Policy Research Working Paper 9767 Sectoral Decomposition of Convergence in Labor Productivity A Re-examination from a New Dataset Alistair Dieppe Hideaki Matsuoka Prospects Group September 2021 Policy Research Working Paper 9767 Abstract This paper investigates how the sector-specific source or the been a significant contributor to aggregate convergence, changing sectoral composition of labor productivity has whereas catch-up in other sectors has only contributed a contributed to aggregate beta convergence, using a newly small amount to convergence. The strong growth of the constructed eight-sector database. The main findings are agriculture sector has been the most important driver of twofold. First, both within and sectoral reallocation have aggregate productivity convergence even though agricul- become important drivers of aggregate convergence in labor tural productivity itself in low-income countries is weakly productivity. Second, agricultural productivity growth has converging to that in advanced economies. This paper is a product of the Prospects Group. 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 alistair.dieppe@ecb.europa.eu and hideaki.matsuoka@mof.go.jp. 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 Sectoral Decomposition of Convergence in Labor Productivity: A Re-examination from a New Dataset* Alistair Dieppe†and Hideaki Matsuoka ‡ Keywords: Labor productivity, Shift-share decomposition, β-decomposition, New sectoral database JEL classification: O1,O11,O4 * This paper should not be reported as representing the views of the European Central Bank nor Ministry of Finance Japan. The views expressed are those of the authors and do not necessarily reflect those of the ECB, Ministry of Finance Japan, the World Bank, its Executive Directors, or the countries they represent. The paper was written when the authors were World Bank’s Prospects Group staff. We thank Kevin Clinton, Gaaitzen de Vries, Noriaki Kinoshita, Akito Matsumoto, Manabu Nose, Christopher Towe, and Kenichi Ueda for valuable comments and Aygul Evdokimova and Xinyue Wang for excellent research assistance. We gratefully acknowledge financial support from the PHRD fund. † European Central Bank, alistair.dieppe@ecb.europa.eu ‡ Corresponding author, Policy Research Institute, Ministry of Finance Japan, hideaki.matsuoka@mof.go.jp 1 Introduction There has been a re-emergence of catch-up in productivity by emerging markets and developing economies (EMDEs) to advanced economies (World Bank (2020)). Understanding how the sector-specific source or the changing sectoral composition (i.e., structural change) has contributed to the aggregate beta convergence in productivity is an area that has so far been under-explored. In low-income countries (hereafter “LICs”), a high share of employment and low labor productivity in agriculture are mainly responsible for low aggregate productivity.1 The average share of employment in the agriculture sector in LICs is high, at over 65 percent in 2018, compared to just 3 percent in advanced economies. The level of agricultural productivity in LICs is only 4 percent of advanced-economy productivity (Figures 1 and 2). This reflects that slow technology adoption in the agriculture sector in LICs is due to the high proportion of smallholder ownership and family farms (Lowder et al., 2016).2 However, even if agricultural labor productivity does not converge to the frontier, the labor reallocation to other sectors with higher productivity levels could be an important engine of aggregate convergence. For example, the East Asia and Pacific (EAP) region has experienced a rapid ‘de-agriculturalization’ over 40 years. Within countries, the productivity gaps across sectors in LICs have remained larger than advanced economies over the last 20 years.3 There is a large body of literature on the determinants of structural change using the multi-sector general equilibrium model. Two traditional explanations for structural change are the representative household with non-homothetic preference (Kongsamut et al. (2001)) and the firms with differential productivity growth rates (Ngai and Pissarides (2007)). Their basic mechanism is that the relative price related to the differential productivity allocates total expenditures across any goods and services. Given these demands, relatively higher productivity growth sheds labor and due to gross complementarity, labor shifts to slower-productivity growth. For example, Alvarez-Cuadrado and Poschke (2011), Duarte and Restuccia (2010), and Herrendorf et al. (2013) 1 Unlessotherwise indicated, productivity is defined in this paper as value added per worker. The classification by income follows World Bank (2021). Low-income countries are part of emerging markets and developing economies (EMDEs). 2 Although mechanization increases agricultural labor productivity due to both capital deepening and total factor productivity(TFP), mechanization in poor countries is hindered by frictions such as untitled land, which is a prevalent feature of poor countries (Chen, 2020). Furthermore, Restuccia et al. (2008) show that agricultural labor productivity is positively associated with the use of intermediate inputs (e.g., modern fertilizers and high-yield seeds) and argue that certain distortions in factor markets may severely dampen the incentives for their use. 3 As agricultural workers often do not work full time in agriculture, the sectoral gap is diminished if productivity is measured per hours instead of per worker (Gollin et al., 2014). However, even after taking hours and human capital per worker by sector, a large sectoral gap remains for a large number of countries Hamory et al. (2021). 2 with non-homothetic preference consider “Engel’s law” which refers to low income elasticity for food produced by the agriculture sector. They show that the productivity improvement in the agriculture sector combined with Engel’s law explains most of the declines in agricultural employment share. Given the ongoing structural change in Africa and low-income countries (as shown by Diao et al. (2017) and Rodrik (2018)), understanding the role of structural change in aggregate convergence is the focus of this paper. In assessing the contribution of structural change to convergence, it is important to recognize that industry and service sectors are made up of a highly heterogeneous set of activities that vary widely in their skill- and capital-intensity as well as their productivity. Understanding these differences is essential to help the policies that can foster sustained productivity growth. This paper investigates how the sector-specific source or the changing sectoral composition (i.e., structural change) has contributed to the aggregate beta convergence. This paper extends the literature in two dimensions: 1. It constructs a new sectoral dataset for 8 sectors and 91 countries over 1995-2018 (and for 60 countries over 1975-2018). This is the first comprehensive database covering a broad range of both advanced economies and emerging and developing economies (EMDEs) over a long time period. This more detailed dataset and a more recent sectoral decomposition improves the scope to assess the contribution of structural change in productivity convergence, particularly as the estimates are sensitive to the level of aggregation (Üngör (2017)). 2. This paper is the first to decompose aggregate beta convergence into contributions from within-sector productivity growth and from between-sector productivity growth, for a large number of countries ranging from advanced economies to low-income countries, whereas Wong (2006) have only focused on advanced economies. The paper starts by describing the new dataset. Then this data is used to decompose aggregate productivity growth into within- and between-sector contributions. The main section examines convergence across countries and examines the extent within and between sectoral reallocation are contributing to convergence. Two robustness exercises are undertaken which support the main findings. The final section concludes with a summary of major findings, policy implications and a discussion of future research directions. 3 2 Data and empirical strategy 2.1 Data The database consists of sectoral and aggregate labor productivity statistics for 91 countries, and 8 sectors covering the period up to 2018.4 Compared with the literature using sectoral datasets, it employs a large and diverse sample of countries.5 The database combines data from the APO Productivity Database, the OECD STAN database, the OECD National Accounts, and the GGDC/UNU-WIDER Economic Transformation Database (ETD, de Vries et al. (2021)) for value-added data and employment. In addition, this database is extrapolated backwards using annual growth rates from KLEMS, the GGDC 10-Sector database (GGDC, de Vries et al. (2015)) or the Expanded Africa Sector Database (EASD, Mensah and Szirmai (2018)) to construct a long time series dataset. Also, if there is no available employment data, ILO modelled estimates are supplementary employed. See the Appendix for more details. 2.2 Within sector and between sector effects Following de Vries et al. (2012), McMillan et al. (2014) and Diao et al. (2017), we start by employing a shift-share-analysis which decomposes aggregate labor productivity into the within sector and between sector effects: k Yj ∆ y j k yj k yj ∆yj ∆y = ∑Y +∑ ∆sj + ∑ ∆sj (1) y j=1 yj j=1 y j=1 y yj Aggregate Within− j Static Dynamic Between where y is aggregate labor productivity, y j is labor productivity of sector j, Y j is initial value-added of sector j, s j is the employment share of sector j. Between sector effects are driven by the change in employment share. They are further decomposed into those which are due to the reallocation of sources to sectors with higher productivity levels (static sectoral effect), and those due to reallocation toward sectors with higher productivity growth (dynamic sectoral effect). Within-sector productivity growth may reflect the effects of improvements in 4 The eight sectors distinguished in the dataset are agriculture, mining, manufacturing, utilities, construction, trade services, transport and financial services, and government and personal services. According to Economic Transformation Database, Business services include “Information and communication” whereas those in other databases are included in transport service.” Hence, we combined transport and financial services to construct a long time series database. 5 McMillan et al. (2014) and Diao et al. (2017) employ 38 and 39 countries; Martins (2019) use 7 sectors and 169 countries, IMF (2018) use 10 sectors and 62 countries. 4 human capital, investments in physical capital, technological advantages, or the reallocation of resources from the least to the most productive firms within each sector. 2.3 Decomposition of aggregate convergence The unconditional (beta) convergence hypothesis suggests that productivity catch-up growth may occur fastest where productivity differentials are the largest across countries. Following Wong (2006), this aggregate beta unconditional convergence can be decomposed into the contribution of the within-sector growth and that of sectoral reallocation.6 The decomposition consists of two steps: First, regressing aggregate labor productivity growth (∆y/y) on the logarithm of initial aggregate labor productivity (y) gives the aggregate beta (βaggregate ) convergence.7 ∆y = αaggregate + βaggregate ln(y) + εaggregate (2) y Second, replacing the dependent variable in the first regression with decomposed components from equation (2), a regression on the logarithm of initial aggregate labor productivity is undertaken:      Within j = αwithin− j + βwithin− j ln(y) + εwithin− j ( j = 1, ...8)   ∑k (3)  j=1 Static j = αstatic + βstatic ln(y) + εstatic    ∑k  j=1 Dynamic j = αdynamic + βdynamic ln(y) + εdynamic  The summation of the left-hand-side in regression (3) is equal to the left-hand-side in regression (2). Hence, rearranging the right-hand-sides in regression (2) and (3) gives: 6 Other studies decomposing convergence employ an accounting approach. They calculate the difference of each component (1) between the frontier and all sample countries. (e.g., Bernard and Jones (1996) and Harchaoui and Üngör (2016) use the United States as the frontier and Caselli and Tenreyro (2005) use France. In contrast, Wong (2006) employs an econometric approach. Its advantage is to understand that the components are statistically significant or not. 7 Following McMillan et al. (2014) , local currency value-added is converted to U.S. dollars using the PPP exchange rate obtained from the Penn World Table for initial labor productivity (y). Van Biesebroeck (2009) builds an expenditure-based sector-specific PPP in OECD countries, using detailed price data. 5 αaggregate + βaggregate ln(y) + εaggregate = ∑k j=1 αwithin− j + αstatic + αdynamic (4) +(∑k j=1 βwithi− j + βstatic + βdynamic )ln(y) + ∑k j=1 εwithin− j + εstatic + εdynamic If αaggregate =∑k k j=1 αwithin− j + αstatic + αdynamic and εaggregate =∑ j=1 εwithin− j + εstatic + εdynamic , k βaggregate = ∑ βwithin− j + βstatic + βdynamic (5) j=1 Between Within Hence, the beta β aggregate coefficients obtained in the first step can be decomposed into a sum of coefficients of the within sector effect ∑k j=1 βwithin− j , the static β static and dynamic β dynamic sector effects. The summation of the static β static and dynamicβ dynamic sector effects is the contribution of between sector effects. We also examine the regressions for sector-specific convergence; ∆yj = α j + β j ln(y j ) + ε j (6) yj Even if sector labor productivity itself has not converged to the corresponding frontier across sectors, the labor reallocation to other sectors with higher productivity levels could be an important engine of aggregate convergence. 3 Results 3.1 Within sector and between sector effects Figure 3 shows the decomposition of the aggregate productivity into within-sector productivity growth and between-sector productivity growth. Productivity growth in advanced economies had been almost entirely driven by within-sector productivity growth mainly in the manufacturing, transport and finance sectors. However, since the 2000s both within-sector and between-sector productivity growth have slowed. In contrast, in EMDEs, productivity growth has been supported by both within-sector and between-sector changes over 40 years. The 6 within sector growth has been broad-based-including in agriculture as well as manufacturing, trade, transport and finance services, while the between-sector productivity gains mainly reflected a move out of agriculture into services. In particular, the share of workers employed in agriculture fell from about 70 percent in 1975 to about 30 percent in 2018. In LICs, between-sector productivity gains in LICs reflected a broad-based shift out of agriculture into services such as trade, transport and finance. During the 2010s, the contribution of between-sector slowed down due to small movement to higher productivity sectors such as manufacturing and trade. Figure 4 shows that contributions of the between-sector effect have been non-negligible in the East Asia and Pacific (EAP), European and Central Asia (ECA), South Asia (SAR) and Sub-Saharan Africa (SSA) regions whereas those in Latin America and Caribbean (LAC) and Middle East and North Africa (MENA) have been small. 3.2 Baseline regression 3.2.1 Aggregate convergence Table 1 and Figure 5 show the results with three different balanced panel datasets: 60 countries in 1975-2018, 60 countries in 1975-1995, and 91 countries in 1995-2018. At the aggregate level, regression (2) shows there has been weak unconditional convergence prior to 1995. However, since the late 1990s aggregate convergence emerges (Table 1 and Figure 5, World Bank (2020)). Over this period, countries with lower initial levels of productivity have begun to catch up to high-productivity economies. Nonetheless, at the estimated rate of convergence it would take about 140 years for countries at the bottom 10 percent of the productivity distribution to reach the level of the top 10 percent.8 3.2.2 Decomposing within and between sector convergence Even though many sectors are not converging to the frontier, the reallocation of labor to other sectors with higher productivity levels could be an important engine of aggregate convergence. Estimating the decomposition of aggregate convergence from regression (3) suggests that since 1995 both within and between sector effects 8 137 years (ln(0.9)/ln(0.1)-1)/0.00695, using Table 1. 7 have become important drivers of aggregate convergence in labor productivity (Table 1 and Figure 5). This reflects larger productivity improvements in many sectors in EMDEs (especially the LICs) compared to advanced economies as well the fact that many EMDEs experienced rapid sectoral shifts from agricultural sectors over the last few decades. Looking across the sectors, agricultural productivity growth has been a significant contributor to aggregate convergence, whereas catch-up in other sectors has only contributed a small amount to convergence. Given the share of value-added in the agriculture sector in LICs is large (Figure 1), the strong growth of the agriculture sector has been the most important driver of the aggregate productivity convergence. Our result is in line with Ivanic and Martin (2018) and Ligon and Sadoulet (2018) which illustrate that the increase in agricultural productivity has a larger poverty-reduction effect than increases in other sectors. 3.2.3 Sectoral convergence The same exercise is undertaken to examine convergence across sectors (Table 2 and Figure 5). Examining this using this study’s extensive data suggests the following: • Agriculture sector: Over the first half of the sample and the over the entire sample, there is no evidence for unconditional convergence in the agriculture sector. This is line with Kinfemichael and Morshed (2019). However, over the second half of the sample (1995-2018) there is convergence. This seems to some degree to be due to the commodity price boom during the 2000s. Nonetheless, the estimated convergence rate is very low, implying that it would take about 750 years for the bottom 10 percent of the productivity level to reach the top 10 percent.9 • Industry sectors (Mining, Manufacturing, Utilities and Construction): There is evidence of unconditional convergence in many of the industry sectors. The finding of unconditional convergence in the manufacturing sector is line with Rodrik (2013) using UNIDO data.10 However, the estimated convergence rate is low. Diao et al. (2021) reveal a dichotomy between larger firms in the manufacturing sector that exhibit 9 750 years (ln(0.9)/ln(0.1)-1)/0.00127, using Table 2. 10 However, Rodrik (2013) acknowledges that the “convergence results that follow should be read as applying to the more formal, organized parts of manufacturing and not to micro-enterprises or informal firms. In developing countries, enterprises with fewer than 5 or 10 employees are often not included.” UNIDO reported that there is a significant difference between UNIDO and the national account. 8 superior productivity performance but do not expand employment much in countries such as Tanzania and Ethiopia. • Service sectors (Trade, Transport and Finance and others): There is evidence of unconditional convergence across many service sectors (IMF (2018); Kinfemichael and Morshed (2019)). The transport and financial services sectors show convergence across three different balanced panel datasets. In contrast, there has been no evidence of convergence in trade services (wholesale, retail trade, accommodation, and food services) over 1995-2018 despite having shown unconditional convergence over an earlier time- period. Lagakos (2016) argued that in the retail trade sector, developing countries rationally choose “traditional technologies” with low measured labor productivity instead of “modern technologies” with high productivity across two dimensions. First, low car ownership rates among households in poor countries cause modern stores to locate further than traditional stores from residential centers less attractive. This situation is related with “appropriate technology” suggested by Basu and Weil (1998) and Acemoglu and Zilibotti (2001). Second, traditional retail technologies offer an opportunity for entrepreneurs to operate informally, thus earning a price advantage over modern retail technologies, which are larger in scale and cannot evade taxes as easily as smaller, traditional stores. 3.3 Robustness analysis 3.3.1 Robustness 1: Time-varying regression The baseline results were based on three sample periods (1970-2018, 1970-1995, and 1995-2018). For robustness and to provide further insights, a rolling window methodology is employed in which regressions (2) and (3) are estimated with OLS over the 10-year rolling window. This results in 34 regressions. Figure 6 shows the time-varying contributions of within and between sector effects on aggregate convergence. The result is line with the baseline regressions. The between sector effects have contributed to aggregate convergence largely and continuously since 1990s. In addition, the within sector growth has played an important role in aggregate convergence since 2000. Furthermore, agricultural productivity growth has been a significant contributor to aggregate convergence since the late 1980s (Figure 6). Finally, due to the commodity price boom during the 2000s, the productivity in the mining sector had also contributed although its contribution subsequently 9 declined. 3.3.2 Robustness 2: Catch-up to the United States Following Bernard and Jones (1996), another robustness check examines the accounting decomposition for each country relative to the United States. A measure of within and between-sector catch-up is computed by subtracting the productivity growth in the other countries for each sector as follows: k k ∆ yother ∆ yus − = ∑ (Withinother, j − WithinUS, j ) + ∑ (Betweenother, j − BetweenUS, j ) (7) yother yus j=1 j=1 where the notations are the same as in equation (1). Figure 7 shows the between sector effects in EMDEs and LICs have contributed to convergence largely since 1995 while that was not the case between 1975 to 1995. This finding is line with the baseline. Figure 8 shows that both East Asia and Pacific (EAP) and South Asia (SAR) experienced both within- and between- sector catch-up whereas there has been divergence of the within sector effect in the Latin America and Caribbean (LAC), Middle East and North Africa (MENA) and Sub-Saharan Africa (SSA) regions before 2010. In those countries, the within sector effects in the manufacturing sector have not been converging to the U.S. (Kinfemichael and Morshed (2019) and Diao et al. (2021)). 4 Conclusion This paper investigates how the sector-specific source or the changing sectoral composition has contributed to aggregate productivity and convergence, constructing a new 8-sector database. The main findings are twofold. First, both within and sectoral reallocation have become important drivers of aggregate convergence in labor productivity. This reflects larger productivity improvements in many sectors in EMDEs (especially the LICs) compared to advanced economies as well the fact that many EMDEs experienced rapid sectoral shifts from agricultural sectors over the last few decades. Second, agricultural productivity growth has been a significant contributor to aggregate convergence. The strong growth of the agriculture sector has been the most important driver of aggregate productivity convergence even though agricultural productivity itself in LICs is weakly converging to advanced economies. Our result is in line with the literature that illustrates that the increase in 10 agricultural productivity has a larger poverty-reduction effect than increases in other sectors. Although the potential productivity gains from sectoral reallocation have become more challenging to achieve, there would still be important payoffs from policies that supported diversification, including developing human capital, including at the tertiary level; promoting good governance and easing the cost of doing business; strengthening institutional capabilities; reducing distortions such as uncompetitive regulations and subsidies; supporting R&D; and promoting exports and developing stronger managerial capabilities. Removing barriers to migration can also help to facilitate structural transformation.11 Given the low level of productivity in EMDE agricultural sectors and its role as the primary employer in LICs, policies to raise agricultural productivity would pay significant dividends. These polices would include improving infrastructure and land property rights. Future research could investigate the endogeneity of sector allocation. As the shift-share decomposition is an accounting exercise, it should be noted that within sector growth could also directly affect sector reallocation. An improvement in agricultural productivity increases incomes and the demand for other goods, encouraging a shift of labor into other sectors (Eberhardt and Vollrath (2018), Gollin et al. (2007), and Diao et al. (2018)). It could result in facilitating between-sector productivity growth. Hence, the contribution of agricultural productivity could be larger and that of sectoral reallocation could be smaller. 11 Artuc et al. (2015) show the estimated labor mobility costs caused by labor market frictions in EMDEs are a larger burden than those in advanced economies, using the data with eight major sectors. Bryan and Morten (2019), using Indonesia data show that reducing migration costs to the US level, a high-mobility benchmark, leads to a 7 percentage point increase in productivity growth. 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Global Productivity: Trends, Drivers, and Policies. Washington, DC: World Bank. 1, 3.2 15 Figure 1: Employment share A. Composition of employment by sector B. Composition of employment by sector (EMDEs Regions) 16 C. Composition of value-added by sector D. Composition of value-added by sector (EMDEs Regions) Notes: “Trans. and Fin.” illustrate transport and finance services; "Other industry" includes utilities and construction; "Others" include government and personal services. Figure 2: Sectoral gap across countries and within countries A. Sectoral productivity gap between advanced economies and LICs B. Agricultural productivity gap C. Agricultural productivity gap (EMDEs Regions) Notes: A. Sectoral gap is defined as the ratio of median of sectoral productivity in LICs to that in advanced economies. B.C Agricultural productivity gap is defined as the ratio of non-agricultural productivity to agricultural productivity. "Others" include government and personal services. 17 Figure 3: Within sector and between sector effects A. Within and between-sector contributions B. Contributions of within-sector growth C. Contributions of between-sector growth Notes: A.B.C. The decomposition is based on the equation(1). Median contribution to productivity growth. “Trans. and Fin.” illustrate transport and finance services; "Other industry" includes utilities and construction; "Others" include government and personal services. 18 Figure 4: Within sector and between sector effects (EMDEs Regions) A. Within and between-sector contributions B. Contributions of within-sector growth (EMDEs Regions) C. Contributions of between-sector growth (EMDEs Regions) Notes: A.B.C. The decomposition is based on the equation(1). Median contribution to productivity growth. “Trans. and Fin.” illustrate transport and finance services;"Other industry" includes utilities, and construction; "Others" include government and personal services. 19 Table 1: Decomposition of aggregate convergence 1975-2018 1975-1995 1995-2018 Sector β R2 β R2 β R2 Aggregate -0.00976** 0.238 -0.00289* 0.088 -0.00695*** 0.266 (0.00484) (0.00155) (0.00234) 1. Agriculture -0.00255*** 0.308 -0.000874*** 0.113 -0.00181*** 0.331 (0.000493) (0.000316) (0.000270) 2. Mining -0.000264 0.025 -2.13e-05 0.000 -0.000388 0.025 (0.000212) (0.000304) (0.000254) 3. Manufacturing -0.00122*** 0.105 -0.000231 0.018 -0.000628* 0.032 (0.000460) (0.000223) (0.000361) 4. Utilities -0.000138 0.033 1.34e-05 0.002 -0.000162** 0.043 (9.67e-05) (4.18e-05) (7.98e-05) 5. Construction -9.95e-06 0.001 2.63e-05 0.002 -4.48e-05 0.004 (5.34e-05) (8.02e-05) (7.22e-05) 6. Trade services -0.000300** 0.097 -0.000174 0.022 -0.000251 0.028 (0.000118) (0.000149) (0.000155) 7. Transport and Finance services -0.000633*** 0.137 -0.000142 0.008 -0.000520** 0.054 (0.000206) (0.000203) (0.000229) 8. Other services -0.000399* 0.046 -7.98e-05 0.004 -0.000507** 0.044 (0.000236) (0.000156) (0.000298) Total within sectoral effect -0.00551*** 0.207 -0.00148 0.039 -0.00431*** 0.171 (1+2+3+4+5+6+7+8) (0.00139) (0.000953) (0.000995) Static sectoral effect -0.00148 0.014 -0.000516 0.001 -0.00277** 0.043 (0.00162) (0.00207) (0.00138) Dynamic sectoral effect -0.00276* 0.050 -0.000890 0.004 0.000134 0.000 (0.00155) (0.00179) (0.00115) Observations 60 60 91 Notes: Regressions (2) and (3) are estimated. The standard errors are reported in parentheses. The constant terms are not reported.∗∗∗ p<0.01, ∗ p<0.05, ∗ p<0.1 Table 2: Sector-specific convergence 1975-2018 1975-1995 1995-2018 Sector β R2 β R2 β R2 Agriculture -0.000588 0.015 0.000587 0.005 -0.00127** 0.022 (0.000621) (0.000885) (0.000623) Mining -0.00377** 0.093 -0.00272 0.027 -0.00681*** 0.123 (0.00152) (0.00165) (0.00220) Manufacturing -0.00244*** 0.130 -0.00175 0.038 -0.00221*** 0.054 (0.000777) (0.00106) (0.000829) Utilities -0.00300** 0.087 -0.00251* 0.045 -0.00463*** 0.091 (0.00132) (0.00147) (0.00172) Construction -0.00172* 0.070 -0.00373* 0.123 3.72e-05 0.000 (0.000982) (0.00208) (0.00113) Trade services -0.00226*** 0.136 -0.00253** 0.077 -0.00157* 0.031 (0.000732) (0.000976) (0.000938) Finance and business services -0.00315*** 0.246 -0.00246** 0.095 -0.00309*** 0.129 (0.000776) (0.00104) (0.000810) Other services -0.00154** 0.096 -0.000230 0.001 -0.00293*** 0.125 (0.000741) (0.000730) (0.00275) Observations 60 60 91 Notes: Regression (6) is estimated. The standard errors are reported in parentheses. The constant terms are not reported.∗∗∗ p<0.01, ∗ p<0.05, ∗ p<0.1 20 Figure 5: Decomposition of aggregate convergence A. Aggregate convergence B. Decomposition of aggregate convergence C. Contributions of sector-specific within effects on aggregate convergence D. Sector-specific convergence 21 Notes: A. Cross-section regressions (2) are estimated with OLS. The sample size is reported in parentheses. Vertical lines denote 90 percent confidence intervals. B.C. The decomposition is based on the Cross-section regressions with OLS (3). “Trans. and Fin.” illustrate transport and finance services;"Other industry" includes utilities, and construction; "Other service" include government and personal services. Residual is the difference between the aggregate beta and the sum of estimated between and within effects. D. Cross-section regressions (6) are estimated with OLS. Figure 6: Robustness check 1: Time-varing contributions A. Decomposition of aggregate convergence B. Contributions of sector-specific within effects on aggregate convergence Notes: A. B. Cross-section regressions (2) and (3) are estimated with OLS over the 10-year rolling window. The sample size varys through overlapping windows. Residual is the difference between the aggregate beta and the sum of estimated between and within effects. B. “Trans. and Fin.” illustrate transport and finance services;"Other industry" includes utilities and construction; "Others" include government and personal services. 22 Figure 7: Robustness check 2: Catch-up to the United States A. Contributions of between-sector growth B. Contributions of within-sector growth C. Contributions of between-sector growth Notes: A.B.C. The decomposition is based on the equation(7). Median contribution to productivity growth. “Trans. and Fin.” illustrate transport and finance services; "Other industry" includes utilities and construction; "Others" include government and personal services. 23 Figure 8: Robustness check 2: Catch-up to the United States (EMDEs Regions) A. Contributions of between-sector growth B. Contributions of within-sector growth C. Contributions of between-sector growth Notes: A.B.C. The decomposition is based on the equation (7). Median contribution to productivity growth. “Trans. and Fin.” illustrate transport and finance services; "Other industry" includes utilities and construction; "Others" include government and personal services. 24 Appendix Table A.1: Comparison with other studies using sectoral dataset Period Country coverage Group coverage This study 1995-2018 91 31 AEs 60 EMDEs 1975-2018 60 17 AEs 43 EMDE IMF (2018) 2000-2010 62 19 AEs 43 EMDEs McMillan et al. (2014) 1990-2005 38 13 AEs 25 EMDEs Diao et al. (2017) 2000-2010 39 13 AEs 26 EMDEs Table A.2: Comparison with other studies decomposing convergence Period Country coverage Group coverage This study 1995-2018 91 31 AEs 60 EMDEs 1975-2018 60 17 AEs 43 EMDEs Wong (2006) 1970-1990 13 13AEs Bernard and Jones (1996) 1970-1987 14 14 AEs Harchaoui and Üngör (2016) 1970-2010 11 11 EMDEs Caselli and Tenreyro (2005) 1960-2000 27 22 AEs 5 EMDEs Notes:AEs=advanced economies, EMDEs=emerging markets and developing economies. LICs: low-income countries. Table A.3: 8-sector categories Sector name Description 1.Agriculture Agriculture, forestry and fishing 2.Mining Mining and quarrying 3.Manufacturing Manufacturing 4.Utilities Electricity, gas, steam and air conditioning supply 5.Construction Construction 6.Trade services Wholesale and retail trade; repair of motor vehicles and motorcycles; Accommodation and food service activities 7.Transport and Transportation and storage; Information and communication; Financial services Financial and insurance activities;Real estate activities; Professional, scientific and technical activities; Administrative and support service activities 8.Other services Public administration and defense; compulsory social security; Education;Human health and social work activities; Arts, entertainment and recreation;Other service activities; Activities of households as employers; undifferentiated goods- and services-producing activities of households for own use; Activities of extraterritorial organizations and bodies Sources: APO; EASD; ETD; GGDC; KLEMS; OECD. 25 Table A.4: Sectoral database Country Group1 Group2 Period Source Australia AEs AEs 1975-2018 APO Austria AEs AEs 1970-2018 OECD STAN/KLEMS Belgium AEs AEs 1970-2018 OECD STAN/KLEMS Czech Republic AEs AEs 1993-2018 OECD National Accounts Cyprus AEs AEs 1995-2019 OECD National Accounts/ILO Denmark AEs AEs 1970-2018 OECD STAN Estonia AEs AEs 1995-2018 OECD STAN Finland AEs AEs 1975-2018 OECD STAN France AEs AEs 1970-2018 OECD STAN/OECD National Accounts Germany AEs AEs 1970-2018 OECD STAN/KLEMS Greece AEs AEs 1995-2019 OECD National Accounts Ireland AEs AEs 1995-2019 OECD National Accounts Israel AEs AEs 1990-2018 ETD Italy AEs AEs 1970-2018 OECD STAN/KLEMS Japan AEs AEs 1970-2018 APO Latvia AEs AEs 1995-2018 OECD STAN Lithuania AEs AEs 1995-2018 OECD STAN Luxembourg AEs AEs 1970-2018 OECD STAN/KLEMS Netherlands AEs AEs 1970-2018 OECD STAN/KLEMS New Zealand AEs AEs 1989-2018 OECD STAN Norway AEs AEs 1970-2018 OECD STAN Portugal AEs AEs 1995-2018 OECD STAN Korea, Rep. AEs AEs 1970-2018 APO Slovak Republic AEs AEs 1995-2018 OECD STAN Slovenia AEs AEs 1995-2018 OECD STAN Spain AEs AEs 1970-2018 OECD STAN/KLEMS Sweden AEs AEs 1970-2019 OECD National Accounts Switzerland AEs AEs 1995-2018 OECD STAN Taiwan, China AEs AEs 1970-2018 APO United Kingdom AEs AEs 1970-2018 OECD STAN/KLEMS United States AEs AEs 1970-2018 OECD STAN/KLEMS Cambodia EMDEs EAP 1970-2018 APO China EMDEs EAP 1970-2018 APO Fiji EMDEs EAP 1970-2018 APO Indonesia EMDEs EAP 1970-2018 APO Lao PDR EMDEs EAP 1970-2018 APO Malaysia EMDEs EAP 1970-2018 APO Mongolia EMDEs EAP 1970-2018 APO Philippines EMDEs EAP 1970-2018 APO Myanmar EMDEs EAP 1970-2018 APO Thailand EMDEs EAP 1970-2018 APO Vietnam EMDEs EAP 1970-2018 APO Bulgaria EMDEs ECA 1995-2019 OECD National Accounts/ILO Croatia EMDEs ECA 1995-2019 OECD National Accounts/ILO Hungary EMDEs ECA 1995-2018 OECD STAN Poland EMDEs ECA 1995-2019 OECD National Accounts Romania EMDEs ECA 1995-2018 OECD National Accounts Russian Federation EMDEs ECA 1995-2018 OECD National Accounts/KLEMS/ILO Serbia EMDEs ECA 1995-2018 OECD National Accounts/ILO Turkey EMDEs ECA 1970-2018 APO 26 Table A.5: Sectoral database (continued) Country Group1 Group2 period Source Argentina EMDEs LAC 1990-2018 ETD Bolivia EMDEs LAC 1990-2018 ETD Brazil EMDEs LAC 1990-2018 ETD Chile EMDEs LAC 1951-2018 GGDC/ETD Colombia EMDEs LAC 1950-2018 GGDC/ETD Costa Rica EMDEs LAC 1950-2018 GGDC/ETD Ecuador EMDEs LAC 1990-2018 ETD Mexico EMDEs LAC 1950-2018 GGDC/ETD Peru EMDEs LAC 1990-2018 ETD Bahrain EMDEs MNA 1970-2018 APO Egypt, Arab Rep. EMDEs MNA 1960-2018 GGDC/ETD Iran, Islamic Rep. EMDEs MNA 1970-2018 APO Morocco EMDEs MNA 1970-2018 GGDC/ETD Oman EMDEs MNA 1991-2018 APO Qatar EMDEs MNA 1986-2018 APO Saudi Arabia EMDEs MNA 1991-2018 APO United Arab Emirates EMDEs MNA 1970-2018 APO Tunisia EMDEs MNA 1990-2018 ETD Bangladesh EMDEs SAR 1970-2018 APO Bhutan EMDEs SAR 1970-2018 APO India EMDEs SAR 1970-2018 APO Nepal EMDEs SAR 1970-2018 APO Pakistan EMDEs SAR 1970-2018 APO Sri Lanka EMDEs SAR 1970-2018 APO Cameroon EMDEs SSA 1965-2018 EASD/ETD Ghana EMDEs SSA 1960-2018 EASD/ETD Kenya EMDEs SSA 1969-2018 EASD/ETD Lesotho EMDEs SSA 1970-2018 EASD/ETD Mauritius EMDEs SSA 1970-2018 EASD/ETD Namibia EMDEs SSA 1965-2018 EASD/ETD Nigeria EMDEs SSA 1960-2018 EASD/ETD Senegal EMDEs SSA 1970-2018 EASD/ETD South Africa EMDEs SSA 1960-2018 EASD/ETD Tanzania EMDEs SSA 1960-2018 EASD/ETD Zambia EMDEs SSA 1965-2018 EASD/ETD Burkina Faso EMDEs (LICs) SSA 1970-2018 EASD/ETD Ethiopia EMDEs (LICs) SSA 1961-2018 EASD/ETD Malawi EMDEs (LICs) SSA 1966-2018 EASD/ETD Mozambique EMDEs (LICs) SSA 1970-2018 EASD/ETD Rwanda EMDEs (LICs) SSA 1970-2018 EASD/ETD Uganda EMDEs (LICs) SSA 1990-2018 ETD Notes:AEs:advanced economies. EMDEs:emerging markets and developing economies. LICs: low-income countries. EAP: East Asia and Pacific, ECA:European and Central Asia, LAC:Latin America and Caribbean, SAR:South Asia, MNA:Middle East and North Africa, SSA: Sub-Saharan Africa 27 Table A.6: Data construction from multiple sources Country Description 1 Austria, Belgium, Germany, Italy, Luxembourg, OECD STAN or National Accounts data is backwards extrapolated Netherlands, Spain, United Kingdom, United States using annual growth rates from EU KLEMS. 2 Chile, Colombia, Costa Rica Mexico, Arab Republic of Egypt, ETD data is backwards extrapolated Morocco, Burkina Faso, Cameroon, Ghana, Ethiopia, using annual growth rates from GGDC or EASD. Kenya, Lesotho, Malawi, Mauritius, Mozambique, Namibia, Nigeria, Rwanda, Senegal, South Africa, Tanzania, Zambia 3 Cyprus, Bulgaria, Croatia, Serbia As OECD Employment data is not available, ILO modelled estimates are supplementarily employed. 4 France OECD STAN data in 2018 is forwards extrapolated using annual growth rates from OECD national Accounts data. 5 Russian Federation OECD National Accounts data is backwards extrapolated using annual growth rates from KLEMS and ILO modelled estimates. 28 Table A.7: Data sources Database URL APO Productivity Database https://www.apo-tokyo.org/wedo/productivity-measurement/ OECD STAN database and National Accounts https://stats.oecd.org/ WORLD KLEMS Data http://www.worldklems.net/data.htm (Russia, March 2017 Release) EU KLEMS Growth and Productivity Accounts http://www.euklems.net/ (November 2009 Release) GGDC/UNU-WIDER Economic Transformation Database (ETD) https://www.rug.nl/ggdc/structuralchange/etd/ GGDC 10-Sector database (GGDC) https://www.rug.nl/ggdc/structuralchange/previous-sector-database/10-sector-2014 Expanded Africa Sector Database (EASD) https://www.merit.unu.edu/docs/EASD.xlsx ILOSTAT databases https://ilostat.ilo.org/data/bulk/ (ILO modelled estimates, Nov. 2020) Penn World Table version 10.0 https://www.rug.nl/ggdc/productivity/pwt/?lang=en