Operationalisation of other key variables

Even more emphasis has been put on what is on the right side of the vote pre-diction equation, i.e. how to measure economic performance. Most individual-level economic voting studies rely on subjective measures of economic percep-tions rather than on actual economic condipercep-tions, but the question of how much people really know about the ‘real’ economy has been raised (see Conover and Feldman 1986; Blendon et al. 1997; Aidt 2000; Paldam and Nannestad 2000).

0 10 20 30 40 50

DE (CDU) DK

(Sd) ES (PP) FR

(PS) GR (ND) IE

(FG) IT (PD) NL

(VVD) PT (PSD) UK

(Con)

vote share (%)

previous national elections the 2014 EP elections

Voters who lack knowledge about the state of the economy cannot be expected to make rational and informed political decisions. With regard to these consid-erations, I conducted a test by comparing aggregated individual-level economic assessments in the EES Voter study with objective economic performance. The results combining the data from 50 surveys show a strong correlation between aggregated economic evaluations and one of the key measures of the actual state of the economy, GDP growth rate (Pearson’s r=0.72). Moreover, regressing aggregated economic perceptions on key macroeconomic indicators (for a sim-ilar approach, see Fraile and Lewis-Beck 2012), demonstrates that all three – GDP growth rate, change in unemployment and in inflation – have a statistically significant effect on subjective assessments on a 99% confidence level with signs in the expected direction (see Table 3). Therefore, individual-level survey data do a good job in reflecting economic realities. Several studies confirm these results (see Nadeau and Lewis-Beck 2001; Bélanger and Lewis-Beck 2004; Fraile and Lewis-Beck 2012), suggesting that voter beliefs about national economies “are grounded in economic reality” (Duch and Stevenson 2010: 113).

Table 3. Effects of macroeconomic indicators on subjective economic evaluations.

GDP growth 0.05***

(0.00) Inflation growth -0.02***

(0.00) Unemployment change 0.00***

(0.00) Constant 0.37***

(0.00) McFadden’s R² 0.55

N 55,731

Source: EES Voter study from 1989, 1994, 2004, 2009 and 2014 for 10 European countries; Eurostat and OECD; author’s own calculations.

Notes: Entries are regression coefficients, standard errors in parentheses. The dependent variable is aggre-gated from individual-level economic perceptions (mean value per country per year).

***p<0.01 **p<0.05 *p<0.1

The main independent variable in the analysis is citizens’ retrospective socio-tropic economic assessments. Retrospective evaluations are based on past as opposed to future economic performance, and sociotropic refers to evaluations of national rather than personal financial situation. Previous results indicate that voters are more influenced by retrospective evaluations than by prospective

ones (Key 1966), and that they form their economic opinions based on the country’s overall economic situation rather than their own pocketbook (Kinder and Kiewiet 1981). Respondents in the EES Voter study were asked to assess on a 5-point scale whether they thought that compared to 12 months previous the general economic situation in the country had gotten a lot better, a little better, stayed the same, got a little worse or a lot worse. This survey item, first proposed by Lewis-Beck in 1988, is the most standard way to measure indi-vidual-level economic perceptions. Because the substantive interest in economic voting studies lies in the distinction between negative, positive and neutral evaluations, the original 5-category variable was recoded into a 3-point scale where 1=worse, 2=stayed the same and 3=better (for a similar approach, see Nadeau, Lewis-Beck, and Bélanger 2013). Being able to select one of five responses is useful for the respondent, but in the analysis we are substantively interested in distinguishing only between three categories: how much more likely are citizens to vote for the incumbent if they move from category “worse”

to “same” or to “better”. Even though broader than the original one, the recoded variable reflects a meaningful division of response categories, while still main-taining the ordinal nature of the data.

In addition to the retrospective dimension, prospective economic evaluations are often used in economic voting studies but show considerably weaker effects.

Here, prospective evaluations were not included due to respective data missing on 1989.

To test the relative impact of economic perceptions, a number of control variables are included in the models. These predictors are held constant throughout the analysis. The basic set of control variables predominantly con-sists of standard determinants known to influence voter political preferences. In the American political system, the key socio-psychological factor influencing electoral choice is party identification (Campbell et al. 1960), with the majority of people having a sense of attachment with one of the two main parties – Dem-ocrats or Republicans. In Europe, where the party landscape is more frag-mented, stronger emphasis is placed on voter ideological identification (Inglehart and Klingemann 1976). The EES Voter study measures voter ideo-logical leaning via respondents’ self-placement on the left-right scale (from 1 to 10, where 1=left and 10=right). A wide range of empirical studies have shown that the left-right continuum is a major ideological dimension along which political life is organised (see Castles and Mair 1984; Warwick 1992). Left-right thinking is a stable feature of political processes, and most citizens in democratic countries are willing and able to place themselves on this scale (Geser 2008). Left-right judgments are a major predictor of voting decisions (see Inglehart and Klingemann 1976; Fleury and Lewis-Beck 1993), and there-fore in this analysis, too, I expect respondent left-right placement to have a strong influence on incumbent support. Additionally, standard socio-demographic indicators that may determine vote preference such as age (in full years), gender (1=male, 2=female), education (age upon leaving full-time

edu-cation,⁹ 0=still studying, 1=up to 15 years, 2=16-19 years, 3=20 years or more), religious attendance (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) are included. All of these items are regularly measured in the EES Voter study surveys. Country and year dummies are used in all single-level models as a control for unobserved hetero-geneity across countries and time.

To ensure correct model specification, respondents’ left-right ideology, social class and attendance of religious services were adjusted to match the ideology of the PM party in office at the time of the fieldwork (for a similar approach, see Nadeau, Lewis-Beck, and Bélanger 2013). For example, the ide-ology scores remained unaltered (1=left, 10=right) if the incumbent PM party was right-wing, but were reversed (1=right, 10=left) if the incumbent PM party was left-wing.¹⁰ In a similar manner, the scores of religious attendance and self-assigned social class were reversed if the governing PM party was left-wing.

This enabled an ambiguous situation to be avoided in combined models where in some elections a positive regression coefficient would indicate higher support for a left-wing PM party and in others for a right-wing one.

In order to account for possible effects of the electoral cycle – the cost of ruling – cabinet time in office is controlled for. It is a robust finding in political science research that government popularity follows a cyclical pattern. Incum-bents begin their terms with high approval rates. The post-election honeymoon period is followed by a decline in popularity by mid-term, which then increases again towards the end of the electoral cycle (see Miller and Mackie 1973; Tufte 1975; Stimson 1976). The average government in an established democracy is thought to lose about 2.25% of votes during a normal election period (Nannestad and Paldam 2002: 17). To capture these effects, I include in the models a measure of the time the government held office. The variable was measured as a number of months from the preceding last national election to the starting date of survey fieldwork. The calculations were based on information available in the EES Voter study methodological reports and in the European Election Database. Because I expect the relationship between the electoral cycle and incumbent support to be nonlinear, logarithmic transformation for the cycle variable was used.

Additional independent variables specific to particular research questions are separately described in each empirical chapter.

9 Data on the highest level of education completed are only available for 2009 and 2014, and coding of the variable is highly diverse across countries. Therefore, age when stopped full education is used instead.

10 PM parties in different countries at different points in time were divided into left and right depending on which side of the midpoint of a typical left-right scale they fell on. In categorising, internet resources (e.g. the Parliaments and governments database at http://parlgov.org/ and the Parties and elections in Europe database at http://

www.parties-and-elections.eu/) and country experts were consulted.