Does inequality foster xenophobia? Evidence from the German refugee crisis

Figures & data

Figure 1. Theoretical framework.

Table 1. Effect of local inequality on perceived relative deprivation.

	DV: Income satisfaction
	(1)	(2)
Local inequality	−1.72${}^{\ast \ast \ast }$	−1.22${}^{\ast \ast }$
	(0.605)	(0.558)
Fixed effects	County × Year	County × Year
Individual covariates	No	Yes
Municipality covariates	Yes	Yes

Note: The Table shows results from OLS models regressing income satisfaction on local inequality. The analysis draws on survey data from the German Socio-Economic Panel (SOEP) using the survey years 2008 – 2020. The outcome variable measures respondents' subjective income satisfaction on a scale from zero (low) to ten (high). Municipality-level covariates are (i) population size 2014, (ii) population density 2014, and (iii) mean net income as recorded in the tax registry data. Individual-level covariates are (iv) gender, (v) year of birth, (vi) education (ISCED-2011), and (vii) monthly household net income. Local inequality is measured using the Gini index (see Section 3.1 for more details). All models include county × year fixed effects. Standard errors are clustered by municipality.${}^{\ast \ast \ast }$ p<.01; ${}^{\ast \ast }$ p<.05; ${}^{\ast }$ p<.1

Figure 2. Local inequality across German municipalities.

The Figure shows the spatial distribution of inequality across German municipalities. The binary measure is based on a within-county median split of the quantitative Gini measure. Missing data is shaded in light gray – this mainly applies to city counties or states that do not contain any constitutive municipalities (e.g. Berlin, Hamburg, and Bremen). Because within-county comparisons are not possible, such units are excluded from my main empirical analysis. Additional details on the calculation of within-municipality inequality based on tax registry data are provided in Sections 3.1 and A.2 in the SI.

Figure 3. Hate crimes in high- and low-inequality localities over time.

The Figure shows the mean rate of hate crimes in low- and high-inequality municipalities in the matched data set (see Section 3.3 for more details).

Figure 4. Effect of inequality on xenophobic hate crimes.

The Figure shows effect estimates from difference-in-differences specifications (Imai, Song Kim, and Wang 2021). Error bars indicate 95% confidence intervals.

Table 2. Effect of local inequality on German nationalism.

	DV: Nationalist identity (0/1)
	(1)	(2)	(3)	(4)	(5)	(6)
Local inequality	0.038${}^{\ast \ast \ast }$	0.045${}^{\ast \ast \ast }$	0.038${}^{\ast \ast }$	0.040${}^{\ast \ast }$	0.045${}^{\ast \ast }$	0.038${}^{\ast }$
	(0.015)	(0.016)	(0.019)	(0.017)	(0.020)	(0.021)
Fixed effects	State	State	State	County	County	County
Individual covariates	No	Yes	Yes	No	Yes	Yes
Municipality covariates	No	No	Yes	No	No	Yes
DV mean	0.08	0.08	0.08	0.08	0.08	0.08
DV s.d.	0.27	0.27	0.27	0.27	0.27	0.27
N	2615	2109	2109	2615	2109	2109

Note: The Table shows the results from linear probability models where the dependent variable is a binary indicator for nationalism. The individual-level control variables are: age in years, education (ISCED scale), gender, household income, and household size. Analoguous to the main results, I control for population density, total population size, and mean net income as recorded in the tax registry data at the municipality-level. The sample only includes native Germans. Respondents with a migration background are excluded. Standard errors are clustered by municipality. The data stems from the the Ethnic Diversity and Collective Action Survey (EDCAS) conducted by Schaeffer et al. (2011). I provide more details on the survey data and data analysis in Section A.6.1 in the SI.${}^{\ast \ast \ast }$ p$<.01$; ${}^{\ast \ast }$ p$<.05$; ${}^{\ast }$ p$<.1$

Imai, Kosuke, In Song Kim, and Erik H. Wang. 2021. “Matching Methods for Causal Inference with Time-Series Cross-Sectional Data.” American Journal of Political Science 67 (3): 499–828.

 Web of Science ®Google Scholar

Schaeffer, Merlin, Ruud Koopmans, Susanne Veit, Mareike Wagner, and Jonas Wiedner. 2011. “The Ethnic Diversity and Collective Action Survey (EDCAS).” Technical Report.

 Google Scholar