Book Project

Cotton farmers

(with Avi Acharya and Maya Sen, Forthcoming, Spring 2018, Princeton University Press)

How did American racial attitudes originate? How can we make sense of consistent and stubborn regional divides on race and race-related questions? Why is the South more conservative than other parts of the country?

This book project tackles these questions by arguing that contemporary politics is shaped in part by the historical persistence of political attitudes. To explain contemporary attitudes on race and politics in the South, we center our argument on the “peculiar institution” that drove the South’s economy and politics for nearly 250 years: chattel slavery. Using extensive quantitative analyses and new sources of data, we show that whites who live in parts of the South that were reliant on slavery are today more conservative, more racially hostile, and less amenable to policies that could promote black progress. We also show that these patterns have persisted historically and are the direct consequences of the slaveholding history of this area, rather than being simply attributable to demographic factors (such as people moving around over time) or the large presence of minority populations in these areas today.

More Information

Working Papers

A Civil Rights March

(with Avi Acharya and Maya Sen)

In Shelby County v. Holder (2013), the Supreme Court struck down parts of the Voting Rights Act of 1965 on the argument that intervening history had attenuated many voting inequalities between blacks and whites. But how, where, and by how much have things changed, and does history still predict voting inequalities today? We show that parts of the American South where slavery was more prevalent in the 1860s are today areas with lower average black voter turnout, larger numbers of election lawsuits alleging race-related constitutional violations, and more racial polarization in party identification. To explain this, we develop a theory of behavioral path dependence, which we distinguish from other theories of path dependence. We show evidence of behavioral path dependence demonstrating that disfranchisement can linger over time and that the effects of restrictions on voting rights can persist culturally.

TSCS data structure

(with Adam Glynn, Working Paper)

Repeated measurements of the same countries, people, or groups over time form the foundation of many fields of quantitative political science. These measurements, sometimes called time-series cross-sectional (TSCS) data, can help researchers answer a variety of causal questions. Repeated measurements, however, can also lead to confusion about what causal question scholars are answering and what methods, data, and assumptions they need to do so. In this paper, we apply the developments in the statistical literature on causal inference to standard TSCS models and clarify how to nonparametrically define and identify certain TSCS quantities of interest within this context. The paper then describes a number of estimation strategies for these quantities, including inverse probability weighting and structural nested mean models. We show that some of these models will, under strong conditions, be equivalent to some traditional econometric models for TSCS data. This result connects two disparate methodological literatures and shows that some traditional TSCS methods can have a valid interpretation in counterfactual/potential outcomes models. We demonstrate these approaches through two empirical examples.

Pooling Poll Trends Across States
Smoothed support trends

The dynamics of presidential elections are usually monitored with state- and national-level polling data. Since polls are not conducted in every state on every day, we usually smooth across time in some way to make inferences; the support for the Democrat probably will not change drastically from day to day. But when support does change, how should it change? I introduce an extension of the usual Dynamic Linear Model to smooth time-trends across the political space. That is, with this model, two states that are politically similar should have similar dynamics of support.

Published Papers


Instrumental Variable Methods for Conditional Effects and Causal Interaction in Voter Mobilization Experiments, Journal of the American Statistical Association, Vol. 112, No. 518 (2017): 590-599
Joint effects push us forward

In democratic countries, voting is one of the most important ways for citizens to influence policy and hold their representative accountable. And yet, in the United States and many other countries, rates of voter turnout are alarmingly low. Every election cycle, mobilization efforts encourage citizens to vote and ensure that elections reflect the true will of the people. To establish the most effective way of encouraging voter turnout, this paper seeks to differentiate between (1) the \emph{synergy hypothesis} that multiple instances of voter contact increase the effectiveness of a single form of contact, and (2) the \emph{backlash hypothesis} that multiple instances of contact are less effective or even counterproductive. Remarkably, previous studies have been unable to compare these hypotheses because extant approaches to analyzing experiments with noncompliance cannot speak to questions of causal interaction. I resolve this impasse by extending the traditional instrumental variables framework to accommodate multiple treatment-instrument pairs, which allows for the estimation of conditional and interaction effects to adjudicate between synergy and backlash. The analysis of two voter mobilization field experiments provides the first evidence of backlash to follow-up contact and a cautionary tale about experimental design for these quantities.

Game changers: Detecting shifts in overdispersed count data, Political Analysis, Forthcoming (2017)

In this paper, I introduce a Bayesian model for detecting changepoints in a time series of overdispersed count data, such as contributions to candidates over the course of a campaign or counts of terrorist violence. While many extant changepoint models force researchers to choose the number of changepoint ex ante, this model incorporates a hierarchical Dirichlet process prior in order to estimate the number of changepoints as well as their location. This allows researchers to discover salient structural breaks and perform inference on the number of such breaks in a given time series. I demonstrate the usefulness of the model with applications to campaign contributions in the 2012 U.S. Republican presidential primary and incidences of global terrorism from 1970 to 2015.

  • Rochester Big Data Forum presentation (Oct 2012): (Video) (Slides)
  • Rochester American Politics Working Group (Mar 2013): (Slides)
A classic example of cognitive dissonance

(with Avi Acharya and Maya Sen)

The standard approach in positive political theory posits that action choices are the consequences of attitudes. Could it be, however, that an individual’s actions also affect her fundamental preferences? We present a broad theoretical framework that captures the simple, yet powerful, intuition that actions frequently alter attitudes as individuals seek to minimize cognitive dissonance. This framework is particularly appropriate for the study of political attitudes and enables political scientists to formally address important questions that have remained inadequately answered by conventional rational choice approaches – questions such as “What are the origins of partisanship?” and “What drives ethnic and racial attitudes?” We illustrate our ideas with three examples from the literature: (1) how partisanship emerges naturally in a two party system despite policy being multi-dimensional, (2) how ethnic or racial hostility increases after acts of violence, and (3) how interactions with people who express different views can lead to empathetic changes in political positions.


Analyzing Causal Mechanisms in Survey Experiments, Political Analysis, Conditionally Accepted (2016)

(with Avi Acharya and Maya Sen)

We present an approach to investigating causal mechanisms in experiments that include mediators, in particular survey experiments that provide or withhold information as in vignettes or conjoint designs. We propose an experimental design that can identify the controlled direct effect of a treatment and also, in some cases, what we call an intervention effect. These quantities can be used in ways to address substantive questions about causal mechanisms, and can be estimated with simple estimators using standard statistical software. We illustrate the approach via two examples, one on characteristics of U.S. Supreme Court nominees and the other on public perceptions of the democratic peace.

The Political Legacy of American Slavery, Forthcoming, Journal of Politics (2016)
The density of slavery in 1860

(with Avi Acharya and Maya Sen)

We show that contemporary differences in political attitudes across counties in the American South in part trace their origins to slavery’s prevalence more than 150 years ago. Whites who currently live in Southern counties that had high shares of slaves in 1860 are more likely to identify as a Republican, oppose affirmative action, and express racial resentment and colder feelings toward blacks. These results cannot be explained by existing theories, including the theory of contemporary racial threat. To explain these results, we offer evidence for a new theory involving the historical persistence of political and racial attitudes. Following the Civil War, Southern whites faced political and economic incentives to reinforce existing racist norms and institutions to maintain control over the newly free African-American population. This amplified local differences in racially conservative political attitudes, which in turn have been passed down locally across generations. Our results challenge the interpretation of a vast literature on racial attitudes in the American South.

PIEP talk slides

Explaining Causal Findings Without Bias: Detecting and Assessing Direct Effects, Forthcoming, American Political Science Review (2016)
Wright's diagram of path analysis

(with Avi Acharya and Maya Sen)

Researchers seeking to establish causal relationships frequently control for variables on the purported causal pathway, checking whether the original treatment effect then disappears. Unfortunately, this common approach may lead to biased estimates. In this paper, we show that the bias can be avoided by focusing on a quantity of interest called the controlled direct effect. Under certain conditions, the controlled direct effect enables researchers to rule out competing explanations—an important objective for political scientists. To estimate the controlled direct effect without bias, we describe an easy-to-implement estimation strategy from the biostatistics literature. We extend this approach by deriving a consistent variance estimator and demonstrating how to conduct a sensitivity analysis. Two examples—one on ethnic fractionalization’s effect on civil war and one on the impact of historical plough use on contemporary female political participation—illustrate the framework and methodology.


A Unified Approach to Measurement Error and Missing Data: Details and Extensions, Sociological Methods & Research, Forthcoming (2015)
Multiple Overimputation

(With James Honaker and Gary King)

We extend a unified and easy-to-use approach to measurement error and missing data. Blackwell, Honaker, and King (2015a) gives an intuitive overview of the new technique, along with practical suggestions and empirical applications. Here, we offer more precise technical details; more sophisticated measurement error model specifications and estimation procedures; and analyses to assess the approach’s robustness to correlated measurement errors and to errors in categorical variables. These results support using the technique to reduce bias and increase efficiency in a wide variety of empirical research.

The missingness continuum

(With James Honaker and Gary King)

Although social scientists devote considerable effort to mitigating measurement error during data collection, they often ignore the issue during data analysis. And although many statistical methods have been proposed for reducing measurement error-induced biases, few have been widely used because of implausible assumptions, high levels of model dependence, difficult computation, or inapplicability with multiple mismeasured variables. We develop an easy-to-use alternative without these problems; it generalizes the popular multiple imputation (MI) framework by treating missing data problems as a limiting special case of extreme measurement error, and corrects for both. Like MI, the proposed framework is a simple two-step procedure, so that in the second step researchers can use whatever statistical method they would have if there had been no problem in the first place. We also offer empirical illustrations, open source software that implements all the methods described herein, and a companion paper with technical details and extensions.


A Selection Bias Approach to Sensitivity Analysis for Causal Effects, Political Analysis, Vol. 22, No. 2 (2014): 169-192
The waving flag of sensitivity analysis

The estimation of causal effects has a revered place in all fields of empirical political science, but a large volume methodological and applied work ignores a fundamental fact: most people are skeptical of estimated causal effects. In particular, researchers are often worried about the assumption of no omitted variables or no unmeasured confounders. This paper combines two approaches to sensitivity analysis to provide researchers with a tool to investigate how specific violations of no omitted variables alters their estimates. This approach can help researchers determine which narratives imply weaker results and which actually strengthen their claims. This gives researchers and critics a reasoned and quantitative approach to assessing the plausibility of causal effects. To demonstrate the approach, I present applications to three causal inference estimation strategies: regression, matching, and weighting.


A Framework for Dynamic Causal Inference in Political Science, American Journal of Political Science, Vol. 57, No. 2 (2013): 504-519
Dynamic Causal Inference

Dynamic strategies are an essential part of politics. In the context of campaigns, for example, candidates continuously recalibrate their campaign strategy in response to polls and opponent actions. Traditional causal inference methods, however, assume that these dynamic decisions are made all at once, an assumption that forces a choice between omitted variable bias and post-treatment bias. Thus, these kinds of “single-shot” causal inference methods are inappropriate for dynamic processes like campagins. I resolve this dilemma by adapting models from biostatistics to estimate the effectiveness of an inherently dynamic process: a candidate’s decision to “go negative.” Using data from U.S. Senate and Gubernatorial elections (2002-2006), I find, in contrast to previous literature and alternative methods, that negative advertising is an effective campaign strategy for Democratic non-incumbents. Democratic incumbents, on the other hand, are hurt by their negativity.