# **I. INTRODUCTION**

Women comprise 50 percent of the agricultural labor force in Sub-Saharan Africa, but manage plots that are 20-30% less productive than male-managed plots (FAO 2011). This agricultural productivity gap contributes to income inequality between women and men. In some cases, the productivity gap is also partially driven by an inefficient over-allocation of inputs to male-managed plots, resulting in large aggregate productivity losses for the agricultural sector (Akresh 2008; Udry 1996). The FAO estimates that closing the agricultural productivity gap globally could increase agricultural output in lower-income countries by 2.5% to 4%, reducing undernourishment by 12% to 17% or 100 million to 150 million individuals (FAO 2011).<sup>1</sup> As sources of inequality and aggregate productivity loss, the country-level magnitude and drivers of the agricultural productivity gender gap (henceforth referred to as “the gap”) are of great interest to policy makers. Investigation of the gap typically takes one of two approaches: 1) Inter-household analysis that uses agricultural production in female and male headed households as proxies for agricultural production on female and malemanaged plots, and 2) Intra-household studies that use production on female and male-managed plots located within the same household as proxies for female and male-managed production in the overall sector.

Inter-household analysis commonly estimates the simple gap in mean yield between male and female households, and then tests whether the gap is driven by differences in resource endowment (distribution of resources) or return to resource endowment (technical efficiency).<sup>2</sup> The most common procedure for this test is to run a multivariate regression on the pooled sample of female and male-headed households and to observe whether there is a statistically significant correlation between gender of household head and yield after accounting for all observable endowments. If so, this remaining correlation is considered to result from a difference in technical efficiency between male and female headed households.

Another possibility is to estimate the technical efficiency of male and female-headed households using a stochastic production frontier and observe whether the mean difference in technical efficiency is statistically significant (Kinkingninhoun-Medagbe _et al._ 2010; Oladeebo and Fajuyigbe 2007).

Some inter-household analyses also test whether resources are allocated efficiently between male and femaleheaded households (allocative efficiency). One approach is to perform the above multivariate regression analysis with individual inputs, such as fertilizer or labor, as the dependent variable in place of yield. If there is a statistically significant correlation between input use and gender of household head after accounting for all observable characteristics, the input is considered to be allocated inefficiently between households. That is, given declining marginal returns to the input in question, redistribution of the input from male to female-headed households with the same characteristics but lower endowment of the resource in question would increase

> 1 It is also commonly argued that redistributing household resources from men to women would improve efficiency through the channel of more optimal investment in human capital of the next generation - such as child health, education, clothing and nutrition (Bank 2011).

> 2 For detailed reviews of the literature, see (Peterman, Behrman, and Quisumbing 2010; Quisumbing 1996)

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