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CHAPTER 4: Explanations for attitude change

In the previous chapter, we observed that, between 2002 and 2007, significant and substantial changes took place in anti-immigration attitudes. We found that one can hardly speak of a uniform trend across Europe: in some countries, attitudes towards immigration became more hostile, while others experienced a significant decrease in anti-immigration sentiment. It is a pressing question to establish the origins of these very diverse patterns of attitude change in Europe. In this fourth and last chapter, we will test a number of possible explanations for attitude change.

Various explanations for attitude change have been suggested in the literature on attitudes towards outgroups. Previous research has shown that cohort replacement can explain a substantial part of long-term evolution in anti-black prejudice among white Americans [Fir88], [Sch97]. The fact that older, more prejudiced cohorts die off and are replaced by younger, less prejudiced and better educated ones can produce a significant evolution towards a more tolerant society. It is very unlikely, however, that cohort replacement is responsible for the attitude changes we observe here. Firstly, the period that we evaluate (2002-2007) is too short for large cohort replacement effects to kick in. Secondly, cohort replacement predicts a uniform trend towards less outspoken anti-immigration attitudes. This is clearly not what the ESS data show us.

Thirdly, important historical events - and the way they are presented in the media - could greatly influence trends in outgroup attitudes. A classic example of this mechanism is the impact of the Civil Rights movement on increasing racial tolerance in the USA [Qui96], [Sch97]. Nevertheless, investigating the link between historical events and attitude changes is a very difficult task. It would require summarising all events covered by the media that took place between 2002 and 2007 in 17 countries, and quantifying these events. Clearly, this falls outside the scope of this module.

Yet another possible explanation for changing attitudes towards outgroups derives from group conflict theory [Bla67], [Olz92], [Qui95]. This chapter provides an empirical test for the group conflict explanation.

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Group conflict theory as a source of attitude change

According to group conflict theorists [Bla67], [Olz92], [Qui95] negative attitudes towards other groups stem from the view that certain prerogatives of the social group in question are threatened by these groups. Negative outgroup attitudes are rooted in perceived threat, i.e. the perception that one’s own group has to compete with other social groups for scarce goods. These scarce goods subject to so-called ethnic competition can relate to material interests, but can also include power and status. Following this argument, negative attitudes to immigration are typically caused by the view that immigrants threaten the opportunities of autochthonous citizens to find a well paid job, an affordable house, or undermine local cultural life.

Blalock [Bla67] proposed that the level of perceived threat is influenced by the context of actual competitive conditions in which intergroup relations take shape. In other words, the more actual ethnic competition, the more people will feel threatened by immigration. In group conflict literature, actual competitive conditions have primarily been operationalised by two variables: the size of the minority group present and the economic conditions. The presence of large immigrant groups and unfavourable economic conditions are expected to induce perceptions of threat and, in consequence, to cause negative attitudes towards immigration. Blalock [Bla67] mentioned two reasons why the presence of large immigrant groups reinforces threat perceptions. Firstly, a more sizeable minority group means a larger number of ethnic competitors and, consequently, a more intense struggle for scarce goods. Secondly, sizeable minority groups can lead to a stronger perceived threat because larger minority groups have more potential for political mobilisation. Group conflict theory predicts that economic context also shapes levels of perceived threat. The logic behind this proposition is that less favourable economic conditions cause the material goods that are the object of competition to become scarcer. In more prosperous times, on the other hand, competition becomes less intense and the perception that majority and minority groups are locked in a zero sum game is reduced [Bla67], [Sch02], [Sem06].

Most empirical tests of group conflict theory have been performed in a static, cross-sectional setting. Various studies have compared countries or regions to find out whether negative attitudes to outgroups are more prevalent in contexts with larger minority groups and less favourable economic conditions. This body of research has provided some empirical support for group conflict hypotheses. Based on a multilevel analysis of 12 European countries, Quillian [Qui95] concludes that prejudice is more widespread in countries with a high proportion of non-EU immigrants and a low gross domestic product (GDP) per capita. Several other studies confirmed the findings that minority group size [Coe04], [Lah04], [Sch02], [Sch08], [Sem04], [Sem08] or economic conditions [Sem08], [Sch08] affect outgroup attitudes. Nevertheless, support for group conflict theory is not unambiguous, as there is also conflicting evidence that cannot be ignored (see for example: [Sid07], [Str08].

The use of data on attitude changes makes it possible to approach group conflict theory from a different point of view, and to test a more dynamic formulation of this theoretical framework. Rather than attempting to link absolute levels of outgroup attitudes to group threat factors, we focus on attitude changes specifically. Following Olzak [Olz92], one can expect attitude changes to be driven by changes in the intensity of actual competition rather than by its absolute level. Following this logic, actual competition could remain constant at a high level without affecting outgroup attitudes. It is only when sudden changes in minority group size or economic conditions occur that outgroup attitudes evolve. Several reasons can be given for the existence of such a process. Rapid changes in immigration or economic conditions might affect the labour, housing and other markets more strongly than slow-paced evolution because of the limited time available to absorb the changes [Olz92]. In addition, sudden changes can have an important impact on popular perceptions because changes in group conflict factors usually receive extensive media coverage.

To summarise, this dynamic formulation of group conflict theory expects changes in actual competition rather than absolute levels of competition to have an impact on changes in outgroup attitudes. This leads to the following research hypotheses:

Hypothesis 1: In countries with a growing (contracting) immigrant population, attitudes to immigration become more (less) restrictive.

Hypothesis 2: In countries with a deteriorating (improving) economic situation, attitudes to immigration become more (less) restrictive.

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An empirical test of dynamic group conflict theory

In order to test these hypotheses, we will perform an aggregate data analysis at country level. Specifically, we will calculate the attitude change between 2002 and 2007 for every country and link these changes to relevant context indicators of change in economic conditions and minority group size.

Exercise 4.1: Constructing an aggregate data set

Before we can test the hypotheses formulated above, an aggregate dataset needs to be constructed. The aggregate dataset should contain 17 records (one for each country), providing information about attitude change as well as about relevant context variables. We will construct such a dataset in two steps.

Step 1: Construct a variable containing information on attitude change between 2002 and 2007.

Create a dataset containing the country means on the REJECT scale for ESS round 1. This aggregate should contain 17 observations, one for each country (hint: make use of the AGGREGATE statement). Then, create a second aggregate dataset containing the country means for ESS round 3. Merge these two datasets into one file by means of the ‘MATCH FILES’ statement. Finally, compute a new variable, ‘REJECT_CHANGE’, which is the difference between the 2006/7 and the 2002/3 average. Save the newly created dataset.

SPSS Syntax

*Weight data, create temporary dataset, aggregate, match files, compute change and save output. *Please do not forget to change the path 'C:\'.

WEIGHT by dweight.

select if essround=1.
aggregate outfile = r1_means.sav
/BREAK = cntry
/meanreject1 = MEAN(reject).

select if essround=3.
aggregate outfile = r3_means.sav
/BREAK = cntry
/meanreject3 = MEAN(reject).

/ FILE = r1_means.sav
/ FILE = r3_means.sav
/ BY cntry.

compute reject_change = meanreject3-meanreject1.

SAVE outfile = 'C:\ESS_country.sav'.

Step 2: Merge the information on attitude change with context data

Next, relevant context data have to be added. Via this link, you can download a small dataset (context.sav) containing information on changes in the level of actual competition:

While flowcap measures changes in immigrant group size, gdp_growth and unemp_change reflect changes in economic conditions. Note that some missing values are included.

Merge ‘context.sav’ with the dataset containing the information on attitude change.

SPSS Syntax

*Merge ‘context_sav’ with the dataset containing the information on attitude change. *Please do not forget to change the path 'C:\'.

/ FILE 'C:\ESS_country.sav'
/ FILE = 'C:\context.sav'
/ BY cntry.

SAVE OUTFILE = 'C:\ESS_country.sav'.

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Exercise 4.2: Correlations between attitude change and context

We use correlation analysis In order to test whether the attitude changes are driven by changing levels of actual competition. Calculate correlation coefficients between ‘REJECT_CHANGE’, on the one hand, and the three contextual variables, on the other.

SPSS Syntax

*Correlation between ’REJECT_CHANGE and the contextual variables.

/variables reject_change flowcap gdp_growth unemp_change.

SPSS Output

Table 4.1. Correlation between 'REJECT_CHANGE' and the contextual variables
reject_change flowcap gdp_growth unemp_change
reject_change Pearsons Correlation1
Sig. (2-tailed)
flowcap Pearsons Correlation.293 1
Sig. (2-tailed).271
N16 16
gdp_growth Pearsons Correlation-.243 -.115 1
Sig. (2-tailed).348 .671
N17 16 17
unemp_change Pearsons Correlation.241 .027 -.589 1
Sig. (2-tailed).368 .924 .016
N16 15 16 16

Weighted by design weight.


  1. Do the correlation coefficients have the expected signs?
  2. Look at the size of the correlation coefficients. Are the correlations between attitude change and change in actual competition substantial?
  3. Are the correlation coefficients statistically significant?
  4. What do you conclude from this analysis?


  1. Based on group conflict theory, we expect anti-immigration attitudes to become stronger in countries where immigration flows are strong, unemployment is increasing and economic growth is low. Positive values for the variable ‘REJECT_CHANGE’ indicate that anti-immigration feelings have become stronger. In consequence, we expect ‘REJECT_CHANGE’ to correlate positively with ‘flowcap’ and ‘unemp_change’, and negatively with ‘gdpgrowth’. The SPSS output shows that the correlation coefficients have the expected signs.
  2. The correlations between attitude change and context variables are all medium size, namely between 0.20 and 0.30 (in absolute value).
  3. The reported p-values show that, although they are substantial, none of the correlation coefficients is statistically significant. This is to a large extent due to the fact that this is an aggregate analysis performed at country level. Consequently, the correlations are based on a very small N, and the test lacks statistical power. Therefore, one should not blindly rely on the statistical tests, but also look at the effect sizes.
  4. It is hard to draw definitive conclusions based on this analysis (this may not be the most satisfactory of situations, but far from uncommon in the life of a social scientist!). On the one hand, we find three medium-size correlations in the expected direction, giving some support to the group-conflict explanation for attitude change. On the other hand, the evidence is far from conclusive: the size of the correlations is not overwhelming, and there is no statistical basis for concluding that a relation is present 1.

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Exercise 4.3: Scatterplots

Graph are very useful tools for studying the relationship between attitude change and context variables in greater detail. Scatter plots, for example, are very effective in detecting outlying countries that might have distorted the correlational analysis. Since our aggregate analyses are performed on a very small number of observations only (17 countries), there is a considerable risk that this is the case.

Drawing scatter plots

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

The Chart Builder wizard pops up. We start by drawing a scatter plot with FLOWCAP on the X-axis and REJECT_CHANGE on the Y-axis. First, define FLOWCAP as a scale variable (now it is still defined as a nominal variable). To do this, right-click the variable name FLOWCAP and select ‘scale’ instead of ‘nominal’.

We continue by defining the graph type. In the lower pane of the gallery, select ‘scatter/dot’. Drag the first icon 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 REJECT_CHANGE to the box next to the Y-axis, and FLOWCAP to the X-axis.

The scatter plot becomes even more informative if we put the country abbreviations next to the dots. Click the ‘Groups/Point ID’ tab in the lower pane of the Chart Builder. Select the box ‘Point ID Label’. Now, an additional box appears in the upper pane (namely Point ID). Drag the variable ‘Country’ into this box.

Click OK. Repeat these steps in order to obtain plots with GDP_GROWTH and UNEMP_CHANGE on the X-axis.

SPSS Output

Figure 4.3. Scatter 1

Figure 4.3. Scatter 2

Figure 4.3. Scatter 3


What do you conclude on the basis of these scatter plots?


The graphs indicate that, for the relationship with at least two of the context variables, the correlational analysis is distorted by a clear outlier, namely Poland. In Poland, we witnessed a marked decrease in anti-immigration attitudes. At the same time, Poland has known high levels of immigration and a remarkable decline in the unemployment rate. As a result of this very particular combination, the correlations between REJECT_CHANGE, on the one hand, and FLOWCAP and UNEMP_CHANGE on the other, are misleading. If Poland were omitted from the analysis, we might find near-zero correlations, or even correlations with the opposite sign. The relationship between REJECT_CHANGE and GDP_GROWTH seems somewhat more stable. These findings further weaken the already shaky evidence for the role of group conflict in attitude change. At the same time, they illustrate the risk of conducting analyses on a small number of observations, and show the usefulness of scatter plots to detect outliers.

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