metadata: authors: 0: author: Cali,Massimiliano
1: author: Francois,Joseph
2: author: Hollweg,Claire Honore
3: author: Manchin,Miriam
4: author: Oberdabernig,Doris Anita
5: author: Rojas Romagosa,Hugo Alexander
6: author: Rubinova,Stela
7: author: Tomberger,Patrick
count: World
docna: 0: docna: The labor content of exports database
docty: Policy Research Working Paper
subtopic: Construction Industry,Common Carriers Industry,Food & Beverage Industry,General Manufacturing,Pulp & Paper Industry,Plastics & Rubber Industry,Labor Markets,International Trade and Trade Rules,Business Cycles and Stabilization Policies
teratopic: International Economics and Trade
abstracts: cdata!: This paper develops a novel methodology
            to measure the quantity of jobs and value of wages embodied
            in exports for a large number of countries and sectors for
            intermittent years between 1995 and 2011. The resulting
            Labor Content of Exports database allows the examination of
            the direct contribution of labor to exports as well as the
            indirect contribution via other sectors of the economy for
            skilled and unskilled labor. The analysis of the new data
            sets documents several new findings. First, the global share
            of labor value added in exports has been declining globally
            since 1995, but it has increased in low-income countries.
            Second, in line with the standard Hecksher-Ohlin trade
            model, the composition of labor directly contained in
            exports is skewed toward skilled labor in high-income
            countries relative to developing countries. However, that is
            not the case for the indirect labor content of exports.
            Third, manufacturing exports are a key source of labor
            demand in other sectors, especially in middle- and
            low-income countries. And the majority of the indirect
            demand for labor spurred by exports is in services sectors,
            whose workers are the largest beneficiaries of exporting
            activities globally. Fourth, differences in the labor value
            added in exports share across developing countries appears
            to be driven more by differences in the composition of
            exports rather than in sector labor intensities. Finally,
            average wages typically increase rapidly enough with the
            process of economic development to more than compensate the
            loss in jobs per unit of exports. The paper also includes
            the necessary information to build the Labor Content of
            Exports database from the original raw data, including stata
            do-files and matlab files, as well as descriptions of the
            variables in the data set.
repnme: repnme: The labor content of exports database
display_title: The labor content of exports database
listing_relative_url: /research/2016/03/26137986/labor-content-exports-database
topicv3: Manufacturing Sectors m1326521 1281,Trade m1326924 698,Jobs m1503931 2670,Labor Earnings m1504041 2738,Labor Demand m1583073 4422
publishtoextweb_dt: 2016-03-28T00:00:00Z
historic_topic: International Economics and Trade
no_of_pages: 59
author_keys: 0: author_key: 1228418
1: author_key: 430606
2: author_key: 1183206
3: author_key: 669937
4: author_key: 1040432
5: author_key: 1289455
6: author_key: 1380844
7: author_key: 1200048
model_extractions: WPS7615 Policy Research Working Paper 7615 The Labor Content of Exports Database _Massimiliano Calì Joseph Francois Claire H. Hollweg Miriam Manchin Doris Anita Oberdabernig Hugo Rojas-Romagosa Stela Rubinova Patrick Tomberger_ Trade and Competitiveness Global Practice Group March 2016
Policy Research Working Paper 7615

# **Abstract**

This paper develops a novel methodology to measure the quantity of jobs and value of wages embodied in exports for a large number of countries and sectors for intermittent years between 1995 and 2011. The resulting Labor Content of Exports database allows the examination of the direct contribution of labor to exports as well as the indirect contribution via other sectors of the economy for skilled and unskilled labor. The analysis of the new data sets documents several new findings. First, the global share of labor value added in exports has been declining globally since 1995, but it has increased in low-income countries. Second, in line with the standard Hecksher-Ohlin trade model, the composition of labor directly contained in exports is skewed toward skilled labor in high-income countries relative to developing countries. However, that is not the case for the indirect labor content of exports. Third, manufacturing exports are a key source of labor demand in other sectors, especially in middle- and low-income countries. And the majority of the indirect demand for labor spurred by exports is in services sectors, whose workers are the largest beneficiaries of exporting activities globally. Fourth, differences in the labor value added in exports share across developing countries appears to be driven more by differences in the composition of exports rather than in sector labor intensities. Finally, average wages typically increase rapidly enough with the process of economic development to more than compensate the loss in jobs per unit of exports. The paper also includes the necessary information to build the Labor Content of Exports database from the original raw data, including stata do-files and matlab files, as well as descriptions of the variables in the data set.

This paper is a product of the Trade and Competitiveness Global Practice 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://econ.worldbank.org. The authors may be contacted at mcali@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **The Labor Content of Exports Database**<sup>**1**</sup>

Massimiliano Calì (World Bank) Joseph Francois (University of Bern and CEPR) Claire H. Hollweg (World Bank) Miriam Manchin (University College London) Doris Anita Oberdabernig (University of Bern) Hugo Rojas-Romagosa (CPB) Stela Rubinova (Graduate Institute) Patrick Tomberger ( University of Bern )

**Keywords:** International trade and labor markets, labor compensation, jobs **JEL classification codes:** J2, J3, F16, F66

> 1 The corresponding author can be contacted at mcali@worldbank.org. The authors thank Paul Brenton, Jakob Engel, Mona Haddad, Tom Farole, Russell Hillberry, and Marinos Tsigas for useful inputs, and Alen Mulabdic for outstanding research assistance.
# **1. Introduction**

Trade – and exports in particular – play a crucial role in supporting jobs and increasing labor earnings, consistent with the fact that 90% of jobs in the developing world are associated with private sector activities (World Bank, 2012). This role of exports is particularly important as jobs and labor earnings are key drivers of poverty reduction and increased shared prosperity in many developing countries (Azevedo et al., 2013). Indeed export growth played a significant role in the dramatic reduction in poverty rates in East Asian countries in recent decades.<sup>2</sup> Despite this, systematic data on the contribution of exports to employment and labor earnings across countries and sectors is not readily available, particularly for developing countries. How much labor income is supported by automotive exports in South Africa? How much of that is via linkages to sectors providing inputs for automotive? How many domestic jobs does $1 million of processed food exports support in Brazil? Is that higher than in comparator countries? Answers to such questions are typically not readily available. Yet this information matters. First, such information would provide some reference to the relative importance of exports for employment and wages in specific countries and sectors. Second, it would shed light on the extent to which the interconnections between sectors matter for jobs and wages supported by exports. However for the majority of governments in developing countries today, it is difficult to know how many jobs and what size of earnings their countries’ exports support domestically, let alone the contribution of individual export sectors.

This paper helps fill this gap by computing the domestic value of labor (i.e. total wages) and the number of jobs contained in exports for a large sample of developed and developing countries and sectors for intermittent years between 1995 and 2011.<sup>3</sup> The resulting Labor Content of Exports (LACEX) database, consisting of two data sets on the labor value added and the jobs content of exports, decomposes the contribution of jobs and wages to exports separately through their direct and indirect components (including both forward and backward linkages), and by skilled and unskilled labor. Doing so illustrates the broader, economy-wide relationship between trade and labor market outcomes. With the LACEX database in hand, the paper then presents stylized facts of how the labor and jobs content of exports have changed over time, and potential drivers of this change by comparing across sectors and countries based on income level and region.

The paper is related to some recent efforts to quantify the labor and jobs content of exports across countries. Timmer et al. (2013) provide an estimation of the number of jobs directly and indirectly related to manufacturing output (rather than exports) using the World Input-Output Database (WIOD). Their focus is almost exclusively on high income countries, and the EU27 countries in particular. Using similar methods and the same data, Jiang and Milberg (2013) estimate the (direct and indirect) labor content of exports for a panel of mainly high income countries at the aggregate level. Stone et al. (2011) compute the factor content of trade, including also skilled and unskilled labor for OECD countries as well as for some emerging countries. Their factor content does not distinguish between domestic and foreign.

Other studies investigate the impact of exports on the number of jobs within countries. The US Department of Commerce (2010) has begun to quantify the number of jobs that exports support in the American economy both directly and indirectly. Kucera et al. (2012) investigate the impact of the recent crisis through exports on employment in India and South Africa by computing the employment content of sectoral exports, including also employment via household expenditure due to export-related earnings.

> 2 For example, the boom in Vietnamese exports to the United States following the US–Vietnam Bilateral Trade Agreement of 2001 led to a large reduction in poverty (McCaig, 2011).

> 3 In the remainder of the paper we refer interchangeably to total wages, total labor value added, total compensation to employees, and total labor contained in exports.

2
This paper expands on this literature by combining data on several cross sections of global input-output (IO) tables, social accounting matrices and exports to construct the labor content of exports, and employment data to construct the jobs content of exports. The first three types of data come from the Global Trade Analysis Project (GTAP), which covers a wide range of countries, including many developing countries. The employment data come from the International Labor Office (ILO) statistics. The combination of these data allows for a wider coverage of developing countries and sectors than previous studies. The labor content of exports data for 2011 cover 57 sectors and 120 countries, 74 of which are developing and comprise over 90% of all developing countries’ population (see Appendix 1 for the sector coverage and Appendix 2 for the country coverage). The sectoral and country coverage is more limited for the jobs content of exports due to the restrictions imposed by employment data availability. The data in this case comprise 73 countries in 2001 but only 66 in 2011 and are available for 11 sectors. The resulting database splits the labor content into skilled and unskilled, as well as into the wages paid and jobs used directly for the production of exports or indirectly via the production of inputs for export production. We also use gross output in place of exports to construct the labor and jobs content of domestic production. (See Appendix 3 for the steps needed to reconstruct the LACEX database from GTAP and Appendix 4 for the list of variables in the LACEX database.<sup>4</sup> ) The resulting LACEX database is a valuable tool to describe the extent to which exports support jobs and wages in an economy, in specific sectors and over time. On the other hand such data are not meant to be used for policy analysis, for example to assess the possible labor market effects of a policy promoting specific sectors’ exports. The data are a representation of equilibrium outcomes, where firms have made input choices, given technology, output prices, labor market institutions as well as other relevant policies, and input prices. Because of that, the data are ill suited to predict the effect of a policy that changed this equilibrium in one way or another.

The analysis using the new LACEX data sets documents a number of new findings. First, the global share of labor value added embodied in gross exports (LVAX share or the labor intensity of exports) has been declining since 1995, when the total value of wages was $46 for each $100 of gross exports while in 2011 the value was $40. This decline is driven by a drop in high income countries as the LVAX share has remained flat in middle income countries, and has increased in low income countries. Overall the number of jobs supported by gross exports (JobX) has declined more rapidly than the labor value added in exports, a result consistent with labor saving technological changes across countries. It has also declined more rapidly in middle income than in high income countries. This decline notwithstanding, our data suggest that exports supported over 20% of total employment for the sample of 66 countries with data available in 2011.

Second, the labor value added directly contained in exports has declined over the past two decades. This result is not a reflection of changes in the share of labor value added in output (LVAD), which in fact has remained stable over the period. This difference between the direct LVAX share and direct LVAD share is thus mainly due to the different changes in the composition of exports versus output. The direct LVAX share has declined in high income and to some extent in middle income countries and has been flat in low income countries. On the other hand the indirect LVAX share has remained flat through the period, slightly declining in high income and increasing in developing countries. This is consistent with two opposite trends over the past two decades: first, countries have overall become more integrated in GVCs, where an increasing share of foreign value added (including labor) is embodied within countries’ exports via imported inputs, which would reduce the indirect labor value added in exports; second, coordinating these inputs requires more intensive use of (mainly domestic) services, which would increase the indirect content of domestic labor. We find a decline in the total value added share in exports, while the total labor share in exported value added increased. In contrast, we find declines in both the direct labor share in exported

> 4 The LACEX data sets can be accessed at: <u>http://data.worldbank.org/data-catalog/lacex.</u>

3
value added and the direct value added share in exports. This suggests that the share of indirect labor value added in total exported value added has increased.

Third, in line with the standard Hecksher-Ohlin trade model, the composition of labor directly contained in exports is skewed towards skilled labor in high income countries relative to developing countries. However that is not the case for the indirect labor content of exports, where the skill composition is similar across income groups. This suggests that sectors providing domestic inputs to exports are less subject to the comparative advantage rule than the export sectors themselves. Thus skilled labor in developing countries appears to be relatively more important for the inputs to the exports than for the exports themselves.

Fourth, much of the indirect demand for labor spurred by exports is in services. Overall, the labor value added in services contained in exports (directly and indirectly) is larger than the entire value of gross services exports. This is consistent with total services value added (Francois et al., 2013). The ratio of the former to the latter has increased rapidly in developing countries over the past two decades, consistent with the increase in indirect labor demand from exports in developing countries. This confirms the increasing importance of services as enablers of export competitiveness in developing countries.

Fifth, manufacturing exports are a key source of labor demand in other sectors, especially in middle- and low-income countries. Each US$1 of manufacturing export supports a higher level of wages and more jobs in sectors providing domestic inputs to manufacturing than in the manufacturing sector itself. This pattern is particularly important in developing countries, where the labor intensity of manufacturing exports has increased markedly between 1995 and 2011 (the opposite trend for high income countries). This finding not only underscores the importance of the manufacturing sector’s backward linkages to support jobs and wages in developing countries, but it also highlights the relevance of skills in other sectors for the development of manufacturing exports. Chinese machinery and equipment exports are a case in point. These dramatically increased the demand for labor particularly in domestic input-providing sectors. As a result, the share of domestic wages per $100 of machinery exports almost doubled from $23 in 1995 to almost $40 in 2011.

Sixth, differences in the LVAX share across developing countries appear to be driven more by differences in the composition of exports rather than in sectoral labor intensities. Indeed the Middle East and Northern Africa (MENA) region has the lowest LVAX share, a result consistent with the dominance of capital intensive extractive exports with little linkages with the domestic economies. On the other hand, the labor intensity among developing countries is highest in East Asia and the Pacific (EAP), consistent with its export sector dominated by labor intensive manufacturing and - particularly in China - increasing linkages with the domestic economy. The share of direct labor value added in exports is highest in South Asia, a reflection of the large share of exports – particularly in India – concentrated in relatively skill intensive services sectors that exhibit a high direct labor content and few backward linkages.

Seventh, unlike the levels of the LVAX share, the change in the LVAX share between 2004 and 2011 was mainly driven by changes in sectoral labor intensities rather than by changes in the sectoral composition of exports. In particular, the sectoral labor intensity of developing countries’ exports generally increased – mostly the result of the increase in labor demand in input providing sectors. In most cases this effect prevailed over the compositional shift towards less labor intensive industries, which is consistent with the increase in minerals’ commodity prices during most of the period.

Eighth, the job intensity of exports is inversely related with a country’s income per capita, while the opposite is true for the wage intensity of exports; as countries develop economically, the labor value added share in their exports increases and the jobs share decreases. These contrasting results suggest that average wages increase rapidly enough with the process of economic development to more than compensate the loss in jobs per unit of exports.

4
The remainder of this paper is organized as follows. The second section discusses the methodology and data used to measure and labor and jobs content of exports to construct the LACEX database. The third section discusses how the labor content of exports has evolved globally, and the fourth section turns attention to the jobs content. These developments are explored across income groups and regions, sectors, as well as at the country level. The concluding remarks are in the fifth section of the paper.

# **2. Data and methodology**

We compute the labor value added and jobs content of exports on the basis of a panel of global IO and other aggregate data from the Global Trade Analysis Project (GTAP). GTAP represents a massive combined effort of international institutions and universities. Over time, the data set has grown to include more countries and more sectors. The latest version, GTAP 9, has data on 129 countries/regions and 57 sectors (Narayanan et al., 2012). Table A1 in Appendix 1 provides the description of the 57 GTAP sectors.

## _<u>Why GTAP</u>_

Although recent years have seen the emergence of different databases of global IO tables, we believe the GTAP data offer a balance between quality and country coverage, making it more suitable than other alternatives for our purposes. There are at least three other databases that could be used to compute the labor contained in exports: the World Input-Output Database (WIOD), the OECD-WTO Trade in Value Added (TiVA) Database and the UNCTAD-Eora Database. These data differ in their coverage of countries, sectors, and years as well as in overall accuracy.<sup>5</sup> WIOD and TiVA include a limited number of developing countries, although they account for most of world trade in value added, which is generated by high income countries. Because they comprise only high income or large middle income countries, these databases tend to have great levels of accuracy. On the other hand, the Eora database has the widest country coverage, with 187 countries in total, but only 25 sectors. However, the data at this stage do not ensure consistency in terms of aggregation and reconciliation of trade flows. This can generate problems of imbalances in the data, which can be particularly relevant for smaller developing countries.<sup>6</sup> GTAP data strike a balance between the accuracy of the data (including a rigorous submission process for national IO tables), the global consistency of the national account and trade data across countries, and the relatively wide coverage of developing countries. GTAP uses various data sources and estimates missing data. The GTAP IO data also include social accounting matrices that cover additional countries where no official IO tables are available. While the global coverage is smaller than Eora (120 versus 187 countries), the countries covered in the GTAP data account for over 90% of developing countries’ population.<sup>7</sup> In addition, GTAP disaggregates the factors of production into skilled and unskilled labor.

There are also certain drawbacks in using GTAP to compute such data, which should be acknowledged upfront. The quality of the data critically hinges on the underlying IO tables and social accounting matrices that each country produces autonomously. While GTAP researchers check and validate these data before using it, their quality cannot be fully observed. That can be a problem especially for certain sectors in specific countries. A second problem relates to the frequency of the data. As the production of GTAP data is complex (it needs global consistency and requires multiple data inputs and iterations), its frequency is not regular and the data suffer from non-negligible time lags. For example, the latest data currently available are from 2011. For these reasons, in the future it will be important to reconcile GTAP with the

> 5 See Tukker and Dietzenbacher (2013) for a review comparing the various databases.

> 6 The Eora developers explain that “for some sectors imbalance remains. Generally this imbalance is small, e.g. <5% for bigger economies, but can be up to 20% (IIRC) in the worst case sectors, e.g. manufactured goods from Mongolia, or similar sectors where we have poor or conflicting input data” (http://worldmrio.com/EoraFAQ.pdf).

> 7 In the longer term, as the database matures, and assuming it shows a pattern of regular updating, one may consider moving partially or fully to Eora for updating of the database developed herein.

5
various existing data, such as TiVA and Eora, from which the labor value added in exports can be extracted, and which can help increase the frequency of the data and improve their quality.

# _<u>Computing the labor value added content of exports</u>_

We work with multiple versions of the GTAP database, benchmarked to 1995, 1997, 2001, 2004, 2007 and 2011. To maintain backward compatibility, we start with the 1995 structure of sectors, and carry this forward in aggregation of more recent iterations of the database. To do so, we aggregate the 57 GTAP sectors into 24 sectors, which also allows ease of exposition of the results. (Table A2 in Appendix 1 provides the concordance between the 24 and 57 sectors.) We also generate the data with the 57 sectors from 1997 onwards. Our database excludes the regional aggregates in GTAP, resulting in 31 countries in 1995 expanding to 120 countries in 2011.

The IO tables in the GTAP data set allow one to exploit a form of social-accounting data – a variation on the social accounting matrix (SAM) where incomes are shown in the rows of the SAM while expenditures are shown in the columns (Hertel, 2013 and McDougall, 2001). The structure of the data provides a comprehensive and consistent record of national income accounting relationships between different sectors of an economy. It is based on a fundamental, general equilibrium principle of economics – every income (receipt) has a corresponding expenditure (outlay). The strength of this framework is that it provides a comprehensive record of the interrelationships of an economy, including intermediate and final demand linkages. For our purposes, it offers the advantage of linking consumption and external trade patterns explicitly to the inter-industry structure of intermediate demand. This allows for a fuller analysis than is possible when working strictly with IO tables.

We also make one adjustment to the data to ensure compatibility of the skilled-unskilled labor split over the years. All the versions of the GTAP data up to 2007 use only two types of labor – skilled and unskilled – while the latest version for 2011 (GTAP 9) classifies labor into five groups of occupational categories, such as officials and managers and service/shop workers (see Table A6 in Appendix 3). We checked that these categories cannot be reconciled with previous versions (see Figure A1 in Appendix 3), and instead exploit the fact that in previous versions of the data set the ratio of skilled-unskilled labor value added is constant over time for a specific country-sector pair.<sup>8</sup> Thus we apply the same skilled-unskilled labor value added ratio for each of these pairs to the 2011 data.

Since the GTAP database tracks interrelationships of an economy both for intermediate use and final demand (including exports), this complex structure of the database allows us to obtain the total value added content of final output and exports, including the compensation of employees’ component. This includes both the direct and indirect value added, based on the linkages of each sector with all other sectors in the economy. We can obtain both forward and backward linkages, where forward linkages count the value added of a given sector that goes into other sectors, while backward linkages count all the value added from other sectors that go into a given sector. Thus we can also account specifically for the direct value added attributable to labor, indirect value added (via backward or forward linkages), and total value added (the summation of direct and indirect value added via backward or forward linkages). We can further distinguish between the labor contributions from skilled and unskilled workers. Finally, we repeat the exercise for total final output as well as final output that is exported. This is explained in what follows more formally.

In order to obtain these labor value added measures, we first need to calculate intermediate multiplier matrixes that will be then used to multiply exports and final outputs. We begin by denoting a representation of intermediate and final demands as follows:

> 8 Results of this comparison are available upon request.

6
(1) Y=Z—AZ In equation (1), the term Y denotes a final demand vector, Z denotes a gross output vector, and A denotes a matrix of intermediate use coefficients, which can be calculated using data from IO tables. Equation (1) therefore defines final output with respect to intermediate input requirements.

The first matrix that is calculated is the widely used Leontief inverse matrix, also known as the multiplier matrix M. With some manipulation we arrive at the Leontief inverse matrix:

# �������<sup>��</sup> ����

The multiplier matrix M measures the inputs contained in a unit of final output. This M matrix contains both direct and indirect inputs. In particular, if we assign the sector indexes i,j to the M matrix, then a representative element of the M matrix, ���, gives the direct and indirect inputs (and thus receipts) of sector i linked to each unit (for example each dollar) of sector j receipts in the data. This implies real production activities measured by value of output. For our purposes, it provides a means to trace through these income flows, the flow of gross activity (and eventually labor value added) from intermediate to final goods and services, ostensibly across borders as well as sectors. Because linkages will vary by industry, each industry will be characterized by different multipliers.

Next, we need to calculate a diagonal matrix that measures the labor value added (compensation of employees) share of total gross output, which can be calculated using data from SAMs. To focus on labor value added, rather than gross activity, we note first that in terms of gross output values Z, some share of this involves labor value added within each sector. We define �<sup>�</sup> as the diagonal matrix indexed over i,j with diagonal elements equal to the compensation of employees’ shares of output Z. These shares can be split between types of workers (e.g. skilled/unskilled).

We then use M and �<sup>�</sup> to provide a breakdown of the flow of labor value added across activities in the form of the matrix V: Similar to the Leontief inverse matrix itself, the V matrix identifies the inputs of labor value added in each sector related to a unit of final demand.

Using the V matrix as a multiplier for exports and final outputs, one can obtain the total labor value added of exports and final outputs, respectively. If we multiply V by the diagonal matrix �<sup>�</sup> whose non-zero elements are the vector of final demand, the matrix yields a breakdown of economy-wide labor value added (both direct and indirect) (the primary component of Gross National Product on a source basis). Similarly, if we multiply V by the diagonal matrix �<sup>�</sup> whose non-zero elements are the national export vector, we can recover the labor value added content of exports X (both direct and indirect).

The G matrix and the H matrix thus give us the set of linkages, both direct and indirect, between value added across sectors for total domestic demand and export demand, respectively.

# _<u>Computing the jobs content of exports</u>_

We are also interested in the jobs content of exports. In order to compute that one would ideally need to split the compensation of employees between the number of jobs and the average wage. However this split 7
is not available in the underlying national account data used in GTAP. The next best thing was to use sectoral employment data that matches the GTAP data.

The United Nations Industrial Development Organization (UNIDO) provides such employment data at a relatively high level of disaggregation (4-digit ISIC sector) for a large number of countries and years but the data are only available for the industrial sector. Therefore we resort to the ILO statistics, which compile sectoral employment data for various countries and years. However the country and sectoral coverage is considerably more limited than that of GTAP, which reduces the number of countries and sectors for which the jobs content of exports is available. In particular, the prevailing aggregation of sectors in the ILO statistics consists of 11 sectors (see Table A3 in Appendix 1), which we match with the original GTAP sectors.

We replace the diagonal matrix of the compensation of employees in total output �<sup>�</sup> with the equivalent matrix of employment in output �<sup>�</sup> , which is obtained by the number of employees per total output Z. By replacing �<sup>�</sup> with �<sup>�</sup> in equation (3) we can compute the J matrix ���<sup>�</sup> � and then derive the counterparts of the _G_ and _H_ matrixes in terms of jobs contained in output and exports. Appendix 3 lists the steps needed to reconstruct the LACEX database from GTAP and Appendix 4 for the list of variables in the labor value added and jobs content of exports data sets.

One drawback of this approach is that the employment data are not always consistent with the compensation of employees data used to compute the labor content of exports. The original source of the former is typically national labor force surveys. These are often not easily reconcilable with national account data, which are the source of compensation of employees data. This discrepancy is particularly acute at the sectoral level also due to the different sectoral classifications of the two sources of data, ultimately making the labor and jobs content of exports data not strictly comparable at the sectoral level.<sup>9</sup> However, this method allows both the labor and jobs content of exports to be consistent with the national account and labor force survey data, respectively.

# **3. Global patterns of wages in exports**

## **3.1. Global patterns of labor value added across income groups and regions**

We start by discussing global patterns of total wages linked to exports, followed by some cross-country comparisons.<sup>10</sup> We examine the labor content of exports both in terms of their direct and indirect value added.<sup>11</sup> The labor content of exports is measured as nominal values in US$ (denoted LVAX), and as a share of gross exports (LVAX share), which is a measure of the labor intensity of a country’s or sector’s exports. We also use our measures of the labor content of domestic output (denoted LVAD).

**Error! Reference source not found.** shows that the global share of total labor value added in the value of gross exports (LVAX share) has been in constant decline since 1995. In 1995, the total (direct plus indirect) value of wages was $46 for each $100 of gross exports and in 2011 the value was $40.<sup>12</sup> The direct value

> 9 To check this we compute the sectoral average wage for laborers by dividing the total compensation of employees by the total number of employees. For several country-sector pairs this computation generates unreasonably high or low average wage values.

> 10 We do not provide global trends for jobs given the limited availability of the data for several countries.

> 11 Where indirect value added can take into account either the indirect contribution via backward or forward linkages to exports, we focus on the backward linkages in this report unless otherwise noted. Total value added accounts for both the direct and indirect value added. At the country level, the indirect value added is the same when considering either the forward or backward linkages.

> 12 According to the TiVA Database, the trade-weighted average share of foreign value added in gross exports across all countries in the world in 2011 is 25%. Combined with our estimates, this suggests the domestic capital value added share of gross exports is 35%.

8
added of labor is roughly half of the total value added of labor in exports and has declined more steeply than the indirect value added, going from $24 for each $100 of gross exports in 1995 to $19 in 2011. This drop in fact explains almost entirely the drop in LVAX share with the indirect share constant. These results are based on the export-weighted average LVAX share at the country level of a balanced sample of 31 mainly large economies for which we have data throughout the period (equivalent to the global sum of LVAX in global exports). The decline would be steeper if we included all countries in the database (results not shown here), implying that the composition of the sample changed to include more countries with relatively lower LVAX shares.

The decrease in the share of direct labor value added in gross exports is consistent with labor saving technological changes across countries, with a substitution away from labor to capital for domestic export production. Conversely the flat trend in the indirect labor content of exports is also consistent with two opposite trends: on the one hand, over the past two decades countries have become more integrated in global value chains (GVCs), where an increasing share of foreign value added (including labor) is embodied within countries’ exports via imported inputs. On the other hand, coordinating these inputs requires more intensive use of (mainly domestic) services, including financial services, transport and logistics, and other business services. The automotive sector in middle-income countries illustrates these concomitant trends well (Farole, 2016).

Within this long-term declining trend, the data also show a slight increase in the total LVAX share between 2007 and 2011. This is consistent with other findings that countries’ domestic value added contained in exports across all factors including labor has tended to decrease over time, but increased during the 200809 global crisis (Stehrer et al., 2012). Notably, however, the direct LVAX share continued to decline even between 2007 and 2011, implying that indirect labor value added became relatively more important to the labor content contained in countries’ gross exports during the crisis period.

**Figure 1. Direct and total LVAX share**

<!-- Start of picture text --> 0.50<br>0.45<br>0.40<br>0.35<br>0.30<br>0.25<br>0.20<br>0.15<br>0.10<br>1995 1997 2001 2004 2007 2011<br>Direct Total<br><!-- End of picture text --> Note: Based on export-weighted data for 31 countries that are present in the database every year since 1995, including: China, India, Sri Lanka, Vietnam, Argentina, Brazil, Chile, Colombia, Indonesia, the Republic of Korea, Morocco, Mexico, Malaysia, Philippines, Thailand, Turkey, Uruguay, República Bolivariana de Venezuela, Australia, Canada, Denmark, Finland, Germany, Hong Kong SAR, China, Japan, New Zealand, Singapore, Sweden, Taiwan, China, UK and USA.

The decline in the share of labor value added in exports in Figure 1 does not reflect a general decline in the share of labor value added in output. Figure 2 shows that the global share of labor value added in the value 9
of gross output (LVA share) has remained fairly constant since 1995, for both the direct and total LVA share. This finding suggests that the determinants of labor shares in exports may be divorced somewhat from those in output, potentially due to different changes in the composition of exports and output.

**Figure 2. Direct and total LVA share**

<!-- Start of picture text --> 0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>total LVA share direct LVA share<br>0<br>1995 1997 2001 2004 2007 2011<br><!-- End of picture text --> Note: Based on export-weighted data for 31 countries that are present in the database every year since 1995, including: China, India, Sri Lanka, Vietnam, Argentina, Brazil, Chile, Colombia, Indonesia, the Republic of Korea, Morocco, Mexico, Malaysia, Philippines, Thailand, Turkey, Uruguay, República Bolivariana de Venezuela, Australia, Canada, Denmark, Finland, Germany, Hong Kong SAR, China, Japan, New Zealand, Singapore, Sweden, Taiwan, China, UK and USA.

In Figure 3 and Figure 4 we split the world into three income groups to understand the drivers of these aggregate trends.<sup>13</sup> In order to gauge the evolution of our indicators over time we draw trend lines using data only for the 31 countries that are present in the database over the whole period 1995-2011, and group countries according to their 1995 income classification. The group of high-income countries is the only group that experienced a considerable and steady decline in both the direct and total LVAX share, and drives the observed declining trend at the global level. In the middle-income group both the direct and the total LVAX share remained rather stable. In the low-income group the total LVAX share increased, notably between 2007 and 2011, despite the direct LVAX share remaining stable. This suggests the indirect linkages have become relatively more important for exports in the low-income group, a result in line with the increasing importance of backward linkages in exports from China (Kee and Tang, forthcoming), the largest country in that group.

For a more accurate comparison of how the direct and total labor value added in exports varies across level of economic development, we use data for the full sample of countries in the database. Because country coverage has increased over the years, to avoid making erroneous comparisons over time we use data only for 2011 represented as dots, and group countries according to their 2011 income classification. In highincome countries the direct labor value added accounts for the greatest share of gross export values (20%). The low-income group has a similar share (18%) while it is the lowest in the middle-income group (13%). This pattern is broadly consistent with the relative export specialization of high-income countries in skilled labor intensive services and of low-income countries in unskilled labor intensive agriculture. On the other

> 13 Income group is defined according to the World Bank country group classification, found at https://datahelpdesk.worldbank.org/knowledgebase/articles/378834-how-does-the-world-bank-classify-countries. A country is low income in 1995 if its 1993 GNI per capita, atlas method (current US$) is $765 or less, middle income if greater than $765 and $9,385 or less, and high income if greater than $9,385. A country is low income in 2011 if its 2009 GNI per capita, atlas method (current US$) is $1,005 or less, middle income if greater than $1,005 and $12,275 or less, and high income if greater than $12,275.

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hand middle-income countries’ exports tend to concentrate more in less labor intensive extractive and manufacturing sectors. As shows below, however, middle-income countries have a relatively low direct labor intensity of exports even for the same sectors.

In terms of the total LVAX share, the high- and low-income groups are again similar (38% and 36%, respectively) and the middle-income group is at a lower level (32%). Other literature has found that emerging economies tend to export relatively more capital and import labor in value terms, with the opposite pattern for advanced economies (Stehrer et al., 2012). This also suggests that while the three income groups differ in the direct LVAX share, the indirect share via the backward linkages is similar between all groups (between 18% and 19%). Comparing the ratios of direct-to-total LVAX shares shows that the indirect linkages are relatively more important for exporting labor value added in middle-income countries than in low- or high-income countries.

**Figure 3. Direct LVAX share by income group**

<!-- Start of picture text --> 0.3<br>0.25<br>0.2<br>0.15<br>0.1<br>1995 1997 2001 2004 2007 2011<br>High, whole sample High<br>Middle, whole sample Middle<br><!-- End of picture text --> Note: Income group is held constant for the time series and is defined according to the World Bank country group 1995 classification. In the subsample of countries present in the database since 1995, low-income countries are represented by China, India, Sri Lanka and Vietnam; middle-income countries by Argentina, Brazil, Chile, Colombia, Indonesia, the Republic of Korea, Morocco, Mexico, Malaysia, Philippines, Thailand, Turkey, Uruguay and República Bolivariana de Venezuela; high-income countries by Australia, Canada, Denmark, Finland, Germany, Hong Kong SAR, China, Japan, New Zealand, Singapore, Sweden, Taiwan, China, UK and USA. Income group for the point comparison is defined according to the World Bank country group 2011 classification.

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**Figure 4. Total LVAX share by income group**

<!-- Start of picture text --> 0.55<br>0.5<br>0.45<br>0.4<br>0.35<br>0.3<br>0.25<br>0.2<br>1995 1997 2001 2004 2007 2011<br>High, whole sample High<br>Middle, whole sample Middle<br><!-- End of picture text --> Note: Income group is held constant for the time series and is defined according to the World Bank country group 1995 classification. In the subsample of countries present in the database since 1995, low-income countries are represented by China, India, Sri Lanka and Vietnam; middle-income countries by Argentina, Brazil, Chile, Colombia, Indonesia, the Republic of Korea, Morocco, Mexico, Malaysia, Philippines, Thailand, Turkey, Uruguay and República Bolivariana de Venezuela; high-income countries by Australia, Canada, Denmark, Finland, Germany, Hong Kong SAR, China, Japan, New Zealand, Singapore, Sweden, Taiwan, China, UK and USA. Income group for the point comparison is defined according to the World Bank country group 2011 classification.

Figure 5 shows the breakdown of the total labor value added share in exports for each income group in 2011. It illustrates differences in the importance of domestic sourcing as opposed to the direct labor value added in export industries, as well as differences in the skill composition of each component.<sup>14</sup> The group of high-income countries has the highest share of labor value added in exports, driven entirely by a relatively large share of direct high-skilled labor value added; this component is smaller by half in the middle- and low-income groups, which on the other hand have the highest component of unskilled labor directly contained in exports. This relationship is a direct implication of the Heckscher-Ohlin theorem, whereby a country will export goods that use its abundant factors intensively. This is also consistent with other literature that finds skills are a necessary factor for countries to increase domestic value addition in their exports (Miroudot and Squicciarini, 2015 and Farole, 2016).

On the other hand that does not apply to the indirect labor content of exports, where the skill composition is similar across income groups, with the skilled labor share around 7% and the unskilled labor share around 11% of gross exports. This suggests that sectors providing domestic inputs to exports are less subject to the comparative advantage rule than the export sectors themselves. Thus skilled labor in developing countries appears to be relatively more important for the inputs to the exports than for the exports themselves.

> 14 The blue parts of the bars represent the direct content and the green parts the indirect content of wages (i.e. the domestic wages paid to those sectors providing inputs for the exports). The darker parts of the bars represent wages paid to skilled workers while the lighter parts represent unskilled workers.

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**Figure 5. LVAX share in exports, by income group, in 2011**

<!-- Start of picture text --> 0.45<br>0.40<br>0.35<br>0.30<br>indirect unskilled<br>0.25<br>indirect skilled<br>0.20<br>direct unskilled<br>0.15<br>0.10 direct skilled<br>0.05<br>0.00<br>High Middle Low World<br>income income income<br><!-- End of picture text --> Note: Income group is defined according to the World Bank country group 2011 classification.

The export-weighted world average LVAX share suggests that unskilled labor is more important than skilled labor for either direct or indirect export production, and that unskilled labor is more important for direct exports than indirect exports. These findings are consistent across all income levels, though to a varying degree:

- In the high-income group the _skilled_ direct and indirect components are the same while the _unskilled_ share is higher for the direct value added than for the indirect value added. This suggests that unskilled workers are relatively more important in the export industries than in the industries that supply them with intermediate inputs.

- In the middle-income group the direct _skilled_ and _unskilled_ labor share is smaller than the indirect one. Nevertheless the proportion of unskilled to skilled is higher in the indirect part of the labor value added in exports than in the direct part. Thus unskilled labor is slightly more important in direct export industries than in the rest of the economy that supplies them.

- The direct and indirect shares of the _skilled_ labor value added are the same in low-income countries as they are in the middle-income group. However, the _unskilled_ direct component is larger than the unskilled indirect component. Therefore, the higher proportion of unskilled labor in export industries compared to their suppliers is much more pronounced than in the middle-income group.

These differences in LVAX shares across income countries do not necessarily extend to domestic production. The share of labor value added in domestic output, as for exports, continues to be the highest in high-income countries. However, unlike for exports, the labor value added share of domestic output is equal in middle- and low-income countries, with similarities in the skilled vs. unskilled and direct vs. indirect splits (Figure 6). As such, we argue that understanding these differences in the LVAX shares across countries is informative about how exports support labor in countries.

13
**Figure 6. LVAD share, by income group, in 2011**

<!-- Start of picture text --> 0.6<br>0.5<br>0.4<br>0.3<br>0.2<br>0.1<br>0<br>High Middle Low<br>direct skilled direct unskilled indirect skilled indirect unskilled<br><!-- End of picture text -->

# **3.2. Global patterns of labor value added across sectors and industries**

One natural follow up question is to what extent the LVAX shares are driven by the composition of the export basket towards more or less labor intensive sectors. In Figure 7 we start investigating this by breaking down the global share of total labor value added in exports by sector. We construct the indicators at the disaggregated level before aggregating into four macro sectors – manufacturing, services, agriculture and extractives (meaning that manufacturing inputs into other manufacturing sectors are still captured as indirect linkages) – and taking the trade-weighted average across the balanced sample of 31 countries.<sup>15</sup> The top panel of Figure 7 considers backward linkages, and the bottom panel forward linkages. The lefthand side figures consider all countries, and the right hand side figures only developing countries (based on 2011 income classification). The bars indicate the export-weighted labor value added of exports (LVAX), and the dots the export-weighted labor value added share in gross exports (LVAX share). The highest share of labor value added in gross exports is in the services sector, followed by agriculture and manufacturing exports, if considering either forward or backward linkages. This shows that services exports are more labor intensive than other sectors’ exports – in large part due to the direct labor value added in the sector, given that services tend to create weak backward linkages with the rest of the economy. At the other end of the spectrum with the lowest LVAX share are extractives, reflecting both a low direct labor intensity and generally little backward linkages in economies.

Once considering forward linkages, services are substantially more labor intensive than other sectors’ exports – for high income and developing countries alike. In 2011 the labor value added from domestic services sectors going into other sectors’ exports was greater than the value of services gross exports for the world as well as for developing countries. This confirms that the largest beneficiaries of exports’ domestic backward linkages are typically the services sectors. While this is consistent with total value added related to forward linkages of services exports in general (Francois et al., 2013), the result is even more evident in this context as labor is the main factor of production for services. The role of services as

> 15 Agriculture includes primary agriculture, forestry and fisheries; extraction includes energy extraction and minerals nec; and services includes electricity, gas and water, construction, trade and transport services, other private services, public administration, defense, health and education, and dwellings.

14
key input providers for other exports is more dominant in high income than developing countries, where the services sector lags behind manufacturing in terms of wages paid to produce exports.

Again the extractive sector instead uses the least amount of domestic labor to produce $1 of gross exports, consistent with the high capital intensity of mining activities. Interestingly, the low LVAX share for manufacturing (on the basis of forward linkages) suggests that not much labor in manufacturing sectors is used to produce domestic inputs for other export sectors, particularly in developing countries.

On average, the nominal value of wages supported by exports has been increasing between 1995 and 2004 across all sectors. The increase is most notable for developing countries in the manufacturing sector, when considering either forward or backward linkages. China is largely driving this trend, where the average developing countries’ LVAX increased 54-fold between 1995 and 2011 including China, and 7-fold excluding China (also aided by China’s large export weight).

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**Figure 7. The evolution of the total LVAX share in exports, by sector**

<!-- Start of picture text --> All countries, backward linkages Developing countries, backward linkages<br>100000 0.6 40000 0.45<br>35000 0.4<br>0.5<br>80000 30000 0.35<br>0.4 0.3<br>60000 25000<br>0.25<br>0.3 20000<br>0.2<br>40000 15000<br>0.2 0.15<br>20000 10000 0.1<br>0.1<br>5000 0.05<br>0 0 0 0<br>agriculture extractive manufacturing services agriculture extractive manufacturing services<br>Values Shares Values Shares<br>All countries, forward linkages Developing countries, forward linkages<br>90000 1.2 20000 1.4<br>80000 1 1.2<br>70000 15000<br>1<br>60000 0.8<br>50000 0.8<br>0.6 10000<br>40000 0.6<br>30000 0.4<br>0.4<br>20000 5000<br>10000 0.2 0.2<br>0 0 0 0<br>agriculture extractive manufacturing services agriculture extractive manufacturing services<br>Values Shares Values Shares<br>LVAX share LVAX share<br>LVAX (million USD) LVAX (million USD)<br>1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011<br>LVAX share LVAX share<br>LVAX (million USD) LVAX (million USD)<br>1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011 1995 2004 2011<br><!-- End of picture text --> Note: Based on export-weighted data for 31 countries that are present in the database every year since 1995. Income group is defined according to the World Bank country group 2011 classification. High-income countries are represented by Australia, Canada, Denmark, Finland, Germany, Hong Kong SAR, China, Japan, the Republic of Korea, New Zealand, Singapore, Sweden, Taiwan, China, United Kingdom, and United States.

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<!-- Start of picture text --> 0.50 High income Middle income Low income<br>0.45<br>0.40<br>0.35<br>0.30<br>0.25<br>0.20-<br>0.15<br><!-- End of picture text --> <!-- Start of picture text --> 0.10<br>0.05<br>0.00 + T r T T T 1<br>vee ft ££ SS SF SF SS<br>°RS # & & -a Bs & & PaS # ros &<br>&&&<br>Bdirect skilled ® direct unskilled<br>B indirect skilled @ indirect unskilled<br><!-- End of picture text -->
<!-- Start of picture text --> Share in exports<br>0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8<br>Electricity, Gas, Water I : : e@ : : : :<br>Construction [if °<br>Beverages and Tobacco Products ii °<br>Mineral Products nec [il °<br>Leather Products [ill °<br>Wood Products [iil °<br>Minerals nec J °<br>Wearing Apparel [US °<br><!-- End of picture text --> <!-- Start of picture text --> Paper Products, Publishing [IM °<br>Manufactures nec [ER °<br>PubAdmin/Defence/Health/Educat —H °<br>Metal Products i i °<br>Textiles [I °<br>Ferrous Metals [GR e<br>Energy Extraction [Ze<br>Metals nec REE °<br>Agriculture, Forestry, Fisheries ll °<br>Processed Foods [UCU °<br>Other Private Services a(REE °<br><!-- End of picture text --> <!-- Start of picture text -->
|<br>Chemical,Trade Rubber,and Transport Plastic ProductsServices —_—___—_—_TM" ®<br>Machinery and Equipment nec iS<br>0 200000 400000 600000 800000 1000000 1200000 1400000 1600000<br>$ million<br>B Direct LVAX skilled @ Direct LVAX unskilled<br>@ Indirect LVAX skilled @ Indirect LVAX unskilled<br>@ Total LVAX share in exports<br><!-- End of picture text -->
above 70% and _energy extraction_ with the share close to only 10%. By and large these shares confirm the importance of labor as a key factor in the direct production of services and agriculture and fisheries. The overall labor intensity of manufacturing exports tends to be slightly lower but the importance of labor value added from other sectors is generally higher.

The labor content of exports shows substantial heterogeneity across developing countries’ region as shown in Figure 10, which reports the export-weighted LVAX share in middle and low income economies by region in 2011. The share of total labor value added in exports was on average the lowest in MENA countries and the highest in EAP, on par with the world average though still below high-income economies. The result for EAP is entirely explained by the large share of indirect labor, which comprises around two third of the labor value in EAP exports, while the direct LVAX share in EAP exports is the lowest across all regions. Export sectors in the EAP region are relatively well connected with the rest of the economy, a result almost entirely explained by China. As the next section will clarify part of this effect is compositional: for example the specialization of MENA countries and of Europe and Central Asian (ECA) countries in capital intensive extractive sectors is less conducive to developing backward linkages than that of EAP countries in manufacturing exports. The labor intensity of exports – both through its direct and indirect components – is low in Sub-Saharan Africa (SSA) relative to most other regions. Coupled with the relatively low value of SSA exports, this result confirms that exports still support only a limited amount of domestic wages in the region.

**Figure 10. LVAX share in exports in middle and low income countries, by region, in 2011**

<!-- Start of picture text --> 0.40<br>0.35<br>0.30<br>0.25<br>0.20<br>indirect unskilled<br>0.15<br>indirect skilled<br>0.10<br>direct unskilled<br>0.05<br>direct skilled<br>0.00<br><!-- End of picture text --> Note: Income group is defined according to the World Bank country group 2011 classification.

# **3.3. Patterns of labor value added across countries**

While these general patterns are interesting, they mask a large heterogeneity among countries. Table 1 starts unveiling that heterogeneity by presenting measures of labor value added at the country level for the 15 countries in the sample with the highest and the 15 countries with the lowest total labor value added in exports (in nominal US$, shown in the first column). Columns two and three show the amount of labor 19
value added directly in the exporting industries by skilled and unskilled labor. The US is the country in the sample with the highest value of wages supported by exports at just over $1 trillion, roughly split between wages paid directly to produce the exports and wages paid by industries providing inputs to exports. Most of these wages paid to skilled workers are in input providing sectors (column 4), while the opposite is true for unskilled workers (column 5). In the US on average $100 of gross exports support $58 in wages (column 6), the highest labor intensity in the sample. However the labor value added in exports accounts for only 10% of domestic labor value in the US (column 7), one of the lowest shares in the sample. That is mainly the reflection of the relatively low values of exports relative to the economy.

China is the country with the second highest nominal LVAX with $795 billion of wages stemming from exports, around 21% of the domestic labor value added. Every $100 of gross exports support $42 in wages in China, more than two-thirds of which are from domestic backward linkages. This is the highest share of indirect LVAX in the sample and explains the increase in labor intensity of Chinese exports since 1995. Kee and Tang (forthcoming) show that the impressive domestic vertical integration of Chinese exports is explained mainly by the intermediate-input industry becoming more competitive and thus allowing for the replacement of imported inputs.

These backward linkages are also an important source of LVAX in countries such as Japan and Brazil. In other countries the importance differs across the skill groups. For example, in the U.S., Japan and Taiwan, China, backward linkages are relatively important for the skilled LVAX while the direct contribution of the exporting industry is more important for the unskilled LVAX. This suggests that exporting industries in these economies rely heavily on domestic skill-intensive services.

A majority of the top 15 countries have relatively high labor intensities of exports (which also tend to be high income economies). The bottom 15 are more heterogeneous, ranging from a high intensity in agricultural exporters, such as Benin ($46 paid in wages for every $100 of exports) or Malawi ($41), to a very low intensity in oil exporters, such as Brunei ($5) or Guinea ($13). The importance of exports as a source of labor demand varies in both groups. Exports play a negligible role in Nepal (responsible for 5% of domestic labor value added) and Brazil (8%) while they are very important in Togo (41%) and Taiwan, China (45%).

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**<u>Table 1. Labor value added in exports (LVAX), 2011, $ million, ratio and share of gross exports</u>**

|||||LVAXbr|eakdown||LVAX|Share<br>||
|---|---|---|---|---|---|---|---|---|---|
||||||R|atio|<br>h|of|I|
|Rank|Country|LVAX|Dir|ect|direct|/indirect|sare<br>in<br>|LVAX<br>in|ncome<br>group|
||||_skilled_|_unskiled_|_skilled_|_unskilled_|exports|LVAD||
|1|United States|1,000,205|109,347|380,793|0.68|1.09|0.58|0.10|HOECD|
|2|China|795,419|69,004|177,323|0.39|0.48|0.42|0.21|LM|
|3|Germany|667,647|163,843|228,359|1.25|1.59|0.43|0.35|HOECD|
|4|Japan|451,779|65,360|114,491|0.49|0.83|0.49|0.15|HOECD|
|5|United<br>Kingdom|276,966|55,780|113,022|1.47|1.61|0.43|0.21|HOECD|
|6|<br>France|270,351|58,713|70,809|0.91|0.93|0.38|0.20|HOECD|
|7|Italy|210,013|47,246|53,385|0.83|1.02|0.36|0.23|HOECD|
|8|Canada|168,277|32,726|53,155|0.92|1.13|0.38|0.17|HOECD|
|9|Korea, Rep.|155,684|43,822|30,716|0.88|0.98|0.27|0.32|HOECD|
|10|Spain|149,242|36,254|48,885|1.21|1.43|0.39|0.19|HOECD|
|11|Switzerland|144,794|38,428|45,765|1.23|1.56|0.44|0.33|HOECD|
|12|Netherlands|120,826|25,528|41,351|1.18|1.28|0.31|0.29|HOECD|
|13|Taiwan, China|117,504|19,743|39,104|0.61|1.50|0.32|0.45|H|
|14|Brazil|109,097|13,178|32,262|0.50|0.87|0.41|0.08|UM|
|15|Australia|105,658|14,094|30,419|0.57|0.84|0.44|0.14|HOECD|
|...<br>106|Madagascar|921|187|303|1.10|1.17|0.34|0.18|L|
|107|Senegal|855|194|308|1.28|1.54|0.24|0.18|LM|
|108|Malawi|758|103|320|0.95|1.41|0.41|0.27|L|
|109|Burkina Faso|754|38|440|0.80|1.93|0.24|0.20|L|
|110|Togo|716|72|434|0.90|3.31|0.40|0.41|L|
|111|Zimbabwe|674|139|244|0.99|1.62|0.26|0.17|L|
|112|Lao PDR|616|60|267|0.80|1.25|0.21|0.20|L|
|113|Armenia|599|150|223|2.13|1.42|0.32|0.15|LM|
|114|Kyrgyzstan|516|72|222|1.34|1.31|0.19|0.24|L|
|115|Benin|503|65|313|3.38|2.98|0.46|0.16|L|
|116|Mongolia|500|91|217|1.04|2.08|0.15|0.17|LM|
|117|Nepal|416|101|151|1.24|1.81|0.31|0.05|L|
|118|Brunei|403|123|165|2.32|2.62|0.05|0.10|H|
|119|Rwanda|390|96|164|1.41|2.69|0.39|0.11|L|
|120|Guinea|215|16|126|0.50|2.91|0.13|0.16|L|

Note: Income group is defined according to the World Bank country group 2011 classification. H-OECD = high income OECD country; H = other high income country; UM = upper middle income country; LM = lower middle income country; L = low income country.

In Table 2 we investigate to what extent LVAX mirrors the stages of economic development. To do so we regress direct (columns 1) and total LVAX share in exports (columns 2) on the log of each country’s GDP per capita measured at PPP current dollars. We find no systematic relationship between level of development and labor intensity of exports. However, once we include country and year fixed effects, a positive and significant correlation is unveiled for both direct and total LVAX share in exports (columns 3 and 4). As countries develop economically, their exports become more labor intensive. As there is no pretense of identifying any causality here, the relationship could also be expressed the other way around: 21
as a country’s exports become more labor intensive, the country’s rate of economic development increases. A 1% increase in a country’s GDP per capita is associated with approximately a 0.3% increase in the direct labor value added of exports as a share of exports and 0.1% increase in the total labor value added of exports as a share of exports. The smaller increase in the total as compared to the direct suggests that the direct becomes relatively more important along the development process, confirming the results in Figure 5. This positive relation is driven entirely by the more than proportionate increase in average wages in the process of economic development, which more than compensate the loss in jobs per unit of exports that we document in Section 4 below.

**Table 2. Labor value** **<u>added in exports (LVAX)</u>**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||ln(direct<br>LVAX share)|ln(total<br>LVAX share)|ln(direct<br>LVAX share)|ln(total<br>LVAX share)|
|ln(GDP per capita)|**-0.007**|**-0.011**|**0.266*****|**0.135****|
||(0.013)|(0.013)|(0.091)|(0.061)|
|Constant|-1.689|-1.022|-4.833|-2.832|
||(0.179)|(0.167)|(1.114)|(0.721)|
|Year dummies|Yes|Yes|Yes|Yes|
|Country dummies|No|No|Yes|Yes|
|R<sup>2</sup>|0.019|0.032|0.855|0.919|
|n|470|470|470|470|

Note: * indicates significance at the 10% level; ** indicates significance at the 5% level; *** indicates significance at the 1% level. Robust standard errors in parentheses.

Table 1 suggests a large heterogeneity across countries in both the LVAX share in exports and the LVAX share in domestic labor value added. These two shares comprise the two key dimensions of the contribution of exports to a country’s labor value added: intensity (US$1 of wages per US$1 of exports) and importance (share of domestic labor paid for by exports). Figure 11 uses these two dimensions to classify countries: the labor intensity of exports (y-axis) and the total domestic labor value added that is generated by exports (x-axis). Black lines indicate the median values for each indicator. Countries that are located on the right of the vertical line have a high share of labor value added that ends up being exported. Countries that are in the upper part of the figure have relatively high labor intensive exports.

The figure confirms the wide dispersion along both dimensions. Countries like Romania and Germany in the upper-right quadrant score high in both dimensions: their exports generate relatively high labor demand and at the same time are also a key source of demand for domestic labor. At the other end of the spectrum countries like Nigeria and República Bolivariana de Venezuela are in the diametrically opposite situation (not surprisingly two oil exporting countries). Countries like Brazil, Ethiopia and Ghana have relatively high labor intensity of exports but their exports explain a small share of domestic labor value added. Finally, in small and medium export oriented EAP economies like Vietnam, Thailand, Malaysia and Singapore, exports are responsible for a large share of the domestic wages but their labor intensity is below the median (a reflection of the relatively low backward linkages in these economies).

Overall, there appears to be no relationship between the labor intensity of exports and the total domestic labor value added that is generated by exports; countries whose exports are an important source of labor demand are no more likely to export products that are labor-intensive.

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<!-- Start of picture text --> ©<br>o # USA<br>* *<br># GHA<br># JPN<br>@ETH bl * *<br>2 + ® BRA # AUS #AReacaR * @ ® DEU * *<br>oo * * * . *<br>3. @ cARERE * # ROU<br>ba @ ITA<br>3 @inpe */DNe@gosZA) . Ps PO Py ry « .<br>a *<br>fe P * *<br>7) @eOL e ® « KOR<br>2 F te aoe e ° . * 6 #MYS<br>AN # VEN T oAfeEx « * * * oven* v *<br>o @ PHL * eTHA e ry #SGP<br>*<br>* * *<br>e**<br>*<br>.<br>@ * *<br>NGA :<br>Qo<br>o<br>0.0 0.2 0.4 0.6<br>LVAX share in total labor VA<br>® High ® Middle ®* Low<br><!-- End of picture text -->
<!-- Start of picture text --> e<br>oe °. e .<br>@ o° e ° °<br>ee e °<br>e@ e<br>oe ee<br><!-- End of picture text --> N N LSror = ~~yy~~ NL; = », ~~ee~~ L, i=1 Xi j=l i=1Xi jrN [es ~~we~~ <u>xj</u> N <u>Xj</u> N <u>Xoj</u> N <u>Xj</u> N <u>Xj</u> N <u>Xj</u> N <u>Xoj</u> DLj=l ~~es~~ <u>Xx1</u> =Dsij= [esor ~~e]~~ J xaed° =|j=l qj ~~Ss~~ X, e]j=l2 [esos Oj <u>eel]X,</u> * [2j=l Oj ~~bo~~ x, redj=l2, OjbsoXo
The first term is the labor intensity effect, the second term is the export composition effect.

Figure 13 plots each of these two components at the country level. The changes in the labor intensity of the exporting industry when the export composition is kept constant is on the vertical axis and the change in the export composition when the labor intensity of the exporting industries is kept constant is on the horizontal axis. The black horizontal and vertical lines divide the plot into negative and positive quadrants. For most countries the change in the LVAX share is explained more by the labor intensity than by the export composition component – or that the LVAX share is changing less because of compositional shifts in the export basket towards more/less labor intensity sectors, but more because the labor intensity of exports within sectors is changing. Furthermore, the majority of low and middle income countries lie in the left quadrants, suggesting a shift in the export composition towards less labor intensive industries. This is consistent with the commodity boom during the 2004-11 period. At the same time the labor intensity of individual export sectors increased for the majority of low and middle income countries. Most of the highincome OECD countries, on the other hand, had small negative changes in the export composition, and negative changes in the labor intensity of exporting industries (meaning they simultaneously shifted towards less labor intensity sectors, which were also experiencing declines in their labor intensity).

The trade-weighted aggregated effect across countries of each of these components is negative, suggesting that the decline in the global LVAX share was driven by declines in both the labor intensity of a sector’s exports, and a shift towards less labor-intensive export sectors. However, the component for the change in export composition was stronger. Only high-income countries saw declines on both of these margins, however. Taking the trade-weighted average, for low-income and middle income countries, the change in the export composition component continued to be negative, while the change in the export intensity component was instead positive.

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ALS = LS, — Ls, = ~~VARA VAS~~ _ ~~LAK VAXo~~ t ° VAX, X, VAX, Xp ~ _ ~~wa~~ LVAX, ~~VAX,~~ X, ~~LVAX,~~ VAX) ~~van)~~ Xy 1 ~~wake~~ VAX, ~~VAX,~~ X, ~~LVAX~~ VAX) ~~Varo~~ Xp
**Figure 14. Decomposition of the change in the LVAX share between 1997 and 2011**

<!-- Start of picture text --> Total LVAX share<br>-.15 -.1 -.05 0 .05<br>change in the labor share in exported value added<br>High Middle Low<br>Direct LVAX share<br>-.08 -.06 -.04 -.02 0 .02<br>change in the labor share in exported value added<br>High Middle Low<br>.1<br>.05<br>0<br>-.05<br>change in the value added share in exports -.1<br>.1<br>.05<br>0<br>-.05<br>-.1<br>change in the value added share in exports<br><!-- End of picture text --> Using a longer time horizon by calculating export-weighted changes in these margins between 1997 and 2011 for the balanced sample of 31 countries, we find that the decline in the total LVAX share is driven by a decline in the total value added share in exports, while the total labor share in exported value added increased. In contrast, the decline in the direct LVAX share is driven by declines in both the labor share in exported value added and the value added share in exports. This suggests that it is the increase in the share of indirect labor value added in total exported value added that explains the rise in the labor share in exported value added.

# **4. Global patterns of jobs in exports**

In this section we turn to the number of jobs that enter export production either directly or indirectly through input sourcing. The jobs content of exports is measured as the number of jobs (denoted JobX), and as a share of gross exports (JobX share), which is a measure of the job intensity of a country’s or sector’s exports. We also use measures of the job content of domestic production (denoted JobD) for comparisons.

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Table 3 presents the number of jobs directly used in export production, the total number of jobs used in export production, and the (direct and total) job intensity of exports. The top panel includes only the subsample of countries that are present every year since 1997, which more accurately captures the evolution of the indicators than the bottom panel, which includes the whole sample (with varying number of countries over the years). Until 2007 both the direct and the total jobs contained in exports were increasing while between 2007 and 2011 both decreased. The job intensity of exports, on the other hand, has been steadily decreasing since 2001. This is different than what we saw for the labor intensity of exports, which has been decreasing since 1995 but experienced a slight increase between 2007 and 2011 (Figure 1). Notably, the change in the job intensity was much more pronounced than the change in the absolute number of jobs – meaning that the number of jobs fell relatively more than any shifts in export values. Again, this is consistent with a transition to labor-saving technologies in production.

**<u>Table 3. Total number of jobs in exports (JobX) and the job intensity of exports</u>**

|1997|2001|2004|2007|2011|
|---|---|---|---|---|
|_Balanced sample - 32 countries_|||||
|**JobX in thousands**|||||
|Direct<br>31,092|38,962|39,850|40,662|39,386|
|Total<br>66,187|87,825|88,311|89,265|83,553|
|**Jobs per $1 mil. of exports**|||||
|Direct<br>11|13|9|6|5|
|Total<br>24|29|19|13|11|
|_Whole sample_|||||
|**Jobs per $1 mil. of exports**|||||
|Direct<br>14|17|11|7|6|
|Total<br>31|38|27|16|14|
|No. of countries<br>36|48|73|65|66|

Note: Based on export-weighted data for 32 countries that are present in the database every year since 1997, including: Australia, Austria, Belgium, Bulgaria, Canada, Switzerland, Czech Republic, Germany, Denmark, Spain, Estonia, Finland, Greece, Bosnia and Herzegovina, Hungary, Ireland, Italy, Korea, Rep., Lithuania, Luxembourg, Latvia, Mexico, Netherlands, New Zealand, Philippines, Poland, Portugal, Romania, the Russian Federation, the Slovak Republic, Slovenia, and Sweden.

Table 4 presents various indicators of the job content of exports but for the 15 richest and 15 poorest individual countries in our database in 2011 based on GDP per capita.<sup>18</sup> While the first two columns show the number of jobs aggregated across all sectors directly and indirectly embodied in exports (in thousands), the third column shows the ratio of the two and thus the relative importance of direct employment compared to the employment generated by backward linkages. The fifth and sixth columns show the total number of jobs per $1 million of gross exports and the share in the economy-wide employment (JobD), respectively. Thus these two columns allow comparisons across countries in terms of the job intensity of exports and how important exports were for generating jobs in total employment.

The data suggest that in 2011 the jobs supported (directly or indirectly) by the exports amounted to 160.7 million for the 66 countries in the sample, or 20.6% of total employment in these countries. The importance of exports as a source of employment does not vary systematically with level of income. Within the high18 The countries in the table are sorted based on the level of development measured by GDP per capita in 2011, starting with the most developed countries in our database.

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income group, it ranges between 12% of total employment in Australia and 49% in Ireland. Within the lowand low-middle-income it ranges from 8% in Ethiopia and 39% in Thailand.

In general countries with lower GDP per capita (and consequently lower wages) tend to have a greater job intensity of exports. For example, while the Philippines had about 105.5 total jobs embodied in $1 million worth of exports in 2011, Belgium had only about 3.4 jobs embodied in the same value of exports. This is in line with standard trade theory, which predicts that exports of countries where labor is relatively cheaper would be more labor intensive, in line with comparative advantage.

There is substantial dispersion in the ratio of direct to indirect jobs embodied in exports across countries. For some countries, it is close to or above one, and thus direct employment plays a relatively more important role in supporting exports. For some countries the ratio is as low as 0.5, which means that compared to the jobs generated in the export industries, twice as many jobs are generated through domestic sourcing of intermediate inputs by the export industries. All of the high-income countries have direct-to-indirect ratios close to or above 1, with the exception of Poland, Italy and the Republic of Korea. A simple regression analysis across countries reveals a negative and mildly significant relation between this ratio and per capita GDP controlling for country size and geographic remoteness,<sup>19</sup> two natural determinants of the extent to which exports may use domestically produced inputs.<sup>20</sup> This suggests that backward linkages for exports are still relatively more important in richer economies.

> 19 Results are not reported here and are available from the authors upon request. In particular the analysis regresses the ratio on a constant, the log of per capita GDP in 2005 PPP terms, the log of the population and the log of market potential measure as a proxy for the country’s remoteness. GDP and population are taken from the World Development Indicators, while the market potential measures are taken from CEPII database and are computed using the Redding and Venables (2004) or the Head and Meyer (2004) method.

> 20 For example, Luxembourg has a very high ratio because a small economy that is integrated into Europe will naturally buy its imports from overseas. Among other explanations, Australia has a fairly low ratio because it is geographically remote and production must use more upstream inputs that are locally sourced.

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**<u>Table 4. Jobs embodied in exports in 2011, in thousands</u>**

|Rk|C|Income||Jobsinex|ports<br>||Share of|
|---|---|---|---|---|---|---|---|
|an|ountry|group|Direct|Indirect|Ratio<br>direct/ indirect|Total jobs in $1<br>million of exports|JobX in<br>JobD|
|1|Australia|H-OECD|638.83|675.21|0.95|5.49|0.12|
|2|Austria|H-OECD|750.76|539.83|1.39|6.12|0.31|
|3|Belgium|H-OECD|824.26|579.37|1.42|3.42|0.31|
|4|Canada|H-OECD|1388.68|1452.60|0.96|6.37|0.16|
|5|Croatia|H-OECD|244.2|256.75|0.95|21.52|0.34|
|6|Czech Republic|H-OECD|877.92|867.80|1.01|11.14|0.36|
|7|Denmark|H-OECD|516.11|336.88|1.53|5.50|0.32|
|8|Estonia|H-OECD|141.45|86.78|1.63|13.09|0.37|
|9|Finland|H-OECD|296.19|256.04|1.16|5.99|0.22|
|10|France|H-OECD|2359.8|2105.64|1.12|6.32|0.17|
|11|Germany|H-OECD|5560.64|5340.57|1.04|6.90|0.27|
|12|Greece|H-OECD|477.14|270.48|1.76|11.52|0.18|
|13|Hungary|H-OECD|882.94|637.98|1.38|13.49|0.40|
|14|Ireland|H-OECD|447.62|451.30|0.99|3.83|0.49|
|15|Israel|H-OECD|340.92|266.49|1.28|7.34|0.20|
|...<br>52|Uruguay|UM|177.1|141.15|1.25|28.48|0.19|
|53|Venezuela, RB|UM|323.46|498.17|0.65|14.80|0.07|
|54|Armenia|LM|109.45|98.33|1.11|112.54|0.18|
|55|Ecuador|LM|581.51|679.81|0.86|64.19|0.20|
|56|Egypt, Arab Rep.|LM|1709.06|858.36|1.99|52.04|0.11|
|57|Guatemala|LM|939.09|615.17|1.53|118.65|0.29|
|58|Moldova|LM|150.92|136.69|1.10|103.49|0.25|
|59|Mongolia|LM|111.95|75.54|1.48|57.95|0.18|
|60|Morocco|LM|861.89|1140.37|0.76|66.84|0.19|
|61|Paraguay|LM|431.82|285.45|1.51|91.74|0.24|
|62|Philippines|LM|2758.88|4281.16|0.64|105.45|0.19|
|63|Thailand|LM|5105.1|10420.23|0.49|63.93|0.39|
|64|Ukraine|LM|2030.03|4128.07|0.49|77.22|0.30|
|65|Ethiopia|L|247.72|181.22|1.37|109.97|0.08|
|66|Zimbabwe|L|1489.62|434.30|3.43|735.12|0.35|

Note: Income group is defined according to the World Bank country group 2011 classification. H-OECD = high income OECD country; H = other high income country; UM = upper middle income country; LM = lower middle income country; L = low income country.

Figure 15 and Figure 16 plot the total number of jobs embodied in exports, split between direct and indirect jobs based on backward and forward linkages, respectively. The job content of exports is broken down by industry in 2011 (services, manufacturing, agriculture, and mining and energy extraction) aggregated across all countries in the database. It also presents the indicators at a more disaggregated level for eight services sectors. Our sample covers 66 countries in 2011, with total employment of 778.6 million, of which 30
73.8 million jobs are directly used to produce exports and 86.9 million are embodied in exports indirectly. The dots in the figures show the total number of jobs per $1 million of gross exports. These are measured domestically within countries then aggregated across all countries in the database.

Worldwide, manufacturing exports are by far the most important source of jobs embodied in exports, both directly and in total when considering backward linkages. It is followed by services, most notably transport and communication services, then agriculture. Direct manufacturing jobs are more important than direct services or agriculture jobs. The indirect component (or the backward linkages) is especially important for manufacturing, which means that the majority of jobs are embodied in the intermediate inputs that are used for the export of manufacturing production.

The ratio of the direct and indirect job content reflects the relative importance of the direct employment compared to the employment generated by backward linkages. Agriculture and most services sectors have ratios above 1, meaning they rely more on direct than indirect jobs to produce their exports. Put another way, these sectors support relatively fewer jobs by sourcing intermediate inputs for exports compared to the number of jobs their exports generate directly. Manufacturing, on the other hand, uses twice as many jobs indirectly through input sourcing compared to its direct jobs content.

Considering backward linkages, exports of the agriculture sector are substantially more job-intensive than manufacturing and services, with close to 60 total jobs created per $1 million of gross exports. In manufacturing it is 13 jobs and services 15 jobs, though there is significant dispersion across services sectors. In real estate, renting, and financial services, for example, less than 10 jobs are supported by $1 million of gross exports, compared to 58 jobs in other commercial services.

Services are instead the most important supplier of jobs embodied in exports if considering forward linkages, driven by the indirect component, followed by manufacturing. Forward linkages present the number of the sector’s jobs used in total exports of all other sectors, showing the importance of a sector as a supplier of jobs embodied in exports. For example in trade, sales and accommodation, relatively little jobs are in export production; the majority of jobs are embodied in exports through supplies of these services to exporting industries. Nevertheless, the distribution of the sources of jobs that are ultimately embodied in exports is much less skewed when considering forward linkages.

By comparing the indirect jobs considering forward versus backward linkages we can gauge the position of the sector in the supply chain. Services have much more jobs in exports embodied through forward linkages (sourced in services and used anywhere) than they use through backward linkages (sourced anywhere and used in services). This means that they are upstream in the supply chain, being a supplier of embodied jobs to other sectors during the production process, rather than a user of embodied jobs from other sectors (with the exception of transport services). The opposite holds for manufacturing, which is a much larger source of demand for jobs than it is a supplier of jobs embodied in exports.

Despite strong forward linkages with export activities, services jobs are supported less by exports than other sectors of the economy. The share in total domestic employment in the sector illustrates how important exports are as a source of jobs in each sector (if considering forward linkages for exports and for domestic production), or the importance of economy-wide exports as a source of jobs in the sector. Seventy-four percent of jobs supplied by mining and energy and 46% of jobs supplied by manufacturing are ultimately embodied in exports while it is only 13% in services.

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**Figure 15: Jobs embodied in exports based on backward linkages in 2011**

<!-- Start of picture text --> Number of jobs per $ million of exports<br>0 10 20 30 40 50 60 70<br>Electricity, gas and water<br>Construction<br>Financial services<br>Public services<br>Real estate, renting<br>Trade, sales and accomodation<br>Other commercial services<br>Mining and Energy<br>Transport and communication<br>Agriculture<br>Total services<br>Manufacturing<br>0 20000 40000 60000 80000 100000<br>Total Number of jobs supported by exports, in thousands<br>Direct jobs Indirect jobs Jobs in $ mil. of exports<br><!-- End of picture text -->

**Figure 16: Jobs embodied in exports based on forward linkages in 2011**

<!-- Start of picture text --> Share of employment<br>0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8<br>Construction<br>Real estate, renting<br>Electricity, gas and water<br>Public services<br>Mining and Energy<br>Financial services<br>Other commercial services<br>Transport and communication<br>Trade, sales and accomodation<br>Agriculture<br>Manufacturing<br>Total services<br>0 10000 20000 30000 40000 50000<br>Number of jobs, in thousands<br>Direct jobs Indirect jobs Share of total jobs in the sector<br><!-- End of picture text --> In Table 5 we further investigate the link between the level of development of a country and the job intensity of a country’s exports, similar to what we did for the LVAX share. The dependent variable in the first column is the log of the number of jobs used directly in exports per $1 million of gross exports (direct JobX share). The dependent variable in the second column is the log of the total number of jobs embodied in exports per $1 million of gross exports (total JobX share). The results indicate a negative and statistically significant correlation between the level of development measured by GDP per capita and the job intensity 32
of exports, regardless of whether we use country and year fixed effects (columns 3 and 4) or not (columns 1 and 2). Developed countries on average have lower job intensity of exports – in line with comparative advantages and with a cursory look what **Error! Reference source not found.** Table 5 suggests. However, this between-country effect is smaller than the within-country effect, which is captured in columns (3) and (4). A 1% increase in a country’s GDP per capita is associated with an approximately 1.44% decrease in the direct number of jobs per $1 million of gross exports and 1.64% decrease in the total number of jobs per $1 million of gross exports. Thus, as countries develop, the job intensity of their exports declines, the opposite result to the LVAX share above.

**Table** **<u>5. Number of jobs in $1 million of gross exports (JobX share)</u>**

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
||ln(direct<br>JobX share)|ln(total<br>JobX share)|ln(direct<br>JobX share)|ln(total<br>JobX share)|
|ln(GDP per capita)|**-1.283*****|**-1.371*****|**-1.444*****|**-1.644*****|
||(0.058)|(0.062)|(0.235)|(0.189)|
|Constant|8.123|9.683|9.659|12.236|
||(0.576)|(0.614)|(1.985)|(1.548)|
|Year dummies|Yes|Yes|Yes|Yes|
|Country dummies|No|No|Yes|Yes|
|R<sup>2</sup>|0.861|0.884|0.975|0.982|
|n|281|281|281|281|

Note: * indicates significance at the 10% level; ** indicates significance at the 5% level; *** indicates significance at the 1% level. Robust standard errors in parentheses.

# **5. Conclusions**

Jobs and labor earnings can be a potential driver of poverty reduction and increased prosperity in developing countries. A channel through which exports can have an impact on poverty is by supporting jobs and increasing earnings for those employed.

In this paper we developed a methodology to measure wage content embodied in exports across countries and sectors. To create this database, we combined different releases of the GTAP database from 1995 to 2011. Although recent years have seen the emergence of different databases of global IO tables, we believe the GTAP data offer a balance between quality and country coverage, making it more suitable than other alternatives for our purposes. The resulting data set includes national IO linkages through trade for a maximum of 120 countries and 57 sectors in 2011. We also use data from the ILO to construct the jobs content of exports, resulting in 66 countries and 11 sectors in 2011. The data sets offer a unique opportunity to examine the value added linkages between different sectors for both skilled and unskilled labor and jobs. We analyze not only the direct contribution of labor to value added contained in a given county’s exports but also the indirect contribution of labor through exports via other sectors of the economy.

In this paper we discuss some global patterns of labor value added, followed by cross-country comparisons both for wages and jobs. We also examine the wage and labor content of exports both in terms of direct and indirect value added. We show that the share of labor costs in the total value of exports has been somewhat declining over time both for skilled and unskilled workers, with the share of total wages declining somewhat more for unskilled than for skilled workers. A similar trend can be observed when looking at only the direct value added of jobs, without taking into account the linkages between sectors in the economy. Moreover, we showed that countries with lower GDP per capita (and consequently lower wages) have more labor embodied in their exports. This is also supported by the results of a simple OLS regression showing the correlation between the level of development and jobs embodied in exports. The results indicate that a 1% increase in GDP per capita is associated with an approximately 1.3% decrease in the number of jobs as a share of exports. This finding is also in line with standard trade theory predicting 33
that for countries where labor is relatively cheaper, exports would be more labor intensive given these countries’ comparative advantage being in more labor intensive products.

The agenda for further research is rich, starting from the need to explore the causal determinants of the patterns identified in the data. This paper has not gone further than basic correlations. More systematic analyses of the determinants of the labor value added and jobs in exports - possibly trying to exploit shocks exogenous to countries – would be welcome. Going forward it will also be important to reconcile the various existing data, such as GTAP, TiVA and Eora, from which the labor value added in exports can be extracted. This would allow filling the existing data gap (for example this data set has still only 60% of the country coverage) as well as improving the quality of the data.

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