The left-right framework

Disagreements about goals are central to most accounts of representative democracy because they motivate parties and electorate alike (Downs Reference Downs1957). Parties pursue ideological goals subject to electoral considerations (Strøm Reference Strøm1990). The electorate wants governments both to produce policy that honours their values and govern with competence (Stimson et al. Reference Stimson, MacKuen and Erikson1995; Erikson et al. Reference Erikson, MacKuen and Stimson2002). Preferences and positions link parties with the electorate and provide the basis for effective communication (Scarbrough Reference Scarbrough1984).

There are a very large number of issues that people disagree about. If we are to understand the interaction between parties and the electorate we need to cut away inessential detail (Budge Reference Budge, McDonald and Ezrow2006). Accordingly, we assume that the interaction between parties and the electorate takes place along a single left-right dimension (Downs Reference Downs1957). The preferences that are of interest to us are those on which the parties have taken positions. “Left” means those preferences expressed or positions adopted by Labour. “Right” means those adopted by the Conservatives (Budge and Farlie Reference Budge1977).

Parties aim to attract voters. They take positions on a wide range of issues. Some are enduring. Some are transient. The “core” issues that divide the major parties relate to economics. Labour has generally supported “more” government activity, “more” collective action and “more” equality. The Conservatives have generally supported “less” (Blais et al. Reference Blais, Blake and Dion1993). Nevertheless, Labour and the Conservatives disagree on other issues relating to law and order, individual freedom, the environment and international affairs. As new issues emerged, the parties have adopted opposing positions, and these have acquired “left” or “right” polarities (Carmines and Stimson Reference Carmines and Stimson1989). It is usual, for example, to label support for shorter sentences, equal rights for gays, environment protection and international cooperation as “left-wing”. This enables analysts to use texts to summarise party positions using both economic and noneconomic positions. The MARPOR “RILE” scores, for example, provide evidence about how parties compete for office (Budge et al. Reference Budge2001; McDonald and Budge Reference Mansbridge2005).

There are doubts whether public preferences can be so simplified (Budge Reference Budge, McDonald and Ezrow2006). Fortunately, it is not necessary to resolve the vexing issue of the “real” structure of public preferences here (Stimson Reference Stimson2004). Our purpose is simply to summarise preferences in such a way as to understand the interaction between parties and the electorate. Accordingly, we focus on the core economic issues that represent the enduring differences between the parties relating to government intervention, collective action and economic equality (Heath et al. Reference Heath, Evans and Martin1994). This decision also simplifies the measurement of policy since it is difficult – if not impossible – to produce annual measures of policy that incorporate both economic and noneconomic issues using textual data.Footnote ⁸

Measuring macrolevel preferences: annual policy mood

If we are to analyse responsiveness we need a public preference time series (Erikson et al. Reference Erikson, MacKuen and Stimson2002). Microlevel theories provide us with good reasons for believing that we can use preferences across a wide range of issues to produce an annual indicator of public preferences. Many issues become “seemingly relate”’ and acquire “left-right” polarities (Carmines and Stimson Reference Carmines and Stimson1989). Since most people have low levels of political awareness, they absorb both “left” and “right” considerations (Zaller and Feldman Reference Zaller and Feldman1992). The typical individual is “ambivalent” about issues. Their responses to questions depend on their predispositions, the issues raised, precise wording and response options offered. Individual predispositions can be viewed as a running tally of “left” and “right” considerations across all issues. If we could aggregate preferences across both individuals and issues, this “double summation” would provide an indicator of the electorate’s aggregate (varying) left-right preferences or “policy mood” (Stimson Reference Stimson1999).

We cannot directly average aggregate responses across survey items. Each survey question has its own biases as a result of the issues that it engages, precise wording and response options provided (Zaller and Feldman Reference Zaller and Feldman1992). Each question has its own metric. Nevertheless, we can use a method – the dyads ratio algorithm – to find a common metric and then aggregate across issues (Stimson Reference Stimson1999). Before we outline this method, however, we describe the data at our disposal.

Content

Our database consists of responses to many controversial issues on which the parties can be expected to take contrasting positions. Some 287 questions in 2,087 separate readings of preferences (39.7% of the total) relate to “core economic” domain, including government intervention, trade unions, public ownership, public spending, taxation, inequality and the trade-off between inflation and unemployment (see Table 1). Another 1,102 readings (21% of the total readings) relate to “social” issues. This domain includes crime and law and order, moral and social attitudes, individual rights, the family and abortion. The “other” category (around 40% of all readings) relate to diverse issues including immigration, international affairs, defence spending, nuclear weapons, Northern Ireland, Europe, national sentiment, the monarchy and left-right self-placement.

Table 1 Items topics for policy mood, 1945–2015

Note:

* Indicates used to estimate the policy mood.

If we were confident that public preferences – like party positions – were undimensional, we could estimate mood using all the available data. Since we are ignorant of the real structure, we estimate mood using only items relating to the “core economic” issues. Even after this self-imposed restriction, however, there here is more than sufficient data to reliably estimate annual mood.

Coding responses

Our focus here is on the “core” left-right issues. Responses are scored from high (most left or Labour) and to low (most right or Conservative) responses. It is straightforward to code these items since the parties have taken consistent (opposing) positions.Footnote ¹¹ Assigning the “wrong” polarities to responses makes no difference to the estimates of mood – it simply results in negative factor loadings that alert us to a coding error (Stimson Reference Stimson1999).

All preferences are expressed as an index of preferences:

$${\rm Index}\,{\rm of}\,{\rm preferences}\,{\equals}\,{{\sum_{i\,{\equals}\,1}^N {\rm Left}\,{\rm preferences}} \over {\sum_{i\,{\equals}\,1}^N {\rm Lef}{\rm t}{\plus}{\rm Right}\,{\rm preferences}}}{\rm }{\times}100$$

These indexes reflect then the balance of left-right preferences on controversial issues. They are fed into the dyads ratio algorithm in order to estimate mood.

Method: the dyads ratio algorithm

The raw index of preferences represents the percentage of all substantive responses that are “left”.Footnote ¹² Each index is then expressed as a ratio at two time points (years).

$$R_{{ij}} \,{\equals}\,{{x_{{t{\rm {\plus}}i}} } \over {x_{{t{\rm {\plus}}j}} }}$$

These dyads have an expected value of 1.0 and can be averaged to produce a rough estimate of underlying preferences (P _t ). The algorithm calculates all the possible dyads for each series x _tk iteratively and averages them:Footnote ¹³

$$P_{t} \,{\equals}\,{{\sum_{k\,{\rm {\equals}\,1}}^N x_{{tk}} } \over N}$$

Since not all items are equally valid indicators of underlying preferences, each series is weighted by their estimated validity hi ².

$$P_{t} \,{\equals}\,{{\sum_{k{\rm \,{\equals}\,1}}^N h_{i}^{2} x_{{tk}} } \over {h^{2} N}}$$

Using ratios causes the original metric to be lost. This is reintroduced by a standardisation of the latent scale in terms of the validity-weighted means and standard deviations of the input items (Stimson Reference Stimson1999). The individual items are scored as per cent left over per cent left plus per cent right. The estimated mood, therefore, has the same interpretation; 50 is the neutral point. Values above 50 indicate net left preferences and those below 50 indicate net right preferences.

Estimates of the policy mood

Figure 1 displays the estimated mood from 1945 to 2015. About 46% of all variance in observed preferences is common to this mood. The remaining variance – some 54% – is either specific to the particular issue domain or is item-specific; that is, a function of the specific wording and measurement error for each question (Erikson et al. Reference Erikson, MacKuen and Stimson2002, 203).

Figure 1 The policy mood, 1945–2015.

Averaging over many items reduces the noise induced by sampling errors (Stimson Reference Stimson1999).Footnote ¹⁴ In the early period from 1945 to 1964, when data are in shorter supply, the series zig-zags (shown by the broken line in Figure 1). In the later period, after 1964, the series is smoother (shown by the unbroken line). Nevertheless, the overall pattern is clear. The series starts off high (left) in the 1950s and then generally drifts down (right) until 1979.Footnote ¹⁵ From 1979 onwards the series tracks back up and to the left, peaking in the early 1990s. This pattern matches standard accounts of public opinion (Kavanagh Reference Kavanagh1988). From 1997 the series drifts down (right) under New Labour. It reaches its lowest point in 2010 when the Conservatives return to power in a coalition government. It then reverses itself under the coalition government between 2010 and 2015. Indeed, by 2015, the mood was as far left as it had been in 2004, the year before Labour won a third election victory.

Broadly speaking, these movements seem to be related to the electoral performance of the parties. This suggests that the mood series has a degree of face validity. In particular, three “turning point” elections were presaged by movements in the mood. The Conservative victory in 1979, New Labour’s landslide in 1997 and the Tories return to power – as the dominant party in the coalition in 2010 – all appear to reflect prior movements. Yet some election outcomes do not seem to be explicable given movements in the mood. The October 1974 and 1979 general elections, for example, produced a Labour victory on the one hand and a Conservative victory on the other, despite the similar levels of mood. Similarly, the 1992 and 1997 elections produced a Conservative victory and a Labour landslide, though the mood was at much the same level. Other factors – social change, party positions and assessments of party competence – must be taken into account.

In order to illustrate the content of mood, we briefly examine the factor loadings for individual items on the estimated mood series. Since there are 287 series, it is not possible to examine all the loadings. And, since the series vary in length, it would be misleading examine the loadings for all the individual series. Table 2 displays the loadings for the items that are entered into the database in at least 10 years and load at 0.5 or above.

Table 2 Item loadings of policy mood, 1945–2015

The items that load on mood relate to trade unions, welfare, tax and spending, and inequality. This is a consequence of our decision to include only the “core” issues. The same items would feature prominently in the equivalent table if we had estimated the mood using all the preferences data.Footnote ¹⁶ The mood estimated from this larger database, moreover, correlates highly with our mood measure (Pearson’s R=0.90). These observations do not resolve the issue of the dimensionality of preferences. They do reassure us that our decision to use only “core” items is not consequential. Averaging a large number of items produces a robust estimator of preferences.

Measuring annual macrolevel policy

In order to assess the interaction between governments and the electorate, it is necessary to develop a measure of government policy analogous to our measure of mood. “Policy” is a course of action or the principles adopted by the government. It is difficult to summarise because governments take lots of action make many statements of principle. They legislate, enter into treaties, make administrative decisions, tax and spend, and issue statements of intent. It is also difficult to summarise because it can be indicated by both words (intentions) and deeds (actions and policy delivery).

Nonmilitary government spending

Spending is a particularly appropriate indicator of delivered policy. It provides a numeraire that gets to the heart of the choice between “more” or “less” (Blais et al. Reference Blais, Blake and Dion1996, 43; Soroka et al. Reference Soroka, Wlezien and McLean2006). The key indicator of total managed expenditure (TME), for example, summarises government activity in an annual time series. This includes Departmental Expenditure Limits that have been allocated to Departments and Annually Managed Expenditure that is not controlled by government departments.Footnote ¹⁷

The parties’ preferences about spending reflect their ideologies. Labour governments prefer “more” spending on welfare, health and transport. Conservative governments prefer “less”. In some domains, however, these preferences are reversed. The most significant case is defence spending. This fluctuated between a high of 9.8% of gross domestic product (GDP) in 1953 to a low of 2% in 2015.Footnote ¹⁸ In general, Conservatives prefer more and Labour prefers less spending on defence. Military spending, moreover, is influenced by perceptions of threat and is less responsive to domestic politics. In order to provide a more accurate indicator of domestic policy, we subtract defence as a percentage of GDP spending from TME to produce nonmilitary government expenditure (NMGE).

Figure 2 displays the NMGE series from 1950 through to 2015.Footnote ¹⁹ This provides evidence of year on year variation but says little about the impact of party control of government over policy. Figure 3 displays changes in NMGE under governments from 1951 to 2015. Dark blocs indicate Conservative governments; lighter blocs indicate Labour. This tells a fascinating story. All Conservative governments, with the single exception of Heath (1970–1974), have reduced NMGE.Footnote ²⁰ All Labour governments have increased it. Our characterisation of elections as a choice between “more” with Labour and “less” with the Conservatives has a degree of face validity.

Figure 2 Nonmilitary government spending, 1948–2013.

Figure 3 Changes in nonmilitary government expenditure by government, 1951–2015.

Average direct taxation

The electorate is collectively ambivalent about the government policy activity. They prefer “more” public services. They also prefer “less” bureaucracy and taxation. They also worry about the impact of welfare on individual incentives (Zaller and Feldman Reference Zaller and Feldman1992). Since it is difficult to produce time series indicators of bureaucracy and incentives, we focus on taxation.Footnote ²¹ Specifically, we incorporate a measure of the average direct tax (income tax plus national insurance) paid by male workers on median incomes (see Figure 4).Footnote ²² This measure does not, of course, cover all taxes but reflects public debates in Britain, which have centred on direct taxation.Footnote ²³

Figure 4 Average direct tax levels (1949–2012).

Methodology

When we inspect the graphs for mood, vote intentions and domestic spending, it is clear that these variables are not stationary. That is to say, they do not oscillate around a single mean, but instead the mean moves over time. Formal stationarity tests confirm this.Footnote ²⁴ Nonstationarity creates problems when analysing time series, in particular the problem of spurious regression (Enders Reference Enders2004). It is quite likely that two nonstationary variables will correlate significantly, even if they are completely unrelated. We need to ensure that our results are not simply the result of such spurious correlation.

One approach to the problem of nonstationarity is to take the first difference of the data (or further differences if necessary), so we have a stationary time series. This deals with nonstationarity but only allows us to draw inferences about the short-term dynamics of the relationship between the variables. An alternative approach is to use the error correction model approach of the type:

$$\Delta {\rm Y}_{{\rm t}} \,{\equals}\,{{\rm b}_{0}}{\plus}{{\rm b}_{1}}\,\Delta {\rm X}_{{\rm t}} {\plus}{\rm a}\left( {{\rm Y}_{{{\rm t}{\minus}1}} -{{\rm g}_{0}}-{{\rm g}_{1}}\,{\rm X}_{{{\rm t}{\minus}1}} } \right)$$

This also allows us to model the long-term relationships between the variables. It assumes that there is a long-term equilibrium relationship between the variables, and that the further out of equilibrium we are the larger the adjustment (the parameter a represents the speed of this adjustment). However, error correction models are only appropriate with nonstationary data if the variables are cointegrated (Engle and Granger Reference Engle and Granger1987). Roughly speaking, this means that the variables move up and down in parallel, and the relationship remains approximately the same over the whole time period.Footnote ²⁵ If the variables are not cointegrated, we again face the problem of spurious regression.

We use the error correction model whenever there is a suitable cointegrating relationship. We test for cointegration in two ways. First, we run an augmented Dickey-Fuller test on the residuals of the regression of the variables in levels. Second, the error correction parameter is itself a test of cointegration. However, when using this as a test for cointegration, it is necessary to use the distribution derived by MacKinnon (Reference MacKinnon1994) and Ericsson and MacKinnon (Reference Ericsson and MacKinnon2002), instead of the normal t-distribution reported by most software packages (see Grant and Lebo Reference Grant and Lebo2016). We find strong evidence of cointegration in our models with mood and vote intentions as the dependent variable, and use error correction models in these cases. However, we do not find sufficient evidence of cointegration for our model explaining NMGE. In this case, we take the first difference of government spending, modelling the change in government spending as a function of mood and economic conditions. This is similar to the approach in Soroka and Wlezien (Reference Soroka and Wlezien2009).

There has recently been a debate about the appropriateness of error correction models in political science. In a special edition of Political Analysis, Grant and Lebo (Reference Grant and Lebo2016) argue that many applications of error correction models are inappropriate because they are applied to data where cointegration is not present. They suggest that the conditions required to use error correction models rarely apply in political science. Others have argued that error correction models are more widely applicable (Enns et al. Reference Enns, Kelly, Masaki and Wohlfarth2016; Esarey Reference Esarey2016; Keele et al. Reference Keele, Linn and Webb2016).Footnote ²⁶ However, there is agreement that if the data are nonstationary, error correction models are only appropriate where cointegration is present. We only use error correction models where there is clear evidence of cointegration, using the tests recommended by Grant and Lebo (Reference Grant and Lebo2016).Footnote ²⁷

Does mood respond to policy?

We now examine the electoral turnover mechanism in two stages. In this section, we examine whether public preferences (as measured by the policy mood) respond to policy. In the next section, we examine whether vote intentions respond to the policy mood.

Previous research has established that preferences for policy (R _ij ) reflect the difference between ideal points (P _i ) and actual policy (P _j ) (Wlezien Reference Wlezien1995).

$${\rm R}_{{{\rm ij}}} \,{\equals}\,{\rm P}_{{\rm i}} \,{\minus}\,{\rm P}_{{\rm j}} $$

When actual policy exceeds the ideal (P _i >P _j ) then R _ij >0 and the electorate signal their preference for less. When a policy is less than ideal (P _i <P _j ) then R _ij <0 and the electorate signal their preferences for “more” activity. Preferences act like a “thermostat” (Wlezien Reference Wlezien1995). The same logic applies to mood. As spending increases the public want less. Accordingly:

The electorate prefers lower levels of direct tax taxation. Accordingly:

Government activity is not the only influence on mood. Exogenous changes in the economy can also shape preferences. Increasing unemployment, for example, will lead the electorate to prefer more activity in order to reduce unemployment.Footnote ²⁸ Thus, independently of policy, the electorate will shift left as unemployment increases. These considerations suggest:

We model mood using an error correction model. Given that our variables are nonstationary and there is a cointegration relation between the dependent and independent variables, this is appropriate.Footnote ²⁹ The dependent variable is a mood. The independent variables are lagged values of average income tax levels, NMGE and unemployment.

Table 3 displays the estimated coefficients from the error correction model. The estimated error correction rate (0.81) implies that over 80% of any deviation of preferences from the target rate in either direction is corrected within one year. The coefficients provide support for Hypotheses 1 and 2. The statistically significant negative coefficients for long-term effects for both NMGE (b=−0.59) and average income tax rates (b=−0.37) clearly suggest thermostatic relationships. There is also the short-term relationship between Δx and Δy for NMGE (b=−0.59). As government activity increases, the electorate moves right and, as government activity decreases, it moves left. The long-term effect for unemployment also provides support for Hypothesis 3 (b=0.86).Footnote ³⁰

Table 3 What drives the policy mood? (error correction model, 1948–2013)

Note: MSE=mean square error.

***p<0.01, **p<0.05.

This evidence confirms the thermostatic hypothesis (Wlezien Reference Wlezien1995). The electorate responds to both policy and economic conditions. Changing preferences communicate a desire to reduce government activity when “too hot” and increase it when “too cold”.Footnote ³¹ This is evidence of the first link in the “chain of responsiveness” and representation. It is characteristic of both electoral turnover and policy accommodation mechanisms (Powell Reference Powell2000). We now examine the next link in the electoral turnover mechanism: whether votes respond to mood.

Policy mood and vote intentions

Before proceeding, we must note that preferences are not the only the plausible influences on the vote: they are also influenced by long-term partisanship (Clarke et al. Reference Christiano2004), policy moderation (Nagel and Wlezien Reference Nagel and Wlezien2010) and evaluations of competence (Green and Jennings Reference Green and Jennings2012).Footnote ³² From this list of “usual suspects” we only have indicators of annual party competence. These are estimated using the dyads ratio algorithm and drawing on responses to survey questions about which party is best able to deal with any particular issue (Green and Jennings Reference Green and Jennings2012).Footnote ³³ Unfortunately, we do not have annual indicators of partisanship or party position at our disposal. Partisanship was rarely measured in the 1950s and 1960s. The “traditional” measure of partisanship is, moreover, influenced by the same short-term factors that it is assumed to shape (Clarke et al. Reference Christiano2004). Accordingly, our vote intention model tests two hypotheses:

Once again we use an error correction model, as there is cointegration between the dependent and independent variables.Footnote ³⁴ Table 4 assesses the relationship between annual Labour vote intentions, mood and evaluations of Labour competence between 1951 and 2015. The error correction term is significant and correctly signed (b=−0.57) suggesting that 57% of any deviation of preferences from the target rate is corrected within one year. Both the long- and short-term coefficients for competence are significant and correctly signed (b=0.62 and b=0.74, respectively) providing support for Hypothesis 4. Crucially, the coefficient for the long-term effect of mood is significant (b=0.17). As the mood moves left, Labour vote intentions increases.Footnote ³⁵ Hypothesis 5 is confirmed.

Table 4 What drives annual vote intentions? (error correction model of Labour vote intentions, 1951–2015)

Note: MSE=mean square error.

***p<0.01, **p<0.05, *p<0.1.

These results suggest that variations in mood have an electoral impact net of evaluations of competence. This provides some evidence for another step in the electoral turnover mechanism. The effect of competence is greater than mood. As a result, a move towards (say) the left in mood will not produce an increase in support for the Labour Party if there is even a small loss of confidence in the competence of the Labour Party.

Party ideology, mood and policy representation

We now examine whether policy responds to party incumbency or mood. If NMGE responds to party incumbency, this will confirm that governments pursue policy consistent with their ideology and provide evidence in favour of the electoral turnover mechanism. If it responds to mood, this will provide evidence in favour of the policy accommodation mechanism.

We did not find an appropriate cointegrating relationship, and so did not use an error correction model. Instead, we took the first difference of NMGE and regressed lagged mood, the change in unemployment and the change in inflation on this. This is similar to the approach in Soroka and Wlezien (Reference Soroka and Wlezien2009). This specification is theoretically appropriate because our operationalisation of mood is intrinsically thermostatic. Respondents are typically not asked to name their ideal level of government spending on a programme; they are asked whether spending is too high, too low or about right. Left-wing mood means that the public demands more public spending than at present. As a result, we would expect the left-wing mood to produce an increase in spending.

If electoral turnover was producing a representation, who is in government should “make a difference” (Rose Reference Rose1980). Labour governments should spend more than Conservative governments other things being equal.Footnote ³⁶ Accordingly, the electoral turnover model suggests:

If policy accommodation was producing a representation, NMGE should respond to the preferences communicated by variations in mood. The government could anticipate that the electorate will punish it if it does not deliver policies compatible with the mood of the electorate. This will lead to the government reacting to the current policy mood, perhaps with a lag. However, it could also anticipate what the mood the public will have at the time of the next election. If it is able to do this, then the future mood will have an effect on current policy. We test both possibilities.

Table 5 displays the coefficients generated by five models. First, we model NMGE as a function of mood in the previous period, the change in unemployment and the previous period’s inflation rate. In the second model, we take into account policy mood in each of the previous four years, to explore the effect of a change in mood over a five-year parliament. We then add political variables to each of these models, producing models 3 and 4. We include a variable representing whether Labour was in government, together with a dummy variable for 1974. Public spending in that year increased by 4.5% of GDP as a result of factors such as the oil crisis and the miners’ strike (see Figure 2). Finally, in model 5 we consider whether the government’s anticipation of the mood at the time of the next election has an effect on policy. We add a variable for the level of mood forecasted in the next period using the data available to that point.Footnote ³⁷

Table 5 What drives annual policy? (modelling domestic government expenditure, 1949–2013)

Note: MSE=mean square error.

***p<0.01, **p<0.05, *p<0.1.

The first thing that is apparent from inspecting the five models is that mood has no significant effect on domestic spending – indeed the estimated coefficients are very close to 0. This is true even if we add the effects of four lags of mood. The mood forecasted for the next period also does not have a statistically significant effect. These results are inconsistent with Hypothesis 8. There is no support for the idea that movements in mood lead to an increase in NMGE, whether we control for the effect of the party in government or not. There is no support for the policy accommodation mechanism.

By contrast, the coefficient for the party in government does have a significant effect. As importantly, this effect is substantially very significant. According to our models, Labour governments raise domestic spending by between 0.51 and 0.67% of GDP per year more than Conservative governments. Thus we find strong support for Hypothesis 8. Essentially parties keep pursuing the policies they are committed to, regardless of changes in public mood.

The effects of the various control variables are as expected. A change in unemployment leads to a very substantial increase in public spending. This is not unsurprising as unemployment directly increases spending on unemployment benefit and other social programmes. Inflation leads to small, but significant fall in spending. Again this is what we would expect as inflation makes it easier for governments to cut programmes by simply not fully indexing them. The dummy variable for 1974 also has the expected effect.

These findings confirm the impression in Figure 3. Simply put, “party matters” (Blais et al. Reference Blais, Blake and Dion1996). Labour’s reputation as the party of “big” or “bigger” government party is based on fact. So is the Conservative party’s reputation as the party of “small” or “smaller” government. This part of the “electoral turnover” mechanism works very well – the parties offer voters a consistent and reliable choice between “more” or “less” government. We would expect that in the long-run changes in mood will lead to a change in government, which will, in turn, lead to a change in policy in line with public preferences. However, as we saw in the last section, the mood only has a weak effect on party support, and this can easily be overwhelmed by considerations of competence.Footnote ³⁸ Although we would expect “the electoral turnover” mechanism to work in the long-run, it may well fail for a considerable time if voters believe the opposition to be incompetent.