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Short-term electoral change in England, 1974
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MM-7185/81/030237-X 0,981 Pergaman
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Short-term
SOXWO Press Ltd.
Electoral Change in England, 1974 R. J. JOHNSTON*,
Sheffield, U.K.
Abstract: The study of short-term electoral change at a fine spatial scale is frequently hampered by the unavailability of suitable ecological data, let alone data on individuals. In Britain, most studies of change have used the measure of swing. An altemative is presented here, using national survey data to predict short-term electoral change in each English constituency. The deviations between the observed and predicted values are the dependent variables in an analysis of the ecological determinants of short-tenn change. Three independent variables are suggested: the neighbourhood effect; campaign spending; and constituency type (reflecting the spatial division of labour). All three contribute substantially in regression analyses, although some of the coefficients relating to campaign spending are difficult to interpret.
Ci = [Ci/(Ci + Lj)] * lo0
Introduction
and
Between any two elections, there is usually spatial variability in the changing pattern of support for the various political parties. This is certainly the case in Great Britain: compared to the 1974 and the 1966 genera1 elections, for example, the 1979 election results were widely interpreted as indicative of a spatial polarisation of partisan support - a north/ south split - within the country (see JOHNSTON, 1979a; TAYLOR, 1979). Unfortunately, data with which to investigate the patterns and processes of change are difficult to obtain, especially if the analyses are to be conducted at a fine spatial disaggregation of the national territory: thus MILLER’s (1977) work on electoral dynamics, for example, is based largely on cross-sectional analyses. The present paper essays an investigation into short-term electoral changes, at the ecological scale, using England as the case study.
Swing
(2)
Li = loO_Ci.
(3)
Swing can also be defined for the entire system-e -as s F,r+l = (C, - &)/2.
(4)
BUTLER and STOKES (1969, 1974), in their detailed analyses of electoral change in Britain, have noted that there is usually a close correspondence between S’ and Se: in other words the national swing tends to be replicated in each constituency. (There is some variation of course, but the standard deviation over the 600+ constituencies is usually small: BUTLER and STOKES, 1974, p. 121.) This replication is somewhat paradoxical since, as has been demonstrated elsewhere (JOHNSTON, 198Oa, 1981), because the parties are stronger in some places than in others, the application of the national swing (S) to individual constituencies should produce major differences between Se and s”. (The national swing is the net result of the gross flows of voters between parties during the inter-election period. These flows form a voter transition matrix. If that matrix is applied to individual constituencies, whose vote distributions, in percentage terms, at election t differ from that for the country as a whole, then s‘ # Se. In general, if there is a national swing to L, then s’ < Se where Li is small, and s’ > Se where Li is large. (See JOHNSTON, 1981; JOHNSTON and HAY, 1981.)
as an Index of Change
The most commonly-used measure of change for British elections is the index of swing. Take a contest between two parties (C and L) in constituency i, at elections t and t+l. The swing (S) is defined as
where
*Department of Geography, University of Sheffield. 237
238 Defining
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Subtraction of the actual proportion from that predicted gives an index of electoral change in each constituency which is not accounted for by the national trend. Across the 452 constituencies the mean values for (observed -predicted) were Conservative Labour Liberal Abstain
Rather than use swing as a measure of change, an alternative index was chosen for the present study. This is the deviation of the actual result in each constituency for a given party from that predicted from a national voter transition matrix. Such a matrix is available for the inter-election period studied here (February 1974-October 1974).[1] It is based on a stratified sample of 1578 voters who lived in English constituencies contested by each of the three main parties at both elections - Conservative, Labour, and Liberal. Nationally, its ‘predictions’ of the percentages voting for those parties and the percentages abstaining at both elections are very good.
-0.0194 -0.0054 -0.0401 0.0228
(standard (standard (standard (standard
The Independent
0.1254) 0.1136) 0.2090) 0.1422)
Variables
To account for the inter-constituency variations in deviations from the predicted vote in October 1974, three factors are presented here. The Neighbourhood
Effect
This is the factor favoured in the only major study of electoral change in Britain, by BUTLER and STOKES (1969, 1974): the literature on the neighbourhood effect is very large (TAYLOR and JOHNSTON, 1979). According to general models of neighbourhood (or structural) effects, spatial variations in the distribution of information about the various parties will influence voter preferences,
Table 1. The Voter Transition
Matrix
Category in October
Voted Conservative Labour Liberal Abstained
deviation deviation deviation deviation
As is frequently the case with opinion polls and voter behaviour surveys, abstention was slightly underpredicted and voting for each of the parties slightly over predicted.
The voter transition matrix (Table 1) shows the proportion of voters in each category (voted for one of the parties or abstained) in February 1974 who were in each category in October 1974.[2] It shows that the national net flow to Labour (which won 27.4% of the voters at both elections; its victory resulted from the decline of the Conservative vote - from 32.2% to 29.3% - and its strength in Scotland and Wales, which are not studied here) reflected the greater loyalty of its February voters and its greater attraction to former abstainers.
in 1974
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The matrix in Table 1 was applied to each of the 452 constituencies in England with a candidate from each of the three main parties at both the February and the October elections.[3] Premultiplying a vector containing the proportionate distribution of the voters between the three parties and the abstainers at the February contest by the matrix produces a prediced proportionate distribution of the votes in October, assuming that the national pattern of gross flows applies equally well in each part of the country.
AS outlined above, the concept of swing is widely used in British electoral studies to index change at both the national and the constituency level. However, both the concept itself and the method of measuring it have been subject to considerable criticism. In the contemporary situation, the main disadvantages refer to the effects of a third party and of abstentions. The swing statistic was developed when two parties - Conservative and Labour - predominated; today the Liberal Party wins up to 20% of the poll nationally (possibly more if the link with the newly formed Social Democratic Party develops), while the existence of Plaid Cymru and the Scottish Nationalist Party make for four-way contests, in Wales and Scotland respectively.
Category February
12/Number
Voted Conservative Labour
0.7453 0.0255 0.1244 0.1029
0.0217 0.7701 0.1295 0.1373
1974 Abstained Liberal
0.0652 0.0365 0.5492 0.0735
0.1553 0.1569 0.1917 0.6618
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especially those of voters who are liable to change their minds and whose main sources of information are local. Thus, according to BUTLER and STOKES (and confirmed by their analyses of survey data), a ‘floating voter’ in a strongly Conservative area is more likely either to be drawn to vote for that party (if he or she either voted for another party or abstained at the previous election) or to remain loyal to it (if he or she voted Conservative previously), than would be the case in a strongly Labour area. Regarding the matrix in Table 1, therefore, the proportions in the first column should all be higher in a strongly Conservative area than they are in a strong Labour or Liberal area, so that the observed proportion voting Conservative at the second election should be greater than that predicted. As a result, there should be a positive correlation between the proportion of a constituency’s electorate voting for a party at the first election and the deviation between its observed and its predicted vote at the second election: parties should perform better than predicted where they were initially relatively strong, but worse than predicted where they were initially weak. According to most theories of the operation of the neighbourhood effect, the process works at very local scales. English constituencies contain more than 60,000 voters on average, and are not socially homogeneous. This may suggest that the information about the different parties will produce countervailing pressures and negate the neighbourhood effect hypothesis. However, within most constituencies, because of residential congregation and segregation (JOHNSTON, 1980b), most voters live in districts in which one party dominates and where the neighbourhood effect process is relevant. Thus the results at the constituency level are the sum of the local effects. If, as is generally the case, one party dominates more districts in a constituency than do the others, the sum of the neighbourhood effects will favour that party. Campaign
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Expenditure
During the campaign period, each party in a British constituency attempts to identify its potential supporters and, on election day, to ensure that they vote. Meanwhile the national campaign, backed by local efforts, seeks to convert those not firmly committed to a particular partisan preference. Much of the local campaigning is undertaken by voluntary although the constituency party may workers, employ a full-time or part-time agent. During the legally defined campaign period (usually three weeks), constituency parties are restricted in the amount that they can spend. The maximum sum is a function of the type of constituency (borough or county-roughly equivalent to urban and rural) and
its population, and the parties are required to make a return of their expenditure. At the October 1974 general election in the 452 constituencies studied here, the mean proportion of the allowed maximum expenditure by each party was: Conservative Labour Liberal
0.8331 0.7143 0.4907
(Standard devitation 0.1832) (Standard deviation 0.2020) (Standard deviation 0.2562)
For all parties, the majority of the declared spending (ca 80%) was on printing and other advertising expenses. Studies in the United States and Canada have shown close correlations between votes won and campaign expenditure (WELCH, 1976; PALDA, 1973); these suggest that the more a party spends, ceterisparibus, the greater the proportion of the votes that it wins, and that the more that its opponents spend, ceteris paribus, the smaller the proportion of the votes that it wins. Similar correlations have been identified in Great Britain (JOHNSTON, 1977, 1979b, 1979~). All of these analyses are based on cross-sectional correlations, however, from which secular processes are deduced. But the English analyses have shown that the proportion of its allowed maximum that a party spends at election t-t1 is correlated with the election result at t. A spurious process correlation may be inferred, therefore, and analyses of changes in spending as causal influences on changes in votes suggest much weaker correlations. Nevertheless, if advertising and publicity are in any way efficacious, then the more that a party spends, ceterisparibus, the more votes it should win.[4] Constituency
Type
The dominant issues at most British general elections in recent decades have related to the economy. At any one time, the various sectors will be performing differentially, with consequent differential effects on various groups within the population. Since the members of those groups tend to be clustered in distinct types of constituencies, then one could expect inter-type variations in the deviations from the predicted. (In a constituency type experiencing an economic boom, for example, the incumbent government party may lose relatively few votes and perform better than it does nationally, whereas in one experiencing a slump, it may lose large numbers of votes and perform less well than predicted by the national voter transition matrix.)
Testing the Model
The previous
sections have outlined a model which
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states, in verbal form, that variations from a party’s predicted performance, by constituency, should be positively related to that party’s proportion of the vote at the previous election (its prior performance), positively related to its campaign expenditure (as a proportion of the maximum allowed), and negatively related to its opponents’ campaign expenditure (as a proportion of that allowed). In addition, there should be significant differences between constituency types in the deviations from the predicted values.
Equation Fit The general fit of the equations at each step was assessed by the multiple correlation coefficient, R’.[5] The results are given in Table 2. For all three parties, each of the components of the model - represented by a separate step - makes a substantial contribution to accounting for spatial variations in the deviations from the predicted inter-constituency pattern of votes. In each case, prior performance accounts for just over half of the variation, indicative of a strong neighbourhood effect. For the two major parties Conservative and Labour - spending accounts for a further 14% of the variation, and constituency type for 9-11%. For the smaller, Liberal party which spent much less (see above), spending was a less important factor, but constituency type accounted for 15% of the variations.
The model was fitted, separately for each party and for abstentions, using regression procedures. These were straightforward for the dependent variable and for the independents referring to prior electoral performance and to expenditure. For constituency type, a series of 2.5 dummy variables was used, with the twenty-sixth type (Inner London, multi-occupied housing areas) being represented in the equation constant.
Regression Equations Correlation coefficients give only an overall impression of a model’s validity. Full evaluation rests on the detail of the related regression equations, especially the size, sign, and statistical significance of the partial regression coefficients.
A stepwise procedure was used. At the first step, the prior electoral performance variable was included as the sole independent variable, as earlier work suggests that this should be the most important. At the second step, the three spending variables were introduced, to see if they accounted for any further variation in the dependent variable. (This ordering was justified by the earlier findings that expenditure at election t+l is closely related to expenditure at
Two sets of regression equations are discussed here. The first refers to step 2 only (Table 3). All twelve of the coefficients shown have statistically significant F-values at the 0.05 level, indicating that every variable makes a substantial contribution to the ‘explanation’ of variability in the dependent variables.[6] For prior performance, the F-values are extremely large. The size of the regression coeffi-
Table 2. Equation fit
1. Prior Performance 2. Spending 3. Constituency Type
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election f. The proportion of the vote won by a party at t+ 1 is closely related to the proportion won at t, so the spending variables were introduced to see if campaign/advertising accounted for a substantial proportion of the variation once the ‘normal vote’ had been accounted for.) Finally at step 3 the 25 dummy variables representing constituency type were introduced. With the analysis of abstentions, the first two steps were omitted since (1) there is little theoretical justification for suggesting that abstention is contagious and (2) the level of party spending should influence partisan choice but not the decision to abstain.
In operational terms, the dependent variables are defined as the deviations between the observed and predicted votes, for each party and for abstainers, at the October 1974 general election in the 452 English constituencies defined above. The independent variable relating to prior electoral performance is, for each party, the proportion of the vote obtained at the February 1974 general election, and for spending the variables are the proportion of the allowed maximum spent by each party at the October 1974 election. For constituency type, a classification of the 635 British constituencies, using 40 variables taken from the 1971 Census, was employed (WEBBER, 1978). Thirty types were defined: four of these are irrelevant to a study of England as they contain only Scottish and/or Welsh constituencies: the other 26 are listed in Appendix 1.
Step
12/Number
: R”
Dependent Variable Conservative Labour Liberal Abstentions 0.55 0.69 0.80
0.51 0.65 0.74
0.51 0.56 0.71
0.59
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241 Table 3. Regression equations
Independent Variable
Conservative
Prior performance Spending by: Conservative Labour Liberal Constant
: Step 2
Dependent Variable* Labour
0.87
(273.3)
0.46
(118.5)
1.33
(86.0)
0.21 0.13 -0.03 -0.62
(59.9) (39.5) (3.6)
0.10 0.17 -0.09 -0.33
(14.9) (68.9) (32.3)
0.16 -0.14 0.19 -0.48
(14.5) (14.6) (24.5)
*The entries are the unstandardised partial regression figures in parentheses are the relevant F-values.
cients varies considerably, however: that for Conservative suggests that the neighbourhood effect for that party is almost twice as great as that for Labour, whereas that for Liberal is nearly three times that for Labour. Apparently, therefore, there are inter-party differentials in the role of local information in both retaining the loyalty of floating voters, who previously voted for the party, and in winning over those who either abstained or supported one of the other parties at the previous contest.
the
The last results are both unexpected and difficult to interpret. They suggest - but no more - that the total volume of spending in a constituency by the two main parties is a significant influence on voting, but that for each party the importance of spending by the one under consideration is most substantial. (In Table 3, the regression coefficient for Conservative spending in the Conservative column is much larger - and more significant-than that for Labour, with the reverse situation in the Labour column.) To try and improve the interpretation, two new independent variables were defined to replace Conservative and Labour spending. They were:
Table 4. Revised regression equations
Prior performance Spending by: Conservative + Labour Conservative/Labour Liberal Constant R’
coefficients;
coefficients for Conservative in the Labour and Liberal equations, have unexpected positive signs. These suggest, for example, that the greater the Conservative expenditure in a constituency, the greater the flow of ‘floating voters’ to both Labour and Liberal.[7]
Turning to the spending variables, although all of the regression coefficients are significant, some have unexpected signs. For each party, the regression coefficient relating to its own spending is the largest of the three, is positive (as expected), and has the largest F-value. Thus for Conservative, for example, every increase of one percentage point in its allowed expenditure, ceterisparibus, produces an increase of 2.1 percentage points in the deviation between actual and predicted percentage of the poll
Independent Variable
Liberal
Conservative
: Step 2
Dependent Variable* Labour
Liberal
0.87
(297.3)
0.46
(131.7)
1.33
(87.1)
0.17 0.03 -0.03 -0.65 0.69
(179.9) (5.4) (3.0)
0.13 -0.10 -0.02 -0.31 0.65
(120.5) (33.6) (3.4)
0.03 0.07 0.20 -0.59 0.56
(1.4) (24.3) (28.2)
*The entries are the unstandardised partial regression parentheses are the relevant F- values. in October, 1974. In the equations for both Conservative and Labour, the regression coefficients for Liberal are negative, as expected: the greater the Liberal campaign expenditure, ceteris paribus, the smaller the number of ‘floating voters’ who vote for either Conservative or Labour. But the coefficient for Labour in the Conservative equation, and the
coefficients;
the figures in
Total Spending by the Main Parties (Labour and Conservative) (as percentages of the allowed total) The ratio of Conservative to Labour Spending The Liberal spending variable was not changed, because it had the correct sign in each equation and was, in aggregate, much smaller. The results of this second round of analysis are given in Table 4. For
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both Conservative and Labour, the regression The full regression equations after step 3 are given in coefficients are as suggested in the previous Table 5. For the first four independent variables, the paragraph. The greater the volume of spending, the only substantial differences from the previous analygreater the deviation between observed and predicses refer to the partial regression coefficients for ted. (With the implication that the smaller the prior performance; the three coefficients are much spending, the greater the number of abstentions. more alike in their magnitude than is the case in This is borne out by a negative regression of deviaTable 3, indicative no doubt of some collinearity tions between observed and expected abstentions: between constituency type and prior perfo~ance. the greater the spending, the greater the number of voters.). And the greater the ratio of Conservative to Turning to the coefficients for constituency type, the Labour spending, the greater the deviation between immediately apparent feature is that virtually all of observed and predicted votes for Conservative, with them in the first three columns are positive (and the opposite relationship for Labour votes. With statistically significant), whereas nearly every one in regard to the two main parties, therefore, the greater the last column is negative (and statistically signifithe volume of spending, the greater the turnout for cant). What this implies is that in virtually all conone of the two: the greater the ratio in spending stituency types, the difference between the observed proportions between those parties, the more and the predicted proportion of the October 1974 favoured is the party with the highest spending. For poll was greater than it was in the baseline type 26, the Liberals, the volume of spending by the two main which was not included in the equation (the Inner parties combined is statistically insignificant as an London areas of multi-occupied housing), and that influence on the deviations between observed and the difference invariably represented more votes predicted: apparently, however, Conservative than predicted. Countering this, the proportionate spending is more conducive to a Liberal vote than is number of abstentions was substantially smaller in Labour spending. Table 5. Regression equations : Step 3 Independent Variable
Conservative
Constant Prior performance Spenkng by: Conservative f Labour Conse~ative/Labour Liberal Constituency type 2 3 4 6 ?
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
-0.61 0.78
(189.3)
0.15 (139.9) 0.02 (4.4) -0.02 (2.4) 0.06 0.10
0.11 0.10 0.11 0.11 0.07 0.12 0.14
(6.8) (23.9) (28.9) (22.8) (23.7) (30.3) (11.1)
Dependent Variable* Liberal Labour -0.46 0.58 (119.1)
-0.48 0.78
(32.7)
0.11 0.01 -0.06
-0.03 0.05 0.25
(2.3) (11.2) (58.2)
(11.5) (23.6) (23.1) (28.1) (3.5) (17.8) (21 .O) (30.7) (45.1) (22.9) (29.3) (24.8) (17.8) (8.9)
0.14 0.17 0.19 0.27 0.28 0.28 0.20 0.20 0.29 0.30 0.27 0.27 0.31 0.21
(8.6) (17.5) (23.1) (44.1) (33.8) (47.7) (22.1) (23.0) (52.9) (59.7) (33.9) (37.7) (38.2) (14.3)
(24.3) (29.7) (14.3) (10.5) (00;;
0.23 0.21 0.16 0.19 -0.20 0.00 0.07 -0.07
(28.7) (30.0) (7.5) (10.7) (2.7) (0.0) (2.8) (1.0)
0.08 0.10
0.10 0.15 0.12 0.16 0.14
;::;; (27.9) (41.9) {30.2j (39.2) (24.0)
0.10 0.12 0.05 0.09 0.10 0.12 0.14 0.10 0.13 0.11 0.11 0.09
0.11 0.10 0.11 0.09 -0.03 -0.03 0.01 0.00
(30.2) (27.8) (14.3) (9.4) (0.3) (0.8) (0.2) (0.0)
0.11 0.11 0.12 0.10 0.01 0.00 0.05 0.01
*The entries are the unstandardised the retevant F-values.
(83.9) (4.7) (15.9)
(4.6) (0.1)
Abstentions 0.25
-0.14 -0.21 -0.22 -0.30 -0.32 -0.32 -0.22 -0.27 -0.37 -0.30 -0.36 -0.29 -0.27 -0.19 -0.15 -0.22 -0.23 -0.26 -0.22 -0.16 -0.17 ;:: -0.04 0.01
(13.2) (41.5) (53.5) (92.0) (78.7) (iO7.2j
(45.9) (67.1) (132.9) (96.7) (95.9) (78.4) (45.7) (17.7) (8.2) (27.7) (40.3) (60.4) (50.8) (12.6) (13.2) ;::; 6.2) (0.0)
partial regression coefficients; the figures in parentheses are
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most constituency types than it was in type 26. The only types to display no substantial differences are 22, 23, 24 and 25. All four also are inner city areas, indicating a common pattern with Inner London. Two interpretations are possible for this. The first is that the inner city areas house the largest proportions of alienated voters, who prefer to abstain. The second is that during the six months between the two elections, the population loss in these areas was substantial; this is shown by a large number of abstentions who have actually moved from the constituencies. Both undoubtedly have some validity: the latter would be more credible, however, were the interelection period longer. Regarding the other twenty-one constituency types, inspection of the first three columns of the table suggests a similar pattern of coefficients. In fact, over the 25 types, the rank correlations between the size of the coefficients are: Conservative-Labour, 0.67, Conservative-Liberal, 0.67; Labour-Liberal, 0.59. If the last four types are excluded because of their insignificant F-values, however, the rank correlations for the other 21 are: Conservative-Labour, 0.45; Conservative-Liberal, 0.45; Labour-Liberal, 0.32. Clearly there are substantial differences between the three columns. The Conservative Party performed much better than expected (regression coefficients exceeding 0.12) in types 13,11,9 and 14, two of which, surprisingly, are coal mining areas. (This suggests an abnormally large vote for Labour in those areas in the February 1974 general election which was held after the national miner’s strike: BUTLER
and
KAVANAGH,
1975.
Presumably,
survey data) of the national gross flow of voters between parties in the intervening period. Such a prediction assumes that the national movement in partisan preferences applies equally in each constituency; the deviation between the prediction and the observed vote indicates the local validity of that assumption. The deviations between observed and predicted for each party provided a dependent variable whose pattern across the entire set of constituencies was to be accounted for. Three independent variables were put forward: the neighbourhood effect; party campaign spending; and constituency type. The importance of the first of these was made clear by the analyses: all three parties showed considerable correlation between prior performance and the deviation (observed-predicted), indicating that party strength is most readily maintained in areas where voter support is already considerable. The constituency types also produced significant findings. Interpretation of some of the regression coefficients was slightly difficult, indicating the need to set the study of change between any one pair of elections in a longer-term perspective. Finally, the findings regarding campaign spending were slightly less clear-cut. The more that the two main parties spent, the more votes each got, relative to the predictions based on the national trends of support; in addition, more spending by one party relative to the other led to it receiving even more votes than predicted. For the smaller, Liberal party, the more it spent, the more votes it got.
in
October 1974 there was some reaction to this earlier major swing to Labour in the mining areas, which accounts for the relatively low regression coefficients for Labour for types 13 and 14[8]) The Labour Party performed much better than expected (regression coefficients exceeding 0.11) in types 9, 11,4,20 and 8; only type 4 (very high socio-economic status areas) is surprising. Finally, the Liberal Party performed much better than expected (regression coefficients exceeding 0.28) in types 13, 10,9 and 17, all of which contain mainly industrial constituencies.
Overall, the results of these analyses have been encouraging. The fit of the models has been substantial and many of the regression coefficients can be sensibly interpreted. Questions still remain to be answered, of course, but a positive contribution has been made to the ecological understanding of short-term electoral change in England.
Conclusions
Notes
This paper has presented a method for identifying spatial variations in short-term electoral change in England (methods that could easily be used elsewhere) and which avoid the relatively crude measure of swing frequently employed in such work. The method allows a prediction to be made of the vote in each constituency at the second of a pair of contests, given information on the distribution of the vote at the first contest and an estimate (obtained from
1. This voter transition matrix was made available to me by Ivor Crewe, British Election Study, University of Essex. I am grateful to Mr. Crewe for his assistance. 2. Two small categories have been omitted: voters who died between the two elections and individuals who become eligible to vote during that period. Both are relatively very small in number for such a short period. They were omitted because of the absence of any data at the constituency level, on deaths. 3. Five constituencies were omitted because of the strong showing of an independent candidate in at least one of
Acknowledgement - A grant from the University Sheffield Research Fund towards the cost of purchasing census data tapes for the parliamentary constituencies gratefully acknowledged.
of the is
244 the elections. 4. A critique of my analysis of the efficacy of advertising has been published by Gordon and Whiteley (1980): a rebuttal is forthcoming. 5. Given the large number of observations relative to no correction factor was independent variables, applied to the R2 values. 6. Although the data refer to a population and not a sample, statistical significance tests can be used to indicate the probability of a chance ordering of the data (or classification of the observations with the dummy variables) producing a regression coefficient of the observed magnitude. 7. There may be a problem of collinearity with the three spending variables. However, the inter-correlations were only: Conservative-Labour, 0.19; ConservativeLiberal, 0.44; Labour-Liberal, -0.13. Attempts to remove the common elements produced no alteration in the findings. 8. Again, there is some collinearity involved. The mining the largest constituencies consistently produce majorities for the Labour Party.
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JOHNSTON, R. J. (1981) Regional variations in British voting trends 1966-1979: tests of an ecological model. Regional Studies 15,2>32. JOHNSTON, R. J. and HAY, A. M. (1981) ‘on the parameters of uniform swing. Envir. Plann. A 13. MILLER, W. L. (1977) Electoral Dynamics. Macmillan, London. PALDA, K. S. (1973) Does advertising influence votes? An analysis of the 1966 and 1970 Quebec elections. Can. J. Political Science 6,638-655. TAYLOR, P. J. (1979) The changing geography of representation in Britain. Area 11,289-294. TAYLOR, P. J. and JOHNSTON, R. J. (1979) Geography of Elections. Penguin Books, Harmondsworth. WEBBER, R. (1978) Parliamentary Constituencies: A Socio-Economic Classification. Occasional Paper 13, Office of Population Censuses and Surveys. London: O.P.C.S. WELCH, W. P. (1976) “The effectiveness of expenditures in State legislative races.“Am. Politics Q. 4,333-3.56.
Appendix
Constituency Types References BUTLER, D. and KAVANAGH, D. (1975) The B&i.& General Election of February 1974. Macmillan, London. BUTLER, D. and STOKES, D. (1969) Political Change in Britain. (first edition). Macmillan, London. BUTLER, D. and STOKES, D. (1974) Political Change in Britain (second edition). Macmillan, London. GORDON, I. and WHITBLEY, P. (1980) Comment: Johnston on campaign expenditure and the efficacy of advertising. Political Studies 28,293-294. JOHNSTON, R. J. (1977) The electoral geography of an electoral campaign. Scott. geogr. Mag. 93,98-108. JOHNSTON, R. J. (1979a) Regional variations in the 1979 general election results for England. Area 11,294-298. JOHNSTON, R. J. (1979b) Campaign expenditure and the efficacy of advertising at the 1974 general elections in England. Political Studies 27, 114-l 19. JOHNSTON, R. J. (1979~) Campaign spending and votes: a reconsideration. Public Choice 33.97-106. JOHNSTON, R. J. (1980a) Testing the Butler-Stokes model of a polarization effect around the natural swing in partisan preferences: England 1979. Br. J. Political Science 11,113-117. JOHNSTON, R. J. (1980b) City and Sociev. Penguin Books, Harmondsworth.
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26.
Centres of learning Service towns Outer London suburbs Very high socio-economic status areas Areas very dependent on agriculture Agricultural areas Resort and retirement areas Areas of intermediate status and modern housing Small industrial towns of Northwest and East Midlands South Midlands growth areas West Midlands growth areas Area of rapid growth Mining areas in conurbations Mining areas The Black Country Steel and chemical towns Poorer urban centres Metropolitan inter-war suburbs Textile areas Maritime industrial areas Peripheral council housing estates in conurbations Inner Liverpool Low socio-economic status inner London Provincial inner city areas High socio-economic status inner London Inner London areas of multi-occupied housing.
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an Great
12, No. 3. pp. 2.37-244.19Rl.
MM-7185/81/030237-X 0,981 Pergaman
Bnmn.
Short-term
SOXWO Press Ltd.
Electoral Change in England, 1974 R. J. JOHNSTON*,
Sheffield, U.K.
Abstract: The study of short-term electoral change at a fine spatial scale is frequently hampered by the unavailability of suitable ecological data, let alone data on individuals. In Britain, most studies of change have used the measure of swing. An altemative is presented here, using national survey data to predict short-term electoral change in each English constituency. The deviations between the observed and predicted values are the dependent variables in an analysis of the ecological determinants of short-tenn change. Three independent variables are suggested: the neighbourhood effect; campaign spending; and constituency type (reflecting the spatial division of labour). All three contribute substantially in regression analyses, although some of the coefficients relating to campaign spending are difficult to interpret.
Ci = [Ci/(Ci + Lj)] * lo0
Introduction
and
Between any two elections, there is usually spatial variability in the changing pattern of support for the various political parties. This is certainly the case in Great Britain: compared to the 1974 and the 1966 genera1 elections, for example, the 1979 election results were widely interpreted as indicative of a spatial polarisation of partisan support - a north/ south split - within the country (see JOHNSTON, 1979a; TAYLOR, 1979). Unfortunately, data with which to investigate the patterns and processes of change are difficult to obtain, especially if the analyses are to be conducted at a fine spatial disaggregation of the national territory: thus MILLER’s (1977) work on electoral dynamics, for example, is based largely on cross-sectional analyses. The present paper essays an investigation into short-term electoral changes, at the ecological scale, using England as the case study.
Swing
(2)
Li = loO_Ci.
(3)
Swing can also be defined for the entire system-e -as s F,r+l = (C, - &)/2.
(4)
BUTLER and STOKES (1969, 1974), in their detailed analyses of electoral change in Britain, have noted that there is usually a close correspondence between S’ and Se: in other words the national swing tends to be replicated in each constituency. (There is some variation of course, but the standard deviation over the 600+ constituencies is usually small: BUTLER and STOKES, 1974, p. 121.) This replication is somewhat paradoxical since, as has been demonstrated elsewhere (JOHNSTON, 198Oa, 1981), because the parties are stronger in some places than in others, the application of the national swing (S) to individual constituencies should produce major differences between Se and s”. (The national swing is the net result of the gross flows of voters between parties during the inter-election period. These flows form a voter transition matrix. If that matrix is applied to individual constituencies, whose vote distributions, in percentage terms, at election t differ from that for the country as a whole, then s‘ # Se. In general, if there is a national swing to L, then s’ < Se where Li is small, and s’ > Se where Li is large. (See JOHNSTON, 1981; JOHNSTON and HAY, 1981.)
as an Index of Change
The most commonly-used measure of change for British elections is the index of swing. Take a contest between two parties (C and L) in constituency i, at elections t and t+l. The swing (S) is defined as
where
*Department of Geography, University of Sheffield. 237
238 Defining
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Subtraction of the actual proportion from that predicted gives an index of electoral change in each constituency which is not accounted for by the national trend. Across the 452 constituencies the mean values for (observed -predicted) were Conservative Labour Liberal Abstain
Rather than use swing as a measure of change, an alternative index was chosen for the present study. This is the deviation of the actual result in each constituency for a given party from that predicted from a national voter transition matrix. Such a matrix is available for the inter-election period studied here (February 1974-October 1974).[1] It is based on a stratified sample of 1578 voters who lived in English constituencies contested by each of the three main parties at both elections - Conservative, Labour, and Liberal. Nationally, its ‘predictions’ of the percentages voting for those parties and the percentages abstaining at both elections are very good.
-0.0194 -0.0054 -0.0401 0.0228
(standard (standard (standard (standard
The Independent
0.1254) 0.1136) 0.2090) 0.1422)
Variables
To account for the inter-constituency variations in deviations from the predicted vote in October 1974, three factors are presented here. The Neighbourhood
Effect
This is the factor favoured in the only major study of electoral change in Britain, by BUTLER and STOKES (1969, 1974): the literature on the neighbourhood effect is very large (TAYLOR and JOHNSTON, 1979). According to general models of neighbourhood (or structural) effects, spatial variations in the distribution of information about the various parties will influence voter preferences,
Table 1. The Voter Transition
Matrix
Category in October
Voted Conservative Labour Liberal Abstained
deviation deviation deviation deviation
As is frequently the case with opinion polls and voter behaviour surveys, abstention was slightly underpredicted and voting for each of the parties slightly over predicted.
The voter transition matrix (Table 1) shows the proportion of voters in each category (voted for one of the parties or abstained) in February 1974 who were in each category in October 1974.[2] It shows that the national net flow to Labour (which won 27.4% of the voters at both elections; its victory resulted from the decline of the Conservative vote - from 32.2% to 29.3% - and its strength in Scotland and Wales, which are not studied here) reflected the greater loyalty of its February voters and its greater attraction to former abstainers.
in 1974
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The matrix in Table 1 was applied to each of the 452 constituencies in England with a candidate from each of the three main parties at both the February and the October elections.[3] Premultiplying a vector containing the proportionate distribution of the voters between the three parties and the abstainers at the February contest by the matrix produces a prediced proportionate distribution of the votes in October, assuming that the national pattern of gross flows applies equally well in each part of the country.
AS outlined above, the concept of swing is widely used in British electoral studies to index change at both the national and the constituency level. However, both the concept itself and the method of measuring it have been subject to considerable criticism. In the contemporary situation, the main disadvantages refer to the effects of a third party and of abstentions. The swing statistic was developed when two parties - Conservative and Labour - predominated; today the Liberal Party wins up to 20% of the poll nationally (possibly more if the link with the newly formed Social Democratic Party develops), while the existence of Plaid Cymru and the Scottish Nationalist Party make for four-way contests, in Wales and Scotland respectively.
Category February
12/Number
Voted Conservative Labour
0.7453 0.0255 0.1244 0.1029
0.0217 0.7701 0.1295 0.1373
1974 Abstained Liberal
0.0652 0.0365 0.5492 0.0735
0.1553 0.1569 0.1917 0.6618
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especially those of voters who are liable to change their minds and whose main sources of information are local. Thus, according to BUTLER and STOKES (and confirmed by their analyses of survey data), a ‘floating voter’ in a strongly Conservative area is more likely either to be drawn to vote for that party (if he or she either voted for another party or abstained at the previous election) or to remain loyal to it (if he or she voted Conservative previously), than would be the case in a strongly Labour area. Regarding the matrix in Table 1, therefore, the proportions in the first column should all be higher in a strongly Conservative area than they are in a strong Labour or Liberal area, so that the observed proportion voting Conservative at the second election should be greater than that predicted. As a result, there should be a positive correlation between the proportion of a constituency’s electorate voting for a party at the first election and the deviation between its observed and its predicted vote at the second election: parties should perform better than predicted where they were initially relatively strong, but worse than predicted where they were initially weak. According to most theories of the operation of the neighbourhood effect, the process works at very local scales. English constituencies contain more than 60,000 voters on average, and are not socially homogeneous. This may suggest that the information about the different parties will produce countervailing pressures and negate the neighbourhood effect hypothesis. However, within most constituencies, because of residential congregation and segregation (JOHNSTON, 1980b), most voters live in districts in which one party dominates and where the neighbourhood effect process is relevant. Thus the results at the constituency level are the sum of the local effects. If, as is generally the case, one party dominates more districts in a constituency than do the others, the sum of the neighbourhood effects will favour that party. Campaign
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Expenditure
During the campaign period, each party in a British constituency attempts to identify its potential supporters and, on election day, to ensure that they vote. Meanwhile the national campaign, backed by local efforts, seeks to convert those not firmly committed to a particular partisan preference. Much of the local campaigning is undertaken by voluntary although the constituency party may workers, employ a full-time or part-time agent. During the legally defined campaign period (usually three weeks), constituency parties are restricted in the amount that they can spend. The maximum sum is a function of the type of constituency (borough or county-roughly equivalent to urban and rural) and
its population, and the parties are required to make a return of their expenditure. At the October 1974 general election in the 452 constituencies studied here, the mean proportion of the allowed maximum expenditure by each party was: Conservative Labour Liberal
0.8331 0.7143 0.4907
(Standard devitation 0.1832) (Standard deviation 0.2020) (Standard deviation 0.2562)
For all parties, the majority of the declared spending (ca 80%) was on printing and other advertising expenses. Studies in the United States and Canada have shown close correlations between votes won and campaign expenditure (WELCH, 1976; PALDA, 1973); these suggest that the more a party spends, ceterisparibus, the greater the proportion of the votes that it wins, and that the more that its opponents spend, ceteris paribus, the smaller the proportion of the votes that it wins. Similar correlations have been identified in Great Britain (JOHNSTON, 1977, 1979b, 1979~). All of these analyses are based on cross-sectional correlations, however, from which secular processes are deduced. But the English analyses have shown that the proportion of its allowed maximum that a party spends at election t-t1 is correlated with the election result at t. A spurious process correlation may be inferred, therefore, and analyses of changes in spending as causal influences on changes in votes suggest much weaker correlations. Nevertheless, if advertising and publicity are in any way efficacious, then the more that a party spends, ceterisparibus, the more votes it should win.[4] Constituency
Type
The dominant issues at most British general elections in recent decades have related to the economy. At any one time, the various sectors will be performing differentially, with consequent differential effects on various groups within the population. Since the members of those groups tend to be clustered in distinct types of constituencies, then one could expect inter-type variations in the deviations from the predicted. (In a constituency type experiencing an economic boom, for example, the incumbent government party may lose relatively few votes and perform better than it does nationally, whereas in one experiencing a slump, it may lose large numbers of votes and perform less well than predicted by the national voter transition matrix.)
Testing the Model
The previous
sections have outlined a model which
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states, in verbal form, that variations from a party’s predicted performance, by constituency, should be positively related to that party’s proportion of the vote at the previous election (its prior performance), positively related to its campaign expenditure (as a proportion of the maximum allowed), and negatively related to its opponents’ campaign expenditure (as a proportion of that allowed). In addition, there should be significant differences between constituency types in the deviations from the predicted values.
Equation Fit The general fit of the equations at each step was assessed by the multiple correlation coefficient, R’.[5] The results are given in Table 2. For all three parties, each of the components of the model - represented by a separate step - makes a substantial contribution to accounting for spatial variations in the deviations from the predicted inter-constituency pattern of votes. In each case, prior performance accounts for just over half of the variation, indicative of a strong neighbourhood effect. For the two major parties Conservative and Labour - spending accounts for a further 14% of the variation, and constituency type for 9-11%. For the smaller, Liberal party which spent much less (see above), spending was a less important factor, but constituency type accounted for 15% of the variations.
The model was fitted, separately for each party and for abstentions, using regression procedures. These were straightforward for the dependent variable and for the independents referring to prior electoral performance and to expenditure. For constituency type, a series of 2.5 dummy variables was used, with the twenty-sixth type (Inner London, multi-occupied housing areas) being represented in the equation constant.
Regression Equations Correlation coefficients give only an overall impression of a model’s validity. Full evaluation rests on the detail of the related regression equations, especially the size, sign, and statistical significance of the partial regression coefficients.
A stepwise procedure was used. At the first step, the prior electoral performance variable was included as the sole independent variable, as earlier work suggests that this should be the most important. At the second step, the three spending variables were introduced, to see if they accounted for any further variation in the dependent variable. (This ordering was justified by the earlier findings that expenditure at election t+l is closely related to expenditure at
Two sets of regression equations are discussed here. The first refers to step 2 only (Table 3). All twelve of the coefficients shown have statistically significant F-values at the 0.05 level, indicating that every variable makes a substantial contribution to the ‘explanation’ of variability in the dependent variables.[6] For prior performance, the F-values are extremely large. The size of the regression coeffi-
Table 2. Equation fit
1. Prior Performance 2. Spending 3. Constituency Type
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election f. The proportion of the vote won by a party at t+ 1 is closely related to the proportion won at t, so the spending variables were introduced to see if campaign/advertising accounted for a substantial proportion of the variation once the ‘normal vote’ had been accounted for.) Finally at step 3 the 25 dummy variables representing constituency type were introduced. With the analysis of abstentions, the first two steps were omitted since (1) there is little theoretical justification for suggesting that abstention is contagious and (2) the level of party spending should influence partisan choice but not the decision to abstain.
In operational terms, the dependent variables are defined as the deviations between the observed and predicted votes, for each party and for abstainers, at the October 1974 general election in the 452 English constituencies defined above. The independent variable relating to prior electoral performance is, for each party, the proportion of the vote obtained at the February 1974 general election, and for spending the variables are the proportion of the allowed maximum spent by each party at the October 1974 election. For constituency type, a classification of the 635 British constituencies, using 40 variables taken from the 1971 Census, was employed (WEBBER, 1978). Thirty types were defined: four of these are irrelevant to a study of England as they contain only Scottish and/or Welsh constituencies: the other 26 are listed in Appendix 1.
Step
12/Number
: R”
Dependent Variable Conservative Labour Liberal Abstentions 0.55 0.69 0.80
0.51 0.65 0.74
0.51 0.56 0.71
0.59
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241 Table 3. Regression equations
Independent Variable
Conservative
Prior performance Spending by: Conservative Labour Liberal Constant
: Step 2
Dependent Variable* Labour
0.87
(273.3)
0.46
(118.5)
1.33
(86.0)
0.21 0.13 -0.03 -0.62
(59.9) (39.5) (3.6)
0.10 0.17 -0.09 -0.33
(14.9) (68.9) (32.3)
0.16 -0.14 0.19 -0.48
(14.5) (14.6) (24.5)
*The entries are the unstandardised partial regression figures in parentheses are the relevant F-values.
cients varies considerably, however: that for Conservative suggests that the neighbourhood effect for that party is almost twice as great as that for Labour, whereas that for Liberal is nearly three times that for Labour. Apparently, therefore, there are inter-party differentials in the role of local information in both retaining the loyalty of floating voters, who previously voted for the party, and in winning over those who either abstained or supported one of the other parties at the previous contest.
the
The last results are both unexpected and difficult to interpret. They suggest - but no more - that the total volume of spending in a constituency by the two main parties is a significant influence on voting, but that for each party the importance of spending by the one under consideration is most substantial. (In Table 3, the regression coefficient for Conservative spending in the Conservative column is much larger - and more significant-than that for Labour, with the reverse situation in the Labour column.) To try and improve the interpretation, two new independent variables were defined to replace Conservative and Labour spending. They were:
Table 4. Revised regression equations
Prior performance Spending by: Conservative + Labour Conservative/Labour Liberal Constant R’
coefficients;
coefficients for Conservative in the Labour and Liberal equations, have unexpected positive signs. These suggest, for example, that the greater the Conservative expenditure in a constituency, the greater the flow of ‘floating voters’ to both Labour and Liberal.[7]
Turning to the spending variables, although all of the regression coefficients are significant, some have unexpected signs. For each party, the regression coefficient relating to its own spending is the largest of the three, is positive (as expected), and has the largest F-value. Thus for Conservative, for example, every increase of one percentage point in its allowed expenditure, ceterisparibus, produces an increase of 2.1 percentage points in the deviation between actual and predicted percentage of the poll
Independent Variable
Liberal
Conservative
: Step 2
Dependent Variable* Labour
Liberal
0.87
(297.3)
0.46
(131.7)
1.33
(87.1)
0.17 0.03 -0.03 -0.65 0.69
(179.9) (5.4) (3.0)
0.13 -0.10 -0.02 -0.31 0.65
(120.5) (33.6) (3.4)
0.03 0.07 0.20 -0.59 0.56
(1.4) (24.3) (28.2)
*The entries are the unstandardised partial regression parentheses are the relevant F- values. in October, 1974. In the equations for both Conservative and Labour, the regression coefficients for Liberal are negative, as expected: the greater the Liberal campaign expenditure, ceteris paribus, the smaller the number of ‘floating voters’ who vote for either Conservative or Labour. But the coefficient for Labour in the Conservative equation, and the
coefficients;
the figures in
Total Spending by the Main Parties (Labour and Conservative) (as percentages of the allowed total) The ratio of Conservative to Labour Spending The Liberal spending variable was not changed, because it had the correct sign in each equation and was, in aggregate, much smaller. The results of this second round of analysis are given in Table 4. For
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both Conservative and Labour, the regression The full regression equations after step 3 are given in coefficients are as suggested in the previous Table 5. For the first four independent variables, the paragraph. The greater the volume of spending, the only substantial differences from the previous analygreater the deviation between observed and predicses refer to the partial regression coefficients for ted. (With the implication that the smaller the prior performance; the three coefficients are much spending, the greater the number of abstentions. more alike in their magnitude than is the case in This is borne out by a negative regression of deviaTable 3, indicative no doubt of some collinearity tions between observed and expected abstentions: between constituency type and prior perfo~ance. the greater the spending, the greater the number of voters.). And the greater the ratio of Conservative to Turning to the coefficients for constituency type, the Labour spending, the greater the deviation between immediately apparent feature is that virtually all of observed and predicted votes for Conservative, with them in the first three columns are positive (and the opposite relationship for Labour votes. With statistically significant), whereas nearly every one in regard to the two main parties, therefore, the greater the last column is negative (and statistically signifithe volume of spending, the greater the turnout for cant). What this implies is that in virtually all conone of the two: the greater the ratio in spending stituency types, the difference between the observed proportions between those parties, the more and the predicted proportion of the October 1974 favoured is the party with the highest spending. For poll was greater than it was in the baseline type 26, the Liberals, the volume of spending by the two main which was not included in the equation (the Inner parties combined is statistically insignificant as an London areas of multi-occupied housing), and that influence on the deviations between observed and the difference invariably represented more votes predicted: apparently, however, Conservative than predicted. Countering this, the proportionate spending is more conducive to a Liberal vote than is number of abstentions was substantially smaller in Labour spending. Table 5. Regression equations : Step 3 Independent Variable
Conservative
Constant Prior performance Spenkng by: Conservative f Labour Conse~ative/Labour Liberal Constituency type 2 3 4 6 ?
8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
-0.61 0.78
(189.3)
0.15 (139.9) 0.02 (4.4) -0.02 (2.4) 0.06 0.10
0.11 0.10 0.11 0.11 0.07 0.12 0.14
(6.8) (23.9) (28.9) (22.8) (23.7) (30.3) (11.1)
Dependent Variable* Liberal Labour -0.46 0.58 (119.1)
-0.48 0.78
(32.7)
0.11 0.01 -0.06
-0.03 0.05 0.25
(2.3) (11.2) (58.2)
(11.5) (23.6) (23.1) (28.1) (3.5) (17.8) (21 .O) (30.7) (45.1) (22.9) (29.3) (24.8) (17.8) (8.9)
0.14 0.17 0.19 0.27 0.28 0.28 0.20 0.20 0.29 0.30 0.27 0.27 0.31 0.21
(8.6) (17.5) (23.1) (44.1) (33.8) (47.7) (22.1) (23.0) (52.9) (59.7) (33.9) (37.7) (38.2) (14.3)
(24.3) (29.7) (14.3) (10.5) (00;;
0.23 0.21 0.16 0.19 -0.20 0.00 0.07 -0.07
(28.7) (30.0) (7.5) (10.7) (2.7) (0.0) (2.8) (1.0)
0.08 0.10
0.10 0.15 0.12 0.16 0.14
;::;; (27.9) (41.9) {30.2j (39.2) (24.0)
0.10 0.12 0.05 0.09 0.10 0.12 0.14 0.10 0.13 0.11 0.11 0.09
0.11 0.10 0.11 0.09 -0.03 -0.03 0.01 0.00
(30.2) (27.8) (14.3) (9.4) (0.3) (0.8) (0.2) (0.0)
0.11 0.11 0.12 0.10 0.01 0.00 0.05 0.01
*The entries are the unstandardised the retevant F-values.
(83.9) (4.7) (15.9)
(4.6) (0.1)
Abstentions 0.25
-0.14 -0.21 -0.22 -0.30 -0.32 -0.32 -0.22 -0.27 -0.37 -0.30 -0.36 -0.29 -0.27 -0.19 -0.15 -0.22 -0.23 -0.26 -0.22 -0.16 -0.17 ;:: -0.04 0.01
(13.2) (41.5) (53.5) (92.0) (78.7) (iO7.2j
(45.9) (67.1) (132.9) (96.7) (95.9) (78.4) (45.7) (17.7) (8.2) (27.7) (40.3) (60.4) (50.8) (12.6) (13.2) ;::; 6.2) (0.0)
partial regression coefficients; the figures in parentheses are
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most constituency types than it was in type 26. The only types to display no substantial differences are 22, 23, 24 and 25. All four also are inner city areas, indicating a common pattern with Inner London. Two interpretations are possible for this. The first is that the inner city areas house the largest proportions of alienated voters, who prefer to abstain. The second is that during the six months between the two elections, the population loss in these areas was substantial; this is shown by a large number of abstentions who have actually moved from the constituencies. Both undoubtedly have some validity: the latter would be more credible, however, were the interelection period longer. Regarding the other twenty-one constituency types, inspection of the first three columns of the table suggests a similar pattern of coefficients. In fact, over the 25 types, the rank correlations between the size of the coefficients are: Conservative-Labour, 0.67, Conservative-Liberal, 0.67; Labour-Liberal, 0.59. If the last four types are excluded because of their insignificant F-values, however, the rank correlations for the other 21 are: Conservative-Labour, 0.45; Conservative-Liberal, 0.45; Labour-Liberal, 0.32. Clearly there are substantial differences between the three columns. The Conservative Party performed much better than expected (regression coefficients exceeding 0.12) in types 13,11,9 and 14, two of which, surprisingly, are coal mining areas. (This suggests an abnormally large vote for Labour in those areas in the February 1974 general election which was held after the national miner’s strike: BUTLER
and
KAVANAGH,
1975.
Presumably,
survey data) of the national gross flow of voters between parties in the intervening period. Such a prediction assumes that the national movement in partisan preferences applies equally in each constituency; the deviation between the prediction and the observed vote indicates the local validity of that assumption. The deviations between observed and predicted for each party provided a dependent variable whose pattern across the entire set of constituencies was to be accounted for. Three independent variables were put forward: the neighbourhood effect; party campaign spending; and constituency type. The importance of the first of these was made clear by the analyses: all three parties showed considerable correlation between prior performance and the deviation (observed-predicted), indicating that party strength is most readily maintained in areas where voter support is already considerable. The constituency types also produced significant findings. Interpretation of some of the regression coefficients was slightly difficult, indicating the need to set the study of change between any one pair of elections in a longer-term perspective. Finally, the findings regarding campaign spending were slightly less clear-cut. The more that the two main parties spent, the more votes each got, relative to the predictions based on the national trends of support; in addition, more spending by one party relative to the other led to it receiving even more votes than predicted. For the smaller, Liberal party, the more it spent, the more votes it got.
in
October 1974 there was some reaction to this earlier major swing to Labour in the mining areas, which accounts for the relatively low regression coefficients for Labour for types 13 and 14[8]) The Labour Party performed much better than expected (regression coefficients exceeding 0.11) in types 9, 11,4,20 and 8; only type 4 (very high socio-economic status areas) is surprising. Finally, the Liberal Party performed much better than expected (regression coefficients exceeding 0.28) in types 13, 10,9 and 17, all of which contain mainly industrial constituencies.
Overall, the results of these analyses have been encouraging. The fit of the models has been substantial and many of the regression coefficients can be sensibly interpreted. Questions still remain to be answered, of course, but a positive contribution has been made to the ecological understanding of short-term electoral change in England.
Conclusions
Notes
This paper has presented a method for identifying spatial variations in short-term electoral change in England (methods that could easily be used elsewhere) and which avoid the relatively crude measure of swing frequently employed in such work. The method allows a prediction to be made of the vote in each constituency at the second of a pair of contests, given information on the distribution of the vote at the first contest and an estimate (obtained from
1. This voter transition matrix was made available to me by Ivor Crewe, British Election Study, University of Essex. I am grateful to Mr. Crewe for his assistance. 2. Two small categories have been omitted: voters who died between the two elections and individuals who become eligible to vote during that period. Both are relatively very small in number for such a short period. They were omitted because of the absence of any data at the constituency level, on deaths. 3. Five constituencies were omitted because of the strong showing of an independent candidate in at least one of
Acknowledgement - A grant from the University Sheffield Research Fund towards the cost of purchasing census data tapes for the parliamentary constituencies gratefully acknowledged.
of the is
244 the elections. 4. A critique of my analysis of the efficacy of advertising has been published by Gordon and Whiteley (1980): a rebuttal is forthcoming. 5. Given the large number of observations relative to no correction factor was independent variables, applied to the R2 values. 6. Although the data refer to a population and not a sample, statistical significance tests can be used to indicate the probability of a chance ordering of the data (or classification of the observations with the dummy variables) producing a regression coefficient of the observed magnitude. 7. There may be a problem of collinearity with the three spending variables. However, the inter-correlations were only: Conservative-Labour, 0.19; ConservativeLiberal, 0.44; Labour-Liberal, -0.13. Attempts to remove the common elements produced no alteration in the findings. 8. Again, there is some collinearity involved. The mining the largest constituencies consistently produce majorities for the Labour Party.
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JOHNSTON, R. J. (1981) Regional variations in British voting trends 1966-1979: tests of an ecological model. Regional Studies 15,2>32. JOHNSTON, R. J. and HAY, A. M. (1981) ‘on the parameters of uniform swing. Envir. Plann. A 13. MILLER, W. L. (1977) Electoral Dynamics. Macmillan, London. PALDA, K. S. (1973) Does advertising influence votes? An analysis of the 1966 and 1970 Quebec elections. Can. J. Political Science 6,638-655. TAYLOR, P. J. (1979) The changing geography of representation in Britain. Area 11,289-294. TAYLOR, P. J. and JOHNSTON, R. J. (1979) Geography of Elections. Penguin Books, Harmondsworth. WEBBER, R. (1978) Parliamentary Constituencies: A Socio-Economic Classification. Occasional Paper 13, Office of Population Censuses and Surveys. London: O.P.C.S. WELCH, W. P. (1976) “The effectiveness of expenditures in State legislative races.“Am. Politics Q. 4,333-3.56.
Appendix
Constituency Types References BUTLER, D. and KAVANAGH, D. (1975) The B&i.& General Election of February 1974. Macmillan, London. BUTLER, D. and STOKES, D. (1969) Political Change in Britain. (first edition). Macmillan, London. BUTLER, D. and STOKES, D. (1974) Political Change in Britain (second edition). Macmillan, London. GORDON, I. and WHITBLEY, P. (1980) Comment: Johnston on campaign expenditure and the efficacy of advertising. Political Studies 28,293-294. JOHNSTON, R. J. (1977) The electoral geography of an electoral campaign. Scott. geogr. Mag. 93,98-108. JOHNSTON, R. J. (1979a) Regional variations in the 1979 general election results for England. Area 11,294-298. JOHNSTON, R. J. (1979b) Campaign expenditure and the efficacy of advertising at the 1974 general elections in England. Political Studies 27, 114-l 19. JOHNSTON, R. J. (1979~) Campaign spending and votes: a reconsideration. Public Choice 33.97-106. JOHNSTON, R. J. (1980a) Testing the Butler-Stokes model of a polarization effect around the natural swing in partisan preferences: England 1979. Br. J. Political Science 11,113-117. JOHNSTON, R. J. (1980b) City and Sociev. Penguin Books, Harmondsworth.
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Centres of learning Service towns Outer London suburbs Very high socio-economic status areas Areas very dependent on agriculture Agricultural areas Resort and retirement areas Areas of intermediate status and modern housing Small industrial towns of Northwest and East Midlands South Midlands growth areas West Midlands growth areas Area of rapid growth Mining areas in conurbations Mining areas The Black Country Steel and chemical towns Poorer urban centres Metropolitan inter-war suburbs Textile areas Maritime industrial areas Peripheral council housing estates in conurbations Inner Liverpool Low socio-economic status inner London Provincial inner city areas High socio-economic status inner London Inner London areas of multi-occupied housing.