Social Sanctioning and Voter Turnout in Emerging Democracies
Danielle F. Jung
Emory University
[email protected]
James D. Long
University of Washington
[email protected]
February 25, 2016
Abstract
Why do citizens in emerging democracies turn out and vote? Particularly given
voting is costly and individuals do not prove pivotal to outcomes. Work has previously
focused on two key explanations for voter mobilization: 1) access additional individual material benefits such as vote-buying through patronage, 2) a sense of duty and
attachment to their ethnic group or political party. We find that in addition to these
explanations there is a third: voters may mobilize to avoid negative social sanctions
from other community members for not voting. Favoring the third explanation, we
argue that voting can be understood as an individual investment in collective goods
and therefore community members must cooperate and coordinate to vote and field
electoral winners. We test our theory using original data from a survey we conducted
after the 2010 Wolesi Jirga parliamentary elections in Afghanistan. We find the avoidance of negative pay-offs from social sanctioning drives mobilization, while strength of
ethnic attachments have no impact on turnout. Vote buying is only a positive predictor
of voting amongst voters who do not trust their neighbors.
Acknowledgments We acknowledge generous funding from Democracy International
(DI) to complete the survey, as well as support from DI staff including Glenn Cowan,
Eric Bjornlund, Alessandro Parziale, Jed Ober, John Gatto, and Jeremy Wagstaff. Mohammad Isaqzadeh and Shahim Kabuli provided invaluable research assistance. We
thank Eli Berman, Karen Ferree, Clark Gibson, Adam Glynn, David Lake, and Jake
Shapiro for comments. Jung acknowledges support from ESOC. All errors remain with
the authors.
Wordcount: 8988
On September 18, 2010, roughly 5.6 million Afghans turned out to vote for members
to the Wolesi Jirga, only the second parliamentary and fourth national election since
the overthrow of the Taliban in 2001.1 Afghanistan holds limited experience with
democratic institutions, lacks established political parties, and suffers high levels of
underdevelopment. Voters went to the polls despite a history of violent election days
and increasing attacks by insurgents during the campaign period.2
Given these enormous challenges, why did many Afghans vote? The extent and
motivations of voter turnout have long bewildered social scientists. According to rational choice approaches, the costs associated with voting are sufficiently large and
the probability any one vote proves decisive sufficiently small that a pure cost-benefit
analysis should yield few, if any, voters (Downs 1957, Riker and Ordeshook 1968).
Yet citizens frequently turn out to vote (Cox 1997, Mackie 2003, Morton 1991, Nickerson 2005), and in emerging democracies at a rate that is sometimes higher than
consolidated democracies (Blaise 2000, Kasara and Suryanarayan 2015, Kostadinova
and Power 2007).
To explain these patterns, scholars offer amendments to cost-benefit calculations,
examining both psychic and material pay-offs including individuals’ desire to adhere
to social norms (Gerber, Green and Larimer 2008, Huckfeldt and Sprague 1995, Sinclair 2012), feelings of “duty” towards democratic principles (Downs 1957, Riker and
Ordeshook 1968), or affective ties of membership to political parties or ethnic groups
(Dickson and Scheve 2006, Horowitz 1985, Uhlaner 1986). Direct contact with political
agents like parties or canvassers may provide voters information encouraging participation (Gerber, Green and Green 2003, Gerber and Green 2000, Gerber, Green and
Larimer 2008, Green and Gerber 2004, Green, Gerber and Nickerson 2003) or gifts like
money or food in exchange for support (Kitschelt and Wilkinson 2007, Kramon 2013,
1
Source: IEC certified results. Afghanistan held presidential elections in 2004, 2009, and 2014; parliamentary elections in 2005 and 2010.
2
Over 500 violent incidents occurred on election day in 2009 (Weidmann and Callen 2012).
1
Posner 2005, Stokes 2005, Wantchekon 2003).
Mapping these insights to motivations for political behavior in transitioning societies like Afghanistan proves particularly difficult, however. On the one hand, citizens
emerging from non-democratic regimes may not yet express significant support for, or
understanding of, the role of the vote and the institutions for which they are selecting. Political parties could face numerous challenges mobilizing voters given a lack of
experience and resources. The threat of violence from incumbents or insurgents may
deter individuals who believe they could be targeted. These factors suggest low levels
of participation. On the other hand, the strength of ethnic or religious groups that
pre-date democracy could serve as important coordinating mechanisms that political
actors exploit to drive turnout. Citizens in new democracies—most of them poor—
could be susceptible to forms of vote-buying, offsetting costs of voting. These realities
indicate high potential for mobilization.
We take a new approach to adjudicate these plausible, but often divergent, predictions regarding levels and motivations for turnout in transitioning democracies. Our
point of departure analogizes voting to a prisoner’s dilemma where individuals vote
to make personal investments in collective goods (Popkin 1994), whose provision they
delegate to leaders at the ballot box (Powell 2000). Voting therefore forms a classic
collective action problem: individuals will enjoy the benefits of the shared goods that
elected politicians provide regardless of whether they pay the individual cost of participation. Citizens in a democracy must therefore cooperate and maintain a level of
turnout that helps support their community’s interests.
To solve this problem, we study the population dynamics of communities in Afghanistan
and first argue that features of communities and the electoral environment in emerging
democracies increase the likelihood that individuals turn up to the polls. Citizens in
poor countries live in highly vulnerable settings, often lacking basic services and suffering perilous security and economic conditions. While communities frequently rely
on self-help for survival, the selection of leaders to democratic institutions provides another important potential avenue for improving welfare where government bodies, like
2
parliament, are tasked with the provision of needed services. However, communities
must solve cooperation problems and have their members mobilize to the polls to create
a democratic mandate by expressing these demands to elected leaders. We therefore
argue there are strong social pressures among community members to vote. We think
fear of exposure as a violator of community norms induces many individuals to behave
in ways they normally would not; for example, voting when they might otherwise stay
home, given the (high) costs to turn out.
To enforce the norm to vote and punish defectors, community members require the
ability to monitor individual behavior. We further argue that the electoral environment
in emerging democracies provides a number of mechanisms for monitoring because
voting remains a highly visible act. In these settings, voters easily observe who turns
out. Communities are clustered tightly around the schools, community centers, or
houses of worship (like mosques) that serve as polling stations and village focal points.3
Voting often requires long queuing and community members can see many of those who
vote. Afghan voters’ fingers are marked with indelible purple ink identifying them as
having voted on election day and for many days after. Because voting is so visible,
community members can pressure people to vote and identify defectors if they do not.
We hypothesize that the fear of sanctioning, driven by community expectations to vote
and the visibility of the act motivates voters to turn out, helps solve the community’s
collective action problem and drives turnout.
Second, we argue that features of the underlying social structure in communities can
either strengthen or attenuate the degree to which community members are likely to
cooperate to vote. Specifically, a community’s level of social capital, measured by trust
between community members, shapes the degree to which coordination is made easier
or harder. We hypothesize that communities with higher levels of social capital need
fewer inducements to punish defectors and therefore produce greater turnout. Conversely, communities with lower levels of social capital have a harder time coordinating
3
In Kabul province, for example, the average geographic area covered by a polling station is 0.2 square
miles.
3
and therefore require more social sanctioning to encourage turnout.
We test our predictions with data from an original survey that we conducted in
all regions of Afghanistan after the 2010 Wolesi Jirga elections. To preview results,
individuals’ perceptions of social sanctioning played an important role in determining
turnout. Neither strong ethnic ties nor expectations of vote-buying consistently explain
participation. Violence decreased the likelihood of turnout, but not consistently. In
a sign that democratic institutions might be taking root in Afghanistan, we find that
respondents who viewed the Wolesi Jirga as an important institution and one that
provides services are more likely to vote, supporting our intuition that citizens vote
to support collective goods. We also find the effect of social sanctioning is strongest
in areas of lower social capital, whereas it is weaker in areas with higher levels of
social capital. Our findings suggest that while there are several important motivations
towards understanding why citizens vote in new democracies, social sanctioning and
levels of social capital prove critical.
We believe our results contribute comparative insights to three literatures. First,
we tackle a long-running puzzle and add to prior studies on the drivers of turnout by
studying a setting, Afghanistan, where citizens pay perhaps some of the highest costs
in the world to vote, yet millions still participate. We depart from standard accounts
from emerging democracies that focus on ethnicity and vote-buying. Instead, we extend insights on the role of social pressure from studies of American voting (Gerber,
Green and Larimer 2008, Huckfeldt and Sprague 1995, Sinclair 2012), but we locate
the source of the norm for voting as a solution to local collective action and account for
the mechanism that allows community members to monitor voters, reflecting the real
electoral environment in countries like Afghanistan.4 While Afghanistan is a distinct
case in many ways, our focus on the population dynamics of local communities and
how these interact with formal institutions broadly reflects many poor and transition4
Gerber, Green and Larimer (2008) ground the enforcement of a social norm for voting as an extrinsic
motivation for voters, but do not say where this norm comes from, why societies would enforce it, and it
only weakly increases turnout in their study. Moreover, their experimental advertisement of possible social
sanctioning does not seem to reflect real mechanisms to monitor and sanction voters in the American system.
4
ing societies that must overcome local collective action problems to express demands
to the government to receive services.5 Second, we join emergent studies in the social
sciences that generate general predictions about the potential for endogenous solutions
to cooperative dilemmas (Berman 2011, Jung and Lake 2011, Shapiro 2012), testing
these predictions with observational data. Last, we contribute to a growing empirical
literature about the opportunities and challenges political actors, policymakers, and
citizens face with contemporary efforts at democratization and development in countries threatened by non-state insurgents (Albertus and Kaplan 2012, Beath, Christia
and Enikolopov 2013, Berman et al. 2013, Blair, Imai and Lyall 2014, Callen et al.
forthcoming, Daly 2014, Steele 2011).
1
Voter Turnout in Emerging Democracies
Classical rational choice approaches to voting in consolidated democracies arrive at
the theoretical prediction that given the costs associated with voting and the nearzero probability that any one vote decides an election, a pure cost-benefit analysis
ought to produce few if any individuals that turn out. Yet citizens frequently vote.
In order to understand the gulf between prediction and reality, scholars argue that
participation is driven by an innate or psychic “duty” towards democratic principles
(Downs 1957, Riker and Ordeshook 1968) or adherence to social norms (Gerber, Green
and Larimer 2008, Huckfeldt and Sprague 1995, Sinclair 2012). Voters may receive
information and messaging encouraging participation from direct contact with parties
or canvassers mobilizing “get-out-the-vote” efforts (Gerber, Green and Green 2003,
Gerber and Green 2000, Gerber, Green and Larimer 2008, Green and Gerber 2004,
Green, Gerber and Nickerson 2003).6
5
Empirical replication in Ghana and Kenya generates similar results presented here (citation redacted).
Approaches that focus on additional motivations can be modeled as parameters entering an individual’s
utility function, following Riker and Ordeshook (1968). Specifically, p is the probability that a voter’s ballot
is decisive and B is the difference in utility from candidates’ positions or characteristics, D is the duty one
feels, and C is the cost of voting.
6
pB + D > C
5
These insights from industrialized democracies provide analogs to transitioning societies, but do not produce a consistent set of predictions on turnout. Citizens in new
democracies could feel a strong duty to vote despite (or even because of) the newness of
democratic institutions (Bratton, Mattes and Gyimah-Boadi 2005), or unfamiliarity or
experiences with institutions like a parliament could suggest disengagement and a lack
of norms encouraging participation. The strength of attachment to one’s ethnic group
could form a duty to vote to express social identity. In the divided societies of much of
the democratizing world, ethnic groups have histories of relationships before the transition to democracy, which may include conflict and civil war. Citizens may vote to
articulate their identity and demonstrate allegiance to co-ethnics (Dickson and Scheve
2006, Horowitz 1985). Ethnic attachments may overlap with feelings of partisanship in
countries where party identification and ethnicity strongly correlate Chandra (2004).7
Contact from political actors may also encourage electoral participation, although
along different dimensions from consolidated democracies. In new democracies, inchoate political parties and candidates frequently lack the organizational strength
and/or experience to mobilize voters through traditional get-out-the-vote efforts (Bratton and Kimenyi 2008). Therefore, political actors may resort to more targeted strategies to identify and mobilize supporters, including individual material incentives in
the form of vote-buying and patronage (Kitschelt and Wilkinson 2007, Kramon 2013,
Posner 2005, Stokes 2005, Wantchekon 2003),8 off-setting the costs of voting. Given
resource constraints among the electorate in poor and under-developed democracies,
With a low value of p and constant B, an individual is likely to vote when their feelings of duty are higher
than costs. Gerber, Green, and Larimer 2008 add to this by dividing the D term and allowing for “intrinsic”
(i.e., psychic) and “extrinsic” (i.e., material) elements:
D = U (DI , DE )
Where D equals the utility of intrinsic DI and extrinsic DE . The value of D, and therefore the likelihood of
voting, can increase with increases in either intrinsic or extrinsic motivations.
7
President Hamid Karzai had issued a decree to make political parties illegal in Afghanistan, and therefore
we focus on ethnic, rather than party, attachments.
8
Much of the literature from emerging democracies argues that vote-buying and patronage occur along
partisan or ethnic lines. The attraction towards parties or ethnic groups from expectations of material
incentives is analytically distinct from affective ties of membership. Therefore, while psychic and material
pay-offs can produce the same observable implication (voting), we treat these as distinct mechanisms.
6
voters could be particularly susceptible to this form of influence.9
We agree that psychic and material incentives in the form of ethnic attachments
and vote-buying could play important roles in elections in emerging democracies. However, we argue these factors on their own do not produce precise or consistent predictions for turnout. Despite the relevance of ethnicity to numerous political processes
in developing democracies, citizens’ actual expressed degree of ethnic attachments are
significantly below observed turnout levels.10 While an individual’s receipt of money
or goods in exchange for a vote plausibly shifts the cost-benefit calculus of voting,11
the amount of payments and the number of voters candidates would need to reach
would be unreasonably high to obtain levels of participation observed in countries like
Afghanistan. With poorly organized parties that lack mobilizing capacities, it seems
extremely unlikely that every voter, or even most voters, receives or expects to receive
gifts or money in exchange for their vote. Additionally, contingent strategies like votebuying like work when politicians can monitor whether and how a person voted.12 On
top of barriers to party organization, a secret ballot makes it very hard for politicians to
confirm whether a person has indeed cast a ballot in their favor (Ferree and Long nd).
While violations of the secret ballot can occur, by and large voters’ privacy is maintained, including in Afghanistan where voters cast ballots behind cardboard screens
(Democracy International 2010).13 Therefore, we think the costs of payments and bar9
Relatedly, other studies look at the correlation between socio-economic characteristics of citizens in poor
democracies and their propensity to vote (Bratton, Mattes and Gyimah-Boadi 2005, Kasara and Suryanarayan 2015).
10
For example, Robinson (2014) uses Afrobarometer data from 16 countries and finds that only about
16 percent of respondents (N=22,115) identify with their ethnicity more than their citizenship. Bratton
and Kimenyi’s (2008) pre-election survey for Kenya’s 2007 election finds 20 percent of respondents identify
themselves ethnically, echoing results from Horowitz and Long (N.d.) and Long (2012). The election had 70
percent turnout. Assuming all of the roughly 20 percent of ethnic identifiers in Kenya voted, that leaves a
50 percentage point gap between strong ethnic identifiers and actual turnout.
11
This could substantially reduce C or reflect a net benefit rather than positive cost, or be modeled as
DE .
12
Rueda (2016) suggests small polling station size makes it more likely for well-organized brokers to monitor
vote-buying, but Afghanistan lacked political parties in this election and candidates could not consistently
place agents at most polling stations to monitor the voting and count processes.
13
In Ghana, exit poll data shows that only 17 percent of voters report secret ballot violations, close to the
average rate reported across Africa in the Afrobarometer (Ferree and Long nd). In our pre-election survey
from Afghanistan, 66 percent of respondents reported they believed their ballot to be secret, 24 percent not
secret, and 11 percent did not know. However, of the people who said not secret, only 38 percent of those
7
riers to monitoring suggest that vote-buying alone does not fully explain variation in
turnout.
Violence may also shape voter behavior. While not all emerging democracies have
violent elections or ongoing insurgency, many developing countries emerge from conflict where elections stoke fears of renewed violence (Ishiyama 2014). Violence should
depress turnout if voters fear negative repercussions from participation (Collier and
Vicente 2012, Condra et al. nd), especially since prior exposure with violence affects
how individuals calculate risk and influences behavior (Callen et al. 2014).14 Yet some
studies find prior exposure may to violence may increase forms of political participation
(Bellows and Miguel 2009, Blattman 2009).
Our approach
To address imprecise and countervailing predictions on turnout in emerging democracies, we adopt a new approach to understanding voter mobilization that we believe
builds on and expands prior approaches, and better accounts for the levels and motivations of turnout. In this theory section, we explore the importance of social sanctioning
within communities to overcome the collective action problem of voting, and we account
for predictions of alternative explanations.15
We depart from prior studies that explicitly or implicitly analogize turnout as a utility maximization problem for individuals. Instead, we analogize voting as a prisoner’s
dilemma where individuals vote to make personal investments in collective goods (Popkin 1994),16 whose provision they delegate to leaders at the ballot box (Powell 2000).
cited a candidate or political agent as the source of the violation, others were more likely to mention family
members.
14
Driscoll and Hidalgo (2014) report that a civic education campaign in Georgia may have suppressed
turnout among opposition supporters. They speculate voters interpreted the campaign as evidence of increased political attention from the regime.
15
We derive our predictions on the levels and motivations of turnout formally from an Agent-Based Model
(ABM). These predictions are generally intuitive. For a full account of the model, we refer readers to the
online description.
16
Analogizing voting as a problem of an individual’s utility maximization focusing on cost-benefit analysis
suggests that the parameters increasing the likelihood of turnout, like D or DI and DE , are act and not
outcome-contingent: an individual would receive the positive pay-off, whether intrinsic or extrinsic, by voting
8
Voting therefore produces a collective action problem: individuals will enjoy the benefits of the shared goods that elected politicians provide regardless of whether they
pay the individual cost of voting.17 Citizens must therefore cooperate and maintain
a level of turnout that helps support their community’s interests and overcome the
coordination problem. Patterns of cooperation and coordination within a population
faced with prisoner’s dilemma-ordered payoffs is therefore analogous to aggregate voter
turnout because individuals have incentives to free-ride.
Table 1 depicts the problem faced by voters according to our framework. We
examine strategies and pay-offs at both the community and individual levels in Table
1 to help frame what drives cooperation. A community has two potential voters, V1
and V2 . Let us first assume neither player turns out, defecting with the action “Stay
Home.” The pay-offs at the community level are negative: there is no community
investment in goods since no one voted to delegate provision to an elected leader.
Without representation and the mandate that comes with high turnout, the community
receives nothing from this level of government.18 Individually, V1 and V2 do not receive
any of the psychic or material benefits that they could gain from voting, they potentially
experience social sanctioning from not voting (we define and examine social sanctioning
below), yet they do not pay any costs (such as losing time or wages, or experiencing
regardless of the outcome and regardless of whether anyone else voted. We analogize voting as an individual
investment in collective goods to explain cooperation to overcome the collective action problem inherent in
turnout. Our approach suggests that part of an individual’s decision to vote is formed by one’s expectations
of others and shows why individual utility maximization through psychic and material benefits does not
predict turnout on its own (although those pay-offs certainly enter a voter’s calculus).
17
In later work, Green and Gerber (2010) refer to adherence to social norms to vote as a side-payment
to solve a collective action problem, but do not explain why voting is a collective action problem or why
communities develop norms to vote.
18
This outcome does not mean that communities necessarily lack services overall— in this stylized setup, we examine voting for a representative as the means to receive services in this particular election
(Afghanistan’s parliamentary race). Were no Afghans to vote and therefore seat no parliamentarians, communities could potentially receive services from other levels of government. The theoretical counterfactual
motivating individuals to vote as investments in collective goods is not necessarily a lack of overall services,
but rather exclusion from the budgetary and policy-making powers of the representative in a given election.
Afghanistan’s electoral rules make the importance of community voting and representation salient: each
province serves as a multi-member district with relatively large magnitude, but winning candidates typically only garner votes from specific locales where they can rely on strong support (Callen and Long 2015).
Therefore, communities who did not cooperate and elect a member would lack representation in parliament
relative to those who had.
9
Table 1: Turnout as a Cooperation Problem (Prisoners Dilemma)
Voter 2
Stay Home
Voter 1
Turnout
Turnout
Stay home
Community outcome
Higher investment
Community outcome
Middling investment
Individual outcomes
Individual outcomes
V1 & V2 :
Benefits: Psychic benefits, potential
social benefits, material benefits
Costs: A day’s wage, risk of violence
V1 :
Benefits: Access community benefits,
psychic benefits, material benefits
Costs: A day’s wage, risk of violence
V2 :
Benefits: Access community benefits,
keep day’s wages, no risk of violence
Costs: no psychic or material
benefits, potential social sanctioning
Community outcome
Middling investment
Community outcome
No investment
Individual outcomes
Individual outcomes
V1 :
Benefits: Access community benefits,
keep day’s wages, no risk of violence
Costs: A day’s wage, risk of violence
V1 & V2 :
Benefits: No lost work or time,
no violence
Costs: no psychic or material benefits
V2 :
Benefits: Access community benefits,
psychic benefits, material benefits
Costs: A day’s wage, risk of violence
violence).
Now we examine what happens if V1 and V2 both cooperate and “Turnout” (upper
left cell).19 The pay-offs for the community include higher investment, equivalent to
19
Our logic here reflects the belief that increasing voter turnout should increase the likelihood that representatives pursue policies in line with the preferences of the median voter, creating more (but not perfect)
pareto-efficient distribution relative to lower voter turnout with skewed ideal points and provision.
10
the community doing as well as it can in terms of receiving collective goods from
representatives given delegation through voting. Both players also receive individual
positive pay-offs including psychic and material rewards, they avoid any negative social
sanctioning, but they pay costs in terms of time and wages, and potentially experiencing
violence.
Last, we view the result if one player cooperates, “Turnout,” while another defects,
“Stay Home,” indicated in the upper right and lower left cells. At the community level
in either scenario, there is middling investment,20 compared to higher investment when
they both turn out or no investment if they both stay home. If one player takes the
suckers pay-offs and turns up, they receive psychic and material rewards, avoid social
sanctioning, and avoid costs but risks violence. The player who does not show up does
not receive additional benefits, potentially receives social sanctioning, but does not pay
costs or risk violence.
Table 1 shows that regardless of the benefits or costs that may accrue to a citizen
from voting, the motivations for turning out also include how electoral outcomes affect
the provision of goods to the community in which individuals live. We recognize that
who and what constitute these environments are highly contextual and undoubtedly
vary across countries, within countries, and over time. However, since democratic lawmakers legislate on the basis of groups or locales and not (solely) individuals, electoral
outcomes affect collectives. Voters therefore face a constant problem of cooperation in
order to ensure that their group or area successfully fields electoral winners. Higher
turnout demonstrates more active participation in creating a democratic mandate relative to lower turnout.
Therefore, we think of voting as both act and outcome contingent. On the one
hand, we focus on explaining the act of voting, rather than the victory of any particular candidate. Our theoretical interest in turnout is driven by understanding the
20
Similar to fn 18, we argue that even if some community members vote, but turnout is lower than if more
individuals coordinated, the likelihood that the community yields a local candidate to parliament and that
the preferences of the median voter are communicated to politicians declines relative to situations in which
there is higher turnout and therefore community investment.
11
puzzle inherent in the collective action problem of why citizens pay the individual costs
of voting even if their vote is unlikely to affect the outcome, and any outcome is shared
among community members, whether they voted or not. Therefore, solving the puzzle
requires explaining an act-contingent behavior. Second, for whom a person votes is
much less visible (if at all) than whether a person votes. The strategies community
members employ to observe a person’s candidate selection therefore requires a separate
line of empirical work that we do not investigate here. However, our assumption driving our analogy for voting—an individual investment in collective goods—necessarily
requires that voters care about electoral results and whether winning candidates will
distribute goods to reflect the preferences of those who voted. While we remain agnostic about the particular identity of electoral winners, we argue that greater turnout
among members of a community should increase the likelihood of distribution of goods
to the community ceteris paribus, compared to lower turnout.
Explaining Turnout
Next, we explore the logic behind how communities overcome collective action problems
and mobilize individuals to cooperate to vote. Critically, we focus on why and how
community members work towards a cooperative equilibrium when both V1 and V2 turn
out, and the desire of both players to avoid the negative costs of social sanctioning if
they defect and stay home.
We first argue that features of communities and the electoral environment in emerging democracies increase the likelihood that individuals turn out. While political parties may be weak in developing democracies, communities and social networks are often
strong (Migdal 1988). In states like Afghanistan, citizens frequently lack services and
survival proves perilous. People depend on their community for basic needs like security, food, and shelter. Therefore, violating community norms and ostracization by
the community entails enormous costs.21 This could include exclusion from social in21
This dynamic also occurs within ethnic groups (Fearon and Laitin 1996) and religious communities
(Berman 2011).
12
stitutions (mosques), services managed by communities (common-pool resources), and
schools; or denial of service by a shopkeeper or shunning from a neighbor. We are agnostic about the precise form of sanction or the rate at which punishment is rendered
(or by whom). Indeed, it likely varies by individual sanctioner and sanctionee as well
as context, and need not be a coordinated effort by the community as a whole, but
rather could operate between two individuals. We think the simple fear of exposure as
a violator of community norms induces people to behave in ways they otherwise would
not, for example voting.
Though communities frequently must rely on self-help and governments face challenges to provide needed services, citizens still use democratic institutions like elections
to voice their demand for goods in developing democracies. Electing members to parliament should increase the likelihood the citizens receive goods and policies within the
purview of that parliament and their local representative.22 But communities must
solve cooperation problems and turn out to vote to delegate and make these demands
of elected leaders. We therefore argue that there are strong social pressures to vote in
states like Afghanistan and violating community norms entails costs.
Even in light of these strong social pressures, what mechanism drives turnout and
how do communities guarantee (high levels of) cooperation? Community members require the ability to monitor individual behavior to enforce norms and punish defectors.
Because voting remains a highly visible act, we argue that the electoral environment
in emerging democracies provides monitoring capacity. In most emerging democracies,
voters can observe who turns up because communities are tightly clustered around
schools, community centers, or houses of worship that serve as polling stations and
village focal points. Voting is logistically difficult and frequently requires long queuing. Polling stations host on average a few hundred voters, many of whom are visible
to other voters and community members as they turn out.23 Even more so than in
22
Despite its youth, many Afghans view parliament as an important government institution. In our survey
(described below), 77 percent of Afghans believe the Wolesi Jirga is very important to improving their life
and 55 percent believe the opportunity to vote for parliament improves the services in their neighborhood.
23
In Afghanistan, we calculate an average of 1000 voters per polling station, and fewer than 500 voters
per polling stream.
13
consolidated democracies, in emerging democracies, community members can see who
votes.24 Moreover, Afghan voters’ fingers are marked with purple indelible ink to show
that they voted. Electoral ink, which can last up to a week, is a feature of election administration in many poor countries to prevent fraudulent double-voting (Ferree et al.
Nd). But ink also allows voters to identify themselves on election day and for many
days after. Because voting is visible, community members can monitor and pressure
people to vote and identify defectors to sanction if they do not, days after the election
itself.25
Second, we argue that features of the underlying social structure in communities
strengthen or attenuate the degree to which community members are more likely to
cooperate to vote or to defect. We contend that a community’s level of social capital,
measured by trust between community members, shapes the extent of coordination.26
In areas with higher social capital where people trust each other more, community
members are more cooperative and therefore require sanctioning less. Despite the fact
that many Afghans typically live and vote in communities with neighbors who come
from similar socio-economic and ethnic backgrounds, decades of internecine conflict
have plausibly broken social bonds in some areas and/or strengthened them in others.
We argue that communities with higher levels of social capital need fewer inducements
to punish defectors (Fearon and Laitin 1996), and therefore have greater turnout. Conversely, communities with lower levels of social capital have a harder time coordinating
and therefore require more social sanctioning to encourage turnout.
24
XX percent of Afghans on our survey report that they perceive community members know whether they
voted or not.
25
The inking of fingers is an iconic image of elections in Afghanistan and Iraq, demonstrated by many
members of the American Congress displaying purple fingers during George Bush’s 2005 State of the Union
address. Inking fingers is common in many consolidating democracies, including Iraq (Al-Jazeera N.d.),
Egypt (Serwer 2014), and India (BBC 2009). The Taliban explicitly threatened to cut off the fingers of
anyone who had ink, a strategy they reprised during the 2014 elections.
26
In the developing world, scholars note a robust relationship between social capital and trust (Fafchamps
2004).
14
Hypotheses
We now offer testable predictions derived from our theoretical framework regarding the
effects of social sanctioning and social capital on turnout. Our approach allows us to
derive predictions about alternative explanations, including the psychic and material
pay-offs from Table 1. From the literature, we specify these as feelings of strong ethnic
attachment (psychic) and vote-buying (material). We conceptualize these effects in
terms of predicting overall turnout and also comparing the strength of the mechanisms
against each other.
We hypothesize that the threats or perception of social sanctions against an individual from other community members drives cooperation (voting). Turnout will
increase as penalties for not turning out become increasingly bad. Social sanctioning
results in a negative payoff, decreasing the value (and net advantages) of staying home
on election day. Within our framework, we think of this as making already negative
payoffs from a lack of community investment increasingly negative for the individual.
As those penalties become smaller, turnout decreases significantly—leaving only those
with a particularly high intrinsic reason to turn out at the polls. The net payoff to such
an outcome need only be slightly less than what the voter would otherwise get from
not voting to induce dramatic increases in predicted turnout overall. The magnitude of
social sanctions (or punishment) therefore need not need be particularly high to have
a dramatic effect.
H1: As individuals’ perceptions of social sanctioning increases, so does their likelihood
of turning out to vote.
Because the intuition behind social sanctioning relies on individuals’ beliefs about
how their neighbors are likely to behave, we explore the effect of variation in levels
of social capital, measured as differential trust in neighbors, on turnout. We believe
the strength of social sanctioning varies inversely with community levels of trust. High
levels of trust indicate an individual’s belief that others in the community will invest
15
in community public goods, whereas low levels of trust indicate a belief that others
in the community will not invest. We predict trust will attenuate the effect of social
sanctioning on voting. Following similar logic, voters who do not trust others in their
community are more likely to rely on psychic or material pay-offs that offset their individual direct costs to turning out.
H2: Higher levels of social capital decrease the strength of social sanctioning on voting.
Alternative Explanations
Voting to receive psychic benefits from expressing ethnic attachments requires participation alongside of co-ethnics with similarly strong attachments. If voters place a
high salience on turning out for reasons of ethnic affinity (independent of the source of
that salience), then the benefits to turning out (particularly mutual turnout) decrease
as either affective ties or the salience of those ties decrease. Regarding Table 1, we
consider this up-weighting or a “bonus” for any individual payoffs for turning out to
vote (or down-weighting staying home).
H3: As individuals’ ethnic attachment increases, so does their likelihood of turning out
to vote.
Vote-buying includes a material good provided by a party or candidate to an individual in exchange for turning out. This is equivalent to adding to the individual voter’s
expected payoff for voting, or increasing the community (and therefore individual) payoff for high turnout relative to staying home. While vote-buying may change certain
individuals’ calculations from “stay home” to “turn out” in circumstances where the
difference between the costs and benefits of voting is relatively small, we find that these
incentives must be significantly large to overcome the direct and opportunity costs of
not voting, and vote-buying must be distributed at very high rates to induce observed
16
turnout levels.
H4: As individuals’ perceptions of the importance of vote-buying increase, so does their
likelihood of turning out to vote.
Our theoretical approach provides important comparative insights towards understanding voting in emerging democracies. Importantly, we differ from standard accounts that focus on the importance of ethnic attachments and vote-buying, although
we account for psychic and material pay-offs into our model. Our theory contributes to
insights from scholars in American politics that analyze whether individuals’ desires to
adhere to social norms drives turnout (Gerber, Green and Larimer 2008, Huckfeldt and
Sprague 1995, Sinclair 2012). However, we provide distinct and important extensions
to this general argument, applying it to a new setting. First, these studies assume
a social norm for voting, but do not say where it comes from or why it is enforced.
While such a norm may exist, there is no automatic reason to assume it extends to
new democracies where citizens are unfamiliar with democratic institutions and practices.27 We locate the voting norm in developing democracies not within democratic
values per se (although those could exist), but rather in the necessity for communities
to overcome local collective action problems to mobilize voters to elect leaders to receive services. Second, we provide a mechanism through which monitoring of the vote
is possible and likely given the visible nature of voting in Afghanistan and other new
democracies. Voting is more private in the US many voters now vote by mail, and the
social clustering around polling stations with electoral ink does not have analogs in the
American system. Americans may announce their voting status on social media (Bond
et al. 2012), but this is a choice and does not map to a person’s local community. The
monitoring and sanctioning capacity mentioned in Gerber, Green and Larimer (2008)
is fairly artificial and does not reflect features of the electoral environment in the US.
27
Social norms could have pushed voters in Afghanistan away from the ballot box in areas where insurgents
exert influence, especially given the Taliban’s threats against voters.
17
This perhaps explains the relatively weak results on the social norm treatment in their
study.28 Conversely, our mechanism is inherent to the electoral environment given
extremely local polling station placement and inking.
Setting
After the US invasion and fall of the Taliban in 2001, Coalition forces established a Constitutional Loya Jirga to create democratic institutions in Afghanistan after decades of
civil war and Taliban rule. The Loya Jirga placed Hamid Karzai in power as president
and created a new parliament (Wolesi Jirga). Afghan voters ratified Karzai in the
first presidential elections in 2004, followed by Wolesi Jirga elections in 2005. In 2009,
the country held presidential elections, with a disputed result between Karzai and his
closest challenger, Dr. Abdullah Abdullah, when Karzai failed to achieve 50 percent
+1 of votes to retain office. Abdullah refused to participate in a run-off leaving Karzai
in the presidency.
In the shadow of this contentious presidential election, Afghans went to the polls
in September 2010 to elect members to parliament. Afghanistan has a unique electoral system and electoral rules. The country is divided into 34 provinces from which
members of parliament are elected in a multi-member provincial-wide district through
a single non-transferable vote (SNTV). Kabul yields the highest seat share (33) and
Panjshir the lowest (2). Even though candidates run at large within the province,
given a large number of candidates for a large number of seats, candidates typically
only receive support from the communities where they come from, where their votes
typically obtain from family members and related co-ethnics. This reality underscores
the salience of our theoretical concern involving the localism of community action towards voting for leaders that represent community interests.
28
Threatening to reveal turnout status to neighbors results in an 8 percent/percentage point increase in
turnout. In our results, while not experimental, we find a much larger average effect of social sanctioning on
voting.
18
Data and Methods
We test our hypotheses using data from an original survey in Afghanistan that we designed and conducted after the September 2010 Wolesi Jirga elections.29 We fielded the
survey directly after the government finished adjudicating electoral disputes and certified winning candidates for parliament. Several challenges affected our ability to draw
a sample. Afghanistan has not conducted a recent census and has no voter registry,
making any proportional distribution of the sample difficult and based on poor estimates. Security problems related to the ongoing insurgency and other violence made it
dangerous to conduct surveys in many districts. Therefore, a nationally-representative
survey of Afghanistan was impossible. As a result, we focused enumeration on areas
within capital cities across 19 of 34 provinces, in all regions of the country, including
all military commands and the capital city Kabul.Within provincial capitals, we used
polling centers as primary sampling units and instituted random walk patterns for selection of households and random selection of respondents (yielding a 50 percent female
sample). In total, we surveyed 3,048 Afghans in 471 polling center catchment areas in
all regions of the country.30 Our sample biases towards more urban and safer areas
under government control. While our results are not representative to the country as
a whole, they are to the 19 provincial capitals sampled.
We note two important and unique elements of our research design critical for
hypothesis testing. First, our core theoretical interest regarding people’s perceptions
of social sanctioning as a motivation for voting requires individual-level survey data
regarding measures of respondents’ beliefs about the likelihood that community members monitor and sanction voting. Second, our sampling procedure enumerated within
polling center precinct areas because our theory involves how voters perceive the likely
behavior of their neighbors when deciding whether to vote, and therefore provides a
more identified effect of social sanctioning with polling centers as focal points, as op29
We join other studies using survey data to understand Afghans’ political and social attitudes (Beath,
Christia and Enikolopov 2013, Blair, Imai and Lyall 2014, Condra et al. nd).
30
The IEC gazetted 5548 polling stations; our sample represents 8.5 percent of the total.
19
posed to studies that do not take this into account. Therefore, our survey provides the
best, and only, data source capable of testing our hypotheses.
Dependent Variable
Our dependent variable is whether or not an individual voted. We rely on self-reported
voting31 and generate our dependent variable from the question: “Did you turn out
and vote in the Wolesi Jirga elections in September 2010?” We code “yes” responses as
1 and “no” as 0. Of our respondents, 67 percent replied that they voted.32 Trying to
employ actual turnout figures incurs a number of challenges in Afghanistan. Lacking
a recent census and because there is no voter registry linking individual voters to a
particular voting station, it is impossible to know within any polling center how many
eligible voters could have voted in relation to how many ballots are cast. Additionally,
any registry that did exist would likely contain significant errors.
We acknowledge opportunities and challenges using self-reported turnout. Our
study requires operationalizing turnout at the individual level to examine how perceptions of social sanctioning affect a person’s propensity to vote. Survey data allows for
this, whereas administrative turnout data would not allow us to examine motivations
at the individual level. However, some respondents may incorrectly report that they
had turned out when they had not, creating bias in one direction potentially artificially inflating the number of possible voters. We believe that if this bias exists, it is
31
This method follows standard approaches of voting behavior from industrialized and emerging democracies employing survey-based measures of turnout (e.g., Bratton, Mattes and Gyimah-Boadi (2005), Kasara
and Suryanarayan (2015)). To our knowledge, it is administratively impossible to follow Gerber, Green and
Larimer (2008)’s method to obtain public records regarding individual-level turnout in Afghanistan, or any
other emerging democracy, and would violate most countries’ election guidelines. It is also nearly impossible to directly observe turnout, with two exceptions. Exit polls, or surveys of voters at polling stations
after they have voted, allow enumerators to validate a respondent’s inked finger (Ferree and Long nd). The
security situation and threats against polling stations prevented us from conducting an exit poll. Second,
a household survey directly after election day when enumerators could view ink on a respondent’s finger
could verify turnout (Ferree et al. Nd). However, given fears of post-election violence and credible threats
against voters with marked fingers by the Taliban, we could not enumerate a survey until the conclusion of
the election process after the ink was no longer visible.
32
We note that the reported turnout number in our Afghan survey falls significantly below many of the
Afrobarometer surveys. We believe turnout reports from Afghanistan could be more accurate than other
countries because there is less social desirability to falsely report turnout given difficulties in voting and the
likelihood of violence.
20
orthogonal to our core theoretical measures driving turnout because our question on
whether a respondent voted came at the very beginning of the survey, before any of the
question that represent our main independent variables or covariates. Therefore, and
critically, any bias would not account for the differences between the potential effects
of these variables. Moreover, while 67 percent could overestimate the likely national
turnout figures from the IEC, we limited our survey to areas that were more urban and
safer, making voting easier and less prone to ballot-stuff than rural and more conflictaffected areas of the country. We are also skeptical that social desirability bias inflates
reported turnout. In a pre-election survey that we conducted in the month before the
election, 76 percent of respondents said they intended to vote. This declines nine points
in the post-election follow-up, asking about actual turnout. If social desirability bias
drives the result, intended and reported turnout should be the same, with no drop-off
between rounds of the survey, which is not the case. Even using official statistics, it is
impossible to know the real turnout rate because without a voter registry there is no
denominator of total possible voters to calculate a proportion. Nonetheless, we performed a validation check described in Appendix B.1 that provides suggestive evidence
that our reported turnout figure does not depart significantly from actual turnout, and
accords with other approximations recorded by independent election observers. While
we cannot completely exclude the possibility that some respondents misreported their
turnout status, we find it unlikely that the true turnout would be dramatically lower
in our sampled areas or systematically correlated to our independent variables.
Independent Variable
To test for the effects of social sanctioning on the propensity to vote, one needs data on
the beliefs and expectations of individuals only possibly elicited directly from a survey.
According to our theory, social sanctioning requires two components: the perception
that other community members believe others should vote and the perception of the
ability for the community to monitor voting. We build our independent variable from
two questions asking respondents whether or not their neighbors expect them to vote
21
even if undesirable candidates appear on the ballot, and whether they think other
members of their community know whether or not they voted. We believe the intersection of these questions highlights both the social context of voting and the visibility
of voting in emerging democracies. We asked “In your opinion, do you think your
neighbors expect you to vote even if you do not like the candidates?” and “Regardless
of whether you actually voted: In your opinion, do your neighbors know if you voted
or if you did not vote?” From these questions, we generate the dichotomous variable
“Social Sanctioning,” which carries a value of 1 if individuals respond “Yes” to both
questions and 0 otherwise.
We believe this question forms a proxy for social sanctioning for four critical reasons.
First, the question allows us to measure to the extent to which voters build expectations
about the behavior of others with whom they will interact (and cooperate with) in order
to succeed both individually and collectively. The question asks specifically whether a
respondent perceives that others have this expectation of them. If they do not have
that perception, then there is not likely sanctioning for defection. Second, the question
wording explicitly imposes a negative cost on voting by specifying that the candidates
are undesirable. This allows us to investigate an important aspect to understanding
the way in which social pressures and sanctioning should drive turnout: the population
of “sanctioners” are those people who believe that members of their community should
always vote, regardless of the desirability of the candidates. Respondents who answer
negatively do not believe that their community members should always vote, or could
be voting because they like the candidates, and are therefore not likely to sanction
defectors for choosing to stay home given the risks if the slate is undesirable. The goal
of the wording is to isolate the conditions under which we believe sanctioning operates:
when others expect one to vote (and pay the associated costs in opportunity cost or
risk) even if a person does not like the candidates, and that individuals think they
know if that person voted or not.33 Third, we ask the question regarding “people in
33
It is important to specify undesirable candidates to minimize the utility gained from closeness of policy
positions, or B, in Riker and Ordeshook (1968), where phrasing the question with respect to “candidates
you do not like” sets a minimum of utility gained from the difference between an individual’s preferences
22
your neighborhood.” Therefore, our measure probes directly at the social and local
act of voting (and follows our sampling procedure of gaining respondents clustered
near polling stations)—which is highly visible to community members regardless of
the benefits conferred to individually privately. This suggests that people who answer
affirmatively to this question are those who are more likely to sanction members of their
communities, although we are agnostic as to the precise method of sanction. People
who answer negatively are not likely to impose sanctions on defectors. Fourth, our
measure includes monitoring capacity by specifying the visibility of voting. Even if
people have the perception that others expect them to vote, if there is no monitoring
it is unlikely that even in the face of possible sanctioning respondents would vote. We
believe our measure provides an important empirical innovation to the study of voting
in emerging democracies, and follows from our theory.
We recognize that the interaction of responses on expectations from social pressure
and visibility is not necessarily a direct measure of sanctioning. We cannot say what the
sanction is exactly and sanctions will vary within and between electoral settings. But
for our purposes, the more relevant factor is to gain a measure of individuals’ perceptions of possible sanctioning, arising from social pressure and the ability of community
members to monitor. We believe the core elements of these features are captured,
without imposing restrictions on the implementation of a sanction. We use the term
sanctioning for ease of exposition because we believe it captures the potential negative
pay-off citizens would receive from their neighbors if they do not vote, but we recognize
that this concept is consistent with something just shy of an actual sanction, like social
pressure.
and those of the candidate. In the formulation: pB + D > C, this effectively reduces B to zero. That then
focuses on when D > C, allowing us to impute extrinsic motivations in the D term through D = U (DI , DE )
following Gerber, Green and Larimer (2008). When voters gain a lot of utility from their closeness to a
candidate’s position, B increases and offsets the importance of D. Similar to other studies, our approach
therefore focuses on the motivations and likelihood of turnout for citizens who would otherwise be the least
likely to vote with respect to gaining (or all together lacking) utility from the policy positions of their
preferred candidate.
23
Covariates
To create a measure for whether affective attachments to one’s ethnic group and the
associated psychic benefits drove a duty to vote, we first ask respondents their language/ethnic group, followed by “Let us suppose you had to choose between being an
Afghan and being a [insert name of language/ethnic group]. Which of these groups
do you feel most strongly attached to?” This question follows similar measures derived from questions on the Afrobarometer survey (Bratton and Kimenyi 2008, Eifert,
Miguel and Posner 2010, Robinson 2014). We create the dichotomous variable “Ethnic
Attachment” which takes a value of 1 for ethnic identifiers who responded that they
felt strongly or mostly attached to their language/ethnic group, and 0 otherwise.34
Measuring the extent of vote-buying in a given election is difficult in a survey
because respondents may be unwilling to give truthful responses given negative perceptions of the practice. For that reason, we do not ask Afghans whether they had
received patronage directly, but rather whether they thought candidates providing gifts
to voters was important: “Thinking about the upcoming elections, candidates may reward their supporters with gifts and money in exchange for support. Do you think it
is very important, somewhat important, or not very important that political parties
reward their supporters with gifts and money in exchange for support?”35 We create
the dichotomous variable “Vote-Buying” to carry a value of 1 responding to positive
responses to this question “very important,” and 0 otherwise. This variable captures
attitudes and expectations about vote-buying—not its de facto level, better fitting our
prediction (contingent strategies like vote-buying only work if voters express that they
desire or expect gifts in exchange for voting, a dynamic captured by our question).
34
Measuring the strength of ethnic attachments poses difficulties given that while ethnic identity itself is
an easy concept to define, how “close” a person feels towards a group is less clear. However, our question and
similar questions are validated against each other by generating similar response frequencies across surveys
in the same country, like Kenya where 17-20 percent of respondents identify closely with their ethnic group
(Bratton and Kimenyi 2008, Horowitz and Long N.d., Long 2012). In a different survey that experimentally
manipulated treatments regarding the degree of ethnic voting, Long (2012) also validates this approach and
finds that about 20 percent of Kenyan voters use primarily ethnic cues when choosing candidates for office
suggesting strong feelings of ethnic attachment.
35
This question construction follows the Afrobarometer, Ferree and Long (nd) and Kramon (2013).
24
We also phrase the question to read as though positive responses were not socially
undesirable.
We include controls that may contribute to turnout in the Afghan context. One
important factor in emerging democracies that could depress turnout is a lack of knowledge or interest in new institutions. In Afghanistan, while a variety of local councils are
common to mediate between local disputants, the Wolesi Jirga as a national institution
with elected members did not exist in Afghanistan before its establishment in 2005.
We ask: “Now I want you to think about role of the Wolesi Jirga in Afghanistan’s
government. Is the Wolesi Jirga very important, somewhat important, somewhat not
important, or not at all important in helping to improve life in your neighborhood?”
We code the variable “Wolesi Jirga Importance” “very” and “somewhat” important
as positive responses as 1, and 0 otherwise. We also probe the perceived link between
voting in the Wolesi Jirga elections and the provision of local services asking: “In your
opinion, does the opportunity to vote in the Wolesi Jirga elections increase the quality
of services in your neighborhood?” We code the variable “Services” 1 if they respond
“Yes” and 0 if they respond “No” or “Don’t know.” In the presence of social sanctioning, we highlight that both of these questions link voting to the provision of collective
goods.
To measure the effects of local violence on a citizen’s calculus of whether or not
to vote, we ask: “Have you lived in a neighborhood that has experienced attacks in
the last 5 years?” We code the variable “Neighborhood Violence” 1 if they responded
“Yes”, and 0 if they responded “No” or “Don’t Know.”36 Given predictions from the
literature about what demographic covariates likely correlate with turnout, we include
controls for whether a respondent is male, urban/rural residence,37 literacy (proxying
education), and access to electricity (proxying income). Given threats made by the
Taliban against voters in predominately Pashtun areas, we also include a dummy for
36
We used data on local attacks in the pre-election period, those results are reported in Appendix B.1.
We note very little variation in this variable given the necessity of enumerating the survey in mostly
urban areas in provincial capitals. So “rural” in our survey really means outside of the direct downtown
area, but still within an urban center.
37
25
whether a respondent was Pashtun given that they may have been less likely to vote.38
Results
To model the choice to vote, the dependent variable is whether or not the respondent
says they voted, corresponding to an individual playing the cooperative “Turn Out”
strategy from Table 1. Our theory predicts that perceptions of social sanctioning
should increase the likelihood of turning out. Table 2 presents ten probit estimations
on the likelihood that a voter will turnout, with marginal effects of coefficients (other
variables held at means) and standard errors (in parentheses).We run all models with
robust standard errors clustered at the primary sampling unit level, the polling center.
In Table 2, models 1-4 test the basic predictions of social sanctioning, ethnic attachments, and vote-buying. Models 5-10 add amendments from Afghanistan to the
specification, including the importance and role of the Wolesi Jirga, violence, and
other demographic controls. First, our key independent variable Social Sanctioning,
is positive, highly significant, and substantively large across all model specifications
predicting turnout, giving support to our first hypothesis. In Model 1, a voter who
perceives social sanctioning is nearly 26 percentage points more likely to turn out than
one who does not. Second, the coefficients for Ethnic Attachment is negative and insignificant across most of the models, and the effects of vote-buying are insignificant
across all models and substantively small. The results on the effects of social sanctioning, ethnic attachment, and vote-buying support the predictions from our model
that social sanctioning remains an important driver of turnout, whereas the promise
of individual pay-offs with psychic and material rewards from ethnic attachments and
vote-buying are not.
38
Appendix A reports descriptive statistics for all variables used in analysis.
26
27
Model 1
0.258
(0.02)
0.019
(0.02)
Model 2
-0.042
(0.04)
Model 3
Model 4
0.259***
(0.02)
0.016
(0.02)
-0.072
(0.04)
Model 5
0.257
(0.02)
0.016
(0.02)
-0.069
(0.04)
-0.034
(0.02)
(0.02)
Model 6
0.242***
(0.02)
0.034
(0.02)
-0.06
(0.04)
-0.148***
(0.03)
Pseudo R2
0.0522
0.0002
0.0003
0.0533
0.054
0.0695
N
3048
3048
3048
3048
3048
3048
Marginal effects of probit regression. Robust standard errors clustered by PSU (polling center)
* p<.05, **p<.01, ***p<.001
Pashtun
Electricity
Literate
Urban
Male
Services
Wolesi Jirga Importance
Community Violence
Ethnic Attachment
Vote Buying
Social Sanctioning
0.290***
(0.02)
0.079***
(0.02)
0.055*
(0.02)
0.124***
(0.02)
-0.006
(0.02)
-0.115***
(0.03)
0.1692
3048
Model 7
0.206***
(0.02)
0.024
(0.02)
0.007
(0.04)
-0.021
(0.02)
Table 2: Probit Model on Likelihood of Voting (=1), Marginal effects
0.1224
3048
0.073***
(0.02)
0.011
(0.02)
0.145***
(0.02)
0.026
(0.02)
Model 8
0.246***
(0.02)
0.007
(0.02)
0.016
(0.04)
-0.045
(0.02)
0.255***
(0.02)
0.1715
3048
Model 9
0.214***
(0.02)
0.007
(0.02)
0.021
(0.04)
-0.052*
(0.02)
0.145***
(0.02)
0.261***
(0.02)
0.073***
(0.02)
0.03
(0.02)
0.126***
(0.02)
0.015
(0.02)
Model 10
0.206***
(0.02)
0.017
(0.02)
0.024
(0.04)
-0.024
(0.02)
0.147***
(0.02)
0.250***
(0.02)
0.079***
(0.02)
0.050*
(0.02)
0.119***
(0.02)
-0.01
(0.02)
-0.117***
(0.03)
0.1797
3048
Next, we examine Models 5-10, which introduce variables specific to the Afghan
context. Wolesi Jirga is positive and significant across all models where it is included
and substantively important. In Model 8, respondents who think the Wolesi Jirga
is important for service provision were 26 percentage points more likely to vote than
citizens who do not find it important. These results hold with the inclusion of Social
Sanctioning, supporting the idea that one of the mechanisms driving perceptions of
enforcement could stem from voting as a concern with collective goods. In Model 7,
these respondents are 29 percentage points more likely to turnout than those who do
not link the Wolesi Jirga with local service provision. We note that these results proxy
for people viewing voting as a substantive transaction, in which they vote, delegate
to leaders, and potentially receive services in return. This confirms our intuition that
investment in institutions works in tandem with social sanctioning.
Though statistically significant in only one specification, voters living in neighborhoods that had experienced violence were less likely to vote. We suspect that this
might be the case because our question on violence asked about attacks within the last
five years.39 This may only serve as a partial proxy for actual violence on or near the
election, but it may also reflect mixed results from other studies on whether exposure
to violence increases or decreased the propensity to vote. Male and urban voters were
more likely to turn out, although their substantive effects are small. As expected,
literate voters are significantly more likely to vote, and access to electricity does not
have an effect. Pashtun voters were consistently less likely to turnout. This may arise
from the credible threats that the Taliban placed on potential voters in predominately
Pashtun areas.
Our results show that citizens in Afghanistan are driven to vote given social pressures to do so, whereas individual pay-offs and strong ethnic ties are not important
factors. Moreover, even with nascent institutions, belief in the importance of the
Wolesi Jirga and belief that voting in Wolesi Jirga elections drive turnout; whereas
39
Using data on local (to the polling station) violence, we find the substantive results reported in Table 2
to be identical (See Appendix B.1).
28
fear of violence partially keeps voters at home.
The Effects of Social Capital
Because much of our intuition regarding social sanctioning relies on individuals’ beliefs
about how their neighbors are likely to behave, we explore our second hypothesis on
the effect of differential levels of extant social capital—measured by levels of trust of
one’s neighbors—on turnout. We conduct a similar analysis as above, but separate the
sample into individuals who trust their neighbors, and individuals who do not. We
asked “How much do you trust your neighbors?” Voters who respond “Very much” or
“somewhat” were coded as trusting. Because “trusting” and “non-trusting” people are
likely playing the game in difference kinds of communities that vary in terms of social
capital, we split the sample into one comprised of only trusting individuals (“trust
sample”), and the corresponding non-trusting sample because we want to measure
the differential effects of moving from a trusting context to an untrusting context to
examine whether the effect of social sanctioning strengthens or attenuates based on
the level of social capital.
29
Table 3: Community Trust on the Likelihood of Voting (=1)
Model 3
Model 4 Model 5
0.316*** 0.198*** 0.152***
(0.04)
(0.02)
(0.02)
Vote Buying
0.069
-0.028
-0.023
(0.04)
(0.03)
(0.03)
Ethnic Attachment
-0.014
-0.04
0.011
(0.07)
(0.05)
(0.05)
Community Violence
-0.062
-0.013
(0.04)
(0.03)
WJ Importance
0.120**
0.161***
(0.04)
(0.03)
Services
0.279***
0.247***
(0.03)
(0.02)
Male
0.124***
(0.04)
Urban
0.064
(0.04)
Literate
0.095**
(0.04)
Electricity
0.052
(0.04)
Pashtun
-0.139***
(0.04)
Trust Sample?
No
No
No
Yes
Yes
Pseudo R2
0.082
0.1707
0.2077
0.0379
0.1314
N
1111
1111
1111
1937
1937
Marginal effects of probit regression. Robust standard errors clustered by PSU (polling
* p<.05, **p<.01, ***p<.001
Social Sanctioning
Model 1
0.355***
(0.03)
0.088*
(0.04)
-0.096
(0.07)
Model 2
0.325***
(0.03)
0.053
(0.04)
-0.053
(0.07)
-0.087*
(0.04)
0.125**
(0.04)
0.294***
(0.03)
Model 6
0.151***
(0.02)
-0.014
(0.03)
0.043
(0.04)
0.002
(0.03)
0.156***
(0.03)
0.229***
(0.03)
0.044
(0.02)
0.037
(0.03)
0.132***
(0.02)
-0.043
(0.02)
-0.086**
(0.03)
Yes
0.1581
1937
center)
Table 3 reports probit regression results on the same dependent variable as Table
2, reporting marginal effects. Models 1-4 include the non-trusting sample, while we
run Models 5-8 on the trusting sample. Similar to the full sample from Table 2, across
all models, Social Sanctioning is a significant, positive predictor of voting. We see
that the effect of social sanctioning is larger in the non-trusting sample than in the
trusting sample, suggesting the visibility of the act of voting is important when trust
is low. This finding supports Hypothesis 2: Higher levels of social capital decrease the
strength of social sanctioning on voting. We note that the results reported in Table
3 also encompass a signaling story in which voters want others in their community to
know they are a type that contributes to community goods provision.
30
Figure 1 reports predicted probabilities on the likelihood of voting, first for the full
sample (table 2, model 10), then for the non-trusting subsample (table 3, model 3),
and finally the trusting subsample (table 3, model 6), holding other variables at the
mean.40
Figure 1: Predicted Probability of Voting
In Table 3, Ethnic Attachment remains an insignificant predictor. We note two
additional interesting differences between the trusting and non-trusting samples. First,
Vote-Buying, which we previously found was not indicative of a higher likelihood of
voting, is a significant, positive predictor of voting, but only within the subset of voters
who respond that they do not trust their neighbors. Among voters who do trust their
neighbors, patronage does not increase the likelihood of voting. This finding suggests
that, in addition to social sanctioning, in neighborhoods where trust is particularly
low, vote-buying may be an effective form of mobilization. This finding also suggests
that there are potentially shorter time horizons in non-trusting neighborhoods, where
more immediate, private, although likely smaller, benefits are important; and/or that
40
We note that holding all variables at their means is likely an unrealistic assumption. Predicted probabilities of logistic regressions with identical specifications.
31
selective incentives are more rampant, and perhaps necessary, in areas with lower levels
of social capital.
Discussion and Conclusion
Why do citizens in emerging democracies like Afghanistan vote? In this paper, we test
the proposition that the core calculus of voting involves an individual investment in
collective goods. For the collective investment analogy to be true and voting understood to produce positive social benefits, it must also incur negative social costs in
case of defection. We discover this through our exploration of the importance of social sanctioning, an effect we are able to measure empirically with robust survey data.
Our findings show that individual motivations, including selective incentives conveyed
through strength of ethnic attachments and vote-buying, do not consistently predict
mobilization. By and large, voting is a social act where individuals monitor and sanction one another to produce better services for the community. These effects grow
stronger in communities that lack social capital.
Theoretically, we emphasize the leverage gained in approaching turnout as a social
phenomenon, across a population, rather than a purely individual one. Although individuals decide to turn out or not according to their personal expected utility, they
do so within a population situated in a specific institutional and social environment.
Population dynamics can, and do, affect the core cooperation problem, and not necessarily in an intuitive way. Individuals who might be predisposed to stay home on
election day may change their behavior as a result of these dynamics.
Empirically, our findings contribute to debates regarding mobilization and turnout
in important ways. First, we add to the general understanding of the anomaly of
voting, including within settings where democratic institutions and practices are new.
We see similar trends in Ghana and Kenya (citation redacted), lending credence to our
belief that the theoretical and empirical insights gained here should apply across a host
of multi-ethnic and emerging democracies, including those with an active insurgency
32
that threatens nascent democratic institutions, like Afghanistan.
We also provide important results for policy-makers and demonstrate the relative
salience of various motivations that may drive voters to the polls in new democracies
beyond social sanctioning. Our results show that despite inchoate democratic institutions and contested legitimacy between insurgents, ethnic leaders, and the state, many
Afghans think the Wolesi Jirga is an important institution to their lives and are willing
to participate in democratic processes like elections. While social sanctioning is still
a positive driver of turnout in Pashtun areas where the Taliban threaten individuals
who vote, Pashtuns are generally less likely to turn out, suggesting less buy-in to the
government in Kabul and institutions of government like the Wolesi Jirga. Given that
NATO’s counter-insurgency strategy requires incorporation of the local Afghan population into the belief that the central government is legitimate, exploring the sources
of democratic participation should contribute important knowledge towards this goal.
33
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39
Appendices
A
Descriptives
Variable
Turnout
Social Sanctioning
Vote Buying
Ethnic Attachment
Community Violence
Wolesi Jirga Important
Services
Male
Urban
Literate
Electricity
Pashtun
Trust Sample (=1)
N
Mean
SD
3048
3048
3048
3048
3048
3048
3048
3048
3048
3048
3048
3048
3048
0.669
0.279
0.246
0.056
0.261
0.776
0.555
0.500
0.495
0.653
0.603
0.325
0.635
0.471
0.449
0.431
0.230
0.439
0.417
0.497
0.500
0.500
0.476
0.489
0.469
0.481
Table A-1: Summary Statistics
40
Province
IEC official vote count
Open Streams
Badakhshan
Badghis
Baghlan
Balkh
Bamyan
Daikondi
Farah
Faryab
Ghazni
Ghor
Helmand
Herat
Juzjan
Kabul
Kandahar
Kapisa
Khost
Kunarha
Kunduz
Laghman
Logar
Nangerhar
Nooristan
Paktia
Paktika
Panjshir
Parwan
Samangan
Sar-i-Pul
Takhar
Urozgan
Wardak
Zabul
Total
209429
118452
222818
248030
117602
134662
18329
237257
212084
205191
32535
188552
104812
511138
191169
74750
32122
96561
121076
74552
25898
284405
24699
146929
105067
21971
113727
104940
134037
161616
19269
129409
15093
4438181
446
237
475
574
259
282
67
469
470
370
112
401
242
1111
417
170
87
200
326
181
70
694
54
278
230
80
252
210
279
337
67
255
44
9726
Table A-2: Number of votes by Province among open polling centers. Source: IEC
41
B
Validation and Robustness Checks
B.1
Turnout
One challenge to studies of voting behavior in Afghanistan involves estimating turnout
without a good measure of voting eligible population. While it is impossible to construct the denominator of turnout given the lack of official registration rolls or a census
to calculate voting eligible population, we have done additional “back of the envelope”
calculations given knowledge of the process and available data.41 This measure gives
us additional confidence in using self-reported turnout.
The IEC gazetted stations prior to election day using their estimates of where voters
were likely to turn out. Each polling center was allocated at least one stream, some
were allocated multiple streams within the center. The cap on the number of voters
at a single stream was 600. Provisions were made to add streams should the number
of voters exceed 600. Using the actual number of streams at a station, rather than
the gazetted number, as well as the number of votes recorded at that center, we are
able to estimate a non-traditional measure of turnout. We use the number of streams
multiplied by 600 to give a measure of the maximum theoretical turnout expected by
the IEC. Next, we divided the total number of votes cast at the polling center by the
calculated maximum theoretical turnout. If that turnout estimate was greater than
one, we know that streams were added in increments of 600, giving us an estimate for
the number of streams, in addition to those gazzetted that were added in that center.
If the turnout estimate was below one, we know that no streams were added. Using the
updated number of streams and the total number of votes, we calculate a new turnout
estimate.
Table A-2 shows the number of votes cast at polling centers that were open on
election day, as well as the final number of streams.
41
Author (name redacted) served as an accredited election observer for the 2009, 2010, and 2014 Afghan
elections working with the largest international election observation mission (name redacted). Our calculations are therefore based on intuitions as well as direct on-the-ground experience working alongside of
electoral administrators, civil society groups, media, and independent observers.
42
If one suspects that there is ballot stuffing that could inflate the number of votes,
this measure is likely to be high, but is the most accurate construction we have. We
calculate an estimated turnout rate of 75 percent nationwide, and 73 percent in Kabul.
We note that after the election, the IEC worked with the Elections Complaint Commission (ECC) to adjudicate the authenticity of various claims of ballot-stuffing. This
process
These figures indicate: (1) that turnout was quite high in this election, and; (2) our
self-reported turnout measure is likely to be slightly low, if biased in any direction.
B.2
Violence
As a measure of violence affecting civilians, we use recently declassified incident reports submitted by ISAF forces and Afghanistan military and police forces that report
combat occurring between ISAF units and insurgents, commonly known as significant
activity or SIGACTs.
As a robustness check to using self-reported perceptions of violence, which for the
reasons discussed above we believe is the correct measure of the concept, we also conducted a robustness check using more recent attacks data, referred to as ”SIGACTS”
(for ”significant activity”). SIGACTs are declassified reports on violent activity between insurgents and US/ISAF forces. We use SIGACTS data that are geo-coded to
the nearest polling center, our primary sampling unit, to measure highly local attacks
within the six months prior to the election.
Table A-3 reports these results. Model 1 is specified identically to Table 2, model
10. Model 2 here is identical to Table 3, model 3– estimated on the non-trusting
subsample. Model 3 is specified as Table 3, model 6, on the trusting subsample. We
note that SIGACTs data are not available for all of the polling centers, so our samples
are slightly smaller than in Tables 2 and 3 above. We note no substantive changes
using this alternative to violence.
43
Table A-3: Likelihood of Voting (=1), Marginal Effects Violence Robustness Check
Social Sanctioning
Vote Buying
Ethnic Attachment
SIGACTs
WJ Importance
Services
Male
Urban
Literacy
Electricity
Pashtun
Constant
Sample
N
Probit, Marginal effects. Errors clustered at the PSU
* p<.05, **p<.01, ***p<.001
C
Model 1
0.683***
(0.07)
0.023
(0.07)
0.115
(0.11)
-0.000***
(0.00)
0.400***
(0.07)
0.729***
(0.06)
0.220***
(0.06)
0.216**
(0.07)
0.346***
(0.06)
-0.035
(0.06)
-0.381***
(0.07)
-0.663
Full
2790
Model 2
0.912***
(0.13)
0.193
(0.11)
-0.073
(0.18)
-0.000**
(0.00)
0.282**
(0.10)
0.735***
(0.10)
0.279**
(0.10)
0.189
(0.11)
0.255**
(0.10)
0.120
(0.10)
-0.456***
(0.10)
-0.776
Not Trusting
1028
Model 3
0.566***
(0.09)
-0.093
(0.09)
0.269
(0.17)
-0.000***
(0.00)
0.472***
(0.09)
0.722***
(0.08)
0.150*
(0.07)
0.212*
(0.09)
0.418***
(0.08)
-0.142
(0.08)
-0.269**
(0.09)
-0.561
Trusting
1762
Description of ABM
We derive our predictions on the levels and motivations of turnout formally from an
Agent-Based Model (ABM). These predictions are generally intuitive. This appendix
is intended to give the reader a better knowledge of the structure and parameters of the
Agent Based Model (ABM or the model) used. The appendix includes the expected
utility calculations used by the agents, the default settings and a discussion of each
parameter, as well as a more in-depth discussion of the predictions of the model. We
divide this appendix into two sections. In section B.1 we give a brief overview of the
44
method and illustrate the simulations that produce the hypotheses we describe above.
In section B.2 we discuss the model and initial settings more specifically.
C.1
Hypotheses
The theory described above the intuitive results of our simulations of various turnout
environments on population levels of turnout. We use an Agent-Based Model (ABM)
of cooperation to manipulate the basic Prisoner’s Dilemma setup described above, and
explained in Table 1 in the text. The discussion here is intended for those interested
in how we derived the predictions.
To derive and test predictions on turnout, we employ an ABM. As described by
Axelrod, agent-based modeling provides a way to do “thought experiments.” In this
paper, most of the propositions are fairly intuitive when thinking about groups of
actors who are incentivized to solve cooperation problems like those faced by voters,
but the simulations serve as a way to verify the underlying intuition. The ABM allows
us to introduce the role of population dynamics and individuals’ reputations within
the population as key characteristics that increase or decrease cooperation (Jung and
Long 2011). These characteristics may include payoffs to the game, individuals’ beliefs
about the population, and affective ties.
We model turnout as a problem of cooperation at its core, and only secondarily a
problem of coordination. This dynamic is captured in a prisoner’s dilemma-like framework, where ideal points are taken into account. Patterns of agent cooperation and
coordination within a population faced with Prisoner’s Dilemma ordered payoffs is
analogous to voter turnout, especially since individuals have incentives to free-ride, as
they will enjoy the benefits of distribution regardless of whether or not they turnout.
Voters also prefer to turnout with others with whom they have strong ties. We are
agnostic as to the source of these ties (they may be ethnic, social, partisan, ideological). In order to capture this concept—of affective ties– theoretically, we subtract a
weighted penalty from the benefits to mutual turnout. Effectively, this means that
cooperation/turnout with people who are unlike you on this dimension provides less
45
utility than cooperation/turnout with people who are similar to you on this dimension.
Our ABM generated hypotheses follow from the same theory as in citation redacted.
A population of voters face a decision to turnout or stay home summarized in Table
A-1. Their payoffs are ordered according to the classic prisoner’s dilemma. These
voters face the cooperative dilemma summarized above, and will pay various costs to
turnout.
Figure A-1: Default payoffs for Turnout simulations
We simulate 100 agent populations where we look at the population effects of pairwise interactions to cooperate (turnout) or defect (stay home), where agents face varying incentives and costs to voting in the face of social sanctions, patronage, ethnic
attachments, and violence. Agents seeking mechanisms to overcome cooperation problems can make use of weak political parties, social networks, as well as the payoffs for
cooperation. For a more detailed description of the model and the emergent properties.
Here, the basic model of cooperation used in Jung and Lake (2011) was modified to
reflect a comparative lack of partisanship and institutionalized parties in Afghanistan,
as well as higher probabilities of both electoral corruption and violence. For even more
detailed information on the underlying structure of the model, and robustness checks,
see the Supplementary Materials in Jung and Lake (2011).
In each simulation we look at the cooperation rates in the population. Because
we see cooperation as analogous to turning out to vote, these correspond to simu-
46
lated turnout rates in the population. Each prediction results from varying the basic
incentives to turnout or stay home. We present comparative statics that sweep these
parameters from low to high and track turnout in that population. The default settings
reflect a weak party infrastructure as well as a relatively low level of partisanship. The
single non-transferable vote with large district magnitudes has impeded a lack of political party development in Afghanistan, and nearly all candidates run as independents.
Therefore, there is no de facto level of partisanship among Afghan voters.
The strategy below is to simulate the various turnout environments we believe
will impact turnout, and track predicted turnout. The underlying pattern of turnout
generated informs out predictions.
C.1.1
Social sanctioning hypothesis
Within the prisoner’s dilemma setup described in Table 1, we think about a social
sanctioning environment being one in which there are increasingly negative payoffs from
a lack of community investment in public goods. The modeling framework allows us
to decrement the payoff for mutual defection over multiple iterations of the simulation
and track the rate at which agents (voters) cooperate.
Figure A-2 shows the turnout on the y-axis as social sanctions for not voting increase
(or the DD payoff becomes worse, read from right to left). Like the figures below, this
is a comparative static result. Moving from right to left, the figure demonstrates
that turnout increases dramatically as the threat or perception of negative payoffs
for staying home increases. Conversely, as those penalties become less costly, turnout
decreases significantly—leaving mainly strong partisans. Indeed, the net payoff to such
an outcome need only be slightly less than what they would otherwise get from not
voting to induce dramatic increases in predicted turnout. Social sanctions of this sort
therefore need not be particularly costly to deliver to have a dramatic effect. We
therefore argue that the social sanctioning mechanism is an important predictor for
explaining the expressed levels of turnout witnessed in Afghanistan.
H1: As social sanctions increase, turnout (cooperation) increases.
47
Figure A-2: Turnout levels as Penalties for not participating become increasingly large.42
C.1.2
Patronage/Vote-buying hypothesis
We assume vote-buying includes a tangible good or service provided by a party or
candidate in exchange for turning out. Within the framework of the PD, this is equivalent to adding to the voter’s expected payoff for turning out, or increasing the payoff
for mutual cooperation. Figure B below illustrates changes in level of turnout created
by simulating increases in the benefits to mutual cooperation (delivering patronage).
Figure A-3 shows the comparative static results of moving both up and down from the
standard payoff of 3, in increments of 0.2. These increases in the payoffs (along the
x-axis) produce dramatic results in the predicted level of turnout, but only as the payoffs for mutual turnout become increasingly large compared to the status quo benefits
to turnout.
Immediately we can see that payoffs need to be unreasonably high to obtain participation above what is observed in Afghanistan. Essentially, ceteris paribus, an added
payoff of about 1.0 unit, or half of the expected long-term communal returns to turnout,
would be needed to achieve high levels of cooperation driven by patronage. The credibility of nascent Afghan parties and politicians to have the resources available to offer
incentives large enough to offset the disincentives to vote seems questionable. Additionally, the human and physical infrastructure to target and identify cooperative voters,
48
and deliver these rewards seems lacking. Therefore, we do not think that patronage
alone, or any marginal payouts through vote-buying, can explain higher levels turnout.
Figure A-3: Cooperation/Turnout as benefits to mutual cooperation increase (patronage) 44
C.1.3
Ethnic Attachment Hypothesis
Figure A-4 simulates the turnout obtained by increasing the weight on affective ties.
Mechanically, this is equivalent to subtracting the weighted difference between agents’
randomly assigned ideological/ethnic values. Theoretically, the larger the weight on the
difference that gets subtracted from any cooperative outcome should decrease turnout
rates. These comparative static results show that high values on the salience on these
affective components should in fact slightly decrease cooperation/turnout, or localize
it. Essentially, when the costs to cooperating with people whose ideal points are distant
from their own increase, cooperation in the population is not significantly affected– people are only willing to cooperate with those who are ethnically very similar. This could
result in pockets of cooperation when the affective ties/ethnic groups are geographically concentrated, but what we see below is that even large increases in the salience
of identity, does not seem to affect turnout. Specifically, H3: A stronger attachment
to one’s ethnic group does not affect turnout (cooperation).
49
Figure A-4: Cooperation/Turnout as the strength of identity increases.
C.2
45
Technical Appendix
In this portion of the appendix, we outline the ABM’s mechanics (rather than the
results) in greater detail. We discuss the types of agents, the setup of the simulations,
expected utility calculations, and the default parameters.
Voters, as agents, play a PD in which they have an assigned strategy: all cooperate
(ALLC), all defect (ALLD) or tit-for-tat (TFT). Agents also have an individual ideal
point [0,1]. This is designed to capture the idea that not all cooperative actions are
created equal—two agents on the far left may view mutual cooperation as more beneficial than one of those agents will feel cooperation with an agent on the far right will
be. To capture this, instances of mutual cooperation can be thought of as conducted
at the midpoint of the two players’ ideological preferences. This weighted difference is
subtracted from the payoff for cooperation.
The model begins with user specification of the parameters. Payoffs are set. Each
of the four outcomes of a PD (i.e., CC, CD, DC, and DD) is specified. In our model,
higher payoffs to the CC outcome are analogous to tangible benefits from voting, such
as personalistic goods like patronage received through vote-buying. They may also be
akin to the positive psychic benefits that an individual feels from voting to affirm their
50
identity or otherwise support their “duty” to vote. We think of the CC outcome as
occurring when an individual and the randomly selected member of her community
both turn up at their polling station. The CC outcome should indicate investment
in the collective goods. Additionally, worse payoffs for not voting, the DD outcome,
are analogous to a social punishment from not voting, in which case sanctioning from
community members drives cooperation. The DD outcome occurs when an individual
actor defects against a randomly chosen member of her community, who also defects.
This community has minimal investment in collective goods. The CD and DC payoffs
are the situation in which free-riding takes place: either the individual or its community
fails to invest, producing a socially sub-optimal investment.
Next we set the population of actors. The number of actors of each strategy type
is allocated to determine the predisposition to cooperation. “Nice” populations are
populated predominantly with ALLC and TFT agents, “nasty” populations are heavy
on ALLD strategy types.
The affective spread is set, but for these examples we do not deviate from a normally
distributed population centered at 0.5. The weight on affective ties is also set. The
higher the weight, the less attractive cooperation with an “unlike” agent becomes. The
focus on “ethnicity” is analogous to the discussion of strong ideological and/or partisan
attachments found in the literature that may drive voting from a sense of duty to one’s
group or achieving psychic benefits from voting. Setting this dynamic allows us to
incorporate psychic explanations for cooperation as a baseline for determining turnout
given hardcore partisans.
To examine turnout, we look at the default rate of cooperation in the population.
Some players will be predisposed to cooperate. Secondly, we will look at the observed
cooperation rate in this simulated world.
Agents begin the simulation randomly paired and playing their default strategy for
a set number of rounds to gather some sense of the population they are in: is it nice
or nasty, are their beliefs relatively moderate, or are they assessed heavy penalties for
defection? These beliefs will continue to be updated as voting is iterative, even though
51
agents (voters) have some baseline beliefs that aid their decision-making. In the case
of voting, this could arise from witnessing turnout in previous elections.
After the short learning phase, agents are given the option of leaving the standard
PD to join either a network or a hierarchy. The network allows them to buy information
about another player—essentially to find out if the person they are paired with in the
next round is likely to cooperate or defect, and if they are likely to have to pay a heavy
penalty ideologically for playing this person. The fee is exogenously set. Communities,
such as villages in Afghanistan, are analogous to potential networks of this kind.
The hierarchy is a way for agents to buy third party enforcement to mandate
cooperation amongst member players. Joining this organization mandates cooperation
amongst members. If an agent is paired with another member of the hierarchy in
a round, it cooperates at the mandated rate, or is assessed a penalty for suckering
someone in its organization. A large number of players using this form of organization
will increase the cooperation rate in the population, particularly if these players are
ALLD types. Hierarchies are exogenously created, at a specified ideal point (at which
cooperation takes place), with a known rate of induced cooperation and penalty. Here,
they are analogous to political parties or ethnic organizations.
After players have chosen their organization, they play a randomly chosen member
of their community according to their strategy as well as their organizational choice.
The simulations that look at rates of cooperation that result from these environments
are detailed in section B.1 of the appendix, and their intuition in the body of the paper.
Expected Utility Calculations This section defines and explains the expected utility
calculations that agents make when deciding to join a market, hierarchy or network.
In addition to the user-defined parameters summarized in Table 1, agents are defined
by their probability of cooperation (γ), which is either fixed (ALLC γ = 1 and ALLD
γ = 0) or variable (TFT γ = 0 or 1). For purposes of calculating an agent’s expected
utility (as opposed to the actual payoffs defined above in the text), kij = w(|pi −ρ—/2),
where ρ is the agent’s belief (continuously updated) about the mean ideal point of the
population. For the hierarchy, kih = w|pi ph |.
52
In addition, the following endogenous variables are created and updated as the simulation
unfolds:
β = the agent’s belief about the cooperation rate of the population
σ = proportion of the population the agent has not already played
For each agent i :
Expected Utility in the MarketThe payoff for a market interaction is essentially the probability
of getting each outcome—based on the probability that the actor itself will cooperate (determined
by their strategy type) multiplied by the probability that they believe their opponent will cooperate
(determined by their beliefs about the cooperation rate in the population).
M = (γβR − kij ) + γS(1 − β) + βT (1 − γ) + P (1 − γ)(1 − β)
(1)
Expected Utility in Network for Fixed Strategy Players
M −φ
(2)
Expected Utility in Network for Contingent Strategy Players The value of the network is essentially likelihood that the player receives information about its current partner that changes its
behavior (in most cases to prevent being suckered, or receiving the CD payoff) plus the likelihood
it does not, less the fee imposed to join the network and gain information (φ).
n
m X γ
σ[
βα )(βR − kij ) + P (1 − β)] + M (1 − σ) − φ
(
n−1
(3)
γ=1
Expected Utility in the Hierarchy The utility for entering a hierarchy will depend on the proportion of the population in the hierarchy the player will join (θ), weighed against the likelihood
of cooperation within the hierarchy (q), the punishment for defection (v), the tax (τ ) and the ideal
point of the hierarchy (ph ).
θ{(q 2 R − kih ) + qS(1 − q) + [qT (1 − q) − v] + [P (1 − q)2 − v]} − (1 − θ)M − τ
53
(4)
Agents choose that organization with the highest expected utility in each round. Actual payoffs
may differ from expected payoffs for any individual agent, but on average will be equal.
Table A-4: Default Parameter Values for Simulations
Parameter
Symbol
Description
Default Value
Times the simulation is run incrementing
20
General
Increments
a parameter
Repetitions
Times the identical simulation is repeated
5
with different random seeds
Rounds
Mean
for
ideal point
Number of rounds of play
20
Distribution of actors’ policy preferences
0.5
in population
Weight on policy preferences
1.0
Learning
Set as either number of rounds or popu-
10 rounds
rounds
lation convergence to within a proportion
Weight
on
W
ideal
of the true population mean
100
Agents
(Total)
All Cooper-
Number of actors of type always cooperate
ate
All Defect
Number of actors of type always defect
TFT
Number of actors playing tit-for tat strategy
Payoffs
R
R
Payoff for CC outcome
3
S
S
Payoff for CD outcome
0
T
T
Payoff for DC outcome
5
54
Table A-4 – Continued
Parameter
Symbol
Description
Default Value
P
P
Payoff for DD outcome
1
θ
Proportion of the population in hierar-
10
Hierarchy
Initial size
chy. In first round of play, this variable is
set exogenously; after the first round, this
variable is endogenous and defined as the
number of players in the previous round.
Penalty
V
Penalty for defection within the hierarchy
0.5
Prob of Co-
Q
Rate at which the agents cooperate with
0.99
operation
other agents in the hierarchy
Tax
τ
Tax assessed on members of the hierarchy
0.2
Ideal point
ph
Ideal point of the hierarchy
0.5
Cost
φ
Fee for joining the network
0.2
Width
α
Number of past cooperative partners each
3
Network
agent i can ask for information about
agent j
Depth
L
Number of levels agent i can survey
3
Memory
mn
How many past moves each agent remem-
5
bers within the network
55