Complementing this aggregate decomposition analysis, we provide a detailed decomposition of the mean gender gap, identifying the contribution of each observable covariate towards the endowment and structure effects. In contrast with the available microeconomic evidence, the detailed decomposition documents, within a partial-equilibrium framework, the relative quantitative importance of each factor in explaining the mean gender differential. This in turn facilitates further analysis to identify the causes of differences in key factors contributing to the gender gap so that the emerging insights could inform the design of policy interventions addressing the gender gap at its roots.<sup>11</sup> The second contribution of our study relates to the application of the decomposition methodology to distributional statistics beyond the mean through the use of recentered influence function (RIF) regressions. Since key contributors towards the gender gap might differ across farmer subpopulations of varying productivity levels, the RIF decomposition is a useful tool for tracing out the heterogeneity in constraints faced by farmers with different gender and productivity profiles, and thus, tailoring better targeted policies that are underlined by analyses that move beyond the “average” male vs. female farmer. Towards this end, we carry out (i) the aggregate decomposition of the gender gap at each decile of the agricultural productivity distribution, and (ii) the detailed decomposition of the gender gap at the 10<sup>th</sup> , 50<sup>th</sup> , and 90<sup>th</sup> percentiles. The paper also discusses the changes in the shares of endowment and structure effects as part of the aggregate decomposition, and the variations in the contributions of key factors towards the endowment and structure effects at selected percentiles.

Finally, the multi-topic and national-representative nature of our household survey data represents the third contribution to the literature on the gender gap in sub-Saharan Africa. The availability of geo-referenced household and agricultural plot locations also allows us to create synergies with geographic information system (GIS) data for the purpose of incorporating relevant geospatial variables into the modelling efforts.

There are five key findings from our study. First, on average, female-managed plots in Malawi are 25 percent less productive than those that are managed by males. Second, 82 percent of the mean gender gap is explained by the differences in observable covariates, i.e. the endowment effect. The direct pay-off to addressing market and institutional failures that affect men and women differentially is economically significant: ensuring that female plot managers have similar years of schooling and apply similar levels of non-labor agricultural inputs, including inorganic fertilizer, pesticides/herbicides, and improved and/or export crop varieties could reduce the mean gender gap by 50 percent. Deficiencies on female-managed plots regarding household adult male labor input and access to agricultural implements are other key

> 11 For instance, if the researcher confirms that female managers, on average, have access to less inorganic fertilizer, and that the gender differences in inorganic fertilizer application is a key contributor towards the gender gap, it becomes crucial to understand why female managers have access to less inorganic fertilizer so that the policy interventions could target the underlying causes of this phenomenon.

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