IMPACT ON COMMERCIALIZATION 41
two procedures described above. Because analytical standard errors are not computable for the kernel density matching method, we use 100 bootstrap replications stratified at the development domain level to compute robust estimates of them. The second estimator is based on ordinary least squares and tobit estimations, including several control variables. To further ensure comparability of the treatment and comparison groups, we restrict the sam- ple to the common support region of the five-nearest-neighbors matching, as described above.
Results: Average Impact of Cooperatives on Their Members Panel A of Table 3.7 gives the nonparametric estimates of the average treat- ment effect on the treated—that is, the mean impact that cooperative mem- bership has had on members’ output commercialization and price. Starting with the price difference indicator, we find that, on average, cooperative members receive between 7.2 and 8.9 percent higher prices for their cereal products than did their nonmember counterparts. This effect is statistically significant and robust across both matching techniques; it is consistent with the idea that cooperatives may increase the returns to commercialization for smallholder farmers. Regarding the share of production sold, however, we find that cooperative membership does not have an impact significantly different from zero.
The implication of these results appears surprising: despite a higher aver- age price received for their outputs, cooperative members apparently do not tend to supply more of their output to the market. Two types of biases may be at play, however. First, the obtained estimates may be influenced by the estimation procedures adopted and the indicators used. Second, given the reduction in the size of the sample, the estimates obtained may lack representativeness of the general situation in Ethiopia. To account for these sources of bias, we present in panels B and C of Table 3.7 a series of robust- ness and representativeness checks.
For instance, one may argue that the use of the sample average as the reference point in the price indicator is problematic. Although our estima- tions control for important determinants of output prices via the develop- ment domains, it could still be the case that, if cooperatives are located in areas with higher prices to start with, a higher price for cooperative members may wrongly be attributed to the presence of cooperatives instead of to local conditions. To avoid such biases, using zonal or regional level aggregates as the reference point instead of the entire sample might be recommended. In this case, however, the relatively small size of our sample at the zonal level as well as in some regions would provide us with imprecise estimates
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