Literature Review

The increasing availability of large-scale cross-national surveys of public opinion such as the European Social Survey (ESS) or the ISSP has spawned a vibrant literature studying the feedback effects of existing institutions and policies on public attitudes towards the welfare state. Pierson’s (Reference Pierson1993, Reference Pierson1994, Reference Pierson2000, Reference Pierson2004) seminal work on ‘policy feedback effects’, building on previous work by Skocpol (Reference Skocpol1992) and Esping-Andersen (Reference Esping-Andersen1990), provided the theoretical framework that inspired this new wave of research in the 2000s and after. Whereas Esping-Andersen (Reference Esping-Andersen1990) was primarily interested in why different types of welfare state regimes have emerged, Pierson was more concerned with explaining why they are sustainable politically in the long term and why large-scale reforms, primarily cutbacks, are increasingly unlikely the longer welfare states persist.Footnote ² Pierson distinguished between resource/incentive effects on the one hand (that is, the effects of institutions on the distribution of material resources across different welfare state beneficiary groups), and interpretive effects on the other, which refer to the effect of institutions on ‘cognitive processes of social actors’ (Pierson Reference Pierson1993, 610) and – one could add – their normative orientations vis-à-vis the welfare state.Footnote ³ He further identified three levels on which feedback effects could materialize: government elites, ‘social groups’ and the mass public (Pierson Reference Pierson1993, 624).

Pierson’s basic argument is that feedback effects reinforce existing institutions, and contribute to their long-term political sustainability and path dependency in policy development. In the literature, this type of feedback is often labeled ‘positive feedback’ (Weaver Reference Weaver2010, 137), since popular attitudes and actors’ strategies positively reinforce the institutional status quo. In Pierson’s original theory (Pierson Reference Pierson2000, Reference Pierson2004), however, the term positive feedback is actually used to describe the process of path formation in the initial stages of development. Since Pierson is (also) interested in explaining the diversity of existing welfare state regimes, he needs to explain why countries, beginning from a similar starting point in the past, move away from this ‘equilibrium’ to develop distinct sets of institutions. In this sense, positive feedback effects occur when the likelihood of moving away from some kind of pre-existing equilibrium increases the further one moves away (a similar use of the term positive feedback can be found in the literature on political agendas, see Baumgartner and Jones Reference Baumgartner and Jones2002).

In the comparative welfare state literature, the notion of reinforcing feedback inspired a new wave of scholarship on the impact of welfare state regimes on public attitudes in the 2000s and beyond, which made use of the newly available cross-national survey data (Andreß and Heien Reference Andreß and Heien2001; Arts and Gelissen Reference Arts and Gelissen2001; Blekesaune and Quadagno Reference Blekesaune and Quadagno2003; Finseraas Reference Finseraas2009; Jæger Reference Jæger2006; Jæger Reference Jæger2009; Jæger Reference Jæger2013; Svallfors Reference Svallfors1997; Svallfors Reference Svallfors2004; Svallfors Reference Svallfors2010; for recent overviews, see Kumlin and Stadelmann-Steffen Reference Kumlin and Stadelmann-Steffen2014a and Svallfors Reference Svallfors2012). This literature starts with the expectation that public attitudes towards the welfare state should be congruent with institutions, either because institutions have shaped attitudes in the way Pierson claimed or because policies and institutions reflect prevailing patterns of public opinion (Rehm Reference Rehm2012). According to this logic, public support for the welfare state should be strongest in the universalist social democratic welfare states in the Scandinavian countries and weakest in the liberal Anglo-Saxon world, with continental European countries located in between (Arts and Gelissen Reference Arts and Gelissen2001). The empirical evidence for this claim is mixed. In a recent update of the literature and using a sophisticated methodological approach, Jæger (Reference Jæger2009), for example, finds little overall support for the expected pattern. One reason for these mixed findings might be that the early contributions to the literature often used simple dummy variables to capture the influence of welfare state regimes, whereas more precise measures of different institutional dimensions of welfare state policies should be used instead (Busemeyer Reference Busemeyer2013; Gingrich and Ansell Reference Gingrich and Ansell2012; Jakobsen Reference Jakobsen2009). Another reason, however, could be that existing institutions do not necessarily and always create patterns of public opinion that are commensurate with the prevailing status quo.

This idea of patterns of public opinion moving in ways that do not reinforce, but instead undermine, existing institutions and policies is often discussed under the label of ’negative feedback effects’ (Weaver Reference Weaver2010; see also Jacobs and Weaver Reference Jacobs and Weaver2015). According to Weaver’s understanding, negative feedback effects occur when existing institutions and policies have ‘negative socio-economic consequences’ that ‘undermine political support for those policies’ (Weaver Reference Weaver2010, 139; see also Fernández and Jaime-Castillo Reference Fernández and Jaime-Castillo2013). This definition is problematic, because whether a certain social phenomenon is considered to be a problem with implications for policy making is itself a matter of public debate. For example, a large and generous welfare state might be perceived as an economic burden in one context or as an important social safety net in another.

The notion of ‘negative feedback effects’ also occupies a central place in the ‘public as thermostat’ model developed by Wlezien and Soroka (Soroka and Wlezien Reference Soroka and Wlezien2005; Soroka and Wlezien Reference Soroka and Wlezien2010; Wlezien Reference Wlezien1995; Wlezien Reference Wlezien2004), but it is used in a very different way compared to Weaver (Reference Weaver2010). The ‘public as thermostat’ approach is rooted in the literature on public opinion and responsiveness of policy making (for example, Erikson, MacKuen and Stimson Reference Erikson, MacKuen and Stimson2002; Page and Shapiro Reference Page and Shapiro1983; Stimson, MacKuen and Erikson Reference Stimson, MacKuen and Erikson1995), and therefore is not well connected to the scholarly debates in the welfare state literature, even though these literatures study similar issues. According to the ‘public as thermostat’ model, the relationship between public opinion on the one hand and the world of policy making on the other is dynamic: citizens perceive and react to changes in public policies by expressing support or opposition, which is then picked up by responsive policy makers. In the words of Wlezien (Reference Wlezien1995, 982): ‘In effect, the public would behave like a thermostat, where a departure from the favored policy temperature (which itself can change) produces a signal to adjust policy accordingly, and once sufficiently adjusted, the signal stops.’ Couched in different terms, this means that if there are feedback effects from policies on public opinion, they should be negative (Soroka and Wlezien Reference Soroka and Wlezien2010, 28): if public spending on a particular policy item is too high/too low, the public will react by expressing support for less/more spending. Policy makers interested in winning elections pick up these signals and change their spending priorities accordingly. Even though the connection between citizen-voters and policy makers is mediated by political institutions and differences in the salience of particular policies, Soroka and Wlezien find strong empirical support for their model in general and across a large number of country cases (Soroka and Wlezien Reference Soroka and Wlezien2010; Wlezien and Soroka Reference Wlezien and Soroka2012; see also Bartle, Dellepiane-Avellaneda and Stimson Reference Bartle, Dellepiane-Avellaneda and Stimson2011).

Theoretical Inconsistencies in the Existing Literature

Previous studies in the comparative welfare state and public opinion research literatures have evaluated both the feedback effects of existing institutions and policies on public opinion, but they come to different – even contradictory – findings. The most obvious contradiction is that researchers working in the tradition of Pierson (Reference Pierson1993) would argue that ‘positive’ (that is, self-reinforcing) feedback effects dominate. In contrast, Wlezien and Soroka (Reference Wlezien and Soroka2012, 1409) expect a dominance of ‘negative’, self-undermining feedback effects. At the same time, the terms ‘positive’ and ‘negative’ have different meanings in these literatures, as we explain in greater detail below. This obvious contradiction, however, is just the starting point and masks deeper underlying challenges for any theory of policy feedback. Some of these challenges are mainly methodological questions of how to measure and operationalize feedback effects (Wlezien Reference Wlezien2016).

While we comment on empirical challenges below, the focus of this article is to point out theoretical inconsistencies and problems that are often overlooked or underestimated in the pertinent literature. Jacobs and Weaver (Reference Jacobs and Weaver2015) and Kumlin and Stadelmann-Steffen (Reference Kumlin and Stadelmann-Steffen2014b) have recently made important contributions to reassessing the conceptual foundations of policy feedback theory. This article builds on these contributions, and moves beyond existing work by developing a more fine-grained and rigorous typology of policy feedback effects, which is applicable in both quantitative and qualitative research designs.Footnote ⁴

In our view, the very success of the concept of policy feedback as it is applied in different literatures has compromised its ‘internal coherence’ and therefore also its differentiation potential, which according to Gerring (Reference Gerring1999) are central quality criterions in concept formation. Even though scholars within the respective literatures introduced above usually managed to provide coherent and convincing definitions of the concept of policy feedback in their respective domains, the ubiquitous usage of the concept has led to blurred conceptual boundaries and confusion. In the next section, we first provide a short account of the most relevant theoretical inconsistencies (from our point of view), followed by a proposal for a more differentiated typology of policy feedback effects, which – we hope – might help to mitigate some of the aforementioned inconsistencies. In this exercise, we side with Collier, LaPorte and Seawright’s (Reference Collier, LaPorte and Seawright2012, 228) claim that the development of typologies can contribute to ‘useful conceptual innovation’ if they are clear about their underlying theoretical concepts and dimensions.

Towards a Synthesis: A Typology of Feedback Effects

The previous section discussed a number of theoretical inconsistencies in the literature, which have emerged as a consequence of conceptual confusion related to the widespread use of the notion of policy feedback in different literatures. We believe the typology of different types of feedback effects, which we develop below, can contribute to ‘conceptual innovation’ (Collier, LaPorte and Seawright Reference Collier, LaPorte and Seawright2012, 226) by improving the internal coherence of the concept of policy feedback and therefore its usefulness in studying different phenomena. In this exercise, we avoid the ambiguous and often misleading labels of ‘positive and ‘negative’ feedback effects, since they often mean different things in different contexts and literatures (Jacobs and Weaver Reference Jacobs and Weaver2015). We explore the empirical relevance and usefulness of this typology in the empirical section of this article.

Devising a more complex and differentiated typology of feedback effects implies that these different types of effects can also be observed in the real world. Instead of assessing whether positive or negative feedback effects dominate in principle or ‘on average’, scholars should instead strive to identify the contextual conditions that explain why certain types of feedback occur in some instances, but not in others. As the example mentioned at the beginning of the introduction shows, different kinds of feedback dynamics might be going on at the same time. The purpose of this article is not to provide a definitive theory that explains these contextual conditions, although we discuss this issue in the concluding section. The goal is more modest – to give scholars new conceptual tools to describe the multitude of feedback effects in a more coherent manner.

Eventually, this conceptual innovation can contribute to a more encompassing synthesis of the core insights of the various literatures outlined above. This will most likely contribute to developing a deeper understanding of the complex linkages between the macro world of policies and institutions and the micro level of attitudes and preferences. In fact, to some extent, Piersonian and thermostatic feedback theory already provide insights that are complementary to each other: the institutional and political mechanisms that Pierson identifies as being responsible for path dependency (Pierson Reference Pierson1993, Reference Pierson2000) can be expressed as thermostatic (or self-correcting) feedback in the frameworks of Soroka and Wlezien (Reference Soroka and Wlezien2010) and Baumgartner and Jones (Reference Baumgartner and Jones2002). Historical–institutionalist approaches may, in turn, be more useful in explaining cross-national differences in ‘preferred levels of policy’, which are largely taken as exogenous in the thermostat model. This is because historical–institutionalist approaches are less concerned with understanding the short-term fluctuations and dynamics of public opinion, and focus instead on the long-term impact of institutions on attitudes, that is, absolute preferences in the sense defined above. Again, developing common conceptual tools help to stimulate this kind of exchange between different literatures.

According to Collier, LaPorte and Seawright, good typologies should clearly spell out the ‘overarching concept’ (Collier, LaPorte and Seawright Reference Collier, LaPorte and Seawright2012, 222), which serves as a reference point for the various subcategories, as well as the dimensions used in the construction of the typology. The overarching concept is the notion of policy feedback, which can be broadly defined as the effect of existing policies and institutions on individual-level attitudes and preferences.Footnote ⁶ Following Pierson (Reference Pierson2006), we conceive of policies as ‘institutions’ in the sense that feedback effects are not caused by the policy output as such (for example, public spending), but by institutional arrangements created and modified by the actions of policy makers. Attitudes refer to general predispositions towards the welfare state, in particular its normative underpinnings, which might not necessarily have immediate policy consequences, for example, support for the principle of distributive justice. In contrast, (policy) preferences are related to concrete policy options and demands, such as support for or opposition to public spending.

The central dimensions in our typology refer to (1) the direction of feedback, (2) its broadness/scope and (3) the time lag of feedback effects. As discussed above, researchers should also pay more attention to the distinction between abstract and relative (or concrete) preferences, but this distinction is not part of the typology, because it refers to characteristics of the dependent variable (attitudes and preferences). Previous contributions by Soroka and Wlezien (Reference Soroka and Wlezien2010), Jacobs and Weaver (Reference Jacobs and Weaver2015) as well as Kumlin and Stadelmann-Steffen (Reference Kumlin and Stadelmann-Steffen2014b) have already alluded to some of these dimensions, but they have not yet been combined into an overarching systematic framework. Instead of drawing a three-dimensional matrix and then discussing potential combinations of feedback types in each individual cell, we discuss each of the three dimensions subsequently and leave it to future researchers to theorize in greater detail about more or less plausible combinations of the different characteristics. Table 1 presents a brief summary of the dimensions.

Table 1 Summary presentation of typology of policy feedback effects

Empirical Implications and Challenges

In the remainder of the article, we turn from theory to empirics. To be clear, the purpose of our empirical analysis is not to provide a definitive (and final) answer as to which type of feedback dominates the others. Rather, we use it to demonstrate the usefulness and applicability of our proposed typology in describing and conceptualizing the multitude of feedback effects observed empirically. Before we do that, we comment briefly on the challenges that emerge when testing the empirical implications of policy feedback theory. Many of these challenges are seldom discussed in the pertinent literature, and we cannot necessarily provide easy solutions at this point. It is still important to think more thoroughly about this topic in order to understand the apparent discrepancies between the different literatures as mentioned above.

The first point to discuss is the challenges of testing long- and short-term feedback effects in quantitative research designs. The most common research design in empirical tests of policy feedback theory is to include a macro-level variable on policies or institutions as an independent variable in regressions of public attitudes. This is plausible in the case of short-term dynamic feedback effects, as respondents are expected to perceive and react to short-term policy changes. But it gets more challenging the longer the time lag. Operationalizing feedback effects as cross-national differences between regimes is the most common way to deal with longer time lags, but it is not a very convincing operationalization of policy feedback theory as it carries the implicit assumption that all kinds of differences between countries are related to feedback of past policy choices. In this broad interpretation, the concept of policy feedback loses analytical rigor and becomes less useful as a conceptual tool to understand the dynamics of the interaction between the mass public and policy makers. Furthermore, it does not seem to make sense – from either a theoretical or empirical perspective – to include levels of spending from the 1970s (or even before?) in regressions of present-day attitudes, because the causal chain between these past decisions and contemporary public opinion is simply too long.

The opposite is also true: including only recent, short-term policy changes in regressions of public attitudes assumes a high level of attentiveness and political interest on the part of citizens. It might well be the case that citizens need some time to perceive and react to policy changes; that is, there might be a significant, but not indeterminate, time lag until policy changes ‘trickle down’ to the level of popular attitudes. But how long is ‘significant’? Obviously, this question is difficult to answer from a purely theoretical perspective, but from an empirical point of view, it is equally problematic to assume that all kinds of feedback effects will materialize immediately in the short term. This is also because it might take some time for policy changes to have real effects on socio-economic outcomes. In established Organisation for Economic Co-operation and Development (OECD) democracies, regular elections typically take place every four or five years. This might serve as a plausible upper limit to the length of lags operationalizing long-term feedback since the link between past policies and attitudes becomes too tenuous otherwise. Recent work by Adams and Somer-Topcu (Reference Adams and Somer-Topcu2009) confirms the importance of mid-range feedback effects, albeit from a different perspective. They show that voters reward political parties electorally for policy moderation at the previous (rather than the current) election, even though the previous election is typically four to five years in the past.

The bottom line of this discussion is that researchers should spend more time thinking about the time dimension when operationalizing feedback effects. The time dimension may also interact with other dimensions of policy feedback. For instance, it may be plausible to assume a short-term dynamic feedback connection in some cases (for instance, in combination with specific feedback effects), but the time lags might be longer in other cases (such as in the case of general feedback effects). Furthermore, testing the historical–institutionalist variety of policy feedback theory in its entirety poses additional challenges as long-term historical feedback effects are difficult to operationalize by way of institutional regime indicators.

The latter is also related to the second point to be discussed: the fact that feedback effects materialize via both macro- and micro-level factors. The common operationalization of policy feedback effects via macro-level variables underestimates the fact that policy feedback also occurs at the micro level. In fact, the longer back we go, the more likely it is that the latter channel gains in importance. For instance, Pierson’s (Reference Pierson1993) argument about resource-based feedback effects simply implies that beneficiaries of welfare state services and benefits develop an interest in their continued provision. In the literature on welfare state attitudes, this is most commonly referred to as ‘material self-interest’ and usually tested at the micro level.

Furthermore, as was also famously argued by Esping-Andersen (Reference Esping-Andersen1990), welfare states change the socio-economic fabric of societies, that is, the size and composition of welfare state beneficiary groups. This (quite obvious) point is often neglected in quantitative analyses of feedback effects since these typically focus on average effects of micro-level variables. Even if the average effect of micro-level variables is similar across regimes, the simple fact that the size of beneficiary groups varies across regimes is an important manifestation of feedback effects as it has implications for the general levels of political support for a particular policy. A common finding in the literature on welfare state attitudes is that attitudes and preferences are also shaped by norms and values above and beyond pure self-interest (Kangas Reference Kangas1997; Margalit Reference Margalit2013). The relative size of partisan constituencies subscribing to a particular ideology is likely to vary across countries (and over time) as well, even if the average effect of left–right ideology on welfare state attitudes and preferences is the same across countries. There might simply be more people identifying themselves with a ‘left-wing’ ideology in a country like Sweden compared to the United States. This is an important empirical implication of policy feedback theory, which gets lost when focusing on the average effects of micro-level variables only or when assuming that the entire feedback effect will only materialize via macro-level variables.

Empirical Analysis

In this section, we perform an empirical analysis of commonly available public opinion data. As mentioned above, the purpose of this analysis is not to settle the debate between historical–institutionalist and ‘thermostat’ perspectives on policy feedback by identifying one (or even: the) dominant type of feedback effect. Rather, we engage in a broad analysis of different types of feedback effects, and demonstrate the applicability and usefulness of the new typology developed above. Thus in some respects the empirical analysis fits well with the theoretical concepts introduced above, while at the same time, we fully acknowledge that the empirical analysis cannot address all the issues and problems discussed before and needs to be complemented with additional analyses.

Regarding aspects where theory and empirics fit well, our analytical set-up allows us to assess the direction of policy feedback by simply reading off the sign of regression coefficients of macro-level variables, which is arguably the easiest point to address. Furthermore, we operationalize the broadness/scope of feedback dimension by employing measures of total social spending and spending on particular social policies. Finally, we address the time dimension by including different time lags of spending levels (as well as long-term averages). Given the intrinsic difficulties of measuring long-term feedback effects in quantitative research designs as discussed in the previous section, this is still an imperfect solution, but it is an improvement on existing research approaches, which rarely consider long-term lag structures.

There are a number of limitations to our analysis, however. First of all, the empirical analysis is more concerned with measuring feedback effects on relative preferences, since the dependent variables used below capture support for more or less public spending in a given area. Mostly for reasons of space, we cannot consider feedback effects on absolute preferences here. Furthermore, our empirical set-up is helpful to identify accelerating and self-undermining feedback, but it has limitations regarding the assessment of genuine self-reinforcing feedback, that is, support for maintaining the status quo. This is, however, also a limitation of much of the existing literature, although it is rarely discussed as such. Below, we present some very basic descriptive statistics to help determine the extent of self-reinforcing feedback, but if taken seriously, more sophisticated and new survey instruments would need to be developed.Footnote ⁸ What is more, we do not have a strong theory about which factors might explain why feedback effects vary across policy fields (that is, why we should expect accelerating, long-term feedback effects in the case of education, but self-undermining, dynamic feedback in the case of unemployment, for instance). In the conclusion we discuss potential conditional factors that might explain which type of feedback is more likely to be observed and how these might be related to policy characteristics. Finally, since our research design is quantitative, we do not illustrate how our typology can be applied in qualitative designs, although we believe it is helpful independently of the choice in methods. We take this point up in the concluding section as well. Despite these limitations, we believe it is useful to confront our theoretical concepts with empirical data.

Methods and Models

In this article, we move beyond existing scholarship by employing a pseudo-panel analysis. This method has several advantages for our purposes compared to simple multi-level models. Instead of using individuals as the basic unit of analysis, we construct groups of individuals with similar individual-level characteristics. Thus it becomes possible to compare groups of individuals across the two different waves, assuming that these represent ‘typical’ cases, even though we do not deal with real panel data, but two repeated cross-sections. Calculating group-level averages instead of using individuals as the basic unit of analysis and including group dummies absorbs a significant amount of variation at the micro level, which is an advantage if one is primarily interested in the effects of macro-level contexts on attitudes as we are here.

To our knowledge, the pseudo-panel approach has not been applied to the study of welfare state preferences, with the sole exceptions of Olivera (Reference Olivera2015) and Jæger (Reference Jæger2013). The choice of variables defining the grouping of observations is crucial to the robustness of the results (Jæger Reference Jæger2013). Hence, we follow the lead of Jæger in using age, education and social class as micro-level variables and socio-demographic characteristics to link the two distinct waves of the ISSP included in the analysis. Each of these three variables is then divided into three categories (see above), which results in a matrix of 3×3×3 subdivisions, that is, twenty-seven distinct groups for each country and year or survey. For each country-year, we therefore calculate twenty-seven groups with different socio-demographic combinations (young, low-educated, low social class; young, low-educated, middle class, etc.). Obviously, not all groups will have the same number of observations and may even have none (there are few young, low-educated, upper-class people around, for instance). Further, some groups are characterized by a lack of variation of the dependent variable (for example, when all respondents in that group demand more or much more spending) and are therefore excluded from the analysis.

Once the different groups are created, the next step is to estimate the mean values of the dependent and independent variables at the micro level for each sub-group. Since age, education and social class are already used to construct the groups, they are not directly included in the regression. However, we include two additional micro-level control variables (income and partisan ideology), for which we again calculate mean values for each sub-group nested in country-years. In the case of the dependent variables, average values were also computed within each sub-group nested in country-years to determine average levels of support for spending.

Due to the clustering of group-level observations within country-years, we apply multilevel modeling techniques using maximum likelihood estimation. Accordingly, we include random intercepts at the country level to account for the fact that the group-level observations share common characteristics within countries. Because we are mostly interested in cross-country variation in our dependent variables, models with country dummies instead of random intercepts are discarded. Year fixed effects are also included to account for wave-specific effects and to isolate the variation of the dependent variable across countries while controlling for its variation over time. Finally, group dummies are included in all models to deal with potential endogeneity problems resulting from unobserved characteristics correlated with the variables used to create the groups.

More formally, the model can be specified as:

(1)$$\bar{A}_{{ijt}} {\equals}\alpha {\plus}\beta _{1} \bar{I}_{{ijt}} {\plus}\beta _{2} \bar{P}_{{ijt}} {\plus}\beta _{3} S_{{jt}} {\plus}\beta _{k} C_{{jt}}^{k} {\plus}\zeta _{j} {\plus}Y_{t} {\plus}G_{i} {\plus}{\varepsilon}_{{ijt}},$$

where i stands for groups, j for countries and t for years. $\bar{A} $ is the average value of preference for more spending for each sub-group, country and year, $\bar{I}$ the mean income (in deciles), while $\bar{P}$ stands for the average of the index of partisanship, S for spending as a percentage of GDP on the different areas of social policy as well as total social spending using the different lags as discussed above. C ^k stands for the k={1-3} control variables at the macro level. Finally, $\zeta _{j} $ are the country random intercepts, Y _t and G _i, the year and group fixed effects, respectively.

In interpreting the coefficient estimates of the macro-level spending variables, we adopt a rather broad operationalization of feedback effects, which focuses on the direction of feedback effects and therefore does not take into account whether respondents generally support or oppose spending in the first place. Thus, a positive coefficient estimate indicates a higher demand for more spending in more generous countries, but also preferences for less spending in countries that tend to spend less (accelerating effect). When the coefficient estimate is negative, individuals respond to high levels of spending by demanding less of it and react to low levels by demanding more spending (self-undermining). In the robustness section below, we also take into account a narrower definition of feedback effects and discuss why this particular operationalization is more problematic than the broader operationalization, which we use in the main analysis.

Findings

Before we discuss the results from the regression analyses in detail, we want to briefly comment on the distribution of respondents across the different categories of the dependent variables (see Figure 1). This is important because a high share of respondents indicating a preference for maintaining current spending levels could be evidence of self-reinforcing feedback. Figure 1 shows that the modal category in these distributions is one, which is significantly above the zero mark – with the notable exception of unemployment spending. For education, health and pension spending, citizens – on average – express a desire to increase current levels of public spending rather than maintaining current levels of cutting back. In the case of unemployment, support is more or less normally distributed around the zero mark. Looking at the relative share of respondents in the middle category using the raw individual-level rather than the aggregated group-level data (see Appendix Table A1) reveals similar findings. These data also show that there is significant variation across countries in the size of the middling category, reaching from 5.95 per cent of respondents supporting the status quo in health care to 60.49 per cent of Danes supporting the existing level of spending on unemployment (both figures are for the 2006 wave).

Figure 1 Descriptive statistics about the distribution of respondents for different policy fields

In sum, these findings indicate that self-reinforcing feedback is indeed a meaningful category, different from accelerating and self-undermining feedback. However, interpreting the share of respondents expressing a preference for maintaining current levels of spending as evidence of self-reinforcing feedback is – at best – an imperfect proxy. First of all, there are methodological concerns as respondents might choose the middling category not because this reflects their actual preferences, but because they are uncertain and ambiguous about how to respond and therefore opt for the middle ground (Goerres and Prinzen Reference Goerres and Prinzen2012, 518). Secondly, the lower average for support for unemployment spending might simply reflect the fact that citizens are less fond of supporting the expansion of welfare state benefits in this area as (potential) recipients are regarded as less ‘deserving’ (Van Oorschot Reference Van Oorschot2006) and not a genuine desire for maintaining the current status quo. Given the currently available data, there are no good alternatives to tackle these problems in a convincing manner. Hence, future research should devote more attention to properly measuring support for maintaining the status quo relative to alternatives, for example by employing factorial survey methods. Alternatively, if panel datasets are available, self-reinforcing feedback can be modeled as a process over time, in which respondents gradually adapt their attitudes after a significant change in spending has occurred.Footnote ¹² Further analyses could also explore whether support for the status quo is in any way related to welfare state institutions. A consistent interpretation of policy feedback theory suggests that there should be no association between welfare state institutions and the extent of self-reinforcing feedback effects as citizens’ attitudes adapt to different institutional contexts.

The first analyses displayed in Table 2 Footnote ¹³ focus on the specific feedback effect across different time lag specifications (from t − 1 until t − 5, plus the five-year average). We include different types of social spending commensurate with the dependent variable, that is, public spending on education, on health care, on pensions and on unemployment as macro-level independent variables affecting micro-level support for more spending on these policies.

Table 2 Specific feedback effects across policy fields with different time lags

Note: standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1

Our first major finding is that the type of feedback effect depends on the field of spending and the time lag. The case of unemployment shows the most consistent and robust evidence of a self-undermining and specific feedback at work. Higher levels of spending on unemployment are associated with lower levels of support for spending increases. The effect holds across different time lags (including the five-year average lag) and different sets of control variables. For education, by contrast, the association between macro-level spending and micro-level support is mostly not significant, except for t − 1, when we find a negative association, that is, evidence of a self-undermining feedback effect in the short term when we include less than the full set of control variables. For health, we also find evidence of a self-undermining (specific) feedback logic, but the association is less consistent and robust compared to the case of unemployment as it varies depending on the controls included in the models. There is no evidence of any statistically significant association between spending on pensions and popular support for more or less spending in this policy field. This could be related to the particularly high degree of institutional inertia in this policy field or to a high degree of support for the prevailing status quo (that is, self-reinforcing feedback in the above defined way).

In the next set of regressions, we focus on general feedback effects. For that purpose, we replace the macro-level variables on spending on specific areas of social policy with total levels of social spending to test whether the feedback effect is specific or non-specific. As before, we use one- to five-year lags of spending levels, as well as the five-year average (Table 3). While the effect is now more consistent across time lags, important differences remain in the support for spending across the different fields included. In the case of education, for example, we find more robust evidence of an accelerating feedback effect, in particular for time lags longer than three years as well as for the five-year average. In contrast, overall social spending is not significantly related to popular support for spending on unemployment at all. In the case of health care, there is evidence of a long-term self-undermining feedback effect, but only in model specifications, which include merely GDP growth as a control. By contrast, we find a statistically significant self-undermining feedback effect in the case of pension spending. The effect is consistently significant across model specifications and different time lags. It is interesting to note that in the case of pensions, overall levels of social spending – general feedback effects – are more relevant than specific feedback effects, whereas we observe the opposite for unemployment. Thus it appears that attitudes towards spending on education and retirement are more strongly linked to overall social spending and in a rather long-term dynamic. On the contrary, demand for more or less spending on unemployment appears to be more sensitive to the specific levels of spending in this field.

Table 3 General feedback effects across policy fields with different time lags

Note: standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1

As briefly mentioned above, we also employ a narrower operationalization of policy feedback.Footnote ¹⁴ It could reasonably be argued that a positive/negative sign of the coefficient estimate is merely a necessary, but not sufficient, condition to document accelerating/self-undermining feedback. This is because accelerating feedback would suggest that citizens are in general supportive of spending increases (that is, the value of our dependent variable should be above zero), because otherwise, the positive coefficient estimate could also mean a weakening of opposition to spending increases and not genuine support for more spending. The same logic holds for self-undermining feedback (in reverse). We believe this narrow interpretation of policy feedback is too stringent and opt for a broader operationalization, which focuses merely on the direction of feedback. Also, as Figure 1 shows, in most cases, respondents are in ‘positive territory’ anyway, expressing some support for spending increases. Hence, a positive coefficient estimate signals a genuine accelerating effect, whereas a negative coefficient estimate signals self-undermining feedback mostly in the sense of weakening opposition to further spending increases. This is plausible in the context of welfare state politics, where citizens rarely express open opposition to welfare state expansion.

Nevertheless, we ran additional analyses applying a narrow operationalization of feedback. More specifically, to measure accelerating feedback, we recoded the dependent variables so that a value of 1 amounts to support for more or much more spending and 0 represents the remaining categories (the same or less). To measure self-undermining feedback, we recorded the dependent variables: 1 captures support for less or much less spending vs. 0 for the remaining categories (the same or more). With these narrower operationalizations, a positive coefficient estimate of the macro-level spending variables constitutes evidence of an accelerating effect in the first case and a self-undermining effect in the second case.Footnote ¹⁵ The number of observations (groups) is significantly reduced in the second case because only a small number of the respondents expressed support for less or much less spending. This is another, more methodological reason why we stick with the broader operationalization in the main part of the analysis. As a consequence, the variable does not vary at all in many groups (because respondents are either happy or want more or much more spending, yielding a value of 0). These groups have to be excluded from the analysis.

In spite of these limitations, Table 4 shows that applying a narrower operationalization yields similar results to the previous analysis. We focus on the two cases of unemployment and education here for reasons of space and since the previous analysis had also revealed relatively strong feedback effects for these two areas. Table 4 confirms the existence of a statistically significant specific self-undermining effect in the case of unemployment and a specific ‘negative accelerating’ feedback – a weakening of accelerating feedback. Put differently, this finding confirms that a weakening accelerating feedback and a genuine self-undermining effect are essentially the same thing; that is, it matters little whether the value of the dependent variable is ‘positive’ or ‘negative’, as the direction of the feedback effect is ultimately the decisive factor. For education, the results in Table 4 confirm previous evidence of a long-term accelerating effect. In this case, the reverse (a weakening self-undermining effect) cannot be observed, most probably for the simple reason that the number of respondents supporting cutbacks in education spending is even lower than in the other policy fields given the strong popularity of education. In short, this additional robustness test strengthens our confidence that the findings obtained using the broader operationalization of feedback in the main part of the analysis are robust to different operationalizations and model specifications.

Table 4 Robustness tests applying a narrower operationalization of feedback effects

Note: standard errors in parentheses. ***p<0.01, **p<0.05, *p<0.1

In order to further boost our confidence in the robustness of the results from the pseudo-panel analysis, we performed the following sensitivity analyses:Footnote ¹⁶

• ∙ A replication of the previous analyses excluding the groups of fewer than twenty;Footnote ¹⁷

• ∙ A replication of the previous analyses including only countries with data from both waves, 1996 and 2006.

The results (see Appendix Tables A2 and A3) are in general consistent with the findings of the previous models. Some differences emerge from the first replication in the case of the general feedback effect: the effect for education is no longer significant, while the one for unemployment gains in significance (Table A2). These differences might be due to the important loss of information that accompanies the exclusion of rare groups with attitudes that are significantly different from the rest of the population. In contrast, replicating the analyses including only countries with data from both waves yielded very similar results to the original analyses (Table A3).

As a final robustness test, we replicated the pseudo-panel analysis using a Bayesian hierarchical modeling framework. We use the same data as before and the same model specification with the same dependent and independent variables. Bayesian hierarchical modeling has become increasingly popular for studying the association between macro-level contexts and micro-level attitudes. A vexing problem in this field of research is that the number of macro-level units (countries) is usually quite small (less than twenty), casting doubt on the robustness and validity of the findings obtained. Recent work by Stegmueller (Reference Stegmueller2013) has shown that Bayesian approaches may be superior to inference-based maximum likelihood estimation methods when the number of country cases is below twenty, since the latter produces standard errors that are too small. Detailed results as well as some background on the methodological choices can be found in Appendix Tables A12–A19.

Figure 2 displays the results from the Bayesian hierarchical analysis. In general, the findings are very similar to the previous analysis. Since we are using the full set of controls in the Bayesian replication, there are slightly more cases where the credible intervals include zero. Nevertheless, the most important findings hold. Again, there is a significant positive association between past levels of social spending and support for further education spending (that is, evidence of an accelerating, general and long-term feedback effect) and a significant negative association between past levels of spending on unemployment and further support for spending increases – this could be interpreted as a specific, self-undermining and long-term feedback effect. Similar to above, the association between spending levels lagged by about three to four years and current support for spending changes seems to be more robust than short-term associations, indicating that in general long-term feedback might be more prevalent than dynamic feedback.

Figure 2 Results from the Bayesian hierarchical analysis. (A) General feedback and (B) specific feedback