VTechWorks Repository :: Browsing by Author "Moeltner, Klaus"

Browsing by Author "Moeltner, Klaus"

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Air quality economics: Three essays
Yao, Zhenyu (Virginia Tech, 2022-06-17)
This dissertation consists of three separate research projects. Each paper uses a different applied econometric technique to investigate problems related to air quality economics. The first chapter is a general introduction to all three studies. The second chapter explores adopting an environmentally-friendly public transportation system in Europe. The Bayesian econometric methods show that willingness to pay for a new public transportation system is primarily driven by improvements to public goods, such as air quality and greenhouse gas emission reduction. The third chapter uses the red tide-related stated experience and satellite imagery of chlorophyll-a concentration as well as field data of respiratory irritation. This chapter illustrates that ancillary scientific information can be efficiently combined with choice experimental data. The fourth chapter uses panel fixed-effect models to investigate the short-term effect of air pollution on students' cognitive performance in China. It is shown that PM2.5 has a significantly negative impact on students' exam performance.
The Amenity Value of Trees: a Meta-analysis of Hedonic, Property-value Studies
Heier, Elizabeth (Virginia Tech, 2012-08-09)
Tree species migration as a result of climate change may alter the composition of trees in local communities. Shifts in tree diversity, stand age, species predominance and the overall number of trees are potential changes. Community tree programs may also change the characteristics of local trees through planting or preservation efforts, but these programs may also mitigate the effects of climate induced tree migration. Numerous hedonic property value studies have estimated the implicit price of tree amenities associated with residential properties. Quantitative analysis of the results from multiple studies valuing trees can identify if the relationship between implicit price and tree amenities extended across these studies. The results of the meta-regression found systematic variation was present across positive implicit prices for local tree cover. The scarcity, age and type of local trees were also significantly related to the implicit price of amenity tree cover. The amenity tree cover findings suggest that county tree canopy cover of about 42% optimizes implicit price. Recent extreme weather events and ownership of trees contributed to negative implicit prices. These results may assist in planning and goal setting for community tree programs to mitigate the effects of climate induced tree migration.
Antimicrobial Resistance Mitigation [ARM] Concept Paper
Vikesland, Peter J.; Alexander, Kathleen A.; Badgley, Brian D.; Krometis, Leigh-Anne H.; Knowlton, Katharine F.; Gohlke, Julia M.; Hall, Ralph P.; Hawley, Dana M.; Heath, Lenwood S.; Hession, W. Cully; Hull, Robert Bruce IV; Moeltner, Klaus; Ponder, Monica A.; Pruden, Amy; Schoenholtz, Stephen H.; Wu, Xiaowei; Xia, Kang; Zhang, Liqing (Virginia Tech, 2017-05-15)
The development of viable solutions to the global threat of antimicrobial resistance requires a transdisciplinary approach that simultaneously considers the clinical, biological, social, economic, and environmental drivers responsible for this emerging threat. The vision of the Antimicrobial Resistance Mitigation (ARM) group is to build upon and leverage the present strengths of Virginia Tech in ARM research and education using a multifaceted systems approach. Such a framework will empower our group to recognize the interconnectedness and interdependent nature of this threat and enable the delineation, development, and testing of resilient approaches for its mitigation. We seek to develop innovative and sustainable approaches that radically advance detection, characterization, and prevention of antimicrobial resistance emergence and dissemination in human-dominated and natural settings...
Cloud-Sourcing: Using an Online Labor Force to Detect Clouds and Cloud Shadows in Landsat Images
Yu, Ling; Ball, Sheryl B.; Blinn, Christine E.; Moeltner, Klaus; Peery, Seth; Thomas, Valerie A.; Wynne, Randolph H. (MDPI, 2015-02-26)
We recruit an online labor force through Amazon.com’s Mechanical Turk platform to identify clouds and cloud shadows in Landsat satellite images. We find that a large group of workers can be mobilized quickly and relatively inexpensively. Our results indicate that workers’ accuracy is insensitive to wage, but deteriorates with the complexity of images and with time-on-task. In most instances, human interpretation of cloud impacted area using a majority rule was more accurate than an automated algorithm (Fmask) commonly used to identify clouds and cloud shadows. However, cirrus-impacted pixels were better identified by Fmask than by human interpreters. Crowd-sourced interpretation of cloud impacted pixels appears to be a promising means by which to augment or potentially validate fully automated algorithms.
Consumer Economic Behavior and the Role of Information: Three Case Studies
Vinoles Gomez, Maria V. (Virginia Tech, 2014-10-13)
The economics of information is a relatively new and important field of economics. This dissertation analyzes the role of information in three case studies within three different branches of economics: health economics, environmental economics, and finance and banking. First I analyze parental nutritional label usage and its effect of children's dietary outcomes (i.e. Health Eating Index and Body Mass Index). I show that parental usage of nutritional labels is associated with a better quality of their children's diet as well as an overall improvement in their health as measured by their Body Mass Index. Secondly, I study the behavioral effect of length of residency on water demand in the arid cities of Reno and Sparks in Nevada. In this case, I observe that social interaction among households affects their water usage. In particular, newcomers' watering behaviors are influenced by the prevailing social norms among neighbors that have lived in the arid area for a longer period of time. Finally, I compare the performance of local versus larger national and regional lending institutions in the years leading to the 2007 mortgage crisis. I find that local or community lenders have a significantly lower foreclosure rate during these years. Local lenders presumably base their origination decisions on an interpersonal relationship with their customers. This provides them with information that is not contained within the standard risk metrics generally used in loan applications. I discuss the policy implications of these results for each case study.
Crowds for Clouds: Using an Internet Workforce to Interpret Satellite Images
Yu, Ling; Ball, Sheryl B.; Blinn, Christine E.; Moeltner, Klaus; Peery, Seth; Thomas, Valerie A.; Wynne, Randolph H. (2014)
A chronologically ordered sequence of satellite images can be used to learn how natural features of the landscape change over time. For example, we can learn how forests react to human interventions or climate change. Before these satellite images can be used for this purpose, they need to be examined for clouds and cloud shadow that may hide important features of the landscape and would lead to misinterpretation of forest conditions. Once clouds and their shadow have been identified, researchers can then look for other images that include the feature of interest, taken a bit earlier or later in time, to fill in the "missing information" for the original image. Therefore, the task of identifying clouds and their shadow is extremely important for the correct and efficient use of each image. Computer algorithms are only imperfectly suited for this task. The aim of this project is to outsource the cloud interpretation task to a global internet community of "turkers" -workers recruited via amazon.com's online job market known as "Mechanical Turk."
The Effect of Mountain Pine Beetle Induced Tree Mortality on Home Values in the Colorado Front Range
Cohen, Jed Jacob (Virginia Tech, 2013-06-06)
Throughout the past decade American pine forests have experienced an epidemic of Mountain Pine Beetle (MPB) induced tree mortality. This thesis estimates the losses to home values caused by deteriorating forest quality in the Front Range Counties of Larimer and Boulder Colorado. We employ a repeat sales model that allows for region specific price indices, and non-linear age-related depreciation in home values. We use the time-invariant existence of pine forest near a home to overcome shortcomings in the measurement of MPB damage. We infer from temporal changes in the marginal "effect of pine trees near a home the approximate MPB "effect . We label this strategy the translating commodity approach. Using this strategy we are able to show that diminished forest quality causes forests to become a dis-amenity that negatively affects nearby home values. The total loss in 2011 home values due to their proximity to dying forest is estimated to be $137 million for all the homes in our sample. Such substantial losses may justify a forest management policy shift in order to better mitigate the risk of future MPB outbreaks.
Essays on Contest Theory Experiments and Revealed Time Preference Models
Zou, Yanyang (Virginia Tech, 2022-08-22)
In this series of essays, we study the influence of weight and group size in the sequential multi-battle contest with laboratory experiences (Chapter 2 and Chapter 3). We then develop an empirical method to model perceptual present and time inconsistency (Chapter 4). Chapter 2 examines how the weight and the ordered weights in battles affect the behavior in sequential multi-battle contests with an experiment. We find robustly that the weight of the current battle consistently influences contestants' efforts. Additionally, we discover the math-point-oriented behavior despite differences in history. In other words, the weight effect is expressed in two ways: influencing the effort of the current battle and transferring a contest to the next battle with a designated intensity. Chapter 3 explores the group size effect and how the contest success functions influence the group size effect in sequential multi-battle contests with an experiment. We capture the negative group size effect on the leaders' efforts, participation and dropout rates; contrarily, the positive effect on the non-leaders' efforts. Compared to the Tullock lottery, the all-pay auction intensifies the group size effect of the high effort in the initial battle. It also enlarges the observed group size effects of the effort gaps between the leaders and the non-leaders. Chapter 4 develops the quasi-hyperbolic discounting model into the general beta-delta model to parametrically detect and measure the inconsistency in revealed time preference. This method empirically classifies time preference into four categories, i.e., time consistent, present bias, future bias, and mixed inconsistent. Then we applied this method to the convex time budget data of seven experiments, including 3670 subjects. We discover empirical evidence supporting perceptual differences in the present-future threshold. Traditional present bias models may interpret the time preference imprecisely.
Essays on the Economics of Climate Change, Water, and Agriculture
Ji, Xinde (Virginia Tech, 2018-08-30)
In an era of global-scale climate change, agricultural production faces a unique challenge due to its reliance on stochastic natural endowments, including temperature, precipitation, and water availability for irrigation. This dissertation presents a series of essays to examine how agricultural producers react and adapt to challenges presented by climate change and scarce irrigation water allocated through the prior appropriation doctrine. The dissertation approaches the problem from three distinct perspectives: institutional differences, climate and water availability, as well as producers' expectation on future endowments. Chapter 2 presents an institutional perspective, in which I investigate how different water allocation mechanisms within the prior appropriation doctrine result in differences in producers' crop allocation decisions. I find that water users in irrigation districts are able to plant more water-intensive crops than farmers outside irrigation districts. Chapter 3 presents the interaction between nature and human systems, in which I examine how the physiological complementarity of temperature and water availability diffuses from crop yield (at the intensive margin) to crop allocation strategies (at the extensive margin). Using a theoretical model I show that the observed complementarity reflects a combination of two mechanisms: yield impact through physiological complementarity, and adaptation response through shifting crop allocation patterns. Using an empirical model, I find that farmers adapt to changing climate conditions by growing more profitable crop mixes when presented with more growing degree-days (GDD), precipitation and groundwater access. Chapter 4 presents a behavioral perspective, in which I test how producers' expectation formation processes lead to short term over-adjustments to weather and water availability fluctuations. Using a fixed-effect regression on lagged weather and water realizations, I find that agricultural producers engage in a combination of cognitive biases, including the availability heuristic and the reinforcement strategy. Adopting these alternative learning mechanisms causes farmers to significantly over-react to more recent fluctuations in weather and water availability when making ex ante acreage and crop allocation decisions.
Essays on Water Quality Management for the Chesapeake Bay Watershed
Xu, Yuelu (Virginia Tech, 2020-02-19)
Water quality management for agricultural production is a complicated and interesting problem. Hydrological and economic factors must be considered when designing strategies to reduce nutrient runoff from agricultural activities. This dissertation is composed of three chapters that investigate cost-effective ways to mitigate water pollution from agricultural nonpoint pollution sources and explore farmers' incentives when participating in water quality trading programs. Chapter 1 investigates landscape targeting of best management practices (BMPs) based on topographic index (TI) to determine how targeting would affect costs of meeting nitrogen (N) loading goals for Mahantango watershed, Pennsylvania. We use the results from two climate models and the mean of the ensemble of seven climate models to estimate expected climate changes and the Soil and Water Assessment Tool-Variable Source Area (SWAT-VSA) model to predict crop yields and N export. Costs of targeting and uniform placement of BMPs across the entire study area (4.23 km2) are compared under historical and future climate scenarios. We find that with a goal of reducing N loadings by 25%, spatial targeting methods could reduce costs by an average of 30% compared with uniform BMP placement under three historical climate scenarios. Cost savings from targeting are 38% under three future climate scenarios. Chapter 2 scales up the study area to the Susquehanna watershed (71,000 km2). We examine the effects of targeting the required reductions in N runoff within counties, across counties, and both within and across counties for the Susquehanna watershed. We set the required N reduction to 35%. Using the uniform strategy to meet the required N reduction as the baseline, results show that costs of achieving a regional 35% N reduction goal can be reduced by 13%, 31% and 36% with cross-county targeting, within-county targeting and within and across county targeting, respectively. Results from Chapters 1 and 2 suggest that cost effectiveness of government subsidy programs for water quality improvement in agriculture can be increased by targeting them to areas with lower N abatement costs. In addition, targeting benefits are likely to be even larger under climate change. Chapter 3 investigates the landowner's nutrient credit trading behavior when facing the price uncertainty given the credits are allowed to be banked for future use. A two-step decision model is used in this study. For the first step, we determine the landowner's application level of a BMP on working land in the initial time period. The nutrient credits awarded to the landowner depend on the nutrient reduction level at the edge of field generated by the BMP application. For the second step, we use an intertemporal model to examine the landowner's credit trading behavior with stochastic price fluctuations over time and with transaction costs. The theoretical framework is applied with a numerical simulation incorporated with a hydro-economic model and dynamic programming. Nutrient Management (NM) is selected as the BMP on working land to generate N credits. We find that gains to the landowner from credit banking increase with higher price volatility and with higher price drift, but that gains are larger with price volatility. However, for a landowner holding a small amount of nutrient credits, the gains from credit banking are small due to transaction costs.
Exploring Alternative Methodologies for Robust Inferences: Applications in Environmental and Health Economics
Kaul, Sapna (Virginia Tech, 2013-10-24)
Researchers often invoke strong assumptions in empirical analyses to identify significant statistical outcomes. Invoking assumptions that do not sufficiently reflect the occurrence of true phenomenon reduces the credibility of inferences. Literature suggests that the potential effects of assumptions on credibility of inferences can be mitigated by comparing and combining insights from alternative econometric models. I use this recommendation to conduct robustness checks of commonly used methods in environmental and health economics. The first chapter proposes a novel nonparametric regression model to draw credible insights from meta-analyses. Existing literature on benefit-transfer validity is examined as an application. Nonparametric regression is found to be a viable approach for drawing robust policy insights. The second chapter proposes an alternative structural and simulations based framework to understand elicitation effects in survey response data. This analysis explains the structural mechanisms in which response anomalies occur and is important for building credible insights from survey data. The last chapter uses methods in program evaluation to investigate the impacts of institutional child deliveries on long-term maternal health in the context of developing countries. The outcomes of this analysis indicate that institutional deliveries positively affect maternal health in lower socio-economic states. Based on the findings of my three chapters, I recommend that researchers should combine insights from alternative models to mitigate the scope of specification bias in empirical outcomes and inform policy about the potential uncertainty that arises in uncovering the truth using statistical methods.
Forest pests and home values: The importance of accuracy in damage assessment and geocoding of properties
Moeltner, Klaus; Blinn, Christine E.; Holmes, Thomas P. (2017-01)
We examine the impact of measurement errors in geocoding of property locations and in the assessment of Mountain Pine Beetle-induced tree damage within the proximity of a given residence on estimated losses in home values. For our sample of homes in the wildland-urban interface of the Colorado front range and using a novel matching estimator with Bayesian regression adjustment we find that both types of errors can lead to substantial biases in estimated losses. Our results confirm that the Forest Service's Aerial Detection Survey is generally too coarse to be informative for property valuation that depends on highly localized spatial data.
Forests and fisheries in the Brazilian Amazon: Understanding incentives to comply with conservation efforts
Schons Do Valle, Stella Zucchetti (Virginia Tech, 2017-08-15)
This PhD dissertation represents an effort to understand individual behavior leading to decisions regarding natural resource use and compliance with conservation policy at the government and at the community levels through the analysis of specific cases in the Brazilian Amazon. I first analyze the case of smallholder land clearing along the Transamazon and BR-163 highways in the face of Brazilian Forest Code enforcement by the federal government. My hypothesis is that smallholder land clearing paths over time are affected by assessments of the probability of being caught violating the Forest Code. I develop a dynamic decision model that considers the potential benefits and costs accrued from land clearing through time by a representative smallholder and include her perception of the probability of Forest Code enforcement, unobserved to the researcher. I apply an endogenous switching regressions econometric model to data collected with a sample of 542 households in 2003 and 2013/14. I find that longer land tenure frontiers where there are opportunities for smallholders to transition to cattle grazing from agriculture deserve the attention of enforcement of land clearing laws and restrictions and that the use of the forest by a smallholder is a protective signal that must be considered and encouraged. My results suggest that alleged government efforts to enforce the Forest Code among smallholders in the sample region have been ineffective. The second case I analyze is that of fisher households that enforce community fishing agreements, known as accords, in the floodplains of the Amazon River surrounding the city of Santarém. My hypothesis is that individual households benefit from their own fishing accords enforcement effort through fishing time savings. A factor demand analysis applied to data collected with over 600 households reveals that statistically important drivers of labor demand and fuel include the level of dedication of a household and its history in implementing fishing accords, the landscape, the flood cycle, the distance to the main regional market and biomass. The average household fishing time savings from enforcing accords range between 59 and 36 eight-hour days for a six-month-period, an important argument for continuing the enterprise.
Harmful algal blooms and toxic air: The economic value of improved forecasts
Moeltner, Klaus; Fanara, Tracy; Foroutan, Hosein; Hanlon, Regina; Lovko, Vince; Ross, Shane D.; Schmale, David G. III (2021-02)
The adverse economic impacts of harmful algal blooms can be mitigated via tailored forecasting methods. Adequate provision of these services requires knowledge of the losses avoided, or, in other words, the economic benefits they generate. The latter can be difficult to measure for broader population segments, especially if forecasting services or features do not yet exist. We illustrate how Stated Preference tools and Choice Experiments are well-suited for this case. Using as example forecasts of respiratory irritation levels associated with airborne toxins caused by Florida red tide, we show that 24-hour predictions of spatially and temporally refined air quality conditions are valued highly by the underlying population. This reflects the numerous channels and magnitude of red tide impacts on locals' life and activities, which are also highlighted by our study. Our approach is broadly applicable to any type of air quality impediment with risk of human exposure.
Hedonic Valuation with Translating Amenities: Mountain Pine Beetles and Host Trees in the Colorado Front Range
Cohen, Jed Jacob; Blinn, Christine E.; Boyle, Kevin J.; Holmes, Thomas P.; Moeltner, Klaus (2016-03)
In hedonic valuation studies the policy-relevant environmental quality attribute of interest is often costly to measure, especially under pronounced spatial and temporal variability. However, in many cases this attribute affects home prices and consumer preferences solely through its impact on a readily observable, spatially delineated, and time-invariant feature of the physical landscape. We label such a feature a "translating amenity." We show that under certain conditions changes in the marginal effect of such amenities on home values over time can be used to draw inference on the implicit price of the unobserved environmental quality of interest. We illustrate this approach in the context of a repeat-sales model and the recently intensified outbreak of the Mountain Pine Beetle in the Colorado Front Range.
Labor Market Dynamics in West Virginia and the Appalachian Region
Beverly, Joshua Paul (Virginia Tech, 2023-01-11)
This dissertation consists of three manuscripts analyzing labor market dynamics in West Virginia and the Appalachian Region. The first manuscript examines the dynamic effects of national, regional, and local labor market shocks on labor force participation rates in Appalachia. A dynamic factor model with time-varying loading parameters and stochastic volatility is used to explore the synchronicity and divergence between state labor force participation rates within and outside the Appalachian region. We find that the choice of time and state is crucial to the relative importance of the level of synchronization on observed change in LFPR variations. Our findings can help better target labor policy by taking advantage of the sensitivity exhibited by each state to various labor market conditions. The second manuscript examines the dynamic effects of state, Metro/Non-Metro, and county labor market shocks on labor force participation rates in West Virginia. In the first stage, using a dynamic factor model, we find that non-metropolitan and county-specific components are dominant contributors to the observed variations in the change in West Virginia LFPRs. In the second stage, using a fixed effects panel model, we find county demographics, education levels, income, access to interstate highways, and industry composition are useful covariates for explaining the variance contributions of the state, metro/non-metro and county factors. The third manuscript uses cointegration analysis in the presence of structural breaks to determine whether the Unemployment Invariance Hypothesis exists in West Virginia. Using monthly labor force data from 1976 - 2022, we find mixed support for the unemployment invariance, added worker effect, and discouraged worker effect hypotheses over multiple sub-sample periods. These results suggest that labor markets are temporally-dynamic, and a one-size-fits-all approach could prove disadvantageous to growth.
Land Use on the Urban Fringe
Rippley, Samantha (Virginia Tech, 2023-06-30)
Due to their location on the urban fringe, many historically agricultural counties face development pressure from a spreading urban core. These local communities must contend with often conflicting objectives of providing economic development opportunities while at the same time protecting their county's natural resources. Land conservation policies incentivize landowners to keep land identified as critical environmental resources in their natural state. In this project, we analyze property-level administrative data to evaluate whether land conservation policies and neighbor land use patterns affect the probability of land parcel development. We find some evidence of the contagion effect and that the county's acknowledgment of Priority Conservation areas impacts development motivation.
Methodological advances in benefit transfer and hedonic analysis
Puri, Roshan (Virginia Tech, 2023-09-19)
This dissertation introduces advanced statistical and econometric methods in two distinct areas of non-market valuation: benefit transfer (BT) and hedonic analysis. While the first and the third chapters address the challenge of estimating the societal benefits of prospective environmental policy changes by adopting locally weighted regression (LWR) technique in an environmental valuation context, the second chapter combines the output from traditional hedonic regression and matching estimators and provides guidance on the choice of model with low risk of bias in housing market studies. The economic and societal benefits associated with various environmental conservation programs, such as improvement in water quality, or increment in wetland acreages, can be directly estimated using primary studies. However, conducting primary studies can be highly resource-intensive and time-consuming as they typically involve extensive data collection, sophisticated models, and a considerable investment of financial and human resources. As a result, BT offers a practical alternative, which involves employing valuation estimates, functions, or models from prior primary studies to predict the societal benefit of conservation policies at a policy site. Existing studies typically fit one single regression model to all observations within the given metadata and generate a single set of coefficients to predict welfare (willingness-to-pay) in a prospective policy site. However, a single set of coefficients may not reflect the true relationship between dependent and independent variables, especially when multiple source studies/locations are involved in the data-generating process which, in turn, degrades the predictive accuracy of the given meta-regression model (MRM). To address this shortcoming, we employ the LWR technique in an environmental valuation context. LWR allows an estimation of a different set of coefficients for each location to be used for BT prediction. However, the empirical exercise carried out in the existing literature is rigorous from a computational perspective and is cumbersome for practical adaptation. In the first chapter, we simplify the experimental setup required for LWR-BT analysis by taking a closer look at the choice of weight variables for different window sizes and weight function settings. We propose a pragmatic solution by suggesting "universal weights" instead of striving to identify the best of thousands of different weight variable settings. We use the water quality metadata employed in the published literature and show that our universal weights generate more efficient and equally plausible BT estimates for policy sites than the best weight variable settings that emerge from a time-consuming cross-validation search over the entire universe of individual variable combinations. The third chapter expands the scope of LWR to wetland meta-data. We use a conceptually similar set of weight variables as in the first chapter and replicate the methodological approach of that chapter. We show that LWR, under our proposed weight settings, generates substantial gain in both predictive accuracy and efficiency compared to the one generated by standard globally-linear MRM. Our second chapter delves into a separate yet interrelated realm of non-market valuation, i.e., hedonic analysis. Here, we explore the combined inferential power of traditional hedonic regression and matching estimators to provide guidance on model choice for housing market studies where researchers aim to estimate an unbiased binary treatment effect in the presence of unobserved spatial and temporal effects. We examine the potential sources of bias within both hedonic regression and basic matching. We discuss the theoretical routes to mitigate these biases and assess their feasibility in practical contexts. We propose a novel route towards unbiasedness, i.e., the "cancellation effect" and illustrate its empirical feasibility while estimating the impact of flood hazards on housing prices.
Modern Econometric Methods for the Analysis of Housing Markets
Kesiz Abnousi, Vartan (Virginia Tech, 2021-05-26)
The increasing availability of richer, high-dimensional, home sales data-sets, as well as spatially geocoded data, allows for the use of new econometric and computational methods to explore novel research questions. This dissertation consists of three separate research papers which aim to leverage this trend to answer empirical inferential questions, propose new computational approaches in environmental valuation, and address future challenges. The first research chapter estimates the effect on home values of 10 large-scale urban stream restoration projects situated near the project sites. The study area is the Johnson Creek Watershed in Portland, Oregon. The research design incorporates four matching model approaches that vary based on the temporal bands' width, a narrow and a wider band, and two spatial zoning buffers, a smaller and larger that account for the affected homes' distances. Estimated effects tend to be positive for six projects when the restoration projects' distance is smaller, and the temporal bands are narrow, while two restoration projects have positive effects on home values across all four modeling approaches. The second research chapter focuses on the underlying statistical and computational properties of matching methods for causal treatment effects. The prevailing notion in the literature is that there is a tradeoff between bias and variance linked to the number of matched control observations for each treatment unit. In addition, in the era of Big Data, there is a paucity of research addressing the tradeoffs between inferential accuracy and computational time across different matching methods. Is it worth employing computationally costly matching methods if the gains in bias reduction and efficiency are negligible? We revisit the notion of bias-variance tradeoff and address the subject of computational time considerations. We conduct a simulation study and evaluate 160 models and 320 estimands. The results suggest that the conventional notion of a bias-variance tradeoff, with bias increasing and variance decreasing with the number of matched controls, does not hold under the bias-corrected matching estimator (BCME), developed by Abadie and Imbens (2011). Specifically, for the BCME, the trend of bias decreases as the number of matches per treated unit increases. Moreover, when the pre-matching balance's quality is already good, choosing only one match results in a significantly larger bias under all methods and estimators. In addition, the genetic search matching algorithm, GenMatch, is superior compared to the baseline Greedy Method by achieving a better balance between the observed covariate distributions of the treated and matched control groups. On the down side, GenMatch is 408 times slower compared to a greedy matching method. However, when we employ the BCME on matched data, there is a negligible difference in bias reduction between the two matching methods. Traditionally, environmental valuation methods using residential property transactions follow two approaches, hedonic price functions and Random Utility sorting models. An alternative approach is the Iterated Bidding Algorithm (IBA), introduced by Kuminoff and Jarrah (2010). This third chapter aims to improve the IBA approach to property and environmental valuation compared to its early applications. We implement this approach in an artificially simulated residential housing market, maintaining full control over the data generating mechanism. We implement the Mesh Adaptive Direct Search Algorithm (MADS) and introduce a convergence criterion that leverages the knowledge of individuals' actual pairing to homes. We proceed to estimate the preference parameters of the distribution of an underlying artificially simulated housing market. We estimate with significantly higher precision than the original baseline Nelder-Mead optimization that relied only on a price discrepancy convergence criterion, as implemented during the IBAs earlier applications.
Opportunity Between the Turbines: A Willingness-to-Pay Experiment Regarding Co-Location Activities with the Coastal Virginia Offshore Wind Farm
Fluharty, Shannon Mae (Virginia Tech, 2021-09-13)
With shipping routes, fisheries, conservation areas, recreation, and other maritime industries competing for space off Virginia's coastline, integrated solutions for marine areas may offer a way to limit conflict and maximize productivity. Countries across the world are researching the different ways in which the space between turbines can be utilized to provide economic and environmental benefits. The act of coupling other maritime activities with offshore wind farms is often referred to as co-location. As Virginia constructs the first offshore wind farm in United States Federal waters, there are new opportunities for co-location that could benefit the Virginia economy. Using data from a choice experiment and random utility modeling, this research quantifies Virginia public preferences for various co-location options within the lease area of the Coastal Virginia Offshore Wind (CVOW) farm. Our estimated WTP values show Virginia's public preference for the addition of co-location to the CVOW lease area to be upwards of $20 per 1,000 acres of activity. Our estimates can be compared to implementation and management costs of each activity to determine potential for incorporation of certain co-location techniques. The experimental design of this study can be applied to other offshore wind installments around the world.

Browsing by Author "Moeltner, Klaus"

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