(2017) find significant inequalities within the household and a sizeable gender gap in consumption. Cells headed by males have approximately 77 percent higher consumption levels than cells headed by females. This difference is largely driven by inequality in non‐food consumption. In Senegal, De Vreyer and Lambert (2017) collect consumption section for small groups of individuals within each household, and use that data to create an individual measure of consumption, leading to findings of within household inequality, and evidence of poor individuals living in non‐poor households. Brown, Ravallion and van de Walle (2017) use nutritional status as a proxy for individual poverty and find evidence of undernourished women and children living in households that are above the bottom 40 percent of the poverty distribution (measured in terms of assets). Studies by Haddad and Kanbur (1990) and Bargain et al (2014) also find significant differences by sex when intra‐household analysis is done. While these findings are important, individual allocations within households are rarely observed. There is no standard process to establish equivalence scales, which means relying on strong assumptions to understand the sharing process (Klasen, 2004; Grown, 2014)<sup>10</sup> . A further method for assessing intra‐household welfare uses data on individual asset ownership for housing, land and business assets. Deere et al. (2012) show that the distribution of asset ownership by sex within households is much more equitable in some Latin American countries than a headship analysis would suggest. It might be the case that women in male‐headed households often own property, either in their own right or as joint property with their spouses. Brown, Ravallion and van de Walle (2017) use asset wealth in households as a proxy for household poverty, and nutritional status to measure individual deprivation of women and children. They find evidence of deprived women in non‐poor households measured by assets holdings. Still, the number of surveys which provide data that can be used for individual measures of poverty is quite limited and thus this approach can only be used in a handful of cases.

Although exploratory work is also ongoing to determine feasible means to measure individual‐level welfare in the context of nationally‐representative surveys, individual‐level welfare data is not yet available for a representative number of countries. Multidimensional approaches to poverty complementing standard measures with non‐monetary metrics have become firmly established as a complement to income‐based approaches. Alkire and Foster (2011) suggest a counting approach based on the concept of capabilities, which allow for both identifying the poor and aggregating the poverty measure. The most well‐ known measure is the Multidimensional Poverty Index (MPI) proposed by Alkire and Santos (2014), including three equally weighted poverty dimensions—health, education and living standards—that are captured by a number of indicators. Several studies have estimated multidimensional poverty among women using the multidimensional approach and comparisons with standard poverty measures (e.g. income and asset poverty) show that considering additional dimensions leads to substantially different results (Batana, 2013). However, these measures, with few exceptions, have focused on a household‐level assessment of poverty and hence, just like monetary approaches cannot provide information on intra‐ household dynamics (Gaddis, 2016). Furthermore, measuring multidimensional poverty at the individual level requires multiple decisions regarding dimensions, indicators, weights and cut‐offs, among others (Ravallion, 2011).

In summary, the present model of welfare measurement may hide differences in poverty by sex by not providing individual‐level information. While the ability to disaggregate or assign household spending or income to individuals is limited, the present paper explores how existing data, globally representative and harmonized at the global level, can shed light on gender differences by looking across the lifecycle and

> 10 The limited agreement on how to estimate equivalence scales (Deaton and Paxon 1996, Deaton 1997) results in a variety of equivalence scales that are not standard and lead to different results across countries, as poverty metrics are sensitive to the equivalence scale used. Varying the economy of scale parameter may change the relative poverty risks of different demographic subgroups of the population, notably the elderly and children (Lanjouw et al. 2000).

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