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Measuring Turnout – Who Voted in 2010? British Election Study - 2010 Harold Clarke, David Sanders, Marianne Stewart, Paul Whiteley, University of Essex.

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Presentation on theme: "Measuring Turnout – Who Voted in 2010? British Election Study - 2010 Harold Clarke, David Sanders, Marianne Stewart, Paul Whiteley, University of Essex."— Presentation transcript:

1 Measuring Turnout – Who Voted in 2010? British Election Study Harold Clarke, David Sanders, Marianne Stewart, Paul Whiteley, University of Essex and University of Texas at Dallas

2

3 Turnout Figures in the 2005 and 2010 British Election Study Surveys for Britain

4 The Measurement of Turnout in Various Studies – Percentages Exceeding Actual Turnout

5 Likelihood of Voting Scale in the Pre-Election Survey ‘Please think of a scale that runs from 0 to 10, where 0 means very unlikely and 10 means very likely, how likely is it that you will vote in the next general election that may be held soon?’

6 Pre-Election Probability of Voting Scale as a Predictor of Post-Election Reported Turnout in 2010 (Eta=0.44)

7 Pre-Election Probability of Voting Scale as a Predictor of Post-Election Reported Turnout in 2005 (eta=0.56)

8 Pre-Election Probability of Voting Scale as a Predictor of Post-Election Validated Turnout in 2005 (Eta=0.43)

9 Validated Vote by Reported Vote in 2005

10 Occupational Status and Reported Turnout in 2010

11 Reported Turnout and Age in 2010

12 Reported Turnout and Income in 2010

13 Reported Turnout and Other Demographics 2010

14 Logistic Regression of Turnout with Demographic Predictors (BES panel data) CoefficientsOdds Ratios Age0.04***1.04 Male Education0.30***1.35 Religion0.70***2.02 Income0.10***1.10 Occupational Status0.28***1.32 Ethnic Minority-0.75***0.47 Single person Nagelkerke R-Squared0.24 p<0.01=***; p<0.05=**; p<0.10=*

15 Rational Choice Model of Turnout Turnout = α 0 + β 1 Efficacy * Collective Benefits - β 2 Costs + β 3 Individual Benefits + β 4 Civic Duty Turnout: Self-reported voting, post-election survey Collective Benefits: Party Differential weighted by Efficacy, pre- election survey Individual Benefits: private benefits of voting, pre-election survey Civic Duty: Perceptions of Duty to Vote, pre-election survey See H.Clarke, D. Sanders, M.Stewart and P. Whiteley, Political Choice in Britain (Oxford University Press, 2004) chapter 8.

16 Collective Benefits Measures -Feeling Thermometers for Labour (Mean = 4.56)

17 Collective Benefits Measures -Feeling Thermometer for the Conservatives Mean = 4.99

18 Collective Benefits Measures -Feeling Thermometers for Liberal Democrats Mean=4.80

19 Collective Benefits – Party Differential Party Differential = (Con – Lab) 2 + (Con – LibDem) 2 + (Lab – LibDem) 2 The greater the party differential the greater the incentive to vote

20 Efficacy in the Rational Choice Model ‘Please use the 0 to 10 scale to tell me how likely it is that the votes of people like you will make a difference to which party wins the election in this constituency’

21 Perceptions of the Costs of Voting ‘People are so busy that they don't have time to vote’.

22 Individual Benefits from Voting ‘I feel a sense of satisfaction when I vote’.

23 Civic Duty and Voting ‘I would be seriously neglecting my duty as a citizen if I didn't vote’.

24 Logistic Model of Turnout with Rational Choice and Demographic Predictors CoefficientsOdds Ratios Weighted Collective Benefits0.002***1.002 Perceptions of Costs-0.15**0.86 Individual Benefits0.26***1.30 Civic Duty0.38***1.46 Age0.03***1.03 Male Education0.25***1.28 Religion0.63***1.88 Income0.09***1.09 Occupational Status0.25***1.29 Ethnic Minority-0.94***0.39 Nagelkerke R-Squared0.34 p<0.01=***; p<0.05=**; p<0.10=*

25 Discrepancy between Pre-Election Likelihood of Voting and Reported Turnout

26 Regression Model of the Discrepancy between Likelihood of Voting and Turnout DemographicsRational Choice Weighted Collective Benefits ** Perceptions of Costs Individual Benefits *** Civic Duty *** Age-0.15***---- Male Education Religion Income-0.05*---- Occupational Status Ethnic Minority R-Squared

27 Conclusions A theoretical model significantly improves the predictive power of a turnout model over and above demographic predictors We might expect nobody with a score of less than 7 or 8 on the pre-election likelihood of voting scale to vote, but they do. If we use demographics to model the discrepancy between the likelihood and actual voting they don’t help very much However, the theoretical model does help to capture this discrepancy and with other theoretical models it can be used to weight the likelihood of voting measure to make it more accurate


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