and more than 90 percent in the October-December period. Both fresh and dry weights were recorded; the correlation between them was over 0.95. We used the dry weights to calculate yields. There were 2,975 crop cuts made in wave 1 and 3,532 in wave 2. We discarded, however, crop cuts for which we had fewer than 150 observations per crop. Ultimately, 5,920 crop cuts remained in the data set, providing yield estimates for 18 crops.<sup>8</sup> For more details on implementation protocols for crop cutting, see Government of Ethiopia, Central Statistical Agency and World Bank (2013).

Farmers reported the harvest per crop and per plot during the third visit. In wave 1 they only reported production for all their plots if a crop cut was implemented on at least one plot, while in wave 2 production was reported for all plots.<sup>9</sup> This explains why there are significantly fewer observations for wave 1 (3,683) than wave 2 (23,638).

Our unit of analysis, plots, cannot be matched across waves. Plots are, however, the smallest unit of a parcel, and parcels can be tracked across waves. Land parcels in Ethiopia are generally small, with mean and median parcel sizes in ESS wave 2 of 0.34 hectares and 0.22 hectares, respectively (and 95 percent of the parcels smaller than 1 hectare). Given the small size of the parcels, we view the parcel fixed-effects estimates as controlling for unobserved soil quality.

By gathering detailed data on labor input per plot, the ESS data also allow us to examine the hypothesis that differences in labor use at the plot level is the main source of the plot-size IR (Barrett et al., 2010). The ESS includes a labor module on planting (first visit) and harvest (third visit) activities. Farmers are asked to report family, hired, and exchange labor by plot, and this information is included in the regression analysis to address the hypothesis that differences in labor inputs across plots is driving the IR. Furthermore, differences in labor input may also explain differences in self-reported yields and crop cuts, a point that will be elaborated in the methodology section.

To examine if crop cuts were indeed random in relation to households and plots, we compare households with at least one plot selected for crop cutting with those without any plot

> 8 Barley, maize, millet, sorghum, teff, wheat, chick peas, haricot beans, horse beans, lentils, field peas, vetch, line seed, ground nuts, nueg, rape seed, sesame, fenugreek.

> 9 Another difference in questionnaire design between the waves is that only in wave 2 were farmers asked to estimate the share of land devoted to each crop if the plot was cultivated in mixed stand. As a result, yields of plots in mixed stand are more precisely estimated in wave 2 than wave 1. To account for this, we included in the regressions an interaction term between the wave and mixed-stand cropping. Moreover, the results do not change when we restrict the sample to pure-stand or mixedstand plots. In addition, defining yields on mixed-stand plots in wave 2 as in wave 1, has a limited effect on the IR. The IR for self-reports in the full sample with household fixed effects changes from

> - 0.397 to -0.402 when estimating yields on mixed-stand plots in wave 2 in the same way as in wave 1 _._

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