Disentangling aporophobia from xenophobia in the EU-15

ABSTRACT

This paper analyses whether the human capital levels embodied in immigrants can explain xenophobic trends for 126 regions in 14 EU-15 countries from 1998 to 2018. It tests if xenophobic regions may be rejecting immigrants because they are poor, a phenomenon recently defined as ‘aporophobia’. The results indicate that larger inflows of low-educated immigrants working in low-skilled occupations are significantly correlated with a higher rejection of migrants, thus confirming the aporophobia hypothesis. The findings in this paper bring light to the discussion of a powerful concept which underpins the need for a more just society.

KEYWORDS:

• aporophobia
• xenophobia
• human capital
• immigration
• European regions

JEL:

• I3
• J15
• R1

ACKNOWLEDGEMENTS

This article is based on an earlier conference paper entitled ‘Disentangling aporophobia from xenophobia in Europe’, presented at the 36th International Association for Research in Income and Wealth (IARIW) Virtual General Conference, 2022. We extend our gratitude to the conference participants for their valuable feedback and insights. We also express our sincere appreciation to the editor and anonymous referees for their constructive comments and suggestions, which greatly contributed to the refinement and expansion of this work. This paper is based on data from Eurostat, European Labour Force Surveys, 1998-2018, Released November 2019, version 2 and DOI 0.2907/LFS1983-2018V.2. The responsibility for all conclusions drawn from the data lies entirely with the authors.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Notes

1. We are very grateful to one of the anonymous referees for raising the distinction between the rational and the irrational fear of low-skilled migrants, in particular during economic recessions. In this situation, natives’ rejection of the poor might be rational and therefore unveil not pure prejudice, but personal fears related to labour market conditions. On the other hand, when this rejection of immigrants comes, for instance, with an association with racialised and ethnic beliefs, we might be facing a situation of discrimination. The literature is rich in examples when the growing criminalisation of unauthorised migrants and racialised beliefs and stereotypes about poor migrants cannot be justified by locals’ rational beliefs (e.g., Lim, 2021; Nuti, 2019).

2. We express our gratitude to one of the anonymous referees who suggested this literature which explores the impact of immigration on the dynamics of labour markets.

3. Aporophobia is a general phenomenon that might be as directed at the migrant poor as it is aimed at the native poor. Here we only tackle the kind of aporophobia directed at the migrant poor. We adopted a multidimensional view of poverty based on a vector of attributes, namely, low educational status, low skilled in occupational terms and unemployment status.

4. We adopted a multidimensional view of poverty based on a vector of attributes, namely, low educational status, low skilled in occupational terms and unemployment status identification of poor and non-poor migrants using occupational skills and education may also introduce endogeneity issues if the mismatch between qualifications and skills is influenced by xenophobia and discrimination.

5. The baseline estimates without NUTS-2 fixed effects are presented in Table B4 in Appendix B in the supplemental data online.

6. Since the origin of the migrants in the EULFS is broadly defined as coming from within the EU-15 and from outside the EU-15, conceptually it is more appropriate to estimate the rejection of these migrant categories using only the EU-15 sample since it would consider EU-15 migrants as intra-regional migrants and non-EU-15 migrants as immigrants. Furthermore, as shown in Table B1 in Appendix B in the supplemental data online, the sampling of skilled migrants in non-EU-15 regions is not representative of the shares reported by Eurostat (see Poland and Slovakia), whereas the sampling of EU-15 countries is very similar to official statistics. Similarly, some non-EU countries have very low numbers of low-skilled migrants working in low-skilled occupations, such as Poland, Romania and Slovakia (see Table B3 online). Nevertheless, results for the full EULFS sample of 29 European countries (209 NUTS-2 regions) are reported in Tables B20–B22 online. The results indicate that xenophobia is lower in areas that have larger shares of migrants, especially high-skilled or college-educated migrants. Although these results partially support contact theory, they are unreliable due to the migrant categories and sampling bias discussed above.

7. The nine ESS waves use new randomised samples, therefore a limitation of analysing at the individual level is that the longitudinal component is lost, and the results presented are pooled estimates.

8. Additionally, since the main independent variable varies at the regional level, the individual data are further estimated using standard errors clustered at the regional level, and are presented in Tables B17–B19 in Appendix B in the supplemental data online. The results from Table B17 online indicate that 40 of the 72 estimates have lost their statistical significance and a further seven estimates have decreased from a 1% to a 5% significance level compared with Table B11 online. Nevertheless, the rejection of low-educated non-EU-15 migrants holds for four of the xenophobia indicators. Furthermore, the estimates for the interaction terms in Table B18 online all remain statistically significant at a 1% level.

9. All GMM estimates have supporting C-tests, Hansen’s J-statistic and robust F-tests to support the specification and strength of instruments.