Measuring Turnout –
Who Voted in 2010?
British Election Study - 2010
Harold Clarke, David Sanders, Marianne Stewart, Paul Whiteley,
University of Essex and University of Texas at Dallas
whiteley@essex.ac.uk
Design of Surveys for the 2010 BES
1. BES 2009/10 CORE FACE-TO-FACE PANEL SURVEY:
Wave 1 Pre-election
Probability Sample,
Face-to-Face N=1,935
200 Primary Sampling
Units
Wave 2 Post-election
Probability Sample, Face-to-Face
N=3075, including top-up, mailback
Scots and Welsh booster samples to
achieve N=800 and 500 respectively
200 Primary Sampling Units
Mode Comparison 1
Mode Comparison 2
2. BES 2009/10 INTERNET AND INTER-ELECTION CAMPAIGN PANEL SURVEY:
BES 2005/06
Internet Panel
Wave 6. Last
interviewed
2008 or 2009.
N = c5000
2009/10 Wave 1
Pre-election baseline
interview. Total N =
16816; approx 4000
from BES 2005/06
Panel; approx 12,000
new respondents
2009/10 Wave 2
Rolling
Campaign
Survey. 522
Interviews per
day. Total N =
14,622
2009/10 Wave 3
Immediate postelection
Interview
N=13,356
Response
Mode bid to
be made at a
later date to
carry the
panel forward
annually to
the next
general
election
3. BES 2009/10 INTERNET CONTINUOUS MONITORING SURVEY:
Monthly Repeated Cross-Section Surveys
Already running April 2004 to May 2008, funded by National Science
Foundation; N=1,000 plus per month
July 2008 ……………………………………………………………….. Dec 2012
Local
Elections
May 2009
Party
Conferences,
Sept/Oct 2009
Unforeseen
Events
Response
Mode bid to
be made at a
later date to
carry the
CMS forward
monthly to the
next general
election and
beyond
Turnout Figures in the 2005 and 2010 British
Election Study Surveys for Britain
100
89.5
90
82.9
76.7
80
71.7
70
65.3
Percentages
61.1
60
50
40
30
20
10
0
Face-to-Face
Internet
2005
2010
Actual
The Measurement of Turnout in Various Studies –
Percentages Exceeding Actual Turnout
2005 BES, internet
21.8
2005 BES, IP
10.6
1964-2001 BES, IP
9.9
2001 BES, RDD
18.7
2000 CES, RDD
21.5
2004 CES, RDD
25.2
2000 ANES, RDD, rev
27.2
2000 ANES, IP, rev
20.2
2002 ANES, RDD, rev
17.8
2002 ANES, RDD, trad
37.9
2004 ANES, IP, rev
17.3
2004 ANES, IP, trad
24.7
2000 NAES, RDD
24.8
2004 NAES, RDD
34.7
0
10
20
30
40
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?’
70
60
Percentages
50
40
30
20
10
0
0
1
2
3
4
5
6
7
8
9
10
Pre-Election Probability of Voting Scale as a Predictor of
Post-Election Reported Turnout in 2010 (Eta=0.44)
100
90
Percentages Voting
80
70
60
50
40
30
20
10
0
0
1
2
3
4
5
6
7
Pre-Election Probability of Voting Scale
8
9
10
Pre-Election Probability of Voting Scale as a Predictor of
Post-Election Reported Turnout in 2005 (eta=0.56)
100
90
80
Percentage Voting
70
60
50
40
30
20
10
0
0
1
2
3
4
5
6
Probability of Voting
7
8
9
10
Pre-Election Probability of Voting Scale as a Predictor of
Post-Election Validated Turnout in 2005 (Eta=0.43)
90
80
Validated Percentages
70
60
50
40
30
20
10
0
0
1
2
3
4
5
6
Pre-Election Probability of Voting
7
8
9
10
Validated Vote by Reported Vote in 2005
Validated Vote By Reported Vote
% of Total
Validated
Vote
Total
1.00 Voted
2.00 Did not vote
3.00 Postal vote
4.00 Proxy vote
5.00 Not registered at
this address
6.00 Address not found
9.00 Missing data
Reported Vote
Did not
vote
Did Vote
.9%
51.0%
17.3%
4.2%
.9%
8.1%
.0%
.8%
Total
51.9%
21.6%
9.0%
.8%
6.2%
4.4%
10.5%
2.0%
.5%
27.8%
2.2%
1.6%
72.2%
4.1%
2.1%
100.0%
Occupational Status and Reported Turnout
in 2010
80
70
Percentages
60
50
40
30
20
10
0
Professional
Manager
Clerical
Sales or
services
Small
business
owner
Foreman or
supervisor
Skilled
manual
Semi or
unskilled
manual
Reported Turnout and Age in 2010
90
80
70
Percentages
60
50
40
30
20
10
0
18-25
26 to 35
36 to 45
46 to 55
56 to 65
66 plus
Reported Turnout and Income in
2010
80
70
Percentages
60
50
40
30
20
10
0
Reported Turnout and Other
Demographics 2010
80
70
60
Percentages
50
40
30
20
10
0
male
female
religious
not religious
ethnic minority ethnic majority
single
not single
Logistic Regression of Turnout with
Demographic Predictors (BES panel data)
Coefficients
Odds Ratios
Age
0.04***
1.04
Male
0.25
1.28
Education
0.30***
1.35
Religion
0.70***
2.02
Income
0.10***
1.10
Occupational Status
0.28***
1.32
Ethnic Minority
-0.75***
0.47
Single person
-0.20
0.82
Nagelkerke R-Squared
0.24

p<0.01=***; p<0.05=**; p<0.10=*
Rational Choice Model of Turnout








Turnout = α0 + β1 Efficacy * Collective Benefits
- β2 Costs + β3 Individual Benefits
+ β4 Civic Duty
Turnout: Self-reported voting, post-election survey
Collective Benefits: Party Differential weighted by Efficacy, preelection 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.
Collective Benefits Measures
-Feeling Thermometers for Labour
(Mean = 4.56)
Feelings-Labour Party
20
Percent
15
10
5
0
0Strongly
dislike
1
2
3
4
5
6
Feelings-Labour Party
7
8
9
10Strongly
like
Collective Benefits Measures
-Feeling Thermometer for the Conservatives
Mean = 4.99
Feelings-Conservative Party
20
Percent
15
10
5
0
0Strongly
dislike
1
2
3
4
5
6
7
Feelings-Conservative Party
8
9
10Strongly
like
Collective Benefits Measures
-Feeling Thermometers for Liberal Democrats
Mean=4.80
Feelings-Liberal Democrat Party
30
25
Percent
20
15
10
5
0
0Strongly
dislike
1
2
3
4
5
6
7
Feelings-Liberal Democrat Party
8
9
10Strongly
like
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
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’
Perceptions of the Costs of Voting
‘People are so busy that they don't have time to vote’.
People Too Busy to Vote
50
Percent
40
30
20
10
0
Strongly agree
Agree
Neither agree nor
disagree
Disagree
People Too Busy to Vote
Strongly disagree
Individual Benefits from Voting
‘I feel a sense of satisfaction when I vote’.
Feel Sense of Satisfaction When Vote
50
Percent
40
30
20
10
0
Strongly agree
Agree
Neither agree nor
disagree
Disagree
Feel Sense of Satisfaction When Vote
Strongly disagree
Civic Duty and Voting
‘I would be seriously neglecting my duty as a citizen if I didn't vote’.
Logistic Model of Turnout with Rational Choice
and Demographic Predictors
Coefficients
Odds Ratios
0.002***
1.002
Perceptions of Costs
-0.15**
0.86
Individual Benefits
0.26***
1.30
Civic Duty
0.38***
1.46
Age
0.03***
1.03
Male
0.17
1.19
Education
0.25***
1.28
Religion
0.63***
1.88
Income
0.09***
1.09
Occupational Status
0.25***
1.29
Ethnic Minority
-0.94***
0.39
Weighted Collective Benefits
Nagelkerke R-Squared

p<0.01=***; p<0.05=**; p<0.10=*
0.34
Discrepancy between Pre-Election Likelihood of
Voting and Reported Turnout
80
70
60
Percentages of respondents with likelihood of voting scores who
reported voting after the election
Percentages
50
40
30
20
10
0
0
1
2
3
4
5
6
7
8
9
10
Regression Model of the Discrepancy between
Likelihood of Voting and Turnout
Demographics
Rational Choice
Weighted Collective Benefits
----
-0.06**
Perceptions of Costs
----
0.00
Individual Benefits
----
-0.23***
Civic Duty
----
-0.13***
Age
-0.15***
----
Male
0.03
----
Education
-0.03
----
Religion
0.03
----
Income
-0.05*
----
Occupational Status
-0.04
----
Ethnic Minority
-0.01
----
R-Squared
0.02
0.12
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