# 1. Introduction

This paper focuses on methodological challenges related to collecting high‐quality individual‐ level employment data through household surveys in rural as well as urban areas of developing countries, paying close attention to issues that disproportionally affect women and young people. Employment data are key to studying a host of issues (Grosh & Glewwe, 2000; IFAD, 2016; World Bank, 2013, 2019). At the macro‐level, the structure of employment defines societies. Relatively slow but far‐reaching evolutions such as the structural transformation, the youth bulge, the demographic dividend and international competitiveness can only be monitored with aggregated labor market statistics that are consistent across countries and over time (Bick et al., 2018). At the micro‐level, employment determines one’s living conditions, as an extensive literature about livelihoods illustrates (Ellis, 1998; Haggblade et al, 2007).

In developing countries, individual‐level employment data are relatively scarce, especially in Sub‐Saharan Africa (SSA) (Headey et al., 2010; Szirmai et al., 2013; World Bank, 2013, p. 34). The ILO, for instance, reports that 37% of all year‐country observations of labor force participation in the period 1990‐2017 are based on actual data such as Labor Force Surveys (LFS), censuses or – in some cases – official government estimates (ILO, 2017). In SSA, the share of real observations to the total number of potential observations drops to 8.4%. Similarly, an ILO review showed that 160 of the 236 countries and territories worldwide (and 36 of 60 African countries) implemented an LFS or a household survey with a labor module from 2000 to 2010 (ILO, 2013b). At the same time, multi‐topic household surveys, such as the LSMS(‐ISA)<sup>4</sup> surveys supported by the World Bank, focus primarily on ‘final’ outcomes such as poverty and food consumption, and focus less on ‘intermediate’ outcomes such as employment (Oya, 2013).

Even when household or labor force surveys are readily available, the quality of the employment data remains a concern. Seemingly minor differences in survey features ‐ questionnaire design, the wording of a question, sampling, coverage, the structure and length of the labor module and its implementation in the field – can have substantial effects on labor market statistics. Fox and Pimhidzai (2013), for instance, show how the absence of screening questions in the labor module affects the employment rate in Uganda. Our analyses for Ethiopia and Malawi reveal that the employment rate can vary substantially depending on the survey instruments used to capture this indicator (Figure A in the Appendix).

Academics and statistical agencies alike have expressed concerns that the accuracy of labor market statistics of women and – although less often emphasized ‐ youth are more sensitive to survey design than labor market statistics of adult men (Gaddis & Palacios‐Lopez, 2018; Verick, 2018; Walsh, 2018). Compelling theoretical arguments support these concerns. First, women and young people engage more frequently in atypical employment (Barrientos et al., 2004). They are more likely to combine a job with other activities such as domestic work or

> 4 <mark>Living Standards Measurement Study‐Integrated Surveys on Agriculture.</mark>

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