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Chapter 1: Anti-immigration attitudes: concepts and measurements

Immigration flows into Europe have increased sharply during recent decades [Hoo08]. During the 1960s, for example, the yearly net migration into the countries that nowadays make up the EU-27 was less than 100,000 on average. In 2006, this figure exceeded 1.6 million. The fact that several European countries adopted more restrictive immigration policies during the 1970s has not prevented an increasing number of persons from settling in Europe, either as economic or labour migrants, political asylum seekers, or to reunify with family members [Cas03]. In recent years, immigration has accounted for the lion’s share of the population increase in the EU-15. Countries such as Sweden, Germany, Greece and Italy would even be facing negative population growth if all migration flows were frozen (European Communities 2004). Europe has de facto become a continent of immigration.

It is no exaggeration to say that the relations between immigrants and European host societies are quite tense at times. The electoral success enjoyed by anti-immigration parties [And96] [Lub02], for example, shows that substantial numbers of European citizens perceive immigration as having negative consequences. Survey research confirms the picture that negative feelings about immigration are quite widespread among the European populations. Inter-ethnic relations can be considered one of the key challenges facing contemporary European societies. It comes as no surprise, therefore, that social scientists from various disciplines have shown great interest in such anti-immigration attitudes.

Anti-immigration attitudes are also the topic of this EduNet module. Specifically, we will investigate how European anti-immigration attitudes have evolved in recent years. Several important research questions will be studied. To what extent are the ESS measures of anti-immigration attitudes comparable across countries and over time (Chapter 2)? How did European attitudes toward immigration evolve between 2002 and 2007 (Chapter 3)? And how can we explain the observed attitude trends (Chapter 4)? Before these questions are tackled, however, we reflect for a moment on how the concept ‘anti-immigration attitudes’ can be defined and operationalised using ESS data.

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What are ‘anti-immigration attitudes’?

Fishbein and Ajzen 1 describe an attitude as ‘a learned predisposition to respond in a consistently favorable or unfavorable manner with respect to a given object'. In other words, attitudes can be seen as more or less consistent tendencies to evaluate particular objects positively or negatively. In this module, we study how Europeans evaluate the object ‘immigration’. Thus, attitudes toward immigration essentially refer to the evaluations that people make in relation to immigration.

According to social-psychological theory, evaluations of objects are based on so-called ‘beliefs’ about these objects. Beliefs are pieces of information that individuals have about the object in question; beliefs are convictions that an object possesses certain characteristics. Individuals may hold a wide range of beliefs on immigration. One can be convinced that immigration causes unemployment for the local population, or, alternatively, that immigration enriches European cultural life. These beliefs can stem from personal experience. But they can also be based on second-hand information - received from acquaintances or from more distant sources, such as mass communication media. People arrive at an overall evaluation of the phenomenon of immigration by balancing all the beliefs they have with respect to immigration, thereby taking their relative importance into consideration. Obviously, beliefs do not necessarily have to be ‘true’ to have an impact on attitude formation.

Previous research has revealed that, in reality, people’s attitudes are often not very consistent, and that, instead, they are strongly dependent on the concrete context in which they are expressed [Tou88] [Kro87]. Attitudes are thus more than a consistent, purely rational calculation based on concrete, well-founded information. Intuitive feelings, superficial impressions, stereotypes and ideological positions play an important role in the formation of attitudes. This is especially the case when one has little personal experience of the object of the attitude (and this is certainly the case with immigration). Attitudes towards a certain object are therefore never completely isolated, but are often strongly connected to attitudes towards objects that are perceived as related or similar. Attitudes to immigration are closely related to attitudes to ethnic minorities or to outgroups in general. Recent empirical evidence has shown that attitudes to diverse groups such as Jews, Muslims, homosexuals, the disabled and homeless persons are indeed very similar in their origins and consequences [Zic08].

This idea is in line with the classical theory of ethnocentrism. As early as 1906, Sumner introduced the notion of ‘ethnocentrism’ to denote the view that one's own social group is the centre of everything. Ethnocentric persons judge outgroups by means of the cultural norms and values of their own group [Sum60]. Ethnocentrism is seen as a complex of two different attitudes: a loyal, uncritical, positive attitude towards one’s own social group (in-group dimension) combined with a hostile, negative attitude towards other groups (the outgroup dimension). Anti-immigration attitudes can thus be seen as a concrete translation of the outgroup dimension of ethnocentrism. Other aspects of ethnocentrism include ethnic prejudice, perceptions of ethnic threat, social distance, and avoidance of outgroup contact [Lev72].

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Antecedents of anti-immigration attitudes

There is now a vast amount of research into the antecedents of negative attitudes to ethnic minorities, immigrants and immigration (see, among others, [Bil95] [Cit97] [Coe03] [Fet00] [Hai07]). Ample empirical evidence has been presented that anti-immigration attitudes are stronger among people with little education, low-skilled workers and those at the lower end of the income distribution. According to self-interest theory, these persons hold similar positions to immigrants, and they are therefore more vulnerable to ethnic competition. As a result, they would be more prone to anti-immigration sentiment. Involvement in religious communities, membership of voluntary organisations, high levels of social trust and the absence of political powerlessness, on the other hand, seem to temper negative attitudes toward immigration. The logic behind these relationships can be traced back to social (contra-) identification theory [Taj82]. According to this theory, people who are socially isolated feel a stronger need to acquire a positive social identity by rejecting outgroups such as immigrants.

More recently, scholars started devoting considerable attention to how anti-immigration attitudes in Europe are influenced by the broader context in which individuals live. Various studies report that anti-immigration attitudes are more widespread in regions or countries with unfavourable economic conditions and sizeable immigrant populations [Qui95] [Sch02] [Sch08] [Sem08]. However, others were not able to replicate these effects [Sid07] [Str08], indicating that effects of economic context and ethnic diversity are still not an open-and-shut case.

Data: anti-immigration attitudes in the European Social Survey

In order to study the evolution of European anti-immigration attitudes, we make use of the first three rounds of the ESS (2002-3, 2004-5 and 2006-7). Seventeen European countries participated in all three ESS rounds. These countries are Austria (AT), Belgium (BE), Switzerland (CH), Germany (DE), Denmark (DK), Spain (ES), Finland (FI), France (FR), Great Britain (GB), Hungary (HU), Ireland (IE), the Netherlands (NL), Norway (NO), Poland (PL), Portugal (PT), Sweden (SE) and Slovenia (SI).

The core module of the ESS (this is the part of the questionnaire that is retaken in every round) contains three items that measure opposition to immigration. Each of the three items asks whether respondents prefer their country to grant access to many or few immigrants from a certain group. The first two items measure the extent to which the respondent believes his or her country should allow people of the same or of a different ethnic group to come and live there. The third question specifically refers to potential immigrants from the poorer countries outside Europe. Respondents indicated their responses on four-point scales, ranging from 1 (allow many) to 4 (allow none). Higher scores thus indicate stronger opposition to immigration to the country. These items can be seen as general indicators of a negative attitude towards immigration 1.

Table 1.1. The immigration items in the ESS. (Introduction to the questions: 'Now some questions about people from other countries coming to live in (country).')
Variable name Literal question
imsmetn To what extent do you think [country] should allow people of the same race or ethnic group as most [country] people to come and live here?
imdfetn How about people of a different race or ethnic group from most [country] people?
impcntr How about people from the poorer countries outside Europe?

Value categories all items: 1 = Allow many to come and live here, 2 = Allow some, 3 = Allow a few, 4 = Allow none

In the remainder of this chapter, we use SPSS to construct and explore the dataset that will be used throughout this module.

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Exercise 1.1. Constructing a pooled dataset

In this exercise, you are taught how to create a dataset with data from several ESS rounds. However, if you don't want to do this work yourself, you may download the manipulated data from the following link.

  1. Download the separate datasets from the first three ESS rounds and save them on your disk. The data can be found on the ESS data website.

    Step by step instructions

    • Click the first dataset in the frame on the left.
    • Click the download icon above the frame on the right:
    • Select SPSS as the dataformat and press ‘Download’. Please register if you are a new user.
    • Save the file to your disk. Repeat for the two next datasets.
  2. Merge the three datasets into one combined dataset containing the data from the three rounds.

    SPSS syntax

    *Start SPSS and open a new syntax window. Copy the syntax below into the syntax window and press ‘run’. *Please do not forget to change ‘C:\’ to the path where you stored the ESS datasets, and to check the names of the datasets. *This will create a dataset with 132,896 rows (units/individuals from three rounds) and 1,076 variables.

    ADD FILES
    /FILE='C:\ESS1e06.sav'
    /FILE= 'C:\ESS2e03.sav'
    /FILE= 'C:\ESS3e03.sav'.
    EXECUTE.
  3. Select cases. Make a selection of the respondents that will be used in this module.
    • Retain only respondents who are living in one of the 17 countries that participated in each of the first three ESS rounds. CNTRY is a character variable containing the country codes. The countries that should be retained (and their respective country codes) are: Austria (AT), Belgium (BE), Switzerland (CH), Germany (DE), Denmark (DK), Spain (ES), Finland (FI), France (FR), Great Britain (GB), Hungary (HU), Ireland (IE), the Netherlands (NL), Norway (NO), Poland (PL), Portugal (PT), Sweden (SE) and Slovenia (SI).
    • In this study, we focus on anti-immigration attitudes among majority group members. The presence of immigrants or ethnic minority group members (who probably have very different views on immigration) in the sample could distort the results. To avoid this, remove all respondents of foreign nationality (variable BRNCNTR) or who belong to an ethnic minority group (BLGETMG).
    • Exclude respondents with missing values for at least one of the three anti-immigration items (IMSMETN, IMDFETN, IMPCRNTR) 1.

    SPSS Syntax

    *Select respondents who live in one of the 17 countries. *This syntax will reduce the number of rows from 132,896 to 98,561.

    SELECT IF
    (cntry = "AT") or
    (cntry = "BE") or
    (cntry = "CH") or
    (cntry = "DE") or
    (cntry = "DK") or
    (cntry = "ES") or
    (cntry = "FI") or
    (cntry = "FR") or
    (cntry = "GB") or
    (cntry = "HU") or
    (cntry = "IE") or
    (cntry = "NL") or
    (cntry = "NO") or
    (cntry = "PL") or
    (cntry = "PT") or
    (cntry = "SE") or
    (cntry = "SI").

    *Select respondents who were born in the country and do not belong to an ethnic minority. *This syntax will reduce the number of rows from 98,561 to 88,134.

    SELECT IF (BRNCNTR=1) AND (BLGETMG=2).

    *Delete observations with missing values. *This syntax will reduce the number of rows from 88,134 to 84,331.

    COUNT missings = IMSMETN IMDFETN IMPCNTR (missing).
    SELECT IF missings = 0.
  4. Save the newly created dataset under a new name. Keep only the following variables in the dataset:
    • The anti-immigration items: IMSMETN, IMDFETN, IMPCRNTR
    • The respondent’s country: CNTRY
    • The ESS round: ESSROUND
    • Weighting variables: DWEIGHT, PWEIGHT

    SPSS Syntax

    *Save the newly created dataset under a new name. *Please do not forget to change ‘C:\’ to the path where you stored the ESS datasets.

    SAVE OUTFILE = 'C:\ESS123_immig.sav'
    /KEEP = CNTRY ESSROUND IMSMETN IMDFETN IMPCNTR DWEIGHT PWEIGHT.
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Exercise 1.2. Data exploration

In order to get to know the data, we start with some data exploration.

  1. Take a look at the means and standard deviations for the three anti-immigration items. Calculate these statistics for the pooled dataset, i.e. without differentiating between countries or time points. Make sure that you have the correct dataset open. Also define DWEIGHT as a weighting variable.

    Question

    Which item has the lowest mean? How do you explain that precisely this item has the lowest mean?

    SPSS Syntax

    *Open the correct dataset. *Please do not forget to change ‘C:\’ to the path where you stored the ESS datasets.

    GET FILE='C:\ESS123_immig.sav'.

    *Define ‘dweight’ as the weighting variable.

    WEIGHT
    BY dweight.

    *Calculate means and standard deviations.

    MEANS IMSMETN IMDFETN IMPCNTR.

    Solution

    Table 1.2. Mean values of the three immigration items
    Variable Mean N Std. Deviation
    Allow many/few immigrants of same race/ethnic group as majority 2.23 84310 0.819
    Allow many/few immigrants of different race/ethnic group from majority 2.50 84310 0.843
    Allow many/few immigrants from poorer countries outside Europe 2.52 84310 0.852

    Weighted by design weight.

    IMSMETN has a lower mean score (2.23) than the other two items. The lower score reflects less opposition to immigration. Apparently, Europeans are less resistant to immigrants with the same ethnicity than to immigrants with a different ethnicity or from poor countries outside Europe (these two latter groups tend to largely overlap). It seems very plausible that attitudes towards immigrants who share some characteristics with the majority population are least negative.

  2. Graphs can be a very useful tool for data exploration. For each of the immigration items, draw a grouped bar chart representing the country means at the three time points. Your bar charts should represent the item averages (on the Y-axis) for all countries and time points (on the X-axis). The bar chart should have the following structure:

    Figure 1.1: Example of a grouped bar chart

    The bar charts can be obtained using SPSS syntax or SPSS ‘Chart Builder’.

    SPSS Syntax

    *Draw a grouped bar chart representing the country-means at the three time points.

    GRAPH
    /BAR(GROUPED)=MEAN(imsmetn) BY cntry BY essround.
    GRAPH
    /BAR(GROUPED)=MEAN(imdfetn) BY cntry BY essround.
    GRAPH
    /BAR(GROUPED)=MEAN(impcntr) BY cntry BY essround.

    * Chart created using Chart Builder.

    GGRAPH
    /GRAPHDATASET NAME="graphdataset" VARIABLES=cntry MEAN(imsmetn)[name="MEAN_imsmetn"] essround[LEVEL=NOMINAL] MISSING=LISTWISE REPORTMISSING=NO
    /GRAPHSPEC SOURCE=INLINE.
    BEGIN GPL
    SOURCE: s=userSource(id("graphdataset"))
    DATA: cntry=col(source(s), name("cntry"), unit.category())
    DATA: MEAN_imsmetn=col(source(s), name("MEAN_imsmetn"))
    DATA: essround=col(source(s), name("essround"), unit.category())
    COORD: rect(dim(1,2), cluster(3,0))
    GUIDE: axis(dim(3), label("Country"))
    GUIDE: axis(dim(2), label("Mean Allow many/few immigrants of same race/ethnic group as majority"))
    GUIDE: legend(aesthetic(aesthetic.color.interior), label("ESS round"))
    SCALE: cat(dim(3))
    SCALE: linear(dim(2), include(0))
    SCALE: cat(aesthetic(aesthetic.color.interior))
    SCALE: cat(dim(1))
    ELEMENT: interval(position(essround*MEAN_imsmetn*cntry), color.interior(essround), shape.interior(shape.square))
    END GPL.

    SPSS Chart Builder

    Click ‘Graphs’ in the main horizontal tool bar, and then ‘Chart Builder’.

    The Chart Builder wizard pops up. The first thing that needs to be done is to change the measurement level of the items from nominal to scale. This is necessary because SPSS does not allow you to calculate the mean of nominal variables. And it is precisely the item ‘means’ that we want to display on the Y-axis. Change the measurement level by right-clicking the variable name (in the upper left corner of the chart builder window) and then selecting ‘scale’ instead of ‘nominal’.

    Next, we define the graph type. In the lower pane of the Chart Builder, select ‘bar’. Drag the second icon (with the green and blue bars next to each other) into the upper pane of the chart builder. Now you can select the variables to be displayed by dragging the variable names (in the upper left corner) into the graph. Drag IMSMETN to the box next to the Y-axis, and CNTRY to the X-axis. The variable ESSROUND should be dragged into the remaining box in the upper right corner (‘cluster on’ - see figure below). The measurement level of the variable ESSROUND must be set to ‘nominal’.

    We still need one more option. Click ‘Element Properties’ in the upper part of the element properties window, select ‘X-axis (Bar 1)’. In the lower part of the screen, select ‘Show only categories present in the data’. Without this option, countries that were excluded from the dataset (but that still have their value labels present) would also be shown in the graph. Click ‘Apply’ and close the ‘Element properties’ window. To get the actual graph, click OK in the ‘Chart Builder’ window.

    Repeat these steps for the other two items (IMDFETN, IMPCNTR).

    SPSS Output

    Questions

    1. In which countries do we witness the strongest resistance to immigration? Which countries have the most immigration-friendly climate?
    2. Is there any evidence that attitudes toward immigration are changing over time? Do all countries under study experience similar evolution?
    3. Do the three items display a similar picture?

    Solution

    1. Portugal and Hungary have the highest mean scores, indicating strong opposition to further immigration to the country. Swedes have by far the most positive attitudes to immigration.
    2. At first sight, attitudes toward immigration seem to be changing in some countries. The over-time evolutions are very different from one country to another, however. In some countries, such as Hungary, Portugal and the Netherlands, there seems to be a rather clear increase in anti-immigration attitudes. In others, such as Poland, we see the opposite trend.
    3. The items referring to immigrants of a different race/ethnic group (IMDFETN) or immigrants from poorer countries outside Europe (IMPCNTR) are very similar. This is not surprising, as there is a big overlap between these two immigrant groups. The item on immigrants from the same race/ethnic group (IMSMETN) leads to somewhat different conclusions, especially with respect to differences between countries. While Hungary clearly has the highest mean score on IMDFETN and IMPCNTR, Portugal scores highest on IMSMETN. The evolutions within countries, however, are largely similar for the three items.
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Exercise 1.3. Exploratory factor analysis

In the previous exercise, it became clear that comparing three different items for 17 countries and three time points can be a daunting task. Rather than working with individual items, we prefer to perform analyses on a single scale that is constructed on the basis of the three items. Working with scales has the additional advantage of higher reliability, because random measurement errors in the different items cancel each other out. However, it needs to be tested whether our three items are sufficiently strongly related to be considered as measuring the same concept. Exploratory factor analysis is one possible way of testing the reliability of the indicators.

Use SPSS to perform an exploratory factor analysis on the three immigration items. Carry out this analysis on the pooled dataset without making distinctions between countries and time points. Use ‘principal axis factoring’ (PAF) as the extraction method and ask SPSS to plot a so-called ‘scree plot’.

SPSS Syntax

*First make sure that the DWEIGHT is on, then run the factor analysis. *This is the end of the exercises in chapter 1. *Save the file to make sure you have an updated version of the file for the exercises in the next chapter. *Please do not forget to change ‘C:\’ to the path where you stored the ESS datasets.

WEIGHT
BY dweight.
FACTOR
/variables IMSMETN IMDFETN IMPCNTR
/print initial correlation extraction univariate
/plot eigen
/extraction paf
/method = correlation.
WEIGHT off.
SAVE OUTFILE='C:\ESS123_immig.sav'
/COMPRESSED.

Questions

  1. Do the three items measure a single dimension?
  2. Can the three items be considered reliable indicators of the intended concept (i.e. anti-immigration attitudes)?

Solution

  1. The number of factors or dimensions measured by the items can be determined in various ways. One possibility is to look at the eigenvalues. The eigenvalues refer to the amount of information that is accounted for by the respective factors, given that every item contributes one unit of information. A common rule of thumb is that only factors with an eigenvalue larger than 1 should be retained. After all, only these factors explain more variance than a single item. The eigenvalue of the first factor is 2.462. Since we have three items, the total variance here equals 3. 2.468, of which three units of variance (i.e. 82.080%) are thus accounted for by factor 1. The second eigenvalue (0.351), on the other hand, is substantially smaller. Since this value is well below 1, the second factor hardly adds anything to the amount of variance explained. Only the first factor should therefore be retained. We conclude that the three items measure a single dimension.
    Table 1.3a. Total variance explained, part 1: Inital Eigenvalues
    Factor Total % of Variance Cumulative %
    1 2.462 82.080 82.080
    2 0.351 11.685 93.765
    3 0.187 6.235 100.000

    Weighted by design weight.

    Table 1.3b. Total variance explained, part 2: Extraction Sums of Squared Loadings
    Factor Total % of Variance Cumulative %
    1 2.212 73.731 73.731
    2
    3

    Weighted by design weight.

    The so-called scree plot confirms the conclusion that the three items are uni-dimensional. This scree plot is a graphical representation of the eigenvalues for the different factors. According to this heuristic, one should look for an ‘elbow’ in the graphic. Only factors on the left hand side of the elbow should be retained, while the rest is ‘scree’ and can be neglected. In this case, the elbow can be found with factor 2. As a result, only the first factor should be retained. By consequence, we can conclude that the three anti-immigration items measure one, single dimension.

  2. In order to judge the reliability of the items, we have to look at the factor matrix containing the factor loadings (note that, since only one factor has been retained, no rotation of the factors was performed). The factor loadings represent how strong the respective items are in relation to the common factor. These factor loadings are essentially correlations, so that they range between -1 and 1. Usually, items with factor loadings higher than .40 (in absolute value) are said to be sufficiently reliable. Here, all three factor loadings are considerably larger, indicating excellent reliability. The first item (IMSMETN) has the weakest factor loading. This is understandable, given that this item mentions a specific immigrant group that is quite different from the groups referred to in the other two items. But even this first item has a very strong factor loading.
    Table 1.4. Factor Matrix
    Variable Factor 1
    Allow many/few immigrants of same race/ethnic group as majority 0.778
    Allow many/few immigrants of different race/ethnic group from majority 0.943
    Allow many/few immigrants from poorer countries outside Europe 0.847

    Weighted by design weight. Extraction Method: Principal Axis Factoring. One (1) factor extracted, 12 iterations required.

    Alternatively, reliability can be judged by looking at the communalities, i.e. the proportions of variance that the indicators share with the common factor. These communalities are the squares of the factor loadings (e.g. for IMSMETN: .778² = .605). All items share at least 60% of their variance with the common factor. Again, this indicates that the items are reliable measurements of the concept of anti-immigration attitudes.

    Table 1.5. Communalities
    Initial Extraction
    Allow many/few immigrants of same race/ethnic group as majority 0.553 0.605
    Allow many/few immigrants of different race/ethnic group from majority 0.715 0.889
    Allow many/few immigrants from poorer countries outside Europe 0.650 0.718

    Weighted by design weight. Extraction Method: Principal Axis Factoring.

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