28 CHAPTER 3


This third source of bias can arise from the likely externalities exerted by cooperatives on the commercialization capacities and choices of non- members. For instance, cooperatives may significantly affect the price offered by local traders to cooperative nonmembers. This effect is likely to be enhanced if nonmembers have the option of using cooperatives as an outlet for their output.


To minimize these biases, we employ propensity score matching tech- niques, extensively used in the recent literature on economic impact evalu- ation (Jalan and Ravallion 2003a). Relevant applications of these techniques include impact assessments of farmers’ field schools (Gotland et al. 2004), community-driven development (Rao and Ibanez 2003), pipe water (Jalan and Ravallion 2003b), and road rehabilitation (Van de Walle and Cratty 2002). Specifically, our approach involves a two-step matching estimator. First, kebeles with cooperatives are matched to similar kebeles without coopera- tives, on the basis of marketing-relevant characteristics, such as remoteness, agricultural potential, and population density. In a second step, we match cooperative members to similar households living in kebeles without coop- eratives. The matching is based on a unique variable, the propensity score, defined as the probability that a given household would participate in a coop- erative, given a set of observable characteristics.1


Overall, controlling for the households’ observable characteristics mini- mizes the incidence of the first bias described above. Furthermore, because our strategy compares cooperative members to similar households located in other kebeles, it is also likely that the third bias is also limited. We are there- fore left with the second source of bias, namely, the effect of nonobservable characteristics influencing both the presence of cooperatives in particular kebeles and households’ decisions to participate.


In Ethiopia, however, most cooperatives were initiated under the influ- ence of an external partner (see Table 2.6). According to data from the ECS (2006), 63 percent of the agricultural marketing cooperatives were created by government institutions, 11 percent by a donor agency or NGO, and only 26 percent by members themselves. Dropping from our sample those kebeles in which cooperatives were member-created, we assume that the establish- ment of cooperatives is exogenous to communities’ unobservable characteris- tics as well as to that of their members. Indeed, it became clear from several discussions with woreda-level cooperative promotion officers that encourage- ment for the creation of cooperatives mostly follows a top-down approach.


1 Rosenbaum and Rubin (1983) show that households with similar propensity scores also have similar distributions of covariates.


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