Matching and mechanisms in protected area and poverty alleviation research.

Excitement among social scientists about the discovery of randomized controlled trials has been tempered by the recognition that experimental research and related designs may be infeasible, prohibitively expensive, or even unsuitable for an enormous range of questions of interest to social science and policy (1–3). Recourse to matching-based statistical approaches can enable more transparent causal inference with observational data. The recent upsurge in environmental science writings that use matching techniques borrows from a long and continuing history of such use in medicine, public health, and economics (4–6) and should be welcomed for demonstrating the utility of another important tool in the search for improved estimation of causal effects of environmental interventions. Ferraro and Hanauer (7), leaders in the application of matching-based techniques to identify impacts of protected areas (PAs), present a fresh innovation for environmental social scientists by supplementing the matching-based approach to estimate the effects of protected areas on poverty in Costa Rica with an analysis of three causal mechanisms that may connect protected areas to observed poverty effects. They found that only ecotourism reduced poverty. Changes in forest cover and infrastructure turned out not to have significant effects. My comment examines issues related to data, theory, and policy relevance that pertain to many recent matching-based studies of the effects of protected areas (7–9).