metadata: authors: 0: author: Dieppe,Alistair Matthew
1: author: Matsuoka,Hideaki
count: World
docna: 0: docna: Sectoral Decomposition of Convergence in
            Labor Productivity : A Re-examination from a New Dataset
docty: Policy Research Working Paper
subtopic: Food Security,Labor Markets,Transport Services,Common Carriers Industry,Food & Beverage Industry,Plastics & Rubber Industry,Business Cycles and Stabilization Policies,Construction Industry,General Manufacturing,Pulp & Paper Industry,Textiles, Apparel & Leather Industry,International Trade and Trade Rules
teratopic: Agriculture,Social Protections and Labor,Transport,Macroeconomics and Economic Growth,Industry,International Economics and Trade
abstracts: cdata!: This paper investigates how the
            sector-specific source or the changing sectoral composition
            of labor productivity has contributed to aggregate beta
            convergence, using a newly constructed eight-sector
            database. The main findings are twofold. First, both within
            and sectoral reallocation have become important drivers of
            aggregate convergence in labor productivity. Second,
            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. The strong growth of the agriculture sector has
            been the most important driver of aggregate productivity
            convergence even though agricultural productivity itself in
            low-income countries is weakly converging to that in
            advanced economies.
repnme: repnme: Sectoral Decomposition of Convergence in
            Labor Productivity : A Re-examination from a New Dataset
display_title: Sectoral Decomposition of
            Convergence in Labor Productivity : A Re-examination from a
            New Dataset
listing_relative_url: /research/2021/09/33382745/sectoral-decomposition-convergence-labor-productivity-re-examination-new-dataset
topicv3: Agriculture and Food Security m1326205 435,Manufacturing Sectors m1326521 1281,Transport m1326661 135,Trade m1326924 698,Labor Productivity m1504060 2756
publishtoextweb_dt: 2021-09-01T00:00:00Z
dois: 10.1596/1813-9450-9767
historic_topic: Agriculture,Social Protections and Labor,Transport,Macroeconomics and Economic Growth,Industry,International Economics and Trade
origu: EFI - Prospects Group (EPGDR)
no_of_pages: 30
author_keys: 0: author_key: 1674011
1: author_key: 1463324
model_extractions: 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 changing sectoral composition of labor productivity has contributed to aggregate beta convergence, using a newly constructed eight-sector database. The main findings are twofold. First, both within and sectoral reallocation have become important drivers of aggregate convergence in labor productivity. Second, 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. The strong growth of the agriculture sector has been the most important driver of aggregate productivity convergence even though agricultural productivity itself in low-income countries is weakly 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<sup>*</sup>

Alistair Dieppe<sup>†</sup> and Hideaki Matsuoka<sup>‡</sup>

**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.<sup>1</sup> 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).<sup>2</sup> 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.<sup>3</sup> 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)

> 1Unless otherwise 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).

> 2Although 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.

> 3As 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.<sup>4</sup> Compared with the literature using sectoral datasets, it employs a large and diverse sample of countries.<sup>5</sup> 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: where _y_ is aggregate labor productivity, _y j_ is labor productivity of sector _j_ , _Yj_ 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

> 4The 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.

> 5McMillan 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.<sup>6</sup> 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.<sup>7</sup> 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: 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:

> 6Other 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.

7Following 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
If _αaggregate_ =∑<sup>_k_</sup> _j_ =1<sup>_αwithin−j_+</sup><sup>_αstatic_+</sup><sup>_αdynamic_and</sup><sup>_εaggregate_=∑</sup><sup>_k_</sup> _j_ =1<sup>_εwithin−j_+</sup><sup>_εstatic_+</sup><sup>_εdynamic_,</sup> Hence, the beta _β aggregate_ coefficients obtained in the first step can be decomposed into a sum of coefficients of the within sector effect ∑<sup>_k_</sup> _j_ =1<sup>_βwithin−j_, the static</sup><sup>_β static_and dynamic</sup><sup>_β dynamic_sector effects.The summation of</sup> the static _β static_ and dynamic _β dynamic_ sector effects is the contribution of between sector effects.

We also examine the regressions for sector-specific convergence; 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.<sup>8</sup>

# **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

> 8137 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.<sup>9</sup>

- **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.<sup>10</sup> However, the estimated convergence rate is low. Diao et al. (2021) reveal a dichotomy between larger firms in the manufacturing sector that exhibit

> 9750 years ≒(ln(0.9)/ln(0.1)-1)/0.00127, using Table 2.

> 10However, 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 timeperiod. 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: 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 betweensector 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.<sup>11</sup> 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.

> 11Artuc 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. Empirically, regress the structural-term on employment rigidity index and raw materials’ share in exports and so on and find that there is a statistically significant impact of the employment rigidity.

11
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- Van Biesebroeck, J. 2009. “Disaggregate productivity comparisons: Sectoral convergence in OECD countries.” _Journal of Productivity Analysis_ 32:63–79. 7

- Wong, W.K. 2006. “OECD convergence: A sectoral decomposition exercise.” _Economics Letters_ 93:210–214. 2, 2.3, 6, A.2

- World Bank. 2021. _Global Economic Prospects,January 2021_ . Washington, DC: World Bank. 1

- —. 2020. _Global Productivity: Trends, Drivers, and Policies_ . Washington, DC: World Bank. 1, 3.2

15
<!-- Start of picture text --> Percent Percent<br>100 100<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>30<br>20<br>10 20<br>0 10<br>pe © SD 2)/ 8 86 SF 2)/ 2 B GS @ 0<br>9 9 SF SFS/B ®B SF F/B B SF S CBE SR Be SR Be eR Ge eR Sle K SSS<br>Advanced economies EMDEs LICs<br>EAP ECA LAC MNA SAR SSA<br>mg Agriculture = Mining = Manuf. = Other industry = Trade = Trans. and Fin. = Others mw Agric. = Mining = Manuf. = Other industry = Trade = Trans. and Fin. = Others<br><!-- End of picture text --> <!-- Start of picture text --> 100<br>100 Percent Percent<br>90 90<br>80 80<br>70 70<br>60 60<br>50 50<br>40 40<br>30 30<br>20 20<br>10 10<br>0 0<br>te) te) fo} © re) w © (oe) wo te) (oe) (oe)<br>~ a = = N fo) ro) — NS fp) ro) — ONOCDNNODANNDODANNODANNHNODANNOM<br>Oo= Oo= fo)nN oOro (o>)= (o>)= oOnN oOnN (o>)= Oo= lo)“ oOnN BwWBROrrMDHDDOODDOCODDOCODHDDCODHRDOCODHCORh OrrROrrROrrThROrr hR Ort<br>Advanced economies; EMDEs LICs TUTNNEAP TH ECATK NNT LACTH NN TH MNAKH NN TOKO SARKHON NTO SSAONG<br>m Agriculture = Mining = Manuf. = Other industry = Trade = Trans. and Fin. = Others m Agric. = Mining = Manuf. = Other industry = Trade = Trans. and Fin. = Others<br><!-- End of picture text -->
<!-- Start of picture text --> 25 Percent<br>20<br>15<br>10<br>0<br>Agric. Mining Manuf. Utilities Cons. Trade Trans. Others<br>and<br>Fin.<br><!-- End of picture text -->

# Percent

<!-- Start of picture text --> 4200 m@1975 91995 m=2010 =2018<br>1000<br>800<br>600<br>400<br>200<br>0<br>Advanced EMDEs LICs<br>economies<br><!-- End of picture text -->

# Percent

<!-- Start of picture text --> 1000 m1975 91995 =2010 =2018<br>800<br>600<br>400<br>200<br>0<br>EAP ECA LAC MNA SAR SSA<br><!-- End of picture text -->
<!-- Start of picture text --> 5 Percentage points<br>4 ae<br>m@ Within sector = Between sector<br>3<br>2<br>1<br>0<br>-1<br>Ke) lo) fee) Ke) [o) oe) Ke) oO foe)<br>(o>) co - (o>) c- - (o>) - -<br>eo &§ fF) eb F& f|/e GF<br>(o>) ike) lo) (o>) ite) [o) (o>) Ke) lo)<br>- DD N - D N - fop) N<br>2 2 2<br>Advanced EMDEs LICs<br>economies<br><!-- End of picture text -->

<!-- Start of picture text --> Pereentagem Agriculturepoints = Mininga = Manuf.<br>m Other industry Trade = Trans. and Fin.<br>2 sm Others<br>1<br>0<br>-1<br>Ke) oO oe) ike) oO foe) Ke) oO ©<br>(o>) c - (o>) c- - (o>) c- -<br>© &§ s)¢ § s)e 8 s<br>(o>) ike) lo) (o>) ike) io) (o>) ike) (oo)<br>- [op) N - [op) N - [op) N<br>2 2 2<br>Advanced EMDEs LICs<br>economies<br><!-- End of picture text --> <!-- Start of picture text --> Percentage points a<br>3 = Agriculture = Mining<br>= Manuf. = Other industry<br>2 = Trade = Trans. and Fin.<br>= Others<br>1<br>0<br>-1<br>Ke) fo) (oe) LO [oo] co ike) oO cO<br>(o>) c - (o>) c- - (o>) c- -<br>© § 3) & $ g 8 g<br>(o>) lO [o) (o>) Ke) fo) (o>) LO fo)<br>- [o>) N - fop) N - [o>) N<br>2 2 2<br>Advanced EMDEs LICs<br>economies<br><!-- End of picture text -->
<!-- Start of picture text --> 5 Percentage points<br>= Within sector =Between sector<br>4<br>3<br>2<br>1<br>0<br>-1<br>oo wWod no DN CO WMD/O OO WA/O OO WOA!\O O WO<br>PSTie®@StriP@SsTrie®striesewesrt<br>RPVYSLRGSRISRISRGVSLRGVES<br>DW OID WOW DID NO DID NYO DIR W DOD/1HD WO O<br>TFT DNIT DNIT DANIrT DNITKT DNIr DON<br>oOo Oo [o>) [o>] [o>) (o>)<br>EAP ECA LAC MNA SAR SSA<br><!-- End of picture text --> <!-- Start of picture text --> Percentage points .<br>@ Agriculture = Mining = Manuf.<br>m Other industry = Trade = Trans. and Fin.<br>3 = Others<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>oowvd no on oo wmonuno woalnonowvd| mn oO &o<br>PS TIPS STP STIG STP Ste@Ssrkt<br>PrYVYSRGIVSRGVSRIFSRLGVSRGVES<br>MDW OID W OID WO DID WMO OD'1HnD YO O/H WW O<br>TFT DODNIT DNITK DNITKT DNIT DANI r DN<br>(o>) [o>) (o>) Oo fo) Oo<br>EAP ECA LAC MNA SAR SSA<br><!-- End of picture text -->

<!-- Start of picture text --> Percentage points<br>3 @ Agriculture = Mining<br>= Manuf. m Other industry<br>2 = Trade = Trans. and Fin.<br>1<br>0<br>-1<br>oondnoon oeodlon 0 oO;/0 0 OO GO &O<br>PStieSTIPaSTIe®@STieSsSregsert<br>KrVYSILVSRGVGSRGISLVISRIVES<br>MDnW OID WMO DID WO DID WM DIM WO O/H WO O<br>TFT DODNITK DNIrTr DNIT DNIrTr DNIr DN<br>Oo Oo Oo [o>] oO Oo<br>EAP ECA LAC MNA SAR SSA<br><!-- End of picture text -->
Table 1: Decomposition of aggregate convergence

||1975-2018||1975-1995||1995-2018||
|---|---|---|---|---|---|---|
|Sector|_β_|_R_<sup>2</sup>|_β_|_R_<sup>2</sup>|_β_|_R_<sup>2</sup>|
|Aggregate|-0.00976**<br>(0.00484)|0.238|-0.00289*<br>(0.00155)|0.088|-0.00695***<br>(0.00234)|0.266|
|1. Agriculture|-0.00255***<br>(0.000493)|0.308|-0.000874***<br>(0.000316)|0.113|-0.00181***<br>(0.000270)|0.331|
|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***<br>(0.000460)|0.105|-0.000231<br>(0.000223)|0.018|-0.000628*<br>(0.000361)|0.032|
|4. Utilities|-0.000138<br>(9.67e-05)|0.033|1.34e-05<br>(4.18e-05)|0.002|-0.000162**<br>(7.98e-05)|0.043|
|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**<br>(0.000118)|0.097|-0.000174<br>(0.000149)|0.022|-0.000251<br>(0.000155)|0.028|
|7. Transport and Finance services|-0.000633***<br>(0.000206)|0.137|-0.000142<br>(0.000203)|0.008|-0.000520**<br>(0.000229)|0.054|
|8. Other services|-0.000399*<br>(0.000236)|0.046|-7.98e-05<br>(0.000156)|0.004|-0.000507**<br>(0.000298)|0.044|
|Total within sectoral effect<br>(1+2+3+4+5+6+7+8)|-0.00551***<br>(0.00139)|0.207|-0.00148<br>(0.000953)|0.039|-0.00431***<br>(0.000995)|0.171|
|Static sectoral effect|-0.00148<br>(0.00162)|0.014|-0.000516<br>(0.00207)|0.001|-0.00277**<br>(0.00138)|0.043|
|Dynamic sectoral effect<br>Observations|-0.00276*<br>(0.00155)|0.050<br>60|-0.000890<br>(0.00179)|0.004<br>60|0.000134<br>(0.00115)|0.000<br>91|

Notes: Regressions <u>(2) and (3) are estimated. The standard errors are reported in parentheses. The constant terms</u> are not reported.<sup>_∗∗∗_</sup> p<0.01,<sup>_∗_</sup> p<0.05,<sup>_∗_</sup> p<0.1 Table 2: Sector-specific convergence

||1975-2018||1975-1995||1995-2018||
|---|---|---|---|---|---|---|
|Sector|_β_|_R_<sup>2</sup>|_β_|_R_<sup>2</sup>|_β_|_R_<sup>2</sup>|
|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.<sup>_∗∗∗_</sup> p<0.01, _∗_ p<0.05, _∗_ p<0.1 20
0.01 0.01 <!-- Start of picture text --> 0.005<br>@ Agriculture = Min. = Manuf.<br>0 m OtherOther serviceindustry = Trade = Trans.and Fin.<br>= a<br>-0.01<br>1975-2017 (60) 1975-1995 (60) 1995-2018 (91)<br><!-- End of picture text --> <!-- Start of picture text --> 0.01<br>-0.01<br>oe aoe<br>0.02<br>= < & = rs} s s Pa<br>cS) = = 5 ~ = B<br>D 2 . o<br><< fe) 2 x<br>O © 5<br>FE<br>mTM 1975-1995 (60) 1995-2018 (91) TM=1975-2018(60)<br><!-- End of picture text -->
0.003 0.0020 -_— _ —_—“= -0.001 -0.002 -0.003 -0.004 -0.005 0.006 mWithin sector mBetween sector mResiduals --Aggregate Beta WOOKR-DDOTKAMNTNOONRDHDOTAMNTOOKRDDOKNMDTOONRWDD<sup>DDO OODWDWDWOWWAODODDODODVODVOVCVCVOTTrT</sup> CCC rrTCO HADWWWWHWWWWWWWDDODDDDDODODVDVDVDVDVODODODVODOO0O00 Te ee ee ee NNN Qn nay Q ay ay ay ql ql Gy NORMRRRRDDOTKAMNTOOKNDDOTKAMNTOOKDDOTKNOSTODDDD DDD DDD HDWDWDWBWOWOODDIHDDDODODODOO0OON CO DNDN HWHWOWWWNWWWWWOWDDADODODOOODOOO rr rr C CCT TTT TTT TTT TTT TTT NNN NNN NANG 0.001 <!-- Start of picture text --> 0 = es : ——_— ew<br>mOthers mTrans. and Fin. “TM<br>-0.003 mTrade mOther industry :<br>mManuf. m=Mining ss<br>-0.004 mAgricultureg -Within sector "<br>LODDOR-DHDOTAMTNOOR-DHOTAYTOOR-DHOMNMODDO HOOHWOOOWHWWWOWODODDODOOVOOCOCOrrrrrAO rrrTOR-CO<br>DKOHODWOWODHWWHWHWWHWOWOHWODODDOODOOOODOODVODODOOOO000<br>ee gag<br>WOOR-OHDOTNMTWMOOR-DHOTMNMOTOOR-ODOM NO ATWO OR- CO<br>MRRMEKDDDDDODDDDD DOD HOOHWWOWWWWODODOODODOOOOO<br>DKOHWD.OWDHWOWHWHODWDWWHWOODHWDHWWOWOWAWDOMDDOODODOODOOO<br>COTOTOT NNN NANNING<br><!-- End of picture text -->
<!-- Start of picture text --> 4 Percentage points<br>3 # Within sector = Between sector<br>2<br>1<br>0<br>-1<br>lo oO (oe) lo oO foe) lo oO foe)<br>@ S 7 @ S 7 @ S T<br>re FY S/R F F/R FF EL<br>Oo ike) [o) Oo ike) lo) Oo LO lo)<br>- (op) N - [op) N - [op) N<br>Oo Oo (o>)<br>Advanced EMDEs LICs<br>economies<br><!-- End of picture text --> <!-- Start of picture text --> Pergentagem Agriculturepoints = Mininga = Manuf.<br>m= Other industry Trade = Trans. and Fin.<br>2 om Others<br>1<br>0<br>-1<br>ike) [o) foe) lo [o) foe) lo oO foe)<br>> S 7 @> S 7 @> S 7<br>re FY F/R FF fSS|R YF LZ<br>© LO oO [o>) LO [o) fo>) LO [o)<br>- fo) N - fo) N - fo)) N<br>2 2 2<br>Advanced EMDEs LICs<br>economies<br><!-- End of picture text -->

<!-- Start of picture text --> Percentage points a<br>3 @ Agriculture = Mining<br>= Manuf. = Other industry<br>2 = Trade = Trans. and Fin.<br>= Others<br>1<br>0<br>-1<br>lO [o) fee) LO lo) foe) lo oO co<br>@ S 7 > S 7 @ S 7<br>re FY FSR FY SIR YF E<br>Oo ike) [o) © LO oO Oo LO oO<br>- [op) N - [op) N - (o>) N<br>2 2 2<br>Advanced EMDEs LICs<br>economies<br><!-- End of picture text -->
<!-- Start of picture text --> 4 Percentage points<br>m= Within sector = Between sector<br>3<br>2<br>1<br>0<br>-1<br>Oo oOo WM N ODO DIN CO WMO 0 WOO OO O/0O CO WO<br>SSeS ease SS esse ages<br>KCVSKGVSILIFSILVIFSILIGSLGES<br>MDW OID WM OID MO DID MO DID WMO O/H YO SO<br>TDNITK DNITKT DNIKTKT DNITK DN!r- DN<br>[o>) [o>) Oo (o>) [o>) (o>)<br>EAP ECA LAC MNA SAR SSA<br><!-- End of picture text --> <!-- Start of picture text --> Percentage points .<br>@ Agriculture = Mining = Manuf.<br>m Other industry = Trade = Trans. and Fin.<br>3 = Others<br>2<br>1<br>0<br>-1<br>-2<br>-3<br>oowWmdnoSS RIP SSISon oOSRPODO OOSSISWOON OSUPWOO SSCO WO<br>PCYSRGVFSRGFSRGVESLPFSLRYIES<br>DWM DIDWMW DID MO DID WMO DID OO O/HD YO O<br>TONIT DNITKT DNITKT DNITKT DN'rK DN<br>[o>) [o>) [o>) [o>) [o>) [o>)<br>EAP ECA LAC MNA SAR SSA<br><!-- End of picture text --> <!-- Start of picture text --> 3 PercentagemAgriculturepoints = Mining_ m= Manuf.<br>m= Other industry m Trade = Trans.and Ein.<br>2 = Others<br>1<br>0<br>-1<br>oomnodn<br>PSTIPeP STIPSTIPoon owvod;onTIPSoO TIPO/0O0 S ODO T&O<br>RGVYSRGVYSRGSRGSRGES|ILMW GES<br>TFT DNITDIDnWMDN!OIDT NYODNO/1HDT OWDN!O/HMDT YODNIir-rO/H YODNO<br>oO [o>) (o>) Oo Oo [o>)<br>EAP ECA LAC MNA SAR SSA<br><!-- End of picture text -->