We investigate how environmental regulation under the U.S. Superfund program and Clean Air Act affected exposures to fine particulate air pollution and hazardous waste for Americans over age 65 during the 2000s. Our research design uses quasi-random features of how the two programs enforce regulations and provide information to estimate their causal effects on migration and pollution exposure. We show that senior Americans' average pollution exposures declined substantially. We also show that spatially heterogeneous improvements in environmental quality had little-to-no effect on residential sorting. This led to relatively large reductions in pollution exposure for seniors living in the dirtiest areas.

The real economic cost of homeownership depends on an intricate system of taxes and subsides that vary over time and across the United States. We incorporate the key features of this system into a framework for measuring the annual user-cost of housing and we use it to document how housing costs and subsidies varied over time, across space, and with household demographics in 2016-2017. Then we examine how the Tax Cuts and Jobs Act of 2017 subsequently reduced subsidies and increased the relative cost of housing. We report how these changes varied by geography, homeownership, race, and voting behavior.

We provide the first evidence on the rate at which spatial variation in all-cause mortality risk is capitalized into US housing prices. Using a hedonic framework, we recover the annual implicit cost of a 0.1 percentage-point reduction in mortality risk among older Americans and find that this cost is less than $3453 for a 67 year old and decreasing with age to less than $629 for an 87 year old. These estimates, while similar to estimates from the market for health care, are far below comparable estimates from markets for labor and automobiles, suggesting that the housing market provides an alternative, substantially cheaper channel for reducing mortality risk. We find this conclusion to be robust to a wide range of econometric model specifications, including accounting for associated expenditures on property taxes and the physical and financial costs of moving.

We consider the implications of unifying the distinct literatures on residential sorting and human capital dynamics. We argue that integrating insights from recent work in both areas has important implications for future research at the intersection of environmental and urban economics. To focus attention on these implications, we summarize stylized facts from recent empirical work on residential sorting and on the effects of exposures to environmental factors on human capital. Then we outline a simple overlapping generations model that reproduces these stylized facts and use it to guide our discussion on directions for future research.

We find that long-term exposure to fine-particulate air pollution (PM2.5) degrades health and human capital among older adults by increasing their risk of developing Alzheimer’s disease and related dementias. We track U.S. Medicare beneficiaries’ cumulative residential exposures to PM2.5 and their health from 2004 through 2013, leveraging within- and between-county quasi-random variation in PM2.5 resulting from the expansion of Clean Air Act regulations. We find that a 1 ig/m3 increase in decadal PM2.5 increases the probability of a dementia diagnosis by 1.68 percentage points. The effects are as large or larger when we adjust for mortality-based sample selection and additional Tiebout-sorting dynamics. We do not find relationships between decadal PM2.5 and placebo outcomes. Our estimates suggest that the federal regulation led to nearly 182,000 fewer people with dementia in 2013, yielding $214 billion in benefits. Further, PM2.5’s effect on dementia persists below the current regulatory thresholds.

We develop a framework for estimating Americans’ implicit expenditures on spatially varying nonmarket amenities. We focus on location-specific factors that affect the quality of life but are not formally traded. Examples include climate, geography, pollution, local public goods, and transportation infrastructure. Households pay for residential access to these amenities indirectly, through housing prices, wages and property taxes. We construct a database of 75 amenities, match it to 5 million households’ location choices, and use hedonic methods to estimate their total amenity expenditures. Our benchmark estimate for the year 2000 is $562 billion--equivalent to 8% of Americans’ personal consumption expenditures.

We describe new data on diversity within the Association of Environmental and Resource Economists (AERE), with a focus on association membership and publication in the association’s flagship journal, the Journal of the Association of Environmental and Resource Economists (JAERE). We use these data to provide an update on the status of women in AERE and to extend the scope of diversity measures to describe the professional position, employer, alma mater, degree year, and degree country of JAERE authors. We find that AERE’s female membership share was approximately 29 percent in 2020. Compared with membership, women served in AERE leadership roles at higher rates and accounted for a smaller share of JAERE authors. In terms of international diversity, 72 percent of JAERE authors were employed in the United States, 78 percent of authors with PhDs earned their degrees from US schools, and 15 percent of authors obtained undergraduate degrees from schools outside the United States, Canada, and the European Union. We also show that 25 percent of JAERE authors were affiliated with 10 employers and 40 percent of authors obtained their highest degrees from 10 schools.

This study examines how the value of residential land and structures evolved during the great housing boom and bust, using data on more than a million residential properties that were sold in 10 metropolitan areas between 1998 and 2009. We use a hedonic estimator to disentangle the market value of land and structures at a local (Census tract) level. Our estimates reveal substantial heterogeneity in the evolution of the market value of land and structures within metropolitan areas. Surprisingly, lowervalue land at the urban fringes of metropolitan areas was the most volatile during the boom-bust. (JEL R14, R21)

School closures are an important public health intervention during epidemics. Yet, the existing estimates of policy costs and benefits overlook the impact of human behavior and labor market conditions. We use an integrated assessment framework to quantify the public health benefits and the economic costs of school closures based on activity patterns derived from the American Time-Use Survey (ATUS) for a pandemic like COVID-19. We develop a policy decision framework based on marginal benefits and costs to estimate the optimal school closure duration. The results suggest that the optimal school closure depends on how people reallocate their time when schools are closed. Widespread social distancing behavior implemented early and for a long duration can delay the epidemic for years, buying time for the development of pharmaceutical interventions and yielding substantial net benefits. Conversely, school closure, with behavior targeted to adjust only to the school closure, is unlikely to provide substantial delay or sufficient net benefits to justify closing schools for pathogen control.

We hypothesize that analyzing individual-level secondary data with instrumental variable (IV) methods can advance knowledge of the long-term effects of air pollution on dementia. We discuss issues in measurement using secondary data and how IV estimation can overcome biases due to measurement error and unmeasured variables. We link air-quality data from the Environmental Protection Agency's monitors with Medicare claims data to illustrate the use of secondary data to document associations. Additionally, we describe results from a previous study that uses an IV for pollution and finds that PM2.5's effects on dementia are larger than non-causal associations.

This study provides the first revealed preference evidence on the value of statistical life (VSL) for US seniors aged 67–97 from the rates at which they choose to consume medical care relative to other private goods, and by the effects of their choices on their survival probabilities. These effects are estimated from individuals’ survey responses linked with their Medicare records. Instrumental variables estimators provide robust evidence that the mean VSL is below $1 million and that it decreases with age, and, given age, increases with income, education, and health and is higher for women and people who never smoked. JEL classification: D90, J14, J17, Q51

The hedonic property-value model has been refined over more than forty years to become one of the premier approaches to valuing environmental amenities. This article presents best practices for hedonic property-value modeling when the goal is to measure households' willingness to pay (WTP) for a change in a spatially varying amenity. The starting point is a research design that identifies a source of exogenous variation in an amenity that is observable by prospective buyers (e.g., air quality). Data on the sales prices and physical attributes of houses, together with location-specific measures for amenities, are then used to estimate a housing-price function. Under ideal conditions, the derivative of this price function can be interpreted as indicating the amenity's implicit price, which can then be used to calculate household marginal WTP for the amenity. In principle, this process is straightforward. In practice, modeling decisions must be made to define variables that measure sale prices and amenities and to select an econometric specification. Although the number of issues to address when developing a "best practices" study may seem daunting, the effort is both worthwhile and important for developing accurate measures of the WTP for environmental quality.

The hedonic property value model is among the most direct illustrations of how private markets can reveal consumers’ willingness to pay for measures of environmental quality. There have been thousands of applications since the model was formalized in the 1970s and, if anything, the pace has accelerated due to advances in data accessibility, econometrics, and computing power. The hedonic model’s enduring popularity is easy to understand. It seems like common sense by beginning with an intuitive premise that is both economically plausible and empirically tractable. The model envisions buyers choosing properties based on housing attributes (e.g., indoor space, bedrooms, bathrooms) and on location-specific amenities (e.g., air quality, park proximity, education, flood risk). In the absence of market frictions, spatial variation in amenities can be expected to be capitalized into housing prices. When buyers face the resulting menu of price-attribute-amenity pairings in the housing market, their purchase decisions can reveal their willingness to pay for marginal changes in each of the amenities.2 In recent years, the prevailing style of empirical hedonic research has evolved to incorporate insights from the “credibility revolution” in applied micro-econometrics. This revolution has raised expectations for data quality and econometric transparency. Recent research has refined our understanding of how parameters identified by quasi-experimental research designs map into welfare measures. This article distills the collective evidence from recent advances in the hedonic

We develop a method that embeds signals about consumers' knowledge to evaluate prospective choice architecture policies. We analyze three proposals for U.S. Medicare prescription drug insurance markets: (i) menu restrictions, (ii) personalized information, and (iii) defaulting consumers to cheap plans. We link administrative and survey data to identify informed enrollment decisions that proxy for preferences of observationally similar misinformed consumers. Results suggest that each policy yields winners and losers, with the menu restrictions harmful to most but personalized information beneficial to most. These results are robust across signals of consumers' knowledge but differ from the benchmark that excludes such signals.

We propose new methods to model behavior and conduct welfare analysis in complex environments where some choices are unlikely to reveal preferences. We develop a mixture-of-experts model that incorporates heterogeneity in consumers' preferences and in their choice processes. We also develop a method to decompose logit errors into latent preferences versus optimization errors. Applying these methods to Medicare beneficiaries' prescription drug insurance choices suggests that: (1) average welfare losses from suboptimal choices are small, (2) beneficiaries with dementia and depression have larger losses, and (3) policies that simplify choice sets offer small average benefits, helping some people but harming others.

Previous studies have used national data to demonstrate that higher annual temperatures negatively affect economic output and growth. Yet, annual temperatures and productivity can also vary greatly across space within countries. With this in mind, we revisit the relationship between temperature and economic growth using subnational short panel data for 10,597 grid cells across the terrestrial Earth. Our estimates from fitting a quadratic model to the data imply that cell-level economic growth in countries with below-median per-capita incomes is concave in temperature, with a maximum at about 16 °C. Our findings suggest that even with similar economic development within a country, climate vulnerability can vary at the regional level. Furthermore, as soon as we take into account the nonlinear relationship between temperatures and economic growth within countries, the impacts of temperature increases are found to be larger, compared to those that disregard such within-country heterogeneity.

A conceptual model of consumer sorting in markets for housing, labor and health care is outlined and used to make three points about how benefit transfers are used for environmental policy evaluation. First, the standard approach to assessing benefits of air quality improvements by transferring the value of a statistical life from labor market studies embeds several untested (but testable) assumptions. Second, if the cost of an environmental policy exceeds its capitalized effect on housing prices, then the capitalization effect is an insufficient statistic for determining whether benefits exceed costs. Third, there are several ways in which equilibrium sorting models may be usefully extended to assess distributional welfare effects of environmental policies.

We develop a structural model for bounding welfare effects of policies that alter the design of differentiated product markets when some consumers may be misinformed about product characteristics and inertia in consumer behavior reflects a mixture of latent preferences, information costs, switching costs and psychological biases.We use the model to analyze three proposals to redesign markets for Medicare prescription drug insurance: (1) reducing the number of plans, (2) providing personalized information, and (3) defaulting consumers to cheap plans.First we combine administrative and survey data to determine which consumers make informed enrollment decisions.Then we analyze the welfare effects of each proposal, using revealed preferences of informed consumers to proxy for concealed preferences of misinformed consumers.Results suggest that each policy produces large gains and losses for some consumers, but the menu reduction would unambiguously harm most consumers whereas personalized information would unambiguously benefit most consumers.

The purpose of this rejoinder is to clarify key areas of agreement and disagreement with Abaluck and Gruber and address aspects of their reply to our comment, both of which appear in the December 2016 issue of the American Economic Review. Readers of our exchange may wonder how we can reach such divergent conclusions from analyzing the same data. In this rejoinder we show how. We demonstrate that Abaluck and Gruber’s criticism of our analysis is based on their mistaken claims about theory and empirics, their omission of key facts, and their emphasis on results that obscure our many areas of agreement.

Managing infectious disease is among the foremost challenges for public health policy. Interpersonal contacts play a critical role in infectious disease transmission, and recent advances in epidemiological theory suggest a central role for adaptive human behaviour with respect to changing contact patterns. However, theoretical studies cannot answer the following question: are individual responses to disease of sufficient magnitude to shape epidemiological dynamics and infectious disease risk? We provide empirical evidence that Americans voluntarily reduced their time spent in public places during the 2009 A/H1N1 swine flu, and that these behavioural shifts were of a magnitude capable of reducing the total number of cases. We simulate 10 years of epidemics (2003–2012) based on mixing patterns derived from individual time-use data to show that the mixing patterns in 2009 yield the lowest number of total infections relative to if the epidemic had occurred in any of the other nine years. The World Health Organization and other public health bodies have emphasized an important role for ‘distancing’ or non-pharmaceutical interventions. Our empirical results suggest that neglect for voluntary avoidance behaviour in epidemic models may overestimate the public health benefits of public social distancing policies.