We construct employment-related indicators directly from primary data sources, including population censuses, labor force surveys, and household surveys from 2001 to 2017. We use about one hundred censuses and surveys containing representative data on employment status (summarized in Table 3<sup>6</sup> ). The targets of the exercise are individuals aged 15 to 64—that is, the working-age population. However, the results do not differ significantly if considering all individuals above 15, as the elderly constitute a minor share of total population in South Asia. First, we categorize respondents into employed, unemployed, and inactive, taking into account survey-specific differences (details below). Then, we further classify employed individuals by age categories (15-24, 25-54, 55-64), type of employment contract (regular, casual, self-employed, and unpaid), sector of activity (agriculture, manufacturing, services, construction, and mining), location (rural or urban), and educational attainment (illiterate, primary, high school, more than high-school).

As shown in Table 3, Sri Lanka and Pakistan have the most frequent and easily accessible households and labor force surveys. Sri Lanka conducts its national Labor Force Survey (LFS) annually and a national Household Income and Expenditure Survey (HIES) every three years. Similarly, Pakistan has carried out the Pakistan Integrated Household Survey (PIHS)/Pakistan Social and Living Standards Measurement (PSLM) survey and the Household Integrated Economic Survey roughly every alternate year since 2001<sup>7</sup> . For 2015/16, Pakistan’s Bureau of Statistics launched the Household Integrated Income and Consumption Survey (HIICS) containing employment data. In addition, Pakistan has conducted its LFS almost every year during the study period considered for this paper.

Data was far less frequent for Nepal and Bhutan, for which we only had three surveys. We used data from the Nepal Living Standards Survey (NLSS). Although the NLSS has three rounds, only the last two were post-2000. Nepal conducts its LFS sporadically, with the latest available from 2008. For Bhutan, we used three waves of the Living Standards Survey (LSS) covering employment characteristics in detail.

In India, the National Sample Survey (NSS) collects labor market information by using a separate employment-unemployment module (Schedule 10) every five years. In the intervening years, the government gathers basic employment information, along with the household consumer expenditure module. The quinquennial surveys have large samples and are referred to as “thick rounds”, whereas intervening surveys are known as “thin rounds” because of their relatively smaller sample size. Overall, data is less frequent in India, and the most recent data point is the “thick round” NSS conducted in 201112. The annual Periodic Labor Force Survey (PLFS), first conducted in 2017, aims to fill this gap by providing frequent, timely, nationally and regionally-representative labor market data.

In Afghanistan we used the 2007-08 and 2011-12 waves of the National Risk and Vulnerability Assessment (NRVA) surveys and its successor Afghanistan Living Conditions Survey (ALCS) of 2013-14. These surveys are the country’s core instrument to collect household level data, and capturing development indicators

> 6Note that all the data used in this paper are identical to those used for employment rate estimates _in Jobless Growth, 2018_ . This was done to obtain consistent estimates for aggregate female employment rates. In addition, definitions for employment status (employed, unemployed, inactive), employment type (regular, casual, unpaid, self-employed or employer), sector of activity (agriculture, manufacturing, services, mining and construction), ownership structure (private or public) and location (rural or urban) are consistent with those used in _Jobless Growth, 2018_ . 7The PIHS was renamed PSLM in 2004.

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