Poverty assessments focus on households, never the individual. So, a person automatically gets classified as either poor or non-poor based on the poverty status of the household to which she belongs. But, do all experience the same deprivation and challenges within the household? Globally, female and male poverty rates\u2014defined as the share of women and men who live in poor households\u2014are very similar (12.8% and 12.3%, respectively, based on 2013 data). Even in the two regions with the largest number of poor people (and highest poverty rates)\u2014South Asia and Sub-Saharan Africa\u2014gender differences in poverty rates are quite small. This approach critically assumes everyone in the household shares equally in household consumption\u2014be they the father, child or daughter-in-law. By design, it thus masks differences in individual poverty within a household. In a report published by The World Bank, Poverty and Shared Prosperity 2018, data has been gathered at an individual level. From these findings, stark differences appear between men and women during the peak productive and reproductive years. The poverty rate of women aged 25-34 in Sub-Saharan Africa and South Asia is, on average, 5.5 percentage points higher than that of men (27.8% vs 22.3%). Factors such as age of marriage and childbearing, the presence of young children in the household\u2014and the related likelihood of women leaving (or not engaging in) economic activities because of the time they allocate to unpaid care and domestic work\u2014can be linked to the gender gap in poverty rates. These factors relate to an unequal distribution of power inside a household, which tends to favour men in many countries. However, data gathering at an individual level is not easy, due to both data and methodological constraints. Lack of data remains a critical problem in many countries\u2014particularly low-income and conflict-affected states. In addition, a renewed focus on intra-household individual-level data collection and methodological research in non-monetary dimensions, such as time-use, violence, access to services and assets is needed. Collecting this data, in turn, can support more effective targeting of social protection and broader development programmes.