As it can be seen, much of the literature on gender differences and poverty are based on specific countries or case studies, and global or regional studies are infrequent<sup>11</sup> . The goal of the present paper is to expand this discussion to the bulk of the developing world and have a more global picture of poverty and gender. At the same time, the study makes regional comparisons that shed further light on critical policy concerns.

# Data sources

This study uses the Global Monitoring Database (GMD), which is a collection of globally harmonized household survey data developed by the World Bank’s Poverty and Equity Global Practice. The GMD contains a combined sample of 104 household surveys containing records representing 6.8 million individuals from 89 developing countries. Full details on the background of the GMD are given in Castañeda et al (2018). A crucial feature of the GMD is that, in general, the welfare aggregates are the same as those used to compute the poverty estimates that the World Bank publishes via PovcalNet and the World Development Indicators.<sup>12,13</sup> These aggregates are based on a money‐metric measure of welfare, either household per capita consumption or income, depending on the concept used to measure national poverty in each country. The analysis in this paper uses those surveys in the GMD collected in 2009 or later in an attempt to balance the competing goals of increasing global coverage while minimizing error that arises from extrapolating poverty figures forward to 2013.<sup>14</sup> The present version of the GMD is missing several labor variables in Sub‐Saharan Africa. To rectify this, a process is underway to integrate an additional data set (the International Income Distribution Dataset) into the GMD. For this study, additional information on labor and education outcomes was merged from this additional dataset for 17 sub‐Saharan African countries.<sup>15</sup> It is important to note that regardless of the source, in several cases the labor information available is of insufficient quality to include the survey in the analysis. As economic empowerment is a key feature of household composition, particularly when the economic typologies of households is applied (see Section 4 below), surveys were discarded if reported labor market outcomes appeared inconsistent with other sources of data for the country.<sup>16</sup> For the 89 countries covered in the GMD a total of 18 were dropped due 11 One of the few examples of regional work include ECLAC (2016) report including the poverty femininity index for Latin America and the Caribbean.

12 Important exceptions are India and China. In China, the analysis is based on the China Household Income Project Data from 2013, which is publicly available. However, the poverty line has been determined such that the poverty rate matches the official World Bank estimates (See Castaneda et al, 2018). For India, the data in this study is taken from schedule 10 of the Indian National Sample Survey, because it contains information on labor market outcomes. As in China the poverty line is set at the percentile corresponding to the official World Bank estimates, which in India are derived from schedule 1 of the National Sample Survey. 13 The term welfare refers to either income or consumption per capita, depending on which measure is used by the country for their national estimates. For more information on how these welfare aggregates are constructed, see Ferreira et al (2015). 14 As the GMD is updated when new surveys become available and are harmonized, it is important to note the ‘vintage’ of the data set used. Specific results of this analysis could vary as new data sets are added to the GMD although it is not expected that the overall story would be affected. The poverty and gender analysis presented here is derived from the September 2016 version of GMD with estimates of poverty figures lined up to (extrapolated to 2013).

15 The countries are: Burkina Faso, Chad, Republic of Congo, Democratic Republic of Congo, Ethiopia, Guinea, Malawi, Mauritius, Niger, Nigeria, Rwanda, Senegal, Sierra Leon, South Africa, Sudan, Tanzania, and Uganda. The I2D2 data set has been used for previous work on gender and labor at the WB.

16 Surveys for five countries were discarded because information on each individual’s primary activity was either not present in the data or missing for more than 30 percent of the observations. Surveys from an additional two countries were discarded because, for more than ten percent of individuals, information on employment status was present even though the person’s primary activity 7