4.2 Second Study: "Immigration, Social Policy and Public Opinion in Western
4.2.1 Data and Methods
In this second study, Eger and I investigate public opinion in 14 Western European countries. Individual-level data come from the European Social Survey (ESS), a comprehensive, biennial multi-country survey covering over 30 countries in Europe from 2002 to 2010. The determinants of public opinion are tested in the 4th round of the ESS (2008) because it includes a special module on social welfare policy. This survey has representative random samples at the regional-level. The regions sampled in the ESS correspond to the European Union’s Nomenclature of Territorial Units for Statistics
(NUTS). These geographical units represent administratively, and often historically distinct political areas in each country. NUTS-level designation (1, 2, or 3) is based on population size (see Technical Appendix Two, Table 36 for more details).
Our research examines how the presence of immigrants impacts native-born public opinion towards social policy; thus, we include only native-born individuals in our sample. We drop all individuals with missing data on any of the variables in our model (about 5%). Our main sample includes all Western European countries in the survey except Portugal due to a lack of compatibility between NUTS regions and Portuguese census regions. In total we analyze 22,835 native-born individuals at the micro-level, nested in 112 regions at the meso-level, in 14 countries at the macro-level; these are Belgium, Denmark, Finland, France, Eastern Germany, Western Germany, Greece, Ireland, the Netherlands, Norway, Sweden, Switzerland, Spain, and Great Britain. Table 15 provides descriptive statistics on the individual-level and regional-level data employed in the current analyses. We split Germany into East and West because public opinion and socioeconomics differs between these two former countries (Roller 1994). We also split Berlin into former East and West in terms of individuals, although measurement limitations force us to capture all of Berlin when measuring meso-level data.
Table 15. Summary Statistics, ESS 2008
Our DVs come from ESS questions about the provision of social policy. We use three questions to measure different public opinions. The first question measures income redistribution asking how much the respondent agrees with the following statement: “The government should take measures to reduce differences in income levels.” Answer choices range from “strongly disagree” through “strongly agree.” The next two questions
Individual-level N Mean S.D. Measure
Redistribution 22,835 69.0 26.1 0strongly
disagree 100strongly
agree The government should take measures to reduce differences in income levels.
Health Care 23,005 85.8 15.5 0none 100fully
responsible How much responsibility governments should have to
…ensure adequate health care for the sick?
Old-Age Welfare
23,003 82.5 16.4 0none 100fully
responsible How much responsibility governments should have to
…ensure a reasonable standard of living for the old?
Education 23,049 12.6 4.01 In years
Age 23,049 48.1 18.3 In years
Female 23,049 0.52 0.50 Female = 1
At Risk 23,049 0.29 0.46 Retired, sick, disabled or
unemployed past 7 days = 1
Union 23,049 0.44 0.50 Currently/formerly in a union = 1
Suburban 23,049 0.14 0.35 Suburban dwelling = 1
Anti-immigrant 23,049 47.8 20.8 Three items immigrants: bad/good, undermine/enrich cultural life, make country worse/better place to live.
Regional-level N Mean S.D. Measure
Foreign-born 112 11.1 7.15 % of population born in a foreign country
Tertiary Degree 112 28.8 6.28 % of population with tertiary degree
GDP Per Capita 112 33.9 16.8 GDP per capita in k Euros
Vote Left 112 43.6 11.7 % of population that voted for
traditional left parties in most recent national election Vote
Neo-National
112 6.66 8.78 % of population that voted for
anti-immigrant parties in most recent national election
12.3 67.5
0.00 37.9
1.98 37.2
14.5 48.3
14.9 120.6
0 1
0 100
Minimum Maximum
0 1
0 1
0 1
Minimum Maximum
0 23
15 85
are from the special welfare state module (to date only asked in 2008) and measure public opinion toward government provision of health care and old-age welfare. The questions are worded similarly and capture attitudes regarding the level of responsibility the government should take to either “…ensure adequate health care for the sick?” or
“…ensure a reasonable standard of living for the old?” A 10-response Likert-scale, ranging from 0, “no responsibility,” to 10, “fully responsible,” captures respondents’
attitudes. For ease of interpretation, all three variables are transformed into equal intervals ranging from 0-100.
Regional-level data come from a unique dataset complied from Eurostat, the European Election Database, and national censes. Although information on the foreign-born population is available for the nation-state in Eurostat and other comparative databases, at the regional level there is no comprehensive source. Therefore, between 2011-2012, we searched national censes’ databases in order to locate this measurement.
Further details on regional data collection are available in Technical Appendix Three, Table 36 and Table 37.
We argue that immigration-generated ethnic diversity reduces public support of social policy. To test this hypothesis we utilize the variable percent foreign-born by region. In general, immigrants are more likely to differ linguistically, culturally, and phenotypically from native-born populations. We argue that these differences are perceptible and thus likely to focus attention on group boundaries and activate in-group bias. Although it is true that other types of ethnic diversity exist in each of these countries (e.g. historic, linguistic, or religious minority populations), our goal is to measure the impact of the ethnic diversity created by immigration across all of the countries. Thus, we use a standard measure that is comparable across Europe. The percentage of the population that is born-abroad captures the proportion of the population that is a
first-generation immigrant, regardless of country of origin, naturalization status, length of stay, or any other way that the immigrant population could be sub-divided. It is important to note that this is the only measure of immigration that is available at the regional-level across these 14 countries.
To test a hypothesis about the effect of a regional variable on an individual-level outcome, we rely on multilevel linear regression modeling. The structure of our dataset is nested, with individuals residing in regions that make up countries. A three-level model takes into account the clustered nature of the data and the repeated observations of characteristics specific to each of the 112 regions. Moreover, this approach assigns a random intercept for each country and region to capture the effects of unobserved heterogeneity. These random intercepts include any unobserved characteristics4, but in particular they allow us to implicitly control for any qualitative features of national social policy systems that structure respective public opinion and might otherwise bias our results (see Rothstein 1998; C. Brooks and Manza 2007; Larsen 2006). Allowing a random intercept uses up only one degree of freedom per level whereas adding a dummy variable for each region and country would use up 112 plus 14 degrees of freedom. Also our method clusters regional-level and country-level standard errors so as not to bias the results by producing significant effects where there are none (DiPrete and Forristal 1994).
In order to test our hypotheses, we use a step-by-step approach to modeling the data (Hox 1995). For each of the three DVs we first run an empty model (1) with no independent variables to test for individual variance at the regional and national levels.
This provides a baseline for comparing subsequent models and model fits. Next in model
4 For example, the regional impact of percent foreign-born on support for health care might be similar in Denmark and Germany; however, overall support is higher in Denmark and lower in Germany (difference in country-level intercepts), all else being equal. Within the Netherlands, individuals in Noord-Holland are more supportive of income redistribution than individuals in Zeeland for example (difference in regional-level intercepts), all else being equal.
(2) we add only the individual control variables to give a baseline effect and to reset the model fit statistics for subsequent comparisons. The next model (3) adds the regional-level control variables to check how they impact the dependent variable and to see if their impact improves the overall GOF. In (4), we add regional percent foreign-born to test our in-group bias hypothesis. To support the hypothesis we expect to find significant negative coefficients for percent foreign-born as well as an improvement in the GOF. To ensure that we are capturing in-group bias and not racism or prejudice, we include individual-level anti-immigrant sentiment in model (5). We expect to find that the effect of in-group bias remains significant while controlling for anti-immigrant sentiment. Finally, we calculate predicted marginal effects to demonstrate the size of the effects for our key independent variables at the different levels.