rather than capital intensive. Artisanal mining is characterized by traditional and often handheld tools and may be of an informal and/or illegal nature. Similarly to large-scale mining, there are taboos regarding women’s participation in underground work, yet women as well as children often engage in other ASM operations. In order to get a more complete picture of the effects of mining, we complement the main analysis using datasets from the U.S. Geological Survey (USGS) and the Center for the Study on Civil War (CSCW) on diamond mines. The USGS data covers a wider variety of mines and deposits beyond those of industrial size, but has the drawback of not including time-varying production levels. Similarly, the CSCW data includes all diamond mines, but no production levels.

# **2.2 DHS data**

We use micro data from the Demographic and Health Surveys (DHS). The DHS data are obtained from standardized surveys across years and countries. We combine the women’s questionnaires from all 67 surveys in Sub-Saharan Africa that contain information on employment and GPS coordinates. The total dataset includes 525,180 women aged 15-49 from 29 countries. They were surveyed between 1990 and 2011 and live in 20,967 survey clusters in 297 sub-national regions.<sup>6</sup> In Figure 1 we show the distribution of the mines and the DHS surveys used across Africa. The data cover large parts of Sub-Saharan Africa; Table A.1 in the Appendix shows the distribution of the sample by country. Table A.2 in the Appendix shows the distribution of the sample by years.

Definitions and summary statistics for our dependent and control variables are shown in Table 1, the occupational status ( _working_ ) relates to whether the respondent had been working during the last 12 months: 66% of the women responded affirmatively. Women who are not working may be engaged in child care, household production, or backyard farming. The information on employment is disaggregated by sector of activity in Table 1. Note that a woman can only belong to one sector, which she states as her main occupation. The main focus of this paper is three occupational categories, given their relative importance. These are agriculture (total 33%), sales (16.8%) and services (3.6%). However, all categories are reported in Table 2 and the results for all categories are also presented for the baseline regressions. The surveys include demographic variables, place of residence, education and religious affiliation. Regarding migration, women state in what year they moved to their current place of residence. However, no information is collected on previous place of residence or place of birth.<sup>7</sup> Table 2 shows labor market outcomes

> 6The cluster sizes range from 1 to 108 women. The mean number of women in a cluster is 25 and the median is 24. In most cases, the regions correspond to the primary administrative division for each country. Where coding into the primary divisions is not possible in the DHS data, due to natural regions being used instead (e.g., North-East, North-West, etc.), we use the existing natural regions. We largely follow Kudamatsu (2012) to make the coding consistent over the years. We complement the classification using Law (2012), which is available on www.statoids.com and which is the updated version of Law (1999). The regions are not of equal sizes; rather, they range from 30 to 22,966 sampled women. The average sample size of a region is 1,769 and the median is 1,201.

> 7Not all survey rounds include information on migration. In the sample, the year of the last move is available

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