My primary area of research is nonmarket valuation in environmental economics, with a focus on hedonic modeling and residential sorting approaches to valuation. My research has examined questions related to air quality, land use, and the siting of noxious facilities.
Amenity Values of Proximity to National Wildlife Refuges: An Analysis of Urban Reidential Property Values , with Xiangping Liu, Laura O. Taylor, and Peter E. Grigelis, Ecological Economics, 94 (2013), pp. 37-43.
Abstract: This research quantifies the property value benefits of National Wildlife Refuges near urban areas on the eastern coast of the U.S.A. Our approach is made possible through access to
confidential U.S. Census data identifying property values surrounding all refuges with high geographic resolution. Results from hedonic property value models suggest that the amenity values of refuges located near urbanized areas are capitalized into the value of homes in very close proximity, averaging $11 million per refuge. These capitalized values add directly to the local tax base and are considerable complements to the annual economic value created by the refuge system.
An Integrated Model of Regional and Local Residential Sorting with Application to Air Quality , with Daniel J. Phaneuf, Journal of Environmental Economics and Management, 74 (2015), pp. 71-93.
Abstract: We examine the interconnectedness of demand for regionally and locally varying public goods using a residential sorting model. We propose a version of the model that describes household choices at the city (MSA) level and, conditional on city, the neighborhood (census tract) level. We use a two-stage budgeting argument to develop an empirically feasible sorting model that allows us to estimate preferences for regionally varying air quality while accounting for sorting at the local level. Our conceptual and empirical approach nests previous sorting models as special cases, allowing us to assess the importance of accounting for multiple spatial scales in our predictions for the cost of air pollution. Furthermore our preferred specification connects the city and neighborhood sorting margins to the upper and lower elements of a nested logit model, thereby establishing a useful correspondence between two stage budgeting and nested logit estimation. Empirically we find that estimates from a conventional model of sorting across MSAs imply a smaller marginal willingness to pay for air quality than estimates from our proposed model. We discuss how the difference is attributable in part to the omitted variable problems arising when tract level sorting is ignored.
Spatial Cost of Living Indices and the Distribution of Public Goods, Land Economics, 91 (2015), pp. 762-782.
Abstract: This paper develops cost of living (COL) indices that vary across space. While conventional indices adjust for differences in prices, the COL defined here also reflects access to public goods. This analysis relies on the structure of a residential sorting model to estimate the COL index for each of 226 metropolitan statistical areas (MSAs) in 1990 and 2000 in the United States. empirical results show significant differences in the COL across the spatial landscape. This paper focuses on the Gini coefficient as a measure of inequality to demonstrate the distribution of public goods across the population.
Bicycle Infrastructure and Traffic Congestion: Evidence from DC's Capital Bikeshare, with Casey J. Wichman, Journal of Environmental Economics and Management, 87 (2018), pp. 72-93.
Abstract: This study explores the impact of bicycle-sharing infrastructure on urban transportation. We estimate a causal effect of the Capital Bikeshare on traffic congestion in the metropolitan Washington, D.C., area. We exploit a unique traffic dataset that is finely defined on a spatial and temporal scale. Our approach examines within-city commuting decisions as opposed to traffic patterns on major thruways. Empirical results suggest that the availability of a bikeshare reduces traffic congestion upwards of 4% within a neighborhood. In addition, we estimate heterogeneous treatment effects using panel quantile regression. Results indicate that the congestion-reducing impact of bikeshares is concentrated in highly congested areas.
Siting Noxious Facilities: Efficiency in Majority Rule Decisions, with Amit Eynan.
Abstract: This paper analyzes the inefficiency of majority-rule decisions in making siting decisions for noxious facilities, such as waste treatment facilities, landfills, or nuclear waste repositories. In particular, we demonstrate in a general context that a majority-rule voting process will lead a locality to make a decision that decreases aggregate welfare. We develop a theoretical model to establish the prevalence of inefficiencies, demonstrate the mechanisms that exacerbate or mitigate inefficiencies, and provide a feasible solution. The model illustrates the roles that population distribution and the nature of disamenity costs play in creating welfare-decreasing outcomes. We use observed population distributions to estimate model parameters and measure the magnitude of possible inefficiencies in 118 US counties. Results suggest potentially large welfare losses are likely to arise with majority-rule decisions.
Using Machine Learning and Google Street View to Estimate Visual Amenity Values, with Erik Johnson.
Abstract: In this paper, we estimate nonmarket values for public parks and open space, distinguishing between recreational use value and visual amenity value. We use machine learning to conduct visual analysis and classify the view of an individual household. In particular, we identify visual characteristics that are similar to those of publicly provided open space and thus provide a similar a visual amenity. This technique allows us to estimate the value of proximity to open space and separately identify values associated with visual amenities versus recreational use. We find positive capitalization rates associated with household views of park-like properties. From a policy perspective, such identification could indicate the optimal size, location, and shape of open space. Furthermore, machine learning methods used in construction of our view variable provide a potentially powerful tool for hedonic analyses.
Work in Progress
Distance-based Amenities in the Hedonic Housing Price Function
Measurement-Error Bias from Spatial Variables
University of Richmond
ECON 270: Introductory Econometrics
Course Description: This course is an introduction to empirical work in economics using multiple linear regression, with a focus on both theory and application. Topics include basic hypothesis testing, estimation and interpretation of linear regression, statistical inference, ordinary least squares assumptions, dummy variables and function form, and violations of assumptions and corrective measures. A component of the course also includes coding regression analysis in the statistical software R.
ECON 330: Environmental and Natural Resource Economic Theory
Course Description: This course provides a rigorous treatment of environmental and resource economics. The course builds off of intermediate microeconomic theory, as we explore theoretical models and results as well as empirical applications. Topics include allocation of public goods, market-based approaches to pollution control, regulation under uncertainty, nonmarket valuation, and management of nonrenewable and renewable resources.
ECON 230: Environmental Economics
Course Description: This is an introductory course to environmental economics that builds a general framework for thinking about environmental problems from the perspective of economic analysis. We apply economic principles to understand some of the causes of environmental issues and to explore potential solutions. The course involves a moderate focus on environmental policy. We look at specific applications, including air pollution, water pollution, land degradation, biodiversity, and climate change. Topics include externalities and public goods, nonmarket valuation, emissions taxes, tradeable emissions permits, and energy production.
BUAD 202: Statistics for Business and Economics
Course Description: This course provides an introduction to applied statistics with business and economic applications. The course also covers basic implementation of statistical techniques and data analysis in Microsoft Excel. Topics include descriptive statistics, probability, inference, and linear regression.