The difference-in-differences (DID) design is widely used in observational studies to estimate the causal effect of a treatment when repeated observations over time are available. Yet, almost all existing methods assume linearity in the potential outcome (parallel trends assumption) and target the additive effect. In social science research, however, many outcomes of interest are measured on an ordinal scale. This makes the linearity assumption inappropriate because the difference between two ordinal potential outcomes is not well defined. In this paper, I propose a method to draw causal inferences for ordinal outcomes under the DID design. Unlike existing methods, the proposed method utilizes the latent variable framework to handle the non-numeric nature of the outcome, enabling identification and estimation of causal effects based on the assumption on the quantile of the latent continuous variable. The paper also proposes an equivalence-based test to assess the plausibility of the key identification assumption when additional pre-treatment periods are available. The proposed method is applied to a study estimating the causal effect of mass shootings on the public's support for gun control. I find little evidence for a uniform shift toward pro-gun control policies as found in the previous study, but find that the effect is concentrated on left-leaning respondents who experienced the shooting for the first time in more than a decade.
We test the relationship between historical immigration to the United Statesand political ideology today. We hypothesize that European immigrants broughtwith them their preferences for the welfare state, and that this had a long-lastingeffect on the political ideology of US born individuals. Our analysis proceedsin three steps. First, we document that the historical presence of Europeanimmigrants is associated with a more liberal political ideology and with strongerpreferences for redistribution among US born individuals today. Next, we showthat this correlation is not driven by the characteristics of the counties whereimmigrants settled or other specific, socioeconomic immigrants’ traits. Finally,we conjecture and provide evidence that immigrants brought with them theirpreferences for the welfare state from their countries of origin. Consistent with thehypothesis that immigration left its footprint on American ideology via culturaltransmission from immigrants to natives, we show that our results are strongerwhen inter-group contact between natives and immigrants, measured with eitherintermarriage or residential integration, was higher. Our findings also indicatethat immigrants influenced American political ideology during one of the largestepisodes of redistribution in US history — the New Deal – and that such effectspersisted after the initial shock.
The presence of a westward-moving frontier of settlement shaped early U.S. history. In 1893, the his-torian Frederick Jackson Turner famously argued that the American frontier fostered individualism.We investigate the Frontier Thesis and identify its long-run implications for culture and politics. Wetrack the frontier throughout the 1790–1890 period and construct a novel, county-level measure of to-tal frontier experience (TFE). Historically, frontier locations had distinctive demographics and greaterindividualism. Long after the closing of the frontier, counties with greater TFE exhibit more pervasiveindividualism and opposition to redistribution. This pattern cuts across known divides in the U.S.,including urban–rural and north–south. We provide evidence on the roots of frontier culture, identi-fying both selective migration and a causal effect of frontier exposure on individualism. Overall, ourfindings shed new light on the frontier’s persistent legacy of rugged individualism.
The premise that constituents hold representatives accountable for their legislative decisions undergirds political theories of democracy and legal theories of statutory interpretation. But studies of this at the individual level are rare, examine only a handful of issues, and arrive at mixed results. We provide an extensive assessment of issue accountability at the individual level. We trace the congressional rollcall votes on 44 bills across seven Congresses (2006--2018), and link them to constituent's perceptions of their representative's votes and their evaluation of their representative. Correlational, instrumental variables, and experimental approaches all show that constituents hold representatives accountable. A one-standard deviation increase in a constituent's perceived issue agreement with their representative can improve net approval by 35 percentage points. Congressional districts, however, are heterogeneous. Consequently, the effect of issue agreement on vote is much smaller at the district-level, resolving an apparent discrepancy between micro and macro studies.
Amid historically low response rates, survey researchers seek ways to reduce respondent burden while measuring desired concepts with precision. We propose to ask fewer questions of respondents and impute missing responses via probabilistic matrix factorization. A variance-minimizing active learning criterion chooses the most informative questions per respondent. In simulations of our matrix sampling procedure on real-world surveys as well as a Facebook survey experiment, we find active question selection achieves efficiency gains over baselines. The reduction in imputation error is heterogeneous across questions and depends on the latent concepts they capture. Modeling responses with the ordered logit likelihood improves imputations and yields an adaptive question order. We find for the Facebook survey that potential biases from order effects are likely to be small. With our method, survey researchers obtain principled suggestions of questions to retain and, if desired, can automate the design of shorter instruments.
Academics and political pundits alike attribute rising support for right-wing political options across advanced democracies to the working classes. In the United States, authors claim that the white working class offered unprecedented and crucial support for Donald Trump in the 2016 election. But what is the evidence for this claim? We examine all of the available academic survey data gathered around the election, along with a number of surveys from prior elections. We test four common claims about the white working class in 2016: (1) that most Trump voters were white working-class Americans; (2) that most white working-class voters supported Trump; (3) that unusually large numbers of white working-class voters switched from Obama in 2012 to Trump in 2016; and (4) that white working-class voters were pivotal to Trump’s victory in several swing states. We find that three of the four are not supported by the available data, and the other lacks crucial context that casts doubt on the idea that Trump uniquely appealed to working-class Americans. White working-class Americans have been supporting Republican presidential candidates at higher rates in recent elections, but that process long predates 2016, and narratives that center on Trump’s alleged appeal obscure this important long-term trend.
Theories of representation suggest that candidates should respond ideologically to their constituency. Two-stage elections like those in the U.S. force candidates to decide which parts of their constituency they should respond to: citizens who are active enough to participate in primaries or those who only participate in general elections. We posit that non-incumbent candidates should mostly focus on the preferences of primary voters while incumbents should be largely unmoved by the preferences of either set of voters. We test these expectations using data from U.S. House and Senate contests and find support for our theory. Our results suggest that scholars should pay closer attention to the two-stage nature of U.S. elections when evaluating electoral responsiveness.
Political commentators have offered evidence that the “polling misses” of 2016 were caused by a number of factors. This project focuses on one explanation: that likely-voter models—tools used by preelection pollsters to predict which survey respondents are most likely to make up the electorate and, thus, whose responses should be used to calculate election predictions—were flawed. While models employed by different pollsters vary widely, it is difficult to systematically study them because they are often considered part of pollsters’ methodological black box. In this study, we use Cooperative Congressional Election Study surveys since 2008 to build a probabilistic likely-voter model that takes into account not only the stated intentions of respondents to vote, but also other demographic variables that are consistently strong predictors of both turnout and overreporting. This model, which we term the Perry-Gallup and Demographics (PGaD) approach, shows that the bias and error created by likely-voter models can be reduced to a negligible amount. This likely-voter approach uses variables that pollsters already collect for weighting purposes and thus should be relatively easy to implement in future elections.
Imagine you’ve scooted into a red booth in an unfussy local diner somewhere in Michigan, not unlike those portrayed in the numerous articles reporters have dispatched from the Midwest since the 2016 election. One booth over, you’re overhearing a middle-aged white man talk about his politics with a buddy of his.
You find out over the course of your meal that he’s a moderate Democrat who wants to keep Obamacare protections in place and opposes concealed-carry, but who also supports mandatory minimum sentencing and favors deporting illegal immigrants. He also happens to mention that he voted for Donald Trump.
This sort of conflicted, “cross-pressured” voter often appears in vigorous debates over swing voters in quasi-hypothetical terms. However, we know from the Cooperative Congressional Election Study (C.C.E.S.), a nationwide scientific survey, that this aforementioned voter in Michigan is a very real, living, breathing man, who was among the roughly 65,000 Americans asked about their identities, policy preferences and voting behavior by the study.
Questions about people’s perceptions of politicians or other political actors are of central interest in a wide variety of research areas. But measuring these perceptions is difficult in part because respondents may use survey response scales in different ways. In a classic article, Aldrich and McKelvey (1977) introduce a method adjusting for such differential item functioning by assuming that all respondents perceive political stimuli identically. I propose a modeling approach built on the Aldrich and McKelvey framework but incorporating anchoring vignettes. This approach allows for scale use adjustments without assuming that all respondents perceive a given politician identically. I apply this model to data on Americans’ perceptions of parties, elected officials, and other political actors, showing that, contrary to previous arguments, most variation in ideology ratings is due not to differing scale use, but to differences in underlying perceptions. Specifically, while perceptions of Republican politicians and the Republican party show no significant differences by respondent partisanship, Democratic and Republican respondents differ strongly in their perceptions of the ideology of Democratic political actors as well as the Supreme Court.
We evaluate whether citizens’ trust in Congress is influenced by perceptions of ideological distance between themselves and their representatives. We argue that citizens view members as the “face” of Congress, and thus trust the institution more when the face of that institution is more ideologically proximal to themselves. We test our hypotheses using responses to survey questions regarding both trust in Congress and perceptions of ideological distance between respondents and members of Congress in the 2008 and 2016 Cooperative Congressional Election Study. We then pair these observational survey data with a survey experiment administered by Qualtrics in 2016. Ordinal logistic regressions from our survey data evince strong empirical support for our arguments, showing that as perceived ideological distance between a respondent and her member of Congress increases, trust in Congress as a whole declines. These observational analyses are corroborated by our survey experiment, which again shows that as perceptions of ideological distance increase, trust in legislatures declines. Our results suggest that a lack of faith in legislative institutions is often the result of a failure of representation. One way to restore Americans’ trust in Congress is for members to demonstrate more fidelity to the ideological leanings of their constituents.
CLOSE OBSERVERS of America know that the rules of its democracy often favour Republicans. But the party’s biggest advantage may be one that is rarely discussed: turnout is just 60%, low for a rich country. Polls show that non-voters—both people uninterested in voting and those blocked by legal or economic hurdles—mainly belong to groups that tend to back Democrats.
What would change if America became the 22nd country to make voting mandatory? To estimate non-voters’ views, The Economist used the Co-operative Congressional Election Study (CCES), a 64,600-person poll led by Harvard University. The survey includes demographic data such as race and age, as well as participants’ recollections of whom they voted for and verified records of whether they voted. In general, voters and non-voters from similar backgrounds had similar opinions. Using a method called “multilevel regression and post-stratification”, the relationships between demography and vote choices can be used to project state-level election results—and to estimate what might have happened in the past under different rules.
Washington and California adopted the Top-Two Primary in 2008 and 2012, respectively. Under this new system, all candidates regardless of party affiliation run against each other, narrowing the field down to the top two for the general election. In some jurisdictions, the general election features two candidates from the same party. Ten percent of California voters chose not to vote in the 2016 U.S. Senate election which featured two Democrats. Using data from the Cooperative Congressional Election Study (2012–2016), I find that among those who vote in the national November elections, orphans, or voters without a copartisan candidate on the ballot are more likely to undervote, opting out of voting in their congressional race. Levels of undervoting are nearly 20 percentage points higher for orphaned voters compared to non-orphaned voters. Additionally, voters who abstain perceive more ideological distance between themselves and the candidates compared to voters who cast a vote. These findings support a multi-step framework for vote decisions in same-party matchups: voters are more likely to undervote if they are unable to vote for a candidate from their party (partisan model), but all voters are more likely to vote for a candidate when they perceive ideological proximity (ideological model).
Recent work on US policymaking argues that responsiveness to public opinion is distorted bymoney, in that the preferences of the rich matter much more than those of lower-income Americans. A second distortion—partisan biases in responsiveness—has been less well studied and is often ignored or downplayed in the literature on affluent influence. We are the first to evaluate, in tandem, these two potential distortions in representation. We do so using 49 Senate roll-call votes from 2001 to 2015. We find that affluent influence is overstated and itselfcontingenton partisanship—party trumps the purse when senators have to take sides. The poor getwhatthey wantmore often fromDemocrats. The rich getwhatthey wantmoreoftenfromRepublicans, butonly ifRepublican constituents side with the rich. Thus,partisanship induces, shapes, and constrains affluent influenc
Debates over the extent to which racial attitudes and economic distress explain voting behavior in the 2016 election have tended to be limited in scope, focusing on the extent to which each factor explains white voters’ two-party vote choice. This limited scope obscures important ways in which these factors could have been related to voting behavior among other racial sub-groups of the electorate, as well as participation in the two-party contest in the first place. Using the vote-validated 2016 Cooperative Congressional Election Survey, merged with economic data at the ZIP code and county levels, we find that racial attitudes strongly explain two-party vote choice among white voters—in line with a growing body of literature. However, we also find that local economic distress was strongly associated with non-voting among people of color, complicating direct comparisons between racial and economic explanations of the 2016 election and cautioning against generalizations regarding causal emphasis.
Previous research shows that people commonly exaggerate the size of minority populations. Theories of intergroup threat predict that the larger people perceive minority groups to be, the less favorably they feel toward them. We investigate whether correcting Americans’ misperceptions about one such population—immigrants—affects related attitudes. We confirm that non-Hispanic Americans overestimate the percentage of the population that is foreign-born or in the United States without authorization. However, in seven separate survey experiments over 11 years, we find that providing accurate information does little to affect attitudes toward immigration, even though it does reduce the perceived size of the foreign-born population. This is true even when people’s misperceptions are explicitly corrected. These results call into question a potential cognitive mechanism that could underpin intergroup threat theory. Misperceptions about the size of minority groups may be a consequence, rather than a cause, of attitudes toward those groups.
This article asks whether legislators are able to reap electoral benefits from opposing their party on one or more high‐profile issues. Using data from a national survey in which citizens are asked their own positions on seven high‐profile issues voted on by the U.S. Senate, as well as how they believe their state's two senators have voted on these issues, I find that senators generally do not benefit from voting against their party. Specifically, when a senator deviates from her party, the vast majority of out‐partisans nonetheless persist in believing that the senator voted with her party anyhow; and while the small minority of out‐partisans who are aware of her deviation are indeed more likely to approve of and vote for such a senator, there are simply too few of these correctly informed citizens for it to make a meaningful difference for the senator's overall support.
Although it is widely known that the self-reported turnout rates obtained from public opinion surveys tend to substantially overestimate the actual turnout rates, scholars sharply disagree on what causes this bias. Some blame overreporting due to social desirability, whereas others attribute it to non-response bias and the accuracy of turnout validation. While we can validate self-reported turnout by directly linking surveys with administrative records, most existing studies rely on proprietary merging algorithms with little scientific transparency and report conflicting results. To shed a light on this debate, we apply a probabilistic record linkage model, implemented via the open-source software package fastLink, to merge two major election studies – the American National Election Studies and the Cooperative Congressional Election Survey – with a national voter file of over 180 million records. For both studies, fastLink successfully produces validated turnout rates close to the actual turnout rates, leading to public-use validated turnout data for the two studies. Using these merged data sets, we find that the bias of self-reported turnout originates primarily from overreporting rather than non-response. Our findings suggest that those who are educated and interested in politics are more likely to overreport turnout. Finally, we show that fastLink performs as well as a proprietary algorithm.
Shrinking audiences and political coverage cutbacks threaten newspapers’ ability to inform the public about politics. Despite substantial theorizing and widespread concern, it remains unclear how much the public can learn from these struggling news sources. I link measures of the newspaper-produced information environment with large-scale surveys that capture the public’s awareness of their member of Con- gress. This shows the contemporary effects of newspapers on representative-specific awareness are one-half to one-third estimates from earlier eras. Despite this decline newspapers remain an important contributor to political awareness in a changing media landscape, even for those with limited political interest. These results estab- lish broader scope conditions under which the public can learn from the media environment.
This article validates donation-based measures of ideology against a rich battery of policy items from the Congressional Campaign Election Study. Donation-based measures are powerful predictors of policy preferences for a wide range of issues and successfully discriminate between donors from the same party. The overall predictive accuracy and relative improvement over party are comparable to what is achieved by scaling roll call votes in legislatures. The results add to an existing body of evidence on the internal validity and reliability of donation-based measures. They also resolve a standing debate in the literature over whether political donations are a valid indicator of donors’ policy preferences.