Economics

Permanent URI for this collectionhttps://digital.lib.washington.edu/handle/1773/4913

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Essays on Health Policy, Consumer Behavior, Social Beliefs, and Education
(2026-04-20) Chen, Cheng; Wen, Quan
This dissertation consists of three essays that study how policies, market environments, and social beliefs shape individual behavior and well-being. The first two essays focus on health policy and consumer behavior in the context of tobacco regulation, while the third essay examines the role of social beliefs within families in shaping educational outcomes.The first essay evaluates the effectiveness of state-level e-cigarette flavor restrictions using retail scanner data and a staggered difference-in-differences design. Using 2018–2019 retail scanner data and a staggered difference-in-differences design, we examine how state size affects policy evasion costs and the effectiveness of e-cigarette flavor restrictions. For every 10-mile increase in population-weighted average distance from borders, cross-border sales decrease by 4.6%. We find that flavor preferences play a greater role than loyalty to the e-cigarette product form in driving substitution: substitution toward menthol cigarettes (the primary flavored alternative) increases with distance from state borders, while substitution toward tobacco-flavored e-cigarettes (the non-flavored baseline permitted under bans) does not. Together, these results indicate that flavor bans’ effectiveness depends on geographic size and consumer access to alternatives, suggesting that evidence from local bans may systematically underestimate harmful substitution toward combustible products when implemented at broader scales. The second essay employs a structural demand model using retail scanner sales data and demographics in four U.S. states to assess the impact of e-cigarette and cigarette bans on different demographic groups. Our findings indicate that low-income consumers exhibit stronger product loyalty toward e-cigarettes, whereas Black consumers show a pronounced preference for flavored and menthol products. Areas with higher poverty rates or populations of young adults exhibit more inelastic demand for both cigarettes and e-cigarettes than areas with lower rates, suggesting that tax policies may be less effective in these populations. Our counterfactual analysis of banning different tobacco products suggests consumer segmentation: some consumers use e-cigarettes primarily for the flavors, while others do so because of the device itself or due to nicotine dependence. The third essay studies students’ second-order beliefs, defined as students’ perceptions of their parents’ beliefs, and their relationship with academic performance and psychological well-being. Using survey data from China, the essay documents that students who perceive their parents as holding gender-biased beliefs perform worse academically and report higher levels of unhappiness, even when parents’ own reported beliefs are not predictive of outcomes. To move beyond observational evidence, the essay proposes and develops a field experiment that measures parents’ and students’ beliefs separately and introduces randomized information revelation. This design provides a framework to study the causal effects of second-order beliefs, belief affirmation, and belief contradiction on students’ academic and mental health outcomes. Together, these essays contribute to a broader understanding of how policy design, market structure, and belief formation influence behavior across health and education contexts.
Bayesian Inference with Applications to Macroeconomics and Financial Market Price Discovery
(2026-04-20) Ma, Wenqiu; Zivot, Eric
This dissertation develops a unified Bayesian framework for analyzing and forecasting macroeconomic time series and for measuring price discovery in fragmented financial markets. Across three papers, it proposes flexible yet disciplined methodologies that exploit Bayesian shrinkage, stochastic volatility, and structural identification to handle large systems, noisy data, and time-varying dynamics while delivering finite-sample-relevant inference.The first chapter introduces a large time-varying parameter vector autoregression (TVP-VAR) with stochastic volatility and informative priors that shrink the richly parameterized model toward a parsimonious benchmark. This Bayesian design stabilizes estimation in high dimensions and mitigates overfitting. Using U.S. macroeconomic data of varying dimensions and levels of aggregation, the chapter provides strong empirical evidence that the proposed TVP-VAR improves forecast accuracy—both point and density—relative to standard benchmarks, thereby validating the model as a practical tool for macroeconomic forecasting. The second chapter turns to price discovery and develops a Bayesian vector error correction (BVECM) framework for structural analysis in cointegrated markets. By imposing an economically interpretable decomposition of shocks into permanent and transitory components, the framework yields structural price discovery measures that depend only on permanent innovations, abstracting from microstructure noise and temporary liquidity imbalances. Shrinkage priors tailored to noisy high-frequency data and full posterior inference provide coherent uncertainty quantification. Simulation studies and an empirical application to S&P 500 ETFs demonstrate that the proposed approach delivers robust, interpretable price discovery rankings and credible assessments of cross-market differences. The third chapter extends the analysis to time-varying price discovery by embedding these structural measures in a Bayesian order-invariant VAR with stochastic volatility. Mapping the reduced-form model into a broad family of price discovery statistics, it shows—both in stylized partial-adjustment settings and in applications—that measures grounded in the permanent component are robust to time-varying volatility and noise, while conventional metrics can be severely distorted. Collectively, the three chapters demonstrate how Bayesian inference provides a powerful and flexible toolkit for macroeconomic forecasting and the measurement of evolving information flows in modern financial markets.
Essays on Macroeconomics and Economic Modeling
(2026-04-20) Chen, Zihao; Chen, Yu-Chin
This dissertation contains three chapters on topics in macroeconomics and dynamic modeling in fishery economics. In Chapter 1, I study the effect of immigration on the natural rate of interest and household welfare in an economy subject to secular stagnation. I build a three-generation overlapping generations model with heterogeneous skills among native and immigrant workers, incorporating higher immigrant fertility and skill complementarity among high-skilled workers. A no-capital version of the model yields closed-form expressions for the natural rate, aggregate supply, and aggregate demand; an extended version with physical capital provides the quantitative analysis. Immigration unambiguously raises the natural rate through population growth: a decomposition of the decline in U.S. real interest rates between 1970 and 2015 shows that the immigration expansion raised the natural rate by 0.22 percentage points relative to a counterfactual with negligible immigration. The welfare implications are regime-dependent. At full employment, immigration lowers native welfare through wage competition, with low-skilled workers bearing the largest losses. Under secular stagnation, the ranking reverses: immigration reduces the severity of the demand shortfall, and the resulting employment gains make it welfare-improving for all household types, including natives. A policy experiment calibrated to a doubling of the H-1B visa cap shows that 85,000 additional high-skilled workers can reduce the initial output gap following a Great Recession--scale deleveraging shock from 7.3% to 1%. In Chapter 2, co-authored with Yu-Chin Chen and Pushpak Sarkar, we use machine learning techniques to re-examine the long-standing difficulty in predicting currency returns with macroeconomic indicators by focusing on three possible causes: the general lack of information in the macro predictors, mis-specifications in the forecasting equations, and inherent instabilities in the relationship between the exchange rate and its macro determinants. Using a large international dataset that captures current macroeconomic conditions as well as forward-looking market expectations and perceived uncertainties, we forecast monthly returns from 1995 onward of four major currencies (AUS, CAD, GBP, and JPY) against the USD. In in-sample regressions, we see that while market expectations embedded in derivatives markets may help predict subsequent exchange rate returns, there is little evidence that they contain predictive content above and beyond what is in the macro indicators themselves. Moreover, both types of predictors perform better in non-linear specifications than under the linear specifications which often deliver adjusted R^2 around zero. We take these findings as indicative that the exchange rate is not disconnected from indicators of the macroeconomy--be their current values or expectations, though their functional relation may be more nuanced than simple linear specifications can capture. Moving the analyses to pseudo out-of-sample (OOS) forecasts, we find that a multilayer perceptron neural network can generate improvements over the long-standing Random Walk benchmark, some of which are statistically significant under the Clark-West test. More prominently, we see that the majority of the ML methods considered do not outperform a RW forecast given our small sample context. In fact, unlike results for other asset returns, ML does not appear to help resolve the FX forecasting puzzle. Nevertheless, our ML explorations unveil significant empirical instabilities, especially around the GFC period. These findings support the views that pseudo-OOS exchange rate forecasting in finite samples can be overwhelmed by inherent statistical issues such as parameter and model instabilities, and that the exchange rate dynamics are inherently difficult to distinguish from a RW process statistically (Engle and West, 2005). They also indicate that predicting exchange rates are a different endeavor from predicting bond yields. Chapter 3, co-authored with Christopher M. Anderson, applies a similar dynamic modeling framework to fishery management, where institutional design shapes economic outcomes even when biological targets are held fixed. The maximum economic yield (MEY) is increasingly adopted as a fishery management objective, yet standard bioeconomic models treat the cost of harvesting as independent of institutional design. We develop a dynamic model in which biological dynamics are common across management regimes while the law of motion for fishing effort depends on the institutional environment. Under open access, profit-driven entry dissipates rents along the extensive margin. Under limited entry, competition shifts to the intensive margin through capacity investment. Under a total allowable catch (TAC), fishers compete for quota shares by expanding capacity, inflating costs even when the biological target is met. Under individual fishing quotas (IFQ), secure harvest shares eliminate the strategic motive for overcapitalization, and fishers instead minimize costs. Phase diagrams, numerical simulations, and comparisons to the static bioeconomic model show that regimes achieving identical biological outcomes can generate very different economic rents. In our calibration, steady state profit under IFQ is roughly double that under TAC at the same stock and harvest level. The entire gap is attributable to endogenous differences in the equilibrium cost structure. We introduce the concept of an implementable steady state set: the frontier of achievable stock-profit combinations specific to each regime. The sole owner MEY may lie outside the feasible set of regimes that leave competitive margins open, making it an unreliable target for decentralized fisheries. Analytically, IFQ can replicate the sole owner optimum when fleet size and harvest cap are jointly chosen, but this level of coordination is rarely available in practice. These results imply that instrument choice matters as much as target-setting for economic outcomes in fisheries.
Inside the Firm: How Employee Knowledge and Ownership Shape Corporate Decisions
(2026-02-05) Karimi, Mahtab; Zivot, Eric; Harford, Jarrad
A large share of a firm’s day to day operations is carried out by rank and fileemployees, yet little is known about how their information and incentives shape important corporate outcomes. In the first chapter, I assemble a new dataset on Employee Stock Purchase Plan activity for a set of public firms and ask whether non executives provide useful signals in mergers and acquisitions. I find that acquirer abnormal returns and combined acquirer target synergies at announcement both increase with the target firm’s non executive ESPP purchase ratio, suggesting that employee trading behavior contains information about the quality of the deal. The second chapter examines whether employee ownership helps protect firms from data breaches. Employees can observe and monitor one another in ways managers cannot, and ownership strengthens this incentive. Using the share of active ESOP participants as a measure of monitoring strength, I find that firms with higher active ratios are less likely to experience data breaches, although this effect weakens in larger firms. I also show that firms increase their active ratio by about three percent after their first breach, and by six to ten percent following breaches with impacts at least as large as the twenty fifth percentile in their industry. Overall, the findings highlight the role of non executive employees in shaping acquisition outcomes and helping firms manage operational risks.
Essays in Contract Theory
(2026-02-05) Choung, Hae Yun; Lawarrée, Jacques; Khalil, Fahad
This dissertation comprises three essays in contract theory on the regulation of risky projects and the optimal organizational form of sequential projects. The first chapter analyzes how a legislature delegates authority to a regulator with expert information and a pro-firm bias to oversee firms that undertake socially risky activities, and shows that the legislature optimally grants more discretion when the regulator is less motivated and has a weaker bias. The second chapter studies the organization of a sequential project with design and construction, in which an owner chooses between unbundling and bundling the tasks; with moral hazard in design effort and private information about construction cost, bundling links the stages by allowing design incentives to depend on the reported cost type. This linkage is valuable when it is optimal to induce effort only from the efficient type, because construction-stage information rents can then be used to motivate design effort, so that bundling can dominate unbundling. The third chapter reviews the theoretical literature on public–private partnerships, summarizing how contractual and political factors influence their efficiency.
Essays on Housing Supply, Affordability, and Policy Incentives: Causal and Quantitative Approaches to Modeling Housing Market Dynamics and Policy Evaluation
(2026-02-05) Ng, Yvonne; Takahashi, Yuya
This dissertation advances the economic understanding of a dual challenge confronting policymakers in many cities with high rent burdens: housing affordability and supply constraints. It provides empirical evidence on the effectiveness of housing policies in addressing these challenges through a combination of descriptive analyses and causal inference methods that exploit spatial and temporal variation in housing and related data. The empirical analyses employ advanced difference-in-differences research designs developed in recent econometric literature. Complementing the empirical work, the dissertation develops a quantitative urban model of affordable housing, augmented with simulation exercises, to examine how such policies redistribute households, housing development, and prices across space, and to evaluate the welfare implications of these redistributions. Future work will structurally estimate this model using empirical data to assess the effectiveness of alternative affordable housing policies in expanding supply and improving affordability. The first chapter examines inclusionary zoning (IZ) policies—an increasingly common tool in many cities that use subsidies and tax exemptions to incentivize the inclusion of income-restricted units in market-rate developments. Focusing on a long-running IZ program in Seattle, we study how such policies shape the spatial distribution of new housing and affect neighborhood outcomes. Although program requirements are uniform, variation in local market conditions generates heterogeneous responses from developers and landlords. Leveraging policy changes over time and detailed rental microdata, we identify a key trade-off: in higher-income neighborhoods, rent discounts for subsidized units are larger, but developer participation is more sensitive to reductions in policy generosity. IZ also lowers nearby rents in lower-income areas but raises them in higher-income areas, consistent with direct competition in the former and building-induced neighborhood changes in the latter. The second chapter develops a quantitative spatial equilibrium model of an urban housing market that allows for features uncovered in the first chapter. The model includes heterogeneous households and profit-maximizing developers to evaluate the general equilibrium effects of an inclusionary zoning policy. The model extends Ahlfeldt, Redding, Sturm, and Wolf (2015) by explicitly modeling developers’ building and participation decisions under endogenous amenities and heterogeneous housing quality. Developers choose whether to construct market-rate or MFTE housing and determine building intensity based on local market rents and program parameters. On the demand side, high- and low-skilled households select residential locations based on wages, amenities, and rents, with low-skilled households eligible for rationed MFTE units. Amenities evolve endogenously with neighborhood housing stock, generating feedback between development and residential demand. The model is closed with equilibrium conditions that ensure market clearing in market-rate housing, rationing in MFTE housing, and dynamic consistency in housing stock evolution. Simulations illustrate how the MFTE program can influence developer entry, clustering of new construction, and access to high-amenity locations for lower-income households, thereby providing a framework to quantify the welfare and spatial redistribution effects of affordable housing policy. The third chapter examines a growing source of pressure on rental housing supply in recent decades: the expansion of investment properties used for short-term vacation rentals. I exploit the economic shocks of the COVID-19 pandemic as a natural experiment to study how property owners reallocated investment homes between short-term rental (STR) markets, such as Airbnb, and long-term rental (LTR) markets. Before the pandemic, many homeowners increasingly used Airbnb to rent investment properties to tourists rather than residents, raising concerns about the platform’s impact on housing affordability in supply-constrained markets. Using a continuous difference-in-differences design, I estimate that a 1 percentage point (pp) greater exposure to pre-pandemic tourism demand led to a 1.2–2.1 pp decline in Airbnb listings on average. I also provide evidence that Airbnb hosts shifted to LTR markets, as reflected in short-run increases in LTR rents during 2020. The effects were strongest among owners of two- to three-bedroom properties in areas with higher ownership costs—such as mortgage payments and property taxes—suggesting possible heterogeneous responses driven by homeowner liquidity constraints or financial leverage.
Essays on Policy, Consumer Behavior, and Health
(2026-02-05) Hong, Lucy; Wen, Quan
My dissertation consists of three chapters that examine how public policies and local markets influence consumer behavior and health. The first chapter examines the effectiveness of e-cigarette flavor restrictions and the role of geographic spillovers in policy evaluation. Using retail scanner data from 2018 to 2019 and a staggered difference-in-differences design, we analyze how state size influences policy evasion costs and the impact of flavor bans. The results show that flavor preferences play a greater role than e-cigarette loyalty in product substitution. Consumers’ distance to the borders affects their substitution toward menthol cigarettes but not toward tobacco-flavored (unflavored) e-cigarettes. Cross-border sales decrease by 4.6% for every 10-mile increase in population-weighted average distance from borders. Our findings suggest that evidence from small geographic areas, where evasion is easier, may underestimate harmful substitution and overstate the effectiveness of broader policy implementation. Building on these findings, the second chapter examines the heterogeneity in consumer responses across demographic groups. Using a structural demand model and retail scanner data with demographics from four U.S. states, this chapter identifies preferences for e-cigarette and cigarette products across demographic groups. Our results indicate that areas with higher poverty rates or larger populations of young adults exhibit more inelastic demand for both cigarettes and e-cigarettes than areas with lower rates, suggesting that tax policies may be less effective in these populations. Our counterfactual analysis of bans on different tobacco products suggests that some consumers use e-cigarettes primarily for their flavors, while others do so because of the device itself or due to nicotine dependence. The third chapter examines how pricing decisions among stores influence one another, as well as how these patterns differ between healthy and unhealthy foods for different store types. I use spatial panel regressions to account for the stores' geographic proximity. The results reveal significant local price interactions among small stores, whereas grocery stores and supermarkets show weak effects. Local price interactions among small stores could exacerbate disparities in access to nutritious foods, particularly in areas with limited supermarket availability and a high concentration of small stores.
Essays on Consumer Preferences and Demand for Aquaculture and Wild Fish
(2026-02-05) Shen, Minyan; Anderson, Chris; Griffith, Alan
This dissertation comprises three empirical chapters investigating U.S. consumers' perceptions and demand for aquaculture products within the broader animal protein market. Using both revealed and stated preference methods, I analyze how environmental concerns, production methods, and ecological shocks influence consumer behavior, while developing improved demand forecasting tools for perishable protein products. \textbf{Chapter 1} addresses a fundamental gap in the animal protein literature by examining consumer choices across fish, chicken, and beef simultaneously, reflecting the real-world choices set for consumer. Based on survey data from 1,274 consumers across seven Seattle farmers markets (June–September 2019), we estimate willingness-to-pay (WTP) for key product attributes using a mixed logit model. The results reveal a clear asymmetry in production method preferences: free-range terrestrial proteins command a premium of \$9.21 per pound relative to conventional alternatives, and wild-caught fish secure an additional \$3.49 premium, whereas farmed fish suffers a distinctive \$2.29 discount. This anti-aquaculture bias is particularly pronounced for whitefish (–\$5.03), while wild steelhead trout accounts for the majority of the wild fish premium. Further heterogeneity analysis indicates that environmentally conscious consumers penalize wild fish due to overfishing concerns, while health-motivated segments disproportionately avoid beef. These findings highlight aquaculture's unique market challenges. \textbf{Chapter 2} leverages grocery scanner data(2016 -2020) from Puget Sound retailer and event study methodology to examine how high profiled salmon related ecological disruptions—a 2017 farmed salmon spill and 2018 orca mourning event—impact Seattle-area salmon demand. While the 2017 Atlantic salmon escape generated no measurable market response, the 2018 orca mourning event triggered immediate and persistent demand shifts, with consumers substituting toward wild-caught alternatives ($\beta = 0.019$, $p<0.001$) and away from farmed salmon ($\beta = -0.014$, $p\approx0$). Counterfactual analysis reveals a 25-40\% sustained reduction in farmed salmon market share, demonstrating how ecological disruptions can reveal latent consumer preferences and reconfigure market structure through aquaculture externality narratives. \textbf{Chapter 3} focuses on accurate demand forecasting for perishable protein products such as meat and fish, where even small errors can lead to spoilage, revenue losses, and environmental costs. This chapter examines the use of machine learning applied to weekly retail scanner data to improve demand prediction for animal protein products. A systematic comparison is made between classical parametric approaches (e.g., SARIMA, regression) and modern non-parametric ensemble methods (e.g., Random Forest, LightGBM, XGBoost), with the objective of capturing nonlinear dynamics and heterogeneity in consumer behavior. The results show that tuned gradient boosting models substantially outperform traditional benchmarks: the best XGBoost specification achieved an RMSE of 119.2 and an $R^2$ of 0.82, compared to SARIMA with an RMSE of 185.3 and $R^2$ of 0.23. In percentage terms, this corresponds to a reduction of forecast error by nearly 70\% relative to baseline seasonal naïve models. An anomalous finding is that a simple median forecast produced the lowest MAPE (2.24\%), outperforming even tuned XGBoost (2.96\%), reflecting the bias of percentage-based metrics in intermittent demand settings. Feature importance analysis confirms the economic relevance of predictors: price and promotions drive short-term fluctuations, lagged sales capture stockpiling and purchase regularity, brand and store identifiers reflect consumer heterogeneity, and seasonal indicators align with calendar-driven consumption cycles. These findings advance the forecasting literature by linking machine learning outcomes with economic interpretation, while also providing actionable insights for retail managers and policy makers. Improved forecasts can enhance pricing and promotional strategies, reduce food waste through better inventory management, and support more sustainable supply chains. The study concludes by noting key limitations—including missing causal variables, evaluation metric biases, and context-specific scope—and outlines future directions involving richer data integration, SHAP-based interpretability, and pipeline approaches to study cross-product substitution effects. Collectively, this research advances understanding of aquaculture demand dynamics, quantifies the market impacts of ecological narratives, and delivers practical forecasting improvements for perishable animal protein products supply chains.
Essays on Self-employment, the Gig Economy, and Labor Market Policy
(2025-10-02) Choi, Kisan; Ghironi, Fabio
What options are available to a worker who loses their job today? For many, self-employment provides an alternative source of income in the absence of wage employment. The recent rise of digital labor platforms has introduced a new form of self-employment—commonly referred to as "gig work"—characterized by immediate and flexible access to income-generating opportunities. A decade ago, most such platforms were virtually nonexistent; today, they account for a sizable share of labor market activity, particularly among lower-income workers. Despite growing attention, the aggregate effects of gig work remain insufficiently understood. This dissertation aims to fill this gap by developing a macroeconomic model and analyzing how the expansion of gig work reshapes economic outcomes and policies. Chapter 1 examines gig work as a novel form of self-employment that offers insurance against labor market risk. I develop a quantitative model that captures distinct characteristics of gig jobs—such as low entry barriers, high flexibility, and relatively low earnings—and calibrate it to replicate key patterns in the U.S. labor market. The results show that the availability of gig work reduces unemployment, particularly benefiting low-skilled and low-wealth individuals. Welfare improves, especially for unemployed workers who are ineligible for unemployment insurance, as gig work provides an alternative form of income support. However, aggregate productivity declines due to a shift of labor into the low-productivity sector. Transition dynamics reveal a trade-off: while gig work cushions the rise in unemployment during economic downturns, it also slows recovery. Policymakers should consider this trade-off when considering how to regulate gig work. Building on these insights, Chapter 2 investigates how the availability of fallback self-employment—especially flexible gig work—alters the effectiveness of labor market policies. I evaluate five key policy instruments: unemployment insurance (UI), firing costs, worker bargaining power (unionization), hiring subsidies, and job-matching efficiency improvements. Counterfactual simulations reveal that policy effects vary significantly depending on whether fallback work is available and how workers transition across occupations. Gig work amplifies the effects of UI benefit changes, mitigates the adverse impact of higher firing costs and stronger bargaining power, and reduces the effectiveness of active labor market programs. These effects operate primarily through gig work's role as an insurance mechanism and a substitute for unemployment. These findings highlight the importance of designing labor market policies that account for the growing prevalence of flexible employment arrangements.
Essays on the Drivers and Impacts of Clean Cooking Energy Transitions in India
(2025-10-02) Ninan, Theradapuzha Varghese; Heath, Rachel M
Household air pollution from cooking with solid fuels remains one of the most significant health hazards in low- and middle-income countries. This dissertation comprises three chapters that explore the underlying reasons behind the persistent use of solid fuels for cooking and examines the potential consequences of transitioning to cleaner alternatives. In chapter 1, I study the impact of providing access to clean cooking technology on learning outcomes of children. About a third of the global population relies on solid fuels for cooking with significant known impacts on health and time use. However, there is limited evidence on the impact of this reliance on further downstream outcomes like children's educational attainment. This paper is the first to examine the impact of gaining access to liquefied petroleum gas (LPG) as a fuel for cooking on children's learning outcomes. Leveraging the variation in the district level intensity of the Pradhan Mantri Ujjwala Yojana (PMUY), one of the world's largest clean cooking transition program, and nationally representative data on foundational learning from India, I find that increased access to LPG leads to improved learning outcomes in both reading and math for primary school-aged children. Specifically, a 30 percentage-point improvement in PMUY coverage leads to an improvement in overall learning of approximately 0.1 standard deviations. This effect is comparable in magnitude to the median impact of of some of the most well-known interventions that directly target learning outcomes in developing countries. I also find larger positive impacts on learning for girls, households in the middle of the wealth distribution, and older children. I provide evidence for the fact that these effects are driven by improved health of children, which may allow them to participate more in activities that aid learning like attending school more regularly. In chapter 2, I explore the link between living in a multi-generational traditional household structure and use of solid fuels for cooking in a patriarchal society. I do this by looking at the effect of the father-in-law's death on the choice of household cooking fuel in India. Using a difference- in-differences model with household fixed effects, I find that the probability of using biomass for cooking is lower by about 6 percent in households where a co-residing father-in-law died compared to a household where they did not. I also find that the probability of collecting every major solid fuel is lower as well. I provide evidence for this effect to be driven primarily by the father-in-law's preference for food that is cooked in the traditional way without the use of modern equipment. In chapter 3, I examine whether providing information about the health risks of cooking with solid fuels for primary cooks and children can change beliefs and behaviors, and whether the identity of the information recipient within the household matters. We test this using a cluster randomized controlled trial with 2000 households in rural India where we inform either the (male) household head or the (female) primary cook about the actual health risks of cooking with solid fuels. An endline survey conducted 16 weeks later shows that information provision leads to belief updating, especially when the primary cook is informed. Information also transmits within households, both from the primary cook to the household head and vice-versa. However, despite these changes in beliefs, we find no evidence of increased adoption of clean cooking fuels or other mitigating behavior. One likely reason that we find in our analysis is that the changes in beliefs are concentrated among primary cooks with low intrahousehold bargaining power at baseline, who are unable to effect change. Together, the three chapters of this dissertation offer novel and important evidence on the drivers and consequences of cooking with solid fuels. They also make significant contributions to the literature on children's learning, intrahousehold dynamics of decision-making and the role of beliefs in shaping behavior in the context of developing countries.
Essays on Large Bayesian VARs with COVID Volatility
(2025-10-02) Joshi, Sudiksha; Zivot, Eric; Giannone, Domenico
Chapter 1 introduces covbayesvar, a Python package for estimating large Bayesian Vector Autoregression(BVAR) models, that account for COVID-induced volatility. The package enables us to estimate the model with hierarchical prior selection when the parameters proliferate, accounting for structural shifts in macroeconomic and financial data during the pandemic. Incorporating various priors, it is versatile enough to answer wide-ranging policy-related questions. With detailed programming examples, I explain how to apply the functions on monthly and quarterly macro and financial data to construct unconditional and conditional forecasts, scenario analysis, assess joint predictive densities of variables, examine structural breaks during and after COVID, and how we can employ entropic tilting to modify the forecast distribution and construct forecasts conditional on long-term targets set by the Federal Reserve Bank. Accessible via PyPI, the package includes extensive documentation and code examples. covbayesvar advances the state-of-the-art in open-source econometric and statistical software, offering researchers a robust tool for analyzing large-scale systems under unprecedented uncertainty. The remainder of the chapters in the dissertation employ the python package detailed in chapter1 to answer a plethora of policy-making questions. For instance, chapter 2 examines the cyclicality of the financial intermediation variables before the 2008 Global Financial Crisis (GFC) using a large BVAR model with 43 variables. To establish stylized facts on the cyclical behavior, I construct reducedform scenario analyses where unemployment rate rises by 1 pp, and illustrate the predictive densities at various quantiles. From the scenario analyses, we observe that M1 behaves counter-cyclically during downturns in business cycles i.e. M1 rises when the real economy contracts such as when the industrial production declines and/ or the unemployment rate spikes. Measures of credit such as real estate loans, real commercial and industrial loans and consumer loans are procyclical. Leverage of securities brokers and dealers, and non-financial businesses are countercyclical in the short run, but procyclical in the medium and long run. On the other hand, tier 1 leverage capital, a measure of capital adequacy, is procyclical in the short run, but countercyclical in the medium and long run. A counterfactual analysis from 2008 to 2024 using a large BVAR with COVID volatility model reveals that financial intermediation variables significantly deviate from historical trends - most prominently evident for monetary aggregates, credit and loan supply, and interest rates. Probing the cylicality of financial intermediary variables is crucial to understand how stress testingscenarios affect the trajectory of financial intermediation, and asses if the responses are consistent with the previously defined stylized facts. The Fed releases stress test projections every February, which contain the projected paths of macro and financial variables for the next 3 years in downturn and severely recessionary environments. Conditioning on these scenarios, they forecast disaggregated data for each bank separately, and sum the values to calibrate the system wide effects. This may miss general equilibrium, spillover and systemic effects. How can we condition the forecasts from a large BVAR on the stress test scenario values for multiple periods at the aggregated level? In chaptr 3, I employ a method known as entropic tilting to alter the forecast distribution to be as close as possible to the scenario paths. I also show how we can extend the entropic tilting method to condition the forecasts of macro and financial intermediary variables on the short and medium-run stress testing scenarios. Then, I show that the forecasts from this novel entropic-tilted approach are very close to the forecasts from the baseline stress testing scenario. How do contractionary monetary policy shocks affect the financial intermediary variables? Answeringthis question is germane in today's environment, where the financial and macroeconomic variables are highly interdependent with some degree of uncertainty around monetary policy actions. Financial intermediaries serve as conduits to effectively transmit monetary policy to the broader economy by not only altering the supply of credit but also impacting asset prices, liquidity, and appetite for risk across sectors. Much of the Bayesian VAR literature has emphasized real macro aggregates such as output, inflation, and employment, overlooking balance sheet variables of financial intermediaries. When the Fed tightens monetary policy, adding the financial intermediary variables is crucial to extricate the elasticities of balance sheets such as loan volumes, leverage, and monetary aggregates. In chapter 4, I examine two scenarios. First, how does a surprise 100bps hike in monetary policy that is structurally recursively identified affect the flow of funds. Second, if agents expect that the Fed will hike the federal funds rate 100 bps 8-12 quarters into the future, how do the responses of the flow of funds compare? Extending the closed-economy analysis of chapter 2, in chapter 5, I broaden the scope to an openeconomicsystem. Now, what is the cyclicality of international economic indicators, and are there any structural breaks? This provides a quick overview of the correlations and co-movements of the international economic variables and sheds light on whether the 2008 GFC altered those patterns. I conclude that there are no structural breaks - treasury securities held by foreign investors and import price index are countercyclical; export price index, real exports and imports are procyclical. Then, with the recent developments in the trade war, I model a few scenarios: What are the implications of the Reverse Greenspan shocks: reduced foreign purchases of US Treasuries? Most importantly, how do the tariffs affect the US aggregate economy? I gauge the effects using a novel approach of changes in the import price index. A cost-push shock, this is a stagflationary scenario analogous to a recursively identified IRF with slow-moving macro and fast-moving financial variables, generating structurally interpretable responses. Then, I extend the analysis using finer-grained sector-specific disaggregated data to evaluate the impact of tariffs on various sectors of the US economy, such as the services, durables, non-durables, retail sector, etc, using a very large dataset of 127 variables.
Bank Competition and Capital Flow Shocks in Open Economies
(2025-08-01) Khurana, Mikita; Ghironi, Fabio
This dissertation examines the interactions between sudden stops in international capital flows, the structure and behavior of the banking sector, and the broader dynamics of external debt across economic sectors in emerging and advanced economies.Chapter 1 investigates the consequences of sudden stops for the competitive landscape of the banking sector and, in turn, how changes in the latter amplify the effects of sudden stops. Using data for 46 emerging economies, I present evidence of a reduction in banking competition following sudden stop episodes. A small open economy model with imperfectly competitive banks that face an occasionally binding collateral constraint can explain this evidence and other standard effects of sudden stops on the economy. Entry and exit of banks influence market power in the banking sector. The diminished availability of external funds during sudden stops causes the sector to contract, resulting in a reduced number of banks. This amplifies market concentration and allows surviving banks to exercise stronger monopoly power. In turn, this results in higher loan rates, exacerbating borrowing costs for firms and households, and amplifying the negative consequences of sudden stops for the aggregate economy. Chapter 2 introduces heterogeneous banks into a small open economy dynamic stochastic general equilibrium (DSGE) framework to study how endogenous market structure interacts with macroeconomic shocks. Banks differ in productivity governed by Pareto distribution. Two structural shocks, a permanent reduction in bank entry costs and a permanent increase in government spending are analyzed to understand their effects on credit markets and aggregate outcomes. Lower entry costs increase selection pressure, raising average bank productivity and expanding credit despite a declining number of operating banks. Conversely, higher government spending boosts loan demand, lowers the profitability threshold for entry, and draws more marginal banks into operation. The degree of productivity heterogeneity, shaped by the thickness of the Pareto tail, governs the intensity of reallocation, market power dynamics, and the resulting changes in lending spreads. Chapter 3 turns to the sectoral composition of external debt. Leveraging a three-state Markov-switching model, I analyze portfolio and other investment debt flows to banks, corporates, and sovereigns across advanced and emerging economies. I uncover pronounced differences in the timing, persistence, and severity of regime shifts across sectors and regions, highlighting the importance of sector-specific vulnerabilities. In sum, this dissertation shows that the macroeconomic consequences of sudden stops are shaped not only by the availability of external finance but also by the structure and granularity of domestic financial intermediation. Bank competition, productivity heterogeneity, and the sectoral allocation of external debt jointly determine how external shocks transmit through the economy. The findings underscore the importance of accounting for institutional features of the financial sector when designing macroprudential and capital flow management policies.
Essays on Online Marketplaces
(2025-08-01) Hager, Lukas Gabriel Heisenberg; Takahashi, Yuya
This dissertation is three chapters on online marketplaces. The first marketplace that I investigate is online auction platforms, where I investigate the impact that default penalties have on bidding in common-value contexts. Due to the existence of the winners curse and the fact that bidders can observe all the bids after the auction closes, winning bidders have an incentive to decline to pay their bids when their signal about the item's value is not representative of the average bid. I build a structural model of bidding, and estiamte the parameters using data scraped from the internet, and calculate counterfactual default policies. The second marketplace that I analyze is Amazon's, where third-party sellers sometimes compete with Amazon itself in the market for a given product. The impact of Amazon participating as a seller in one of the markets that it hosts has received a lot of theoretical attention, but limited applied analysis. Using a large sample of Amazon products, we leverage differences-in-differences designs to isolate the impact of Amazon participating in such a market and find that Amazon entry drives prices down and increases sales, while not driving third-party sellers out of the market. These results suggest that entry by Amazon is beneficial for customers on its platform, potentially at the expense of third-party sellers. I conclude by formulating a dynamic model to answer the question of how third-party sellers should set prices and invest in their products given the presence of Amazon. This model builds off of the conclusions of the empirical work in Chapter 2, which motivates expanding the static analysis presented there to a dynamic setting to answer important questions about how third-party sellers operate in hybrid marketplaces, how the platforms enforce price discipline, and the welfare consequences thereof.
Essays on Applied Econometrics
(2025-08-01) Carneiro de Figueredo, Felipe; Heath, Rachel
This dissertation consists of three essays in applied econometrics that explore experimental, quasi-experimental, and observational methodologies to study political behavior, infrastructure-driven labor market change, and price elasticity of demand estimation. The first chapter examines the causal impact of spatial proximity on legislative behavior by exploiting the randomized allocation of offices in the Brazilian Chamber of Deputies. The analysis finds that legislators assigned to neighboring offices are significantly more likely to vote alike in contested decisions—an effect amplified when at least one legislator is a policy expert, such as a committee member. The results provide empirical support for cue-taking theories of legislative decision-making, suggesting that informal physical proximity enhances the influence of expertise, particularly in closely divided votes. The second chapter evaluates the labor market impacts of Brazil’s broadband expansion policy using a regression discontinuity design (RDD) on about 2,000 municipalities covered by microwave radio technology. The findings reveal heterogeneous effects: while the policy stimulates job creation among low-educated workers and in commerce-related sectors, it simultaneously reduces hours worked and wages, especially for highly educated individuals, skilled occupations, and women in services. These findings highlight the inherent trade-offs of digital infrastructure policies, wherein thesame technological improvements that foster inclusiveness and job growth can also precipitate labor market disruptions, possibly through automation and substitution effects. The third chapter applies a new approach to estimating price elasticity of demand by combining double machine learning (DML) with multimodal embeddings derived from product descriptions and images. This combination offers two key advantages. First, the embeddings capture rich, high-dimensional signals of product quality that are often unobserved but crucial in shaping both prices and consumer demand. By leveraging visual and textual features, the method provides a data-driven way to control for latent quality differences across products. Second, DML allows for flexible, machine-learning-based estimation of complex relationships—such as how price and demand depend on covariates—while still delivering valid causal estimates. Together, these tools offer a powerful solution to the endogeneity problem in demand estimation, substantially reducing bias from unobserved quality. Together, these essays demonstrate how causal inference can be empirically applied to diverse data environments to uncover the mechanisms shaping legislative peer effects, labor market outcomes, and consumer behavior.
Scaling Econometrics: Text Processing, Distributed Computing, and Experimental Design
(2025-08-01) Okar, Yigit; Fan, Yanqin
This dissertation develops new methodological approaches to address three fundamentalchallenges in modern econometrics: computational scalability in choice models, experimental design in digital markets, and the integration of unstructured text data. The first chapter addresses the computational challenges in estimating multinomial logistic regression mod- els with large choice sets. We introduce an iterative distributed computing estimator that dramatically reduces computational burden while preserving statistical efficiency. This estimator, when initialized with a consistent preliminary estimate, achieves asymptotic efficiency under a weak dominance condition. We develop a parametric bootstrap procedure for statistical inference and establish its consistency. Through extensive simulation studies, we demonstrate that our method achieves substantial computational gains while maintaining estimation accuracy, making it particularly valuable for applications in industrial organization and marketing where researchers face increasingly large choice sets. The second chapter tackles the methodological challenges inherent in e-commerce pricing experiments. While cluster randomization is necessary to prevent bias from spillover effects between substitute products, it introduces additional variation that can compromise statistical power. We develop a comprehensive analytical framework for understanding and managing these variance components. Our methodology makes several contributions: first, we provide a detailed decomposition of variance components in cluster randomized experiments; second, we introduce a novel binned estimator specifically designed for the high-kurtosis data common in e-commerce settings; and third, we evaluate various approaches to variance reduction including matched-pair designs, stratified randomization, and covariate adjustment. Through simulation of e-commerce data, we demonstrate that our proposed methods can improve power while maintaining robust inference. The binned estimator proves particularly effective, though we carefully describe the conditions under which it maintains unbiasedness. The third chapter presents a methodological breakthrough in the integration of textual data into econometric analysis. We develop a two-stage text regression methodology that leverages recent advances in transformer-based language models to capture rich semantic information and contextual nuances. The first stage employs state-of-the-art natural language processing techniques to represent textual data in a lower-dimensional space while preserving semantic relationships. The second stage develops an econometric framework for estimating the association between these text-derived features and economic outcomes. We demonstrate the methodology’s effectiveness through an application to online economics forums, showing substantial improvements in both predictive accuracy and interpretability compared to traditional bag-of-words approaches. This methodology opens new avenues for research across various subfields of economics, from labor economics to finance, where textual data may provide crucial insights into economic behavior and outcomes. Collectively, these chapters advance the frontier of empirical methods in economics by developing scalable solutions for modern data challenges. The methodological innovations presented here enable researchers to handle larger datasets, conduct more precise experiments, and incorporate richer forms of information into their analyses. While each chapter addresses a distinct challenge, they are united by a common theme: expanding the scope of feasible empirical research through methodological innovation. The tools and frameworks developed in this dissertation con- tribute to the growing toolkit available to empirical researchers, particularly those working with large-scale, complex, or unstructured data.
From Whales to Fish: Three Essays on Marine Resource Economics
(2025-08-01) Schamp, Abby; Anderson, Christopher M; Griffith, Alan
My dissertation considers how marine policies aimed at protecting vulnerable human communities and endangered ecological populations impact socio-economic outcomes. The first chapter uses a discrete choice experiment to estimate how changes in tour attributes affects Salish Sea tourist willingness to pay for whale watching tours. I find that tourists are willing to pay the most to view orcas from a close viewing distance for at least 40 minutes with less boats in proximity. My sample exhibits clear heterogeneity for the number of boats in proximity and the viewing distance, which I investigated with a latent class model. My second chapter develops a theoretical model to investigate how processor centered cooperatives and processor-allocated quota impact ex-vessel prices, market share, and quasi-rents for fishery harvesters and processors post-rationalization. My third chapter analyzes a behavioral experiment of my theoretical predictions from chapter two, using experimental data collected during research sessions with undergraduates acting as fishery harvesters and processors. Fisheries rationalization through individual harvest rights increases overall industry rent while also transferring rent from the processing sector to the harvesting sector. Both my theoretical model and experiment show that fishing cooperatives and allocating some harvest rights to processors both transfer rent from the harvesting sector to the processing sector, though this this rent transfer is not split equally within the processing sector. My research shows that processor centered cooperatives and processor-allocated quota both benefit low-cost processors, but only a combined policy with both cooperatives and processor quota benefits high-cost processors in a meaningful way. Managers and researchers designing community protections for high-cost processing plants providing local jobs should carefully consider these differential impacts. My research uses several different methods, including a discrete choice experiment, a theoretical model, and a behavioral experiment, to discuss policy protections for vulnerable human and endangered animal populations.
Essays on Role Models, Beliefs, and Aspirations of Secondary School Students
(2025-08-01) Di, Aurelia Aochun; Heath, Rachel M
Stereotypes about gender and ability can suppress female students’ aspirations, limit their academic performance relative to their true potential, and divert them from science fields regardless of their actual ability, leading to lower-paying jobs later. Role models have been shown to be a cost-effective way to counteract these. Further understanding on factors that influence the role model effectiveness is crucial for better designing and implementing these interventions. My first and second chapters contribute to this. In the first chapter, I study how role models with different success levels affect students’ academic performance and mental health. In a field experiment, I randomly assigned middle school students in China to see an interview with different types of role models during a weekly class meeting or participate in a non-academic class meeting in the same week. I find that students exposed to higher-achieving role models improved test scores by 0.07-0.18 s.d., whereas those exposed to moderately achieving role models experienced an average of 28.8% and 26.6% reduction in the likelihood of feeling depressed and stressed, respectively. Higher-achieving role models improve low-performing female students’ academic outcomes but negatively affect their mental health, as these female students invested more effort but found their improved performance still falling short of their elevated aspirations. This study highlights the negative impacts of role models on mental health as a cost for enhancing performance in an underperforming subgroup, emphasizing the need to consider mental health when implementing role model interventions. Role models challenge stereotypes and shape behaviors. Are these impacts simply driven by knowing role models’ real-life experiences? Can these effects be further enhanced by explicitly sharing anti-stereotyping views and practical strategies? In Chapter 2, I test this using a randomized controlled trial with 2719 middle school students in China. Treated classrooms were randomly assigned to see role models who discuss learning strategies, those who share anti-stereotyping perspectives, or those who combine both. I find that combining strategies and perspectives improves first-year students’ math scores by 0.07-0.10 s.d. Purely anti-stereotyping messages improve first-year girls’ academic outcomes but take longer to exert significant effects. Sharing only learning strategies does not raise academic outcomes and even increases mental health burdens. Second-year students did not derive any academic or psychological improvements from these treatments, suggesting the need to consider timing – introducing role models after students’ workloads and stress intensify can minimize potential benefits. Individuals tend to overestimate the stereotypes or conservativeness of people around them, leading to self-limiting behaviors. Do peer parents’ beliefs influence how students perceive their own parents? In Chapter 3, I investigate the impacts of peer parents’ gender-math stereotyping beliefs on academic performance and perceptions. Peer parents’ stereotyping beliefs negatively affect female students’ math scores of female students relative to males. These negative effects are significantly larger when stereotypes are held by parents of same-gender peers, causing female students to perceive math as more difficult and feel less confident about their future. However, these parent stereotypes of female peers do not change the students’ own parents’ stereotyping beliefs; instead, parents increase both time and monetary investments in their daughters. This paper highlights the importance of considering students’ perceptions of others when addressing gender-ability stereotypes in educational settings.
Essay on structural estimation of entry games in oligopoly
(2025-05-12) Inosaki, Ken; Takahashi, Yuya
This dissertation introduces a novel approach for estimating the structural parameters of demand, cost,and entry costs in a differentiated products model where product characteristics and input cost data are not observed for non-entrants. Traditional methods for entry game estimation rely on the product characteristics that are used as instruments to be observable for both entrants and non-entrants — a scenario that is uncommon in practice. I first provide an extension of the standard identification strategy that does not require such observability condition, but also demonstrate based on identification analysis as well as Monte-Carlo study that such an approach requires impractically large sample size. To overcome this limitation, I use the instrument-free methods proposed by Byrne et al. (2022) and Imai et al. (2024), which allow estimation of the demand and cost function by addressing the endogeneity of price using entrants' cost data. Building upon this foundation, I extend their framework to incorporate entry-exit decisions. My findings indicate that using both demand and cost data offers a more practical and effective estimation approach. I propose a data-augmented Markov Chain Monte Carlo (MCMC) estimationmethodanddemonstratethroughMonteCarlosimulationsthatthisapproachyieldsconsistent estimates. Furthermore, I apply the estimation techniques developed in this research to estimate the structural parameters of the Wisconsin nursing home market and discuss the social welfare implications of the Certificate of Need (CON) law. Counterfactual simulations reveal that abolishing the CON law would increase consumer and producer surplus by $868 million and $165 million, respectively, while government spending would rise by $700 million. I also estimate important market structures, such as labor/capital elasticities, entry costs, and the difference in the distribution of service quality between entrants and non-entrants.
Essays on Risk-Return Relation and Asset Pricing
(2025-05-12) Choi, Changhun; Kim, Chang-Jin
In this dissertation, I empirically examine stock market valuations, with a particular focus on the risk-return relation and the respective roles of cash flow and discount rate news.In Chapter 1, I empirically investigate the risk-return relation under the investors' subjective volatility expectations, which deviate from the rational expectations. I first derive the objective risk premium under the slow-moving subjective volatility expectations based on the theoretical model of Lochstoer and Muir (2022) and show that the slow-moving volatility expectation generates a lead-lag specification in the objective risk-return relation. Then, I develop and estimate an empirical model by employing the log-linear present value framework. The empirical results using U.S. monthly excess stock returns suggest that the slow-moving feature of volatility expectations and the lead-lag structure in the objective risk-return relation are both significantly identified from the data. The parameter estimates suggest that while the objective risk-return relation can be negative, the subjective risk-return relation remains strongly positive, aligning with the key prediction and assumption in Lochstoer and Muir (2022). Moreover, I find that incorporating subjective expectations that deviate from rational expectations helps explain the variation in the Sharpe ratio. In Chapter 2, I examine the subjective risk-return relation from the observed stock return data in the presence of information rigidity in investors' volatility expectations. Based on the present-value approach of Campbell and Shiller (1988), I develop an empirical model for excess stock returns by introducing the sticky information model of Mankiw and Reis (2002) into aggregate subjective volatility expectations, while using realized volatility to capture time-varying risk. The estimation results based on U.S. monthly excess stock returns and realized volatility suggest that a significant information rigidity component and a positive and statistically significant subjective risk-return relation are identified from the observed stock return data. Meanwhile, the restriction among parameters implied by the present-value approach is rejected, indicating that other factors may influence stock return variations. I suggest that investors' overextrapolative belief may help explain the rejection of the restriction. I also find a state-dependent information rigidity: it increases during a period with lower macroeconomic volatility. Consistent with the findings of Coibion and Gorodnichenko (2015), the estimation results indicate that the degree of information rigidity increased during the Great Moderation period. Chapter 3 explores the regime dependency and time variation of the relative importance of cash flow news and discount rate news in explaining excess stock return variance. To this end, I apply the variance decomposition method of Campbell and Ammer (1993) to the threshold VAR (TVAR) and the time-varying parameter VAR with stochastic volatilities (TVP-VAR-SV). To identify the regimes in the stock market, I use the Chicago Fed's financial condition index and investor sentiment index constructed by Baker and Wurgler (2006). The variance decomposition results using TVAR suggest that the contribution of discount rate news increases during tight financial conditions or high investor sentiment regimes. The result of TVP-SV-VAR indicates that cash flow news has become more important than discount rate news after the 1990s. I propose possible explanations for the results. First, the regime-dependent relative importance may be associated with the change in attention allocations of investors and the asymmetric stock return predictability across the regimes. Second, the reversal of the relative importance after the 1990s may be attributed to the less volatile discount rate news caused by increased information rigidity and changes in the return-earnings relationship after the onset of the Great Moderation.
Essays on Tribes and States
(2025-01-23) Makan, Resem; Heath, Rachel; Griffith, Alan
This dissertation comprises three chapters that study the interactions of institutions, culture, and economic outcomes. In Chapter 1, I ask what happens when a state absorbs a historically stateless, tribal society, and what the consequences and mechanisms are of such a process. At the turn of the 19th century the British Empire in the northeastern front of India drew an imperial border that divided a tribal people into administered versus un-administered regions. Using a spatial regression discontinuity design (RDD) to study the long-run effects of state exposure in the region, I find that the areas falling within the former British administrative border have higher years of schooling, higher rates of literacy, and more wealth today. Villages in the formerly administered regions also have better public goods/services and a smaller agricultural share in the labor force. Using census data I am also able to study time varying effects of this historical state exposure—gaps in literacy rates are very persistent with little signs of convergence even 70 years after independence in 1947. In uncovering deeper channels that are potentially driving these results, I find evidence of the emergence of pro-social traits: those formerly under the British state identify more strongly with non-kin members, reflecting an expansion of the in-group. This chapter thus contributes to our understanding of the immediate changes that occur in a society transitioning from tribe to state. Chapter 2 examines the impacts of a forced urbanization program implemented by the Indian government on the citizens of Mizoram, a mountainous, tribal state in the country's northeast. In response to an insurgent uprising in the 1960s, the government enacted a policy that forcibly relocated residents from over 500 villages into 103 designated “Grouping Centers” (GCs) to facilitate surveillance and control, while approximately 110 villages remained ungrouped. Official reports suggest that this policy was also intended to promote economic development within the largely rural population. Using a historical difference-in-differences approach between grouped and ungrouped villages, this study finds that the policy resulted in significant population divergence lasting into the long run. The analysis further reveals a modest reduction in agricultural employment share in the GCs, suggesting the possibility of structural transformation even in a highly agrarian, low-state capacity setting. Upon further investigation, I also find evidence that the institutional capacity of the GCs predict their ability to absorb the refugees, highlighting the importance of pull factors in achieving successful urbanization. In Chapter 3, I show how customary laws around land inheritance can shape spatial growth and polity size. Looking at two sets of tribes in India's Manipur state, I find that among the group practicing a chief-based custom of land inheritance, there is a tendency for villages to fragment into smaller ones. Sons of chiefs who are not in line to inherit land split up to establish villages of their own. The consequences of having to build villages from scratch are smaller village size and fewer public good amenities for the chief-based villages. This chapter therefore highlights the effect culture has on agglomeration and space. Lastly, Chapter 4 asks if geography in historical contexts can be treated as an endogenous left-hand-side variable. This paper provides evidence that groups can and do selectively migrate based on certain pre-existing practices. It examines the historical migration of two language families from Southern China, the Kra-Dai and Hmong-Mien. Due primarily to Han Chinese expansion, these groups moved into the Zomia Highlands of Southeast Asia. Despite facing the same pressure, however, their migration patterns differed: the Kra-Dai, traditionally practicing wet-rice cultivation along river valleys, resettled in flatter areas, while the Hmong-Mien, practicing slash-and-burn agriculture on hilly slopes, moved to rugged mountains. Furthermore they carried with them other institutions to the new lands. This chapter thus highlights the possibility of geography being endogenous in the sense that pre-determined factors at the group-level shape subsequent movements across time and space.

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