labor). They use recentered influence function decomposition to show that these results hold throughout the productivity distribution.

This analysis is performed on cross-sectional data, however, that does not include within-household variation in gender of plot manager. In that sense, the decomposition is still an arbitrary procedure in that it relies on the assumption that the mean of unobservable characteristics conditional on observable characteristics is equal to zero – the zero conditional mean assumption (Fortin, Lemieux, and Firpo 2010) - and it is not clear which characteristics must be included in the analysis for this assumption to hold.

To increase the likelihood that zero conditional mean holds, we introduce panel-data methods to the O-B decomposition of the gender gap in agricultural productivity to account for unobserved community, household and farmer characteristics.

A potential drawback of our approach is that it cannot separately account for the contribution of observable household and farmer-level characteristics. To address this limitation, we first estimate the relative contribution of these characteristics by reducing the specification to control only for unobserved regional characteristics. This approach, however, suffers from the same omitted variable bias the fixed effects were meant to obviate.

As a robustness check, we therefore decompose the household and community-season fixed effects extracted from our full specification into components accounted for by specific observable variables. We are therefore able to simultaneously control for unobservable variables at the person, household, and community levels and to quantify the relative contribution of observable household, person and community characteristics to the gap.

# **II. Ugandan Context**

The agricultural sector employs 66% of Uganda’s working population and accounts for one-fourth of its GDP (World Bank Development Indicators 2013). The population growth rate of Uganda is one of the highest in the world (the UN predicts the population will double by 2050 in the most conservative scenario).

But despite the sector’s clear importance to poverty reduction, inclusive economic growth and food security, agricultural productivity increases have stalled in recent years (2.2%) – below the population growth rate (3.2%), and well below the Comprehensive Africa Agriculture Development Programme (CAADP) target (6%). To address weakness in the sector, the government launched the 5 year Agricultural Sector Development Strategy and Investment Plan (DSIP) in 2010, which targets improvements in rural household incomes, food security, and malnutrition.

Women make up the majority of Uganda’s agricultural labor force (53%) and a higher proportion of female workers are employed in agriculture (76%) than are male workers (62%). Thus the gap in productivity between male and female managed plots is of meaningful concern both to GDP growth and to the distribution of income between men and women.

Using a 2003 cross section collected from 123 communities in 8 of Uganda’s 111 districts, (Peterman _et al._ 2011) estimate the size of the raw gender gap (the difference in mean value of crop production per acre between female and male farmers) for these areas to be 50%. This productivity gap remains statistically significant even 5