Studying economic voting

In order to estimate the association between economic variables and political support, quantitative econometric data analysis is typically used. Both macro and micro-level approaches most often employ either linear regression if the outcome variable is continuous, or logistic regression if the dependent variable is dichotomous. Regression analysis applies statistical functions to estimate the relationship between two variables. Data structure in such analyses is ordinarily cross-sectional, i.e. characterises different populations at a single time point, time-series, i.e. observes one population over time, or both. If data are clustered, e.g. the answers are correlated because the respondents come from the same country, multilevel modelling is often used. In spite of various similarities, there are, naturally, also vast differences across studies, most notably in how the vari-ables are operationalised and how multivariate models are specified.

The first research decision of many is whether to define incumbent support via vote or popularity. Vote choice is measured as an actual political decision executed by individuals. At the macro level this can be done by using aggre-gated election results, and at the individual level by asking respondents in post-election studies who they voted for in most recent post-elections. Popularity is meas-ured in population surveys by asking individuals who they would vote for if the elections were held tomorrow or whether they approve of the work the govern-ment has done. Macro-level popularity-functions utilise an aggregated estimate of these figures. Despite voting being considered the ultimate dependent vari-able in political behaviour (Campbell et al. 1960), political popularity actually shows better fits with the economy. The final vote can be a mixture of various

elements from campaign influence to strategic voting (see van der Eijk et al.

2006), and popularity is therefore considered a purer function of economic fac-tors. On the other hand, polling results are more volatile. Popularity is a riskless non-binding way to signal one’s attitudes, whereas vote is the ‘real thing’

(Nannestad and Paldam 1994).

The next step is to determine whose support one wishes to study. Most commonly in economic voting studies the dependent variable is defined as sup-port for incumbents rather than for the political system, political institutions or another actor. But who are we actually talking about when we talk about incumbents? In the United States, studies often focus on presidential or gov-ernmental support, whereas in Europe the government, the Prime Ministerial (PM) party or less often the party holding the portfolio of the Finance Minister is considered responsible for the economy. In two-party systems the situation is relatively clear: the government is tasked with economic management and is held responsible for poor economic circumstances, whereas the opposition may gain from economic hardship. Things get more complicated in the case of multi-party systems and coalition governments. Do people in such cases attribute responsibility to the entire government or only to the leading party? It has become a common practice in economic voting models to reduce the outcome to a dichotomous choice between government and opposition in order to allow consistency in coding across surveys (Duch and Stevenson 2008), but govern-ments can be very diverse both in size and composition. Van der Brug, van der Eijk and Franklin (2007) have argued that discrete choice models like these neglect the possibility that parties are not affected the same way by the omy. Different coalition partners have dissimilar responsibilities for the econ-omy and may therefore not suffer or gain equally from economic changes. The authors also claimed that focusing on electoral choice overlooks the competition between parties, and proposed an alternative approach of electoral utilities, using a more sophisticated research design of stacked data matrix. The under-lying logic of this concept is that in reality people do not vote for or against the government, but are rather engaged in a two-step decision-process, where they first assess their support for each party and only then choose the party they will actually vote for. Instead of the typical vote choice question, the authors pro-posed that respondents be presented with a list of parties and be asked to indi-cate their propensity to vote (PTV) for each of these parties. This interval-level measure of electoral utility overcomes many limitations related to the nominal nature of the dependent variable of electoral research in multi-party systems, allowing researchers to model vote choices with a higher degree of methodo-logical accuracy (van der Eijk 2002). Like many other approaches, however, this one is not without limitations, the main one being the lack of independence between the vote propensity scores given by the same person for different par-ties (van der Brug, Hobolt, and de Vreese 2009). When observations related to the same respondent are correlated, the independence assumption of regression analysis is violated, possibly leading to biased estimates and inaccurate results.

Furthermore, when PTVs are measured in post-election surveys, such as the

EES Voter study, they are likely to be endogenously produced – or ‘colored’ – by actual voting behavior in the past election, thus undermining the validity of the PTV question which, ideally, should not be related to any specific election (De Angelis and Garzia 2012). Last but not least, vote intention or choice on the one hand, and PTVs on the other, measure conceptually different things. While the two former map respondent’s current political preference, the latter looks at the likelihood to ever vote for a party. From the economic voting perspective, current preference is of higher relevance as its exhibits a straightforward theo-retical link to economic perceptions: short-term changes in economic opinions induce provisionary changes in electoral support patterns. Conversely, we can-not assume a similar individual-level mechanism to explain change in PTVs, as willingness to ever vote for a party can be linked to much more longstanding and fundamental attitudes.

Survey instruments used to study economic voting on the individual level also vary a great deal. Due to data limitations, researchers are often constrained by the survey questions that already exist instead of being able to choose ones they would actually need or prefer. In their literature overview, Bellucci and Lewis-Beck (2011) counted at least eight different ways to operationalise the dependent variable in economic voting studies. Analyses on the United States typically rely on presidential approval, which measures individuals’ support for the incumbent president as regards the latter’s success in their job. In Europe, a similar approach can be used to measure the approval of the PM party, the gov-ernment, etc. Depending on the research design, another common option is to ask respondents to indicate which party they voted for in last elections (past vote recall) or which one they would vote for if the elections were held the fol-lowing day or week (vote intention). Studies also differ in terms of which elec-tions are considered when asking people to express their political support. Ordi-narily, party preference in general elections is preferred, but some studies look at party support in other elections, for instance the European Parliament (EP) elections. However, previous work indicates that EP elections are second-order elections, where the accountability attribution is different from that in national elections (Reif and Schmitt 1980). As for survey measurement of the explanatory variable, national economic perceptions on the subjective level are most commonly measured by asking respondents whether the country’s economy has in their opinion improved, worsened or remained unchanged over the preceding year.

Voting behaviour is of course not determined by the economy alone. There-fore, statistical models include a number of other predictors that typically influ-ence political preferinflu-ences. While these are not a substantive concern in eco-nomic voting studies, the inclusion of control variables helps us to determine the relative effect of the economy on political support. In order to better under-stand the effect of one particular variable, all other factors in the model are held constant. Failing to account for essential indicators that are related to vote can lead to the omitted variable bias, which may cause the economic effect to be overestimated. The underlying idea in economic voting studies is that vote choice is a function of three essential elements: social cleavages, political

ideol-ogy and the economy (Lewis-Beck 1988). In other words, the basic model specification typically includes data on voter demographics and socio-economic status (e.g. age, social class, ethnicity, income, education, religiosity), self-placement on a left-right scale (or in the United States party identification), and economic perceptions. Social background and political predispositions are con-sidered long-term forces of vote choice, which stay relatively stable over time.

Economic considerations, on the other hand, are treated as a short-term factor, which can vary from one election to the next and may thereby help explain electoral change. Furthermore, the decline of cleavage voting and of ideological leaning is thought to have increased the importance of economic assessments (see Bellucci 2012). In addition to individual-level controls, most economic voting models also include a variable measuring the electoral cycle to account for the broadly demonstrated cyclical pattern in political support (see Miller and Mackie 1973; Tufte 1975; Stimson 1976). Other aggregate-level controls may contain party characteristics (e.g. size, role, and ideology), information about institutional and political context (e.g. party system, system clarity), etc.