attending school, to engage in temporary or seasonal work or to engage in informal activities. Atypical work is likely to be underreported in household surveys. Second, proxy reporting is common in household surveys and as the proxy respondent is often the male household head, data on youth and women may disproportionally suffer from ‘proxy response’ bias. The respondent, in fact, may accurately report his own activities, but under‐ or overreport activities of other household members, especially of household members who primarily engage in atypical work (Bardasi et al., 2011; Dillon et al, 2012). Third, many household surveys rely on the notion of the main and second activity and do not use appropriate screening questions to define ‘activity’ (Benes & Walsh, 2018c; Fox & Pimhidzai, 2013), which might again be problematic for household members engaging in atypical work such as contributing family members. Fourth, the sampling framework is more likely to miss individuals who (temporarily) migrate to other regions to find a job (Mueller & Chan, 2015).

Few empirical studies have examined the sensitivity of women’s employment to survey design and, as far as we know, not a single study has focused on young people (Koolwal, 2019). One robust finding in this literature is nevertheless that women, when responding to simple keyword question such as _‘Did you work in the last seven days?’,_ tend to report domestic work as employment, which upwards biases the female employment rate (Bardasi et al., 2011; Benes & Walsh, 2018d). Studies evaluating the sensitivity of women’s employment to other survey design features provide mixed results. Comparing self‐reporting versus proxy reporting in Tanzania, Bardasi et al. (2011) conclude that proxy reporting has no effect on the female employment rate, but substantially lowers the male employment rate. Exploiting variation within surveys, Comblon and Robilliard (2015) compare the effect of several survey design issues, including the wording of questions, on labor market statistics. Although the effects are substantial, they do not differ by sex. Interviewing the same population of women in the Arab Republic of Egypt, Langsten and Salen (2008) show that the female employment rate is substantially higher when an activity list is used rather than identifying workers with a single keyword question about their ‘main’ occupation. Finally, Gaddis et al. (2018) study recall error in labor input at the plot level in agriculture. Recall error substantially affects measurement of labor input for both male and female workers, but – contrary to their expectations ‐ the authors do not find larger recall bias for women.

This background paper aims to take stock of current approaches used in nationally representative household surveys to collect individual‐level employment data and attempts to evaluate how survey design features affect women’s and youth labor market statistics. It is structured along four questions: (1) what should we measure, (2) what do we measure, (3) how do we measure and, to conclude, (4) how and what will we measure in a changing labor market?

# 2. What should we measure?

Before delving into the specifics of household surveys, we need to agree on a common set of labor market indicators that are indispensable to study labor markets. While there are many 3