Research and Software
Bayesian Adaptive Choice Experiments (with Fernando Payró, Neil Thakral, and Linh T. Tô) Draft
Abstract: We propose the use of a dynamic choice experiment method, which we call Bayesian Adaptive Choice Experiment (BACE), to elicit preferences efficiently. BACE generates an adaptive sequence of menus from which subjects will make choices. Each menu is optimally chosen, according to the mutual information criterion, using the information provided by the subjects' previous choices. We provide sufficient conditions under which BACE achieves convergence and show that its convergence rate significantly improves upon existing discrete choice methods with randomly generated menus. We show that it achieves the highest possible rate of convergence whenever preferences are deterministic. Beyond efficiency gains, BACE addresses a bias in estimating population-level average preference parameters stemming from using combined data across individuals when individuals differ in their tendency to be inconsistent in their choices. Given that BACE requires the calculation of a Bayesian posterior as well as the solution to a non-trivial optimization problem, several computational challenges arise. We address such challenges by using Bayesian Monte Carlo techniques and provide a package for researchers to employ. The separation between a front-end survey interface and a back-end computational server allows the BACE package to be portable for research designs in a wide range of settings.
Software: BACE Package BACE Manual.
This package helps researchers implement and run their own Bayesian Adaptive Choice Experiments (BACE). BACE allows researchers to elicit preferences quickly and efficiently using a dynamic experimental framework. Researchers specify a model that they want to estimate, prior beliefs over the model’s parameters, and questions (or “designs”) that can be shown to respondents.
Wage Differentials and the Price of Workplace Flexibility (with Neil Thakral and Linh T. Tô) Draft available upon request.
Abstract: This paper studies the distribution of workplace flexibility in the labor market and its sources. We collect information on which workers have workplace flexibility along three dimensions, flexible location, flexible scheduling of hours, and flexible total number of hours worked. Low-wage workers can choose to work part-time more flexibly but have a significant disadvantage when it comes to flexible location and scheduling. We overcome challenges in measuring individual-level willingness-to-pay (WTP) by using an adaptive discrete choice experiment, finding WTP for all three dimensions of workplace flexibility to be relatively flat across the wage distribution. Differences in preferences thus cannot account for the facts, suggesting that workers may face unequal compensating prices to obtain the same workplace amenity. We microfound this relationship with a model in which workplace flexibility interacts with production processes that utilize labor inputs at different skill levels. Using a structural model of compensating differentials, we quantify that unequal amenity prices explain 7.2% of wage inequality and 8% of total utility inequality. The results highlight that the unequal distribution of workplace flexibility contributes to the widening of wage inequality even in the presence of compensating differentials.
Social Insurance Programs and Preferences for Redistribution: A Bayesian Adaptive Choice Experiment (with Neil Thakral, Linh T. Tô, and Valeria Zurla)