Peter M. Aronow

Field Experiments and the Study of Voter Turnout

Although field experiments have long been used to study voter turnout, only recently has this research method generated widespread scholarly interest. This article reviews the substantive contributions of the field experimental literature on voter turnout. This literature may be divided into two strands, one that focuses on the question of which campaign tactics do or do not increase turnout and another that uses voter mobilization campaigns to test social psychological theories. Both strands have generated stubborn facts with which theories of cognition, persuasion and motivation must contend.

Does Knowledge of Constitutional Principles Increase Support for Civil Liberties? Results from a Randomized Field Experiment

For decades, scholars have argued that education causes greater support for civil liberties by increasing students’ exposure to political knowledge and constitutional norms, such as due process and freedom of expression. Support for this claim comes exclusively from observational evidence, principally from cross-sectional surveys. This paper presents the first large-scale experimental test of this proposition. More than 1000 students in 59 high school classrooms were randomly assigned to an enhanced civics curriculum designed to promote awareness and understanding of constitutional rights and civil liberties. The results show that students in the enhanced curriculum classes displayed significantly more knowledge in this domain than students in conventional civics classes. However, we find no corresponding change in the treatment group’s support for civil liberties, a finding that calls into question the hypothesis that knowledge and attitudes are causally connected.

A General Method for Detecting Interference Between Units in Randomized Experiments

Interference between units may pose a threat to unbiased causal inference in randomized controlled experiments. Although the assumption of no interference is often necessary for causal inference, few options are available for testing this assumption. This article presents an ex post method for detecting interference between units in randomized experiments. With a test statistic of the analyst’s choice, a conditional randomization test allows for the calculation of the exact significance level of the causal dependence of outcomes on the treatment status of other units. The robustness of the method is demonstrated through simulation studies. Moreover, using this method, interference between units is detected in a field experiment designed to assess the effect of mailings on voter turnout in a U.S. primary election.

Sharp bounds on the variance in randomized experiments

We propose a consistent estimator of sharp bounds on the variance of the difference-in-means estimator in completely randomized experiments. Generalizing Robins [Stat. Med. 7 (1988) 773-785], our results resolve a well-known identification problem in causal inference posed by Neyman [Statist. Sci. 5 (1990) 465-472. Reprint of the original 1923 paper]. A practical implication of our results is that the upper bound estimator facilitates the asymptotically narrowest conservative Wald-type confidence intervals, with applications in randomized controlled and clinical trials.

Estimating average causal effects under general interference, with application to a social network experiment

This paper presents a randomization-based framework for estimating causal effects under interference between units, motivated by challenges that arise in analyzing experiments on social networks. The framework integrates three components: (i) an experimental design that defines the probability distribution of treatment assignments, (ii) a mapping that relates experimental treatment assignments to exposures received by units in the experiment, and (iii) estimands that make use of the experiment to answer questions of substantive interest. We develop the case of estimating average unit-level causal effects from a randomized experiment with interference of arbitrary but known form. The resulting estimators are based on inverse probability weighting. We provide randomization-based variance estimators that account for the complex clustering that can occur when interference is present. We also establish consistency and asymptotic normality under local dependence assumptions. We discuss refinements including covariate-adjusted effect estimators and ratio estimation. We evaluate empirical performance in realistic settings with a naturalistic simulation using social network data from American schools. We then present results from a field experiment on the spread of anti-conflict norms and behavior among school students.

A Class of Unbiased Estimators of the Average Treatment Effect in Randomized Experiments

Abstract We derive a class of design-based estimators for the average treatment effect that are unbiased whenever the treatment assignment process is known. We generalize these estimators to include unbiased covariate adjustment using any model for outcomes that the analyst chooses. We then provide expressions and conservative estimators for the variance of the proposed estimators.

Combining List Experiment and Direct Question Estimates of Sensitive Behavior Prevalence

Survey respondents may give untruthful answers to sensitive questions when asked directly. In recent years, researchers have turned to the list experiment (also known as the item count technique) to overcome this difficulty. While list experiments are arguably less prone to bias than direct questioning, list experiments are also more susceptible to sampling variability. We show that researchers need not abandon direct questioning altogether in order to gain the advantages of list experimentation. We develop a nonparametric estimator of the prevalence of sensitive behaviors that combines list experimentation and direct questioning. We prove that this estimator is asymptotically more efficient than the standard difference-in-means estimator, and we provide a basis for inference using Wald-type confidence intervals. Additionally, leveraging information from the direct questioning, we derive two nonparametric placebo tests for assessing identifying assumptions underlying list experiments. We demonstrate the effectiveness of our combined estimator and placebo tests with an original survey experiment.

Field Experimental Designs for the Study of Media Effects

Field experimentation is a promising but seldom used method for studying the effects of media messages on political attitudes and behavior. The practical challenges of conducting media experiments in real-world settings often come down to securing cooperation from research partners, such as political campaigns. To do so, researchers must be prepared to adapt their experimental designs to satisfy the constraints imposed by research partners and the media environment in which they operate. This article provides an overview of some alternative field experimental designs that allow researchers to maintain the advantages of random assignment while addressing practical considerations. Examples are drawn from the growing field experimental literature examining the effects of television, radio, and online communication.

The Effects of Aid on Rights and Governance: Evidence from a Natural Experiment

Does foreign aid promote good governance in recipient countries? We help arbitrate the debate over this question by leveraging a novel source of exogeneity: the rotating presidency of the Council of the European Union. We find that when a countrys former colonizer is the president of the Council of the European Union during the budget-making process, the country is allocated considerably more foreign aid than are countries whose former colonizer does not hold the presidency. Using instrumental variables estimation, we demonstrate that this aid has positive effects on multiple measures of human rights and governance, although the effects are short-lived after the shock to aid dissipates. We then disaggregate aid flows, present evidence for the causal mechanism at work, and offer directions for future advances.

Ideologically Extreme Candidates in U.S. Presidential Elections, 1948–2012

Scholars routinely cite the landslide defeats of Barry Goldwater and George McGovern as evidence that American electorates punish extremism in presidential politics. Yet systematic evidence for this view is thin. In this article we use postwar election outcomes to assess the electoral effects of extremism. In testing ten models over the seventeen elections, we find scant evidence of extremism penalties that were either substantively large or close to statistical significance.