metadata: authors: 0: author: Beyer,Robert Carl Michael
1: author: Chocce Falla,Milagros Alejandra
2: author: Rama,Martin G.
count: South Asia
docna: 0: docna: Employment in South Asia : A New Dataset
docty: Policy Research Working Paper
subtopic: Labor Markets,Economic Growth,Industrial Economics,Economic Theory & Research,Employment and Unemployment,Food Security
teratopic: Industry,Macroeconomics and Economic Growth,Social Protections and Labor,Agriculture
abstracts: cdata!: This paper presents a new dataset of
            comparable employment indicators for South Asian countries,
            constructed from more than 60 primary data sources from 2001
            to 2017. The main contribution of the paper is to curate the
            information provided by individual respondents to censuses
            and surveys, in a way that is consistent across countries
            and over time. The usefulness of the dataset is illustrated
            by conducting a rigorous assessment of employment
            characteristics, of changes in employment over time, and of
            the short- and long-run relationship between economic growth
            and employment growth in South Asia. The exercise shows that
            agriculture still employs the majority of the working-age
            population across the region and, except in Sri Lanka, more
            than half of the employment is self-employment or unpaid
            family work. The paper also shows that employment rates are
            generally decreasing in South Asia, and that in some
            countries female employment rates are falling rapidly.
            Seasonal growth patterns are shown to affect the composition
            of employment, while non-seasonal changes in short-run
            growth affect the overall level of employment. The paper
            estimates that, in the long run, one percentage point growth
            of gross domestic product has led on average to a 0.34
            percent increase in employment.
repnme: repnme: Employment in South Asia : A New Dataset
display_title: Employment in South Asia : A New Dataset
listing_relative_url: /research/2019/03/30899232/employment-south-asia-new-dataset
topicv3: Agriculture and Food Security m1326205 435,Economic Growth m1326241 471,Jobs m1503931 2670,Self-Employment m1504000 2698,Employment Rate m1504079 2895
publishtoextweb_dt: 2019-03-13T00:00:00Z
historic_topic: Industry,Macroeconomics and Economic Growth,Social Protections and Labor,Agriculture
no_of_pages: 29
author_keys: 0: author_key: 1501628
1: author_key: 1559867
2: author_key: 95769
model_extractions: Policy Research Working Paper 8779 Employment in South Asia A New Dataset _Robert C. M. Beyer Milagros Chocce Martin Rama_ South Asia Region Office of the Chief Economist March 2019
Policy Research Working Paper 8779

# **Abstract**

This paper presents a new dataset of comparable employment indicators for South Asian countries, constructed from more than 60 primary data sources from 2001 to 2017. The main contribution of the paper is to curate the information provided by individual respondents to censuses and surveys, in a way that is consistent across countries and over time. The usefulness of the dataset is illustrated by conducting a rigorous assessment of employment characteristics, of changes in employment over time, and of the short- and long-run relationship between economic growth and employment growth in South Asia. The exercise shows that agriculture still employs the majority of the working-age population across the region and, except in Sri Lanka, more than half of the employment is self-employment or unpaid family work. The paper also shows that employment rates are generally decreasing in South Asia, and that in some countries female employment rates are falling rapidly. Seasonal growth patterns are shown to affect the composition of employment, while non-seasonal changes in short-run growth affect the overall level of employment. The paper estimates that, in the long run, one percentage point growth of gross domestic product has led on average to a 0.34 percent increase in employment.

This paper is a product of the Office of the Chief Economist, South Asia Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/research. The authors may be contacted at rcmbeyer@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
# **Employment in South Asia: A New Dataset**<sup>*****</sup>

**Robert C. M. Beyer**

## **Milagros Chocce**

**Martin Rama**

**Keywords:** employment, labor force surveys, South Asia, data harmonization

**JEL Classification:** J21, O17, C80

_____________________________________________________________________________________

* The authors thank Esha Chhabra and Zetianyu Wang for excellent research assistance and Urmila Chatterjee, Rinku Murgai, and David Newhouse, as well as participants of the _Regional Workshop on Jobless Growth in South Asia_ and of the _2nd SANEM-World Bank North America Discussion Forum,_ for comments and suggestions. This paper builds on work initiated for the Spring 2018 edition of _South Asia Economic Focus,_ entitled ‘Jobless Growth?’ (World Bank 2018a). All authors are with the office of the Chief Economist for South Asia at the World Bank. The corresponding author is Robert C. M. Beyer, at rcmbeyer@worldbank.org, 1818 H Street NW, Washington DC, USA.
# **1. Introduction**

Employment is at the center of multiple debates across South Asia. Are sufficient jobs being created to absorb the numerous young entrants to the labor force? To what extent is economic growth translating into employment growth? Is all employment growth happening in the services sector, or are manufacturing jobs growing too? How many of the jobs being created involve regular wage employment? And is the female share of employment declining with economic development?

If anything, the debate is made more acrimonious by the scarcity of reliable employment data. Highfrequency labor force surveys are still uncommon in the region. Employment data is typically generated out of statistical instruments that are better geared to measuring population, economic activity, poverty or human development. These instruments rely on different definitions of key employment indicators, making comparisons difficult. And often the data is released years after it is collected.

In this paper we try to address these shortcomings by generating comparable employment figures across South Asian countries from 2001 to 2017, out of over 60 primary data sources.<sup>1</sup> We curate the data directly from population censuses, household surveys, and labor force surveys, in a transparent and replicable way. Each individual respondent to these censuses and surveys is classified as employed, unemployed or inactive based on standard labor definitions, matched as closely as possible to the corresponding questionnaires. In addition, we include information on the type of the employment, the sector of activity, and the institutional sector – public or private.

Part of the paper is devoted to explaining in some detail the data harmonization process on which our dataset is built. We also assess differences between the resulting indicators and the readily-available national estimates and model-based estimates reported by the International Labour Organization (ILO). By going into this detail, we hope to reassure readers and potential users about the reliability of the dataset, while at the same time encouraging researchers to further improve it.

The paper also aims to show the usefulness of the dataset to analyze some of the questions recurrently arising in policy debates around the region. In that spirit, we provide a rigorous assessment of the employment characteristics, of changes in employment over time, and of the relationship between economic growth and employment growth in South Asian countries.

Ours is certainly not the first attempt to shed light on employment trends and characteristics in the region. For instance, the World Bank regularly conducts Jobs Diagnostics that discuss labor market issues in selected countries. These multisectoral studies aim at identifying country priorities regarding their labor markets, and often propose policy actions to address them.<sup>2</sup> The World Bank also regularly publishes reports dealing with broader economic issues but touching on employment trends.<sup>3</sup> Other government agencies and international organizations produce similar reports. And numerous academic papers examine specific topics in South Asian labor markets, from the impact of schooling on earnings to youth

> 1  We define South Asia as including Afghanistan, Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka. The number of observations varies for each country.

> 2  For example, a _Jobs Diagnostic_ for Bangladesh was recently published (Farole and Cho 2017).

> 3  In South Asia’s case, _Development Updates_ have recently been produced for Afghanistan (World Bank 2018b), Bangladesh (World Bank 2018c), Bhutan (World Bank 2018d), India (World Bank 2018e), Nepal (World Bank 2018f), Pakistan (World Bank 2018g) and Sri Lanka (World Bank 2018h).

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unemployment to female labor force participation. While the literature is vast, papers, studies and reports tend to build on one or just a few country-specific datasets. Cross-country studies on employment exist, but they often rely on national estimates or ILO model-based estimates, without scrutinizing the differences between the primary data on which these estimates are based.

An exception is the continuous World Bank effort to harmonize the surveys used in its _World Development Reports_ (WDR), starting from the 1990 WDR on _Poverty_ (World Bank 1990). More recently, Montenegro and Hirn (2009) constructed a harmonized dataset of a few labor market indicators using primary data from surveys – including 19 from South Asian countries – for the 2009 WDR on _Reshaping Economic Geography_ (World Bank 2009). Similarly, a global harmonized household survey dataset called the International Income Distribution Dataset (I2D2) was developed for the 2013 WDR on _Jobs_ (World Bank 2012, Gindling and Newhouse 2012).

The dataset presented and analyzed in this paper is built in a similar spirit. It was developed for the Spring 2018 edition of _South Asia Economic Focus_ (World Bank 2018a), which explored the relationship between growth and employment in the region. To our knowledge, our dataset is the only harmonization effort for South Asia that ensures full transparency and replicability by explaining the coding based on actual questionnaires. The resulting database, identified in what follows as the South Asia Employment Dataset, is available upon request.

# **2. Sources and indicators**

# _2.1. Available primary data sources_

South Asian countries have conducted close to 100 population censuses, household surveys and labor force surveys that include questions related to employment since 2001. Pakistan and Sri Lanka have the most frequent and easily accessible surveys. Pakistan has carried out the Pakistan Social and Living Standards Measurement (PSLM) survey and the Household Integrated Economic Survey every alternate year since 2004-05 and, except for a few years, the labor force survey (LFS) has been conducted on an annual basis between 2001 and 2017. Similarly, Sri Lanka has a triennial Household Income and Expenditure Survey (HIES) and an annual Labor Force Survey (LFS) which is nationally and quarterly representative.

In India, labor market information is collected using the National Sample Survey (NSS), which every five years includes a separate employment-unemployment module (Schedule 10). In the intervening years, basic employment information is gathered together with the household consumer expenditure module. The quinquennial surveys have large samples and are referred to as the ‘thick rounds’, whereas the intervening surveys are known as the ‘thin rounds’ because of their relatively smaller sample size. An annual Periodic Labor Force Survey (PLFS) was started in 2017 to provide more frequent and timely labor market data that is nationally and regionally representative.

Primary data from these various sources is not always publicly available. Table 1 lists all the relevant censuses and surveys conducted in South Asian countries between 2001 and 2017 and highlights in bold the 62 we got access to. Of these, 19 are from Pakistan, 17 from Sri Lanka, 9 from India, 8 from Bangladesh and 3 each from Afghanistan, Bhutan, and Nepal.

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<!-- Start of picture text --> Labor force<br>status<br>Employed Unemployed<br>Type of job Sector ofactivity Institutionalsector<br>[rate |<br>[Pate<br>pe Construction<br>employed<br><!-- End of picture text -->
2003-04 and 2010-11 (for Nepal) and the LFS (for Pakistan) count activities such as fetching water and collecting firewood and dung as employment, whereas sources in other countries do not. To ensure comparability, we excluded such activities in all countries.

The structure and framing of employment questions may vary even within the same primary source. In Afghanistan and Pakistan, for example, employment modules are different for males and females. In Bangladesh, the HIES asks employment questions in two different modules. A first set of questions is asked in the roster module while a second set appears later (module 5: A & B). This breakdown sometimes results in inconsistent responses.

A second difference in the definition of employment across data sources concerns the identification of the main activity of the respondent. For example, in the Bangladesh HIES each respondent lists several activities he or she was involved in, but only the activity absorbing the most time is registered. In India, on the other hand, the NSS makes a distinction between ‘principal’ and ‘subsidiary’ activities based on the duration of the work. We include only the ‘principal’ status in our dataset.

The recall period is a third important difference between data sources. For example, in Afghanistan the 2007-08 NRVA uses a recall period of one month when asking respondents whether they are employed, while the 2011-12 NRVA has a recall period of one week. The India NSS includes a weekly status, but it is constructed based on questions about time use over half-days. The yearly status, on the other hand, is identified based on a specific question to the respondent. The Pakistan LFS uses a recall period of one week, but the period is one month in the Pakistan HIES. In Sri Lanka, some of the surveys do not specify a recall period at all when inquiring about employment. The majority of Sri Lanka’s HIESs have information about the usual employment status, while the LFS reports the weekly employment status.

Fourth, the dates when information was collected vary, even within the same type of instrument. For instance, in Afghanistan and India (NSS thin rounds), most of the analyzed surveys did not follow a constant data collection pattern. Another example is the Bangladesh LFS, for which data for the 2002-03 round was collected during a short span (less than six months) while data for the 2005-06 round was collected during a whole year (from October 2005 to September 2006). If there is seasonality in employment, figures from surveys with different data collection periods are not strictly comparable.

In some cases, the dates when information was collected allow generating quarterly employment data, in addition to annual estimates. Censuses are conducted over a very short period of time, and most of the household and labor force surveys record the month when a respondent was interviewed. In these cases, individual observations can be mapped to specific quarters. However, this approach is only used in the case of surveys in which interviews were spread across space and over time in a relatively even way.

Because of these four differences, the way we defined employment varied across countries and sources, as shown in Table 2. We provide even more detailed information in Table A2 in the Appendix, where we report the exact language used in the corresponding questionnaires.

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**Table 2.** Definition of employment indicators across countries.

||**Employed**|**Employment type**|**Public/Private**|
|---|---|---|---|
|**Afghanistan**|<br>Those who worked for any organization or any individual, have<br>been involved in agricultural or non-agricultural work, or had<br>permanent or long-term jobs (NRVA 07-08 / ALCS 13-14), or<br>have found a job but it was going to start later, or had a job but<br>were temporarily absent, or that were apprentice (ALL), or did<br>any work for pay/profit/family gain (NRVA 11-12).|Regular: salaried workers.<br>Casual: day laborers.<br>Self-employed: self-employed, employers.<br>Unpaid: unpaid family workers.|-|
|**Bangladesh**|<br>Those who worked or worked for livelihood (ALL HIES), or for<br>at least one hour for pay, or who had a job but was temporarily<br>absent, or who have found a job but it was going to start later, or<br>had any kind of business, or did any work for wage, or helped<br>without being paid in any kind of business run by her/his<br>household (ALL LFS).|Regular: employee (agri. and non-agri.).<br>Casual: day laborer (agri. and non-agri.).<br>Self-employed: self-employed and employer<br>(agri. and non-agri.).<br>Unpaid: unpaid family workers.<br>(Unpaid worker not available for all HIES, not<br>available in the Census too).|Public: Govt. organization, Autonomous body, Public<br>mill/factory, Local govt.<br>Private: Private mill/factory, NGO, Household, Others,<br>Private office. Also coding private for self-employees<br>and employers.<br>(Not available in Census)|
|**Bhutan**|Those who did agricultural work, or who worked for money, or<br>had profitable business, or did any unpaid work (ALL).|<br>Regular: paid employee.<br>Casual: casual paid employee.<br>Self-employed: own account worker and<br>employers<br>Unpaid: unpaid family worker.<br>(Casual employment types not available for 2003)|Public: government office or State enterprise.<br>Private: cooperatives, collective farm, joint stock<br>company, Joint venture, private non-farm enterprise,<br>private farm, Foreign company/organ, Family business.<br>(Not available for LSS 2012)|
|**India**|Those who worked in a household enterprise as an own account<br>worker or as a helper, or as an employer, or as a regular salaried<br>worker, or as a casual wage labor, or in other types of work<br>(ALL).|Regular: salaried/ wage employee.<br>Casual: casual wage labor in public works, in<br>other types of work.<br>Self-employed: workers in H.H. enterprise (own<br>account worker), and employers.<br>Unpaid: helper in H.H. enterprise.|Public: government/public sector.<br>Private: proprietaries, partnerships, cooperatives,<br>household workers.<br>(Not available for the Thin-rounds)|
|**Nepal**|Those who worked as wage employed in agriculture/non-<br>agriculture, or as self-employed, or who had a job but was<br>temporarily absent; or who were receiving any pay or other<br>returns from a job or business while not at work (LFS), or who<br>have found a job but it will start later; or who worked between 1<br>to 105 hours a week; or who worked between 1 to 117 hours a<br>week (ALL LSS).|<br>Regular: paid employee.<br>Casual: daily payment.<br>Self-employed: operating own business or family<br>business with and without regular paid employee.<br>Unpaid: family worker.<br>(No unpaid information available for all LSS).|Public: government service, financial public<br>corporation, non-financial public corporation.<br>Private: NGOs/INGOs, private registered financial<br>company, private registered non-financial company,<br>private unregistered organization, other.<br>(Not available for LSS 2003-04)|
|**Pakistan**|<br>Those who did any work for pay/profit/family gain, or who had a<br>job or enterprise, or who worked at least one hour for earning<br>money, or who worked without a wage at family farm, business<br>(ALL PIHS); or who worked at least one hour for earning money<br>(ALL PSLM/HIES/ HIICS).|Regular: paid employee.<br>Casual: contract cultivator.<br>Self-employed: owner cultivator, share cropper,<br>livestock (only), and employers.<br>Unpaid: family worker.|-|
|**Sri Lanka**|<br>Those who were employed as usual status (ALL HIES), or who<br>were involved in an economic activity, or who worked for<br>pay/profit/family gain, or who had a job or enterprise at which<br>she/he did not work when interviewed, or who were engaged,<br>even for a few hours in paid employment, own account profitable<br>work or contributing family work as an economic activity<br>(including employer), or who already got a job and ready to<br>go/already plan for self-employment activity (ALL LFS).|<br>Regular: employees that didn't receive a daily<br>payment.<br>Casual: employees that did receive a daily<br>payment.<br>Self-employed: own account worker, and<br>employers.<br>Unpaid: unpaid family worker.<br>(No employment type for HIES 2002)|Public: government employee and semi-government<br>employee.<br>Private: other type of ownership.<br>(No public/private for HIES 2002)|

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# _2.3. Employment outcomes across sources_

Given the data disparities across countries and over time, it is not surprising that employment outcomes depend very much on the data source and do not necessarily coincide with those reported by the ILO. Figure 2 compares observations in our dataset with both national estimates and model-based estimates publicly available from the ILO. The focus is on employment rates, defined as the ratio of total employment to the working-age population, defined as all individuals aged 15 and above.

In Bangladesh, employment rates from the HIES are much lower than those from the LFS. This is in part because the HIES does not fully account for unpaid work while the LFS does. Another difference between the LFS and the HIES that may explain the disparity is the procedure for employment data collection. While the HIES asks about employment directly, in the LFS the employment variable is constructed from activity descriptions. There are also differences with the ILO data. The national estimate reported by ILO shows a decline of the employment rate from 2010 to 2013 but an increase from 2013 to 2016. The LFS, on the other hand, shows a steady decrease. For the last observation, the national estimate reported by the ILO is 3 percentage points above ours.

In India, the census delivers much lower employment rates than the LFS, a disparity that has been extensively discussed (e.g. Hirway 2012, Bardhan 2013, Shaw 2013, Papola 2014, Kasturi 2015). A first explanation for the gap is that the census classifies the population binarily into workers and non-workers while in the NSS a person’s ‘activity status’ can be either employed, unemployed, or not being in the labor force. Second, the NSS underestimates the total population compared to the Census. Third, there are differences in the definition of economic activity. And fourth, there are differences in the minimum time a person has to spend working before being considered employed.

In Sri Lanka, Nepal and Pakistan, the ILO model-based estimates are generally above both the national estimates and our own data points. There are also differences among the latter. In the case of Sri Lanka, the main difference between data sources concerns the recall period, as discussed above. In Nepal and Pakistan, it relates to the treatment of activities such as fetching water or collecting firewood.

Our employment breakdowns by type of job and sector of activity are not identical to those reported by the ILO either. To begin with, our breakdowns are more disaggregated as we differentiate between regular and casual wage employment, as well as between manufacturing and construction. A comparison between our dataset and ILO estimates thus requires some prior aggregation. But even then, some differences emerge for specific countries, as shown in Table A3 in the Appendix. For example, in Bangladesh, India, Nepal and Pakistan, self-employment accounts for a slightly lower share of total employment in our dataset than in the ILO estimates. Conversely, our estimates for wage employment are slightly higher than those of the ILO. But differences can be wide in some cases. In Bhutan, wage and salaried employment is 7 percent of the total in our data, compared to 20 percent for the ILO. The biggest gaps concern the breakdown of employment by sector of activity. In Nepal, for instance, agriculture accounts for 41 percent of total employment in our dataset, but for 60 percent according to the ILO. In Afghanistan, the industry sector accounts for 10 percent of employment in our case, but for only 3 percent in the ILO estimates.

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**Figure 2.** Employment rate by source.

<!-- Start of picture text --> Bangladesh Bhutan India<br>Percent Percent Percent<br>65 80<br>60 75 60<br>55 70<br>50 65 50<br>45 60<br>40 55 40<br>Modeled ILO estimate<br>Modeled ILO estimate National estimates (WDI)<br>Modeled ILO estimate Thin rounds: weekly status<br>National estimates (WDI) Thin rounds: yearly status<br>LFS: weekly status National estimates (WDI) Thick rounds: weekly status<br>HIES: weekly status LSS: weekly status Thick rounds: yearly status<br>Census: year<br>Nepal Pakistan Sri Lanka<br>Percent Percent Percent<br>95 60 60<br>90<br>55<br>85<br>55<br>80<br>50<br>75<br>50<br>70<br>45<br>65<br>60 45 40<br>Modeled ILO estimate Modeled ILO estimate Modeled ILO estimate<br>National estimates (WDI) National estimates (WDI) National estimates (WDI)<br>LSS: weekly status LFS: weekly status LFS: weekly status<br>LSS: weekly status (official report) HIES/PSLM: weekly status HIES: usual status<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br><!-- End of picture text -->

**Source:** International Labor Organization, World Development Indicators **,** South Asia Employment Dataset.

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# **3. Employment in South Asia**

# _3.1. Current employment structure_

For the overview of employment in South Asia, we rely on information only from sources that allow the construction of comparable data.<sup>4</sup> As shown in Figure 3, agriculture continues to employ a majority of the working-age population across the region. This is especially so in Nepal and Bhutan, but the share is high in Afghanistan and India as well. The second most important source of jobs is the services sector. Sri Lanka is the only country in the region where the services sector employs more people than agriculture, and in Bangladesh the two sectors are even. On the other hand, the share of the manufacturing sector is relatively low across all countries in the region, ranging from 3 percent in Bhutan to 9 percent in Sri Lanka. In many countries in the region, construction employs as many people as manufacturing.

**Figure 3.** Employment rate by sector of activity.

<!-- Start of picture text --> Percent<br>80<br>60<br>40<br>20<br>0<br>Nepal Afghanistan Bhutan Bangladesh India Sri Lanka Pakistan<br>Agriculture Manufacturing Service Construction<br><!-- End of picture text -->

**Source:** South Asia Employment Dataset. **Note:** Period of reference specified in footnote 4.

Regarding the type of jobs, it is important to keep in mind that regular wage employment is not the same as formal employment. It only means that there is a stable and predictable employer-employee relationship, and that the worker can expect to still have the job the following month. But even with this definition, only around one-tenth of the working-age population has a regular job across South Asia, as shown in Figure 4. The share is higher in Bhutan, but it still reaches only 16 percent. Even casual wage work, which is defined as a temporary employer-employee relationship, is relatively uncommon across the region. The corresponding share of the working-age population is highest in India (15 percent) and in Sri Lanka (15 percent), and lowest in Bhutan (11 percent).

Except for Sri Lanka, the sum of self-employment and unpaid family work accounts for more than half of employment across the region. Among the countries for which a breakdown is possible, unpaid family workers account for a quarter of the working-age population in Bhutan and a fifth in Afghanistan. At around one-tenth, the share is lower in India and Pakistan.

> 4  For the analyses in this section we use the following surveys: Afghanistan 2013/14 ALCS; Bangladesh 2005-2015 LFS; Bhutan 2003-2012 LSS; India 2004/05-2011/12 NSS-Thick, Pakistan 2005/06-2014/15 LFS; and Sri Lanka 2006-2015 LFS.

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**Figure 4.** Employment rate by type of job.

<!-- Start of picture text --> Percent<br>80<br>60<br>40<br>20<br>0<br>Nepal Afghanistan Bhutan Bangladesh India Sri Lanka Pakistan<br>Regular employee Casual employee Self-employed worker Unpaid family worker<br><!-- End of picture text -->

# **Source:** South Asia Employment Dataset.

**Note:** Period of reference specified in footnote 4. The survey design of Nepal did not allow us to separate unpaid family workers from self-employed workers.

The public sector accounts for less than one-tenth of total employment across South Asia with the exception of Sri Lanka, where the share attains 15 percent (Figure 5). Bangladesh is at the other extreme, with only 3 percent of the employment being in the public sector. However, a high share of regular wage employment is accounted for by the public sector in all South Asian countries. More than half of the regular wage jobs in Sri Lanka are in the public sector and the share is also high in India and Pakistan. At around 15 percent, Bangladesh has the lowest share of public sector employment among regular wage employment in South Asia.

**Figure 5.** Employment by institutional sector.

<!-- Start of picture text --> Percent<br>60<br>40<br>20<br>0<br>Share of total employment Share of regular jobs<br>Sri Lanka Pakistan India Bangladesh<br><!-- End of picture text -->

**Source:** South Asia Employment Dataset. **Note:** Period of reference specified in footnote 4.

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# _3.2. Changes over time_

Almost all South Asian countries have a very large number of young people reaching working age every year. Between 2005 and 2015, the number of South Asians aged 15 and above grew by 1.8 million per month, a trend that will only moderate gradually over time. In relative terms, the share of the working-age group in the total population increased by over 1 percent per year in Bhutan, and by around 0.5 percent in Bangladesh, India, and Pakistan (Figure 6). At the same time, the employment rate decreased on average more than 1.5 percent per year in Bhutan and India and by more than 0.5 percent per year in Bangladesh. Since income per capita grew considerably in all these countries from 2005 to 2015, some decline in the employment rates could be anticipated. However, the declines in employment rates are much larger than can be explained by increasing incomes per capita alone (World Bank 2018a). This contrasts with the experience of many East Asian countries like the Philippines, Vietnam, Malaysia, or Indonesia, for which the employment rate even increased between 2005 and 2015.

**Figure 6.** Changes in the working-age share and the employment rate from 2005 to 2015.

<!-- Start of picture text --> Annual change in percent<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>-1.5<br>-2.0<br>Working age rate Employment rate Predicted on GDP per capita growth<br><!-- End of picture text -->

**Source:** South Asia Employment Dataset.

**Figure 7.** Changes in employment rates from 2005 to 2015, by gender.

<!-- Start of picture text --> Annual change in percent<br>2<br>0<br>-2<br>-4<br>-6<br>Male Female<br><!-- End of picture text -->

**Source:** South Asia Employment Dataset.

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The decline in employment rates was not even for men and women. Between 2005 and 2015, the male employment rate in India decreased by 1 percent per year, while the female employment rate decreased by nearly 5 percent per year (Figure 7). In Bhutan, the male employment rate decreased at 0.2 percent per year, while the female employment rate decreased by over 3 percent per year. However, not all countries in the region display the same pattern. In Bangladesh, male employment declined by close to 1 percent per year, whereas female employment remained constant. And in Pakistan, female employment even increased at 0.6 percent per year – though from a very low level.

As economies develop, individuals usually move out of agriculture into more productive, non-agricultural employment. South Asia is not an exception to this pattern, but the pace of structural transformation varies across countries. Such transformation took place at a rapid pace in Bhutan, where there was a clear shift in employment from agriculture to services between 2005 and 2015 (Figure 8). While agricultural employment decreased by 20 percentage points, services sector employment increased by 8 percentage points. The transformation was slower in Bangladesh and India. The agricultural employment rate decreased by around 7 percentage points in both countries; but while manufacturing employment increased by 3 percentage points in Bangladesh, it did not vary in India. In Pakistan, structural changes in employment have been almost inexistent.

**Figure 8.** Change in employment rates by sector of activity from 2005 to 2015.

<!-- Start of picture text --> Percentage points<br>15<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>-25<br>Bangladesh Bhutan India Pakistan Sri Lanka<br>Agriculture Manufacturing Construction Services<br><!-- End of picture text -->

**Source:** South Asia Employment Dataset.

The region has not experienced a considerable creation of more regular employment between 2005 and 2015. As shown in Figure 9, the only strong expansion in regular employment happened in Bangladesh, where the share of regular wage jobs in the working-age population increased by 4.5 percentage points. Regular wage employment also increased in Bhutan, but only by 2 percentage points. In terms of other types of employment, casual work and unpaid employment declined across the region in the same period. In Pakistan, for example, casual employment decreased by almost 8 percentage points. In India and Bangladesh, unpaid employment decreased by 4 and 5 percentage points respectively.

Worldwide, a vast majority of jobs is created by the private sector. And the public sector can create only a limited number of jobs in countries where government revenue is modest, as is the case in South Asia. In Bangladesh and Pakistan, the share of public sector employment in the working-age population declined 13
by around 0.5 percentage points between 2005 and 2015 (Figure 10). In India and Sri Lanka, on the other hand, the share of public sector employment increased during this period. The change was a modest 0.25 percentage points in India, but a more substantial 1.5 percentage points in Sri Lanka, making it the only country with a substantial increase in the share of public sector employment.

**Figure 9.** Change in employment rates by type of job from 2005 to 2015.

<!-- Start of picture text --> Percentage points<br>10<br>5<br>0<br>-5<br>-10<br>-15<br>-20<br>-25<br>-30<br>Bangladesh Bhutan India Pakistan Sri Lanka<br>Regular Casual Self-employed Unpaid<br><!-- End of picture text -->

**Source:** South Asia Employment Dataset. **Note:** For Bhutan the comparison of unpaid family work between the two surveys is not comparable due to a reframing of the questionnaire.

**Figure 10:** Change in the employment share of the public sector from 2005 to 2015.

<!-- Start of picture text --> Percentage points<br>2.0<br>1.5<br>1.0<br>0.5<br>0.0<br>-0.5<br>-1.0<br>Bangladesh Pakistan India Sri Lanka<br><!-- End of picture text -->

**Source:** South Asia Employment Dataset.

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# **4. Economic growth and employment growth**

In South Asia, like in the rest of the world, politicians and researchers are concerned about job creation, and the kind of economic growth needed to absorb all the new young labor force entrants brought by the demographic transition. We use our dataset to estimate the short-term and long-term relationship between employment and growth to try to address this concern. In doing so, we note an important trade-off.

In the short term, growth can boost employment rates as greater labor demand pulls people out of unemployment and inactivity. Growth can also lead to better jobs, for example when farm employment is replaced by work in factories and offices. In the long term, on the other hand, growth could reduce employment rates. When comparing employment rates across countries with different levels of income per capita, a U-shaped curve emerges. Many of the poorest countries in the world have very high employment rates. In these countries, people start working young, and do not have the means to be unemployed or retired. Not only adult men, but also women, the young and the elderly are part of the labor force. As countries become richer and living standards improve, families can afford to keep their children in school longer, the ill and the disabled can stay home, old-age pension programs are put in place, and women may withdraw from the labor force. But this downward trend reverses at higher levels of income per capita. In richer countries a growing number of youths reach tertiary education, and they are keen to work in the field of their study (Goldin 1994, Mehrotra and Parida 2017). Higher wages, safer transportation and workplaces, and more easily available childcare also bring large numbers of women back into the labor force (World Bank 2018a). In assessing whether growth is jobless or not, it is therefore important to distinguish between the short- and long-run effect of economic growth on employment growth (World Bank 2018a).

Estimating the short-term relationship between economic growth and employment rates has been the subject of a vast literature in advanced economies. This short-term relationship has often been interpreted in connection with Okun’s Law, which posits that when growth accelerates above potential, the unemployment rate falls below its natural level (Lee 2000, Ball et al. 2017). In advanced economies, the consensus is that rapid economic growth reduces unemployment rates in the short term, while slowdowns are associated with increases in unemployment. The literature is much scanter in developing countries, partly due to data limitations. Ball et al. (2016) conclude that the relationship in developing countries is only half as strong as in advanced countries and varies considerably across countries. The weakness of the relationship in these countries is not surprising as many are too poor to afford being idle and unemployment is hence a poor indicator of the situation in the labor market.

Instead of analyzing unemployment, we correlate quarterly economic growth with quarterly changes in employment. Some sources, such as the Sri Lanka LFS, are explicitly designed to generate quarterly data. For other sources, the month of the interview can be relied upon to disaggregate annual data by quarters. This approach can be used for Bangladesh, India and Pakistan. Combining genuine quarterly employment data with constructed breakdowns by quarter, 123 observations on quarterly changes in employment can be generated. As for GDP data, both India and Sri Lanka publish quarterly series, whereas Bangladesh and Pakistan do not. For the latter two countries we used the Denton method to interpolate annual GDP with quarterly industrial production. The Denton method imposes the constraint that the interpolated series adds up to the annual GDP totals.<sup>5</sup>

> 5  The Denton method aims to achieve consistency between time series related to the same target variables but measured at different frequencies (Scholtus 2014).

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We report the short-term correlations between quarterly GDP growth and employment growth in South Asia in Table 3. The results suggest that seasonal variation in economic growth affects the composition of employment. With higher seasonal growth, self-employment decreases while employment in other types of jobs increases. But these changes net out and we do not find evidence of an overall increase in employment with economic growth if the latter is not seasonally adjusted. Conversely, seasonally-adjusted economic growth has no impact on the composition of employment, but it has an impact on overall job creation. With each additional annualized percentage point of seasonally adjusted quarterly growth, employment increases by 0.16 percent.

More distant points are needed to assess how much employment is created per percentage point of economic growth in the longer term. We follow a four-step procedure to this effect. First, we compute comparable employment rates for all observations in our dataset. Second, we apply these employment rates to consistent estimates of the working-age population, obtained from readily-available demographic series, to obtain employment levels. The advantage of using demographic series is that they are comparable over time. Third, we compute percentage changes in the resulting employment for all the pairs of observations available for each country. And fourth, all the percentage changes in employment levels are divided by the percentage change in GDP over the corresponding period.

**Table 3.** Correlations of quarterly changes in employment rates and GDP growth.

|**South Asia**|**Correlation coefficient**|
|---|---|
|total employment|-0.03<br>(0.7206)|
|Between non-seasonally adjusted GDP and<br>self-employment|-0.30***<br>(0.0006)|
|rest|0.24***<br>(0.0078)|
|total employment|0.16*<br>(0.0794)|
|Between seasonally adjusted GDP and<br>self-employment|0.14<br>(0.1302)|
|rest|0.04<br>(0.6353)|

**Note:** *** p<0.01, ** p<0.05, * p<0.10. Number of observations (n=123). **Source:** South Asia Employment Dataset.

Each of the resulting figures is an estimate of the elasticity of employment growth to economic growth, or the percentage change in employment for every percentage point of GDP growth. We summarize the distribution of these elasticities for South Asian countries in Table 4. On average, one percentage point of economic growth has led to an increase of employment by 0.34 percent. When only considering the data points furthest away from each other, the elasticity decreases to 0.19 as in the long-run rising GDP per capita negatively affects the employment rate (World Bank 2018a). Using these long-run estimates, Bangladesh has generated 110,000 jobs per percentage point of growth, India 750,000, Pakistan 200,000 and Sri Lanka 9,000.

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**Table 4.** Distribution of employment elasticities in South Asia.

|**South Asia**|**Elasticity**|
|---|---|
|25<sup>th</sup> percentile|0.10|
|Median|0.20|
|75<sup>th</sup> percentile|0.60|
|Mean|0.34|
|Long-run|0.19|

**Source:** All comparable surveys highlighted in Table 1.

We report employment elasticities by country, sector of activity and type of job in Table 5, while also providing a breakdown by gender. Except for Bhutan, faster growth in South Asian countries has translated into more employment. The employment elasticity has been very low in India, driven by a negative elasticity in the case of female employment. Conversely, the elasticity is high in Pakistan, driven by a positive elasticity for female employment. However, the sectoral breakdown shows that in the case of Pakistan, the growth elasticity of female employment in agriculture is huge, whereas it is negative in India.

For most South Asian countries, the employment elasticity was strongest for the manufacturing sector. While jobs created in the manufacturing sector benefitted mainly male workers, the opposite was true in the services sector. In Sri Lanka, for example, the employment elasticity of the services sector was zero for men, but 0.5 for women. The elasticity of regular wage employment was especially high in Bangladesh, which can partly be explained by the large growth of manufacturing employment in urban areas (Farole and Cho 2017). In all countries the elasticity of regular wage employment is higher for women than for men.

**Table 5.** Employment elasticities by sector of activity and type of job.

|**C**|**Y**|**S**||**El**||**Indu**|**stry**|||**Emplo**|**yment ty**|**pe**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**ounr**|**ears**|**urve**||**momen**|||||||||
||||||||||||**elf-**||
||||||**Agriculture**|**Manufacturin**|**g**<br>**Service**|**Construction**|**Regular**|<br>**Casual**|**employed**|**Unpaid**|
||||Total|0.23|-0.05|0.84|0.37|1.00|0.96|0.07|0.23|-0.26|
|Bangladesh|<br>2002 -<br>2015|LFS|Male|0.17|-0.18|1.09|0.34|1.01|0.84|0.05|0.11|-0.44|
||||Female|0.43|0.35|0.46|0.52|0.80|1.40|0.19|1.09|-0.13|
||||Total|-0.04|-0.26|2.37|1.07|1.26|n/a|n/a|-0.19|-0.34|
|Bhutan|2003 -<br>2012|LSS|Male|0.08|-0.21|3.04|0.98|1.10|n/a|n/a|-0.24|-0.30|
||||Female|-0.17|-0.30|1.41|1.32|2.91|n/a|n/a|-0.12|-0.38|
||||Total|0.07|-0.16|0.16|0.28|1.16|0.41|0.08|0.12|-0.28|
|India|2004 -<br>2011|NSS-<br>Thick|Male|0.18|-0.03|0.21|0.29|1.15|0.40|0.24|0.14|-0.15|
||||Female|-0.20|-0.38|0.01|0.26|1.25|0.45|-0.27|0.01|-0.40|
||||Total|0.86|0.81|1.24|0.68|1.47|0.67|0.89|1.23|0.59|
|Pakistan|2005 -<br>2014|LFS|Male|0.70|0.36|1.18|0.67|1.49|0.65|0.82|0.94|0.02|
||||Female|1.57|1.83|1.50|0.68|-0.41|0.78|1.36|4.07|1.28|
||||Total|0.21|0.09|0.17|0.15|n/a|-0.10|0.75|0.33|0.02|
|Sri Lanka|2001 -<br>2015|LFS|Male|0.16|0.06|0.12|0.04|n/a|-0.16|0.67|0.24|-0.29|
||||Female|0.31|0.13|0.23|0.52|n/a|-0.01|0.99|0.71|0.19|

**Note:** n/a: information not available. **Source:** South Asia Employment Dataset.

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# **5. Conclusion**

While the dataset presented in this paper certainly has room for improvement, the figures emerging from it seem meaningful and provide valuable insights on some of the employment issues more hotly debated in South Asia these days. The contribution of the dataset is to provide employment information that is curated in a systematic way from individual responses to over 60 primary data sources in seven countries from 2000 to 2017. The coding of the dataset is fully transparent and replicable, as we report how the different questionnaires have been used to generate information on the employment status, the sector of activity, the type of job and the institutional sector of all respondents. We show that employment outcomes are sensitive to data sources, even within the same country. But we also show that comparable data from our new dataset shed light on some of the ongoing debates in South Asia.

Thus, we show that the agricultural sector still employs the most people in all countries in the region with the exception of Sri Lanka. We also show that the employment share of the manufacturing sector is relatively low across the region, and in many countries is even matched by the employment share of the construction sector. Probably related to this pattern, only around one-tenth of the working-age population has a regular job across South Asia and even casual wage employment is relatively uncommon. Instead, self-employed and unpaid family work are the predominate forms of employment. In addition, a high share of regular wage employment is accounted for by the public sector in all South Asian countries.

The dataset confirms that the number of people at work in South Asia has not increased in line with the working-age population and that employment rates mostly declined during the last decade. This contrasts with the experience of many East Asian countries, in which the employment rate increased during the same period. The decline in overall employment rates was mostly driven by declining employment rates of women.

Last but not least, our dataset provides valuable insights on the relationship between economic growth and employment growth in South Asia, both in the short term and in the long term. We show that seasonal growth patterns affect the composition of employment, while non-seasonal changes in short-run growth affect its overall level. In the long-run, every percentage point of economic growth leads on average to an increase of employment by 0.34 percent.

The new dataset presented in this paper can be used to deepen these insights. For example, it would be interesting to analyze the determining factors of the employment structure and the drivers of structural employment changes. In addition, researchers may be interested in studying how the relationship between employment growth and economics growth is influenced by the institutional characteristics of countries, and the composition of their economic growth. We hope that our dataset will help provide reliable answers to these and other pressing employment-related questions debated in South Asia these days.

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