Daniel J. Hopkins

Bio: Daniel J. Hopkins is an academic researcher from University of Pennsylvania. The author has contributed to research in topics: Population & Public opinion. The author has an hindex of 34, co-authored 103 publications receiving 7058 citations. Previous affiliations of Daniel J. Hopkins include Cornell University & Harvard University.

Public Attitudes Toward Immigration

Abstract: Immigrant populations in many developed democracies have grown rapidly, and so too has an extensive literature on natives' attitudes toward immigration. This research has developed from two theoretical foundations, one grounded in political economy, the other in political psychology. These two literatures have developed largely in isolation from one another, yet the conclusions that emerge from each are strikingly similar. Consistently, immigration attitudes show little evidence of being strongly correlated with personal economic circumstances. Instead, research finds that immigration attitudes are shaped by sociotropic concerns about its cultural impacts—and to a lesser extent its economic impacts—on the nation as a whole. This pattern of results has held up as scholars have increasingly turned to experimental tests, and it holds for the United States, Canada, and Western Europe. Still, more work is needed to strengthen the causal identification of sociotropic concerns and to isolate precisely how, when,...

Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments

Abstract: Survey experiments are a core tool for causal inference. Yet, the design of classical survey experiments prevents them from identifying which components of a multidimensional treatment are influential. Here, we show how conjoint analysis, an experimental design yet to be widely applied in political science, enables researchers to estimate the causal effects of multiple treatment components and assess several causal hypotheses simultaneously. In conjoint analysis, respondents score a set of alternatives, where each has randomly varied attributes. Here, we undertake a formal identification analysis to integrate conjoint analysis with the potential outcomes framework for causal inference. We propose a new causal estimand and show that it can be nonparametrically identified and easily estimated from conjoint data using a fully randomized design. The analysis enables us to propose diagnostic checks for the identification assumptions. We then demonstrate the value of these techniques through empirical applications to voter decision making and attitudes toward immigrants.

Politicized Places: Explaining Where and When Immigrants Provoke Local Opposition

Abstract: In ethnic and racial terms, America is growing rapidly more diverse. Yet attempts to extend racial threat hypotheses to today’s immigrants have generated inconsistent results. This article develops the politicized places hypothesis, an alternative that focuses on how national and local conditions interact to construe immigrants as threatening. Hostile political reactions to neighboring immigrants are most likely when communities undergo sudden influxes of immigrants and when salient national rhetoric reinforces the threat. Data from several sources, including twelve geocoded surveys from 1992 to 2009, provide consistent support for this approach. Time-series cross-sectional and panel data allow the analysis to exploit exogenous shifts in salient national issues such as the September 11 attacks, reducing the problem of residential self-selection and other threats to validity. The article also tests the hypothesis using new data on local anti-immigrant policies. By highlighting the interaction of local and national conditions, the politicized places hypothesis can explain both individual attitudes and local political outcomes.

Politicized Places: Explaining Where and When Immigrants Provoke Local Opposition

Abstract: In ethnic and racial terms, America is growing rapidly more diverse. Yet attempts to extend racial threat hypotheses to today's immigrants have generated inconsistent results. This article develops the politicized places hypothesis, an alternative that focuses on how national and local conditions interact to construe immigrants as threatening. Hostile political reactions to neighboring immigrants are most likely when communities undergo sudden influxes of immigrants and when salient national rhetoric reinforces the threat. Data from several sources, including twelve geocoded surveys from 1992 to 2009, provide consistent support for this approach. Time-series cross-sectional and panel data allow the analysis to exploit exogenous shifts in salient national issues such as the September 11 attacks, reducing the problem of residential self-selection and other threats to validity. The article also tests the hypothesis using new data on local anti-immigrant policies. By highlighting the interaction of local and national conditions, the politicized places hypothesis can explain both individual attitudes and local political outcomes.

A Method of Automated Nonparametric Content Analysis for Social Science

Abstract: The increasing availability of digitized text presents enormous opportunities for social scientists. Yet hand coding many blogs, speeches, government records, newspapers, or other sources of unstructured text is infeasible. Although computer scientists have methods for automated content analysis, most are optimized to classify individual documents, whereas social scientists instead want generalizations about the population of documents, such as the proportion in a given category. Unfortunately, even a method with a high percent of individual documents correctly classified can be hugely biased when estimating category proportions. By directly optimizing for this social science goal, we develop a method that gives approximately unbiased estimates of category proportions even when the optimal classifier performs poorly. We illustrate with diverse data sets, including the daily expressed opinions of thousands of people about the U.S. presidency. We also make available software that implements our methods and large corpora of text for further analysis.

The Nature and Origins of Mass Opinion.

Abstract: Originally published in Contemporary Psychology: APA Review of Books, 1994, Vol 39(2), 225. Reviews the book, The Nature and Origins of Mass Opinion by John Zaller (1992). The author's commendable effort to specify a model of mass opinion formation offers readers an introduction to the prevailing vi

Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Review

Abstract: Recently there has been a surge in econometric work focusing on estimating average treatment effects under various sets of assumptions. One strand of this literature has developed methods for estimating average treatment effects for a binary treatment under assumptions variously described as exogeneity, unconfoundedness, or selection on observables. The implication of these assumptions is that systematic (for example, average or distributional) differences in outcomes between treated and control units with the same values for the covariates are attributable to the treatment. Recent analysis has considered estimation and inference for average treatment effects under weaker assumptions than typical of the earlier literature by avoiding distributional and functional-form assump- tions. Various methods of semiparametric estimation have been proposed, including estimating the unknown regression functions, matching, meth- ods using the propensity score such as weighting and blocking, and combinations of these approaches. In this paper I review the state of this literature and discuss some of its unanswered questions, focusing in particular on the practical implementation of these methods, the plausi- bility of this exogeneity assumption in economic applications, the relative performance of the various semiparametric estimators when the key assumptions (unconfoundedness and overlap) are satise ed, alternative estimands such as quantile treatment effects, and alternate methods such as Bayesian inference.

From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series

Abstract: We connect measures of public opinion measured from polls with sentiment measured from text. We analyze several surveys on consumer confidence and political opinion over the 2008 to 2009 period, and find they correlate to sentiment word frequencies in contemporaneous Twitter messages. While our results vary across datasets, in several cases the correlations are as high as 80%, and capture important large-scale trends. The results highlight the potential of text streams as a substitute and supplement for traditional polling.