Research

Publications 

1.  "Modeling Behavioral Response to Infectious Diseases Under Information Delay” (with Haosen He and Frederick Chen) Review of Economic Design (2025): 1-22.

2. "Observational Studies Generate Misleading Results About the Health Effects of Air Pollution: Evidence From Chronic Air Pollution and COVID-19 Outcomes." (with Marc N. Conte, Matthew Gordon,Nicole A. Swartwood,and Rachel Wilwerding) Plos one 19.1 (2024): e0296154.

3.  "An Agent-based Model of Elephant Crop Consumption Walks Using Combinatorial Optimization" (with Haosen He, Erin Buchholtz, Frederick Chen, and Susanne Vogel) Ecological Modelling  (2022): 464, 109852.  

4. "COVID-19's U.S. Temperature Response Profile"  (with Richard Carson, Samuel Carson, Thayne Dye, Samuel Mayfield, and Daniel Moyer) Environmental and Resource Economics (2021): 1-30. 

Working papers

"Air Pollution Exposure and Donation to Its Victims: Evidence from Online Charitable Giving "  with Peng Shen, Xincheng Wang, Yinxiao Wang, Yucheng Wang and Shuhuai Zhang. Revise and resubmit at the Journal of Environmental Economics and Management.

This study examines the role of charitable giving in mitigating environmental damages. We study donations to the potential victims of air pollution - particularly, patients with respiratory diseases - when donors are exposed to varying levels of air pollution. Using unique data from a major online medical crowdfunding platform in China, our empirical design compares the differential impact of air pollution exposure on donations across different disease categories. Our findings indicate that a one-standard-deviation increase in ambient PM2.5 exposure leads to a significant 20.8% increase in donations toward respiratory disease patients relative to non-respiratory disease patients. We provide evidence suggesting that these effects are primarily driven by direct physical exposure to local air pollution, rather than by disseminating pollution information. For respiratory diseases, the charitable contributions induced by air pollution are quantitatively comparable to the additional medical expenses caused by air pollution.

"Exploring the Sources and Nature of Temperature Measurement Error in Estimating Climate Related Impacts" with Richard T. Carson. Under review.

Thousands of studies estimate prospective climate change impacts by examining how temperature affects a wide range of outcomes of interest. Potential measurement error in temperature variables is routinely ignored, and when acknowledged, typically assumed to be small in magnitude with a classical (i.i.d. normal) distribution, leading estimates to be attenuated toward zero. We formally document the sources of errors introduced in the construction of temperature proxy variables and show this widely held assumption is wrong using a best-case approach that treats readings at 1,120 U.S. weather stations as truth. Overall, the average downward bias in estimated impacts is substantial. However, due to the non-classical nature of the measurement errors, overestimates are not rare. Estimation problems are concentrated where attention focuses—the tails of the temperature distribution.

"Multidimensional Environmental Injustice: Air Pollution and Emergency Medical Care in NYC" with Marc N. Conte and Rachel Wilwerding. 

This study explores three dimensions of environmental injustice that have been relatively under-explored in the literature on environmental hazards. Our focal hazard is air pollution, and we use 8.5 million Emergency Medical Service (EMS) records in New York City and wind instruments in a 2SLS framework to highlight the magnitude of these inequities. First, we address gaps in information access by analyzing how measurement limitations in traditional pollution monitoring networks can systematically underestimate exposure in vulnerable communities, which are defined in our context as disadvantaged based on a New York State classification. Second, we examine healthcare access disparities by analyzing how emergency services utilization patterns, response quality, and associated costs differ between disadvantaged and non-disadvantaged communities during pollution events. Third, we investigate differential access to defensive behaviors, particularly how socioeconomic factors influence communities' capacity to protect themselves during air quality emergencies. Using high-resolution PM2.5measurements and wind instruments, we find that a one-unit increase in PM2.5 leads to 0.471 additional EMS dispatches per 100,000 residents daily, resulting in approximately 48.5 million in additional annual healthcare costs citywide. The burden falls disproportionately on disadvantaged communities, with the effect of PM2.5 on EMS dispatches 74% higher in these areas, creating an annual cost difference of $9.94 million between community types.

Work in progress

"Smoggy Instruments: A Breath of Bias in Air Pollution Impact Studies" with Zhenxuan Wang

"Unintended Digital Precaution in Extreme Weather" with Tin Cheuk Leung

"Measuring Growing Degree Days: A Crop Yield Response to Temperature  Estimation" 

Resting papers

"Estimating the Impact of Climate Change: An Exploration of the Bin Regression Model" with Richard T. Carson and Dalia Ghanem

The “bin” regression model has been put forward as a flexible semi-parametric method for representing a climate variable and it has emerged as the workhorse approach for empirical work (e.g., Deschênes and Greenstone, 2011). Our paper is the first to formally explore econometric properties of the bin regression approach. We show that, although the bin regression approach often produces reasonable results, that the approach produces consistent parameter estimates only under very stringent and highly unlikely assumptions about the true data generating procedure. Problems with the bin regression approach are likely to be most severe in the tail bin categories, where most policy interest with respect to climate change impacts lies. We propose alternatives to bin model for the climate change impacts that produce consistent estimates and generally have better efficiency properties.