Third, because household surveys are conducted in two stages, we report robust standard errors clustered over districts.

Table 2 presents the White-corrected, district clustered, OLS estimates of the determinants of net revenues per hectare using two models. Our specification is entirely driven by the literature on the Ricardian technique and the literature on the determinants on farm net revenues. Model 1 presents the marginal impacts of the quarterly precipitation rates and annual temperatures, while model 2 also includes socioeconomic characteristics. Demeaning the climate variable does not correct for multicollinearity associated with the linear and quadratic variables for quarters 1 and 4 respectively. We exclude the quadratic precipitation measures on the grounds of collinearity as well as insignificance<sup>5</sup> .

Several key insights permeate through these regressions. In Model 1, precipitation in the 2<sup>nd</sup> and 3<sup>rd</sup> quarters exerts a significant impact on net revenues per hectare along with annual temperature. Precipitation has a concave effect in the 2<sup>nd</sup> quarter (indicating diminishing returns) and a convex effect in the 3<sup>rd</sup> quarter.

Model 2 presents the impacts of climate and socioeconomic characteristics on net revenues per hectare<sup>6</sup> . We find that the R<sup>2</sup> more than doubles from 0.14 when only climate is used to 0.30 when climate and socioeconomic characteristics are included. The inclusion of many of the socioeconomic characteristics is jointly highly significant<sup>7</sup> . We believe that our finding of high explanatory power of the included socioeconomic characteristics is not a special case and is likely to be found in other countries. However, the significance of marketing channels as an explanatory variable of net revenues has not been considered previously in Ricardian studies to examine climate impacts.

The importance of precipitation in the second quarter gathers significance when its quadratic is removed<sup>8</sup> . Three specific variables, irrigation, land tenure and cropping intensity, render the precipitation effect in the 3<sup>rd</sup> quarter insignificant. The strong negative impact of temperature remains important in model 2. The importance of precipitation in quarters 2 and 4 is not surprising given that these periods correspond with the beginning of the main agricultural seasons.

Due to the importance of the interpretation of the climate variables on net revenues per hectare, two tests were conducted to validate the robustness of the chosen measure of climate. First, we compare the same regressions using temperature and

> 5  The collinearity between the linear and quadratic terms of the demeaned climate variables in quarters 1 and 4 is very strong with correlation coefficients of 0.98 and 0.99.  The collinearity between the 2<sup>nd</sup> and 3<sup>rd</sup> quarter is weak with correlation coefficients of 0.84 and 0.83, respectively. As a result, the precision with which the coefficients can be estimated makes inferences unreliable.

> 6  Although we only report 2 models, numerous other variants were attempted to assess the stability of the estimated coefficients.  There were no interesting differences between coefficients across models and in no case did a variable go from being positively significant to being negatively significant and _vice versa_ . 7  The F-statistic of the test of the joint significance of the climate variables are 1,211 and 11,833 – both of which are highly significant at the 1 percent level.

> 8  However, the convex relation to precipitation in the 3rd quarter (as observed in Model 1) looses significance when other socioeconomic variables are included.  This implies that other included variables are capturing the effect that the negative impact of precipitation in the 3<sup>rd</sup> quarter exhibited when only climate variables are included.

17