Estimation of Regression Discontinuity and Kink Designs with Multiple Running Variables

Estimation of Regression Discontinuity and Kink Designs with Multiple Running Variables - A Design Having Potential for Health System Research Description: The regression discontinuity (RD) design is a popular method used in a wide range of policy evaluations. Its popularity owes to the simplicity, credibility, and wide applicability of its design: individuals are assigned to treatment based on whether the value of their running variable exceeds a known threshold, effectively creating a local randomized experiment in a neighbourhood of the cutoff as long as individuals cannot precisely manipulate their running variable. With the growing availability of rich datasets, RD designs with multiple running variables (MRD) have become increasingly common, and yet unlike for single-dimensional RD designs, there is little consensus over how to conduct estimation in such settings. In this webinar, we cover the basics of the MRD design, and discuss a simple estimation method. We also discuss potential applications in healthcare settings, such as studying the effect of Medicaid when eligibility depends on income and age, and health effects of higher education when financial aid eligibility depends on test scores and family income. Featuring speaker: Alden CHENG is a postdoctoral research associate at the National Bureau of Economics Research and Gies College of Business, University of Illinois Urbana-Champaign, with research interests in health economics, applied econometrics, and applied microeconomics. His research includes topics involving nursing homes for the use of novel statistical techniques to improve empirical research. He obtained his B.A. in economics, applied mathematics, and statistics from the University of California at Berkeley in 2016, and awarded in 2023, his PhD. in economics from the Massachusetts Institute of Technology.