Responsiveness and the macro-origins of immigration opinions: Evidence from Belgium, France and the UK

Abstract

Throughout recent decades, social science studies have systematically reported that citizens respond to their macro-social environments. While this is typically true in highly visible and salient policy domains, scholarship remains ambiguous about which macro-environmental factors are at the origins of citizens’ opinions on immigration. We contribute to this debate by theorising three factors that have the potential to move immigration opinions and subsequently testing their empirical relevance. We most notably emphasise the role of immigration itself and ask whether and how increasing immigration levels affect immigration opinions. We then examine to what extent the regional power structure and economic hardship interplay with this relationship. Through the dyadic ratios algorithm, we estimate a unique set of immigration opinion measures across regions in Belgium, France and the UK between 1990 and 2015. When modelling these measures, our findings are threefold. First, citizens are responsive to their environments, and specifically to immigration. Second, citizens become more favourable towards immigrants when immigration levels increase. Third, we find evidence that decentralisation (regional power) conditions this empirical relationship, while there is little to no indication that economic conditions affect immigration opinions, either directly or indirectly.

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Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Notes

  1. 1.

    Throughout this study, the term ‘immigration opinions’ refers to an opinion spectrum that ranges from left (low values, indicating more open immigration positions) to right (high values, favouring more restrictive positions). While we realise the label ‘anti-immigrant’ might be preferred by many, we explicitly choose not to use an ‘anti’ label for the phenomenon under analysis.

  2. 2.

    For a meta-analysis of a group threat approach, we refer to Riek et al. (2006). At the same time, several studies that test group threat do not find empirical support for its rationale (e.g. Hainmueller and Hiscox 2007, 2010; Hjerm 2007; Sides and Citrin 2007).

  3. 3.

    For a meta-analysis of the contact perspective, we refer to Pettigrew et al. (2011). At the same time, some studies that test the contact hypothesis find no evidence for it (e.g. Rustenbach 2010) or find evidence in support of group threat (e.g. Schlueter and Scheepers 2010).

  4. 4.

    When public opinion data are available, the representativeness of samples collected in Corsica (part of the Méditerranée region) is questionable. Therefore, we exclude Corsica from our estimation of the immigration opinions in the Méditerranée region.

  5. 5.

    For the Belgian regions, we rely on surveys from the BNES, ESS and EVS. For Flanders and Wallonia, we also include the SCV and BSW, respectively. For France, we use the BPF, DEM, DREES, DYNAMOB, ESS, EVS, FES, FF, ISSP, LW, OIP, PEF and WVS surveys. For the UK, we rely on the BES, BSA, ESS, EVS, LW and WVS surveys. We refer to Table A.1 in the Appendix for bibliographic details.

  6. 6.

    The items we select concern all questions with reference to positions towards immigration or immigrants, positions towards government policy regarding immigration, positions towards immigrants or other general non-native minorities, economic or cultural implications of immigrants or immigration, xenophobia and prejudice. We exclude items that inquire about racism, Muslims, refugees, asylum seekers and illegals. For more details regarding the individual items we included, the question wording, the years of measurement and the degree of repetition, we refer to the online Appendix.

  7. 7.

    For the Belgian regions, measures go from 1990 to 2015. (We initially included the 1980s as well, but estimations are not sufficiently reliable.) For French regions, measures go from 1988 to 2017. For UK regions, measures go from 1983 to 2015. Data are limited for Northern Ireland. We are only able to estimate a reliable measure from 2003 onwards. Following the limited number of data points, we exclude the latter region from inferential analyses.

  8. 8.

    Existing studies use this method to construct measures of presidential approval (Carlin et al. 2015a, b), European integration (Guinaudeau and Schnatterer 2017), environmental concerns (Brulle et al. 2012), support for nuclear energy (Brouard and Guinaudeau 2015), gender equality (Koch and Thomsen 2017; Tapia Velázquez and Van Hauwaert 2018) and redistributive preferences (Romero and Van Hauwaert 2018).

  9. 9.

    For a more extensive and in-depth discussion of the model’s formal estimation procedure, we refer to Stimson (1991, 2018) and McGann (2014).

  10. 10.

    The item loadings and descriptive variable information for all regions are available from the lead author.

  11. 11.

    For France, we use CENSUS data (1990, 1999, 2006 and 2011) to estimate annual regional foreign-born population. For the UK, we use CENSUS data (1991, 2001 and 2011) and the International Migration Database (2004–2014) by the Office of National Statistics to estimate annual regional foreign-born population. For Belgium, we constructed our own yearly and regional database, drawing mostly from data provided by the Ministry of Economic Affairs.

  12. 12.

    This is the percentage of unemployed (15+) over the labour force (15+).

  13. 13.

    We rely on linear multinomial imputation, using the ‘predict’ function in R’s stats package, to complete some of the missing values on our predictors. Existing research indicates this is appropriate and accurate for our particular purpose, namely to predict few estimates over a short range of time within a more extensive longitudinal series (King et al. 2001).

  14. 14.

    We include descriptive statistics in Table A.2 of the Appendix. For more information, see the OECD Statistics website (http://stats.oecd.org/Index.aspx?DataSetCode=REGION_DEMOGR).

  15. 15.

    We use a GLS instead of an OLS model because the residuals of the OLS model show signs of autocorrelation.

  16. 16.

    At this point, we exclude Northern Ireland from the analysis due to a limited time series on our dependent variable (data only available from 2003 onwards). We also exclude 2015 from our inferential analysis because the last point of time-series estimations can be difficult to interpret. For full models, we refer to Table A.3 in the Appendix.

  17. 17.

    We also estimated the impact of immigration rates, rather than the absolute immigration levels we account for in the models. This does not substantively alter the results.

  18. 18.

    We use the GLS model as the foundation of Fig. 5. We refer to Table A.4 in the Appendix for the full set of models.

  19. 19.

    The unemployment coefficient is significant for the Belgian regions (p < 0.05), approaches statistical significance in France (p < 0.1) and fails to reach significance in the UK regions.

  20. 20.

    We only examine regions with political power because Fig. 5 suggests there might not be a relationship between immigration levels and opinions in regions without political power. An analysis of all regions, however, confirms results from Table 2. We refer to Table A.5 in the Appendix for full models.

  21. 21.

    Following Brambor et al. (2006) and Berry et al. (2012), we further explored this conditional effect to potentially account for the clustering of the interaction in specific contexts. While results remain the same across France and the UK, the interaction term returns significantly for the Belgian regions.

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Acknowledgements

Results presented in this study have been obtained as part of the Global Public Opinions Project (GPOP), which receives funding from the Alexander von Humboldt Foundation. Data collection for the regional immigration opinions was done by Andrea Y. Tapia Velázquez and Katia Guzmán Martinez. We thank them for their continued investment and research excellence. Earlier versions of this manuscript have been presented at the 2017 ‘State of the Federation’ and the 2016 MPSA conference, as well as the DEP departmental seminar at CIDE. We are grateful to participants and panel members for their feedback, comments and suggestions. In particular, we thank the anonymous reviewers and editors of Comparative European Politics, Ryan Carlin, Manlio Cinalli, Robert Huber, Xavier Romero, Matt Singer and Tom Verthé for their valuable comments and insights.

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Appendix

Appendix

See Tables A.1, A.2, A.3, A.4 and A.5.

Table A.1 Data overview
Table A.2: Descriptive statistics
Table A.3:  Full regression models
Table A.4:  Full regression models, with regional power dummy interaction
Table A.5:  Full regression models, with immigration and unemployment interaction

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Van Hauwaert, S.M., English, P. Responsiveness and the macro-origins of immigration opinions: Evidence from Belgium, France and the UK. Comp Eur Polit 17, 832–859 (2019). https://doi.org/10.1057/s41295-018-0130-5

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Keywords

  • Public opinion
  • Immigration
  • Responsiveness
  • NUTS region
  • Dyadic ratios algorithm
  • Time-series cross-sectional (TSCS)