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With the ever-increasing availability of large, high-dimensional datasets, there is a growingneed for econometric methods that can handle such data. The last decade has seen the development of many such methods in computer science, but their applications to economic models have been limited. In this thesis, I investigate whether modern tools in (exact and approximate) Bayesian inference can be useful in economics. In the three chapters, my coauthors and I develop and estimate a variety of models applied to problems in organizational economics, health, and labor. In chapter one, joint with Andrew Olenski, we estimate a mortality-based Bayesian model of nursing home quality accounting for selection. We then conduct three exercises. First, we examine the correlates of quality, and find that public report cards have near-zero correlation. Second, we show that higher quality nursing homes fared better during the pandemic: a one standard deviation increase in quality corresponds to 2.5% fewer Covid-19 cases. Finally, we show that a 10% increase in the Medicaid reimbursement rate raises quality, leading to a 1.85 percentage point increase in 90-day survival. Such a reform would be cost-effective under conservative estimates of the quality-adjusted statistical value of life. In chapter two, joint with Laura Battaglia and Stephen Hansen, we demonstrate the effectiveness of Hamiltonian Monte Carlo (HMC) in analyzing high-dimensional data in a computationally efficient and methodologically sound manner. We propose a new model, called Supervised Topic Model with Covariates, that shows how modeling this type of data carefully can have significant implications on conclusions compared to a simpler yet methodologically problematic two-step approach. By conducting a simulation study and revisiting the study of executive time use by Bandiera, Prat, Hansen, and Sadun (2020), we demonstrate these results. This approach can accommodate thousands of parameters and doesn’t require custom algorithms specific to each model, making it more accessible for applied researchers. In chapter three, I propose a new way to estimate a two-way fixed effects model such as Abowd, Kramarz, and Margolis (1999) (AKM) that relaxes the stringent assumptions concerning the matching process. Through simulations, I demonstrate that this model performs well and provide an application to matched employer-employee data from Brazil. The results indicate that disregarding selection may result in a significant bias in the estimates of location fixed effects, and thus, can contribute to explaining recent discoveries about the relevance of locations in US labor markets. The three chapters demonstrate the usefulness of modern Bayesian methods for estimating models that would be otherwise infeasible, while remaining accessible enough for applied researchers. The importance of carefully modeling the data of interest instead of relying on ad-hoc solutions is also highlighted, as it has been shown to significantly impact the conclusions drawn across a variety of problems.

This dissertation contains four essays in spatial and international economics. Chapter 1 investigates how housing variety varies across space. Housing costs are key in understanding real income differences across space and time. Standard measures of housing costs do not account for availability differences, where some housing varieties are available in certain cities or time periods but not others. When households have idiosyncratic preferences over housing units, the set of available housing varieties in a city matters. This paper develops theoretically-founded housing price indices to measure housing costs that account for availability differences. To allow for flexible substitution patterns, I propose a method to jointly estimate the nests that varieties belong to and the elasticity of substitution across varieties within each nest. I find that households in larger cities benefit from having access to varieties not available in smaller cities. Utility-consistent housing prices reduce the elasticity of housing prices with respect to population by a half. Since housing is a third of household expenditure, this implies that we have systematically underestimated real income and overestimated residual amenities in larger cities. In contrast to previous estimates, I find that real income is increasing in city size after accounting for availability differences. Chapter 2 investigates the factors that cause incomplete pass-through of exchange rate shocks into border prices. This paper examines the role of decreasing returns to scale, a channel that has received limited empirical and theoretical attention. Based on a first-order approximation to a firm's optimal price, I show that 1) decreasing returns to scale interacts with variable markups, imported inputs, and destination non-traded costs to generate incomplete pass-through, 2) there is asymmetry between importer currency and exporter currency shocks due to imported inputs, and 3) strategic complementarity matters, where firms adjust their prices in response to competitor prices. I propose a new estimation method for key demand and supply parameters that govern the degree of markup and marginal cost adjustments. Using the estimated parameters, I find that decreasing returns to scale is the dominant factor in generating incomplete pass-through, with variable marginal costs contributing to over 90% of the incomplete pass-through, while variable markups account for less than 10%. Chapter 3 analyzes the determinants of exporter size. Theories of comparative advantage and product differentiation have emphasized productivity and quality differences. This paper shows that incorporating decreasing returns to scale matters for understanding the determinants of exporter size. Exogenous marginal cost differences affect equilibrium quantities but do not necessarily appear in prices since lower exogenous marginal costs (a lower cost curve) are offset by higher endogenous marginal costs (movement along the cost curve). As a result, standard approaches that assume constant returns to scale underestimate the contribution of marginal cost differences and overestimate the contribution of quality differences. Based on bilateral trade flow data between 1997 to 2016 for over 200 countries and 3000 products, I find that standard approaches attribute almost no variation in exporter size to cost differences. In contrast, after incorporating decreasing returns to scale, I estimate that 58% (65%) of the variation in exporter size is attributed to fundamental cost differences in the time series (cross-section). Chapter 4 models and quantifies the dynamic gains from exporting. I develop a dynamic trade model where firms innovate and learn from other firms in the destinations they sell to. The evolution of a country's stock of knowledge can be expressed as a function of export flows and the stocks of knowledge of their trading partners. I find evidence that countries in Asia, North America, and Europe, as well as countries in the top two quartiles of TFP growth were able to better absorb foreign insights than other countries. I evaluate whether there are dynamic gains from trade with two counterfactual exercises. First, I measure the impact of changing trade costs between 1962 and 2000. I find small static gains but zero dynamic gains for the world economy. Second, I quantify the dynamic gains from export-induced foreign knowledge flows by simulating a counterfactual where there is no learning from foreign sources. I find that domestic learning compensates for foreign learning: there are large dynamic gains from exporting when there is no domestic learning and small dynamic gains when there is domestic learning.

The three chapters of my dissertation study household finance, with a particular interest in financial institutions and poor households. Specifically, I study constraints households have that are unobservable to institutions yet impact their interaction and the effectiveness of government interventions that aim to ease or facilitate these exchanges. Chapter 1 studies the behavior of payday loan borrowers by using bank-transaction level data on household spending, income, and loan activity to document three novel stylized facts. They suggest that a payday borrower is poor, has volatile income, and uses payday loans to smooth baseline consumption needs after an adverse idiosyncratic income shock. Chapter 2 builds on these findings to motivate a short-term lending model. The equilibrium contract under realistic frictions matches the observed payday loan contract on multiple dimensions, specifically when borrowers have low expected income and high-income volatility. I then calibrate my model using my bank-transaction dataset and find welfare increases between 5% and 28.7% when rollover fees decrease and initial fees increase. Chapter 3 studies the efficiency of government healthcare subsidization schemes, with a specific interest in the underlying mechanisms that financially motivate hospitals to serve the uninsured. We study a Disproportionate Care Hospital (DSH) payment scheme that supports hospitals treating a disproportionately higher fraction of uninsured patients. We demonstrate that DSH payments lead to social welfare loss, especially in a system with large and small hospitals, compared to the second-best mechanism. We then use the setting of the Global Payment Program (GPP) program, which compliments DSH by providing primary care, to show that direct and assured payment for primary care improves social welfare. Overall, my dissertation seeks to understand the development of an equilibrium contract structure between financial institutions and the poor, examine methods of greater efficiency, and evaluate the impact of government interventions to alleviate tensions between these parties.

<p>It is widely recognized that a requisite aspect of addressing climate goals is to develop a more sustainable transportation sector. One initiative towards this is the federal administration’s stated goal that 50% of all new vehicle sales will be electric by the year 2030. However, it is a common consensus that this will not occur without significant changes in electric vehicle (EV) adoption trends. In order to meet this goal and significantly diminish transportation greenhouse gas emissions, it is critical to better understand EV adoption at scale. To do this, we must understand at the system level what the progression of adoption will look like and what factors influence that adoption.</p> <p>This problem requires a more granular analysis than has been previously performed. We analyze adoption at the ZIP code level in four US states (CA, CO, NY, WA) with historical data dating to 2011. To understand the progression of adoption, we consider two adoption models (the logistic model and the Bass model) to forecast future EV levels in ZIP codes. We find that the logistic is better for the data that is currently publicly available.</p> <p>We additionally find that EV forecasts must be decomposed into both battery electric vehicle (BEV) and plug-in hybrid electric vehicle (PHEV) forecasts. There is sufficient evidence that the adoption processes for these two types of EVs differ.</p> <p>Critically, we extend this analysis to consider the factors influencing adoption. Utilizing the adoption forecasts, we perform spatial regression analyses on the parameters that define the forecast shapes. We examine how multiple sociodemographic, land use, and charging measures correlate with the rate of EV adoption and the lateral shift of early EV adoption.</p> <p>Crucially, we find that multiple measures of charging infrastructure availability correspond with increased adoption; of these, a variation on the distance to fast-charging stations is the most consistent metric across final models. We additionally find that land use type is indeed relevant to adoption. Finally, we are able to corroborate at a granular spatial level numerous sociodemographic variables from the literature.</p> <p>Ultimately, this research can provide valuable insights into adoption trends at a local level and what factors may be best leveraged to promote adoption.</p>

This thesis explores several aspects of state and local governments' finances and its interaction with the real economy. The first chapter explores the question of what the fiscal position of local governments is and how the financial market assesses it. I find that a large share of municipalities operate with a negative net position-akin to a negative book equity position in the corporate context. I find that most of the decline in the fiscal position originates from the accumulation of legacy obligations, i.e. pensions and other post-employment benefits (OPEBs); this is recognized by municipal bond markets through higher credit spreads. While accounting values from the annual comprehensive financial reports are informative, they are based on book valuations which potentially convey limited information about the economic value of assets and liabilities. Thus, I turn to the market valuation of local governments' equity by estimating an stochastic discount factor that matches the valuation of a wide range of assets in the economy to prices future tax and expenditure claims. Using market prices for tax and expenditure claims, and market valuations of liability positions I find that the market values of equity are highly correlated with the book values. The negative equity position-in terms of book and market values-for some local governments suggests the presence of implicit insurance by state and federal governments. In the second chapter I utilize quasi-experimental variation in Connecticut to causally estimate the policy response of local governments and the migration response of residents to a large fiscal shock. I find that local governments adjust tax rates to maintain stable tax revenues; there is no change in public employment levels and limited adjustments of public services. The micro data on people's location further allows me to causally estimate the migration elasticity to a change in property tax rates. I find evidence of inter-state migration in response to an increase in property tax rates; and no statistically significant response of intra-state migration. Detailed property and location choice data reveal the elasticity of migration with regard to the property tax bill. An increase in the property tax bill by ten percent leads to an average increase in the migration propensity by about 1.5%. In the third chapter I explore the contribution of the local fiscal constraint channel on the local economy. I show that the observed general equilibrium response to local labor market shocks contains an economically important amplification effect through local financial constraints. At the center of the local fiscal constraint channel is the housing market. Local governments in the United States receive a median share of 63.13% of own source revenues from property taxes. I show that exogenous shocks to local labor markets affect the housing market and exerts fiscal pressure on local government finances. Local governments-on average-increase property taxes and cut amenities. Both policy responses affect the relative attractiveness of a location which amplifies the initial shock. I estimate a multiplier of 1.7x through this local financial constraint channel for employment.

Tourism - in particular its determinants and effects - is an issue presently attracting much attention worldwide. International tourism is considered to be the largest single item of the world's foreign trade and for some countries it is the most important export industry and earner of foreign exchange. In addition to its economic significance, tourism contributes to the quality of life. It produces intangible benefits which are directly related to the physical and psychological health of people, and the enjoyment of the right to rest and free time. This applies with equal validity to both domestic and international tourism; the latter establishing international economic, political and socio-cultural links, as well as strengthening the domestic character of a nation. At the individual level, tourism satisfies the need to travel in search for relief from the stress of work and the routine of daily life in the big urban centres. At macro (country) level, tourism is, therefore, a human and economic activity which concerns most of us in many parts of the world, directly or indirectly. One country for which foreign tourism is of considerable importance is Greece. In order to identify likely supply constraints (e. g. tourist accommodation and basic infrastructure) and to establish the major market segments of Greek tourism, the growth and structure of foreign tourism in Greece between 1960 and 1984 is examined. Special attention is given to the magnitude of tourism in Greece and its economic effects on the national economy. The non-economic effects of tourism are also considered. This is followed by the construction of a tourist profile so that the types of foreign visitors that go to Greece are identified. Subsequently, an econometric model is developed and empirical results provided to explain foreign tourist arrivals in Greece and to assess the impact of promotional expenditure by the Greek National Tourist Organisation in a number of foreign tourist generating markets. Finally, a tourism marketing planning model is devised which highlights the main variables affecting the international tourism marketing policies of the Greek National Tourist Organisation and, in particular, empirical results are used in conjunction with- a tourism market choice matrix for selecting market targeting strategies. The major conclusion emerging from the research is that as the tourist industry in Greece is of vital importance, the adoption of a strategic, interdisciplinary and integrated tourism planning process along with the establishment of a tactically orientated task unit could provide important improvements in the effectiveness and contribution of tourism in Greece. A few proposals regarding future policies by the Greek authorities are made, such as the establishment of a co-ordinating body orchestrating the efforts of the appropriate groups relevant to the multifaceted nature of tourism. / Economic and Social Research Council (ESRC)

<p dir="ltr">This study aims to predict the environmental, organizational, and managerial factors that determine the adoption of cloud computing in U.S. healthcare delivery systems. The premise of the analysis is that several internal and external factors determine a health provider’s transition to cloud computing. The U.S. government has funded healthcare providers through HITECH <a href="" target="_blank">(Health Information Technology for Economic and Clinical Health) </a>to implement electronic health records (EHR) which is considered as an important first step in transitioning to cloud computing. This study investigated whether there is a significant difference between hospitals and providers that received HITECH funding to enhance their EHR infrastructure and those that did not in terms of their external environmental complexities, internal organizational structure, and quality of healthcare services they provide. A stratified random sample was applied to select a cohort of 3,385 hospitals from the American Hospital Association (AHA) 2022 roster for the period 2018- 2021 to test the study hypothesis. The sampled hospitals were linked with claim, administrative, cost, and ICD-10 clinical data files to capture variables of interest repeatedly over the study period. The analysis modeled for selected external (location, market concentration as measured by Herfindahl Index), internal (number and composition of staff – physicians, nurses, technicians, etc.) demographic, clinical and financial factors. Quantitative methods such as generalized estimating equations (GEE), logistic regression, and generalized linear mixed model (GLMM) were applied within the framework of unified technology acceptance theory (UTAT), accounting for both discrete and continuous response variables while modeling for possible between-subject heterogeneity and within-subject correlations. The analysis is based on publicly available data sources that are systematically linked to address the research questions. The portion of the HITECH funding that is applied for cloud computing is calculated from the hospital’s EHR funding. This is one of the very few longitudinal time series studies of cloud computing in healthcare since almost all previous studies on American hospitals are cross-sectional. The findings of this study show statistically significant differences between hospitals that received government funding in terms of internal organizational structure, environmental complexity, and quality of healthcare provided. The analysis identified management and quality metrics that help to gauge continuously changing organizational needs and identify emerging trends. This study proposes specific topics that future researchers can consider promoting a successful implementation of cloud computing.</p>