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second reason is that the Great Recession was not a mere local recession but an international one that began outside of Europe. This being the case, it is possible that voters in high-clarity countries might find it difficult to hold their own governments to account for a crisis that those governments arguably had very little control over.

4.4

How clarity affects the prime minister’s party

The economic voting models developed in the previous chapter have been extended in order to measure how clarity of responsibility impacts the economic vote. This analysis is divided into two parts. The first part focuses on the head of government’s party in each country and specifically those factors affecting the degree to which relative support for that party8 was influenced by economic perceptions. This single-party approach has the advantage of producing relatively simple models which are straightforward to interpret but they do not take advantage of all the data available. The second part of this analysis uses this extra data by looking at all parties together, not just the dominant government parties. The resulting model is inevitably more complex than the single-party model but it offers a different view of the evidence. Taken together, these two approaches allow for greater insight than either approach used alone.

The first clarity of responsibility model, Model 4A, is based on Model 3A from the previ- ous chapter, which predicts support for the head of government’s party in each country based primarily on the left–right distance between the voter and the party, whether that voter iden- tifies with the party and, crucially, the voter’s prospective economic assessment. The effect of this economic assessment on support for the prime minister’s party is a measure of the overall level of economic voting. In order to test the hypothesis that clarity of responsibility influences the economic vote, therefore, this model must be extended to include an interaction between the economic assessment predictor and the chosen measure of clarity of responsibility. This al- lows the model to estimate different levels of economic voting for different levels of clarity. As the underlying model is a multilevel model, the fact that clarity of responsibility is a country- level variable is accounted for. The new model includes predictor terms for both institutional

8_{As in the previous chapter, the dependent variable is a voter’s level of support for a particular party, centred} around that voter’s mean level of support for all parties. This means that even the models that focus on prime ministers’ parties only still take some account of voter attitudes towards other parties, which is appropriate because a voter is not expected to switch their vote unless their support for a new party exceeds their support for the party they previously supported.

Table 4.2: Alternative government clarity models

Fixed effect Mod. 4A Mod. 4B Mod. 4C

Intercept 0.54 (0.24) 1.91 (0.41) 1.40 (0.32) Year 2009 0.33 (0.17) 0.31 (0.17) 0.34 (0.18) Year 2014 0.04 (0.13) −0.06 (0.14) −0.06 (0.14) Prospective assessment 0.13 (0.09) ₋0.13 (0.14) ₋0.11 (0.10) Time in office (PM) 0.02 (0.02) 0.01 (0.02) 0.01 (0.01) Government clarity −0.89 (0.29) Single-party government 0.21 (0.19) Absence of cohabitation ₋0.27 (0.21) Ideological cohesion ₋1.58 (0.32) ₋1.39 (0.30)

Dominance of main party ₋0.14 (0.34)

Institutional clarity 0.05 (0.26) ₋0.15 (0.26) ₋0.08 (0.24) Prosp. assess._×year 2009 ₋0.08 (0.03) ₋0.07 (0.03) ₋0.07 (0.03) Prosp. assess._×year 2014 ₋0.07 (0.03) ₋0.04 (0.03) ₋0.03 (0.03) Prosp. assess._×time in office 0.01 (0.00) 0.01 (0.00) 0.01 (0.00) Prosp. assess._×govt clarity 0.19 (0.09)

Prosp. assess.×single party −0.05 (0.06) Prosp. assess._×no cohabitation ₋0.03 (0.07)

Prosp. assess._×cohesion 0.39 (0.09) 0.38 (0.08) Prosp. assess.×dominance 0.04 (0.11)

Prosp. assess._×inst. clarity 0.14 (0.12) 0.19 (0.12) 0.15 (0.11)

Comparison of clarity of responsibility models. The dependent variable is the individual’s support for the current head of government’s party. Only the key fixed effect coefficients are shown here, with standard errors in brackets. The full results can be found in Appendix B. Source: EES, ParlGov & PDY

and government responsibility, as well as time in office.9 These three variables account for the different types of clarity discussed in this chapter. The model furthermore includes inter- actions between each of these and the voter’s prospective economic assessment. This model has been estimated and the key results are summarised in the first column of Table 4.2.

Before interpreting any figures in detail, it is worth discussing some alternative model spe- cifications. The government clarity index consists of four component variables, as discussed earlier. Model 4B replaces the index with its four components so that their relative impact can be assessed.10 The key results from this model form the second column of Table 4.2. Com- paring the two models, it is striking that the interaction between economic assessment and ideological cohesion in the second model appears to be stronger than that between economic 9_{This is specifically the number of years this party has held the office of prime minister, even if they held other} cabinet posts before that.

10_{Multicollinearity might be a concern, as the components of an index can be expected to be correlated with} each other. In fact, the components are not so strongly correlated as to cause serious difficulties. The highest correlation is between dominance of the main governing party and single-party government variables (r=0.70) but these variables are dependent since single-party government prevails if and only if the prime minister’s party completely dominates the government. The second highest correlation is between dominance and ideological cohesion (r=0.31).

4.4. HOW CLARITY AFFECTS THE PRIME MINISTER’S PARTY 101 assessment and the government clarity index. Although it is not straightforward to compare model coefficients directly, given the similar scale of the two variables, this difference is sug- gestive. It is also surprising that the interactions involving the other three component variables are not even close to significance. This evidence is hardly conclusive but it does suggest that ideological cohesion alone may be a better predictor of economic voting than the complete index. In order to test this hypothesis, Model 4C was estimated, in which ideological cohe- sion alone replaces government clarity. The key results from this model make up the third column of Table 4.2. Comparing this to the other models shows that this model fits the data better (∆AIC=26,∆BIC=27) than the government clarity model (Model 4A) and that the components model (Model 4B) does not improve the model fit enough to justify the extra complexity relative to this model (∆AIC=7,∆BIC=61).

These results support the hypothesis that ideological cohesion alone is a better predictor of economic voting levels than the complete index. Consequently, Model 4C forms the basis of this analysis. As in the previous chapter, post-estimation simulation is used to derive key quantities of interest rather than interpreting model coefficients directly. This chapter’s first hypothesis is that governments that have been power for some time experience greater levels of economic voting than governments which have been recently elected. The level of eco- nomic voting predicted from the model can be measured by the difference in support for the prime minister’s party between a highly optimistic individual and an otherwise similar11highly pessimistic individual. Using this definition, the predicted level of economic voting for a gov- ernment that has just been elected is 1.23 (SE=0.16, p<0.001). A party that has been in power for a full decade, on the other hand, has a predicted economic vote of 1.50 (SE=0.18, p<0.001). This amounts to a difference of 0.26 points (SE=0.15, p=0.07), which is not statistically significant. In other words, it is possible that a longer time in office is associated with a higher level of economic voting but the effect is weak if it exists at all.

The second hypothesis is that clarity of responsibility increases the level of economic vot- ing. This has two sub-hypotheses, relating to government and institutional clarity respect- ively. Since government clarity is measured in this model by ideological cohesion alone, the

11_{Unless otherwise stated, all predictions in this chapter are made for a context in which ideological cohesion,} institutional clarity and time in office are held at their means. This is 0.86 for cohesion and 0.61 for institutional clarity. The mean time in office is 4.03 years where this refers to the time the party has held the office of prime minister, as in this case, or 4.92 years where this refers to the time the party has been part of the governing coalition, which becomes relevant later in the chapter. As in the previous chapter, the individual is an employed 40 year old male, living in a town, who has completed high school but not university.

first sub-hypothesis is tested by comparing high-cohesion to low-cohesion contexts.12 The pre- dicted economic vote for a low-cohesion country is 0.79 (SE=0.21, p<0.001) whereas the predicted economic vote for a high-cohesion country is 1.54 (SE = 0.15, p < 0.001). This corresponds to a difference of 0.75 points (SE = 0.16, p < 0.001) between high- and low- cohesion countries. In other words, the economic vote is almost twice (1.94,[1.39, 3.54]13) as high when the ideological cohesion of the government is high than when it is low. The second sub-hypothesis pertains to institutional clarity. The predicted economic vote in high- clarity countries14 is 1.08 (SE= 0.24, p <0.001) and the predicted economic vote in low- clarity countries is 1.58 (SE= 0.23, p < 0.001). The difference of 0.50 points (SE= 0.38, p=0.19) is not significant. Based on this model then, it appears that government clarity, and in particular ideological cohesion, is the strongest predictor of economic voting in a country.

The third hypothesis is that these clarity of responsibility effects were weaker during the recession than at other times. As the models introduced so far assume that these effects are static over time, a new model is needed to test this hypothesis. Model 4D was created by extending Model 4C with interactions between the time dummy variables and the clarity of responsibility and time in government measures, thus allowing each of these effects to vary in strength over time. This model was used to predict the level of economic voting under different clarity contexts in each year. Figure 4.1 compares the predicted economic vote for a government that has just been elected with that of a government that has held office for a full decade. This plot shows that countries whose governments have held power for some time experienced a greater level of economic voting than countries who have experienced a recent change in government. This effect changes over time, appearing to vanish in 2009 and return much stronger in 2014. These impressions are mostly borne out by a numerical analysis except that the effect falls just short of significance (∆=0.77, SE=0.40, p =0.06) in 2004. The effect is clearly not significant (∆=0.12, SE=0.19, p =0.52) in 2009, and the difference between the two years is also not significant (∆=0.64, SE=0.42, p=0.13). By 2014, on the other hand, the decade in office accounts for a 1.98 point (SE=0.43,p<0.001) increase in the economic vote and this effect is significantly stronger than both earlier years (compared to 2004, ∆ =1.22, SE =0.58, p= 0.04). In other words, time in office was only a strong 12_{High cohesion means a cohesion measure of 1, indicating no differences in ideology among government} parties. Low cohesion means a cohesion measure of 0.5, indicating that half of the government-held seats are held by parties not sharing an ideology with the prime minister’s party. This value has been chosen because lower values are unlikely, since the prime minister’s party is typically to be the largest governing party. It is also the lowest value occurring in the dataset but it is by no means an outlier.

13_{Square brackets indicate 95% confidence intervals.}

14_{High clarity means an institutional clarity measure of 1 and low clarity a measure of 0.18, these being the} highest and lowest scores assigned to any country by Hobolt, Tilley and Banducci (2013).

4.4. HOW CLARITY AFFECTS THE PRIME MINISTER’S PARTY 103 Figure 4.1: Economic vote for dominant government party by time in office

0.5 1.0 1.5 2.0 2.5 2004 2009 2014

year

economic vote

time in office

Predicted level of economic voting for the head of government’s party according to the time that the party has held office and the survey year. The economic vote is the difference in sup- port for the party from a highly optimistic and a highly pessimistic voter based on predictions from Model 4D. Ideological cohesion and institutional clarity are held at their means. Source: EES, ParlGov & PDY

predictor of economic voting in 2014. The fact that this effect was so weak in earlier years may explain why no effect was found when it was assumed not to vary over time.

Figure 4.2 shows the predicted level of economic voting for a high-cohesion and a low- cohesion context in each year. A high-cohesion context is a country in which all of the gov- erning parties are ideologically similar and a low-cohesion context is one in which half of the government-held parliamentary seats belong to parties not sharing the ideology of the prime minister’s party. This shows a similar pattern to the previous figure, in that there is an apparent difference between the two groups in 2004, which closes in 2009 before widen- ing again in 2014. These observations are supported by numerical analysis, which shows that in 2004, the economic vote experienced in a high-cohesion context was 1.65 points (SE = 0.41, p < 0.001) higher than in a low-cohesion context. By 2009, this difference had fallen (∆=1.37, SE=0.47,p<0.01) to 0.29 points (SE=0.27,p=0.28) and by 2014 had risen once again (∆=0.68, SE=0.33,p=0.04) to 0.97 points (SE=0.24,p<0.001). In other words, high ideological cohesion was associated with more economic voting before

Figure 4.2: Economic vote for dominant government party by ideological cohesion 0.0 0.5 1.0 1.5 2004 2009 2014

year

economic vote

cohesion

Predicted level of economic voting according to the ideological cohesion of the incumbent government and the survey year. The economic vote is the difference in support for the prime minister’s party from a highly optimistic and a highly pessimistic voter based on predictions from Model 4D. Institutional clarity and time in office are held at their means. Source: EES, ParlGov & PDY

and after but not during the Great Recession. This supports the third hypothesis with respect to government cohesion.

Similarly, Figure 4.3 shows the relationship between institutional clarity and economic voting by comparing the difference between high and low scores on the institutional clarity index. This figure shows little if any difference between high- and low-clarity contexts in 2004 and 2009 but a large difference in 2014. Numerical analysis confirms that there was no significant effect in either 2004 (∆ = −0.19, SE = 0.51, p = 0.71) or 2009 (∆= 0.43, SE=0.53,p=0.42), nor is there a significant difference between these two years (∆=0.62, SE=0.47,p=0.18). In 2014 on the other hand, the economic vote was considerably stronger among high-clarity countries than low-clarity countries (∆ =1.67, SE =0.50, p < 0.001), which is a significant increase over both other years (compared to 2009,∆=1.24, SE=0.47, p<0.01). This means that high institutional clarity was not a predictor of economic voting either before or during the Great Recession but it did become one in the aftermath of the recession. This evidence provides only qualified support for the third hypothesis with respect

4.5. THE EFFECT OF CLARITY ON OTHER PARTIES 105