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This dissertation presents a three-part study in modern empirical environmental economics. In these three studies, I focus on five core economic issues—equity, incentives, environmental quality, consumer behavior, and causality—and ask what environmental economics can teach us about three common topics: energy consumption, cannabis legalization, and pesticide application. The first chapter examines how residential natural gas consumers respond to changes in the price of natural gas. With 70 million consumers, residential natural gas has grown to a first-order policy issue. This first chapter provides the first causally identified, microdata-based estimates of residential natural-gas demand elasticities using a panel of 300 million bills in California. To overcome multiple sources of endogeneity, we employ a two-pronged strategy: we interact (1) a spatial discontinuity along the service areas of two major natural-gas utilities with (2) an instrumental-variables strategy using the utilities' differing rules/behaviors for internalizing upstream spot-market prices. We then demonstrate substantial seasonal and income-based heterogeneities underly this elasticity. These heterogeneities suggest unexplored policies that are potentially efficiency-enhancing and pro-poor. The second chapter explores what may be unintended—or unconsidered—results of cannabis legalization. Cannabis legalization advocates often argue that cannabis legalization offers the potential to reduce the private and social costs related to criminalization and incarceration—particularly for marginalized populations. While this assertion is theoretically plausible, it boils down to an empirically testable hypothesis that remains untested: does legalizing a previously illegal substance (cannabis) reduce arrests, citations, and general law-enforcement contact? The second chapter of this dissertation provides the first causal evidence that cannabis legalization need not necessarily reduce criminalization—and under the right circumstances, may in fact increase police incidents/arrests for both cannabis products and non-cannabis drugs. First, I present a theoretical model of police effort and drug consumption that demonstrates the importance of substitution and incentives for this hypothesis. I then empirically show that before legalization, drug-incident trends in Denver, Colorado matched trends in many other US cities. However, following cannabis legalization in Colorado, drug incidents spike sharply in Denver, while trends in comparison cities (unaffected by Colorado's legalization) remain stable. This spike in drug-related police incidents occurs both for cannabis and non-cannabis drugs. Synthetic-control and difference-in-differences empirical designs corroborate the size and significance of this empirical observation, estimating that Colorado's legalization of recreational cannabis nearly doubled police-involved drug incidents in Denver. This chapter's results present important lessons for evaluating the effects and equity of policies ranging legalization to criminal prosecution to policing. Finally, the third chapter investigates the roles pesticides play in local air quality. Many policymakers, public-health advocates, and citizen groups question whether current pesticide regulations properly equate the marginal social costs of pesticide applications to their marginal social benefits—with particular concern for negative health effects stemming from pesticide exposure. Additionally, recent research and policies in public health, epidemiology, and economics emphasize how fine particulate matter (PM2.5) concentrations harm humans through increased mortality, morbidity, mental health issues, and a host of socioeconomic outcomes. This chapter presents the first empirical evidence that aerially applied pesticides increase local PM2.5 concentrations. To causally estimate this effect, I combine the universe of aerial pesticide applications in the five southern counties of California's San Joaquin Valley (1.8M reports) with the U.S. EPA's PM2.5 monitoring network—exploiting spatiotemporal variation in aerial pesticide applications and variation in local wind patterns. I find significant evidence that (upwind) aerial pesticide applications within 1.5km increase local PM2.5 concentrations. The magnitudes of the point estimates suggest that the top decile of aerial applications may sufficiently increase local PM2.5 to warrant concern for human health. Jointly, the three parts of this dissertation aim to carefully administer causally minded econometrics, in conjunction with environmental economic theory, to answer unresolved, policy-relevant questions.
Three chapters are presented in this thesis, each in the field of environmental economics. The first chapter studies voting outcomes when people differ in their private expectations about marginal damages and the policy maker proposes an externality pricing instrument that is either based on a static political compromise or on a state-contingent updating rule (McKitrick, 2010). We examine cases in which voters are honest (they prefer the socially-optimal price based on their expectation of marginal damages) or dishonest (they prefer either a zero tax or a maximum tax on a priori grounds, irrespective of marginal damages) or combinations of these two. We show that when all voters are honest a standard pricing mechanism that aims at minimizing political losses may never obtain majority support, but implementation of the state-contingent pricing rule always obtains majority support. We then examine whether dishonest voters would prefer implementation based on the static rule or the state-contingent rule. The second chapter examines the relationship between the social discount rate (a rate chosen by a social planner) and the pure rate of time preference (a rate chosen by a representative agent). We accomplish this by examining the slope of famous 'Ramey equation' with respect to the pure rate of time preference. In the literature, it is typically assumed the value of the slope to be equal to one, which is equivalent to treating the consumption growth rate to be independent of the pure rate of time preference. Our general equilibrium framework shows that this relationship is rather complex, and depends on the relative signs and sizes of elasticities of consumption and the growth rate of consumption with respect to the pure rate of time preference. We then examine a closed form solution case where we observe their relationship depends on exogenous parameters of the model, such as initial capital stock, technology parameter as well as time index. Finally, we empirical estimate the relationship by using the Time-Varying Semiparametric Smooth Coefficient model using U.S data from 1930-2014. We find that the estimate of the slope of the social discount rate with respect to the pure rate of time preference to be 0.84 for the United States. The social discount rate based on Ramsey equation is critically dependent upon future consumption growth rates and the pure rate of time preference. In practice, however, the pure rate of time preference is not observable, unlike with the consumption growth where past rates are known with certainty and future projections can be formed with some uncertainty. Ironically, numerous papers have incorporated and examined the effects of the consumption uncertainty but treat the pure rate of time preference to be constant and certain in social discount rates. The third chapter investigates the latter issue for various underlying cases in restricted and unrestricted frameworks. Overall, we find that the effects of uncertainty on the certainty-equivalent discount rate are critically dependent on the source of uncertainty and its assumed properties.
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.