Entrepreneurial Migration

We use cross-state business registrations to track the geographic movement of startups with high growth potential. In their first five years, 6.6% percent of these startups move across state borders. Though startup births are concentrated geographically, hubs like Silicon Valley and Boston on net lose startups to entrepreneurial migration. A revealed preference approach nonparametrically identifies the average utility of cities to migrant founders. University towns and startup hubs have low relative utility. This pattern is due neither to vertical sorting nor industrial specialization. The higher-quality startups move to lower-tax, business-friendly cities, while less growth-oriented startups move to low-tax, high-amenity cities.

State of the Art of Hedonic Pricing

Hedonic pricing methods have become a staple in the environmental economist’s toolkit for conducting nonmarket valuation. The hedonic pricing method (HPM) is a revealed preference approach used to indirectly infer the value buyers and sellers place on characteristics of a differentiated product. Environmental applications of the HPM are typically focused on housing and labor markets, where the characteristics of interest are local environmental commodities and health risks. Despite the fact that there have been thousands of hedonic pricing studies published, applications of the methodology still often grapple with issues of omitted variable bias, measurement error, sample selection, choice of functional form, effect heterogeneity, and the recovery of policy-relevant welfare estimates. Advances in empirical methodologies, increased quality and quantity of data, and efforts to link empirical results to economic theory will surely further the use of the HPM as an important nonmarket valuation tool.

Bounded rationality and limited data sets

Bounded rationality theories are typically characterized over exhaustive data sets. We develop a methodology to understand the empirical content of such theories with limited data, adapting the classic revealed‐preference approach to new forms of revealed information. We apply our approach to an array of theories, illustrating its versatility. We identify theories and data sets testable in the same elegant way as rationality, and theories and data sets where testing is more challenging. We show that previous attempts to test consistency of limited data with bounded rationality theories are subject to a conceptual pitfall that may lead to false conclusions that the data are consistent with the theory.

Inferring Risk Perceptions and Preferences Using Choice from Insurance Menus: Theory and Evidence

Demand for insurance can be driven by high risk aversion or high-risk. We show how to separately identify risk preferences and risk types using only choices from menus of insurance plans. Our revealed preference approach does not rely on rational expectations, nor does it require access to claims data. We show what can be learned non-parametrically about the type distributions from variation in insurance plans, offered separately to random cross-sections or offered as part of the same menu to one cross-section. We prove that our approach allows for full identification in the textbook model with binary risks, and extend our results to continuous risks. We illustrate our approach using the Massachusetts Health Insurance Exchange, where choices provide informative bounds on the type distributions, especially for risks, but do not allow us to reject homogeneity in preferences.