 5 41 St. Lucia Latin America and Caribbean 4 42 St. Vincent and the Grenadines Latin America and Caribbean 4 43 St. Suriname Latin America and Caribbean 4 58 E. Additional countries to include in the LTGM toolkit: small population (50) 44 Eswatini Sub-Saharan Africa 3 45 Timor-Leste East Asia and Pacific 2 46 Tonga East Asia and Pacific 3 47 Trinidad and Tobago Latin America and Caribbean 5 48 Tuvalu East Asia and Pacific 3 49 Vanuatu East Asia and Pacific 3 50 Virgin Islands (U.S.) Latin America and Caribbean 5 F. Countries for the statistical analysis with WDI dataset in Appendix E (96) ID Country Region Income quintile* 1 Albania Europe and Central Asia 3 2 Argentina Latin America and Caribbean 5 3 Australia OECD Not applicable 4 Austria OECD Not applicable 5 Bangladesh South Asia 1 6 Belgium OECD Not applicable 7 Benin Sub-Saharan Africa 2 8 Bolivia Latin America and Caribbean 3 9 Botswana Sub-Saharan Africa 4 10 Brazil Latin America and Caribbean 5 11 Bulgaria Europe and Central Asia 4 12 Burkina Faso Sub-Saharan Africa 1 13 Burundi Sub-Saharan Africa 1 14 Cambodia East Asia and Pacific 1 15 Cameroon Sub-Saharan Africa 2 16 Canada OECD Not applicable 17 Central African Republic Sub-Saharan Africa 1 18 Chile Latin America and Caribbean 5 19 China East Asia and Pacific 3 20 Colombia Latin America and Caribbean 4 21 Congo, Dem. Rep. Sub-Saharan Africa 1 22 Côte d'Ivoire Sub-Saharan Africa 2 23 Denmark OECD Not applicable 24 Dominican Republic Latin America and Caribbean 4 25 Ecuador Latin America and Caribbean 4 26 Egypt, Arab Rep. Middle East and North Africa 3 27 El Salvador Latin America and Caribbean 3 28 Finland OECD Not applicable 29 France OECD Not applicable 30 Germany OECD Not applicable 31 Ghana Sub-Saharan Africa 2 32 Greece OECD Not applicable 33 Guatemala Latin America and Caribbean 3 34 Honduras Latin America and Caribbean 3 35 Hong Kong SAR, China East Asia and Pacific 5 36 Hungary Europe and Central Asia 5 37 India South Asia 2 38 Indonesia East Asia and Pacific 3 39 Iran, Islamic Rep. Middle East and North Africa 4 40 Ireland OECD Not applicable 41 Israel Middle East and North Africa 5 42 Italy OECD Not applicable 43 Jamaica Latin America and Caribbean 4 44 Japan OECD Not applicable 45 Jordan Middle East and North Africa 3 46 Kenya Sub-Saharan Africa 2 47 Korea, Rep. East Asia and Pacific 5 59 F. Countries for the statistical analysis with WDI dataset in Appendix E (96) 48 Lao PDR East Asia and Pacific 2 49 Liberia Sub-Saharan Africa 1 50 Madagascar Sub-Saharan Africa 1 51 Malawi Sub-Saharan Africa 1 52 Malaysia East Asia and Pacific 5 53 Mali Sub-Saharan Africa 2 54 Mauritania Sub-Saharan Africa 2 55 Mexico Latin America and Caribbean 5 56 Mongolia East Asia and Pacific 3 57 Morocco Middle East and North Africa 3 58 Mozambique Sub-Saharan Africa 1 59 Namibia Sub-Saharan Africa 4 60 Nepal South Asia 1 61 Netherlands OECD Not applicable 62 New Zealand OECD Not applicable 63 Nicaragua Latin America and Caribbean 3 64 Niger Sub-Saharan Africa 1 65 Nigeria Sub-Saharan Africa 3 66 Norway OECD Not applicable 67 Pakistan South Asia 2 68 Panama Latin America and Caribbean 4 69 Paraguay Latin America and Caribbean 3 70 Peru Latin America and Caribbean 4 71 Philippines East Asia and Pacific 3 72 Poland Europe and Central Asia 5 73 Portugal OECD Not applicable 74 Qatar Middle East and North Africa 5 75 Rwanda Sub-Saharan Africa 1 76 Senegal Sub-Saharan Africa 2 77 Sierra Leone Sub-Saharan Africa 1 78 Singapore East Asia and Pacific 5 79 South Africa Sub-Saharan Africa 4 80 Spain OECD Not applicable 81 Sri Lanka South Asia 3 82 Sudan Sub-Saharan Africa 2 83 Sweden OECD Not applicable 84 Switzerland OECD Not applicable 85 Tanzania Sub-Saharan Africa 1 86 Thailand East Asia and Pacific 4 87 Togo Sub-Saharan Africa 1 88 Tunisia Middle East and North Africa 3 89 Turkey Europe and Central Asia 5 90 Uganda Sub-Saharan Africa 1 91 United Kingdom OECD Not applicable 92 United States OECD Not applicable 93 Uruguay Latin America and Caribbean 5 94 Vietnam East Asia and Pacific 2 95 Zambia Sub-Saharan Africa 2 96 Zimbabwe Sub-Saharan Africa 2 * 1: <$583 (constant 2010 US$), 2: $583-$1,347, 3: $1,347-$3,243, 4: $3,243-$6,837; 5: ≥$6,837 60 Appendix C. Average values of the determinant indexes and their indicators over 1985−2014 Table C.1 shows the average values of the rescaled subcomponent and overall determinant indexes from 1 (the worst) to 100 (the best) and their indicators over 1985─2014. The individual indicators are shown without any transformation or standardization. Missing indicators are imputed if eligible as described in the section 3.3. Table C.1. TFP determinant indexes and their indicators for OECD countries (21) and non-OECD countries (115), average (standard deviation), 1985−2014 OECD Non-OECD by region East Asia and Europe and Latin America Middle East and Sub-Saharan Pacific Central Asia and Caribbean North Africa South Asia Africa Overall determinant index 74.38 (11.66) 35.87 (21.91) 41.13 (11.85) 32.10 (9.00) 35.55 (14.52) 20.76 (6.62) 18.16 (7.79) I. Innovation index 37.49 (16.06) 11.95 (19.73) 9.45 (7.19) 4.46 (2.80) 12.32 (15.85) 3.59 (2.00) 3.74 (1.62) Research and development expenditure (% 1.85 (0.85) 0.50 (0.76) 0.53 (0.40) 0.26 (0.22) 0.70 (0.88) 0.28 (0.21) 0.29 (0.14) of GDP) Number of patents (per 100 people) 0.13 (0.14) 0.08 (0.16) 0.03 (0.02) 0.01 (0.01) 0.05 (0.08) 0.00 (0.00) 0.00 (0.01) Number of journal articles (per 100 people) 0.09 (0.05) 0.01 (0.04) 0.02 (0.03) 0.00 (0.01) 0.02 (0.03) 0.00 (0.00) 0.00 (0.00) II. Education index 61.12 (12.88) 33.62 (22.01) 49.87 (12.10) 31.54 (10.69) 33.47 (12.48) 18.88 (8.05) 15.28 (7.82) Government expenditure on education, total 5.21 (1.15) 3.11 (1.38) 4.19 (1.24) 3.83 (1.80) 4.83 (1.93) 2.75 (0.81) 4.02 (2.54) (% of GDP) Percentage of population aged 25-64 with 30.10 (11.93) 16.96 (12.49) 42.78 (16.05) 16.91 (7.93) 16.61 (8.74) 12.69 (9.97) 7.42 (7.08) completed secondary schooling Percentage of population aged 25-64 with 14.06 (6.42) 6.90 (7.65) 10.17 (4.77) 7.10 (4.55) 7.39 (6.01) 4.20 (2.17) 1.17 (0.84) completed tertiary schooling PISA, average of math, science, and 503.47 (21.43) 438.37 (76.21) 427.63 (44.57) 408.83 (25.57) 405.57 (30.81) 370.94 (14.62) 368.65 (2.82) reading III. Market efficiency index 82.94 (9.90) 54.04 (19.74) 52.06 (12.42) 47.12 (10.67) 45.67 (14.09) 38.55 (5.92) 34.47 (12.49) a. World Bank Doing Business scores 72.37 (9.61) 48.02 (19.82) 45.33 (18.13) 44.14 (14.21) 43.33 (15.88) 38.45 (13.60) 31.87 (15.26) b. IMF Financial Development Index 0.66 (0.16) 0.34 (0.24) 0.20 (0.15) 0.22 (0.11) 0.29 (0.14) 0.22 (0.09) 0.11 (0.08) c. Labor market index -0.92 (0.37) 0.01 (0.87) -0.67 (0.48) 0.13 (0.54) 0.64 (0.83) 0.99 (0.46) 0.58 (1.17) - Ratio of minimum wage to value added 0.25 (0.14) 0.35 (0.26) 0.29 (0.13) 0.56 (0.39) 0.45 (0.41) 0.38 (0.33) 0.64 (0.56) per worker - Severance pay for redundancy dismissal 4.33 (5.81) 20.26 (15.25) 8.74 (4.59) 17.72 (7.73) 14.12 (9.70) 25.89 (13.58) 14.52 (16.67) (weeks of salary) - Share of women in wage employment in 45.37 (4.39) 39.52 (5.50) 44.23 (8.35) 40.10 (5.47) 20.25 (10.64) 20.47 (7.12) 27.74 (10.74) the nonagricultural sector (% of total nonagricultural employment) IV. Infrastructure index 71.55 (9.53) 33.55 (16.91) 47.64 (10.39) 36.80 (9.50) 42.24 (12.60) 24.04 (8.77) 18.94 (7.37) 61 OECD Non-OECD by region East Asia and Europe and Latin America Middle East and Sub-Saharan Pacific Central Asia and Caribbean North Africa South Asia Africa Fixed telephone subscriptions (per 100 48.40 (10.77) 13.76 (18.29) 18.81 (10.67) 11.05 (8.13) 14.04 (12.30) 2.04 (3.25) 1.21 (1.99) people) Mobile cellular subscriptions (per 100 53.97 (49.71) 33.76 (49.96) 37.77 (49.52) 31.96 (44.52) 34.36 (45.99) 15.78 (26.73) 15.21 (28.23) people) Electricity production (kWh per 100 932462.60 199921.20 355155.10 174065.30 356559.90 28440.79 35189.09 people) (559809.30) (251765.20) (167625.10) (173809.20) (456594.60) (21837.76) (80191.89) Paved roads (km per 100 people) 1.20 (0.48) 0.07 (0.07) 0.51 (0.37) 0.14 (0.15) 0.17 (0.07) 0.11 (0.13) 0.06 (0.08) Improved sanitation facilities (% of 98.48 (2.51) 62.70 (30.08) 88.30 (10.93) 71.56 (19.84) 82.82 (17.96) 38.58 (23.20) 24.55 (15.58) population with access) Improved water source (% of population 99.60 (0.96) 74.15 (22.66) 91.02 (11.69) 85.85 (10.22) 89.76 (12.24) 71.65 (20.94) 59.81 (17.14) with access) V. Institutions index 89.37 (7.19) 54.84 (17.71) 51.30 (15.58) 54.15 (12.67) 52.73 (12.22) 41.83 (12.48) 43.90 (13.32) Voice and accountability 1.39 (0.22) -0.46 (0.85) -0.23 (0.89) 0.03 (0.67) -0.64 (0.53) -0.58 (0.70) -0.70 (0.71) Control of corruption 1.76 (0.61) -0.16 (1.01) -0.47 (0.66) -0.27 (0.72) -0.12 (0.69) -0.77 (0.55) -0.59 (0.65) Government effectiveness 1.69 (0.44) 0.02 (0.94) -0.35 (0.74) -0.20 (0.64) -0.06 (0.70) -0.67 (0.65) -0.70 (0.63) Political stability 1.05 (0.42) -0.21 (0.92) -0.25 (0.86) -0.34 (0.66) -0.50 (0.95) -1.45 (0.72) -0.71 (1.02) Regulatory quality 1.42 (0.38) -0.02 (1.06) -0.22 (0.89) 0.05 (0.73) -0.21 (0.74) -0.72 (0.71) -0.65 (0.66) Rule of law 1.54 (0.35) -0.17 (0.87) -0.46 (0.78) -0.46 (0.71) -0.08 (0.67) -0.59 (0.72) -0.77 (0.68) 62 Appendix D. Regression with different time-lags for robustness check We run regressions with different time lags of 3 and 7 years for a robustness check of the fixed-effect model. Table D.1 shows that the sign and the significance of coefficients under the models using 3- and 7-year lags are consistent with those using 5-year lags. Table D.1. Regression with different time lags Dependent variable: ℎ , , Number of observations: 869 477 379 Number of groups 98 98 98 (countries): Time lag (α): 3 years 5 years 7 years Regressors (below): Coefficient (SE) Coefficient (SE) Coefficient (SE) , 0.039 (0.0138) *** 0.050 (0.0183) *** 0.025 (0.0074) *** , -0.120 (0.0119) *** -0.099 (0.0151) *** -0.102 (0.0100) *** Constant -0.143 (0.0470) *** -0.180 (0.0636) *** -0.094 (0.0244) *** : Within 0.2729 0.3048 0.4535 Between 0.2669 0.2749 0.6002 Overall 0.1457 0.1586 0.3841 SE = Standard error; *: significant at 10%; **: significant at 5%; ***: significant at 1% level Note. The coefficients of time dummies are not listed. 63 Appendix E. Robustness check using World Development Indicators for TFP calculation As a robustness check, we obtain results using World Development Indicators (WDI) as a primary source for TFP calculations and compare them with those presented in the main text, which use PWT 9.0 as primary data. Specifically, we use WDI for real GDP, capital formation, population, and employment rate (World Bank 2017d, 2017g, 2017m, 2017n, 2017b) and the PWT 9.0 (Feenstra, Inklaar, and Timmer 2015) and GTAP 9.0 for country- and time-specific labor shares. Because the employment rate in WDI is available from 1991 and onward, we impute missing values for 1985─90 applying the annual change of the employment rate in PWT. To calculate the physical capital stock, we use the perpetual inventory method (Barro and Sala-i-Martin 2004) with a depreciation rate of 4% and an initial year of 1960 or the earliest year with available data. The list of countries included in the analysis using the WDI data set is presented in table B.1. 1) Total factor productivity Figure E.1 shows the trend of average annual TFP growth rate over decades calculated using PWT 9.0 and WDI as primary sources. Figure E.1. Comparison of average annual TFP growth rates using PWT 9.0 and WDI for all, OECD, and developing countries Average anuual TFP growth rate 1.0% 0.8% 0.6% 0.4% 0.2% 0% -0.2% -0.4% -0.6% -0.8% -1.0% 85-94 94-04 04-14 85-94 94-04 04-14 85-94 94-04 04-14 All OECD Developing PWT 9.0 WDI PWT 9.0 WDI PWT 9.0 WDI Source: Authors' calculation, using PWT 9.0, GTAP (for a few cases), and WDI data. Note: The OECD group includes high-income countries that have been members of OECD for more than 40 years. 64 The results from the two sources are similar in terms of signs and magnitude for the period 1985─2004 for all, OECD, and developing countries. For the period 2004─14, the average TFP growth rate calculated using WDI is much lower than that using PWT, especially for the developing countries. This is driven by the gap in the average TFP growth rate between the two sources for Europe and Central Asia and Sub-Saharan African for the recent decade as shown in figure E.2. For Europe and Central Asia, WDI has missing values for several countries unlike PWT. For Sub-Saharan Africa, some countries such as Mozambique and Zimbabwe have quite low average TFP growth rates (less than -3 percent). Figure E.2. Comparison of average annual TFP growth rates using PWT 9.0 and WDI for developing countries by region Average annual TFP growth rate 3% 2% 1% 0% -1% -2% -3% 85 94 04 85 94 04 85 94 04 85 94 04 85 94 04 85 94 04 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 East Asia Europe Latin America Middle East South Sub-Saharan and and and and Asia Africa Pacific Central Asia Caribbean North Africa PWT 9.0 WDI Source: Authors' calculation, using PWT 9.0, GTAP (for a few cases), and WDI data. Figure E.3 shows the comparison of average annual TFP growth rates weighted by real GDP using PWT 9.0 and WDI. The results are similar in terms of signs. However, the magnitude tends to be bigger when WDI is used as the primary source for East Asia and Pacific, Latin America and Caribbean, and Sub-Saharan Africa. 65 Figure E.3. Comparison of average annual TFP growth rate weighted by real GDP using PWT 9.0 and WDI for developing countries by region 3% average weighted by GDP Annual TFP growth rate, 2% 1% 0% -1% -2% -3% 85 94 04 85 94 04 85 94 04 85 94 04 85 94 04 85 94 04 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 East Asia Europe Latin America Middle East South Sub-Saharan and and and and Asia Africa Pacific Central Asia Caribbean North Africa PWT 9.0 WDI Source: Authors' calculation, using PWT 9.0, GTAP (for a few cases), and WDI data. 2) Statistical analysis The relative contribution of the main determinants to total factor productivity We conduct the variance decomposition of TFP growth rates using PWT 9.0 and WDI as primary sources and compare the results. The list of developing countries with available TFP growth rates is different between PWT 9.0 and WDI; therefore, we conduct the analysis for the full sample of countries for each source and for the common sample (60 developing and 21 OECD countries). Figure E.4 shows, for the OECD group, the largest contributor is market efficiency using PWT 9.0 and innovation using WDI for the recent decade, and the smallest contributor is infrastructure using the both data sources. For the developing countries, the largest contributor is institutions in the period 1985─94; however, in the period 2004─14, it varies among education, market efficiency, and institutions depending on a set of countries and data sources. The common feature among the four sets of results for the developing countries is that innovation and infrastructure have a lower contribution compared to other determinants in the last decade. 66 Figure E.4. Comparison of the variance decomposition of TFP growth rates into the determinant indexes using PWT 9.0 and WDI by decade for all, OECD, and developing countries, controlling for the TFP level lagged by five years and time- effects World 1985-1994 1994-2004 2004-2014 0.06 0.05 0.09 0.06 0.08 0.07 Innovation 0.06 0.38 0.09 0.09 0.30 0.12 0.13 0.45 0.36 0.13 0.25 0.30 Education 0.06 0.11 0.19 0.11 0.08 0.17 0.14 0.13 0.18 0.16 0.23 0.24 Market efficiency 0.13 0.18 0.14 0.17 0.22 0.19 0.19 0.12 0.17 0.18 0.13 0.21 Infrastructure 0.09 0.12 0.18 0.20 0.17 0.23 0.48 0.25 0.19 0.48 0.30 0.19 Institutions 0.66 0.21 0.39 0.42 0.22 0.28 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 OECD 1985-1994 1994-2004 2004-2014 0.16 0.09 0.21 0.16 0.09 0.21 Innovation 0.10 0.13 0.38 0.10 0.13 0.38 0.11 0.20 0.16 0.11 0.20 0.16 Education 0.12 0.27 0.19 0.12 0.27 0.19 0.31 0.18 0.45 0.31 0.18 0.45 Market efficiency 0.37 0.17 0.14 0.37 0.17 0.14 0.18 0.37 0.08 0.18 0.37 0.08 Infrastructure 0.26 0.21 0.04 0.26 0.21 0.04 0.24 0.16 0.11 0.24 0.16 0.11 Institutions 0.16 0.22 0.26 0.16 0.22 0.26 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 Developing 1985-1994 1994-2004 2004-2014 0.02 0.03 0.07 0.02 0.03 0.05 Innovation 0.03 0.28 0.09 0.04 0.26 0.13 0.13 0.47 0.49 0.12 0.27 0.38 Education 0.04 0.07 0.24 0.08 0.06 0.21 0.08 0.17 0.15 0.10 0.28 0.23 Market efficiency 0.11 0.19 0.21 0.13 0.23 0.33 0.20 0.12 0.14 0.20 0.15 0.17 Infrastructure 0.08 0.23 0.10 0.31 0.36 0.11 0.56 0.22 0.15 0.55 0.28 0.17 Institutions 0.75 0.23 0.36 0.44 0.09 0.23 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 PWT, full sample PWT, common sample WDI, full sample WDI, common sample Source: Authors' calculation. Note: The OECD group includes high-income countries that have been members of OECD for more than 40 years. 67 The relationship between the overall determinant index and total factor productivity We run regressions for the same model (equation 2) using PWT 9.0 and WDI as primary sources and compare results. The list of countries with available TFP growth rates is different between PWT 9.0 and WDI; therefore, we run regressions for the full sample of countries for each source and the common sample (81 countries). For the pooled OLS, random-effect, and fixed-effect models, shown in table E.1-E.3 respectively, the results are consistent in terms of sign and significance between PWT 9.0 and WDI for the full and the common sample of countries. These results suggest that the fixed-effect model with the five-year lag fitted to the full sample of PWT 9.0, the model we choose for simulation and the extended LTGM, is robust. Table E.1. Comparison of the regression results using PWT 9.0 and WDI, pooled OLS Dependent ℎ ; , variable: Country effects: None (pooled OLS) Primary source: PWT 9.0 WDI Number of 477 400 468 394 observations: Number of groups 98 81 96 81 (countries): Country sample: Full Common Full Common , 0.004 (0.0011) *** 0.004 (0.0012) *** 0.006 (0.0023) ** 0.004 (0.0013) *** , -0.082 (0.0052) *** -0.087 (0.0076) *** -0.089 (0.0189) *** -0.070 (0.0063) *** Constant -0.021 (0.0055) ** -0.020 (0.0062) *** -0.030 (0.0113) *** -0.019 (0.0062) *** : 0.4022 0.3564 0.4260 0.4073 Results are expressed as coefficient (standard error). *: significant at 10%; **: significant at 5%; ***: significant at 1% level. The coefficients of time dummies are not listed.; We use country-cluster robust variance estimation. 68 Table E.2. Comparison of the regression results using PWT 9.0 and WDI, random effect Dependent ℎ ; , variable: Country effects: Random Primary source: PWT 9.0 WDI Number of 477 400 468 394 observations: Number of groups 98 81 96 81 (countries): Country sample: Full Common Full Common , 0.004 (0.0011) *** 0.004 (0.0012) *** 0.006 (0.0023) *** 0.004 (0.0013) *** , -0.082 (0.0052) *** -0.087 (0.0076) *** -0.089 (0.0189) *** -0.070 (0.0063) *** Constant -0.021 (0.0055) *** -0.020 (0.0062) *** -0.030 (0.0113) *** -0.019 (0.0062) *** : Within 0.2784 0.3245 0.4003 0.2158 Between 0.8573 0.7482 0.4639 0.4779 Overall 0.4022 0.3564 0.4260 0.4073 Results are expressed as coefficient (standard error). *: significant at 10%; **: significant at 5%; ***: significant at 1% level. The coefficients of time dummies are not listed. Table E.3. Comparison of the regression results using PWT 9.0 and WDI, fixed effect Dependent ℎ ; , variable: Country effects: Fixed Primary source: PWT 9.0 WDI Number of 477 400 468 394 observations: Number of groups 98 81 96 81 (countries): Country sample: Full Common Full Common , 0.050 (0.0183) *** 0.055 (0.0197) *** 0.098 (0.0268) *** 0.062 (0.0233) *** , -0.099 (0.0151) *** -0.124 (0.0171) *** -0.116 (0.0351) *** -0.071 (0.0143) *** Constant -0.180 (0.0636) *** -0.198 (0.0684) *** -0.333 (0.0894) *** -0.219 (0.0803) *** : Within 0.3048 0.3660 0.5050 0.2776 Between 0.2749 0.2193 0.1499 0.1376 Overall 0.1586 0.1238 0.1371 0.1089 Results are expressed as coefficient (standard error). *: significant at 10%; **: significant at 5%; ***: significant at 1% level. The coefficients of time dummies are not listed. 69