metadata: authors: 0: author: Kim,Young Eun
1: author: Loayza,Norman V.
count: World
docna: 0: docna: Productivity Growth : Patterns and
            Determinants across the World
docty: Policy Research Working Paper
subtopic: Educational Sciences,Economic Theory & Research,Economic Growth,Industrial Economics,Judicial System Reform,International Trade and Trade Rules,Legal Reform,Regulatory Regimes,Social Policy,Common Property Resource Development,Legal Products,Legislation
teratopic: Social Development,Industry,Rural Development,Education,International Economics and Trade,Macroeconomics and Economic Growth,Law and Development,Governance,Public Sector Development
abstracts: cdata!: This is the background paper for the
            productivity extension of the World Bank’s Long-Term Growth
            Model (LTGM). Based on an extensive literature review, the
            paper identifies the main determinants of economic
            productivity as innovation, education, market efficiency,
            infrastructure, and institutions. Based on underlying
            proxies, the paper constructs indexes representing each of
            the main categories of productivity determinants and,
            combining them through principal component analysis, obtains
            an overall determinant index. This is done for every year in
            the three decades spanning 1985–2015 and for more than 100
            countries. In parallel, the paper presents a measure of
            total factor productivity (TFP), largely obtained from the
            Penn World Table, and assesses the pattern of productivity
            growth across regions and income groups over the same
            sample. The paper then examines the relationship between the
            measures of TFP and its determinants. The variance of
            productivity growth is decomposed into the share explained
            by each of its main determinants, and the relationship
            between productivity growth and the overall determinant
            index is identified. The variance decomposition results show
            that the highest contributor among the determinants to the
            variance in TFP growth is market efficiency for Organisation
            for Economic Co-operation and Development countries and
            education for developing countries in the most recent
            decade. The regression results indicate that, controlling
            for country- and time-specific effects, TFP growth has a
            positive and significant relationship with the proposed TFP
            determinant index and a negative relationship with initial
            TFP. This relationship is then used to provide a set of
            simulations on the potential path of TFP growth if certain
            improvements on TFP determinants are achieved. The paper
            presents and discusses some of these simulations for groups
            of countries by geographic region and income level. An
            accompanying Excel-based toolkit, linked to the LTGM,
            provides a larger set of simulations and scenario analysis
            at the country level for the next few decades.
repnme: repnme: Productivity Growth : Patterns and
            Determinants across the World
display_title: Productivity Growth : Patterns and
            Determinants across the World
listing_relative_url: /research/2019/05/31060879/productivity-growth-patterns-determinants-across-world
topicv3: Justice m1326106 840,Economic Growth m1326241 471,Personal Property Rights m1326571 1907,Trade m1326924 698,Education m1327048 470
publishtoextweb_dt: 2019-05-10T00:00:00Z
historic_topic: Social Development,Industry,Rural Development,Education,International Economics and Trade,Macroeconomics and Economic Growth,Law and Development,Governance,Public Sector Development
no_of_pages: 71
author_keys: 0: author_key: 1462381
1: author_key: 97109
model_extractions: 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 of the World Bank’s Long-Term Growth Model (LTGM). Based on an extensive literature review, the paper identifies the main determinants of economic productivity as innovation, education, market efficiency, infrastructure, and institutions. Based on underlying proxies, the paper constructs indexes representing each of the main categories of productivity determinants and, combining them through principal component analysis, obtains an overall determinant index. This is done for every year in the three decades spanning 1985–2015 and for more than 100 countries. In parallel, the paper presents a measure of total factor productivity (TFP), largely obtained from the Penn World Table, and assesses the pattern of productivity growth across regions and income groups over the same sample. The paper then examines the relationship between the measures of TFP and its determinants. The variance of productivity growth is decomposed into the share explained by each of its main determinants, and the relationship between productivity growth and the overall determinant index is identified. The variance decomposition results show that the highest contributor among the determinants to the variance in TFP growth is market efficiency for Organisation for Economic Co-operation and Development countries and education for developing countries in the most recent decade. The regression results indicate that, controlling for country- and time-specific effects, TFP growth has a positive and significant relationship with the proposed TFP determinant index and a negative relationship with initial TFP. This relationship is then used to provide a set of simulations on the potential path of TFP growth if certain improvements on TFP determinants are achieved. The paper presents and discusses some of these simulations for groups of countries by geographic region and income level. An accompanying Excel-based toolkit, linked to the LTGM, provides a larger set of simulations and scenario analysis at the country level for the next few decades.

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 World Bank Norman V. Loayza 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 longrun 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 taxfinanced 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),<sup>1</sup> 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
<!-- Start of picture text --> NA NA<br>jt jt<br>NA NA Jt,t-1<br>jt-1 jt-1<br><!-- End of picture text -->

> ota — jt,t-1 = ~~=~~ <u>5</u> ((LABSHj,jt + LABSHjjt-) ~~t-1)(——-GP Hej~~ + [1 — ~~+~~ <u>2</u> (LABSHjejt + LABSHje-1)|Jt-1 ~~(5)~~ <u>RKMA,</u>

NA NA NA NA NA LABSH: The share of labor income of employees and self — employed workers in GDP
In(rtfpna,) — In(rtfpnaz_;).

<!-- Start of picture text --> A. Factor analysis B. Principal component analysis<br>Sub-<br>component Oger!<br>index index<br>+ Common variance |<br>+ Distinct variance +<br>Indicator1 Indicator2 Indicatorn Subindex 1 Subindex 2 Subindex 5<br><!-- End of picture text -->
possible in a single index (figure 1B) (Jolliffe 2002).<sup>2</sup> 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.<sup>3</sup> 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.<sup>4</sup> 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**

_<u>The relative contribution of the main determinants to the variance of total factor productivity growth</u>_ 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 PISA�,� �β� �β� ln�GDP per capita��,��� �δ�, c: country �1, ... ,76�, t: year �2003/06/09/12/15�; β� �187.1*, β� �28.7 *** (***: p-value<0.01), R<sup>�</sup> �0.444

17
ct) ct {.}, {educ, ;},..., {inst,¢},feduce ctp {infragt erp educe ct ctf {educet ct ct) {educ.¢ ct ct cot} (original index for country c and time t — lowest index) /(highest index — lowest index) * (100-1) +1
<!-- Start of picture text --> c(tt—s) = Po + BilnUndex,,-5) + Baln(rtfpna)et—-5 + Oc + 6 + Ect<br>c,(t,t—5)<br>c,t-5<br>cts: TFP level (2011 = 1)<br>Cc<br>t<br>ct<br><!-- End of picture text -->
**_Figure 2. Annual TFP growth rate for all, OECD, and developing countries, median and simple average by decade_**

<!-- Start of picture text -->
1.0%<br>0.8%<br>0.6%<br>0.4%<br>0.2%<br>0%<br>-0.2%<br>-0.4%<br>-0.6%<br>-0.8%<br>-1.0%<br>85-94 94-04 04-14 85-94 94-04 04-14 85-94 94-04 04-14<br>All OECD Developing<br>Median Average Median Average Median Average<br>Source: Authors' calculation, using PWT 9.0 data complemented, in a few cases, with GTAP data.<br>Note: The OECD group includes high-income countries that have been members of OECD for more than 40 years;<br>the former Soviet Union countries are excluded in the period 1985-94 considering their independence in the early 1990s.<br>Figure 3. Annual TFP growth rate for developing countries, median and simple average by region and decade<br>3%<br>2%<br>1%<br>0%<br>-1%<br>-2%<br>85 94 04 85 94 04 85 94 04 85 94 04 85 94 04 85 94 04<br>-94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14<br>East Asia Europe Latin America Middle East South Sub-Saharan<br>and and and and Asia Africa<br>Pacific Central Asia Caribbean North Africa<br>Median Average<br>Annual TFP growth rate<br>Annual TFP growth rate<br><!-- End of picture text -->

**_Figure 3. Annual TFP growth rate for developing countries, median and simple average by region and decade_**

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_**

<!-- Start of picture text --> 3%<br>2%<br>1%<br>0%<br>-1%<br>-2%<br>85 94 04 85 94 04 85 94 04 85 94 04 85 94 04 85 94 04<br>-94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14<br>East Asia Europe Latin America Middle East South Sub-Saharan<br>and and and and Asia Africa<br>Pacific Central Asia Caribbean North Africa<br>Source: Authors' calculation, using PWT 9.0 data complemented, in a few cases, with GTAP data.<br>Note: The former Soviet Union countries are excluded in the period 1985-94 considering their independence in the early 1990s.<br>4.2 Main determinant indexes<br>Figure 5 shows the median of the subcomponent indexes representing the main categories of TFP determinants, as<br>well as the median of the overall index, for all, 21 OECD, and 115 developing countries by decade. All the<br>median indexes are lower for the developing countries as compared to the OECD countries. A noticeable<br>difference is that the innovation index stays at the lowest level for the developing countries, whereas it increases<br>in the OECD group over time. For both groups, the subcomponent indexes of education, market efficiency, and<br>infrastructure increase over decades, whereas that of institutions stays at the same level.<br>Figure 5. Median of subcomponent and overall determinant indexes for all, OECD, and developing countries by decade<br>World<br>85 95 05 85 95 05 85 95 05 85 95 05 85 95 05 85 95 05<br>-94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14 -94 -04 -14<br>Innovation Education Market Efficiency Infrastructure Institutions Overall<br>Annual TFP growth rate,<br>average weighted by GDP<br>100<br>80<br>60<br>40<br>20<br>1<br><!-- End of picture text --> 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_**

21
ct = 0.41 * 2(R&D_z) + 0.34 * z(patent,;) + 0.39 * z(article,,) Z(X) . . ~~X-mean(X)~~ is standardized X, <u>Sandard deviation</u>
ct = 0.20 * z(eduexper) + 0.36 * z(secondary¢,) + 0.39 * 2(pisacr) + 0.36 * 2(tertiaryc,) ct = 0.43 * z(business, ) + 0.43 * z(financial, ;) — 0.34 * z(labor, x), ct = 0.45 * z(minwage, ) + 0.47 * z(severance,,) — 0.47 * z(women,,) ct = 0.23 « z(tele,,) + 0.14 * z(mobile,,) + 0.21 * z(road,,) + 0.21 * z (elec_(c,t)) + 0.22 * z(water,,) + 0.23 * z(sanit,+)
<!-- Start of picture text --> ct = 0.18 * 2(vacr) + 0.19 * 2(cCer) + 0.19 * 2(gect) + 0.16 * 2(pSct) + 0.18 * 2(rdct) + 0.19 * z(rl¢t).<br><!-- End of picture text --> <!-- Start of picture text --> ct = 0.43 * z(Innov,,) + 0.44 * z(Edu,,) + 0.46 * z(Ef fice) + 0.47 * z(Infra,,) + 0.43 * z(Inst,,).<br><!-- End of picture text -->
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_**

<!-- Start of picture text --> World<br>1985-1994 1994-2004 2004-2014<br>Innovation 0.06 0.05 0.09<br>Education 0.13 0.45 0.36<br>Market efficiency 0.14 0.13 0.18<br>Infrastructure 0.19 0.12 0.17<br>Institutions 0.48 0.25 0.19<br>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<br>OECD<br>1985-1994 1994-2004 2004-2014<br>Innovation 0.16 0.09 0.21<br>Education 0.11 0.20 0.16<br>Market efficiency 0.31 0.18 0.45<br>Infrastructure 0.18 0.37 0.08<br>Institutions 0.24 0.16 0.11<br>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<br>Developing<br>1985-1994 1994-2004 2004-2014<br>Innovation 0.02 0.03 0.07<br>Education 0.13 0.47 0.49<br>Market efficiency 0.08 0.17 0.15<br>Infrastructure 0.20 0.12 0.14<br>Institutions 0.56 0.22 0.15<br>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<br><!-- End of picture text --> Source: Authors' calculation. Note: The OECD group includes high-income countries that have been members of OECD for more than 40 years.

25
# _<u>The relationship between the overall determinant index and total factor productivity growth</u>_

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:|Annualized TFPgrowt|h�,����,��||
|---|---|---|---|
|Number of observations:|477|||
|Number of groups<br>(countries):|98|||
|||Country effects:||
||None|Random|Fixed|
|Regressors (below):|Coefficient (SE)|Coefficient (SE)|Coefficient (SE)|
|ln�Index�,����|0.004(0.0011)***|0.004(0.0011)***|0.050(0.0183)***|
|ln�TFP level��,���|-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)***|
|R<sup>�</sup>:||||
|Within|Not applicable|0.2784|0.3048|
|Between|Not applicable|0.8573|0.2749|
|Overall|04022|04022|01586|
|<br>SE = Standard error; *: si<br>Note: Hausman test reject<br>Chi-square 22.90 and p-<br>explanatory contribution o<br>In the regression with no<br>clusters.|.<br>gnificant at 10%; **: signifi<br>s the null hypothesis (Ho: c<br>value less than 0.01. The<br>f the country-specific const<br>country effects, we use clus|.<br>cant at 5%; ***: significant at 1%<br>oefficients are consistent under b<br>R<sup>�</sup>in the case of the fixed-effect<br>ants (which is why its overall valu<br>tered robust (White-Huber) varian|.<br>level<br>oth random and fixed effects) with<br>s estimator does not consider the<br>e is lower than in the other cases).<br>ce estimation, treating countries as|

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 middleincome 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.

# _<u>Scenario I: Improving to the highest TFP determinant index in the region</u>_

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
<!-- Start of picture text --> 3.0%<br>2.5%<br>2.0%<br>—— ee<br>~ ne<br>1<br>1.0% g<br>0.5% £ ae eer<br>—<br>1 Ha<br>y<br>0.0% oF te<br>o~<br>-0.5%<br><!-- End of picture text --> <!-- Start of picture text -->
-1.0%<br>0 5 10 15 20 25 30 35<br>East Asia and Pacific —«#— Europe and Central Asia<br>—@® Latin America and Caribbean Middle East and North Africa<br>—4— South Asia —e— Sub-Saharan Africa<br><!-- End of picture text -->
<!-- Start of picture text --> 2.0%<br>1.5%<br>0.0% 7 oe<br>0.5% [we<br>-1.0%<br>) 5 10 15 20 25 30 35<br>—m—East Asia and Pacific —«— Europe and Central Asia<br>—e@Latin America and Caribbean —<— Middle East and North Africa<br>—4— South Asia —e— Sub-Saharan Africa<br><!-- End of picture text -->
# _<u>Scenario III: Improving to the highest TFP determinant index among all developing countries</u>_

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
<!-- Start of picture text --> 4.0%<br>9 a Seg<br>2.5% go —_»® i = S~o.,<br>2.0%<br>: fa” ee ee gg<br>1.5% jf<br>1.0%<br>i “fp Zk<br>0.5% “gd Ae<br>0.0% fb<br>-0.5%<br>-1.0%<br>0 5 10 15 20 25 30 35<br>—m—East Asia and Pacific —*— Europe and Central Asia<br>—e—Latin America and Caribbean Middle East and North Africa<br>—— South Asia —e— Sub-Saharan Africa<br><!-- End of picture text --> <!-- Start of picture text --> 4.0%<br>3.5%<br>3.0%<br>2.5%<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>-0.5%<br>0 5 10 15 20 25 30 35<br>—low -#®LowMiddle -*—Upper Middle<br><!-- End of picture text -->
_<u>Scenario IV: Following the trajectory of the most improving TFP overall index among all developing countries</u>_ 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
<!-- Start of picture text --> 2.5%<br>2.0% 4 eB LS L-O-9-9-5. .<br>0 MD a Hae a2. —...“Soo, -<br>0.5% ", i”, yo!:<br>0.0% 9%<br>-0.5%<br>-1.0%<br>0 5 10 15 20 25 30 35<br>—m—East Asia and Pacific —*— Europe and Central Asia<br>—@Latin America and Caribbean Middle East and North Africa<br>—4— South Asia —e Sub-Saharan Africa<br><!-- End of picture text --> <!-- Start of picture text --> 2.5%<br>2.0%<br>1.5%<br>1.0%<br>0.5%<br>0.0%<br>-0.5%<br>0 5 10 15 20 25 30 35<br>—lLow -#® Low Middle -*—Upper Middle<br><!-- End of picture text -->
# **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. The relative contribution of the determinant indexes to the variance of TFP growth and the impact of the overall determinant index on TFP growth could be 36
different for each country and region, generally depending on the level of economic development and the nature of their political and social environment. Despite these limitations, we expect that this paper and accompanying toolkit can be a starting point – an international benchmark – for researchers and policy makers in their analysis of productivity and growth for particular countries.<sup>6</sup>

> 6 See, for instance, Céspedes, Loayza, and Ramírez, forthcoming.

37