Method of analysis

Most economic voting studies rely on quantitative analysis, which uses numeri-cally measureable data and employs statistical analytical techniques. These studies offer an understanding of the relationships between political phenomena based on large-scale empirical evidence, where generalisations are made from sample to population. The generalizability of quantitative results comes with a trade-off: being less in-depth than qualitative research, such studies may miss contextual details that help to describe underlying meanings and patterns of these relationships. Nevertheless, the focus here is on identifying the overall associations between economic opinions and political preferences across Europe, as well as variation in these relationship and trends over time. For this reason, quantitative data and statistical analysis methods are a logical choice.

The purpose of the analysis is to estimate how explanatory variables, specifi-cally economic perceptions, affect incumbent support. I utilise a variety of sta-tistical methods, which best fit the data and the research question at hand. In social sciences, the relationship between variables is commonly estimated using regression analysis. Regression analysis helps us to estimate the statistical rela-tionship between two or more variables. In this work, it helps us to understand how the typical value of incumbent support changes when economic percep-tions vary, while the values of control variables are held constant.

When the dependent variable is scalar, its statistical relationship with one or more explanatory variables is estimated with linear regression. Here, however, the dependent variable is dichotomous – the observed outcome can only take on two possible values, 1 if the respondent would vote for the incumbent PM party in subsequent national elections and 0 if the vote would go to any other party.

The binary response means that the relationship between the variables is non-linear, and this requires the use of logistic regression (Pampel 2000). The coefficients in logistic regression models will indicate the increase or decrease in probability of voting for the incumbent due to a one-unit change in a given independent variable. The baseline multivariate model includes economic assessments as its main explanatory variable, a number of control variables to test the relative impact of the economy on incumbent popularity, and, addition-ally, dummies for country and year fixed effects in order to account for unmeasured time-constant country-specific factors that may influence incum-bent vote probability for individuals and that may correlate with other covari-ates. Interaction effects between micro- and macro-level variables are intro-duced where necessary to determine how individual-level economic effects vary depending on higher-level factors.

The results are presented as average marginal effects, which express the population average effect of on (Mood 2010). Marginal effects can be inter-preted as discrete change in predicted probabilities when independent variables change from their minimum to their maximum value. Logistic regression coef-ficients can be estimated in various ways, e.g. probability, odds or odds ratio (Pampel 2000; Menard 2002). In order to linearise the nonlinearity in regression

models with binary outcome, further logit transformation is used. Most statisti-cal packages by default produce logistic regression results as logged odds.

Unfortunately, while this transformation improves linearity, it carries with it the loss in interpretability since logged odds can be less intuitive to understand (Pampel 2000). Moreover, interpreting logged odds or odds ratios as substantive effects and comparing these effects across models, samples or groups can be problematic because of unobserved heterogeneity: differences in coefficients may not be due to differences in actual effects but variation in the dependent variable caused by omitted variables (see Allison 1999; Mood 2010). The recommended solution is to transform coefficients into changes in probability, for example marginal effects, based on derivatives of the prediction function (Mood 2010). This is the approach used in this study.

When conducting data analysis, special considerations are taken with the specifics of the data used. In particular, in some empirical chapters the data have a hierarchical structure. Voters in Europe are not independent individuals, existing in vacuum, but are nested in countries and in different points in time.

For example, survey respondents in France in 2009 are more likely to be similar to each other than to those from France in 2014, or to voters from Italy at any time point. If the answers are correlated because units of observations are simi-lar, one of the main assumptions of classical single-level regression models is violated – namely, that scores of individual observations in the data pool must be independent from each other. In order to address this problem, I use multi-level analysis, which accounts for the variability in each multi-level of nesting. A multilevel approach enables relaxation of assumption of statistical independ-ence and allows for correlation among responses for units that belong to the same group (Luke 2004; Rabe-Hesketh and Skrondal 2008). At the same time, using multilevel modelling ensures that the researcher does not ignore the con-text and wrongly assume that political processes operate in a similar matter in different environments (Luke 2004). This is an important concern in social sciences, not only in general, where researchers are dealing with open systems affected by outside influences (Luke 2004), but also in economic voting studies in particular, where we know from previous works that individual-level eco-nomic effects can vary quite notably depending on the political and institutional context (see section 2.2.2). In this dissertation, I use 3-level models where indi-viduals are nested in country-years at level 2 and country-years are nested in countries at level 3. The slope of the economic coefficient is allowed to vary randomly across all three levels in order to account for the variation in eco-nomic effects.

Specific statistical models employed in different chapters are discussed sepa-rately in that chapter. The models were fitted with maximum likelihood estima-tion in Stata version 12.0 (StataCorp. 2011) using logit and xtmelogit com-mands.

Table 4. Research question, theoretical focus and method of the empirical chapters. Research question Theoretical focus Individual-level data Aggregate-level data Method Chapter 4How robust are economic effects on incumbent support?

There is a strong link between the economy and elections, but empirical evidence for responsibility attribution lacks stability. This chapter tests the robustness of the overall mechanism of economic voting in Western Europe.

The EES Voter study from 1989, 1994, 2004, 2009 and 2014 for Denmark, France, Germany, Greece, Ireland, Italy, the Netherlands, Portugal, Spain, and the UK Macroeconomic data for countries in survey years from Eurostat and the OECD

Logistic regres- sion, multilevel logistic regression Chapter 5Did the global crisis influence the relationship between economic performance and voting? If so, how?

The financial and economic crisis led to major political and economic instability. This chapter explores the question of whether economic voting also changed. It tests two alternative arguments: increased punishment of incumbents as suggested by the asymmetry hypothesis, or decreased punishment as proposed by the clarity of responsibility hypothesis.

The EES Voter study from 1989, 1994, 2004, 2009 and 2014 for Denmark, France, Germany, Greece, Ireland, Italy, the Netherlands, Portugal, Spain, and the UK Macroeconomic data for countries in survey years from the OECD

Logistic regression Chapter 6How do voters respond to eco- nomic policies in general and during an economic crisis in particular?

The crisis was met by national countermeasures, ranging from radical austerity programs to fiscal expansion. The focus of public discourse turned to the government response to the crisis, and it is feasible to expect these government decisions to have influenced political support for incumbents. This chapter extends the focus beyond the classic mechanism of economic voting and examines the new dimension of economic voting, the role of attitudes to economic policies in voter behaviour.

The EES Voter study from 2004, 2009 and 2014 for Austria, Belgium, Cyprus, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Latvia, Lithuania, Luxembourg, the Netherlands, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden, and the UK Macroeconomic data for countries in survey years from Eurostat and the IMF

Multilevel logistic regression