Although the estimates of gender differences in agricultural productivity (henceforth referred to as the gender gap)<sup>9</sup> across sub-Saharan Africa range widely from 4 to 40 percent, the majority cluster around 20 to 30 percent. The studies that compare productivity outcomes on female- vs. male-managed plots across and within households provide further support for the presence of systematic and persistent gender differences in agricultural productivity in the region (Akresh, 2005; Alene et al., 2008; Gilbert et al., 2002; Goldstein and Udry, 2008; Moock 1976; Peterman et al., 2011; Oladeebo and Fajuyigbe, 2007; Quisumbing et al., 2001; Saito et al., 1994; Tiruneh et al., 2001; Udry, 1996; Vargas Hill and Vigneri, 2011). The major reasons for the observed gender gap have been identified as gender differences in (i) access to and use of agricultural inputs, (ii) tenure security and related investments in land and improved technologies, (iii) market and credit access, (iv) human and physical capital, and (v) informal institutional constraints affecting farm/plot management and marketing of agricultural produce.<sup>10</sup> Regardless of whether the comparisons are made across or within households, the common thread across the relevant literature is that the gender gap disappears or diminishes significantly once the researcher controls for the factors discussed above.

Despite what could be perceived as a well-established evidence base on the extent and proximate causes of the gender gap across sub-Saharan Africa, the overwhelming majority of empirical studies on the topic have used data from small-scale surveys that were limited in terms of geographic coverage, topic, or attention to intra-household dynamics (or, in some cases, all three). With the exception of Akresh (2005), none of the above-referenced papers rely on nationally-representative survey data. Dearth of nationally-representative, methodologicallysound data collected in heterogeneous settings across sub-Saharan Africa has in turn inhibited the computation of externally-valid, rigorous estimates. Our study seeks to start filling this gap by providing a nationally-representative analysis of the gender gap in Malawi, using a different econometric approach than existing studies.

Our econometric approach is underlined by the use of an identification strategy that has been utilized extensively in labor economics since the seminal studies of Oaxaca (1973) and Blinder (1973), most notably in the analyses of the gender wage gap, union wage gap, and growing wage inequality. Specifically, we decompose the average difference in agricultural productivity between male-managed and female-managed plots into (i) the portion that is driven by gender differences in levels of observable attributes (i.e. _the endowment effect_ ), and (ii) the portion that is driven by gender differences in returns to the same set of observables (i.e. _the structure effect_ ). To our knowledge, this is the first time this method has been applied to understanding the gender gap.

> 9 Agricultural productivity is commonly proxied by major crop production quantity per hectare or gross value of crop output/profit per hectare.

> 10 Cultural roles that are assigned to males and females regarding domestic duties and those that may underlie the gender segregation in crop production (i.e. staple vs. cash crop cultivation, high-yielding vs. low-yielding variety cultivation, etc.) could be thought of as informal institutional constraints.

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