Research
Fields of Interest: Public Goods Valuation, Environmental Economics, Prediction Markets
Beyond the Neighborhood: Regional Goods in Equilibrium Sorting Models
[PDF coming soon]
Equilibrium sorting models treat amenities as localized; they are enjoyed equally within a geographic boundary and not at all outside of it. However, many environmental amenities generate diffuse recreation benefits across broad regions. Incorporating such amenities into sorting models requires solving a fundamental problem: what is the market extent, and how do benefits attenuate with distance inside of it? I address this problem by integrating a recreation demand study directly into a random utility sorting framework. Because the value is estimated from revealed-preference data, it endogenizes distance decay and market extent rather than allowing the researcher to choose arbitrarily. I estimate a model of residential sorting across Iowa and find that households are willing to pay $5,925 for a one standard deviation improvement in lake quality-accessibility. The index's multidimensionality enables policy counterfactuals that traditional measures cannot support: simulating the effects of a lake cleanup proposal on Secchi depth, I estimate an implied total welfare change of ¢18 per person, generating a total value of about $100,000.
Darkening the Mood: Are People Willing to Pay for Dark Skies?
[PDF coming soon]
The invention of the lightbulb is widely credited as the primary driver of widespread residential electrification; to this day, when new communities are connected to power grids, the first purchases are often lighting devices. Indeed, light is so correlated with wealth and productivity that economists regularly use remote sensing night sky brightness as a proxy for GDP in hard-to-reach locations. However, the popular use of the term light pollution implies some amount of social unease in the trend of lighting up the night. This paper uses cell phone mobility data to quantify willingness to pay for sky darkness using a recreation demand study. I utilize spatial variation in sky brightness using remote sensing data and temporal variation in brightness due to moon phase to precisely estimate a near-zero valuation of dark skies on normal nights, but find that during meteor showers willingness to pay for dark skies increases to $35 for a one unit increase in darkness, measured by mag/arcsec².
When Can We Trust Prediction Markets?
[PDF coming soon]
Prediction markets are increasingly cited as real-time probability estimates, but existing evaluations have been limited to small numbers of markets or comparisons against a single alternative forecast. This paper proposes a marketwide bias index, varying between 0 (perfect probability prediction) and 1 (as good as random guessing), that measures systemic miscalibration, and applies it to a sample of Polymarket contracts: 43,028 markets closing between April 15 and September 15, 2025, with 82.5 million minute-level price observations. I find substantial miscalibration overall (0.172) alongside striking heterogeneity across categories — crypto markets are nearly perfect (0.096) while golf markets are severely biased (0.601). Generally, sports markets underperform non-sports markets throughout. Bias also falls as markets approach resolution and as trading volume rises. I undertake an exercise to determine optimal observations by volume, time to close, and a sports indicator to minimize systemic miscalibration, and find optimal weighting cuts miscalibration by roughly in half (0.074), suggesting that prediction market prices are best treated not as probabilities but as signals whose reliability is predictable from observable market characteristics.