Jens Hainmueller

Educated Preferences: Explaining Attitudes Toward Immigration in Europe

Recent studies of individual attitudes toward immigration emphasize concerns about labor-market competition as a potent source of anti-immigrant sentiment, in particular among less-educated or less-skilled citizens who fear being forced to compete for jobs with low-skilled immigrants willing to work for much lower wages. We examine new data on attitudes toward immigration available from the 2003 European Social Survey. In contrast to predictions based on conventional arguments about labor-market competition, which anticipate that individuals will oppose immigration of workers with similar skills to their own but support immigration of workers with different skill levels, we find that people with higher levels of education and occupational skills are more likely to favor immigration regardless of the skill attributes of the immigrants in question. Across Europe, higher education and higher skills mean more support for all types of immigrants. These relationships are almost identical among individuals in the labor force (that is, those competing for jobs) and those not in the labor force. Contrary to the conventional wisdom, then, the connection between the education or skill levels of individuals and views about immigration appears to have very little, if anything, to do with fears about labor-market competition. This finding is consistent with extensive economic research showing that the income and employment effects of immigration in European economies are actually very small. We find that a large component of the link between education and attitudes toward immigrants is driven by differences among individuals in cultural values and beliefs. More educated respondents are significantly less racist and place greater value on cultural diversity than do their counterparts; they are also more likely to believe that immigration generates benefits for the host economy as a whole.The authors would like to thank Beth Simmons, Shigeo Herano, Mike Tomz, James Alt, Jeffry Frieden, Ron Rogowski, Ken Scheve, Torben Iversen, Andy Baker, and Peter Gourevitch for helpful comments on earlier drafts.

Comparative Politics and the Synthetic Control Method

In recent years, a widespread consensus has emerged about the necessity of establishing bridges between quantitative and qualitative approaches to empirical research in political science. In this article, we discuss the use of the synthetic control method as a way to bridge the quantitative/qualitative divide in comparative politics. The synthetic control method provides a systematic way to choose comparison units in comparative case studies. This systematization opens the door to precise quantitative inference in small‐sample comparative studies, without precluding the application of qualitative approaches. Borrowing the expression from Sidney Tarrow, the synthetic control method allows researchers to put “qualitative flesh on quantitative bones.” We illustrate the main ideas behind the synthetic control method by estimating the economic impact of the 1990 German reunification on West Germany.

MPs for Sale? Returns to Office in Postwar British Politics

Many recent studies show that firms profit from connections to influential politicians, but less is known about how much politicians financially benefit from wielding political influence. We estimate the returns to serving in Parliament, using original data on the estates of recently deceased British politicians. Applying both matching and a regression discontinuity design to compare Members of Parliament (MPs) with parliamentary candidates who narrowly lost, we find that serving in office almost doubled the wealth of Conservative MPs, but had no discernible financial benefits for Labour MPs. Conservative MPs profited from office largely through lucrative outside employment they acquired as a result of their political positions; we show that gaining a seat in Parliament more than tripled the probability that a Conservative politician would later serve as a director of a publicly traded firm—enough to account for a sizable portion of the wealth differential. We suggest that Labour MPs did not profit from office largely because trade unions collectively exerted sufficient control over the party and its MPs to prevent members from selling their services to other clients.

On the Validity of the Regression Discontinuity Design for Estimating Electoral Effects: New Evidence from over 40,000 Close Races

The regression discontinuity (RD) design is a valuable tool for identifying electoral effects, but this design is only effective when relevant actors do not have precise control over election results. Several recent papers contend that such precise control is possible in large elections, pointing out that the incumbent party is more likely to win very close elections in the United States House of Representatives in recent periods. In this article, we examine whether similar patterns occur in other electoral settings, including the U.S. House in other time periods, statewide, state legislative, and mayoral races in the U.S. and national or local elections in nine other countries. No other case exhibits this pattern. We also cast doubt on suggested explanations for incumbent success in close House races. We conclude that the assumptions behind the RD design are likely to be met in a wide variety of electoral settings and offer a set of best practices for RD researchers going forward.

Validating vignette and conjoint survey experiments against real-world behavior

Significance Little evidence exists on whether preferences about hypothetical choices measured in a survey experiment are driven by the same structural determinants of the actual choices made in the real world. This study answers this question using a natural experiment as a behavioral benchmark. Comparing the results from conjoint and vignette experiments on which attributes of hypothetical immigrants generate support for naturalization with the outcomes of closely corresponding referendums in Switzerland, we find that the effects estimated from the surveys match the effects of the same attributes in the behavioral benchmark remarkably well. We also find that seemingly subtle differences in survey designs can produce significant differences in performance. Overall, the paired conjoint design performs the best. Survey experiments, like vignette and conjoint analyses, are widely used in the social sciences to elicit stated preferences and study how humans make multidimensional choices. However, there is a paucity of research on the external validity of these methods that examines whether the determinants that explain hypothetical choices made by survey respondents match the determinants that explain what subjects actually do when making similar choices in real-world situations. This study compares results from conjoint and vignette analyses on which immigrant attributes generate support for naturalization with closely corresponding behavioral data from a natural experiment in Switzerland, where some municipalities used referendums to decide on the citizenship applications of foreign residents. Using a representative sample from the same population and the official descriptions of applicant characteristics that voters received before each referendum as a behavioral benchmark, we find that the effects of the applicant attributes estimated from the survey experiments perform remarkably well in recovering the effects of the same attributes in the behavioral benchmark. We also find important differences in the relative performances of the different designs. Overall, the paired conjoint design, where respondents evaluate two immigrants side by side, comes closest to the behavioral benchmark; on average, its estimates are within 2% percentage points of the effects in the behavioral benchmark.

ebalance: A Stata Package for Entropy Balancing

The Stata package ebalance implements entropy balancing, a multivariate reweighting method described in Hainmueller (2012) that allows users to reweight a dataset such that the covariate distributions in the reweighted data satisfy a set of specied moment conditions. This can be useful to create balanced samples in observational studies with a binary treatment where the control group data can be reweighted to match the covariate moments in the treatment group. Entropy balancing can also be used to reweight a survey sample to known characteristics from a target population.

Do concerns about labor market competition shape attitudes toward immigration? New evidence

Are concerns about labor market competition a powerful source of anti-immigrant sentiment? Several prominent studies have examined survey data on voters and concluded that fears about the negative effects of immigration on wages and employment play a major role generating anti-immigrant attitudes. We examine new data from a targeted survey of U.S. employees in 12 different industries. In contrast with previous studies, the findings indicate that fears about labor market competition do not appear to have substantial effects on attitudes toward immigration, and preferences with regard to immigration policy, among this large and diverse set of voters.

Electoral balancing, divided government and ‘midterm’ loss in German elections

This paper takes a fresh look at the midterm loss in German elections and argues that government type is a crucial determinant of midterm loss. Using panel regressions on a newly compiled data set covering all state elections during the period 1949–2004, we find that systematic midterm losses occur only when both chambers of the federal legislature (Bundestag and Bundesrat) are controlled by one party or a party coalition. Prior research has failed to discover this important regularity. These findings lend strong support to electoral balancing models while calling into doubt more traditional explanations of midterm loss.

Kernel Regularized Least Squares: Reducing Misspecification Bias with a Flexible and Interpretable Machine Learning Approach

We propose the use of Kernel Regularized Least Squares (KRLS) for social science modeling and inference problems. KRLS borrows from machine learning methods designed to solve regression and classification problems without relying on linearity or additivity assumptions. The method constructs a flexible hypothesis space that uses kernels as radial basis functions and finds the best-fitting surface in this space by minimizing a complexity-penalized least squares problem. We argue that the method is well-suited for social science inquiry because it avoids strong parametric assumptions, yet allows interpretation in ways analogous to generalized linear models while also permitting more complex interpretation to examine non-linearities and heterogeneous effects. We also extend the method in several directions to make it more effective for social inquiry, by (1) deriving estimators for the pointwise marginal effects and their variances, (2) establishing unbiasedness, consistency, and asymptotic normality of the KRLS estimator under fairly general conditions, (3) proposing and justifying a simple automated rule for choosing the kernel bandwidth, and (4) providing companion software. We illustrate the use of the method through several simulations and a real-data example. Jens Hainmueller, Department of Political Science, 77 Massachusetts Avenue, Cambridge, MA 02139. E-mail: jhainm@mit.edu. Chad Hazlett, Department of Political Science, 77 Massachusetts Avenue, Cambridge, MA 02139. E-mail: hazlett@mit.edu. Authors are listed in alphabetical order and contributed equally. We thank Jeremy Ferwerda, Dominik Hangartner, Lorenzo Rosasco, Marc Ratkovic, Teppei Yamamoto, and participants in seminars at NYU, MIT, the Midwest Political Science Conference, and the European Political Science Association Conference for helpful comments. Companion software written by the authors to implement the methods proposed in this paper in R, Matlab, and Stata can be downloaded at http://www.mit.edu/ jhainm/software.htm. The usual disclaimer applies.We propose the use of Kernel Regularized Least Squares (KRLS) for social science modeling and inference problems. KRLS borrows from machine learning methods designed to solve regression and classification problems without relying on linearity or additivity assumptions. The method constructs a flexible hypothesis space that uses kernels as radial basis functions and finds the best-fitting surface in this space by minimizing a complexity-penalized least squares problem. We argue that the method is well-suited for social science inquiry because it avoids strong parametric assumptions, yet allows interpretation in ways analogous to generalized linear models while also permitting more complex interpretation to examine nonlinearities, interactions, and heterogeneous effects. We also extend the method in several directions to make it more effective for social inquiry, by (1) deriving estimators for the pointwise marginal effects and their variances, (2) establishing unbiasedness, consistency, and asymptotic normality of the KRLS estimator under fairly general conditions, (3) proposing a simple automated rule for choosing the kernel bandwidth, and (4) providing companion software. We illustrate the use of the method through simulations and empirical examples.

Consumer demand for fair trade: Evidence from a multistore field experiment

We provide new evidence on consumer demand for ethical products from experiments conducted in a U.S. grocery store chain. We find that sales of the two most popular coffees rose by almost 10% when they carried a Fair Trade label as compared to a generic placebo label. Demand for the higher-priced coffee remained steady when its price was raised by 8%, but demand for the lower-priced coffee was elastic: a 9% price increase led to a 30% decline in sales. While consumers attach value to ethical sourcing, there is significant heterogeneity in willingness to pay for it.