QMSA Thesis Projects – 2025 Cohort

Multi-site randomized control trials are increasingly prevalent in the social sciences, particularly for studying the effects of educational interventions. This paper synthesizes and evaluates approaches to char- acterizing how the effects of an intervention vary across sites and individuals, extending beyond the typical approach of estimating an average treatment effect. Working with data from a nationwide evaluation of charter schools, we deploy existing empirical Bayes and fully Bayesian methods to further an understanding of the distribution of site-specific effects. We then use randomization-based methods a to understand the distribution of individual-level effects within sites. The paper provides a novel comparison of the application of model-based and randomization-based inferential methods to the study of a multi-site experiment in edu- cation. Ultimately, this paper continues an ongoing push for a focus on heterogeneity in causal inference and introduces methods that grant researchers the ability to study individual-level variation alongside site-level variation.

This paper examines the relationship between corporate tax competition and income inequality using panel data from 243 prefecture-level cities in China over 15 years. The results suggest an association between lower corporate tax rates and higher income inequality. Spatial analysis reveals significant clustering of corporate tax rates across regions, implying that local tax decisions may be influenced by neighboring cities. Estimates from the Spatial Durbin Model indicate the presence of spatial spillover patterns: lower corporate income tax rates in adjacent areas are correlated with lower local inequality. These patterns remain consistent across several robustness checks, including the inclusion of control variables and alternative model specifications. Moreover, the association between corporate tax rates and inequality appears to differ by wage level. In high-wage regions, higher corporate tax rates are linked to higher income inequality, while in low-wage regions, the relationship may differ.

Does decriminalizing hard drugs affect the risk of car accidents? The answer is hard to predict because the consequences of such a policy can run in different directions. To the extent that hard drugs impair coordination, judgment, and reaction time, legalization may increase accidents by making the drugs more easily available. But decriminalization might reduce accidents if people replace alcohol with substances that make them less likely to drive dangerously or drive at all. The net effect is an open question. This paper aims to answer it by looking at Oregon’s Measure 110 as a natural experiment, using a synthetic difference-in-differences (DID) approach to compare accident trends before and after its implementation. The findings suggest that decriminalization had no detectable effect on the rate of car accidents.

This study systematically examines the determinants and pathways of intergenerational income elasticity (IGE) in China, highlighting the explanatory power of parental occupations. Using data from the China Family Panel Studies (CFPS) (2010–2022), we employ decomposition frameworks and layered mediation analysis to disentangle the complex mechanisms underlying income persistence. The findings reveal that parental income alone explains a limited portion of income mobility, while parental occupations emerge as significant independent contributors, even after controlling for education, urban-rural status, and regional economic conditions. Through mediation analysis, education years, industry selection, and social capital are identified as key channels through which parental occupations influence child income. The results underscore the importance of addressing structural advantages embedded in occupational hierarchies, suggesting that policy interventions should extend beyond educational improvements to include targeted measures addressing occupational barriers and inherited advantages.

This paper investigates the effect of incentives for educational achievement on parental investment decisions when children derive intrinsic benefits from skill acquisition. I incorporate endogenous child motivation into Berry’s (2015) model of the educational production process to investigate the effect of a conditional in-kind incentive on parental input decisions in secondary school contexts. Using individual-level data from India’s National Sample Survey and a differences-in-differences (DiD) design with propensity score matching, I analyse the implementation of the Tamilnadu Free Laptop Scheme (TFLS), which provided students of government-funded schools with free laptops conditional on passing their Higher Secondary Certificate Examination. I compare eligible government-school students to ineligible private-school students in the same year cohort to estimate the effect of the TFLS on household education expenditures. 

This thesis investigates the relationship between educational mismatch—specifically overqualification—and its implications for firm-level productivity and financial market performance in high-skill sectors across OECD countries. As labor markets become increasingly dynamic due to technological change and globalization, the misalignment between workers’ educational attainment and job requirements has become more prevalent, particularly in sectors such as finance and information technology. Despite growing attention to education mismatch in labor economics, limited empirical work explores how these inefficiencies extend beyond individual labor outcomes to affect firm productivity and stock market valuation. Using a panel dataset constructed from Eurostat and OECD STAN industry data spanning 2015–2023, this study employs a combination of Propensity Score Matching with Difference-in-Differences (PSM-DiD), Generalized Method of Moments (GMM), and Structural Equation Modeling (SEM) to assess both the direct and indirect pathways linking mismatch to productivity and financial performance. The results reveal that high levels of overqualification are significantly associated with lower firm productivity, which in turn is correlated with lower stock returns. These findings highlight the need for more responsive educational policies and labor allocation strategies in high-skill sectors to sustain both economic and financial stability.

Legal status operates as a powerful social marker in the U.S. immigration context, shaping public perceptions of migrants’ competence, trustworthiness, and societal value. While prior research has examined how static legal categories influence migrant experiences, less attention has been paid to how transitions between visa statuses affect public evaluations. This study investigates how different visa trajectories—upward, downward, and lateral shifts within the immigration hierarchy—shape stereotype attribution. Using a survey-based experimental design, respondents evaluated fictional migrant profiles undergoing various legal transitions. Drawing on the Stereotype Content Model (SCM), the study measures perceived competence and warmth, employing cluster analysis, propensity score matching, and mediation analysis to uncover how these social perceptions mediate the relationship between visa mobility and policy support. Special attention is paid to gendered dynamics, revealing how evaluations differ for female versus male migrants following the same visa pathways. The findings contribute to a more nuanced understanding of how both legal mobility and gender stratify public perceptions of migrants, informing debates on equity in immigration policy. By foregrounding the symbolic significance of visa transitions, this research challenges static classifications of migrant identity and highlights the socio-political consequences of legal status fluidity.
Keywords: Migrant

Global equity in vaccination represents a persistent challenge that transcends national boundaries and healthcare systems, particularly the lasting disparate access to COVID-19 vaccines during the pandemic. Despite increasing research attention to vaccine equity, this field lacks a comprehensive understanding of the evolution of research concepts and methodologies and their dynamic relationship with the impact of journals and authors.

Drawing from Web of Science and Scopus databases, a systematic analysis of peer-reviewed articles on vaccine equity will be conducted, employing natural language processing (NLP) for topic modeling on abstracts and structured extraction of methodological approaches from method section. The extracted information forms the foundation of constructing a multilayer network model that captures the network-based dynamics through intra- and interconnected layers representing research topics, methodology frameworks, and author and journal-related impact dynamics.

Our analytical approach proceeds in two steps. The first step examines the temporal evolution within individual network layers by tracking the evolving process of methodologies and research concepts for every two-year lag. The second step investigates inter-layer dynamics, focusing on the interplay between influential papers and authors. By constructing a neural network, we predict the methodology and research concepts through network-based metrics and pre-defined factors.

This study aims to illuminate historical patterns and propose evidence-based predictions for future trajectories in vaccine equity research.