30 CHAPTER 3 Matching


Matching Kebeles Among the 293 kebeles in the sample, 94 had at least one cooperative at the time of the survey. However, not all of them satisfy the identification assumption that the present spatial distribution of cooperatives is exogenous. Specifically, this assumption may not hold for kebeles with member-created cooperatives; such kebeles were therefore removed from our sample. In addi- tion, in some kebeles without cooperatives, it was reported that households had access to one in a nearby kebele. To further add to the robustness of our estimates, these kebeles were also removed from the sample. The remaining sample consists of 68 treatment kebeles, where at least one cooperative can be found, and 134 comparison kebeles where no cooperatives exist. The next step is to ensure that the treatment kebeles are sufficiently similar to the comparison ones. To do so, we apply the notion of develop- ment domains, as adapted to Ethiopia by Chamberlin, Pender, and Yu (2006). Development domains are defined as geographic locations sharing broadly similar rural development constraints and opportunities. The classification is based on four characteristics that best capture livelihood heterogeneity among smallholders in Ethiopia: altitude, population density, distance to the closest market, and moisture reliability. Their aggregation is based on thresholds spe- cifically established to maximize the predictive power of the domains.3 In our sample, kebeles can be classified into 22 different domains. To test the validity of these domains as predictors for the existence of externally created cooperatives, we use a probit estimation, where the dependent vari- able is the existence or absence of a cooperative and the independent variables are dummy variables for each of the domains. Overall, this test performs relatively well: domains successfully predict 70 percent of the incidence of cooperatives.


Next, according to our matching procedure, we need to ensure that a suf- ficient number of treatment and comparison kebeles exist in each domain. The distribution is reported in Table 3.1, showing that five domains (1, 2, 5, 12, and 15) capture more than 70 percent of the kebeles with at least one externally created cooperative, while the remaining 30 percent are dispersed among 12 of the remaining 17 domains. It appears that each of the five pre- dominant domains includes enough comparison kebeles to perform the analy- sis. Finally, although selective, these five domains are quite heterogeneous:


3 Whereas Chamberlin, Pender, and Yu (2006) conduct the necessary computation at the woreda level, the present analysis is based on the analogous computation at the kebele level.


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