report Lee bounds which indicate a decline in yields in the range 240 Kg/ha to 130 Kg/ha. This suggests that the drought was severe enough to change yield expectations.

Column 1 in Table 8 presents the self-reported measure of yields for the full sample of 678 farmers. Yields for control farmers were 2, 360 Kg/ha. The provision of recommendations and extension services increased yields by 210 Kg/ha (a 9% increase relative to the control mean) but the estimate is not statistically significant at conventional levels. The in-kind grant, however, increased yields significantly (both in an economic and statistical sense) relative to those in the control group. For farmers with the inflexible in-kind grant and cluster level recommendations ( _T_ 2), yields rose by 280 Kg/ha (a 12% increase relative to the control mean). The corresponding treatment effect for farmers who received the flexible grant was 360 Kg/ha (a 15% increase). The two numbers are not statistically distinguishable, and our conclusion is that providing farmers with autonomy, did not decrease yield or profits. Yields for farmers with plot level recommendations ( _T_ 1) rose by 400 Kg/ha (a 17% increase), but given the standard errors, this is not statistically different from the yields for arms _T_ 2 or _T_ 3.<sup>42</sup> While the results on yields are encouraging, it is perhaps more important to evaluate impacts on profits. Measuring and valuing both inputs and output, however, is extremely challenging for smallholder farmers (see e.g. Foster and Rosenzweig, 2010). We measured revenues and expenditures on a comprehensive set of agricultural inputs using frequent, detailed surveys throughout the growing season.<sup>43</sup> To calculate revenues we multiplied the price received in the sale of maize by the self-reported quantity harvested.<sup>44</sup> Revenues are reported in column 2 and not surprisingly, show a similar pattern to that for yields in column 1. Expenditures are reported in column 3. For each stage of the growing season – soil preparation, sowing, plant maintenance, and harvesting – we measured labor days in the one hectare subplot, whether it was provided by a family member or hired-in labor, and the wage paid for hired labor. We also measured other inputs, including seeds, fertilizers, sowing machinery, pesticides, herbicides, and harvest machinery and whether the cost was paid by the farmer or by the research team (i.e. we include the up to 2, 000 peso subsidy in the costs and impute harvesting costs if they were paid by the research team).<sup>45</sup> Column 3 shows that grant recipients in _T_ 2 invested 623 more pesos/ha than farmers in _T_ 4 or the control group, who spent on average 5, 280 pesos/ha. Column 4 reports profits as the difference between revenues and costs. Although all the point estimates are positive – and suggest profit increases in the range of 10% (T3), 12% (T2), 20%

> 42In fact, we cannot reject the joint null hypothesis that the treatment effects are the same for all three arms ( _T_ 1, _T_ 2 and _T_ 3).

> 43Enumerators from each of the seven teams lived less than a thirty minutes drive from the study plots assigned to them. Plots were visited several times by the team during the year. Unpaid labor is however not taken into account in our calculations because of the difficulty in imputing a shadow wage.

> 44Only about 70% of farmers sold maize, and we imputed the price of maize for the remaining farmers using the median price in their cluster.

> 45Appendix Table A10 disaggregates expenditures into different categories. Note however, that we do not value unpaid (typically family) labor in labor costs.

22