aggregate decomposition would remain valid as long as overlapping support and ignorability assumptions are tenable.

In exploring the existence and extent of the gender gap in a multivariate framework, the validity of findings largely depend on the plausibility of ignorability and zero conditional mean assumptions, i.e. the extent to which the identification strategy addresses possible unobservable household-/plot-level heterogeneity that jointly determines plot agricultural productivity and observable covariates, including whether a plot is managed by a female. While the most rigorous studies on the gender gap recognize the need for an instrumental variable strategy to deal with potentially endogenous observables, recovering instrumental variables that predict endogenous covariates without directly influencing the outcome is often not possible. A subset of the studies that are reviewed in Section 2 and that feature plot-level analyses have attempted to address the potential bias by controlling for direct measures of plot soil quality and household-crop fixed effects. With panel data, time fixed effects have also been included in the specifications.

Furthermore, to deal with the possibility that the male and female plots might be physically systematically different from each other along the dimensions that are finer than the observable variations in soil physical infrastructure and quality, an alternative identification strategy has been to rely on a spatial fixed effects estimator that allows for local neighborhood effect in unobserved land quality that could be correlated with the gender of plot manager and the other regressors. The spatial fixed effects are differenced out by modeling the difference between the plot-level outcome and the average of the outcome across plots from other households within a critical distance as a function of a vector of plot-/household-level variables that are differenced from their matched plot-/household-level neighborhood averages (see Goldstein and Udry (2008) for an example of this).

We lack, in our case, plot-level measures of soil quality, and the nature of farm organization as captured in the IHS3 data does not allow us to feature household fixed effects as a central piece of our empirical strategy. The average number of plots cultivated by Malawian farming households is 1.76, significantly less than the comparable statistics from West African settings that have largely informed the analysis of the gender gap in sub-Saharan Africa thus far. The managers identified across agricultural plots cultivated by a given household also correspond to the head of household in an overwhelming sample of households that report to be cultivating multiple plots. Specifically, there are only 109 households that cultivate multiple plots and exhibit within-household variation in terms of the gender of the plot managers, corresponding to 1.7 percent of our plot sample. Nevertheless, we attempt to lend as much support to the overlapping support, ignorability, and zero conditional mean assumptions as possible by relying on all available data and econometric methods at our disposal. These sensitivity analyses are presented later in Section 5.3.

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