Declining Turnout and Increasing Party Responsiveness

We assume that parties are vote seeking and that the largest changes in turnout patterns occur among moderate voters (Rodon Reference Rodon2017). Declining turnout in a focal election sends a clear signal to political parties to respond to the median voter in an effort to mobilize disaffected voters for the next election. This expectation is based on numerous studies which report that when parties appeal to citizens in terms of policy, they will be more likely to turnout to vote (see, for example, Adams, Dow, and Merrill Reference Adams, Dow and Merrill2006; Dreyer and Bauer Reference Dreyer and Bauer2019; Hobolt and Hoerner Reference Hobolt and Hoerner2020; Lefkofridi, Giger, and Gallego Reference Lefkofridi, Giger and Gallego2014; Plane and Gershtenson Reference Plane and Gershtenson2004; Reher Reference Reher2014).

Persuasive arguments suggest that shifts in the median voter position will influence parties' policy shifts because they seek to maximize votes. Prominent researchers of mainstream parties have observed that these parties have expanded their range of ideological appeals and that they have shed their “ideological baggage” (Kirchheimer Reference Kirchheimer, La Palombara and Weiner1966; see also Kitschelt Reference Kitschelt, Rhodes, Heywood and Wright1997; Van Kersbergen Reference Van Kersbergen and Kitschelt1999). The result is that mainstream parties are sensitive to shifts in public opinion (see, for example, Adams et al. Reference Adams2004). Maximizing votes may not be an end goal in itself; rather, vote maximization is an efficient strategy for office- and policy-seeking parties (see Müller and Strøm Reference Müller and Strøm1999). For an office-seeking party in a multiparty system, increased vote shares enhance its position for postelection coalition negotiations. In the latter case, for a policy-seeking party, its leverage to pull the governing coalition's policy in its preferred direction will increase as its electoral strength increases as well.Footnote ⁶ Thus, there are theoretical arguments to suggest that parties—whether they are vote, office, or policy seeking—will be responsive to changes in the median voter position in the general electorate (see, for example, Adams and Merrill Reference Adams and Merrill2009; Downs Reference Downs1957; Huber and Powell Reference Huber and Powell1994; McDonald and Budge Reference McDonald and Budge2005; Stimson, MacKuen, and Erikson Reference Stimson, Mackuen and Erikson1995).Footnote ⁷

If turnout decisions are based on how close citizens are to the most proximate party in policy terms (Adams, Dow, and Merrill Reference Adams, Dow and Merrill2006; Dreyer and Bauer Reference Dreyer and Bauer2019; Hobolt and Hoerner Reference Hobolt and Hoerner2020; Lefkofridi, Giger, and Gallego Reference Lefkofridi, Giger and Gallego2014; Plane and Gershtenson Reference Plane and Gershtenson2004; Reher Reference Reher2014), political parties will be more sensitive to the left–right preference of the median voter after voter turnout decreases. Previous turnout decline signals that there are more moderate disaffected voters that may potentially vote in the following election. One reason why parties appeal to moderates in these circumstances is because there are typically more voters in the middle than there are at the extremes (see Adams and Somer-Topcu Reference Adams and Somer-Topcu2009b). Furthermore, the variation in individual turnout is lower at the extremes. Distinctly noncentrist voters also tend to hold a higher intensity of ideology (Rabinowitz and Macdonald Reference Rabinowitz and Macdonald1989), and these citizens (of which there are fewer) tend to be more interested and passionate about politics (Adams and Ezrow Reference Adams and Ezrow2009). Furthermore, these voters have been found to be more stable in their vote intention at the national level (Dassonneville Reference Dassonneville2012), and they report voting at higher rates than moderate voters.

The arguments summarized earlier are consistent with the findings of Rodon (Reference Rodon2017), who reports stark empirical differences in voter turnout between moderates and other voters. In particular, he attributes the difference to partisanship levels, which are much weaker in the center than to the left or right. Rodon (Reference Rodon2017, 150) writes the following on partisanship levels for the center, left, and right:

He also reports that low partisanship levels in the middle have the predictable consequence that turnout is lower for moderate citizens.Footnote ⁸

Although the aforementioned studies are insightful and lend preliminary support to the claim that changes in centrist citizens' turnout rates drive overall changes in turnout, to the best of our knowledge, no study has directly tested whether this actually occurs. Since our argument about party responsiveness to public opinion hinges on this assumption, we report findings in the empirical section that support the finding that changes in rates of turnout are predominantly driven by changes in centrist citizens' turnout decisions.

The theoretical expectation that political parties will be more responsive to public opinion after elections that exhibited a decrease in voter turnout may also vary across types of political party.Footnote ⁹ For example, some parties may even further prioritize vote or office seeking, such as “dominant” parties, that is, parties that have previously governed, when compared to the “challenger” parties that have not (De Vries and Hobolt Reference De Vries and Hobolt2020). In a similar vein, Klüver and Spoon (Reference Klüver and Spoon2016) and Meguid (Reference Meguid2005; Meguid Reference Meguid2008) analyze how mainstream and niche parties represent voters differently (see also Bischof and Wagner Reference Bischof and Wagner2019). Bawn and Somer-Topcu (Reference Bawn and Somer-Topcu2012) report different election strategies for governing and opposition parties. Relatedly, vote-losing and vote-gaining parties have different incentives to change their policy positions (Somer-Topcu Reference Somer-Topcu2009). It is possible that other party distinctions matter, such as whether they are centrist or extreme, or large or small. The empirical section reports analyses which suggest that the theory applies to most of these parties and that significant differences only occur in few instances (for challenger and extreme parties). The theory thus has implications for which party types are “center-oriented” and hence more responsive to previous changes in turnout.

To summarize, the preceding discussion of party-type effects notwithstanding, political parties will seek to mobilize moderate voters after elections that are characterized by relatively low voter turnout by appealing to them in policy terms.

The preceding discussion motivates the following declining turnout hypothesis:

Exploring this hypothesis enhances our understanding of party competition, and if it is supported, it suggests that an important underemphasized effect of decreased turnout is increased party responsiveness to public opinion in the following election.

Data and Measurement

To test whether changes in turnout influence party responsiveness, we develop longitudinal, cross-national measures of voter turnout, the median (or mean) voter position,Footnote ¹⁰ and parties' policy positions. Our cases include West European democracies for which Eurobarometer mean voter data are available. Countries and years include Austria (1999–2017), Denmark (1977–2015), Finland (2003–15), France (1981–2017), Germany (1980–2017), Great Britain (1979–2017), Greece (1985–2015), Ireland (1981–2016), Italy (1983–2018), the Netherlands (1981–2017), Portugal (1991–2015), Spain (1989–2016), and Sweden (2002–18).Footnote ¹¹

Independent Variables: Changes in Voter Turnout, the Mean Voter Position, and Their Interaction

To test the declining turnout hypothesis, it is necessary to measure voter turnout. Most comparative studies of turnout emphasize the importance of cross-national comparability of the measure. Given the longitudinal nature of this study, it is important that our measure of turnout is consistent over time within each country. The longitudinal measure of voter turnout for our sample is from a dataset provided by the WZB Berlin Social Science Center.Footnote ¹³

Estimates of the turnout variable, stratified by country, are presented in Figure 1. The dots represent turnout levels for each election that is included in the empirical analyses, and there are several important patterns to identify. Few countries have constant turnout rates. In France, Portugal, and Italy, participation rates have declined consistently since the 1990s. Other countries, such as Denmark, the Netherlands, or Spain, show fluctuating trends, where turnout decreases and increases alternate. Lastly, in some countries, downward trends have been met with an upward movement, particularly in the most recent elections under study (for example, Austria, Germany, the UK, and Sweden). Overall, there is no common trend that applies to every European country.Footnote ¹⁴

Fig. 1. Voter turnout, 1977–2018.

Notes: The turnout estimates are based on the numbers of eligible voters. The time periods correspond to the coverage of the Eurobarometer surveys (see later).

Data on mean voter preferences come from the Eurobarometer public opinion surveys that ask respondents to place themselves on a left–right scale from 1 (left) to 10 (right). We focus on voters' (and political parties') left–right positions instead of more detailed conflict dimensions for two reasons. First, comparative survey data for citizens' positions on subdimensions of political conflict (such as the economy or cultural issues) are scarce. Hence, only left–right positions are available for a broad set of countries over extended time periods. Secondly, and more importantly, recent research suggests that left-right positions represent a crucial orientation for voters. As a consequence, left–right congruence between voters and parties is the stronger predictor of vote choice if compared with congruences on political subdimensions (Lichteblau, Giebler, and Wagner Reference Lichteblau, Giebler and Wagner2020; see also Lachat Reference Lachat2018).

As the Eurobarometer does not survey at the exact time of national elections, but rather every six months, we consider only those surveys that were conducted at maximum of one year before the election in question.Footnote ¹⁵ Figure 2 gives an overview of the distribution of the mean voter position for all countries under observation. Some countries have seen considerable variation in mean voter positions over time. For instance, the position of the mean voter in Ireland has moved from 6.2 in 1981 to 5.12 in 2016. Similar levels of variation are observable in other countries, such as Germany, Italy, or the UK. Other countries, like Finland, exhibit less variation over time.

Fig. 2. Mean voter positions, 1975–2018.

Notes: Mean voter position estimates are based on Eurobarometer surveys, which ask respondents to place themselves on a left–right scale from 1 (left) to 10 (right). The boxplots report mean voter positions for each country election included in the study. The boxes show the interquartile ranges of the mean voter positions, with the middle line in each box showing median values. The ends of the whiskers are minimum and maximum values.

Testing Centrist Turnout as a Driver of Overall Turnout

Before we evaluate the declining turnout hypothesis, we first test the crucial assumption that changes in a country's turnout are affected by changes in turnout among moderate voters. We test for this relationship by making use of election studies provided by the Comparative Study of Electoral Systems (CSES), which comprises postelection surveys for the majority of West European countries since 1996 (CSES 2018; CSES 2020). Relying on a question asking survey respondents whether they turned out to vote in the most recent general election, we estimate the parameters of multilevel logit model specifications for the 39 elections in the 13 countries that are covered in the analyses of party responsiveness in the following. We estimate random effects for each country and each survey (Schmidt-Catran and Fairbrother Reference Schmidt-Catran and Fairbrother2016), and we use CSES design and sample weights. We estimate a respondent's left–right extremism score by calculating the distance between their left–right position and the mean voter position. This measure takes the value of 0 if the respondent's left–right position is identical to the mean voter position. The higher the value, the greater is the distance to the mean voter position, and the maximum value of the resulting scale is 6. The results are similar if we use a binary indicator for centrist (distance 0–1) and noncentrist (distance 2–6) respondents, and these are reported in the Online Supporting Information (Table A2 and Figure A1). We further add a variable at the election level that denotes the change in turnout between the election in question and the previous election. Interacting the variables allows us to evaluate centrist individual turnout probabilities in contexts of declining turnout and then to compare it to estimates for more extreme citizens.

Table 1 reports the corresponding regression results. Models 1 and 2 evaluate whether left–right extremism influences a citizen's probability of voting independent of overall turnout. Model 2 includes a series of additional individual-level covariates that are likely to affect citizens' voting behavior.Footnote ¹⁶ These controls include sociodemographic characteristics (age, gender, education, household income, union membership, and employment status) and attitudinal variables (external political efficacy and satisfaction with the working of democracy).

Table 1. Citizen ideology, turnout, and changes in turnout (individual-level analyses)

Notes: Sample and demographic weights used. Left–right extremism = distance to mean voter position. *** p < 0.01; ** p < 0.05; * p < 0.1.

Sources: CSES (2018) and CSES (2020).

The positive coefficients on left–right extremism confirm that the probability of voting increases for more extreme citizens, which is consistent with previous research (Rodon Reference Rodon2017). Next, Models 3 and 4 report the left–right extremism coefficients that are conditional on changes in turnout. The interaction effect is negative, which suggests not only that centrist voters abstain more often, but also that the gap between the abstention of centrist and extreme voters widens in elections with declining turnout. If declining turnout were not driven by centrist abstention, the estimate on the interaction variable would be insignificant and the probability of voting would decline uniformly for all voters.

Figure 3 clearly depicts this finding using predicted probabilities comparing centrist and extreme citizens. In elections with increasing turnout, the probability of voting is nearly identical for all voters independent of their distance to the mean voter position. If turnout decreases, however, the likelihood of voting only decreases for centrist citizens. Alternatively, turnout change has no discernible effect on voting for extreme citizens. As a consequence, the models indicate that declining turnout is driven to a substantial extent by centrist citizens deciding to abstain. Conversely, increasing turnout is marked by centrist citizens deciding to turn out. We note that the effect sizes should be interpreted with caution, as survey respondents overstate their turnout likelihood in pre- and postelection surveys. Nevertheless, the results indicate that the effect for centrist citizens is substantively significant because the gap in the probability of voting between centrist and extreme respondents varies between 5 and 10 percentage points, depending on the size of the negative turnout change. We thus find support for the assumption that changes in centrist turnout drive overall turnout changes, which is part of the central argument that political parties have a strong incentive to respond to moderate (disaffected) voters in times after declining turnout. On this basis, we proceed.

Fig. 3. Predicted probabilities of voting based on individual-level analyses.

Note: Confidence bands show 95 per cent confidence intervals. Estimates are based on Model 3 in Table 1.

Testing the Declining Turnout Hypothesis

It should be recalled that the declining voter turnout hypothesis predicts that parties will respond to changes in the mean voter position after an election that has experienced a decrease in voter turnout. We estimate parameters of “cross-national” ordinary least squares (OLS) regression models to evaluate whether this relationship between responsiveness and turnout is present in our data. This “cross-national” specification is:

$$\eqalign{\Delta Party\,Position\,( t) = \beta _0 + \beta _1[ \Delta \,Mean\,Voter\,( t) ] + \beta _2[ \Delta \,Turnout\,( t-1) ] & \cr + \beta _3\,[ \Delta \,Mean\,Voter\,( t) ] \,\times \,[ \Delta \,Turnout\,( t-1) ] + \beta _4\,\gamma + \alpha + \varepsilon}$$

where Δ Party Position (t) is defined as the difference between a party's left–right position at election t and its position at the previous election (t – 1). Δ Mean Voter (t) denotes the difference between the position of the mean voter in the current election t and the mean voter in the previous election (t – 1). The lagged Δ Turnout (t – 1) variable is defined as the difference between turnout in the previous election (t – 1) and turnout two elections ago (t – 2). Moreover, we added country fixed effects (α) to control for time-constant factors (such as electoral system characteristics). Finally, γ denotes a vector of time-varying covariates and ɛ is the error term.

We add a number of covariates that potentially influence changes in parties' left–right positions. First, the change in a party's vote share may have an influence. In particular, previous election results might constitute different incentives for parties to respond to the mean voter position. For example, the literature on decision rules suggests that vote-winning parties might be inclined to shift in the same direction as in the previous election and vote-losing parties might strategically decide to switch to the opposite direction (Adams et al. Reference Adams2004; Budge Reference Budge1994; Somer-Topcu Reference Somer-Topcu2009). We thus control for parties' vote changes at t – 1.

Secondly, parties in government—constrained by the responsibility of policy and decision making—might be restricted in their flexibility to respond to public opinion in order to maintain their programmatic credibility in the face of previous government decisions. Accordingly, we include parties' governing status (that is, governing or opposition) in the model specification. Thirdly, macroeconomic factors possibly restrain parties' ability to maneuver, as parties potentially refrain from proposing positions that are considered unrealistic under economic globalization (Hellwig Reference Hellwig2014). We add a measure of the difference in globalization from the current election and the previous election, provided by the KOF Globalization Index (Sturm, Haelg, and Gygli Reference Sturm, Haelg and Gygli2018).Footnote ¹⁷ Lastly, economic performance could influence party responsiveness. It has been shown that if the economy performs well, parties tend to promote more rightist economic policies if compared with periods when the economy slows down. As a result, we control for the log-transformed change in gross domestic product (GDP) per capita (World Bank 2018) between t – 1 and t.

In total, we estimate a first-difference model that focuses on the impact of changes in our independent variables on the changes in parties' left–right positions. Focusing on changes makes sense theoretically when investigating party responsiveness, and using a first-difference model comes with the advantage of addressing several common problems related to working with (quasi-)panel data. In particular, it addresses the stationarity of the time series and party- and country-specific heterogeneity. Still, serial correlation remains a possibility. Accordingly, we add a lagged dependent variable to the regression equation (Beck and Katz Reference Beck and Katz1995).Footnote ¹⁸ Moreover, the error terms might be correlated within units (parties) or time (elections), which could yield unreliable standard errors. We cluster the standard errors based on both types—parties and elections—to account for this potential problem. In total, the analysis encompasses 117 elections.

Differential Effects across Party Groups

Previous research has indicated that not all parties tend to respond to the mean voter to the same extent. Different party types have been assumed to be constrained by their programmatic structure or the composition of their supporters, and, thus, to be less attentive to shifts in the positions of the mean voter. De Vries and Hobolt (Reference De Vries and Hobolt2020), for instance, propose that parties that had government experience in the past should be encouraged to prioritize vote- or office-seeking goals, while challenger parties are expected to politicize new issues rather than cater to the center of the left–right dimension (see also De Vries and Hobolt Reference De Vries and Hobolt2012). As a consequence, we expect challenger parties to pay less attention to the mean voter position even when voter turnout has previously decreased. Similarly, smaller parties with less electoral support should be inclined to focus on particular subgroups in the electorate, rather than shifts in the mean voter position. Niche parties, such as radical left, radical right, green, and ethno-territorial parties, have been found to represent voters who care about single policy issues (Adams et al. Reference Adams2006; Klüver and Spoon Reference Klüver and Spoon2016; Meguid Reference Meguid2005; Meguid Reference Meguid2008). As a consequence, niche parties might be less incentivized to focus on broader shifts in the electorate. Also, we might expect that the electoral support of extreme parties is not dependent on centrist voters and, thus, that these parties should be less attentive to the mean voter. Bawn and Somer-Topcu (Reference Bawn and Somer-Topcu2012) have argued that election strategies differ for governing and opposition parties, and this distinction could potentially matter for our results. Lastly, political parties that have experienced vote losses in a previous election are expected to be less risk averse and, thus, more inclined to change their policy positions (Janda et al. Reference Janda1995; Somer-Topcu Reference Somer-Topcu2009). We provide information on the exact operationalization of these variables in the Online Supporting Information.

We test for the differential responsiveness of all of these party types by adding three-way interactions to the main model presented in Table 2. Table 3 shows the corresponding regression results, and the marginal effects plots can be found in the Online Supporting Information (see Figure A3). It should be noted that the standard errors need to be interpreted with caution due to small group sizes in some specifications. The coefficients of Δ Mean Voter Position (t) * Δ Turnout (t – 1) show the marginal effect for those party groups coded as “0” and the three-way interaction—Δ Mean Voter Position (t) * Δ Turnout (t – 1) * Party Type—estimates whether the effect is conditioned by party groups coded as “1.”

Table 3. Empirical analyses of different party types

Notes: Two-way clustered standard errors in parentheses. Country fixed effects, control variables, and lagged dependent variable not shown. Information on the operationalization of the different party categories can be found in the Online Supporting Information. *** p < 0.01; ** p < 0.05; * p < 0.1.

Overall, we find support for the idea that challenger parties, as defined by De Vries and Hobolt (Reference De Vries and Hobolt2020), are less responsive to the mean voter position. The three-way interaction in Model 1 is positive and statistically significant at the 0.05 level, which implies that challenger parties pay less attention to the mean voter position even when voter turnout decreases. A similar effect is visible for extreme parties, which seems plausible because we might expect that most challenger parties are also political parties with more extreme positions on the left–right scale. For the remaining party groups, the three-way interaction estimates are insignificant. The corresponding marginal effects that estimate the conditioning effects of previous turnout on party responsiveness are not significantly different for governing and oppositions parties, large and small parties, or parties who lost or gained electoral support in the previous election. The triple interaction effect for niche parties is also insignificant. One set of arguments suggests that these “ideological” parties have been shown to respond more to their supporters than to the mean voter position (Ezrow et al. Reference Ezrow2011). However, recent research suggests that the programmatic “nicheness” of parties is a dynamic rather than a manifest feature of parties. In particular, larger and older niche parties are expected to focus on voter groups beyond their core supporters (see, for example, Bergman and Flatt Reference Bergman and Flatt2020; De Vries and Hobolt Reference De Vries and Hobolt2020) and could thus be more responsive to shifts in the mean voter position. Our estimates corroborate this latter set of findings.

Table 3 indicates that the marginal effect is only significant for centrist parties after a previous decrease in turnout. The estimated effect of responsiveness to the mean voter for extreme and challenger parties is not statistically significant across the whole range of the change in turnout in the previous election (see Figures A3.1 and A3.6 in the Online Supporting Information). In total, these additional analyses lend considerable support to the declining turnout hypothesis. With few exceptions, such as with challenger and extreme parties, the idea of increasing responsiveness after turnout decline in the previous election applies to several party categories.

Robustness

We have conducted a number of robustness tests to make sure that our findings are not due to decisions related to modelling strategies or to the operationalization of the variables of interest. We investigated whether the findings are dependent on modelling decisions. Table A4 in the Online Appendix presents a number of alternative specifications. First, we tested whether adding party fixed effects or omitting fixed effects changes the results (Models 1 and 2). Secondly, we omitted the lagged dependent variable to investigate whether potential Nickell bias influences the results (Model 3) and use lagged levels of the dependent variable, as left–right shifts might be restricted by the party's “starting point” on the left–right scale (Model 4). Next, applying clustered standard errors with a reduced number of clusters might unexpectedly deflate the standard errors. For that reason, we ran additional models for which we used only party-clustered (Model 5), only election-clustered (Model 6), and no clustered standard errors (Model 7). The parameter estimates for each of these model specifications continue to support the central conclusion that we report.

Additional robustness analyses are also reported in the Online Supplementary Information. We estimate parameters for: models in which parties' left–right shifts are based on a bipolar measure (Laver and Budge Reference Laver and Budge1992), rather than a logit-transformed scale (see Table A4, Model 8); an error-correction model (see Table A5); low- and high-turnout contexts in Table A6 (see, respectively, Dreyer and Bauer Reference Dreyer and Bauer2019; Hooghe, Dassonneville, and Oser Reference Hooghe, Dassonneville and Oser2019), models that account for trending (see Table A7); jackknife analyses (see Figure A4); models that estimate the mean voter position based on different “windows” before the election in which public opinion was collected (see Table A8); omitting observations for which materials other than manifestos (for example, party bloc programs) were used to estimate party positions (see Table A9); models that address very small changes in public opinion (see Table A10); models that control for several additional independent variables that potentially influence turnout and party responsiveness simultaneously, including globalization, competitiveness, and polarization (see Table A11); and, following Ferland (Reference Ferland2020; see also Adams et al. Reference Adams2004), models that control for the direction of public opinion shifts in relation to each political party (see Table A12 and Figure A5).