UC Berkeley

This dissertation combines three empirical studies of human behaviors as they relate to environmental economics and the valuation of non-market environmental goods like air quality and climate. The first studies the effect of a large-scale program that aimed to improve air quality in New York City, and its effect on people’s valuation of residential real estate. The second explores how people respond differently to heat waves, with multiple consecutive days of high temperatures, than to single days with high temperatures, by increasing their demand for air conditioning and electricity use. In the third, we estimate household preferences over local climates, and use the estimates to project the welfare loss due to climate change by the year 2100.

In Chapter 1 I present evidence of inefficiency in the valuation of an important non-market good, air quality, in New York City. Large buildings burning fuel oil for heat are a major contributor to particulate matter and other air pollutants in NYC. A recent NYC policy that rapidly phased out certain types of fuel oil boilers, combined with a randomly-assigned compliance deadline, gives me exogenous variation in air quality. Using the universe of home sales between 2003 and 2014, I test whether the conversion of a dirty oil boiler affects nearby home prices, and furthermore, whether this effect is different during different months of the year. In the absence of information asymmetry and projection bias in the housing market, I would expect to find that an oil boiler conversion increases nearby housing prices, and that this effect is nearly constant across the year. Instead, I find that while there is a positive effect on housing prices, the effect is much stronger for sales that occur during winter months, when boilers are running and emissions are visible. This indicates that home buyers are either uninformed about local air quality during seasons different than that in which they made the purchase decision, or suffer from projection bias. I interpret this as evidence of inefficiency in the housing market and in the valuation of air quality.

In Chapter 2 I conduct a panel-data analysis, presenting evidence that high temperatures have a lagged effect on residential electricity use. The effects of temperature on contemporaneous residential electricity demand have been well studied. However, little research has been done on how high temperatures may also affect electricity use that occurs several hours later, due to physical or behavioral effects. I find that the direct effect of lagged temperatures does not seem to be important, but that interactions between current and previous temperatures are significant, suggesting that consecutive days of high heat do effect electricity use.

Chapter 3 studies household preferences over temperatures and climate, in a joint work with David Albouy, Ryan Kellogg, and Hendrik Wolff. We present a hedonic framework to estimate U.S. households’ preferences over local climates, using detailed weather and 2000 Census data. We find that Americans favor a daily average temperature of 65 degrees Fahrenheit, that they will pay more on the margin to avoid excess heat than cold, and that damages increase less than linearly over extreme cold. These preferences vary by location due to sorting or adaptation. Changes in climate amenities under business-as-usual predictions imply annual welfare losses of 1 to 4 percent of income by 2100, holding technology and preferences constant.