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Elections in the Digital Age 4 th International Electoral Affairs Conference Professor Rachel K. Gibson (University of Manchester)

Elections in the Digital Age 4 th International Electoral Affairs Conference Professor Rachel K. Gibson (University of Manchester)

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Elections in the Digital Age

4th International Electoral Affairs Conference

Professor Rachel K. Gibson (University of Manchester)

E-voting – the basic arguments

The pro’s– Increasing voter turnout (convenience, lower costs, mobile access)– Increasing administrative efficiency and lowering costs of elections– Alignment of voting system with societal modernization

The con’s– Increased security risks and reduced perceptions of integrity of voting

process– Privacy and anonymity concerns – exposing voters to undue

influence/pressure– Undermining the reflective quality of participation – Undermining the collective quality of participation

E-voting in a wider context

1. Voting as a form of participation is clearly important in building attachment to the political system, however, losing appeal particularly among younger people alongside conventional politics more generally.

2. Since 2000 when it was trialled in a binding public election – the U.S. Democratic primaries its use has been debated and become controversial. Voters’ perceptions of security are vital to the integrity of elections and acceptance of the outcome. No evidence of sustained and significant increases in voter turnout due to new mode?

3. There are more exciting types of online participation that have emerged, particularly since 2004 and the rise of ‘social media’ that could more effectively encourage participation in the campaign, particularly among younger citizens .

Number of individuals voted in national elections as proportion of voting-age population in selected OECD

countries 1945-2005

Source: International Institute for Democracy and Electoral Assistance (IDEA), Stockholm Reported by the OECD Society at a Glance http://caliban.sourceoecd.org/vl=1676525/cl=24/nw=1/rpsv/society_glance/30.htm

30

40

50

60

70

80

90

100

1945 1955 1965 1975 1985 1995 2005

US Japan Germany Austria New Zealand Switzerland

Number of individuals voted in national elections as proportion of voting-age population in selected OECD countries 1945-2005

Source: International Institute for Democracy and Electoral Assistance (IDEA), Stockholm Reported by the OECD Society at a Glance http://caliban.sourceoecd.org/vl=1676525/cl=24/nw=1/rpsv/society_glance/30.htm

40

50

60

70

80

90

100

Number of individuals voted in national elections as proportion of voting-age population: UK 1945-2010

Source: International Institute for Democracy and Electoral Assistance (IDEA), Stockholm Reported by the OECD Society at a Glance http://caliban.sourceoecd.org/vl=1676525/cl=24/nw=1/rpsv/society_glance/30.htm

United Kingdom

Norway

France

Sweden

Ireland

Switzerland

Denmark

Netherlands

Germany

0 10 20 30 40 50 60 70

% Decline

% Decline

% Change in party membership size in selected European countries: 1980-2009 (van Biezen et al. 2009)

Change in party membership size in selected European countries: 1980-2009 (van Biezen et al. 2009)

Biezen, Mair and Poguntke. 2009. ‘Going going ….gone: Party Membership in the 21st century’ van Biezen, I., Mair, P. and T. Poguntke. Paper presented at the ECPR Jt Sessions, Lisbon

Rise in % population participating in demonstrations in selected OECD countries(Sources: Barnes & Kaase, 1979 and World Values Surveys)

Nether-lands

Sweden France Ireland United States

Norway05

101520253035404550

Early 1980sEarly 1990s1999 to 2001

Source: Norris, P. , Walgrave, S. and P. Van Aelst. 2006. ‘Does Protest Signify Disaffection? ‘ in Torcal & Montero (eds). Political Disaffection in Contemporary Democracies.

Rise in political protest for selected countries: 1980-2001

Engagement in the 2010 e-campaign (% internet users)(Source: BMRB Survey, N = 1,643 20.05.10-26.05.10)

Type of activity0

5

10

15

20

25

30

35

40

Party sitesE-newsParty toolsMSM sitesOnline videoSNS Post commentFoward contentEmbed content

E-participation: 4 Modes

1 E-formal

E-targeted

E-expressive

E-communication

Register

Join sns

E-discuss

Sites

Videos

Tools

E-donation

Forward

E-petition

E-contact

Post

Embed

1

1

1

News

E-expressive participation

Particularly interesting in that it doesn’t fit easily into established categorisations of political participation.– Voting– Contacting– Campaigning– Communal /civic activities– Protesting /violence– Discussion/attention to news

E-expressive participation – what is it?

Not as instrumental as voting, signing a petition, campaigning for a party but not simply casual political ‘talk’

A deliberate public statement of one’s political opinion or views, with the intent to influence ‘other’ - not necessarily government policy.

Pre-internet = political speech or letter to the editor. Internet = posting comments on blog, forwarding political

content to friends and family, tweeting or retweeting political message, embedding the logo or banner of a political organization on a site. Social media oriented.

Informal activity but occurring within and supportive of, formal political environment, i.e. elections

E-expressive participation – who engages in it and why should we care about it?

Closer analysis of predictors :younginternet skillssome political interestnot partisannot high trust or efficacy not heavily involved in community activities.(Not so for e-formal/party activists.)

So potential for mobilization exists.....This is where we should be focusing our attention in elections in the digital

era if we want to make a difference in terms of increasing engagement. Cant simply do it by switching mode, have to first generate interest.

Getting the parties, election commission, pressure groups to create dynamic viral content that people want to circulate and talk about is key.

Table 1: A typology of online participation Method of influence

REPRESENTATIONAL EXTRA-REPRESENTATIONAL

EXIT-BASED

eVoting

eBoycott/Boycott (CONSUMERISM)

VOICE-BASED

Non-targeted PARTY eJoin, edonate, evolunteer

Non-targeted PROTEST eJoin, edonate, esign up Promote /coordinate strike, demo, illegal protest Hacktivism, e-disturbance, electronic sit ins.

TARGETED eContact email politican, org, party)

Online participation Offline participation e-communication e-targeted e-formal e-informal Contact Petition Donation Gender (female) -0.177 0.179 -0.296 0.028 0.077 -0.016 0.026 (0.133) (0.178) (0.295) (0.307) (0.184) (0.184) (0.292) Age -0.017*** 0.008 -0.026** -0.026* 0.017** 0.007 0.048*** (0.004) (0.006) (0.009) (0.011) (0.006) (0.006) (0.010) Education 0.063 0.021 -0.042 0.105 -0.022 0.170* 0.426*** (0.049) (0.065) (0.101) (0.117) (0.065) (0.067) (0.111) Free time -0.011 -0.004 0.000 0.012 0.012 -0.001 -0.034 (0.023) (0.031) (0.048) (0.051) (0.031) (0.031) (0.051) Civic skills 0.059 0.150* 0.164 0.060 0.332*** 0.118 0.334** (0.046) (0.062) (0.095) (0.103) (0.073) (0.069) (0.112) Online skills 0.227*** 0.449*** 0.284* 0.625*** -0.072 0.216** 0.157 (0.053) (0.078) (0.111) (0.134) (0.087) (0.080) (0.133) Internal efficacy 0.005 0.081* -0.049 0.057 -0.005 0.032 0.006 (0.025) (0.034) (0.053) (0.058) (0.034) (0.035) (0.054) Trust in politicians 0.054 0.039 0.130* 0.048 0.066 0.009 0.200** (0.030) (0.039) (0.066) (0.068) (0.042) (0.042) (0.071) Interest in politics 0.115*** 0.123** 0.224*** 0.154* 0.184*** 0.162*** 0.067 (0.028) (0.040) (0.064) (0.066) (0.039) (0.040) (0.067) Partisanship strength 0.061 0.259** 0.458** 0.151 0.306** 0.313** 0.535** (0.073) (0.099) (0.167) (0.169) (0.106) (0.106) (0.177) Media exposure (TV) -0.005 -0.050 0.090 0.017 0.060 -0.047 0.084 (0.026) (0.041) (0.047) (0.062) (0.034) (0.039) (0.055) Constant -2.500*** -5.491*** -5.403*** -6.002*** -5.597*** -5.411*** -10.956*** (0.389) (0.564) (0.845) (0.967) (0.572) (0.570) (1.088) Observations 1,116 1,121 1,116 1,124 1,609 1,609 1,609 Pseudo-R2 0.101 0.159 0.171 0.173 0.112 0.111 0.223

Standard errors in parentheses, *** p<0.001, ** p<0.01, * p<0.05Models predicting online participation: Poisson regressions, only internet users. Models predicting offline participation: binary logistic regressions, full sample.

Table 4: Regression Models predicting E-participation and Offline Participation