Resource information
This paper develops and applies a new approach for analyzing the spatial aspects ofindividual adoption of a technology that produces a mixed public-private good. The technologyis an animal insecticide treatment called a “pouron” that individual households buy and apply totheir animals. Private benefits accrue to households whose animals are treated, while the publicbenefits accrue to all those who own animals within an area of effective suppression.A model of household demand for pourons is presented. As for a private good,household demand for the variable input depends upon output price, input cost, and householdcharacteristics. Input costs for pouron treatments include both the market price of the pouronsand the transaction costs that the household must incur to obtain the treatments. Demand alsodepends upon the way that each household expects its neighbors to respond to one’s ownbehavior. Free-riding is expected in communities with no tradition or formal organization tosupport collective action. Greater cooperation is expected in communities that haveorganizations that reward cooperative behavior and punish deviant behavior.Data for estimation of the model were collected for all of the 5,000 households thatreside within the study area of 350 square kilometers in southwest Ethiopia. Geographicreference data were collected for every household using portable Geographic PositioningSystem units. GIS software was used to generate spatial variables. Variables for distance fromthe household to the nearest treatment center and number of cattle-owning neighbors within a 1-kilometer radius of the household were created. The density of cattle-owning neighbors wasused as a measure of the potential benefits from cooperation; this variable was expected to havea positive effect on household pouron demand in communities able to support effectivecollective action and a negative effect in communities not able to support effective collectiveaction. A set of community binary variables was interacted with the density variable to capturedifferences between communities. The results confirm the importance of the household-levelvariables. The results also indicate large differences in ability to cooperate between localadministrative units. Everything else equal, the areas least able to cooperate were locatedfarthest from the treatment center, were ethnically heterogenous, and had a different ethniccomposition than areas around the treatment centers.
