Data, methods and model specification

To empirically test the robustness of economic effects, I use individual-level data from the EES Voter study (for study description, see section 3.3). The study is a post-election survey conducted shortly after EP elections every five

11 Don’t knows, refusals, respondents who said they would not vote if the elections were held the following day, would spoil their vote or vote blank, and missing answers are all included in this category.

28,6 20,8 19,7 20,7 17,7

0 20 40 60 80 100

1989 1994 2004 2009 2014

%

Incumbent support Worse Same Better

years since 1979. In every country that was included in a given survey wave, a representative sample of voters aged 18 and over was interviewed. The analysis presented in this chapter includes respondents from 10 Western European countries in 1989, 1994, 2004, 2009 and 2014, with a total of 50 cross-sections (n≈1000 interviews per survey per country; for more information on case selec-tion, see section 3.3). The countries covered are Denmark, France, Germany, Greece, Ireland, Italy, the Netherlands, Portugal, Spain, and the United King-dom. In order to maximise the variation in economic and political conditions and consequently more accurately estimate economic effects, data for five sur-vey waves and 10 countries were pooled into one dataset. Such an approach is made possible by extensive similarities in study design, sample set-ups, inter-viewing procedures and questionnaires across all 50 surveys. The final data pool has a total of N=55,371 respondents, providing ample statistical power to explore the individual-level relationship between variables. Weights were not applied because no continuity exists in the weight variables for separate study waves and countries.

The outcome variable in the analysis is incumbent support, measured as vote intention in the subsequent respective national election (for more information on operationalising the dependent variable, see section 3.4). Respondents in the EES voter study were shown a list of parties and asked who they would vote for if the general election were held the following day. The answers were recoded as 1 if the respondent intended to vote for the incumbent PM party and as 0 in the case of any other vote intention. Don’t knows, refusals, respondents who stated that they would not vote, would spoil their vote or vote blank, and miss-ing answers were excluded. In the pooled dataset of five survey years and 10 countries, 21.3% of respondents indicated their support for the incumbent PM party, 49.5% for some other party, and 29.2% expressed no clear party prefer-ence. Typically for economic voting studies, the economy was operationalised through subjective retrospective evaluations as previous findings show that when evaluating incumbent performance, citizens are more influenced by retro-spective than proretro-spective considerations (Key 1966; Fiorina 1978); for more information on operationalising the independent variables, see section 3.5).

Survey respondents were asked to assess whether they thought compared to 12 months previous, that the general economic situation in their country had gotten better, stayed the same or gotten worse. In a combined data pool of 50 country-years, 43% of individuals negatively evaluated national economic develop-ments, 25.8% positively and 29.1% stated that they felt that no change had occurred over the course of a year. The attitudes of 2.1% were not revealed.

The control variables added in the models are age (in full years), gender (1=male, 2=female), education (age when stopped full-time education, 0=still studying, 1=up to 15 years, 2=16-19 years, 3=20 years or more), attendance of religious services (1=several times a week, 2=once a week, 3=few times a year, 4=once a year or less, 5=never) and subjective placement into social class (1=working class, 2=middle class, 3=higher class). To account for potential effects of the electoral cycle, cabinet time in office in months is included. In

single-level models, country and year dummies are used to account for un-observed heterogeneity across nations and time. Left-right self-placement, class affiliation and religiosity are adjusted to account for government ideology: the scores remain unaltered (0=left, 10=right) when the government is right from the centre, and are reversed (0=right, 10=left) when the government is left-wing. For electoral cycle, log transformation is used because I expect its effect on incumbent support to be non-linear (for more information on these decisions, see section 3.5).

Finally, in order to carry out robustness checks for economic effects, a num-ber of additional variables are utilised. Past vote choice is used to capture indi-cators potentially missing from the model specification and in order to chal-lenge economic effects with conservative over-controlling. The variable is coded as 1 if in the preceding national elections the respondent voted for the PM party in office during the conducting of the survey fieldwork and as 0 for any other party. The same variable is later used in endogeneity tests. The macro-level model of incumbent support is estimated using an annual national average of three macroeconomic indicators: GDP growth, change in inflation and change in unemployment. Macroeconomic data are obtained from the Euro-stat and the OECD online databases. For the sake of within-model comparison, all predictors are recoded on a scale from 0 to 1. Descriptive statistics of all variables used in this chapter, as well as question wording, are shown in Appendix 2.

Due to the dichotomous nature of the dependent variable, I use logistic regression analysis to estimate the effect of economic perceptions on voter political preferences (for more information on the choice of analytical tech-nique, see section 3.6). The function is specified as follows:

pˆ

pˆ ^{= + + +. . . + (2)}

wherepˆ is the probability of voting for the incumbent, is the intercept,

− are regression coefficients, is economic perceptions, and − are control variables.

The results are presented as average marginal effects, which indicate discrete changes in predicted probabilities of incumbent support when independent vari-ables change from their minimum to their maximum value.

In addition to single-level logistic regression, multilevel regression analysis will be employed to test the robustness of the results. Multilevel analysis is typically used for analysing data with a focus on nested sources of variability.

Ignoring any sources of variability may lead incorrect conclusions being drawn (Snijders and Bosker 2011; see also section 3.6).

4.3. Empirical results