<div>State and local adoption/repeal of highly polarized policies causes migration responses both out of and into the affected region. Interpreting the responses as revealed policy pref?erences leads to the conclusion that marijuana legalization and abortion waiting periods had been favored nationally, while gay marriage had been opposed. Policy preferences are geographically heterogeneous, which leads to different responses across counties. From 1992- 2017, these policy changes reduced domestic migration by two percent, which is approxi?mately 20% of the total migration decline. The migration changes, via partisan sorting, accounted for a significant share of the increased political polarization from 2012-2016 in western, urban, and swing counties. <br></div><div><br></div><div>In cases where unmarried parents have joint physical custody of their child(ren), there is a wide range of default relocation restrictions that depend on their state of origin. Using IRS county-to-county migration data, demographic data from the ACS, and state relocation restrictions gathered from divorce law websites, I study the impact of these default reloca?tion restrictions on domestic US migration. Results from both regression discontinuity and selection on observables designs, find about 10% - 30% less migration to counties that are outside the allowed relocation range. This migration friction is shown to strengthen from 1992 - 2012, as both joint physical custody and unmarried parents became more common, thereby contributing to the decline in domestic US migration. <br></div><div><br></div><div>In the United States, between 2004 and 2008, 28 states increased their minimum wage; the national minimum wage was increased in 2007. The average migration response to these increases was a 3% change in migration away from a one dollar increase. These effects are not distributed evenly across the population. People from more impacted demographic groups are more likely to move away from minimum wage increases.</div>

<p dir="ltr">In this thesis, we delve into the exploration of several nonparametric and semiparametric econometric models within the Bayesian framework, highlighting their applicability across a broad spectrum of microeconomic and macroeconomic issues. Positioned in the big data era, where data collection and storage expand at an unprecedented rate, the complexity of economic questions we aim to address is similarly escalating. This dual challenge ne- cessitates leveraging increasingly large datasets, thereby underscoring the critical need for designing flexible Bayesian priors and developing scalable, efficient algorithms tailored for high-dimensional datasets.</p><p dir="ltr">The initial two chapters, Chapter 2 and 3, are dedicated to crafting Bayesian priors suited for environments laden with a vast array of variables. These priors, alongside their corresponding algorithms, are optimized for computational efficiency, scalability to extensive datasets, and, ideally, distributability. We aim for these priors to accommodate varying levels of dataset sparsity. Chapter 2 assesses nonparametric additive models, employing a smoothing prior alongside a band matrix for each additive component. Utilizing the Bayesian backfitting algorithm significantly alleviates the computational load. In Chapter 3, we address multiple linear regression settings by adopting a flexible scale mixture of normal priors for coefficient parameters, thus allowing data-driven determination of the necessary amount of shrinkage. The use of a conjugate prior enables a closed-form solution for the posterior, markedly enhancing computational speed.</p><p dir="ltr">The subsequent chapters, Chapter 4 and 5, pivot towards time series dataset model- ing and Bayesian algorithms. A semiparametric modeling approach dissects the stochastic volatility in macro time series into persistent and transitory components, the latter addi- tional component addressing outliers. Utilizing a Dirichlet process mixture prior for the transitory part and a collapsed Gibbs sampling algorithm, we devise a method capable of efficiently processing over 10,000 observations and 200 variables. Chapter 4 introduces a simple univariate model, while Chapter 5 presents comprehensive Bayesian VARs. Our al- gorithms, more efficient and effective in managing outliers than existing ones, are adept at handling extensive macro datasets with hundreds of variables.</p>

<p dir="ltr">Retail prices for eggs surged during the period from early 2022 to mid-2023 in the U.S. Eggs are important to a wide range of people because of their nutritional benefits and cost relative to other protein sources. Thus, rapidly increasing egg prices can cause risks to numerous people. Using social media listening data, we analyzed the relationship between egg prices and online and social media attention and the relationship between egg prices and online and social media sentiment. Our findings suggest that egg prices are associated with the sentiment of the public as expressed in online media. However, the relationship between egg prices and online and social media attention is complex when studying the timing of increased concern with the timing of online news media coverage. Importantly, by leveraging a method of regression discontinuity in time, we show that online and social media conversations about eggs and egg prices tend to increase after the rapid rise in online news coverage. Similarly, online and social media conversations about eggs and egg prices tend to decrease after the rapid rise in online news coverage. This research also provided an example of how a total number of statements and sentiment score of social media listening data can be utilized to capture people’s attention levels, overall sentiment, and how they change over time.</p>

<p dir="ltr">This dissertation advances the field of causal inference by developing methodologies in the face of assumption violations. Traditional causal inference methodologies hinge on a core set of assumptions, which are often violated in the complex landscape of modern experiments and observational studies. This dissertation proposes novel methodologies designed to address the challenges posed by single or multiple assumption violations. By applying these innovative approaches to real-world datasets, this research uncovers valuable insights that were previously inaccessible with existing methods. </p><p><br></p><p dir="ltr">First, three significant sources of complications in causal inference that are increasingly of interest are interference among individuals, nonadherence of individuals to their assigned treatments, and unintended missing outcomes. Interference exists if the outcome of an individual depends not only on its assigned treatment, but also on the assigned treatments for other units. It commonly arises when limited controls are placed on the interactions of individuals with one another during the course of an experiment. Treatment nonadherence frequently occurs in human subject experiments, as it can be unethical to force an individual to take their assigned treatment. Clinical trials, in particular, typically have subjects that do not adhere to their assigned treatments due to adverse side effects or intercurrent events. Missing values also commonly occur in clinical studies. For example, some patients may drop out of the study due to the side effects of the treatment. Failing to account for these considerations will generally yield unstable and biased inferences on treatment effects even in randomized experiments, but existing methodologies lack the ability to address all these challenges simultaneously. We propose a novel Bayesian methodology to fill this gap. </p><p><br></p><p dir="ltr">My subsequent research further addresses one of the limitations of the first project: a set of assumptions about interference structures that may be too restrictive in some practical settings. We introduce a concept of the ``degree of interference" (DoI), a latent variable capturing the interference structure. This concept allows for handling arbitrary, unknown interference structures to facilitate inference on causal estimands. </p><p><br></p><p dir="ltr">While randomized experiments offer a solid foundation for valid causal analysis, people are also interested in conducting causal inference using observational data due to the cost and difficulty of randomized experiments and the wide availability of observational data. Nonetheless, using observational data to infer causality requires us to rely on additional assumptions. A central assumption is that of \emph{ignorability}, which posits that the treatment is randomly assigned based on the variables (covariates) included in the dataset. While crucial, this assumption is often debatable, especially when treatments are assigned sequentially to optimize future outcomes. For instance, marketers typically adjust subsequent promotions based on responses to earlier ones and speculate on how customers might have reacted to alternative past promotions. This speculative behavior introduces latent confounders, which must be carefully addressed to prevent biased conclusions. </p><p dir="ltr">In the third project, we investigate these issues by studying sequences of promotional emails sent by a US retailer. We develop a novel Bayesian approach for causal inference from longitudinal observational data that accommodates noncompliance and latent sequential confounding. </p><p><br></p><p dir="ltr">Finally, we formulate the causal inference problem for the privatized data. In the era of digital expansion, the secure handling of sensitive data poses an intricate challenge that significantly influences research, policy-making, and technological innovation. As the collection of sensitive data becomes more widespread across academic, governmental, and corporate sectors, addressing the complex balance between making data accessible and safeguarding private information requires the development of sophisticated methods for analysis and reporting, which must include stringent privacy protections. Currently, the gold standard for maintaining this balance is Differential privacy. </p><p dir="ltr">Local differential privacy is a differential privacy paradigm in which individuals first apply a privacy mechanism to their data (often by adding noise) before transmitting the result to a curator. The noise for privacy results in additional bias and variance in their analyses. Thus, it is of great importance for analysts to incorporate the privacy noise into valid inference.</p><p dir="ltr">In this final project, we develop methodologies to infer causal effects from locally privatized data under randomized experiments. We present frequentist and Bayesian approaches and discuss the statistical properties of the estimators, such as consistency and optimality under various privacy scenarios.</p>

<p dir="ltr">As nations strive to reduce greenhouse gas emissions, the transformation of energy-intensive industries will significantly impact job quality and worker well-being. This thesis investigates the critical intersection of employment opportunities and just energy transitions in the context of industrial decarbonization, focusing on the U.S. steel sector. We address the challenge of balancing economic, environmental, and social considerations in the shift towards low-carbon manufacturing processes. Semi-structured interviews inform the development of a choice-based conjoint survey of Indiana steelworkers, which helps quantify worker preferences for various job attributes such as shift patterns, overtime hours, and wages. The analysis employs willingness-to-pay models to elucidate the complex relationships between compensation and working conditions in the context of potential changes brought about by renewable energy integration and electrification of steel production. Key findings reveal significant disutility associated with increased overtime hours and an unexpected preference for night shifts over day shifts among respondents. The research also highlights the importance of sociotechnical solutions that account for worker needs in designing decarbonized manufacturing processes. While acknowledging limitations such as potential sample bias, this thesis contributes to the development of integrated modeling approaches that combine worker preferences with operational constraints and energy costs. The results inform strategies for achieving a just energy transition in the steel industry, emphasizing the need for policies that prioritize worker well-being alongside decarbonization goals.</p>

No description available.

Notable structural changes have taken place in the world sheepmeat market over the 1960-80 period. Imports into the major consuming countries of the EEC are declining as a result of changing tastes, higher import barriers and other factors. World exports have steadily increased however, and sales diversified into a number of alternative, expanding markets. Little quantitative information exists on these markets. An econometric model was constructed to analyse the changes on a global basis. The model covers production, consumption and trade in the main importing and exporting regions over a twenty one year period. These components form a dynamic, simultaneous system which solves for the world price. It allows the impact of changes in any particular market to be evaluated in terms of the effect on other markets and international prices. Simulation analysis is employed to test the effects of various shocks to the market, and to evaluate the impacts of certain policy changes, such as those recently implemented in the EEC. The changes are assessed against a Base simulation, which also provides a forecast of the market situation through the 1980's. From the conclusions various policy implications are drawn with respect to NZ's exports.

<div> <p>First Essay: We study the effect of entry of ethanol plants on the spatial pattern of corn prices. We use pre- and post-entry data from corn elevators to implement a clean identification strategy that allows us to quantify how price effects vary with the size of the entrant (relative to local corn production) and with distance from the elevator to the entrant. We estimate Difference-In-Difference (DID) and DID-matching models with linear and non-linear distance specifications. We find that the average-sized entrant causes an increase in corn price that ranges from 10 to 15 cents per bushel at the plant’s location, depending on the model specification. We also find that, on average, the price effect dissipates 60 miles away from the plant. Our results indicate that the magnitude of the price effect as well as its spatial pattern vary substantially with the size of the entrant relative to local corn supply. Under our preferred model, the largest entrant in our sample causes an estimated price increase of 15 cents per bushel at the plant’s site and the price effect propagates over 100 miles away. In contrast, the smallest entrant causes a price increase of only 2 cents per bushel at the plant’s site and the price effect dissipates within 15 miles of the plant. Our results are qualitatively robust to the pre-treatment matching strategy, to whether spatial effects are assumed to be linear or nonlinear, and to placebo tests that falsify alternative explanations.</p><p><br></p></div> <p>Second Essay: We estimate the cost of transporting corn and the resulting degree of spatial differentiation among downstream firms that buy corn from upstream farmers and examine whether such differentiation softens competition enabling buyers to exert market power (defined as the ability to pay a price for corn that is below its marginal value product net of processing cost). We estimate a structural model of spatial competition using corn procurement data from the US state of Indiana from 2004 to 2014. We adopt a strategy that allows us to estimate firm-level structural parameters while using aggregate data. Our results return a transportation cost of 0.12 cents per bushel per mile (3% of the corn price under average conditions), which provides evidence of spatial differentiation among buyers. The estimated average markdown is $0.80 per bushel (16% of the average corn price in the sample), of which $0.34 is explained by spatial differentiation and the rest by the fact that firms operated under binding capacity constraints. We also find that corn prices paid to farmers at the mill gate are independent of distance between the plant and the farm, providing evidence that firms do not engage in spatial price discrimination. Finally, we evaluate the effect of hypothetical mergers on input markets and farm surplus. A merger between nearby ethanol producers eases competition, increases markdowns by 20%, and triggers a sizable reduction in farm surplus. In contrast, a merger between distant buyers has little effect on competition and markdowns.</p><p><br></p> Third Essay: We study the dynamic response of local corn prices to entry of ethanol plants. We use spatially explicit panel data on elevator-level corn prices and ethanol plant entry and capacity to estimate an autoregressive distributed lag model with instrumental variables. We find that the average-sized entrant has no impact on local corn prices the year of entry. However, the price subsequently rises and stabilizes after two years at a level that is about 10 cents per bushel higher than the pre-entry level. This price effect dissipates as the distance between elevators and plants increase. Our results imply that long-run (2 years) supply elasticity is smaller than short-run (year of entry) supply elasticity. This may be due to rotation benefits that induce farmers to revert back to soybeans, after switching to corn due to price signals the year the plant enters. Furthermore, our results, in combination with findings in essay 2 of this dissertation, indicate that ethanol plants are likely to use pricing strategies consistent with a static rather than dynamic oligopsony competition.

<p>This dissertation address two significant challenges in the causal inference workflow for Big Observational Data. The first is designing Big Observational Data with high-dimensional and heterogeneous covariates. The second is performing uncertainty quantification for estimates of causal estimands that are obtained from the application of black box machine learning algorithms on the designed Big Observational Data. The methodologies developed by addressing these challenges are applied for the design and analysis of Big Observational Data from a large public university in the United States. </p> <h4>Distributed Design</h4> <p>A fundamental issue in causal inference for Big Observational Data is confounding due to covariate imbalances between treatment groups. This can be addressed by designing the study prior to analysis. The design ensures that subjects in the different treatment groups that have comparable covariates are subclassified or matched together. Analyzing such a designed study helps to reduce biases arising from the confounding of covariates with treatment. Existing design methods, developed for traditional observational studies consisting of a single designer, can yield unsatisfactory designs with sub-optimum covariate balance for Big Observational Data due to their inability to accommodate the massive dimensionality, heterogeneity, and volume of the Big Data. We propose a new framework for the distributed design of Big Observational Data amongst collaborative designers. Our framework first assigns subsets of the high-dimensional and heterogeneous covariates to multiple designers. The designers then summarize their covariates into lower-dimensional quantities, share their summaries with the others, and design the study in parallel based on their assigned covariates and the summaries they receive. The final design is selected by comparing balance measures for all covariates across the candidates and identifying the best amongst the candidates. We perform simulation studies and analyze datasets from the 2016 Atlantic Causal Inference Conference Data Challenge to demonstrate the flexibility and power of our framework for constructing designs with good covariate balance from Big Observational Data.</p> <h4>Designed Bootstrap</h4> <p>The combination of modern machine learning algorithms with the nonparametric bootstrap can enable effective predictions and inferences on Big Observational Data. An increasingly prominent and critical objective in such analyses is to draw causal inferences from the Big Observational Data. A fundamental step in addressing this objective is to design the observational study prior to the application of machine learning algorithms. However, the application of the traditional nonparametric bootstrap on Big Observational Data requires excessive computational efforts. This is because every bootstrap sample would need to be re-designed under the traditional approach, which can be prohibitive in practice. We propose a design-based bootstrap for deriving causal inferences with reduced bias from the application of machine learning algorithms on Big Observational Data. Our bootstrap procedure operates by resampling from the original designed observational study. It eliminates the need for additional, costly design steps on each bootstrap sample that are performed under the standard nonparametric bootstrap. We demonstrate the computational efficiency of this procedure compared to the traditional nonparametric bootstrap, and its equivalency in terms of confidence interval coverage rates for the average treatment effects, by means of simulation studies and a real-life case study.</p> <h4>Case Study</h4> <p>We apply the distributed design and designed bootstrap methodologies in a case study involving institutional data from a large public university in the United States. The institutional data contains comprehensive information about the undergraduate students in the university, ranging from their academic records to on-campus activities. We study the causal effects of undergraduate students’ attempted course load on their academic performance based on a selection of covariates from these data. Ultimately, our real-life case study demonstrates how our methodologies enable researchers to effectively use straightforward design procedures to obtain valid causal inferences with reduced computational efforts from the application of machine learning algorithms on Big Observational Data.</p> <p><br></p>

<p>Platforms are now everywhere in our society. Some platforms share real-time information such that people can refer to many aspects, i.e., transportation, weather, news, etc. For example, online learning platforms can play a significant role in accelerating learning through things like providing more real-time feedback loops. Due to the recent innovation in mobile devices as well as faster networks, live streaming platforms become a new trend. Several usages of live streaming platforms are gaming experience sharing such as Twitch, or shopping experience like Amazon Live. My dissertation studies the strategic designs of different online platforms, especially how information affects users’ strategic behaviors and how it creates<br> different market outcomes.</p>