10:30-11:30 Yuta Toyama (Waseda Univesity), “Designing Nonlinear Electricity Pricing with Misperception: Evidence from Free Electricity Policy” (with Ngawang Dendup)
11:35-12:35 Katalin Springel (HEC Montréal)), “Pass-through and Incidence of U.S. Electric Vehicle Purchase Incentives” (with David Scolari and Jing Li)

Abstract: This paper develops semiparametric Bayesian methods for estimating the average treatment effect on the treated (ATT) in difference-in-differences (DiD) designs. We propose two Bayesian procedures with frequentist validity. The first places a Gaussian process prior on the conditional mean function of the control group. The second is a double-robust Bayesian approach that adjusts the prior on the conditional mean function and then corrects the posterior distribution of the ATT. We establish a semiparametric Bernstein¨Von Mises theorem, showing the asymptotic equivalence between our Bayesian procedures and efficient frequentist estimators. For the second approach, the result holds under double-robust smoothness conditions: the lack of smoothness in the conditional mean function can be compensated by high regularity of the propensity score, and vice versa. Monte Carlo simulations and an empirical application demonstrate strong finite-sample performance of our Bayesian DiD methods. We also extend the Bayesian framework to staggered DiD designs.

Abstract：In low- and middle-income country cities, poor households often reside in unattractive locations, including flood-prone areas. This can be due to poor information about flood risks or acceptance of these risks in the face of lower housing prices. Poor households are also more vulnerable to floods than richer households given the low-quality housing they occupy. Does information on flood risks help households make better location and housing choices? To what extent will these choices be revised with increased flood risks from climate change? To answer these questions, we develop a polycentric land use model with heterogeneous income groups, formal and informal housing, and flood risks. The model is calibrated to the city of Cape Town (South Africa) and simulations are run to assess the impact of flood risks on land values and income segregation within the city, distinguishing between the effects of three types of flooding (fluvial, pluvial, and coastal). Although total damages from floods are greater for rich households, they represent a larger relative share of poor households’ incomes. Better information encourages the adaptation of poor households up to a certain point, and this allows them to mitigate most of the adverse consequences from climate change. Considering the different nature of flood types is key to understanding their responses.