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Does Information Change Attitudes Toward Immigrants?

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Demography

Abstract

Strategies aimed at reducing negative attitudes toward immigrants are at the core of integration policies. A large literature shows that misperceptions about the size and characteristics of immigrants are common. A few studies implemented interventions to correct innumeracy regarding the size of the immigrant population, but they did not detect any effects on attitudes. We study whether providing information not only about the size but also about the characteristics of the immigrant population can have stronger effects. We conduct two online experiments with samples from the United States, providing one-half of the participants with five statistics about immigration. This information bundle improves people’s attitudes toward current legal immigrants. Most effects are driven by Republicans and other groups with more negative initial attitudes toward immigrants. In our second experiment, we show that treatment effects persist one month later. Finally, we analyze a large cross-country survey experiment to provide external validity to the finding that information about the size of the foreign-born population is not enough to change policy views. We conclude that people with negative views on immigration before the intervention can become more supportive of immigration if their misperceptions about the characteristics of the foreign-born population are corrected.

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Data Availability

The data are available on the Open Science Framework on the following link: https://osf.io/rhz8n/.

Notes

  1. Throughout the text, we define immigrants as people living in the country but who were not born in that country.

  2. A large literature has studied the determinants of people’s attitudes toward immigrants (Alba et al. 2005; Hainmueller et al. 2015; Scheve and Slaughter 2001). Previous studies have focused on characteristics such as age, media exposure, competition in the labor market, exposure to immigrants, education, or income that are correlated with people’s attitudes toward immigrants (Card et al. 2012; Citrin et al. 1997; Facchini and Mayda 2009; Haaland and Roth 2018; Mayda 2006). Others have included the real or the perceived size of the immigrant group as a key correlate (Gallagher 2003; Hjerm 2007; Hooghe and de Vroome 2015; Semyonov et al. 2004).

  3. Although our results are in line with the notion of a “deserving” immigrant category, our interventions do nothing to encourage moralizing classifications or to advocate support for only one category of immigrants. Andrews (2018) studied how the combination of expanded immigration enforcement and good/bad moralizing classifications can affect undocumented immigrants. Menjívar and Lakhani (2016) showed how the process of applying for legal status can trigger enduring changes by which immigrants try to behave according to the “deserving” immigrant profile.

  4. An influential paper has documented the existence of backfire effects (Nyhan and Reifler 2010), where people’s beliefs actually are reinforced in the face of contradictory evidence. However, recent evidence indicates that these types of backfire effects might not be so common (Guess and Coppock 2018; Wood and Porter 2019).

  5. A precise definition of these families of outcomes can be found in the online appendix.

  6. Previous literature has cast doubt on whether interventions can have persistent effects on beliefs. For example, Flores (2018) found that the effect of anti-immigrant rhetoric by political elites does not persist more than two weeks and attributed them to social desirability bias. In contrast, Herda (2017, 2019a) showed that a classroom activity can correct misperceptions among students, with effects persisting five weeks later; Herda did not, however, examine whether the correction generated changes in attitudes or policy preferences.

  7. Because the sample is not drawn from a probability-based sample, it is not representative in terms of variables not targeted by the quota.

  8. TNS provided us with 1,193 observations rather than 1,000 as we had specified in the pre-analysis plan because they made an error in a count variable, which meant that they underestimated the number of observations and therefore accidentally provided us with a larger sample.

  9. The attrition rate was very low (smaller than 2%). We find no evidence of differential attrition across treatment arms.

  10. Our survey question includes mutually exclusive options for White, Hispanic, Black, Asian, or other ethnicity. Therefore, our White category includes those identifying as Whites, and we cannot distinguish between Hispanic and non-Hispanic Whites.

  11. We chose these statistics for two main reasons. First, as we described in the related literature section, evidence shows that people are particularly concerned about these issues. Americans prefer immigrants who are employed, speak English, and are documented. Second, census data are available on these issues, which increases the reliability of the information we provide.

  12. Only participants with a link can see the petition until at least 150 people sign it, after which it becomes public. Moreover, if the petition reaches 100,000 signatures in 30 days, it is entitled to receive an official reply from the White House.

  13. Robust standard errors are used throughout the analysis.

  14. Among pre-specified covariates, we include measures of prior beliefs, which are missing for less than 2% of our respondents. We impute their values using the set of pre-specified controls displayed in the balance table. In an earlier working paper (Grigorieff et al. 2016), we showed that results were very similar when we did not include the covariates Xi in the regression.

  15. For each family of outcomes, we control for a false discovery rate of 5% (Anderson 2008).

  16. We found that more educated people, males, and people who live in zip code areas with a small share of immigrants tend to have less biased beliefs about the share and characteristics of immigrants (Grigorieff et al. 2016). These findings are consistent with previous research on the determinants of innumeracy in the United States (Alba et al. 2005; Laméris et al., 2018a; Nadeau et al. 1993) and Europe (Herda 2010).

  17. On average, Republicans have a significantly more negative view of immigrants than Democrats for all our outcomes. This is in line with evidence that immigration enforcement is higher in states with a larger share of Republican constituents (Moinester 2018).

  18. We asked respondents some additional questions on the respective contributions of legal and undocumented immigrants, for which we find consistent effects. These estimates are reported in Grigorieff et al. (2016).

  19. About 10% of our sample signed the petition, suggesting that we had sufficient variation to detect treatment effects.

  20. The number of people who reported having signed the petition (25%) is higher than the number of signatures, which can partly be explained by the fact that signing the petition was a multistage process. People who signed the petition received a confirmation email containing a link that they had to click to confirm their signature. If they did not complete this second step, their signature was not counted. People’s intention to sign the petition and their self-reported signature are strongly correlated with their self-reported support for increasing the number of green cards for immigrants.

  21. Another piece of evidence indicating that MTurk data are of high quality is a very high correlation (of around .80) between responses in the follow-up and in the main survey among control group participants.

  22. We randomized the order of the sections in the survey. Half of the sample estimated the five statistics first and then answered the set of self-reported questions on immigration, and the other half answered the self-reported questions first. We find no significant order effects.

  23. People in the control group did not update their beliefs in the follow-up, indicating that they did not make the effort to look up the information we provided to the treatment group.

  24. Our measures of beliefs and attitudes toward immigrants are strongly correlated with people’s self-reported policy preferences regarding immigration. These results were not pre-specified and are available upon request.

  25. Republicans represent 28% of the pooled sample (32% of the TNS sample and 23% of the MTurk sample); the share of Democrats is 45% in the TNS sample and 58% in the MTurk sample. Both the share and observable characteristics of Republicans and Democrats in treatment and control groups are well balanced.

  26. In Grigorieff et al. (2016), we employed a machine learning algorithm to identify the most significant sources of heterogeneous treatment effects (Athey and Imbens 2016). The algorithm confirmed that political affiliation is the factor that most strongly predicts heterogeneous responses to the treatment.

  27. In an additional pre-specified analysis, we examine heterogeneous treatment effects by participants’ biases in beliefs using three different definitions of biases. We find that people with larger biases in beliefs seem to respond more strongly to information. However, this effect is not statistically significant for most families of outcomes, which could be due to measurement error given that we do not know how people weigh the biases for the five statistics we measure. Results are available upon request.

  28. The experiment was designed by the German Marshall Fund of the United States, and the main results were graphically reported in Wunderlich et al. (2010) and Stelzenmueller et al. (2015); those reports did not include any regression or heterogeneity analysis.

  29. In Germany and in the United Kingdom, only people with access to a landline were surveyed. In Poland and Russia, participants were randomly selected from the general population, and face-to-face interviews were conducted. Response rates for phone interviews ranged from 4% in France, the United Kingdom, and the Netherlands, to 27% in the United States. Face-to-face interviews had higher response rates: 49% in Russia and 40% in Poland (Stelzenmueller et al. 2015; Wunderlich et al. 2010).

  30. To obtain a sample that is as representative as possible for each country, we use the probability weights constructed by the Transatlantic Trends Survey. Our results are not affected by the use of these weights.

  31. Results are robust to the inclusion or exclusion of control variables, and wave and country fixed effects. The sample is well balanced across the treatment and control group, as is highlighted in Table A2 in the online appendix.

  32. Flores (2018) showed that altering the source of negative statements about immigrants does not have differential effects on attitudes, but changing the polarity of the message does: only negative messages have an effect.

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Acknowledgments

We thank all the participants of the NBER political economy summer institute (2017); ESA conference in Bergen; the applied coffee at Bonn; the conference on Managing Migration in Siracusa; the EPEC Workshop in Political Economy; and seminars at Bocconi University, Columbia University, IIES Stockholm, the University of Oxford, and Uppsala University. Financial support from IGIER (Bocconi) and the Oxford Economic Papers fund is gratefully acknowledged. Christopher Roth acknowledges funding under the grant Policy Design and Evaluation Research in Developing Countries Initial Training Network (PODER), which is funded under the Marie Curie Actions of the EU Seventh Framework Programme (Contract Number: 608109). Alexis Grigorieff would like to thank the Economic and Social Research Council for their financial support (Grant No. SSD/2/2/16). Diego Ubfal acknowledges funding from the Bocconi Young Researcher Grant. We also thank the Centre for Experimental Social Sciences at the University of Oxford for their help as well as Ornella Bissoli for administrative assistance.

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Authors and Affiliations

  1. Department of Economics, University of Oxford, Oxford, UK

    Alexis Grigorieff

  2. Department of Economics, University of Warwick, Coventry, UK

    Christopher Roth

  3. Institute on Behavior and Inequality (BRIQ), Bonn, Germany

    Christopher Roth

  4. Centre for Economic Policy Research, London, England

    Christopher Roth

  5. Department of Economics, Bocconi University, Milan, Italy

    Diego Ubfal

  6. Abdul Latif Jameel Poverty Action Lab (JPAL), Cambridge, MA, USA

    Diego Ubfal

  7. Innocenzo Gasparini Institute for Economic Research (IGIER), Milan, Italy

    Diego Ubfal

  8. Laboratory for Effective Anti-poverty Policies (LEAP), Milan, Italy

    Diego Ubfal

Authors
  1. Alexis Grigorieff
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All authors contributed equally to the study conception and design, material preparation, data collection, and analysis. The first draft of the manuscript was co-written by all authors. All authors read and approved the final manuscript.

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Correspondence to Diego Ubfal.

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Grigorieff, A., Roth, C. & Ubfal, D. Does Information Change Attitudes Toward Immigrants?. Demography 57, 1117–1143 (2020). https://doi.org/10.1007/s13524-020-00882-8

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