<p>The goal of this dissertation is to provide a coherent and computationally feasible basis for the analysis of the dynamics of both housing supply and demand from a microeconomics perspective. The dissertation includes two papers which incorporate unique micro data with new methodological approaches to examine housing market dynamics. The first paper models the development decisions of land owners as a dynamic discrete choice problem to recover the primitives of housing supply. The second paper develops a new methodology for dynamically estimating the demand for durable goods, such as housing, when the choice set is large.</p><p>In the first paper, using the new data set discussed above, I develop and estimate the first dynamic microeconometric model of supply. Parcel owners maximize the discounted sum of expected per-period profits by choosing the optimal time and nature of construction. In addition to current profits, the owners of land also take into account their expectations about future returns to development, balancing expected future prices against expected future costs. This forward looking behavior is crucial in explaining observed aggregate patterns of construction. Finally, the outcomes generated by the parcel owners' profit maximizing behavior, in addition to observable sales prices, allow me to identify the parameters of the per-period profit function at a fine level of geography.</p><p>By modeling the optimal behavior of land owners directly, I can capture important aspects of profits that explain both market volatility and geographic differences in construction rates. In particular, the model captures both the role of expectations and of more abstract costs (such as regulation) in determining the timing and volatility of supply in way that would not be possible using aggregate data. The model returns estimates of the various components of profits: prices, variable costs, and the fixed costs of building, which incorporate both physical and regulatory costs.</p><p>Estimates of the model suggest that changes in the value of the right-to-build are the primary cause of house price appreciation, that the demographic characteristics of existing residents are determinants of the cost environment, and that physical and regulatory costs are pro-cyclical. Finally, using estimates of the profit function, I explain the role of dynamics in determining the timing of supply by distinguishing the effects of expected future cost changes from the effects of expected future price changes. A counterfactual simulation suggest that pro-cyclical costs, combined with forward looking behavior, significantly dampen construction volatility. These results sheds light on one of the empirical puzzles of the housing market - what determines the volatility of housing construction?</p><p>In the second paper, I outline a tractable model of neighborhood choice in a dynamic setting along with a computationally straightforward estimation approach. The approach allows the observed and unobserved features of each neighborhood to evolve in a completely flexible way and uses information on neighborhood choice and the timing of moves to recover semi-parametrically: (i) preferences for housing and neighborhood attributes, (ii) preferences for the performance of the house as a financial asset, and (iii) moving costs. In order to accommodate a number of important features of housing market, this approach extends methods developed in the recent literature on the dynamic demand for durable goods in a number of key ways. The model and estimation approach are applicable to the study of a wide set of dynamic phenomena in housing markets and cities. These include, for example, the analysis of the microdynamics of residential segregation and gentrification within metropolitan areas. More generally, the model and estimation approach can be easily extended to study the dynamics of housing and labor markets in a system of cities.</p> / Dissertation

<p>This dissertation considers incentives arising from equilibrium influences that affect the sequence of decisions that lead to family formation. The first chapter examines how state regulations directly aimed at abortion providers affect the market for abortion in the United States. Estimates from a dynamic model of competition among abortion providers show that regulations' main impact is on the fixed costs of entry for providers. Simulations indicate that the removal of regulations would promote entry and competition among abortion providers, and because abortions are found to be price sensitive, this would lead to increases in the number of abortions observed. The second chapter tests if an important negative externality of abortion access exists, namely whether abortion access makes prospective fathers more likely to leave pregnant women. Designing a number of empirical tests, I confirm that in some areas where abortion is more accessible women who give birth are more likely to be single mothers, rather than sharing parental responsibility with the biological father. The final chapter, which is joint work with Peter Arcidiacono and Marjorie McElroy, examines how gender ratios influence bargaining power in romantic relationships between men and women. Gender ratios, by influencing the prospects of matching, allow us to estimate preferences for various match characteristics and activities. We find men prefer sexual relationships more than women at high school ages, and that men and women trade off their preferred partner for an increased chance of matching.</p> / Dissertation

Thesis advisor: Andrew Beauchamp / Thesis advisor: Peter Gottschalk / This dissertation examines how caregiving for an elderly parent affects an adult child's labor supply and wages. In the first chapter (co-authored with Courtney H. Van Houtven and Norma B. Coe) we identify the relationship between informal care and labor force participation in the United States, both on the intensive and extensive margins, and examine wage effects. We control for time-invariant individual heterogeneity; rule out or control for endogeneity; examine effects for men and women separately; and analyze heterogeneous effects by task and intensity. We find modest decreases--1.4-2.4 percentage points--in the likelihood of working for caregivers providing personal care. Male and female chore caregivers, meanwhile, are more likely to retire. For female care providers who remain working, we find evidence that they decrease work by 3-10 hours per week and face a 2.3-2.6 percent wage penalty. We find little effect of caregiving on working men's hours or wages except for a wage premium for male intensive caregivers. In the second chapter I formulate and estimate a dynamic discrete choice model of elder parent care and work to analyze how caregiving affects a woman's current and future labor force participation and wages. Intertemporal tradeoffs, such as decreased future earning capacity due to a current reduction in labor market work, are central to the decision to provide care. The existing literature, however, overlooks such long-term considerations. I depart from the previous literature by modeling caregiving and work decisions in an explicitly intertemporal framework. The model incorporates dynamic elements such as the health of the elderly parent, human capital accumulation and job offer availability. I estimate the model on a sample of women from the Health and Retirement Study by efficient method of moments. The estimates indicate that intertemporal tradeoffs matter considerably. In particular, women face low probabilities of returning to work or increasing work hours after a caregiving spell. Using the estimates, I simulate several government sponsored elder care policy experiments: a longer unpaid leave than currently available under the Family and Medical Leave Act of 1993; a paid work leave; and a caregiver allowance. The leaves encourage more work among intensive care providers since they guarantee a woman can return to her job, while the caregiver allowance discourages work. A comparison of the welfare gains generated by the policies shows that half the value of the paid leave can be achieved with the unpaid leave, and the caregiver allowance generates gains comparable to the unpaid leave. / Thesis (PhD) — Boston College, 2012. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.

<p>In the first chapter of this dissertation, I outline a hedonic equilibrium model that explicitly controls for moving costs and forward-looking behavior. Hedonic equilibrium models allow researchers to recover willingness to pay for spatially delineated amenities by using the notion that individuals "vote with their feet." However, the hedonic literature and, more recently, the estimable Tiebout sorting model literature, has largely ignored both the costs associated with migration (financial and psychological), as well as the forward-looking behavior that individuals exercise in making location decisions. Each of these omissions could lead to biased estimates of willingness to pay. Building upon dynamic migration models from the labor literature, I estimate a fully dynamic model of individual migration at the national level. By employing a two-step estimation routine, I avoid the computational burden associated with the full recursive solution and can then include a richly-specified, realistic state space. With this model, I am able to perform non-market valuation exercises and learn about the spatial determinants of labor market outcomes in a dynamic setting. Including dynamics has a significant positive impact on the estimates of willingness to pay for air quality. In addition, I find that location-specific amenity values can explain important trends in observed migration patterns in the United States.</p><p>The second chapter of this dissertation describes a model which estimates willingness to pay for air quality using property value hedonics techniques. Since Rosen's seminal 1974 paper, property value hedonics has become commonplace in the non-market valuation of environmental amenities, despite a number of well-known methodological problems. In particular, recovery of the marginal willingness to pay function suffers from important endogeneity biases that are difficult to correct with instrumental variables procedures [Epple (1987)]. Bajari and Benkard (2005) propose a "preference inversion" procedure for recovering heterogeneous measures of marginal willingness to pay that avoids these problems. However, using cross-sectional data, their approach imposes unrealistic constraints on the elasticity of marginal willingness to pay. Following Bajari and Benkard's suggestion, I show how data describing repeat purchase decisions by individual home buyers can be used to relax these constraints. Using data on ozone pollution in the Bay Area of California, I find that endogeneity bias and flexibility in the shape of the marginal willingness to pay function are both important.</p><p>Finally, in the third chapter of this dissertation, I combine the insights of the Bajari-Benkard inversion approach employed in second chapter with more standard estimation techniques (i.e., Rosen (1974)) to arrive at a new hedonic methodology that allows for flexible and heterogeneous preferences while avoiding the endogeneity problems that plague the traditional Rosen two-stage model. Implementing this estimator using the Bay Area ozone data, I again find evidence of considerable heterogeneity and of endogeneity bias. In particular, I find that a one unit deterioration in air quality (measured in days in which ozone levels exceed the state standards) raises marginal willingness to pay by $145.18 per year. The canonical two-stage Rosen model finds, counter-intuitively, that this same change would reduce marginal willingness to pay by $94.24.</p> / Dissertation

<p>In this dissertation, I discuss two important factors in individuals' decision-making processes: subjective expectation bias and time-inconsistent preferences. In Chapter I, I look at how individuals' own subjective expectations about certain future events are different from what actually happens in the future, even after controlling for individuals' private information. This difference, which is defined as the expectation bias in this paper, is found to have important influence on individuals' choices. Specifically, I look into the relationship between US elderly's subjective longevity expectation biases and their smoking choices. I find that US elderly tend to over-emphasize the importance of their genetic makeup but underestimate the influence of their health-related choices, such as smoking, on their longevity. This finding can partially explain why even though US elderly are found to be more concerned with their health and more forward-looking than we would have concluded using a model which does not allow for subjective expectation bias, we still observe many smokers. The policy simulation further confirms that if certain public policies can be designed to correct individuals' expectation biases about the effects of their genes and health-related choices on their longevity, then the average smoking rate for the age group analyzed in this paper will go down by about 4%.</p><p>In Chapter II, my co-author, Hanming Fang, and I look at one possible explanation to the under-utilization of preventive health care in the United States: procrastination. Procrastination, the phenomenon that individuals postpone certain decisions which incur instantaneous costs but bring long-term benefits, is captured in economics by hyperbolic discount factors and the corresponding time-inconsistent preferences. This chapter extends the semi-parametric identification and estimation method for dynamic discrete choice models using Hotz and Miller's (1993) conditional choice probability approach to the setting where individuals may have hyperbolic discounting time preferences and may be naive about their time inconsistency. We implement the proposed estimation method to US adult women's decisions of undertaking mammography tests to evaluate the importance of present bias and naivety in the under-utilization of mammography, controlling for other potentially important explanatory factors such as age, race, household income, and marital status. Preliminary results show evidence for both present bias and naivety in adult women's decisions of undertaking mammography tests. Using the parameters estimated, we further conduct some policy simulations to quantify the effects of the present bias and naivety on the utilization of preventive health care in the US.</p> / Dissertation

<p>This dissertation consists of three separate essays that use dynamic models to better understand the human capital accumulation process. First, I analyze the role of migration in human capital accumulation and how migration varies over the business cycle. An interesting trend in the data is that, over the period of the Great Recession, overall migration rates in the US remained close to their respective long-term trends. However, migration evolved differently by employment status: unemployed workers were more likely to migrate during the recession and employed workers less likely. To isolate mechanisms explaining this divergence, I estimate a dynamic, non-stationary search model of migration using a national longitudinal survey from 2004-2013. I focus on the role of employment frictions on migration decisions in addition to other explanations in the literature. My results show that a divergence in job offer and job destruction rates caused differing migration incentives by employment status. I also find that migration rates were muted because of the national scope of the Great Recession. Model simulations show that spatial unemployment insurance in the form of a moving subsidy can help workers move to more favorable markets.</p><p>In the second essay, my coauthors and I explore the role of information frictions in the acquisition of human capital. Specifically, we investigate the determinants of college attrition in a setting where individuals have imperfect information about their schooling ability and labor market productivity. We estimate a dynamic structural model of schooling and work decisions, where high school graduates choose a bundle of education and work combinations. We take into account the heterogeneity in schooling investments by distinguishing between two- and four-year colleges and graduate school, as well as science and non-science majors for four-year colleges. Individuals may also choose whether to work full-time, part-time, or not at all. A key feature of our approach is to account for correlated learning through college grades and wages, thus implying that individuals may leave or re-enter college as a result of the arrival of new information on their ability and/or productivity. We use our results to quantify the importance of informational frictions in explaining the observed school-to-work transitions and to examine sorting patterns.</p><p>In the third essay, my coauthors and I investigate the evolution over the last two decades in the wage returns to schooling and early work experience. </p><p>Using data from the 1979 and 1997 panels of the National Longitudinal Survey of Youth, we isolate changes in skill prices from changes in composition by estimating a dynamic model of schooling and work decisions. Importantly, this allows us to account for the endogenous nature of the changes in educational and accumulated work experience over this time period. We find an increase over this period in the returns to working in high school, but a decrease in the returns to working while in college. We also find an increase in the incidence of working in college, but that any detrimental impact of in-college work experience is offset by changes in other observable characteristics. Overall, our decomposition of the evolution in skill premia suggests that both price and composition effects play an important role. The role of unobserved ability is also important.</p> / Dissertation

[pt] Os mercados de carbono oferecem uma promissora abordagem para enfrentar as mudanças climáticas. No entanto, seu avanço encontra desafios, especialmente na medição precisa da redução de emissões provenientes de atividades relacionadas à floresta. Este artigo apresenta um modelo dinâmico de escolha discreta adaptado para avaliar tais emissões, utilizando uma nova base de dados de dados em painel sobre o uso da terra em propriedades privadas, contendo suas características e participação em projetos de carbono. Nossa análise revela que aproximadamente 23 por cento dos estoques de carbono dentro de projetos de carbono florestal em propriedades privadas na Amazônia brasileira não têm exposição a riscos de desmatamento e, portanto, não devem ser negociados como créditos de carbono. Através de cenários simulados, demonstramos que maiores preços de carbono ou menores custos de participação nesses projetos poderiam aumentar substancialmente a oferta de emissões de carbono evitadas. Intervenções como redução de custos, subsídios de preço ou melhoras regulatorias poderiam recrudescer a oferta e contribuir para os esforços de mitigação das mudanças climáticas. Por fim, identificamos propriedades adequadas para participação futura em projetos, com o objetivo de mitigar os riscos de investimento e otimizar os retornos esperados. / [en] Carbon markets offer a promising avenue for tackling climate change, yet their advancement encounters challenges, notably in accurately measuring emissions avoidance from forest-related activities. This paper introduces a dynamic discrete choice model tailored for assessing such emissions, using a novel database of panel data on private property land use, characteristics, and carbon project participation. Our analysis reveals that approximately 23 percent of carbon stocks within forestry carbon projects on private properties in the Brazilian Amazon lack exposure to deforestation risks and should therefore not be tradable as carbon credits. Through simulated scenarios, we demonstrate that elevated carbon prices or reduced participation costs in these projects could substantially augment the supply of avoided carbon emissions. Interventions such as cost reductions, price subsidies or regulatory improvements could bolster supply and contribute to climate change mitigation efforts. Lastly, we identify suitable properties for future project participation, aiming to mitigate investment risks and optimize expected returns.

<p> The sales of deer licenses, one of the most important revenue sources for wildlife management at the Indiana Department of Natural Resources (IDNR), have been declining for a decade. To increase its funds, the agency is considering launching a new lifetime deer license, which would allow hunters to harvest deer (and possibly other species) each year for the rest of their lives in exchange for a large, up-front fee. The forward-looking nature of the decision to buy a lifetime license means hunters’ choice behavior is necessarily dynamic. We estimate a dynamic discrete choice model using data from a discrete choice experiment (DCE) to capture this forward-looking choice behavior and to estimate hunters’ preferences for different lifetime license designs. We find that our dynamic model better fits our data than a standard, static choice model. We also find that hunters prefer licenses that allow (i) harvest of antlered and antlerless deer to one that only allows harvest of antlerless deer and (ii) harvest of additional species beyond just deer. We use our model to estimate the price of lifetime licenses that maximizes IDNR revenues. This is the first study to estimate the value of lifetime deer hunting licenses using a dynamic approach. This dynamic approach can help improve the IDNR’s decision-making to maximize its revenue and stabilize wildlife management funds.  </p>

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La modélisation du choix d'itinéraire est un sujet de recherche bien étudié avec des implications, par exemple, pour la planification urbaine et l'analyse des flux d'équilibre du trafic. En raison de l'ampleur des effets que ces problèmes peuvent avoir sur les communautés, il n'est pas surprenant que plusieurs domaines de recherche aient tenté de résoudre le même problème. Les défis viennent cependant de la taille des réseaux eux-mêmes, car les grandes villes peuvent avoir des dizaines de milliers de segments de routes reliés par des dizaines de milliers d'intersections. Ainsi, les approches discutées dans cette thèse se concentreront sur la comparaison des performances entre des modèles de deux domaines différents, l'économétrie et l'apprentissage par renforcement inverse (IRL). Tout d'abord, nous fournissons des informations sur le sujet pour que des chercheurs d'un domaine puissent se familiariser avec l'autre domaine. Dans un deuxième temps, nous décrivons les algorithmes utilisés avec une notation commune, ce qui facilite la compréhension entre les domaines. Enfin, nous comparons les performances des modèles sur des ensembles de données du monde réel, à savoir un ensemble de données couvrant des choix d’itinéraire de cyclistes collectés dans un réseau avec 42 000 liens. Nous rapportons nos résultats pour les deux modèles de l'économétrie que nous discutons, mais nous n'avons pas pu générer les mêmes résultats pour les deux modèles IRL. Cela était principalement dû aux instabilités numériques que nous avons rencontrées avec le code que nous avions modifié pour fonctionner avec nos données. Nous proposons une discussion de ces difficultés parallèlement à la communication de nos résultats. / Route choice modeling is a well-studied topic of research with implications, for example, for city planning and traffic equilibrium flow analysis. Due to the scale of effects these problems can have on communities, it is no surprise that diverse fields have attempted solutions to the same problem. The challenges, however, come with the size of networks themselves, as large cities may have tens of thousands of road segments connected by tens of thousands of intersections. Thus, the approaches discussed in this thesis will be focusing on the performance comparison between models from two different fields, econometrics and inverse reinforcement learning (IRL). First, we provide background on the topic to introduce researchers from one field to become acquainted with the other. Secondly, we describe the algorithms used with a common notation to facilitate this building of understanding between the fields. Lastly, we aim to compare the performance of the models on real-world datasets, namely covering bike route choices collected in a network of 42,000 links. We report our results for the two models from econometrics that we discuss, but were unable to generate the same results for the two IRL models. This was primarily due to numerical instabilities we encountered with the code we had modified to work with our data. We provide a discussion of these difficulties alongside the reporting of our results.