Political Interest Furthers Partisanship in England, Scotland, and Wales


1. Introduction

In the field of public opinion and voting behaviour, few concepts have stimulated as many analyses and have aroused as much debate as that of party identification. The idea of a durable “identification” with a political party has been a staple in American electoral studies since the 1940s and is at the core of the Michigan model of electoral choice (Campbell et al., 1960). But with the proliferation of Michigan-inspired voting studies during the late 1960s and early 1970s (see Miller, 1994) scholars tried to apply the concept to political systems other than the US and began to couch their analysis of voting behaviour in Western Europe in terms of stabilizing party attachments and disruptive short-term factors. Roughly about the same time, the concept came under criticism both in the US and abroad.

A first strand of this critique accepts the basic premises of the Michigan model but holds that their empirical findings were dependent on the unusually quiet and stable political setting of the 1950s. According to this school, the political unrest of the 1960s and 1970s greatly reduced the prevalence and electoral importance of partisanship (Abramson, 1976; Nie et al., 1976). A second and related line of argument questions the validity of the indicators, which is of particular importance for the application outside the US. Critics objected that in political systems other than the US indicators for party identification merely presented another measure for vote intentions (Schleth & Weede, 1971; Thomassen, 1976; LeDuc, 1981; Küchler, 1986). Other challenges focus on the fact that the original Michigan indicator was based on a single continuum spanned by the two relevant American parties, whereas the vast majority of European polities feature multi-party systems (Katz, 1979).

The third and most fundamental line of criticism addresses the meaning and theoretical status of party identification. The original Michigan model as outlined in the American Voter was firmly grounded in then-contemporary assumptions about human behaviour that have been subsumed under the label of a “homo sociologicus of sociological empiricism” (Lindenberg, 1985, p.102): Voters were perceived as actors who “form opinions about everything”, are “easily influenced by others”, and “act directly on the basis of … [their] opinions” (Lindenberg, 1985, p.102). Consequentially, the attachment to one of the major parties would have a powerful direct impact on one’s political behaviour and would operate as a “perceptual screen” that biases the reception and the processing of political information (Campbell et al., 1960, p.133).

According to the classic Michigan model of political behaviour, citizens do not apply an explicit decision rule to maximise their utility from voting but rather react in a semi-conscious and stochastic way to political stimuli. This runs against the grain of a competing “model of man” which holds that citizens act rationally, i.e. actively choose the political alternative that is optimal given their general preferences. Starting out from Downs’ instrumental interpretation of party identification (Downs, 1957), authors such as Samuel L. Popkin and Morris P. Fiorina developed a competing view of partisanship. According to them, party identification is nothing but a cost-saving device for voters in a situation where it would be irrational to gather too much political information (Popkin et al., 1976; Fiorina, 1977; Fiorina, 1981).

While the question of whether humans act rationally in politics or merely follow their attitudinal dispositions is obviously a deep and complex one, in the case of party identification both competing views have observable implications that lend themselves to an empirical test. While the Michigan school never claimed that party identification is perfectly stable (Campbell et al., 1960, pp. 135, 165), it follows from their presentation in the American Voter and subsequent accounts that one’s identification is normally acquired early in life and then retained for years if not decades unless the political landscape changes dramatically. On the other hand, the “revisionist school” claims that party identification is nothing but a “running tally of retrospective evaluations of party promises and performance” (Fiorina, 1981, p. 84) that is frequently updated and changes accordingly as new political information comes in (see Milazzo et al. 2012 for a more subtle development of this idea in the British context).

On the basis of panel data that track the party identifications of individuals over time, it should be fairly straightforward to establish which of the competing views more adequately reflects political realities: if many electors change their identifications over a comparatively short period of four or five years, that would constitute rather strong evidence of rational updating in the light of new information. If, on the other hand, identifications are more or less stable over this period, this would prima facie support the classic view, although proponents of the revisionist school could still argue that political circumstances have simply not changed enough to trigger significant updating.

The issue is further complicated by random measurement error: respondents will get tired and bored during the course of a lengthy interview and give an incorrect or random answer to get over with the procedure, and interviewers will mishear or mistype their utterances. Therefore, not all apparent changes in the answers to a survey question reflect a true shift in the underlying attitude. Put differently, the correlation between measurements at time t and time t+1 will be attenuated.i Therefore, results from panel analyses of partisan change and stability that do not account for random measurement error will be seriously biased in favour of the revisionist school (Green et al., 2002).

As regards party identification in Britain, to our best knowledge only the analyses by Clarke and his colleagues (Clarke et al. 2004, Clarke et al. 2009, Clarke & McCutcheon 2009) deal adequately with this problem. Their main finding is that even if measurement error is taken into account, the “substantial latent-level dynamics in party identification in recent years … [are] inconsistent with the high levels of partisan stability as argued by Green et al. and other proponents of Michigan-style partisanship” (Clarke et al., 2004, p. 193; see also Clarke et al. 2009, pp. 327330; Clarke & McCutcheon 2009).

While we agree with Clarke et al. that party identification in Britain is not completely stable, we disagree with some of their interpretations of their findings. More importantly, we argue that their model should be extended to include political interest as a key factor that can explain stability of partisan ties. This reflects the crucial role political interest plays in another strand of the literature that harks back to Dalton’s (1984) seminal contribution on cognitive mobilisation and partisan dealignment. Although both strands of the literature should obviously complement each other, they are by and large disjoint. Our own model aims to narrow this gap.

In the remainder of this paper, we first review very briefly the existing evidence on the status of party identification in Britain. Then, we present findings from a model that differs from the specification chosen by Clarke et al. by introducing one additional variable and by defining the analytical sample in a slightly more restrictive way. These two innocuous modifications lead us to substantively different conclusions that are much more in line with the classic model of party identification. Finally, we discuss how our findings relate to the broader discussion on the nature and stability of party identification.

2. Previous Research

The Michigan-inspired concept of party identification (labelled as “partisan self-image”) was introduced by Butler and Stokes (1969; 1974) to the analysis of public opinion and voting behaviour in Britain. While building on the American concept, the authors found considerable Anglo-American differences. Though highly stable in absolute terms, partisan self-images in Britain turned out to be much more likely than their American counterparts to travel in tandem with vote choice over time. Given these findings, some scholars discarded the concept arguing that in Britain party identification is not as independent from voting behaviour as implied by the Michigan school. Others related the Anglo-American differences to features of the political institutions or even to measurement problems and concluded that the concept could be applied to Britain in principle (LeDuc, 1981; Mughan, 1981; but also see Crewe et al., 1977, p. 141; Cain & Ferejohn, 1981). Following the latter account, many scholars used the concept of party identification to analyse public opinion and voting behaviour in Britain.

Subsequent analyses, however, yielded more and more evidence that seemed at odds with the traditional concept. Beginning in the 1970s, Britain underwent a period of partisan dealignment, i.e. the strength of party attachments decreased considerably (Crewe et al., 1977). What is more, the decline in intensity resulted from party attachments’ responsiveness to retrospective evaluations of party performance on salient political issues and party leader evaluations (Clarke & Stewart, 1984; see also Johnston & Pattie, 1996). Expanding on this line of research, Clarke and colleagues (e.g. Clarke et al. 1997, 1998) argue in a number of contributions that the directional component of British partisanship also responds to short-term forces like performance evaluations and party leader images. Summarising these findings, Clarke et al. conclude that this level of aggregate and individual volatility cannot be reconciled with the classic model. Therefore, party identification in the sense of the Michigan model should be replaced by the Fiorina-inspired concept of “valenced partisanship” (Clarke et al., 2004, p. 211; Clarke et al. 2009; Whiteley et al. 2013).ii Specifically, employing fractional cointegration methods (e.g., Box-Steffensmeier & Smith 1996), Clarke and his colleagues suggest that party leader images and personal economic evaluations exert effects on aggregate-level party support in Britain (Clarke & Lebo 2003; see also Lebo & Young 2009; Pickup 2009).

But this conclusion has not gone uncontested. Several scholars claimed that the apparent instability of party identification in Britain is the result of an inappropriate survey instrument. Since the traditional indicator prompts respondents with a list of party labels and does not offer them an explicit non-identity option, respondents who lack the sense of durable attachment that is implied by the concept will in all likelihood answer the question on the basis of their present (but not necessarily stable) party preference. This will result in an inflated figure for the percentage of partisans in Britain as well as in a deflated estimate of partisan stability.

Cross-sectional analyses have largely supported the view that the traditional indicator has problems (Brynin & Sanders, 1997; Bartle, 1999; Bartle, 2003; Blais et al., 2001; Sanders et al., 2002; see also Sanders, 2003; see for a similar argument on the effects of question order Heath & Pierce, 1992). When it comes to the stability issue, however, the findings are somewhat mixed. Comparing the traditional and a revised measure (the so-called “supporter question”), Clarke et al. can demonstrate that both indicators result in identical rates of stable responses. At the same time, the revised indicator leads to considerably lower rates of inter-party change than the traditional instrument (Clarke et al., 2004, pp. 196199).

A related methodological objection against the notion that party identification in the UK is merely a “running tally” was raised by Donald Green and his colleagues. Using a simple dummy indicator specification for party identification, Green et al. show that party attachments in Britain turn out to be very stable over time if one controls for random measurement error. What is more, they demonstrate that short-term forces do not seem to affect party identification in Britain if random error is accounted for. Accordingly, Green et al. conclude that evidence in favour of the running tally account of party identification is based on methodologically flawed analyses (Schickler & Green, 1997; Green et al., 2002).

Clarke and his colleagues (e.g. Clarke & McCutcheon 2009, pp. 711-714), however, argue that Green et al.’s approach to measurement error is methodologically flawed and suggest to take an alternative route based on Mixed Markov Latent Class (MMLC) analysis a more adequate method for dealing with categorical latent variables that estimates probabilities for transition from one latent group to another and obtain very different results. Their analyses of panel data collected between 1963 and 2006 reveals considerable change in party identification at the latent-variable level even when allowing for measurement error. Testing four different specifications, they find that a Mixed Markov Latent class model with time homogeneous measurement error rates fits the data for Britain (and also Canada and the USA) best. In such a model, the measurement of partisanship is assumed to be affected by random error that is homogeneous (constant) over time. Moreover, one group of respondents is assumed to have perfectly stable partisan orientations (the “stayers”), whereas members of a second group change their orientations randomly from one wave to the next (the “movers”). The model is related to Converse’s “black-white” model but differs in one important aspect: the assumption that these switches occur with equal probability is relaxed. In Britain, the estimated size of the “mover” group varies between 29 and 37 per cent (Clarke & McCutcheon 2009, pp. 721). Therefore, they suggest discarding the original Michigan concept in favour of the running tally model (Clarke et al., 2004, pp. 194195; Clarke et al., 1999, pp. 97-101; Clarke et al. 2009, pp. 327330).

Clarke et al.’s works are an important methodological and substantive step forward, although we think that they do not represent the final word on the “Michigan vs Running Tally” debate. To be sure, the probabilities for retaining one’s party identification do not equal unity. Perfect stability, however, is not implied by the Michigan model. Rather, this would be a feature of the “unmoved mover” caricature of the original concept (Converse, 2006, p. 5).iii The finding that about two thirds of the respondents in the UK are “stayers” with perfectly stable partisan orientation, combined with the fact that estimated retention rates amongst the “movers” are also relatively high could easily be read as an endorsement of the Michigan model.

However, the debate on the relative merits of both models has become somewhat stale. In reality, electorates are comprised of stable partisans, party switchers, and apartisans, and investigating the scale, the sources and the consequences of this unobserved heterogeneity seems to open up promising new avenues for research (Neundorf, Stegmueller & Scotto 2011). In this article, we suggest adding political interest as an explanatory variable into Clarke et al.’s model to re-examine the over-time stability of party identification in Britain in the late 1990s. This seemingly innocuous modification leads to an important new insight that helps us to discriminate more clearly between the traditional interpretation of partisanship and the revisionist one. In addition, it has some bearing on a related conflict within the traditionalist camp.

The rationale is simple: From the ideas of Downs, Fiorina and Popkin it follows that citizens who are interested in politics and hence exposed to political information will update their “running tally” more frequently than their compatriots with lower levels of political interest. Being interested in politics should therefore have a positive effect on the probability of being in the “mover” class. Moreover, since party identification is merely a function of prior information, interest in politics will raise levels of exposure to political information and should hence be associated with higher levels of partisanship.iv

From the point of view of the traditionalists, expectations regarding the role of political interest are less clear-cut. On the one hand, starting from ideas originally formulated in the American Voter, the thrust of Dalton’s (1984) seminal “cognitive mobilisation” thesis is that low-interest citizens will rely on partisan cues, whereas cognitively mobilised citizens have no need for these cost-saving devices. Cognitive mobilisation will therefore undermine partisan ties and contribute to dealignmant (e.g. Dalton 2014).v For individual respondents, this implies that political interest should once more increase the probability of being the mover group. Moreover, it follows that political interest should have a negative effect on the likelihood of identifying with a political party.

On the other hand, Campbell et al. (1960, p. 133) have famously argued that party identification acts as a perceptual screen that affects the way political information is selected, processed, and stored (see also Bartels 2002). This view is in line with modern theories of motivated reasoning, which suggest that voters with high levels of interest in politics and high levels of political knowledge already stored in their long-term memory are more likely to select information that bolsters their preferences and to counter-argue information that runs contrary to their existing preferences (confirmation and disconfirmation bias). In effect, additional information is likely to support, rather than undermine, existing preferences. Among low-interest voters, this effect is smaller (e.g., Taber and Lodge 2006; see somewhat relatedly Converse, 1962; Zaller, 1992). Political interest should therefore be associated with higher levels of partisanship (e.g. Albright 2009), as well as with a higher probability of being in the stayer class.

To summarise, the three different theoretical perspectives lead to diverging expectations regarding the effect of political interest:

– Table 1 about here –

3. Data and Model

It is not our aim to fully replicate the various analyses by Clarke and his colleagues or to refute their findings. Rather, we want to demonstrate how adding a single variable to their model can shed some new light on the heterogeneity of partisanship stability in Britain and beyond. We therefore focus on data that were collected for the British Election Panel Survey from 1997 to 2000. There are three reasons for re-analysing these data that were collected more than a decade ago. First, this was the last face-to-face panel study of the BES, which began to employ internet (access) panels from 2005 on. While these are cost-efficient and can offer comparable data quality, the bulk of the data analysed comes from traditional surveys, and we would rather avoid mode effects. More specifically, we are worried that “professional respondents” who participate in many surveys may be more interested in politics in the first place and will develop more consistent attitudes over time, which would bias the results in favour of the traditionalist camp. However, an additional analysis of the 2005-2009 internet panel (documented in the online appendix) leads to essentially identical findings.

Second, the 1997-2000 panel (just like the 2005-2009 study) is a “truncated” panel, as far as party identification is concerned, because the last measurement was taken well before the beginning of the election campaign in the following year. Hence, it will not be contaminated by any campaign effects that may prompt voters to revert to their initial identification (e.g. Finkel 1993), while still giving us the benefits of analysing four measurements that span almost the complete life of a parliament.

Third, and most importantly, Clarke & McCutcheon (2009, p. 721) flag up the 1997-2000 panel as the UK’s least stable in modern times. This is confirmed by our own analyses of the 1963-2009 BES panel data: While 80 per cent of the respondents reported the same identification in the last wave in 2000 as they did when they were first interviewed in 1997 (compared to 74 per cent in the 1992-95 and 84 per cent in the 2005-08 periods), the year-to-year changes (documented in the online appendix) are higher than most of those that have been previously observed. To summarise, re-analysing the 1997-2000 data stacks the odds against finding stable identifications of traditionalist lore and is therefore a sensible modeling choice.

The volatility of the 1997-2000 period does not come as a surprise. After all, “New Labour” won the 1997 election by a landslide following the abolition of the old Clause IV (that called for nationalisation) in 1995 and an “Americanised” campaign, which played down ideological differences between the parties. Subsequently, Labour implemented a flurry of constitutional changes, but carried on with many of the previous government’s fiscal and economic policies and established a leadership style that was widely seen as presidential and tried to appeal to the broadest possible constituency (Bevir & Rhodes, 2006). Therefore, the early Blair years should provide a litmus test for the hypothesis of (largely) immutable identifications: if identifications resist the momentum of these events, if they remain stable although Britain has, according to Clarke et al., entered an era of valence politics, this would constitute strong evidence in favour of the classic model.

Unlike Clarke et al., we analyse the 1064 respondents from the smaller nations (882 from Scotland and 182 from Wales separately). Since the 1970s, both Scotland and Wales have featured party systems that are clearly distinct from the national party system, with Labour as the dominant party (until 2014/15) and comparatively strong nationalist parties that represent the centre-periphery cleavage. Devolution of the Scottish Parliament and the Welsh Assembly in 1999 has made these differences even more salient (Bohrer & Krutz, 2005; Lynch, 2007). In our view, pooling respondents from the three nations would lead to more unobserved heterogeneity instead of controlling for sources of heterogeneity, as the presence of the nationalist parties (taking seats in the Scottish Parliament and the Welsh assembly from 1999) will alter the meaning and possibly the stability of the “other” category. Moreover, the stability of mainstream party identifications might be lower in the periphery because respondents may well hold diverging party preferences and loyalties at the national and the sub-national level (see for evidence on the US and Canada Niemi et al., 1987; Stewart & Clarke, 1998).

The second important difference between the analysis by Clarke et al. and our approach is the specification of the model: As outlined in the previous section, we introduce self-stated political interest as an additional variable, which is correlated with both the mover/stayer property and the initial latent identification. The model’s parameters were estimated with MPlus 7.11 assuming that data are missing at random (MAR). Apart from considering political interest, our model is identical to the Mixed Markov model championed by Clarke et al.: We distinguish between Labour identifiers, Conservative identifiers, and all others. Like Clarke & McCutcheon, we further assume that reliability of the indicator is stable across the 1997-2000 period but place no restrictions on the transition matrices within the mover group.

4. Findings

As outlined in the previous section, we set up the model separately for a) England and b) the devolved nations. To establish a baseline, we first estimate the model without considering political interest and its correlations with the latent classes. In line with our expectations, restricting the sample to English respondents results in a higher estimate for the proportion of stayers (75 per cent vs. 66 per cent). Like Clarke et al., we note that some (about one third) of those placed in the mover category do not actually change their identification, bringing the total number of respondents with perfectly stable party identification to 83 per cent. This finding alone is a powerful re-assertion of the idea of largely stable party alignments.

In 1997, 40 per cent of the respondents were Labour identifiers, and 32 per cent identified with the Conservatives. Amongst the initial Labour identifiers, only one quarter are movers. For the Conservatives, the rate is even lower (one fifth), but for the “other” group, it approaches one third. Across all respondents, the probability of retaining one’s identification from one wave to the next varied between 0.94 and 0.99 for Labour and between 0.86 (between 1997 and 1998) and 0.97 for the Tories. For the heterogeneous “other” group, the retention rate is markedly lower (0.74) in 1998 and somewhat lower (0.92) in 1999 and 2000.

For Scotland and Wales combined, the estimate for the stayer group is also somewhat higher than 70 per cent and thus tends to exceed the figure reported by Clarke & McCutcheon. While this difference is small and within the margin of error, the discrepancy underlines that it is worthwhile to disaggregate the data. Once more, about one third of the “movers “(most of them Labour identifiers) retain their initial identification over the four waves, bringing the total number of stable partisans close to 80 per cent. Across all respondents, retention rates are slightly lower than in England and vary between 87 per cent (Labour from 1998 to 1999, the year of the first elections to the Scottish Parliament and Welsh Assembly) to 97 per cent (the Conservatives 1999 to 2000).

Next, we introduce political interest as an additional variable. Interest was measured in every wave using a five-point scale. While we could have constructed another latent variable from these four measurements (see, e.g., Prior 2010), this would have made the estimation prohibitively expensive in terms of computation, and would have led to a further proliferation of parameters. Instead of trying to purge random measurement error from this variable, we simply use the single measurement from the first wave as a rough indicator for general political interest. This is actually a conservative modelling strategy: Any random noise in the 1997 measurement (which could be due to higher levels of interest during the campaign) will dilute the relationship between political interest on the one hand and partisanship and its stability on the other.

  • Table 2 about here –

Including political interest in the model involves estimating three additional parameters – one for membership in the mover group, and two for initially identifying with Labour or the Conservatives, respectively. The change in the Bayesian Information Criterion (BIC) shows that this additional complexity is well-warranted: The BIC (which aims to strike a balance between model fit and parsimony) drops from 11,024 to 10,996 in England and from 4,386 to 4,378 in Scotland/Wales, which indicates a modest improvement over the pure Mixed Markov model (see Table 2).

  • Table 3 about here –

In both regions, including political interest in the model has hardly any effect on the estimated sizes of the mover/stayer classes. However, higher levels of political interest seem to reduce the probability of being a mover. The effects are sizable at about -0.2, meaning that each one-point increase in interest will reduce the odds of being a mover by roughly 18 per cent, but are not statistically significant by conventional standards (p=0.06 in England, 0.09 in Scotland/Wales). Hence, the findings (narrowly) fail to support the “motivated reasoning” perspective on partisan stability, but also clearly contradict the “running tally” and “cognitive mobilisation” perspectives, which predict a positive coefficient.

Regarding the effect of political interest on partisanship, the results are less equivocal. Political interest has a statistically significant and substantial positive effects on both identification with the Labour and the Conservative party (see Table 3). They are equivalent to changes in the odds ranging from 19 to 43 per cent. The somewhat weaker effects in the devolved nations are compatible with a lower number of apartisans in the “other” category due to the relevance of the nationalist parties.

  • Table 4 about here –

Moreover, there is a strong correlation between membership in the mover/stayer groups and the direction of the (initial) party identification. In England, stayers are considerably more likely to identify with one of the two major parties (see Table 4) than movers. From the relative size of the groups it follows that only about 19 per cent (a quarter of 75 per cent) of the respondents remain in the “other” group over the course of the survey. In Scotland and Wales, however, stayers predominantly identify with none or one of the nationalist parties, whereas more than two thirds of the movers identified with Labour in 1997 but may have changed their allegiance further down the line.

  • Table 5 about here –

Taken together, the results imply that overall retention rates are high in some, but not in all circumstances as Table 5 reveals. In particular, we can see that Labour experiences a significantly lower rate of retention outside England while the situation is reversed for the Tories and to a lesser extent for the “other” parties, both of which see a higher levels of loyalty among supporters in Scotland and Wales than in Englandvi These findings suggest that some of the late-1990s support for Labour in Scotland and Wales may have been rather instrumental in nature, and that devolution may have facilitated and accelerated the emergence of subnational party systems in the devolved nations.

5. Conclusion

In many ways, the discussion between proponents of the Michigan model (that has been updated and revised many times since its first inception in the 1950s) and their revisionist critics could be described as a dialogue of the deaf. This is in part because both models will lead to very similar empirical findings under most circumstances. In this contribution, we have tried to bring some fresh air to this otherwise stale debate by taking a closer look at the role of interest in politics in affecting the prevalence and stability of party attachments. Relying on this perspective, we derived three models with clearly distinguishable predictions: the “running tally” model, the “cognitive mobilisation” model, and the “motivated reasoning” model, which is well in line with the traditional notion of party identification.

The evidence gleaned from the 1997-2000 BES panel survey suggests that in Britain the traditional model is better suited to describe the role of interest in politics in affecting the prevalence and stability of party attachments than its contenders. Rather than providing evidence for a frequent “updating” of identifications amongst those who are interested in politics, our results for England and Scotland and Wales support the classic view that a change in party identification is a comparatively rare event amongst both high- and low-interest citizens, and it is even rarer among the former than among the latter. Our findings thus support the classic notion of party identification (Campbell et al., 1960; see also Bartels 2002), which implies that party attachments serve as perceptual screen. In particular, they suggest that the affective nature of party identification is conducive to motivated reasoning and thereby lends considerable stability to party attachments. In line with recent findings in political psychology (e.g., Taber and Lodge, 2006), these self-stabilizing effects of party attachments are particularly strong among high-interest citizens. This finding in turn fits nicely into Zaller’s (1992) RAS model which posits that high levels of political awareness is valuable in identifying and refuting information that contradicts existing predispositions.

In accordance with the latter model, our findings also have implications for the dynamics of party identification at the aggregate level, the so-called macro-partisanship (e.g., MacKuen et al. 1989; Clarke et al. 2001; Clarke & Lebo 2003). They suggest that the dynamics in the aggregate-level distribution are primarily driven by voters who are not heavily interested in politics. To be sure, this finding on voter heterogeneity does not imply that these dynamics are indicative of some kind of irrationality (see on this debate, e.g., Page and Shapiro 1992). However, it contradicts the notion that it is highly involved voters who cause shifts in macro-partisanship, a notion that would appear to be desirable from a democratic accountability perspective. Thus our findings can be seen as supportive of the well-known paradox whereby individuals with less than ideal citizenship traits actually seem to make important contributions to the functioning of democratic political systems (Berelson et al. 1954: 316; Neuman 1986).

As is the case for most empirical studies of political behaviour, this paper is subject to several limitations.vii First, we studied the stability of party attachments in a specific period in time. The 1997-2000 period is probably atypical in that citizens were provided with much information that could make them switch party allegiances. While this characteristic made a good test case for the hypotheses it also limits the generalizability of our findings, although our additional analysis of the 2005-2009 data leads to essentially identical results. Also, we did not take into account the durability of changes in party attachments. With data from multi-wave panel surveys covering longer time periods, future research may also be able to distinguish short-term fluctuations from more permanent changes in party attachments and hence explore whether political interest affects the durability of shifts in party attachments. Moreover, we have to keep in mind that we simplified our model by treating political interest not as latent variable. Yet, this strategy is likely to have diluted the impact of political interest on the stability of party attachments. Utilizing more sophisticated techniques would probably yield evidence that supports our conclusions even more strongly. So, we are confident that our evidence lends considerable support to the classic notion of party identification in Great Britain.


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i While it is possible that random measurement error masks some true changes at the micro-level, the correlation of measurements at occasions t and t+1 will underestimate stability in the aggregate.

ii Similarly, Richardson (1991) claimed that in Britain partisanship did not resemble affective-laden identifications but rather cognitive partisanship.

iii Campbell et al (1960, p.135) proposed non-recursive rather than recursive effects between party identification and short-term attitudes, though they found party identification to be the predominant factor in the 1950s.

iv As Fiorina (1981: 90) suggests that party identification may also have non-political roots, this relationship might be attenuated.

v Dalton’s original measure of of cognitive mobilisation is an additive index of political interest and levels of formal education. We focus on political interest because it is more closely related to Fiorina’s idea of updating one’s identification based on the influx of new information, and also because average levels of formal education have risen sharply in Britain so that educational attainment is now closely linked to birth cohort membership.

vi It should be noted that the retention rates reported here refer only to those respondents who (net of any measurement error) were deemed to hold a perfectly stable identification across all four years. While this is a more conservative measure than looking just at consistency over the two ‘end’ points it does mean we exclude those too young to be included in all waves which may lead to some under-estimation of volatility levels, given the higher rates of switching commonly found among younger voters.

vii More recently, social identity theory has become quite popular in addressing party identification (e.g., Greene 2004), but we obviously have to rely on the traditional BES indicator..


Table 1: Theoretical Perspectives on the Expected Effects of Political Interest




Running tally”


Cognitive Mobilisation”

Motivated Reasoning”



Table 2: Model Fit


Free Parameters


Adjusted BIC


Mixed Markov




Mixed Markov + Interest




Scotland & Wales

Mixed Markov




Mixed Markov + Interest




Table 3 Effects of Political Interest






On Mover




On Lab-Identification




On Con-Identification




Scotland + Wales

On Mover




On Lab-Identification




On Con-Identification




Table 4: Mover/Stayer Property and Initial Party Identification (row percentages)

















Scotland + Wales











Table 5: Estimated Retention Rates 1997 → 2000 (per cent)









Scotland + Wales




Geolocation and voting: candidate-voter distance effects on party choice in the 2010 General Election in England




The role of geographical distance in candidate evaluations by voters and subsequent vote choice remains one of psephology’s relatively untested hypotheses. Theories of representation would suggest that, in systems where constituency representatives vie for local inhabitants’ support in elections, candidates living closer to a voter should have a greater probability of receiving that individual’s support, other things being equal. Yet, to date there have only been qualitative or inferential, indirect tests of this hypothesis in the UK, and relatively little research on other countries. This has principally been due to insufficient data to allow the measuring of distance from voter to candidate in any meaningful manner.

Advances in open source geographical data and Geographical Information Systems (GIS) software, together with publicly available election data, mean that such hypotheses are now more easily testable. In this paper, we present a first empirical analysis using constituency data from the British General Election of 2010 and the British Election Survey (BES), together with geographical data from Ordnance Survey, to test the hypothesis that candidate distance matters in voters’ choice of candidate. We map constituency residence of Parliamentary candidates and where possible calculate a distance measure to voters sampled by BES living in their constituency.

We find that, in English constituencies, distance between a voter and candidates from the three main parties (Conservative, Labour and Liberal-Democrat) does matter, even when controlling for traditional predictors of voting, such as party feeling and incumbency advantage. This suggests that candidates living closer to their voters enjoy a small but significant electoral advantage over rivals living further afield, and provides further confirmation of previous research which has found that the localism of a candidate matters to voters.

Locality and distance

Why should the relative distance of candidates to voters in a constituency matter? There is very little work specifically testing this hypothesis, and in the UK case, none of it does so directly. We build upon an existing body of research which suggests that a candidate who is more proximate to the constituency, and by extension its voters, will enjoy characteristics which will resonate positively with those voters and which, other things being equal, suggest more effective representation for that constituency.

The most specific test of such a dynamic to date has been in Ireland. In their study of canvassing effects in the 2002 Irish General Election, Gorecki and Marsh (2012) factor in geographical distance between voter and candidate, citing the friends and neighbours hypothesis first posited by Key (1949) and Putnam’s local effect (1966), and find that other things being equal, likelihood of vote does indeed reduce as geographical distance increases. That this should be the case in Ireland but not in the UK would coincide with traditional views of differences between the two systems. As Parker noted, Ireland, with its STV electoral system, represents a particularly propitious case for studying the effects of electoral geography, in apparent contrast to the UK case which “often yield[s] unknown and inaccessible public representatives, who are often voted for merely because they are standing for a particular political party.” (1986: 2)

Broader tests of localism are more common. Johnston’s work on New Zealand local elections found limited evidence of a local effect (1973a: 422) but again surmises that at national elections, “[v]oters are unlikely to cross party lines to support a local candidate” (420). Hypothetical mapping of the distance effect through residential and work location states most clearly a candidate-oriented methodological perspective (Johnston, 1973b: 75). Cox’s seminal work on spatial effects included study of distance effects, such as centre-suburban location of London constituencies (1968), but investigation of his influential concept of “neighbourhood effect” has been more prevalent in the UK, with different studies concluding that social interaction as an effect does not have a significant impact (Curtice, 1995) or precisely that conversations with family and friends will influence individuals as to how they should vote (Pattie and Johnston, 2000). This follows the extensive literature on peer socialisation, opinion leaders and group interests from Lazarsfeld et al’s work onwards (1948, 1954).

Ecological models of vote looking for evidence of distance decay similar to Cox’s neighbourhood effect have been carried out on the American case, in particular testing ‘home state advantage’ (Lewis-Beck and Rice, 1983; Garand, 1988). This builds on Key’s assertion that candidates for state office will do much better in their home counties (Key, 1949). Lewis-Beck and Rice’s work finds that presidential candidates will win a premium beyond their expected vote in their home state, not enormous but sufficient to matter in a close race (1983: 551). They also find that three other key variables mediate this effect – size of state, with smaller states providing opportunity for greater levels of contact, peer networks and knowledge of the candidates; the party affiliation of the candidate, to allow for differential turnout between Democrats and Republicans; and an incumbency effect, with incumbents securing higher turnouts. Garand’s test is more mixed in its outcome, finding evidence of home-state but not of regional advantage (1988: 96), with Democrat candidates seeming to do worse in their home region (1988: 101). Rice and Macht (1987a) consider whether this advantage accrues from otherwise non-voters being mobilised by the local candidate, or by vote-switchers choosing the local against their normal party loyalty, and find that both play their part. Home-stage advantage is sufficiently well established to be used in forecasting models of US presidential elections to factor in the local premium candidates receive (Rosenstone, 1983; Campbell, 1992). Also in the US, Gimpel et al (2008) look at the distance between gubernatorial candidates’ hometowns and other counties in the State, hypothesising that there is a non-linear relationship between distance and trust, and thus to vote, and find at the meso level that this relationship does pertain.

Previous work, then, has not pursued the UK case as a likely example to show a distance effect at work. However, other related approaches to candidate evaluation and voter perceptions suggests this may be an oversight. Research into the so-called ‘politics of presence’ considers the reasons for voters preferring candidates whose profile matches that of their eventual constituents in terms of being ‘local’ as well as other characteristics (Childs and Cowley, 2011; Evans 2011). Johnson and Rosenblatt show, using the British Social Attitudes Survey and Hansard / Electoral Commission Audit of Political Engagement, that relatively consistently across time, voters have identified localness – being from the local area – as one of the most important attribute for their MP to have (Johnson and Rosenblatt, 2007: 166). Other work extends the notion of locality from the individual to the concept of constituency itself and notions of territorial constituencies (Rehfeld, 2005). A much broader literature looks at the supply side of candidate selection by parties in the UK and beyond (Denver, 1988; Pedersen et al, 2007). Rush has looked at the number of MPs with direct constituency connections – not just living in the constituency, but also place of birth, education, public service and so on – and found that the highest levels are found amongst Labour and Liberal Democrat MPs, with much lower levels amongst Conservatives (Rush, 2001; Rush in Childs and Cowley, 2011: 6). In earlier literature, there is some consideration of candidate residence (Katz, 1980; Crewe, 1985). More recent experimental tests of relative salience of candidate characteristics in voter preference have shown that the attribute ‘local’ has a greater differential effect than age, gender or occupation (Campbell and Cowley, 2012).

Research into the ‘personal vote’ provides additional evidence that the localism of a candidate may matter. A candidate who is rooted in the immediate vicinity of his / her voters may be expected to be in a position to carry out eventual constituency service more effectively – public participation is more convenient, surgeries will be less disruptive for the MP, and therefore more productive, during periods away from Westminster, and so on. Largely written off in the past as a marginal activity reaping few rewards in electoral terms, both by politicians and political scientists (Norton and Wood, 1990), an increase in constituency service by MPs saw a revised assessment of its importance in securing a small but significant share of the vote additional to that secured by more standard vote explanations, not least partisanship. Comparative work found evidence of incumbency advantage through ‘constituency attentiveness’ in the British case, although not as strongly as in the US House of Representatives (Cain, Ferejohn and Fiorina, 1984: 115).

There is therefore strong evidence that voters prefer local candidates. In that sense, we are interested in measuring empirically varying localness between the voter and the respective candidates, and as a first step most likely a distance measure. The most obvious loci for measuring relative locality between candidate and voter should be residence. Simply put, if localness matters for the reasons outlined above, then ceteris paribus a voter should prefer a candidate who lives closer to them than one who lives at a greater distance. This is intuitively appealing. As Lewis-Beck and Rice noted, a candidate in closer proximity to a voter will be more likely to be known to some degree ‘personally’ to the voter, can be expected to have similar concerns to the voter at local level, and will see the community resonate with them (1983:552). Johnston endorses the latter two of these arguments – “The candidate wins the voter’s support because a local representative is considered desirable, regardless of party, because he would fight for local causes, or because of the voter’s pride in the local boy and his hope for reflected glory.” (1973: 42) – although he steers away from a widespread effect of personal contact with the candidate due to its limited range.

Distance itself is a complex affair, but one well explored in physical and human geography. Building upon distance as commonly defined, ie. Euclidean distance between two points, geographers have identified more appropriate measures to be used according to context (Gatrell, 1983: 29). ‘Straight-line distance’ or the ‘as the crow flies’ metric is often replaced by taxi-cab, city-bloc metrics or route metrics – road distance covered, for instance. Distance as measured by time, for example using so-called ‘isochrones’, are fundamental to traffic analysis (Clark, 1977). Economic distance sees cost incurred to cover the space between two locations as a key metric (Lowe and Moryadas, 1975). The psychologically informed metric of ‘cognitive distance’, which taps respondents’ estimates of distance between locations, may differ from travel time and Euclidean distance (Canter and Tagg, 1975; MacEachran, 1980). In our study, all these distance metrics may be relevant for how voters are to be placed relative to their Parliamentary candidates.

In social science terms, distance could also be interpreted as indicating a relative position based upon a socio-economic index such as class, relative district wealth or another comparator. The role of social and locational context in determining voting behaviour has been well studied elsewhere, finding voters to be as influenced by their social environment and territorial position as by individual characteristics (e.g.; Johnston et al, 2001). In the context of voting behaviour, relative indicators would be likely to influence electoral choice: we might expect voters to favour candidates with less socio-economic distance between them, in terms of occupational status, residential area or indeed individual prosperity. To ensure that a geographical measure does not unwittingly tap socio-economic distance, then, it is important to control for this possible covariation. Lastly, returning to more commonly held notions of distance, the ‘true’ measure may not be one based upon a ratio scale, but rather a step-change based upon areas of proximity, e.g. ‘my street’, ‘my ward’, ‘my constituency’, ‘a neighbouring constituency’, ‘my region’, and so on.

Empirically, we restrict ourselves here to testing whether simple distance, as an objective proxy for a multiplicity of perceptions of localness, influences the probability of an individual voting for a candidate in an UK general election, other things being equal. Unlike US studies of localism, we do not predicate the distance hypothesis on the strength of local ties that a candidate may have, and the relationship this may have with size of population in the relevant agglomeration (Rice and Macht, 1987b: 450). Of course, local ties will matter, both directly – involvement in the community – and indirectly – perception of ‘localness’ through place of birth, length of residence, and so on. However, such indicators of localness and local involvement are not easily quantified, so we must necessarily leave these to one side.

One potential issue is whether voters know where the candidates live. Collecting survey data to enquire whether an individual knows these addresses would be an unsatisfactory method of tapping this information. A simple ‘yes / no’ response to multiple requests re individual candidates in a survey will not yield data for which we can have confidence in its validity. Asking respondents to give an actual address sets the bar unattainably high. From the perspective of first principles, then, we need to assume that, if voters are aware of where candidates live, and this matters to them, this will be reflected in their likelihood of voting for the candidate.1 We do not expect that voters know the distance to each candidate’s residence. Rather, we wish to see if there is evidence that relative distance of candidates influences the party choice of voters to any degree.

It is certain at least that all voters have the opportunity to be aware of their candidates’ respective residential locations, as these are printed on all ballot papers. Whether voters recall seeing this information, or consciously use it in their selection is unknown – that the information is freely available to every voter is known. With one specificity of the 2010 General Election, which we will consider below, we therefore potentially have a dataset which gives full information for candidates contesting the election.

Data and method

The analysis uses a range of datasets. To map constituency boundaries across England, the open-source OS OpenData Boundary-Line ESRI shapefile is essential. Candidate addresses were collected using the notices of poll published four weeks before the election. All 650 UK constituencies were covered, with notices returned either directly or downloaded from local authority websites. The postcode for each candidate was recorded, where given. It is important here to note that the requirements for statement of residence of the 2010 election were different to previous elections held over the last 140 years, as candidates were not required to record their home address on the notice of poll, and were given the option of stating only their constituency of residence.2 Precise locations of candidate residences were identified using the Code-Point® point data file and GoogleMaps, which provide latitude / longitude coordinates for every GB postcode.

Voter-related data were taken from the short-term in-person panel component of the British Election Survey 2010. For reasons detailed below, we model electoral choices for the three main parties in England as self-reported after the election while controlling for pre-campaign feelings. Northern Ireland had to be excluded ab initio due to an absence of the 2010 constituency boundaries in the necessary ESRI format, while Scotland and Wales were excluded due to having different party choice sets, including significant nationalist parties (the Scottish National Party and Plaid Cymru, respectively).The total sample size of the panel component is 1498. Because of the oversampling of the UK’s smaller nations, restricting the sample to English voters excludes about 23 per cent of the panellists, with self-declared non-voters and voters of smaller parties making up roughly 10 per cent of the remainder, leaving us with 887 cases. These cases represent 146 of the 149 English constituencies that were covered by the BES short-term panel.

We start by considering a simple diagnostic of candidate location – whether they live in their constituency or not. Figure 1 provides a choropleth of English constituencies graded by the number of candidates for the three main parties who live within the constituency boundary. The modal number of main party candidates living in the constituency where they stand is 2 (42 per cent). For 34 per cent of the English constituencies, only one of the main party contenders live within their boundaries. Having none of the candidates living in the constituency is unusual (7 per cent), while 17 per cent of the constituency have three resident main party candidates. These numbers are essentially unrelated to the constituency’s size (r=0.1) or its log size (r=0.13). Moran’s I is 0.06, indicating that there is very little evidence for positive spatial autocorrelation (clustering).3

Candidate locations by constituency

Location of voters is less easily tapped. Whilst we have (almost) complete data for candidates, we need to rely upon survey data to identify the residential location of a small sample of voters. The British Election Study provides the obvious source of data in this regard, but unfortunately – if understandably – it does not provide the full postcode for respondents, only the first letter(s) and digit(s), i.e. the postcode area and district. There are currently roughly 2,900 postcode districts in use in the UK, and almost all of them are far too large to locate voters with any reasonable degree of accuracy.

Fortunately, the BES does provide a code for the respondents’ electoral ward or ‘electoral division’ (in the new Unitary Authorities). The Office for National Statistics’s most recent (December 2010 edition) file lists 7,681 English wards, most of which are rather small. Our 887 respondents live in 271 of these wards. Figure 2 indicates the location of these wards within the 146 constituencies.

Figure 2 about here

Similarly to Gorecki and Marsh in their study of Irish voting (2012), we then use the centroid – the notional centre of balance of a polygon – of each ward to estimate the location of the voter and consequently calculate, using Google Geocoder API, the route distance between this position and the locations of the relevant candidates to generate a set of distances from a voter to each of their three candidates.4 Using centroids instead of the voters’ exact positions introduces some statistical noise into our model, but we believe that the effects are moderate: 50 per cent of our wards cover an area of 4.4 square kilometres or less, with 75 per cent being smaller than just above 13.5 square kilometres. The distribution is, however, heavily skewed to the right: the top five per cent of the wards cover areas between 60.3 and 95.6 square kilometres. We provide a diagnostic test of this effect later.

Ward locations of analytical sample

Lastly, then, we simply wish to look at whether distance between the voter and candidate location has an effect on likelihood of voting for that candidate. Our hypothesis is the following:

Other things being equal, the likelihood of an individual voting for a candidate decreases as distance from the individual’s residence to the candidate’s residence increases.

To test this robustly, we need to include the distance measure in an appropriate model controlling for other standard explanations of vote. Clearly, a fully specified model of vote along the lines of Michigan is not feasible given the analytical sample size. We therefore choose a basic thermometer of party feeling as our key control, hypothesising that all prior causes of vote are likely to manifest themselves through this pseudo-instrument. We use party feeling from before the campaign, to ensure that this is free from campaign effects, band-wagoning from knowing the outcome of the election and other similar biases. We also expect that, prior to the campaign, knowledge of candidates’ residential whereabouts will be at its lowest, with all voters having similar access to this information only at the stage of balloting.5 We do need to acknowledge that voters may well have received information regarding candidates’ localness, or otherwise, in the so-called ‘long campaign’ leading up to the election, where voters are primed with literature detailing localness prior to the campaign proper concentrating on policy issues. This still relatively understudied phenomenon has been noted in particular for continuous Liberal Democrat campaigning, between general and local elections, for example (Cutts, 2006: 75). Within constituency, we would expect uniform levels of information, but are unable to control easily for cross-constituency variation in information. We return to the implications of this ‘long campaign’ in the discussion.

Party feeling covers the majority if not all of the variables squeezed through the funnel of causality. However, given the importance of constituency service to our hypothesis – there may be considerations of effective representation for voters, as well as a sense of shared proximity – we must additionally control for incumbency advantage. If constituency service picks up additional variance beyond the party feeling instrument, this may simply be a function of a sitting MP, irrespective of distance to his / her voters, who has developed a personal vote through such activity. Desposato and Petrocik have shown, through tests using redistricting in the US case, that such an advantage works through constituency service anchoring non-partisan voters, rather than as an automatic ‘bonus’ (2003: 19). We therefore need to control for incumbency to ensure that our distance effect is not confounded by non-distance related anchoring covarying through incumbents living closer to their constituencies.

Similarly to the personal vote, past work on incumbency advantage has disagreed on its effect. Despite consistently stronger partisan foundations to vote than in the US, Cain et al (1987) found evidence of Labour incumbency effects, and increasing importance for incumbency generally (although not at US levels). Following from more robust models of incumbency advantage in the US (e.g. Gelman and King, 1990), Katz and King found that incumbency advantage matters differentially for the main parties in the UK, mattering least for the Conservatives, but that there was no evidence of it increasing in importance (1999: 29-30). Fieldhouse and Cutts more recently found, however, that the Labour party ran significantly stronger campaigns in constituencies with incumbent candidates (2009: 382). Gaines, on the other hand, found incumbency advantage in the UK to be strongest for Liberals – a finding supported by Denver et al (1998) – but with little or no effect for Conservatives or Labour (Gaines 1998; see also Ansolabehere and Gerber 1997 for a discussion of incumbency advantage and its effect on minority parties).

We condition incumbency effect in two ways. Firstly, we control for personal incumbency advantage. Secondly, we include a party incumbency variable, where incumbent candidates have stood down, to test if there is any residual ‘bonus’ which a party receives from having held the seat in the previous legislature, despite the incumbent standing down. Desposato and Petrocik’s rejection of the notion of an automatic bonus in the US case suggests that this variable will not pick up any variance, but we feel it is worth checking anyway for the UK case.

Lastly, we control for socio-economic distance using the English Indices of Deprivation (2010) for voter and candidate location. This composite measure is based on a broad and multi-faceted notion of resources (e.g. adequate access to the job market, housing, education, social conditions etc.) proposed by Townsend in his seminal 1987 paper. While Townsend’s original concept chiefly refers to individual deprivation, it has been usefully applied in small area statistics to capture crucial differences in living conditions between local neighbourhoods.

Building on previous work dating back to the 1990s, the Oxford Institute of Social Policy at the Department of Social Policy and Intervention on behalf of the Department for Communities and Local Government has recently updated the Indices of Deprivation for 32,482 Lower layer Super Output Areas (LSOAs). While the actual calculations are complex (McLennan et al., 2011), the measure essentially aggregates objective information on deprivation across seven domains – income, employment, health, education, housing and services, living environment and crime – into a single figure that can be used to assess the degree of deprivation of a given area.6 LSOAs are very small, homogeneous areas that were specifically constructed for census purposes. On average, just 1500 people live within a LSOA.

For candidates’ residences, their full postcodes uniquely identify the encompassing LSOAs so that the assignment of a deprivation score is straightforward. Electoral wards very rarely correspond to a single LSOA. Therefore, we calculated averages of those LSOAs with whom a ward overlaps, with weights proportional to the sizes of the overlapping areas. The GeoConvert service provided by MIMAS (http://geoconvert.ds.man.ac.uk/) greatly facilitated these calculations.

While many publications focus on the relative rank of a given location (i.e. its place in a league table based on deprivation scores), we look at the differences between voters’ and candidates’ deprivation scores. If voters are selecting candidates on the basis of the similarity in socio-economic status, we would expect there to be a negative association between vote probability and this differential index. Its inclusion is principally to allow for possible covariance between this and geographical distance, consequently we include it in a final step to our model, to see if geographical distance indeed washes out.

We model party support including the above variables using a conditional logit model. Unlike more common binomial and multinomial logit models, the conditional logit model (Long, 1997: 178 ) can estimate effects of alternative-specific variables (i.e. distances between a voter and each candidate). Put differently, we estimate a single coefficient for the effect of distance, but the values of this variable differ within subjects (voters) for each category (party choice) of the dependent variable and are potentially different for each voter, depending on their precise location. Using the Labour candidate as the reference, the model will estimate the likelihood of a Conservative or Liberal Democrat vote, with single control estimates for incumbency, party feeling and driving distance, measured in kilometres. We present three nested models, showing the effects of incumbency when added to the model.

Missing data is a relatively minor problem for our analysis, as we only use four survey variables from the BES panel. Almost all respondents who reported a vote for the three main parties in the second wave also rated them in the first wave. As regards the candidates’ positions, between 78 (Conservative) and 87 (LibDems) per cent of the candidates provided their full addresses on the ballot. We refrained from substituting these missing addresses with the centroid of the respective constituency of residence, because the mean constituency area in our sample is 245 square kilometres. Instead, alternatives where either the pre-campaign rating of the respective party or the address of the respective candidate are missing were excluded from the analysis.7


Table 1 Conditional logit model of party support





Conservative Party









Liberal Democrats









Party Feeling (pre)









(Driving) Distance












Incumbent Party



Incumbent Candidate





Deprivation Distance








Pseudo R2










Standard errors in parentheses

*p < 0.05, **p < 0.01, ***p < 0.001

Table 1 presents the conditional logit model of party distance effect on relative support for the three main parties in England.8 The model includes two constants that capture any differences in the baseline probabilities of voting for the three parties (after controlling for the independent variables). Everything else being equal, a Tory vote is significantly more likely than a vote for Labour, whereas thedifference between the Liberal Democrats and Labour is not significant.

As expected, by far the best predictor for party choice is pre-campaign party feeling. Across the 11-point range, the logit increases by 0.8 for every one-point increase. Note that the model is ‘alternative-specific’, so the thermometer effect is the same for all parties, but for each respondent the direction and intensity of each voter’s feelings are obviously likely to differ across parties. Although relatively small, the driving distance effect is significant, and in the expected direction: as distance between voter and candidate increases, so likelihood of vote for that candidate decreases.

In Model 2, we include incumbency status as a simple index ranging from -1 (non-incumbent) to +1 (incumbent candidate), with the value of 0 representing party-only incumbency. Even after controlling for pre-campaign party feeling, the effect of incumbency is also significant. This is intuitively appealing, the coefficient reflecting the effects of political learning throughout the local campaign, where incumbent candidates will focus on the experience gained during their previous term(s), and their achievements for their constituencies – in other words, constituency service. Again, incumbency status is an alternative-specific variable, i.e. we treat it as a feature of the candidate that has a uniform positive effect, regardless of the candidate’s party affiliation.

Including incumbency status in the model slightly reduces the estimate for the effect of distance. This is due to the fact that incumbent candidates live an average 8.9 kilometres closer to their potential voters, presumably because non-incumbents will often have not moved into the constituency.9

If we unpack incumbency status by replacing the index with two separate dummies, as we do in Model 3, it is easy to see that its effect has nothing to do with a party carrying a constituency. Rather, this is a personal (and strong) effect. Moreover (and most importantly for our research question) controlling for personal incumbency advantage does not reduce the importance of distance. Finally, it is clear from Model 4 that geographical distance is not related spuriously to vote probability through socio-economic distance – indeed, the deprivation index shows no effect whatsoever.10 Apparently, this element of vote choice is being picked up in the party thermometer.

So far, we have demonstrated that personal incumbency and spatial distance have effects on the vote that are consistent across a range of specifications. Up to now, however, we have made two assumptions regarding the functional forms that might be simplistic: that personal incumbency can be treated as dichotomous, and that distance has a linear impact on the logit. After all, the effect of distance could well level out once a threshold value is passed. Similarly, the effect of parliamentary service could peak after two or three terms and possibly even decline after some turning point where voters grow tired of perpetual incumbents.

To test for different functional forms, we first replaced the personal incumbency dummy by a count of each incumbent’s years of continuous parliamentary service. Following the procedure outlined in Royston & Altman (1994), and Royston & Sauerbrei (2008), we then replaced both variables by a series of fractional polynomials and estimated the corresponding models to find each variable’s best-fitting non-linear transformation. However, no transformation significantly improves the model fit, so we retain the original parsimonious specification (Model 4).

We perform one final diagnostic test to check for effect of ward size on the model. Because we are unable to identify precisely where a voter lives, the ward centroid provides an estimator which inevitably introduces random error. Given that a number of principally rural wards are relatively large, we want to ensure this ‘louder’ statistical noise is not biasing our findings significantly. In Table 2, then, we report a reduced sample model including only those voters who live in wards under 65 square kilometres in area.

Table 2 Conditional logit model of party support (only constituencies < 65 square kilometres)





Conservative Party









Liberal Democrats









Party Feeling (pre)









(Driving) Distance












Incumbent Party



Incumbent Candidate





Deprivation Distance








Pseudo R2










Standard errors in parentheses

*p < 0.05, **p < 0.01, ***p < 0.001

It is clear from this table that, whilst the largest wards do introduce some bias into the model, this makes no substantive difference to the findings. Given the political colour of the larger, rural wards, unsurprisingly the Conservative baseline loses significance. However, the driving distance parameter increases in size slightly.

How, then, do these models translate into ‘quantities of interest’, i.e. wins/losses for the parties? One thing that we should keep in mind here is that we are looking at voters for the three main parties only. Moreover, the sample is certainly biased, because we can only look at English panellists who responded to both pre- and post-election waves, to allow us to measure pre-campaign feeling and actual vote, and we have not applied any weighting. In the end, however, we are not interested in levels of vote, but rather in marginal change.

Table 3 Scenarios of vote distribution with variable candidate distance




Party Feeling (pre)




Incumbent Candidate




(Driving) Distance




Deprivation Difference








Scenario 1




Scenario 2




Scenario 3




Scenario 4




Table 3 provides some simulations of scenarios of three-party competition including candidates based at different distances from the ‘average’ voter. The upper half shows the real distribution of the independent variables, i.e. average feeling for the three parties in the sample, the proportion of respondents for whom the respective candidate/party is the incumbent, average deprivation differential, and the average geographical distance (in km). By and large, candidates are local on average (19-27 km away), and Labour is by far the least popular party. Below the line, the ‘Real’ row shows the expected probabilities of a Conservative/Liberal Democrat/Labour vote, conditional on the distribution of the independent variables.

Scenario 1 assumes that on average, all candidates are equidistant (in this case, local: 26 km away). The impact here is negligible (basically, a minuscule exchange from Labour to Liberal Democrat), which makes sense because on average, candidates are local. Scenarios 2-4 are more interesting. These keep two candidates local (still at 26 km from the voter) while parachuting in the third candidate from 120km away. Such a strategy would cost the Tories 16 percentage points, while the Liberal Democrats (coming from a lower level) would lose only 10. Labour would lose nine. Generally, parties which are not doing well anyway will suffer a little less, while parties that come close to a majority are more affected by marginal changes.11

Overall, then, to answer the question posed by Pedersen et al (2007), “Which candidate will – or should – the local leadership prefer – the local resident/native son or the candidate from outside, the parachutist?”, the evidence indicates that parachuting in outsiders is risky, unless the constituency is very safe.


We set out to test whether there is evidence that distance between candidates and voters in UK elections influences vote-likelihood. As a first-principles test of geographical distance, there clearly remain a large number of refinements to be made to the model. However, the findings thus far are clear and appealing. Candidate distance does matter, with voters finding distant candidates less appealing than local ones, even when pre-campaign party feeling and personal incumbency effects are controlled for. This confirms the findings of the Gorecki and Marsh test (2012) but overturns others’ notion that this is an Irish finding that would not replicate in a UK setting. Admittedly, the effect is relatively small. In a safe constituency, residency is not game-changing. In a marginal constituency, however, the small distance effect could prove more decisive. Given we have a number of reasons to believe our model is conservative, this also represents the minimum effect of geographical distance.

Certainly, as our simulations show, local is better. Of course, local is not always possible. Moreover, candidates cannot live close to all voters, particularly in single-member constituencies, unlike multi-member counterparts where candidates can be located strategically. In that respect, our findings do not represent any transforming ‘How To’ for political parties. What they do indicate, however, is that the thus-far largely speculative evidence for the importance of localism bears out in a relatively stringent empirical test of an important aspect to this localism. Voters do have a sense of who is where, and this influences their vote accordingly.

As with research into the personal vote, there is a temptation academically to overlook the small effect of distance. However, as Cain et al noted re the personal vote, “[W]hat is of importance to tenured professors seeking to explain variance, and what is of importance to elected officials seeking to win re-election may not correspond very closely.” (1984: 122). Our model shows that geographical distance does matter to voters. Whilst parties cannot use this information to win constituencies which are otherwise beyond their grasp, ignoring this information in candidate selection, for example, is a certain means of putting a constituency further beyond their grasp. That parties have understood this since the 1960s is clear from Norton and Wood’s work: “The position changed significantly in the 1960s. New Members were increasingly expected by local parties to live in their constituencies […] some would-be candidates failed to secure adoption because they disclaimed knowledge of the locality.” (1990: 197-8). In that sense, our contribution is important because it is the first robust test of the distance hypothesis that does not rely upon voter or MP perceptions, and includes all mainstream candidates, successful or otherwise.

Our findings also suggest a paradox in political elite behaviour. On the one hand, the locating of candidates in constituencies, and the importance accorded to knowledge of localness by parties in the ‘long campaign’, demonstrate that party leaderships are aware that such considerations matter to voters. Even if such awareness only results in lip service being paid to candidate presence in the constituency, this still indicates a conviction that not to play the local card may jeopardise their electoral fortunes. Yet, simultaneously, MPs prior to the 2010 election precisely voted to remove the obligatory indication of addresses on the ballot paper. Constituency of residence is still given, providing a level of geographical information, but nevertheless this represents a step in the wrong direction, away from a cognitive link between candidate and voter through awareness of the former’s location within the neighbourhood.

The next step in refining the model is to refine the definition of ‘local’. As the research by Childs, Campbell, Cowley and others has shown, voters do gravitate to someone local, but this is not merely tapped by someone’s residence. Place of birth, regional identity and other dimension of localness all matter. Some of these are potentially, if arduously, quantifiable, and may indeed matter more than geographical distance. Distance also needs refining. Other socio-economic differences, beyond those tapped by a deprivation index, may colour voter perceptions of candidates. If addresses do register with voters when they look at the ballot paper, individual streets within wards may matter just as much. Again, such nuances are quantifiable, and indeed work on social delineations and economic geography are common in sociology and human geography, if less developed to date in political science. There is a good deal further work to be done to refine a distance test to check that it belongs in a ‘full model’ of voting. However, that work appears to be worth the candle in the UK cases on the basis of the first cut of the data.


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Thanks to Will Jackson for his work on collecting the notice of poll data, and Eva Frischmann, David Hammann and Paul Ruenz for their work on collecting incumbency data. Thanks also to Philip Cowley, David Denver, Ron Johnston, Charles Pattie, Michael Thrasher and Jonathan Tonge who commented on previous versions of the paper. We also acknowledge the support of the Faculty of Arts, Media and Social Sciences of the University of Salford which provided the pilot-funding to carry out the data collection.


1 This also addresses the issue of candidates who rent properties close to or in the constituency for the duration of the election, and list this as their residence. Assuming such practices occur close to the constituency – candidates would be unlikely to rent at a distance – this will inevitably bias our model, but by rendering it more conservative. If anything, we will underestimate the importance of geographical distance on this basis. Ideally we would be able to control for ‘true’ residence, but the available data do not allow this.

2 Consultation on the publication of candidates’ addresses at UK Parliamentary elections was held at the end of 2008 (Consultation Paper CP(L) 30/08) and a clause added to the Political Parties and Elections Bill in March 2009 allowing candidates to withhold their full address on their nomination paper, and instead identify their residential constituency (SN/PC/05004). A new and confidential ‘home address form’ now accompanies the nomination paper.

3Moran’s I was calculated for contiguity neighbours, with weights scaled so that they sum up to unity for each constituency. The Isle of Wight (which is a Westminster constituency) was excluded, because it has no neighbours. The difference between the Moran statistic and its expected value (-0.002) is statistically significant, but of little substantive interest.

4 As discussed in the theoretical section, there are potentially a number of ways of calculating the distance between two points, the three most common being straight-line distance, route distance and time travelled. We calculated all three for each distance. However, given there was a very high correlation between all three (Pearson’s r > 0.90) we use distance by car, as we believe that this comes closest to the psychological rationale that voters might employ when – if – thinking spatially.


5 Those opting to cast their ballot by post do potentially have much longer to consider ballot-paper information, and indeed to trawl for more candidate information, than a voter going to the polling station.

6 The index gives more weight to Income and Employment Deprivation than to other domains (McLennan et al., 2011, 18).

7This alternative-wise deletion does not necessarily imply that the whole case is lost: If the voter reported a vote for one of the remaining candidates and if information on these alternatives is complete, this choice still contributes to the likelihood function.

8 The n is higher than the number of respondents, because in the alternative-specific perspective, every choice for or against a given candidate is an observation, while the calculation of standard errors reflects the “nesting” of choices within persons. We further correct the standard errors upwards to account for the nesting of voters within constituencies with the same set of candidates. This is roughly equivalent to specifying an even more complex conditional logit multi-level model.

9 While the median distance for incumbents and non-incumbents are almost identical, the proportion of candidates who live far (more than 45 km) away from their (prospective) constituents is roughly three times higher for non-incumbents.

10To protect the privacy of citizens living in sparsely populated areas, deprivation indices are not published for a small number of LSOAs. Therefore, the number of observations is slightly smaller for model 4. This reduction of sample size is less pronounced in table 2, because very large wards contain more of these problematic LSOAs.

11This differential impact is a consequence of the models inherent non-linear structure. Because probabilities are restricted to the unit interval, the negative impact of distance cannot grow without bounds.