managers are considered for the analysis, of which 1,277 are male (84.1 percent) and 241 are female (15.9 percent).<sup>15</sup> Households were visited at three points in time: (i) the first visit (post-planting) occurred in September and October 2011 to gather information on planting activities; (ii) the second visit occurred in November 2011 to collect information on livestock activities; and (iii) the final visit (post-harvest) occurred in February and March 2012 to gather information on harvest activities and production. The household and community questionnaires were also administered during the third visit. For agricultural households, detailed data were collected on crops grown, land size, inputs, production, yields (on selected crops) and other plot characteristics.

The empirical analysis presented here is done at the manager level. Broadly speaking, the manager is an individual within the household in charge of the management and decision making of a given piece of land – the _parcel_ .<sup>16</sup> The agricultural survey asks each manager to report the final use given to their agricultural production on a crop-by-crop basis. Using that information, a productivity measure<sup>17</sup> is generated by calculating the value of production per hectare during the last agricultural season. Gender differences in productivity will be estimated using this measure. Additional covariates that might explain different levels of productivity, including manager characteristics, labor and non-labor inputs, and land characteristics will be added to the analysis.

Table 1 provides some descriptive statistics of the variables to be included in the empirical analysis. Also, it gives an initial idea of the differences in productivity (unconditional difference), showing that female managers are 23 percent less productive on average than male managers (difference significant at the 1 percent level). In addition, Table 1 contains information about individual characteristics, access to resources (e.g. labor, technology, agricultural inputs) and land characteristics.

Based on information from Table 1, the following individual attributes differentiate female and male managers:<sup>18</sup> (i) female managers are 4.4 percentage points less likely to be household heads (99 percent of male managers and 95 percent of female managers are household heads); (ii) female managers are 70 percentage points less likely to be married, which reflects that the majority of female managers are widowed (60 percent) or divorced (12.4 percent); (iii) female managers

> 15 The sample contains 3,198 managers (2,521 males and 677 females). Of those, 2,388 report some production: 1,969 males (82.5 percent), 419 females (17.5 percent). Those observations with productivity above the 99th percentile or below the 1st percentile are excluded, leaving 2,340 observations. Finally, 822 observations are excluded for not having information available for some covariates to be included in the analysis. This yields the final 1,518 manager dataset. Table A.1 in the Appendix illustrates the selection patterns in terms of observables that result from the three different restrictions imposed on managers: (i) ability to estimate productivity, (ii) excluding outliers, and (iii) covariates availability. The strongest restriction –measured by proportion of covariates with significant differences between managers excluded and those that remain in the sample– is the ability to estimate productivity, followed by covariate availability and removing outliers. Managers with smaller managed land size are left out of the sample. Small differences in labor and non-labor inputs are observed between managers excluded and those remaining in the sample. The restrictions are more distorting for male managers.

> 16 Each parcel is subdivided into fields, which are the most disaggregated unit of land identified in the dataset. The agricultural survey has information about each of the crops planted on each field.

> 17 Productivity is measured as the sum of the total value of production divided by the total size (in hectares) of land managed. Appendix 1 gives a detailed explanation of how the production variable (in kilograms) is estimated based on the questions from the ERSS survey's crop disposition section. To determine the value of production, prices of each product are required. The prices are estimated using manager's self-reported information for sales. The median price is computed at an EA-product basis. If less than 10 observations for a given product at a given Enumeration Area (EA) are obtained, the price is estimated at the next geographical level in terms of aggregation. The purpose is to use the most disaggregated geographical level for which at least 10 observations of the sale price are available. The geographical units from less to more disaggregated are: nation, region, zone, _woreda_ , _kebele_ , and EA.

> 18 The significantly different characteristics will be highlighted in the section. However, Table 1 contains a more comprehensive set of variables that characterize female and male managers.

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