metadata: authors: 0: author: Burgi,Constantin Rudolf Salomo
1: author: Hovhannisyan,Shoghik
2: author: Mondragon-Velez,Camilo
count: World
docna: 0: docna: GDP-Employment Elasticities across
            Developing Economies
docty: Policy Research Working Paper
abstracts: cdata!: Economic growth is often associated with
            welfare gains through job creation. However, the number and
            quality of new job opportunities created in a growing
            economy vary across countries and sectors, due in great part
            to changes in labor productivity. This paper provides
            estimates of country and sector-specific GDP-employment
            elasticities based on data from the past two decades,
            including an evaluation of the predictive power among
            alternative methodological approaches. The results show that
            employment elasticities of growth vary significantly across
            countries and sectors, but are in most cases below 1.0,
            implying that employment grows less than GDP due to
            increasing productivity. Across sectors, agriculture has
            mostly lower elasticity values, becoming negative for more
            than one-third of developing countries. In addition,
            increases in labor productivity are associated with
            reductions in informal employment. These empirical results
            are in line with the implications of a theoretical model
            about the relationship between GDP growth, job creation, and
            labor productivity in economies with varying levels of
            productivity and informality.
display_title: GDP-Employment Elasticities across
            Developing Economies
listing_relative_url: /research/2024/12/34431146/gdp-employment-elasticities-across-developing-economies
topicv3: Jobs m1503931 2670,Jobs and Development m1503932 2671,Employment Elasticity of Growth m1503948 2677,Jobs Diagnostics m1503991 2689
publishtoextweb_dt: 2024-12-02T00:00:00Z
dois: 10.1596/1813-9450-10989
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 43
author_keys: 0: author_key: 1588858
1: author_key: 808825
2: author_key: 674223
model_extractions: Policy Research Working Paper 10989 GDP-Employment Elasticities across Developing Economies _Constantin Burgi Shoghik Hovhannisyan Camilo Mondragon-Velez_ International Finance Corporation December 2024
Policy Research Working Paper 10989

# **Abstract**

Economic growth is often associated with welfare gains through job creation. However, the number and quality of new job opportunities created in a growing economy vary across countries and sectors, due in great part to changes in labor productivity. This paper provides estimates of country and sector-specific GDP-employment elasticities based on data from the past two decades, including an evaluation of the predictive power among alternative methodological approaches. The results show that employment elasticities of growth vary significantly across countries and sectors, but are in most cases below 1.0, implying that employment grows less than GDP due to increasing productivity. Across sectors, agriculture has mostly lower elasticity values, becoming negative for more than one-third of developing countries. In addition, increases in labor productivity are associated with reductions in informal employment. These empirical results are in line with the implications of a theoretical model about the relationship between GDP growth, job creation, and labor productivity in economies with varying levels of productivity and informality.

This paper is a product of the International Finance Corporation. It is part of a larger effort by the World Bank Group 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 SHovhannisyan@ifc.org and CMondragonvelez@ifc.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
# **GDP-Employment Elasticities across Developing Economies**<sup>**1**</sup>

Constantin Burgi<sup>2</sup> Shoghik Hovhannisyan and Camilo Mondragon-Velez<sup>3</sup> Keywords: Employment Elasticity of Growth, Jobs, Jobs and Development, Informal Sector, Jobs Diagnostics JEL: E24, J46, O17, O41

> 1 The views expressed in this paper are those of the authors and do not necessarily represent those of the International Finance Corporation, the World Bank Group, or their management. We would like to thank Aart Kray, Emelly Mutambatsere, Marcio Cruz, Pablo Fajnzylber, and Xiao Jiang for very useful comments and suggestions.

> 2 University College Dublin, corsbu@gmail.com

> 3 International Finance Corporation – World Bank Group, SHovhannisyan@ifc.org and CMondragonvelez@ifc.org.
# **1. Introduction**

The effect of economic growth on job creation has been a prominent topic in macroeconomics and development economics. It is particularly important in the context of developing countries where job creation is viewed as a critical outcome of investment activity - both public and private - driving poverty reduction and welfare improvements. However, while there is a large body of literature studying this relationship, from Solow (1956) to Acemoglu and Restrepo (2018), most theoretical models and empirical estimates are based on the experience and data from developed countries. When focusing on employment growth generated by private investment activities, there is expected variation across countries and sectors. Along these lines, this paper introduces a theoretical model to understand the relationship between GDP growth, job creation, and labor productivity in economies with different levels of productivity and informality. The model is used to interpret the implications of empirically estimated GDP-employment elasticities as well as the estimated effects of labor productivity improvements on informality. In this regard, differentiating the types of employment generated by economic growth is especially relevant in the context of developing economies, where a high share of the working poor is not able to meet their daily needs despite engaging in informal activities that are characterized by belowsubsistence remuneration and provide limited or no employment benefits.

The theoretical model expands the literature on modeling informality, including Lucas (1978), Rauch (1991), Aghion et al. (2005), Gollin (2008), Sharma (2009), and De Paula and Scheinkman (2011). It follows existing studies on ability or labor productivity that drive labor market participants’ decisions on whether to become: (i) formal sector entrepreneurs, (ii) workers in the formal sector, or (iii) informal sector entrepreneurs; and provides a theoretical framework to understand the impact of productivity improvements on output and employment across the formal and informal sectors in developing economies. In summary, the model shows that in economies with sizable informality, employment growth is lower than economic growth, and that there is a negative relationship between informality and labor productivity.

This paper implements alternative methodologies to estimate the GDP-employment elasticities empirically for three aggregate sectors (agriculture, manufacturing, and services) across developing economies. First, a Social Accounting Matrix (SAM) multiplier approach (Khan et al., 1989), which under strong assumptions (i.e., fixed technology, prices and capital output ratios) implies that GDP growth leads to proportional changes in employment, and therefore the GDP-employment elasticity equals 1. This is an estimation approach used by many practitioners working with Input-Output (IO) and SAM frameworks to 2
assess the effects of different shocks to the economy. However, this approach tends to significantly overestimate employment growth relative to GDP growth across sectors and countries.<sup>4</sup> The second method uses an econometric-based approach to estimate the relationship between employment growth and GDP growth. This follows various studies in the literature. For instance, Kapsos (2005) applies a panel estimation in levels with country and sectoral dummies, Sahin et al. (2015) estimate a cointegration relationship, while Crivelli et al. (2013) use a panel in levels with lags of the dependent variable and country specific dummies.<sup>5</sup> The estimated elasticities in these studies differ substantially, influenced by the specific country and sector of analysis and the chosen regression specification. While the results of these studies vary substantially, it is important to note that the key challenge of these approaches is the endogeneity bias in the estimators generated by the simultaneous causality of employment and economic growth.

The third estimation method is derived from distributional properties of employment and GDP. Following Klenow and Rodriguez (1997), it can be shown that the expected growth in employment conditional on observed growth in GDP equals the covariance of employment growth and economic growth divided by the variance of GDP growth.<sup>6</sup> Finally, the fourth estimation approach follows a direct and straightforward calculation of the GDP-employment elasticities as the average ratio of annual percentage growth in employment to the annual percentage growth in GDP. This method does not impose any structural assumptions on the underlying variables and can be implemented for time series of different length.<sup>7</sup> To evaluate these estimation methods’ predictive power, a cross-country data panel for developing economies in the period 2000-2016 was used based on the value added and employment data from the World Development Indicators and International Labor Organization. Within-sample as well as out-ofsample GDP-employment elasticity estimates were produced under each methodology and used to estimate employment growth based on observed GDP growth. The results of this analysis show that the fourth approach based on average ratio of annual percentage changes in employment and GDP exhibits

> 4 For example, following this approach (using macro data from Eurostat and the World Bank World Development Indicators), the observed 50 percent of GDP growth in European Union countries during 1995-2005 implies 50 percent growth in employment, in contrast with the 10 percent actual growth in employment observed over the same period.

> 5 Other works in the area include Kaldor (1966), Parikh (1978), Rowthorn (1975a,b), Saget (2000), Döpke (2001), Kapsos (2006), Pattanaik & Nayak (2014), or Olusoji (2016). 6 We thank Aart Kraay for his help on identifying this alternative estimation method. It is important to note that this approach is underlined by the assumption that the growth of employment and productivity are bivariate normal.

> 7 Okun’s law (Okun, 1962) offers an alternative way to compute employment elasticities based on the documented inverse relationship between the unemployment rate and GDP growth (see Ball et al., 2017 for a recent summary). While there have been sector-focused versions (Goto and Bürgi, 2020), one key challenge in applying this approach in the context of developing countries is that unemployment rates alone do not describe labor market supply-demand gaps, given sizable informal employment and inefficient labor use across different sectors. In this case, GDP growth leads to a combination of job creation and increased labor productivity. In developing countries significantly lagging behind the frontier economies, employment effects would be dampened.

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superior predictive power relative to other methodologies. Furthermore, the results show that average GDP-employment elasticities vary substantially across countries and sectors – ranging between 0.0 and 1.0 for more than 80 percent of countries, and between 0.25 and 0.75 for more than half of countries – in manufacturing and services. Agriculture shows a different pattern: estimated average elasticities show lower values than for manufacturing and services, 60 percent of countries have values less than 0.25 (and negative for about one-third of countries), while only 30 percent of countries have values ranging between 0.25 and 0.75. This is explained largely by the dominance of smallholder farming in agriculture across developing economies, which have in general low productivity and follow different dynamics related to labor demand, compared to other sectors.

In addition to the GDP-employment relationship, there are several studies focused on the relationship between employment levels and labor productivity. One distinct feature highlighted by these studies, in the context of developing countries, is the role of informal employment. Capp et al. (2005), Elstrodt et al. (2002), Farrell (2004), Kenyon et al. (2005), and Palmade (2005) found that informality is among the key factors driving productivity differences between developing and developed countries as it affects investment decisions and reduces growth potential of economies. In this context, increases in GDP can be decomposed into productivity and employment gains (World Bank, 2010). While productivity gains increase wages and could lift people from informal jobs into formal ones, they might not necessarily increase employment. Maloney (2001) and Loayza and Rigolini (2011) found strong and negative relationships between informality and productivity.

To complement the analysis of GDP-employment elasticities, the paper estimates the relationship between labor productivity and informal employment. For this purpose, a consistent measure of informality is used across countries which includes the self-employed and non-paid employees (which can account for more than 60 percent of total employment in low-income countries).<sup>8</sup> This measure is subsequently applied to a new standardized global micro dataset (I2D2) harmonized and assembled by the World Bank Group (see Appendix 1).<sup>9</sup> The empirical results show that an increase in labor productivity is associated with a reduction in informality, consistent with the theoretical model predictions.

> 8 This measure does not comprehensively capture the extent of informal employment in developing countries, as it does not take into consideration those workers who work for informal enterprises or formal enterprises while still maintaining informal employment status. In practice, informality measures vary across developing countries and are heavily influenced by the availability of relevant indicators in labor force surveys. The definition of informality used in this paper has the advantage of being consistent across countries and has been widely adopted in studies that employ cross-country data.

> 9 The I2D2 covers 147 countries and includes agriculture, manufacturing, and services as sectors of occupation. I2D2 distinguishes four employment categories: paid employee, non-paid employee, employer and self-employed.

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The remainder of the paper is structured as follows: section 2 describes the theoretical model, section 3 discusses the empirical approach, section 4 describes the data and descriptive statistics, section 5 presents the empirical results, and section 6 concludes.

# **2. Model**

The paper introduces a theoretical model to understand the relationship between GDP growth, job creation, and labor productivity in economies with different levels of productivity and informality. The model is used to interpret the implications of empirically estimated GDP-employment elasticities as well as the estimated effects of labor productivity improvements on informality.

The model uses an economy with potential entrepreneurs, drawing from the research of Aghion et al. (2005), Gollin (2008), and Sharma (2009). It is assumed that there are two types of agents in this economy in period t: H with high entrepreneurial ability H and with low entrepreneurial ability . While ability captures multiple factors, ability and productivity will be used interchangeably in the model. High LLHH LLHH NN AA NN AA ability agents create firms with high productivity H and low ability agents can only start firms with low productivity H .<sup>10</sup> Agents have three possible employment options. First, they can become selfAA employed in the informal sector. Second, agents can start a formal business as entrepreneurs and employ LLHH AA < AA other workers. Third, they can become paid workers in formal firms where they earn the _“formal wage”_ ( ~~w~~ , where > 0 reflects mandatory taxes and/or contributions for formal employment), higher than the reservation wage jjHH + ττjjHH ττjjHH ~~w~~ j of worker j (which is equal to self-employment income). The production function of firms is shown below: (1) αα iiHH iiHH iiHH where is output of firm _i_ in period _t_ ; is productivity linked to entrepreneurial ability of a firm’s YY = AA LL owner; iiHH is the number of workers with ability iiHH _j_ employed by firm _i_ ; and ensures that there YY AA is an optimal number of workers. It is assumed that workers’ ability mix does not impact the productivity ii,jj,HH LL 0 < αα< 1 of the firm, determined only by the entrepreneur’s ability.

Agents maximize their payoff by choosing between becoming an entrepreneur, a wage worker or selfemployed. The equilibrium in this economy is determined by the (formal) wage, (formal) firms’ profits, and agents’ choices. Conditional on the number of high and low ability agents there are three possible scenarios. In the first scenario, all agents with high entrepreneurial ability become entrepreneurs and

> 10 This setup of firms is similar to that in Gollin (2008), but it combines productivity and capital in A.

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create exactly as many jobs as there are agents with low entrepreneurial ability. In the second scenario there are too many high ability agents and thus (formal business) profits decrease to their reservation wage and a fraction of high ability agents become self-employed. In the third scenario there are too many low ability agents and thus formal wages fall to the low ability formal reservation wage and a fraction of low ability workers become self-employed. The details and derivations of each scenario is detailed in the Appendix 2. Note that as the variables can change over time, economies can transition from one scenario into another.

This economic model can be used to assess the impact of GDP growth on employment; where GDP growth is driven by increases in formal sector firms’ capital or productivity H ) , through changes in output and employment. For the purpose of this analysis, only one scenario is of interest, where a fraction of (AA low ability workers opts for informal self-employment (scenario 3). The output per worker is HH YY disaggregated into formal and informal production: where s is a share of a formal sector employment and (1-s) is a share of informal sector workers in total. Variables with subscript F relate to the formal sector and I to the informal one. In scenario 3, both formal sector employment and informal sector employment include only low ability agents. Since all informal workers in scenario 3 are self-employed, an output per worker in the FFHH IIHH LL LL informal sector is simply I , which we assume not to change.<sup>11</sup> Under this assumption, there are I YY LLHH only two possible channels through which productivity in the economy can increase. The first channel is LL = AA an increase in the productivity of formal firms ( H ) and the second channel is an increase in the number of agents with H .<sup>12</sup> In both cases, the number of informal sector workers decreases, either because each AA firm hires more workers or because there will be more firms. This means that regardless of the channel, AA any increase in overall labor productivity goes in tandem with a decrease in the share of informal employment (under the assumption that is fixed). This negative relationship is estimated and confirmed in the empirical section of this paper. LL AA

> 11 In line with Loewenstein and Bender (2017).

> 12 Small increases in H will allow profit maximizing firms to hire more workers to increase their production and profits, given unchanged wages. AA

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This model can also help explain differences in GDP-employment elasticities across countries with and without informal employment – or in economies with higher or lower informality (see the Appendix). In scenario 3, where an economy has an informal sector mainly composed by low ability agents – as is the case in most developing countries - GDP growth does not imply an increase in formal sector wages. Instead, increases in productivity lead to an expansion of the formal sector given an abundant supply of low ability agents willing to work in the formal sector.<sup>13</sup> This implies a negative relationship between productivity growth and the rate of informality. In this case, net job creation in the economy will be less than (gross) job creation in the formal sector, as a fraction of new formal workers move from the informal to the formal one. Thus, increases in employment originate from the unemployed entering the formal sector. It is important to note that if all new jobs are taken by the unemployed, the change in employment would be higher than in the case with informality, assuming wages remain constant.<sup>14</sup> However, if some of the new workers move from the informal sector, then employment growth will be relatively lower than in the former case.

The paper draws on model implications for the empirical approach. Specifically, the relationship between GDP and employment has two components. Firstly, increases in GDP can increase employment due to unemployed people becoming employed and secondly, through a shift from the informal sector to the formal sector which does not increase employment but increases wages. GDP growth can thus be decomposed into these two employment related effects.

# **3. Empirical Approach to Estimate GDP-Employment Elasticities**

Four alternative methodologies are implemented to generate estimates of GDP-employment elasticities (i.e. the economic growth elasticity of employment) for three aggregate sectors (agriculture, manufacturing, and services) across developing economies: a SAM multiplier approach, a cross-country regression-based approach, a (country-sector-specific) growth decomposition derived approach, and a direct calculation of average annual elasticities from country-sector time series.

> 13 It can be shown that dd d = dd d = 1 . This implies that dd dd = 0 , which is consistent with invariant formal wages to formal sector ddYYHHII ddLLHHII HHII productivity under scenario 3 (14 Unemployed earn 0 wages and do not produce anything. Self-employed earn their informal sector wages and produce some output (GDP). If HHII HHII (1−αα)) . HHII HH LL LL an informal worker becomes a formal sector worker, his previous job disappears and hence there is no increase in employment and output only ww = AA + ττ increases by the difference between the formal job output and the informal job output.

7
= In(X ) ~~—--- 4 (—~~ =0) ~~(— —)~~ ~~(— —)~~ Aln(l )=c+aAln(y )+ oX Aln(l_ ) Aln(y_ )
in ~~(—)~~ +a in ~~(—)~~ FA [4 = efA te EA) <u>cov[A</u> <u>]</u>
This decomposition approach can be applied to a large number of individual countries and sectors to capture country-sector-specific trends.

Similarly, within-sample estimates are produced using data for years 1992-2016, and out-of-sample estimates are produced with data for years 1992-2011 (and later compared to actual employment growth for years 2012-2016).

# **3.4. Average Annual Elasticities Calculated from Country-Sector Data**

Two versions of these estimates are produced to test if more up-to-date representation of the economic structure and technology embedded in the data increases predictive power. One taking the full time series available (“Simple Full”, using annual data since year 1992) and another taking only recent history into account (“Simple Short”, using annual data since year 2000). This estimation approach can also be easily applied to a large number of individual countries and sectors to capture country-sector-specific trends.

# **4. Data and Descriptive Statistics**

World Bank World Development Indicators (WDI) and International Labor Organization’s (ILO) datasets are used for GDP-employment elasticities analysis using different methods. The data on gross value added for agriculture, manufacturing, and services were obtained from the WDI in 2010 US dollars. The ILO provides data on sectoral employment for the same three sectors. According to the ILO, employment includes all persons of working age who are in the following categories: paid employment (whether at work or with a job but not at work); or self-employment (whether at work or with an enterprise but not at work). The availability of time series data on employment only for agriculture, manufacturing, and services limits the analysis to these three aggregate sectors. Given the mix of data sources, the combined employment data across all sectors from the ILO was compared with the total employment from the WDI.

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The latter is computed as a sum of population 15-64 years old and above 65 years old multiplied by the employment-population ratio of 15+ years old. The comparison indicates that some countries have more than a 10 percent deviation in employment data during one or more years over the period of 2000-2016.<sup>16</sup> Countries with more that 10 percent deviations are excluded from the analysis. Country and sectorspecific annual GDP-employment elasticities for average elasticities approach are constructed by dividing annual employment growth by GDP growth. Similarly, changes in labor productivity are divided by GDP growth to discount the share of economic growth explained by improvements in labor productivity, following the decomposition approach. Finally, average of annual elasticities over the period 2000-2016 are used as estimates of country and sector GDP-employment elasticities.

To study the relationship between labor productivity and informal employment, sector-specific informal employment shares (in agriculture, manufacturing and services) were computed using harmonized labor force surveys using the World Bank’s International Income Distribution Data Set (I2D2).<sup>17</sup> Informal employment is defined for the purposes of this paper as those individuals that declare to be self-employed or are non-paid employees.<sup>18</sup> Several stylized facts emerge when comparing informal employment across countries, country income groups and sectors; such as prevalence of informality in low-income countries and in the agriculture sector. Figure 1 shows that lower-income countries tend to have higher informal employment than highincome ones; and this pattern is consistent across all aggregate sectors. Low-income countries have sizable employment informality and thus under-utilized labor due to the limited number of formal jobs available in the economy relative to the size of the working-age population. In most cases, informal jobs do not provide sufficient income, benefits, or certainty for workers who are either poor or vulnerable to poverty in the face of economic shocks. In addition, the share of informal employment is the highest in agriculture, reaching on average 70 to 80 percent in low-income and lower-middle-income countries. In low-income countries most of these workers are part of subsistence farmer households that are either poor or vulnerable to poverty. Manufacturing and service sectors have similar patterns across income groups, although service sectors have on average slightly higher informality compared to manufacturing.

> 16These countries are excluded from the analysis and include Albania, Angola, Bangladesh, Bosnia and Herzegovina, Botswana, Burkina Faso, Cabo Verde, Comoros, Republic of Congo, Djibouti, Dominican Republic, Equatorial Guinea, Fiji, Gabon, Ghana, Guam, Guinea, Iraq, Lesotho, Mali, Mauritania, Moldova, Montenegro, Myanmar, Namibia, New Caledonia, Niger, Oman, Peru, Samoa, Serbia, Sierra Leone, Solomon Islands, Somalia, St. Lucia, Tajikistan, Turkmenistan, Uganda, and Yemen.

> 17 The I2D2 a global harmonized labor force survey database that provides comparable indicators on both household and individual levels across countries and over time, from 600 surveys for 120 countries.

> 18 While there are alternatives definitions of informal employment in the literature, the ability to measure it varies substantially across countries, depending on quality and availability of related indicators in labor force surveys.

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Informal employment in manufacturing changes significantly across income groups, dropping from 77 percent in low-income to 11 percent in high-income countries.<sup>19</sup>

**Figure 1. Average Share of Informal Employment by Income Groups and Sectors.**

<!-- Start of picture text --> All Sectors Agriculture<br>1 1<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0 0<br>High Income Upper Middle Lower Middle Low Income High Income Upper Middle Lower Middle Low Income<br>Income Income Income Income<br>Manufacturing Services<br>1 1<br>0.8 0.8<br>0.6 0.6<br>0.4 0.4<br>0.2 0.2<br>0 0<br>High Income Upper Middle Lower Middle Low Income High Income Upper Middle Lower Middle Low Income<br>Income Income Income Income<br><!-- End of picture text --> _Source: Authors’ computations using the I2D2 dataset._ Other indicators from the WDI and Penn World Tables are included as control variables in the econometric analysis. Among these variables are shares of sectoral value added, shares of import and export in GDP, human capital, dependency ratio, population, and real interest rates. The sectoral shares of the value added, import and export shares in GDP, and human capital index are obtained from the Penn World Table database (version 8). The human capital index is based on the average years of schooling from Barro and Lee (2013) and an assumed rate of return to education is based on Mincer equation estimates from Psacharopoulos (1994).<sup>20</sup> Data on dependency ratio, population, and real interest rate are taken from the WDI.

> 19 An important caveat worth mentioning is that many self-employed workers in high-income countries are not necessarily informal workers although their labor productivity is low compared to the rest of the workers. Similar patterns emerge in the service sectors.

> 20 Human Capital in PWT 9.0: https://www.rug.nl/ggdc/docs/human_capital_in_pwt_90.pdf.

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# **5. Empirical Results**

This section describes the empirical results of GDP-employment elasticities estimations across countries and sectors using the alternative methodological approaches described in section 3, as well as the estimated relationship between productivity growth and informality. As described in section 3, GDPemployment elasticities estimates were produced based on data for two different time periods, up to 2016 for within-sample estimations and up to 2011 for out-of-sample estimations. The estimated elasticities were compared for their predictive power on employment levels.<sup>21</sup>

# **5.1. GDP-Employment Elasticities**

The section analyzes the range of GDP-employment elasticities estimates obtained from the different methodologies discussed in section 3. Figure 2 shows the distribution of elasticities across developing countries using available data up to year 2011 (used for out-of-sample estimations), for manufacturing, services, and agriculture.<sup>22</sup> For manufacturing, the average annual elasticities resemble closely a normal distribution around a median value of 0.40 (see Appendix 4 for GDP-employment elasticities for each country and sector using average annual elasticities approach). The distribution for services is less symmetric, with a median value around 0.55. 84 and 93 percent of countries’ average elasticities for manufacturing and services, respectively, range between 0.0 and 1.0; while more than half of countries’ average elasticities range between 0.25 and 0.75. On the other hand, growth decomposition-based estimates show a higher share of low elasticity values below 0.25, including more countries with negative values than those observed for average elasticities estimates.<sup>23</sup> Regression-based elasticity estimates for manufacturing and services are more widespread, with a significantly higher share of negative elasticity values around 30 percent. For agriculture, the distribution of estimated average annual elasticities has its mean and median around 0.15, as its highest mass is in negative values (36 percent) followed by low positive elasticity values below 0.25 (23 percent).<sup>24</sup> The decomposition and regression-based estimates are even more concentrated in negative and low-positive values. 75 and 90 percent of countries have regression and decomposition-based elasticity values below 0.25, respectively (with 40% negative values). The negative elasticities observed in the agriculture sector in many developing countries can be attributed to the prevalence of high levels of informality or subsistence farming, where employed individuals exhibit

> 21 For a detailed description of data sources and construction of underlying variables, see Appendix 3.

> 22 See Appendix 4 for details of the coefficients from the regression-based approach.

> 23 For manufacturing 48 percent of countries have growth decomposition-based elasticity values below 0.25 (where 16 percent are negative values), while for services 70 percent of countries have growth decomposition-based elasticity values below 0.25 (where 19 percent are negative values).

> 24 The distribution of average annual elasticities for agriculture also exhibits a close-to-symmetrical distribution when increasing the granularity of the histogram bins.

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<!-- Start of picture text --> Average Annual<br>GDP-Employment Elasticities<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>a eee<br><0.00 (0.00, 0.25] (0.25, 0.50] (0.50, 0.75] (0.75, 1.00] > 1.00<br><!-- End of picture text --> <!-- Start of picture text --> Average Annual<br>GDP-Employment Elasticities<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>10<br>— Zz<br><0.00 (0.00, 0.25] (0.25, 0.50] (0.50, 0.75] (0.75, 1.00] > 1.00<br><!-- End of picture text --> <!-- Start of picture text --> Average Annual<br>GDP-Employment Elasticities<br>35<br>30<br>%<br>20<br>15<br>10<br>0<br><0.00 (0.00, 0.25] (0.25, 0.50] (0.50, 0.75] (0.75, 1.00] > 1.00<br><!-- End of picture text --> <!-- Start of picture text --> Growth Decomposition<br>GDP-Employment Elasticities<br>45<br>40<br>35<br>30<br>25<br>20<br>45<br>10<br>0 | ——<br><0.00 (0.00, 0.25] (0.25, 0.50] (0.50, 0.75] (0.75, 1.00] > 1.00<br><!-- End of picture text -->

<!-- Start of picture text --> Growth Decomposition<br>GDP-Employment Elasticities<br>45<br>40<br>35<br>30<br>25<br>20<br>45<br>10<br>— —<br>7 <0.00|(0.00, 0.25] (0.25, 0.50] (0.50, 0.75] (0.75, 1.00] > 1.00<br><!-- End of picture text -->

<!-- Start of picture text --> Growth Decomposition<br>GDP-Employment Elasticities<br>35<br>30<br>*<br>20<br>15<br>10<br>0 |<br>| <0.00 (0.00, 0.25] (0.25, 0.50] (0.50, 0.75]|(0.75, 1.00] > 1.00<br><!-- End of picture text -->
<!-- Start of picture text --> Regression-based<br>GDP-Employment Elasticities<br>35<br>30<br>25<br>20<br>15<br>10<br>;5 |<br><0.00 (0.00, 0.25] (0.25, 0.50] (0.50, 0.75] (0.75, 1.00] > 1.00<br><!-- End of picture text -->

<!-- Start of picture text --> Regression-based<br>GDP-Employment Elasticities<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>5<br>:Ue L |Beas<br><0.00 (0.00, 0.25] (0.25, 0.50] (0.50, 0.75] (0.75, 1.00] > 1.00<br><!-- End of picture text -->

<!-- Start of picture text --> Regression-based<br>GDP-Employment Elasticities<br>45<br>40<br>35<br>30<br>25<br>20<br>15<br>5<br>:© LJTT<br><0.00 (0.00, 0.25] (0.25, 0.50] (0.50, 0.75] (0.75, 1.00] > 1.00<br><!-- End of picture text --> <!-- Start of picture text -->
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# **5.2. Predictive Power of Estimated GDP-Employment Elasticities**

To evaluate the predictive power of the four estimation approaches discussed in section 3, this section uses the elasticities estimated using data until year 2011 (described in the previous section), to perform an out-of-sample evaluation of predicted employment growth based on the estimated elasticities and observed GDP growth. These predicted employment growth values are benchmarked against observed employment growth for years 2012-2016. In addition, the predictive power of average annual elasticities estimated from more recent data (years 2000-2011) is also tested. To assess and compare predictive power, Mean Absolute Prediction Percentage Error (MAE) and Root Mean Squared Prediction Percentage Error (RMSE) are computed for each of the country-sector employment predictions generated with the elasticity estimation approaches. Table 1 shows the results of this analysis.

Table 1. GDP-Employment Elasticities Predictive Power ( **bolded numbers** identify the estimation methodology with highest predictive power in each case)

||Average Ann<br>Full<br>Sample|ual Elasticities<br>Recent<br>Sample|Decomposition<br>based<br>Approach|Regression<br>based<br>Approach|SAM Multiplier<br>Approach<br>(EYL,i,j,H= 1.0)|
|---|---|---|---|---|---|
|Manufacturing||||||
|MAE|**2.34**|**2.50**|3.47|2.73|<br>8.64|
|RMSE|15.39|15.56|21.56|**11.28**|47.96|
|Services||||||
|MAE|**0.38**|**0.43**|0.83|1.06|1.39|
|RMSE|**0.54**|**0.58**|1.22|1.54|2.94|
|Agriculture||||||
|MAE|1.51|1.73|**1.33**|2.50|7.10|
|RMSE|4.31|4.78|**2.11**|8.23|17.59|

The results imply that the average annual elasticity estimates have the highest predictive power, by at least one of the performance measures utilized in the evaluation, for the manufacturing and services sectors. For agriculture, the decomposition-based approach has the highest predictive power, but with MAE and RMSE values closely followed by the average annual elasticity estimates. Therefore, across sectors the average annual elasticity estimates have either the highest or second highest predictive power among the methods tested. It is worth noting that using the full or more recent (short) sample to produce 17
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high-income, upper-middle and lower-middle-income countries in the sample show a reduction in employment accompanying growth in the agriculture sector. These results are in contrast with manufacturing and services.

Figure 6. Contribution of Job Creation and Improvements in Labor Productivity to GDP Growth <!-- Start of picture text --> Manufacturing<br>1.0<br>0.8<br>0.6<br>0.4<br>0.2 Job Creation<br>0.0<br>-0.2<br>-0.4<br>Services<br>1.0<br>0.8<br>0.6<br>0.4<br>Job Creation<br>0.2<br>0.0<br>-0.2<br>MOZ NPL ZWE UGA MLI TGO MDG GTM MDA LAO PHL YEM NGA BGD SLV BOL JOR IDN EGY MUS HRV CHN ECU JAM BLZ BGR CRI IRN IRQ PRY GRC FIN LTU CZE SVN ARE HUN HKG LVA DNK ISL SAU LUX PRT<br>MWI ETH MLI CAF NPL SLE TZA MDA IDN IND SDN VNM BGD HND MNG EGY COG LSO BOL SRB FJI ALB MUS MDV DZA ECU THA PER CRI NAM LTU DNK CHL CHE POL GBR SGP GRC SVN PRT SWE ISL NLD ESP<br><!-- End of picture text --> <!-- Start of picture text --> Agriculture<br>1.0<br>0.8<br>0.6<br>0.4<br>Job Creation<br>0.2<br>0.0<br>-0.2<br>-0.4<br>-0.6<br>-0.8<br>-1.0<br>NER GMB ZWE TGO MLI BEN COD ZMB SLV HND IDN NGA UKR COG SDN SWZ YEM LAO CMR MEX CHN NAM BLZ MUS DZA SRB CRI ECU PRY VEN CHL ESP JPN HUN CZE NZL AUT LVA ARE EST LTU NLD SVK KOR<br><!-- End of picture text --> 20
Note: Authors’ calculations using the average contributions of the employment and labor productivity to GDP growth (%Y=%L +%(Y/L)) over the period of 2000-2016 based on data from the WDI and ILO.

Second, Figure 7 shows the relationship between informality and value added per worker in the manufacturing and service sectors across countries. These data suggest that countries and sectors with higher labor productivity have less informality in both manufacturing and service sectors.

Figure 7. Informal Employment and Value Added per Labor in Manufacturing and Services <!-- Start of picture text --> Manufacturing<br> 70,000<br> 60,000<br> 50,000<br> 40,000<br> 30,000<br> 20,000<br> 10,000<br> -<br>0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00<br>Informal Employment Share in Total<br>Services<br> 35,000<br> 30,000<br> 25,000<br> 20,000<br> 15,000<br> 10,000<br> 5,000<br> -<br>0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00<br>Informal Employment Share in Total<br>Value added per labor, USD (2017)<br>Value added per labor, USD (2017)<br><!-- End of picture text --> To estimate the relationship between productivity growth and informality, the study follows the specification proposed by Maloney (2001) and runs the following regression model: 21
where is total employment in the economy, is informal employment, is value added, are IIddII control variables in sector jjHH _j_ and year _t_ , and and jjHH are sector time fixed effects, respectively. For this jjHH jjHH LL LL YY XX estimation informal employment includes those who are self-employed and non-paid employees. jj HH μμ λλ Capturing non-paid employees is important, especially for low-income countries, as their number can reach more than 60 percent of total employment in countries such as Liberia, as well as in the agriculture sector where labor activities for smallholder farming are spread among household members.<sup>27</sup> Harmonized labor force surveys collected by the World Bank are used to derive a measure of the informal sector for this estimation, substantially expanding the sample size and including countries outside the Latin America and Caribbean region, when compared to other studies. The sample includes 87 countries, where each country has more than one survey (see Appendix 1).<sup>28</sup> Labor productivity is measured as a logarithm of the value added of the sector in constant prices divided by the number of total (formal and informal) workers.

The empirical results in Table 2 are in line with the theoretical model and the stylized facts discussed above, showing that productivity growth is associated with a decrease in informal employment across the three sectors considered (agriculture, manufacturing, and services). These results are consistent with Maloney (2001) and Loayza and Rigolini (2011). Columns (1)-(3) provide estimates for equation (14) without control variables for the three sectors, while columns (4)-(6) show regression results with control variables, such as export and import shares in GDP, human capital, the dependency ratio, the real interest rate, the log of population, and country fixed effects. The regression results indicate that there is a significant negative relationship between labor productivity and share of informal employment across all sectors; and the results hold in both specifications – with and without the use of control variables. This confirms that enhancements in labor productivity are associated with reduced informality and higher

> 27 Informal employment using this definition differs from the ILO’s reported statistics on informality as the latter includes other types of informal workers, such as paid workers who work for either formal or informal sectors. Country-specific employment informality definitions are based on the data availability about job characteristics, such as having a remuneration, contract, and social security among other benefits. Since the coverage and quality of data vary significantly across countries, this paper uses a definition that can be harmonized across countries. While this definition does not capture the entire informal sector, it allows to measure informality in a comparable way; and improves over previous definitions used by other studies like Maloney (2001) and Loayza et al. (2011) which only include self-employed workers.

> 28 It is worth noting that in high-income countries, the self-employed are not necessarily part of the informal economy. Rather, they reflect economic inefficiencies or low productivity of workers that can be improved with more capital and economies of scale, ultimately affecting an overall aggregate productivity at the sector level.

22
earnings for workers who were previously employed in informal sectors. The estimated coefficients range from (-0.054) to (-0.077). The coefficients are slightly higher for agriculture compared to manufacturing and services in the specifications with control variables, reflecting large inefficiencies in the sector. Finally, these coefficients are lower than those reported by Maloney (2001) and Loayza and Rigolini (2011) for Latin American countries.

Table 2. Regressions results of informality and labor productivity.

||(1)<br>Agriculture|(2)<br>Manufacturing|(3)<br>Services|(4)<br>Agriculture|(5)<br>Manufacturing|(6)<br>Services|
|---|---|---|---|---|---|---|
|Labor|-0.07***|-0.077***|-0.055***|-0.064***|-0.054**|-|
|Productivity|(0.017)|(0.022)|(0.019)|(0.018)|(0.029)|0.056***<br>(0.021)|
|Controls|No|No|No|Yes|Yes|Yes|
|Country fixed|||||||
|effects|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|545|543|544|532|530|531|
|R-squared|0.37|0.34|0.55|0.04|0.104|0.25|
|# Countries|87|87|87|79|79|79|

Note: The first three columns show panel data regression results for each sector. The last three columns include control variables such as export and import as shares of GDP, human capital, the dependency ratio, the real interest rate and the log of population. Standard errors in brackets; * shows significance at 10% level, ** at 5% and *** at 1%.

# **6. Conclusion**

This paper provides a theoretical framework and empirical evidence to understand and estimate the relationship between economic growth and employment growth. The results show that employment growth resulting from economic growth varies substantially across developing countries and across sectors, reflecting considerable differences in production technologies and informality. The paper 23
estimates the GDP-employment elasticities following alternative methodological approaches and shows that elasticities estimated as the average ratio of annual percentage changes in employment and GDP based on historical data have superior performance in terms of predictive power among all the methods evaluated. The estimated elasticities using this approach imply that employment in manufacturing and services grows less than GDP growth in these sectors across developing economies, given consistent complementary growth in labor productivity. Employment growth in agriculture follows similar dynamics. Given the dominance of smallholder farming across many developing economies, large gains in productivity may imply decreasing employment as economic activity in the sector grows, and thus, a higher number of countries have negative GDP-employment elasticities for this sector. In addition, the paper shows that labor productivity growth is associated with declining informality, implying further welfare effects for households through access to better jobs.

The results of this paper have important implications for estimations of job creation and improvements in job quality resulting from policy or investment interventions to grow economic activity in different sectors across developing economies characterized by high informality. Conditional on data availability, further research in this area could explore the variation of GDP-employment elasticities for more disaggregated sectors and different kinds of occupations or worker profiles; as well as by documenting value-added employment elasticities at the firm level, for companies operating in different sectors across developing economies.

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