Runsheng Yin
Department of Forestry, Michigan State University

If a public program, project, or policy can be viewed as a treatment, then evaluating the treatment effect becomes an important endeavor of economic research. Indeed, applied economics has recently witnessed tremendous expansion in treatment effect analysis. Of course, if the treatment has already been done, all an analyst can do is to design a plan for data collection and analysis in assessing the treatment effect. Otherwise, if the treatment has not been done, field experiment of plausible scenarios can be considered to implement the treatment and thus to generate data for analysis. Regardless, the central question facing the analyst is how to determine the counterfactual, that is, what would have happened to the participants without the treatment. In the latter case, a randomized assignment strategy can be adopted to produce a close-to-perfect control group and thus alleviate the difficulty in estimating the treatment effect econometrically. In the former case, however, the focus is to identify a reasonable comparison group, which is not always a simple task. So, a great deal of modeling work has gone into this effort even before quantifying the difference between the treatment and comparison groups. This presentation will (1) introduce recent advances in field experimentation and econometric approaches to treatment effect analysis; (2) highlight how an empirical study can be properly done with some local cases; and (3) elaborate the significance of taking a broader perspective of the social-ecological systems under investigation in conducting field work and getting quality data.