1. Compulsory voting and political knowledge: testing a ‘compelled engagement’ hypothesis
Jill Sheppard
Centre for Applied Social Research Methods
The Australian National University

2. Background Assumptions about compulsory voting But evidence too:
turnout rate (Franklin 1999)
equality between voters and non-voters
engagement (Lijphart 1997)
party stability (Mackerras and McAllister 1999)
policy outcomes (Fowler 2013)

3. Compulsory voting Variations in enforcement
formal sanctions (e.g. fines)
informal sanctions (e.g. job exclusions)
‘compulsion’ without sanction (e.g. statute but no enforcement)
“Compulsory voting” is heterogeneous

4. Or, in an array… Sanctions Sanctioned Unsanctioned Form of obligation
Formal
Sanctioned compulsion (Australia)
Unsanctioned compulsion (Mexico, Venezuela)
Informal
Sanctions/benefits but no ‘compulsion’ (Russia)
No compulsion, little pressure (NZ, US, UK…)

5. Research questions
Does sanctioned compulsory voting have positive effects on engagement?
- specifically, on political knowledge?
Do any effects also occur in the absence of sanctions?
- does the *idea* of compulsion matter?

6. Switzerland (Schaffhausen)
Data and methods
CSES Modules 1-4 pooled
OLS and LME models (LME4 package in R)
Strong sanctions
(elections=15)
Moderate sanctions
(elections=8)
No sanctions
Australia
Brazil
Greece
Uruguay
Chile
Mexico
Belgium
Italy
Switzerland (Schaffhausen)
Thailand
Peru
Degrees of enforcement come from CSES investigators’ advice
Countries with voluntary voting in the sample: Albania, Austria, Bulgaria, Belarus, Canada, the rest of Switzerland, the Czech Republic, Germany, Denmark, Spain, Estonia, Finland, France, Great Britain, Croatia, Hungary, Ireland, Israel, Japan, Kyrgyzstan, South Korea, Lithuania, Montenegro, Netherlands, Norway, New Zealand, Philippines, Poland, Portugal, Romania, Russia, Serbia, Slovakia, Slovenia, Sweden, Taiwan, Ukraine and United States
Iceland and Hong Kong excluded as no Polity data available for them
In total, 38 countries and 95 elections

7. CSES information questions (examples)
Australia: Australia became a federation in 1901 (T/F). The longest time allowed between Federal elections for the House of Representatives is four years (T/F).
Brazil: The president has a 4 year mandate (T/F). Geraldo Alckmin belongs to the PTB (T/F). The deputies of the House of Representatives are elected by majoritarian system (T/F).
Greece: Could you tell me how many parties are represented in parliament today? Based on the current electoral law, what percentage of votes constitutes the threshold for entry of a political party into parliament? (T/F)
Mexico: Which are the chambers of Mexico’s Congress? Could you tell me the name of the governor of your state? On the whole, how many years does a Deputy stay on his charge? (T/F)
Note measurement errors inherent to comparative design (and the CSES project more generally)

8. Mean responses by CV type
All diffs sig (p=.00) except ‘Strong’ and ‘Weak’ for ‘Don’t know’

9. Partial effects by CV type
Voluntary
(n=122,839)
No sanctions
(n=11,663)
Moderate sanctions
(n=11,685)
Strong sanctions (n=15,166)
Male
.28 (.01)
.26 (.02)
.22 (.02)
Party ID (binary)
.18 (.01)
.10 (.02)
.29 (.02)
.00 (.02)
Age
.00 (.00)
.01 (.00)
.02 (.00)
Education
.09 (.00)
.20 (.01)
.09 (.01)
.10 (.00)
Household income
.07 (.00)
.03 (.01)
.10 (.01)
.11 (.01)
Constant
-.19 (.02)
-.13 (.05)
-.09 (.05)
1.30 (.19)
Adj r2
.07
.16
.11
.26
Note lack of party ID effect in strongly enforced compulsory countries, but overall higher model fit
Bit all over the shop, if anything

10. Interactions: education * CV
Model A
Model B B SE
Country-level
CV (strong)
.24
.06
.30
.07
CV (mod)
-.32
.29
-.20
CV (weak)
.16
.26
.00
Individual-level
Education
.68
.01
.71
Education*CV (strong)
-.04
.03
Education*CV (mod)
-.17
.04
Education*CV(weak)
.19
Constant
-1.49
0.80
.80
Residual
.77
0.88
0.77
.88
Controls all modelled as fixed effects – Polity IV, days since election (for time-contingent effects of media coverage), district magnitude (as a proxy for electoral systems and consequent differences in constituent-representative linkage), party ID (which has a moderate positive effect) age (which has a positive effect), gender (being male has a strong positive effect, which is covered extensively in literature elsewhere on measurement error in knowledge studies), and household income (weak positive effect).
Intercepts are modelled as random effects, controlling for clustered standard errors by election and interview mode of administration

11. Interactions: education * CV

12. Interactions: gender * CV
Model A
Model B B SE
Country-level
CV (strong)
.24
.06
.07
CV (mod)
-.32
.29
-.16
CV (weak)
.16
.26
.27
Individual-level
Male
.30
.01
.48
.04
Male*CV (strong)
.02
Male*CV (mod)
-.10
Male*CV(weak)
-.07
Constant
-1.49
0.80
-1.76
.80
Residual
.77
0.88
0.77
.88
Controls all modelled as fixed effects – Polity IV, days since election (for time-contingent effects of media coverage), district magnitude (as a proxy for electoral systems and consequent differences in constituent-representative linkage), party ID (which has a moderate positive effect) age (which has a positive effect), gender (being male has a strong positive effect, which is covered extensively in literature elsewhere on measurement error in knowledge studies), and household income (weak positive effect).
Intercepts are modelled as random effects, controlling for clustered standard errors by election and interview mode of administration

13. Interactions: gender * CV

14. Interactions: age * CV Model A Model B B SE Country-level CV (strong)
CV (strong)
.24
.06
.03
.07
CV (mod)
-.32
.29
-.03
CV (weak)
.16
.26
.27
Individual-level
Age
.01
.00
.04
Age*CV (strong)
Age*CV (mod)
Age*CV(weak)
Constant
-1.49
0.80
-1.76
.80
Residual
.77
0.88
0.77
.88
Controls all modelled as fixed effects – Polity IV, days since election (for time-contingent effects of media coverage), district magnitude (as a proxy for electoral systems and consequent differences in constituent-representative linkage), party ID (which has a moderate positive effect) age (which has a positive effect), gender (being male has a strong positive effect, which is covered extensively in literature elsewhere on measurement error in knowledge studies), and household income (weak positive effect).
Intercepts are modelled as random effects, controlling for clustered standard errors by election and interview mode of administration

15. Interactions: age * CV

16. Conclusion and implications
Strongly enforced CV has an overall positive effect on political knowledge
But effects are disparate:
- education is less important for knowledge
- has no effect on knowledge by gender
- increases the concentration of knowledge with age
Why?
- effects of habitual voting?
- heterogenous effects of no GOTV?
Compelled engagement may not be so direct…
Strong effect of the strongest levels of enforcement undermined somewhat by apparent negative effects of moderate enforcement
Weak enforcement also throws argument – turnout is relatively low in the two weakly enforced countries (Mexico and Greece), but they’ve got other things going on. Possible to control for this, without introducing correlations among the independent variables?
Next step is to look more closely at the countries by level of enforcement