Conservation impact evaluation using remotely sensed data
Alberto Garcia, University of California, Santa Barbara, albertogarcia@ucsb.edu
Robert Heilmayr, University of California, Santa Barbara, rheilmayr@ucsb.edu (Presenter)
The application of quasiexperimental impact evaluation to remotely sensed measures of deforestation has yielded important evidence detailing the effectiveness of conservation policies. However, researchers have paid insufficient attention to the binary and irreversible structure of most deforestation datasets. Using analytical proofs and simulations, we demonstrate that many commonly employed panel econometric models are biased when applied to binary and irreversible outcomes. The significance, magnitude and even direction of estimated effects from many studies are likely incorrect, threatening to undermine the evidence base that underpins conservation policy adoption and design. To address these concerns, we provide guidance and new strategies for the design of panel econometric models that yield more reliable estimates of the impacts of forest conservation policies.
Poster: Poster_Garcia_1-10_135_35.pdf
Associated Project(s):
Poster Location ID: 1-10
Presentation Type: Poster
Session: Poster Session 1
Session Date: Tue (May 9) 5:00-7:00 PM
CCE Program: LCLUC