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Section 5 of the 1965 Voting Rights Act required covered jurisdictions—those deemed perniciously politically discriminatory to minorities—to preclear changes to their voting practices with the Department of Justice. By exploiting the use of a federally imposed threshold for how Section 5 coverage was applied in North Carolina, this article estimates the effect of coverage using a difference-in-differences design. This article finds that Section 5 coverage increased black voter registration by 14–19 percentage points, white registration by 10–13 percentage points, and overall voter turnout by 10–19 percentage points. Additional results for Democratic vote share suggest that some of this overall increase in turnout may have come from reactionary whites. This article finds that Section 5 coverage had a statistically and substantively meaningful effect on enfranchisement, although an effect consistent with the more modest of extant estimates in the literature.

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The 1965 Voting Rights Act (VRA) fundamentally changed the distribution of electoral power in the US South. We examine the consequences of this mass enfranchisement of Black people for the use of the carceral state—public and police—post-VRA. Did mass enfranchisement lead to increases in incarceration of Black people? We test this with new historical data on state and county prison intake data by race (1930–1985) in a series of difference-in-differences designs. We find that Section 5 of the VRA increased incarceration systematically in proportion to the electoral threat posed by Black voters. Our findings are robust to several tests of validity and temporal policy comparisons.

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A large literature considers the mid-century a key turning point in punitive public opinion in the United States. This article examines racial and geographic heterogeneity in changing public opinion during the mid-century using data on death penalty support from as early as 1953. I find that the punitive turn is characterized by divergence in death penalty support between Black and White people, and that White Southerners grew more supportive than Whites in the non-South from before to after the turn.

Creating Contestation: Adversarial Politics and Institutional Change in Early Modern England asks where one of the defining features of modern elections—public, adversarial political choice—came from. For much of England's early parliamentary history, representatives were not selected through competitive elections in the modern sense. Instead, candidates, patrons, and other local elites frequently bargained over who would be returned to Parliament, with the electorate largely affirming choices already made. During the long seventeenth century, these arrangements began to break down and contested elections became increasingly common.

I argue that this transformation cannot be understood simply as the electoral expression of a singular social or ideological cleavage. Contests emerged from failures of coordination among political elites, particularly conflicts among patrons whose authority to nominate representatives had previously rested on relatively stable hierarchies. The book investigates why those arrangements became harder to sustain, examining changes in economic power, religion, Crown–elite relations, the value of parliamentary office, and beliefs about hierarchy, deference, and the legitimacy of open political conflict. The paper uses extensive quantitative data on contestation, candidates, their challengers and the social and economic conditions of England's long 17th century drawn from primary and secondary sources.

The book uses this history to develop a broader account of how decentralized, informal institutions change. Institutional theories often identify conditions under which one arrangement should replace another without explaining how that transition actually unfolds, particularly when institutions are not centralized. I distinguish between the breakdown of an established practice within particular political relationships and the subsequent process through which new behavior spreads, generates new expectations, and becomes regularized. The English case is especially revealing because this transformation was not linear: electoral contestation expanded dramatically, then receded, and eventually became widespread again under different political and social conditions. By tracing these changes from individual electoral disputes to broader patterns of political behavior, the book explains the origins a central institution of democracy, but also the dynamic process through which institutions emerge, stabilize, and transform again.

How should a scientific community allocate its finite capacity for inquiry? The questions researchers ask condition what they can learn about the world. In political science, one widely invoked criterion for question selection is whether a question is motivated by a “puzzle,” often understood as an empirical observation that contradicts prevailing expectations. This heuristic can generate important insights, but it also shapes inquiry in ways that this paper argues are not fully appreciated. I argue that widespread reliance on this criterion creates a form of selection bias into inquiry. Questions expected to revise existing understanding receive attention, while questions expected primarily to confirm, refine, or increase confidence in existing knowledge are comparatively neglected. A discipline can accumulate an expanding collection of revisions without establishing how much confidence should be placed in the claims being revised. I illustrate this argument using a simple formal model of Bayesian learning. The model demonstrates how puzzle-, and surprise-driven research more broadly, privileges belief revision over the systematic refinement of existing claims with implications for how knowledge accumulates.

This paper examines how population growth and urbanization—core processes of modernization—shaped subnational political development in 19th-century Britain. Using new data on the universe of Members of Parliament, I show that constituencies with larger and denser populations followed sharply different political trajectories, experiencing greater electoral contestation and the declining presence of traditional elites. Democratic reform amplified divergent constituency trajectories; particularly by reallocating geographic political power toward growing urban areas, rather than expanding the electorate. By comparing estimation approaches that capture both (i) historical divergence and (ii) marginal causal effects, the paper shows that population is strongly associated with political development in the aggregate, yet has limited causal leverage at the margin. The population revolution mattered for Britain’s political modernization, not because it reshaped local politics through incremental population change, but instead via cumulative, long-run processes that jointly produced both dense, urban populations and a new political class.

Political scientists increasingly use conversations as treatments. Sometimes these conversations are conducted by humans, but often and increasingly they will be conducted by generative artificial intelligence (AI). AI makes it possible to scale treatments that are responsive, and rich in real-world relevance. But this same interactivity creates specific challenges for causal inference. A respondent may be randomly assigned to a conversational condition, but the conversation that follows is not merely received by the respondent. It is generated jointly by the respondent and the conversational agent and is thus endogenous to who the respondent is. Consequently, when the theoretical object of interest is the conversation itself – or particular messages, features, or other attributes of that conversation – randomization of assignment does not necessarily identify the causal quantity the researcher seeks to estimate. This paper develops a potential outcomes framework for causal inference with conversations, with broad application but particular relevance to AI-mediated interaction. We distinguish among several causal objects: assignment to a conversational condition, assignment to a conversational policy, opening messages, messages within a conversation, realized conversational features, and the full realized conversation. Each corresponds to a distinct estimand and a different set of identifying assumptions. While some of these quantities are identified by standard randomized designs, others require additional assumptions or research designs, including sequential assumptions, representations of conversational histories, or explicit message-level interventions. The framework clarifies these distinctions and provides a common language for defining, interpreting, and designing conversational experiments as conversations increasingly become objects of causal inquiry.

This is a description of your talk, which is a markdown file that can be all markdown-ified like any other post. Yay markdown!

This course was taught for an accredited prison university program–Mount Tamlpais College (formerly the Prison University Project)–in San Quentin State Prison in San Quentin, California. This course is a survey of Latin American history. It briefly considers the colonial period, and then spends most of its time on the modern era since independence. Drawing from primary source documents and the writings of historians, students explore the role of race, class, and gender in the formation of nations, and consider the ideas of “progress” and “backwardness,” “civilization” and “barbarism” in the political, economic, intellectual, and cultural development of the region. Through a comparative approach, the class provides students a broad understanding of the origins and the historical trends shaping modern Latin American states.

This course was taught for an accredited prison university program–Mount Tamlpais College (formerly the Prison University Project)–in San Quentin State Prison in San Quentin, California. This course introduces students to the process and problems of research, broadly defined. At the same time, students improve their critical reading, critical thinking and writing skills in preparation for more advanced coursework. The course examines research in both theory and practice–from what it means to search for fact (or truth ) across different disciplines–to the methods that researchers employ in their work. Our readings will focus on completed works of academic research, texts about the research process, as well as texts that allow us to improve our written communication skills. Through these readings and a series of guest presentations by practicing researchers in a variety of fields, we’ll ask ourselves what questions interest researchers in different disciplines, what problems or concerns arise in their research, and how they and their discipline search for fact, or truth. How, in essence, do they go about knowing what they know?

How do political scientists know what they know? How can you apply these strategies to answering a question about the political world that you are curious about? This course is an introduction to research design in political science for honors students. Students learn to evaluate the scientific research through the written work of other scholars. Students also develop a research proposal of their own—beginning with identifying a research question that is answerable and worth answering, reading the existing scholarly literature, and proposing a plan for answering that question.

Taught as part of a prison university teaching program (Tennessee Initiative for Higher Education) through Nashville State Community College at Turney Center Industrial Complex, a state prison in Only, Tennessee. Course provides an introduction to economic thinking and standard concepts and principles of the macroeconomy. By the end of the course, students understand inflation, money, debt, deficits, the unemployment rate, and economic growth. Problem sets and exams focus on students’ application of knowledge to analyzing how changes in the macroeconomy affect these concepts.

First year PhD course in political science. Course considers theoretical approaches and empirical designs for making causal inferences with data. The focus is on estimands and the logic of comparisons rather than the statistical implementation of particular designs. Course covers the potential outcomes framework, Lewis’ counterfactual theory, randomized experiments, instrumental variables, regression discontinuity designs, difference-in-differences and matching. Companion course to POLISCI 611 Intro to Theory.

This course answers the question of why political science is a science. The course answers this question by introducing students to the systematic study of politics. In the course, we investigate subtantive questions and problems in the areas of of governance, poverty and war. We ask why democracy and autocracy are distributed globally and historically in the way that they have been; we ask why some countries redistribute wealth more or less than others; and we ask why wrs are fought instead of being solved with a bargain. Along the way, we introduce key concepts and methods in political science. These include wht it means to build theories and generate hypotheses, how we test hypotheses with evidence, and how we use that evidence as a basis for policy recommendations.

I informally title this course Understanding Institutions to Change the World. This course is an undergraduate seminar that introduces students to the mechanics of specific institutions; theories of institutional origin, evolution and change; and asks students to engineer their own institution to solve a real-world problem that matters to them. Students consider both major works in institutional theory (e.g., North, Greif, Bicchieri), and readings on specific examples of institutions (e.g., prison gangs, car-parking, foot-binding, peace accords, and corruption).

This course is part of the First Year Constellation ‘How Does Social Control Operate in the Modern World?’. In the course, students and I consider the contemporary U.S. police and prison system from a political science perspective. Our units cover: police behavior (through theories of bureaucratic control), carceral contact and political participation (through theories of voter behavior), and prison gerrymandering (allowing us to explore the nature and geography of political representation).

This course is a graduate-level introduction to conducting research in political science. In the course, we focus on selected topics in the philosphy of science, question asking, theory building, and empirical research design, including designs for causal inference, selecting cases, and designing archival and field research. Though we can’t cover every topic in research design, students should leave the class with foundational skills and an understanding of what they don’t know, but could learn more about. In addition to the philosophical and conceptual work, students design and present a complete research proposal by the end of the term.

Practical Data Science I is a flipped-classroom, exercise and project-focused course. It requires zero prior experience with programming and begins with an introduction to Python, computational thinking, and the principles of good programming using the 7 Steps method. The class focus then shifts to data analysis with an emphasis on the type of analyses of interest to social scientists and public policy students. The course provides students with experience manipulating and analyzing real (often messy, error-ridden, and poorly documented) data using the full range of Python data science tools (like the command line, git, VS Code, numpy, pandas, matplotlib, statsmodels, and more).

Practical Data Science II is a flipped-classroom, exercise and project-focused course. Building on the computational thinking skills developed in Practical Data Science I, this course introduces students to a range of computational inquiry methods, including network analysis, geospatial analysis, advanced plotting, and natural language processing (NLP). Throughout, the focus will be on developing hands-on experience implementing these methods with messy real-world data to ensure students are prepared to deploy these tools to answer the questions they care about. Requirements: Practical Data Science I.