Post on 15-Apr-2017
Beyond digital crowdsourcing -
how the Estonian People’s
Assembly solved a crisis of
democracy
Nele Leosk
Alexander H.Trechsel
European University Institute
April 27, 2016
Crowdsourcing
is defined as a public invitation by an actor
or a group of actors for any person or
organization to participate in a process
geared at finding solutions to a societal
problem (adopted Brabham 2008; Howe 2006; Landemore 2014)
Contributions
• Staging the process
• Impact analysis
- Inclusiveness
a) Preconditions
b) Who gets involved?
c) Whose voice is heard?
• Political outcomes
- institutional changes
Topics
• Financing and financial reporting (of political
parties)
• Political parties and party system
(establishment, membership)
• Public participation in policy making between
the elections (open policy making, petitions,
etc)
• Electoral system regulation
• Political patronage and corruption
• Varia
Crowdsourcing: 1970 registered
users 0
100
200
300
400
Fre
que
ncy
1/7/2013 1/30/2013Day of registration
Type of participation Obs Mean Median SD Min Max
Voted 1568 40.43878 13 79.13851 1 1226
Commented 394 7.241117 2 18.74878 1 258
Presented Ideas 644 3.031056 1 12.40387 1 276
05
01
00
150
200
250
Fre
que
ncy
0 20 40 60 80 100Votes (for and against)
05
01
00
150
Fre
que
ncy
0 10 20 30 40Comments
0
100
200
300
400
Fre
que
ncy
0 5 10 15 20 25Ideas
Discussion seminars by actors’
group 1. Participation 2. Financing 3. Political
patronage
4. Parties 5. Elections Total
Politician 8
(25%)
10
(29.4%)
9
(30%)
7
(30.4%)
10
(31.3%)
44
(29.1%)
Expert 14
(43.8%)
12
(35.3%)
13
(43.3%)
10
(33.5%)
12
(37.5%)
61
(40.4%)
Citizen 9
(28.1%)
11
(32.4%)
7
(23.3%)
6
(26.1%)
9
(28.1%)
42
(27.8%)
Media 1
(3.1%)
1
(2.9%)
1
(3.3%)
0
(0%)
1
(3.1%)
4
(2.6%)
Total 32
(100%)
34
(100%)
30
(100%)
23
(100%)
32
(100%)
151
(100%)
Rep. by political party Seminar 1 Seminar 2 Seminar 3 Seminar 4 Seminar 5
Participation Financing Political
patronage
Parties Elections Total
SDE 2
(22.2%)
3
(27.3%)
2
(22.2%)
1
(9.1%)
4
(33.3%)
12
(23.1%)
RE 1
(11.1%)
2
(18.2%)
2
(22.2%)
3
(27.3%)
1
(8.3%)
9
(17.3%)
IRL 0
(0%)
1
(9.1%)
0
(0%)
1
(9.1%)
1
(8.3%)
3
(5.8%)
KESK 3
(33.3%)
3
(27.3)
3
(33.3%)
3
(27.3%)
3
(25%)
15
(28.8%)
EER 1
(11.1%)
1
(9.1%)
0
(0%)
0
(0%)
0
(0%)
2
(3.8%)
ERKE 1
(11.1%)
1
(9.1%)
2
(22.2%)
2
(18.2%)
3
(25%)
9
(17.3%)
DEM 1
(11.1%)
0
(0%)
0
(0%)
0
(0%)
0
(0%)
1
(1.9%)
Total 9 11 9 11 12 52
Representation at the DDay Participants Population
1. Gender (n=299)
Male 45% 45%
Female 55% 55%
2. Age (n=294)
18-35 18% 34%
36-55 32% 34%
56+ 50% 32%
3. Education (n=299)
Elementary 4% 17%
Secondary 18% 27%
Vocational 26% 33%
Higher 52% 24%
4. Living place (n=296)
Town 64% 70%
Country 36% 30%
Transparency
• Guaranteed at most stages
- Transparent at the initial crowdsourcing and
the final Deliberation Day phase
- failed at the ideas systemisation and
analysis phase
• National public broadcasting company as media
partner
- local media less involved
• Mainly new technologies and social media
dependent: People’s Assembly portal, online
streamings, FB
Inclusion
• Aimed at all inclusiveness
• Random, targeted and self-selection
mechanisms used
• Politicians, experts (public officials,
representatives of civil society
organisations, academia), and the public
involved at all stages
- Yet, not equal at all stages
• Full representativeness not reached
Political outcomes • Out of the 15 presented ideas:
– Two fully implemented (regulation on popular
initiatives, lowering the no needed for
establishing a pol. party)
– Four partly implemented
– Three included in the Coalition Agreement of
the Government (assumed office March 2014)
Longer term impact:
- Vabaerakond established in 2014 with 650
members, 8 seats in the Parliament (2015)
- www.rahvaalgatus.ee (2016)
Some conclusions • Crowdsourcing suitable for addressing
highly salient societal issues:
– tensions smoothened by involving the
public;
- can have considerable political impact
• Various selection mechanisms needed to
improve representativeness (gender and
age)
• Cooperation betw. different societal
groups