Agricultural & Resource Economics
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Item Essays in nonmarket valuation and energy economics(2016) Miller, Mark Vincent; Alberini, Anna; Agricultural and Resource Economics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation comprises three essays, relating to negative externalities in economics. The first essay concentrates on residential electricity consumption. In the economic literature, price elasticity of demands estimates for this market vary widely. In this essay, I seek to explain these findings using three nationwide datasets – the American Housing Survey, Forms EIA-861, and the Residential Energy Consumption Survey – from the U.S. I examine the role of the sample period, level of aggregation, use of panel data, use of instrumental variables, and inclusion of housing characteristics and capital stock. The findings suggest that price elasticities have remained relatively constant over the time period considered, from 1997 to 2009. Upon splitting my panel datasets into annual cross sections, I do observe a negative relationship between price elasticities and the average price. Whether prices are rising or falling appears to have little effect on the estimates. I also find that aggregating our data can result in both higher and lower price elasticity estimates, depending on the dataset used, and that controlling for unit-level fixed effects with panel data generally results in more inelastic demand functions. Addressing the endogeneity of price and/or measurement error in price with instrumental variables has a small but noticeable effect on the price elasticities. Finally, controlling for housing characteristics and capital stock produces a lower price elasticity. My second essay focuses on personal vehicular transportation, which is the source of several externalities, including congestion and conventional air pollutant and greenhouse gas emissions. In this work, I examine the geographical distribution of carbon dioxide emissions from cars. Specifically, I focus on rural and urban households in the UK, using repeated cross sections from the UK National Travel Survey. I contrast driving behavior with new vehicle purchases, and ask three related questions: First, do rural households purchase more vehicles, and/or vehicles with higher emissions rates? Second, do they drive more miles than their urban counterparts? Third, how do the “carbon footprints” from these two groups compare? To answer these questions, I first model the number of vehicles chosen by each household, and the emissions type of each. I then model the travel demand for that vehicle, conditioning on the latter choice. I use these results to estimate the contribution of rural and urban residents to total CO2 emissions. I find that rural households own more vehicles than urban households, and that these vehicles have higher emissions rates. Rural vehicles are also driven 12.9% more than urban vehicles. Lastly, I estimate a carbon footprint that is 58% higher for rural households than urban. My third essay considers an application of the hedonic price models, which are widely used in nonmarket valuation to find the value of environmental quality, changes in health risks, etc. The approach relies on property values. It is less commonly employed, however, to assess the value of certain amenities like crime mitigation. One problem with deploying hedonics in this area is that crime tends to be correlated with unobserved neighborhood attributes, and for that reason regression coefficients on crime may be biased. This chapter considers one particular set of crimes, methamphetamine laboratories, on property values. While methamphetamine labs are more likely to be established and discovered in some neighborhoods more than others, I follow previous literature by arguing that, within the confines of the neighborhoods they are discovered in, they are as good as randomly assigned and can thus be regarded as locally exogenous in regression analysis. I use three registers – one federal, two local – to identify the locations of past discoveries, in Summit County, OH, and categorize the discoveries by scale (e.g., a small discovery of meth in a dumpster versus an actual meth lab). I find mixed evidence that the value of homes closest to a discovery are negatively affected, either directly or in terms of relatively higher properties for properties that are somewhat further away from such discoveries. The effect, when identified, is slightly stronger for large-scale discoveries. I also consider the effect of information disclosure, and find that, for repeat sales observations, property sales that occur following a discovery within 200 meters and after public registers are available observe a sizable loss in value. The robustness each of these findings, however, appears questionable, since their magnitude and statistical significance are sensitive to model specification.Item Essays in Personal Transportation Demand and Consumer Finance(2016) Evans, Jaclyn; Williams, Roberton C; Agricultural and Resource Economics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation is composed of three essays covering two areas of interest. The first topic is personal transportation demand with a focus on price and fuel efficiency elasticities of mileage demand, challenging assumptions common in the rebound effect literature. The second topic is consumer finance with a focus on small loans. The first chapter creates separate variables for fuel prices during periods of increasing and decreasing prices as well as an observed fuel economy measure to empirically test the equivalence of these elasticities. Using a panel from Germany from 1997 to 2009 I find a fuel economy elasticity of mileage of 53.3%, which is significantly different from the gas price elasticity of mileage during periods of decreasing gas prices, 4.8%. I reject the null hypothesis or price symmetry, with the elasticity of mileage during period of increasing gas prices ranging from 26.2% and 28.9%. The second chapter explores the potential for the rebound effect to vary with income. Panel data from U.S. households from 1997 to 2003 is used to estimate the rebound effect in a median regression. The estimated rebound effect independent of income ranges from 17.8% to 23.6%. An interaction of income and fuel economy is negative and significant, indicating that the rebound effect may be much higher for low income individuals and decreases with income; the rebound effect for low income households ranged from 80.3% to 105.0%, indicating that such households may increase gasoline consumption given an improvement in fuel economy. The final chapter documents the costs of credit instruments found in major mail order catalogs throughout the 20th century. This study constructs a new dataset and finds that the cost of credit increased and became stickier as mail order retailers switched from an installment-style closed-end loan to a revolving-style credit card. This study argues that revolving credit's ability to decrease salience of credit costs in the price of goods is the best explanation for rate stickiness in the mail order industry as well as for the preference of revolving credit among retailers.Item THE INTERACTION BETWEEN DISTANCE TO WORK AND VEHICLE MILES TRAVELED(2008-01-31) Gonzalez, Hernan Mauricio; Horowitz, John K.; Agricultural and Resource Economics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Economists have long been concerned with the externalities generated by automobiles, such as traffic congestion and air pollution. Since many of these externalities are closely bound up with the number of miles being driven, economists have been much interested in the behavior of what is known as vehicle miles traveled (VMT). Planners believe that land use can be manipulated to serve congestion management, air quality or related transport planning goals. The underlying idea is that household location may have a big impact on its transportation demand, including car ownership. In this context, I focus on distance to work (DTW) as the measure of household location. I chose a continuous measure of household location instead of a discrete one because, besides being easily measured, it matches better the data available for this study and it has a very straightforward interpretation--it allows me to calculate the contribution of commuting miles to total miles driven. Despite the clear conceptual connection between DTW and VMT, and the constraining nature of household location, little is known about their joint behavior. City and household level attributes that may lead households to live close or far from their work may also lead them to drive few or many miles for non-commuting purposes. This effect must be accounted for when measuring the behavior of VMT conditional on DTW. I develop two models to analyze: (i) the role of city characteristics in explaining households' distance to work, (ii) the effect of distance to work on VMT and car ownership, (iii) the effect of city level attributes on VMT, conditional on DTW, (iv) the unobserved taste for driving, (v) differences between workers and non-workers. I find that: (i) City characteristics expected to affect commutes have a small effect on households' DTW, (ii) DTW provides an important effect on car ownership levels and VMT, (iii) City characteristics expected to influence non-commute miles have a small impact on VMT, (iv) taste for driving has a small but significant effect on VMT, and (v) non-workers are much less responsive to gas prices than workers.