Nationalism and Social Sanctioning Across Ethnic Lines€¦ · fects sanctioning in Kenya { namely increased punishment of outgroup violations against ingroup members { is at odds

Nationalism and Social Sanctioning Across Ethnic Lines:Experimental Evidence from the Kenya-Tanzania Border∗

Sangick JeonIndependent Researcher

[email protected]

Tim JohnsonAssistant Professor

Atkinson Graduate School of ManagementWillamette University

[email protected]

Amanda Lea Robinson†

Assistant ProfessorThe Ohio State University

2130 Derby Hall540 N. Oval Mall

Columbus, OH 43210P: 614-292-5210 — F: 614-292-1146

[email protected]

Word Count: 4718

∗This research was conducted with generous support from the Russell Sage Small Grants Program in Behav-ioral Economics, the Global Underdevelopment Action Fund at the Freeman Spogli Institute for InternationalStudies at Stanford University, and the Stanford Center for Philanthropy and Civil Society. Valuable researchassistance was provided by Chacha Maroa, Daniel Merengo, Geoffrey Ochieng, and Wango Chaula Wango, andthe Shirati Health, Education, and Development group and Kenyatta University provided research affiliationsfor this work. The research also benefited from helpful feedback at the 2013 Midwest Group in African PoliticalEconomy meeting.†Corresponding author. Authors’ names appear in alphabetical order to reflect their equal contributions.

mailto:[email protected]



Abstract

Past research shows that ethnic diversity reduces the ability to sanction norm violators,ultimately undermining cooperation. We test this directly by experimentally varying theethnic composition of groups playing a dictator game with third-party punishment amongtwo ethnic groups along the Kenya-Tanzania border. We also implement a structurallyidentical game where the endowment division is randomly determined in order to isolatea punishment motivation from the motivation to rectify income inequality. While costlyincome adjustment in both games is driven primarily by norm violations and inequalityaversion, the ethnic composition of groups also influences sharing and sanctioning behav-ior in Kenya but not Tanzania, consistent with documented differences in the strengthof nationalism across the two countries. However, the way in which shared ethnicity af-fects sanctioning in Kenya – namely increased punishment of outgroup violations againstingroup members – is at odds with theories that anticipate that costly sanctioning willprimarily target coethnics.

Societies require communally-determined standards of conduct – i.e., social norms – to

function (Sober and Wilson 1998; Fehr and Fischbacher 2003; Richerson and Boyd 2005),

and individuals thus sanction violations of those norms (Boyd and Richerson 1992), even when

doing so is costly (Fehr and Gachter 2002; Fehr and Fischbacher 2004; Henrich et al. 2006).

However, scholars have argued that enforcing social norms is more difficult in ethnically-

diverse communities because individuals are less willing or able to effectively sanction across

ethnic lines (Fearon and Laitin 1996; Miguel and Gugerty 2005; Shinada et al. 2004;

Habyarimana et al. 2009).

We conduct experiments designed to test this directly, among members of two ethnic

groups in East Africa, the Luo and the Kuria. Participants in our experiments completed

dictator games with third-party punishment and random income games with third-party in-

come adjustment. The latter game is structurally identical to the dictator game except that

a randomizing device allocates income to players, thus allowing us to distinguish between

the punishment of norm violators and efforts to reduce inequality. Random assignment to

groups for each game generated variation in the ethnic make-up of experimental groups, which

allowed us to identify shared ethnicity’s role in social sanctioning and inequality aversion.

Furthermore, we conducted the behavioral games among members of the Luo and Kuria

ethnolinguistic groups living on both sides of the Kenya-Tanzania border, to assess whether

supra-ethnic nationalism can facilitate sanctioning across ethnic lines, ultimately improving

cooperation in diverse settings. Because nationalism has been much stronger in Tanzania

than Kenya (Barkan 1994; Miguel 2004), we expect that ethnic differences will be a greater

barrier to cooperation and sanctioning in Kenya than in Tanzania.

In general, we find that across all coethnicity treatments in both countries, the degree

of norm violation and income inequality are by far the strongest and most robust predictors

of costly sanctioning. We also report fairly weak but detectable differences in the degree

to which ethnicity shapes sharing and (to a lesser degree) costly sanctioning, but only in

Kenya. This suggests that Tanzania’s strong nationalism may indeed alleviate ethnic barriers

to cooperation. However, the patterns of play suggest that when punishment is conditioned on

1

ethnicity, it tends to be focused on outgroup members who fail to share with the third party

punisher’s coethnic. This is at odds with conventional political science theories of ethnic-based

sanctioning, which anticipate that costly sanctioning will be reserved for ingroup members

who violate social norms of cooperation with other ingroup members (Miguel and Gugerty

2005; Habyarimana et al. 2009).

Research Design

Our research design includes two behavioral economic games designed to separate distinct

motivations for social sanctioning. Social sanctioning that is motivated by punishment of

a norm violation is captured using a a classic dictator game with third party punishment

(DG3) (Fehr and Fischbacher 2004), while the desire to rectify inequality is isolated using

the random income game with third party income adjustment (RIG3), a modification of the

game employed in Dawes et al. (2007). Each game has three roles – A, B, and C. The two

games are depicted graphically in Figure 1.

In the first stage of the DG3, A is endowed with 10 tokens and told that she can either

keep the 10 tokens or she can divide them – in any manner – between herself and B. After A

makes her decision, C is endowed with 5 tokens, informed of A’s sharing decision, and offered

the opportunity to spend some of his 5 tokens to reduce the final income of A; for every 1

token C spent, A’s final income is reduced by 3 tokens. In this game, A’s decision about

how much to share with B indicates adherence to a sharing norm, while C’s costly decision

to punish indicates a willingness to sanction a norm violation. The RIG3 works exactly the

same way except that the initial division of the 10 tokens between A and B is determined

randomly rather than by A, and C has the opportunity to reduce the income of either A or

B. As a result, in RIG3, C’s costly decision to adjust incomes is an indicator of inequality

aversion.

Following the framework of Bernhard et al. (2006), we also manipulated the ethnic make-

up of the game partners in order to identify the impact of shared ethnicity on cooperation and

social sanctioning in DG3 (and on inequality aversion in RIG3). We implemented these games

2

Dictator Game withThird Party Punishment

(DG3)

A Bx

Stage 1

C

A

pa

B

Stage 2

PayoffsA = 10 − x− 3paB = xC = 5 − pa

Random Income Game withThird Party Punishment

(RIG3)

Nature

A

10 − x

B

xStage 1

C

A

pa

B

pbStage 2

PayoffsA = 10 − x− 3paB = x− 3pbC = 5 − pa − pb

Figure 1: Games: Each game is played in two stages. In the first stage of the DG3, playerA transferred some amount, x, to player B, keeping 10 − x for herself. PlayerC observed the amount transferred in the first stage, x, and then decided howmuch, if any, to pay to reduce the income of player A. Whatever punishment paidwas tripled and deducted from player A. RIG3 is structurally equivalent to DG3except that x was determined randomly and C was allowed to reduce the incomeof players A or B.

with the Luo and the Kuria, two ethnic groups who reside in southwest Kenya and northwest

Tanzania. In this sense our study is a lab-in-the-field experiment (Grossman 2011). The four

treatment groups, depicted in Figure 2, had the following ethnic compositions:

1. All players A, B, and C are of the same ethnic group.

2. Players A and B are of the same group while C is of another group.

3. Players A and C are of the same group while B is of another group.

3

4. Player B and C are of one group while A is of another group.

Note that each treatment group could be constituted two different ways given that we have

two different ethnic groups.

This design allows us to evaluate not just whether shared ethnicity impacts cooperation

and sanctioing, but also competing explanations about how. Miguel and Gugerty (2005) and

Habyarimana et al. (2009) argue that higher rates of cooperation among coethnics result from

costly sanctioning being ethnically-bound, with evidence coming from Kenya and Uganda,

respectively. These findings suggest that punishment, and thus cooperation, will be more

common when A and C are coethnic (conditions ABC and AC), and especially so when B is

also a coethnic (ABC> AC > AB=BC=0). Fearon and Laitin (1996) are motivated instead

by the surprisingly high rates of interethnic cooperation, and propose two different strategies

that could support cooperation in diverse contexts.1 In the in-group policing model, pun-

ishment is only targeted at coethnics, and especially when coethnics fail to cooperate with

non-coethnics, with the expectation that non-cooperation by non-coethnics will be sanctioned

by other non-coethnics (AC>ABC>AB=BC=0). In the spiral model, by contrast, punish-

ment is directed at both coethnics and non-coethnics who defect against one’s own coethnics

(ABC and BC), but not transgressions against non-coethnics (ABC=BC>AB=AC=0). Fi-

nally, Bernhard et al. (2006), directly evaluated the role of shared ethnicity in sanctioning

behavior among members of two tribes in Papau New Guinea. Contrary to all but one of

the theoretical expectations outlined above, they find that punishment is harshest for trans-

gressions against coethnics (ABC and BC), but also some leniency for coethnic transgressors

(BC>ABC>>AB=AC).

1Fearon and Laitin (1996) propose these strategies in contexts of repeating interaction.While our one-shot games do not allow such repeated play, we nevertheless derive expectationsfor behavior based on Fearon and Laitin’s strategy profiles with the expectation that subjectsoften play one-shot behavioral economic games as if they are in a context of repated socialinteractions (Hoffman et al. 1996; Habyarimana et al. 2007).

4

AB

C

C

AB

AB C

AB

AC C

AB

BC C

AB

Fig

ure

2:

Eth

nic

Con

figu

rati

on

Tre

atm

ent

Gro

up

s:S

had

edci

rcle

sre

pre

sent

pla

yer

sfr

omth

esa

me

eth

nic

grou

p.

5

We also evaluate whether the effects of ethnic affiliations are moderated by the presence

of a strong, supra-ethnic national identity. To do so, our research design exploits the natural

experiment afforded by the political border between Kenya and Tanzania, which was deter-

mined by colonial authorities in the 19th century. Miguel (2004) argues that the arbitrary

nature of this border creates laboratory-like conditions to test the effects of nation-building on

interethnic cleavages domestically, since communities share the same objective cultural differ-

ences, geography, and history on both sides of the border, but differ radically in their exposure

to nation-building policies. While ethnicity has played a central role in post-independence

Kenyan politics, concerted efforts at nation-building in Tanzania – including a common na-

tional language and public education emphasizing a common Tanzanian history and culture

– resulted in a stronger sense of a common Tanzanian identity (Barkan 1994; Miguel 2004).

Thus, implementing the lab-in-the-field experiments with the Kuria and Luo living on each

side of the border permits us to examine how different levels of popular nationalism affect sanc-

tioning patterns across an identical set of ethnic divisions.2 While the differences in national

identification between the two countries was our primary motivation in comparing behavior

on each side of the border, we recognize that there are many other differences between the

two countries that could also shape behavior (see Dunning (2012) for a discussion of “bundled

treatments” within natural experiments in general, and McCauley and Posner (2015) for a

specific discussion of the use of African borders as sources of natural experiments). We are

thus cautious in interpreting any national differences as resulting solely from differences in

nationalism.

While building on past research, our research design is novel in two important ways. First,

our study adds to the designs of Habyarimana et al. (2009) and Bernhard et al. (2006) by

incorporating the RIG3, which allows us to evaluate the effect of coethnicity on egalitarian

2In nationally representative surveys in 2011/2012, Tanzanians were more likely thanKenyans (96% vs. 91%) to say they identified with their national identity at least as much astheir ethnic identity (Afrobarometer 2012). We find similar differences in our sample usingthe same question (98% vs. 92%, t=3.5, p<0.01), as well as finding that Tanzanians weremuch more likely than Kenyans to agree that “even though there is a lot of cultural variety inTanzania [Kenya], we are more the same than we are different” (87% vs. 64%, t=6.6, p<0.01).

6

Figure 3: Map of Experiment Sites. Dotted polygon shows the ancestral homeland of theLuo, and the striped polygon shows the ancestral homeland of the Kuria (Gordon2005). Black circles indicate the two experiment sites.

motives separately from punishment of non-cooperation. Second, we build on Miguel’s (2004)

observational finding that interethnic cooperation is more robust in Tanzania than Kenya by

experimentally manipulating coethnicity directly among members of the exact same ethnic

groups on each side of the border.

Experimental Protocols and Data Collection

The behavioral economic games were implemented separately on each side of the international

border in a rural town – Karamu, Kenya and Sombanyasoko, Tanzania – near the intersection

of the Kuria and Luo homelands (see Figure 3). Participants were recruited using door-to-door

canvassing of randomly selected households in 12 largely ethnically homogenous villages near

the experiment sites. To generate ethnic diversity in each experimental session, participants

7

were recruited from 1-2 villages Luo villages and 1-2 Kuria villages for each session. Potential

participants were informed that they would receive a show-up fee approximately equal to

one day’s wages in the informal economy (300 Kenyan Shillings (KES) or 5,000 Tanzanian

Shillings (TZS) and that it would be possible to earn additional money during the study

depending on the decisions made by participants. Interested individuals were scheduled to

attend one study session and were reminded the day before via text message, when possible.

Of the 672 individuals recruited to participate, 596 (89%) showed-up, 558 were randomized

into treatment groups, and behavioral decisions from 501 were analyzed. Figure A.1 in the

appendix shows the CONSORT diagram for the study.

Research assistants explained both the DG3 and RIG3 games to the 20-30 participants

in each session as a group, and worked through examples to ensure comprehension (see the

appendix for the exact wording and examples used). To generate the random division of the

10 tokens in the first stage of the RIG3 game, we used a wheel that contained 11 values

on its face (in one unit increments from 0 to 10) and a “respin” value (see Figure A.6 in

the appendix). For each RIG3 game, Player C would spin the wheel once to determine the

incomes of A and B in the game. Participants were informed that they would not play with

actual Kenyan/Tanzanian shillings but with tokens that would be exchanged at a rate of 1

token equal to 10 Kenyan shillings or 200 Tanzanian shillings.3

Participants were then randomized into groups of three and into particular roles within

each group by drawing numbers from a hat.4 This random assignment of individuals to

groups generated the assignment to the four different coethnicity treatments in Figure 2.5

333% of our sampled reported that their household had no cash income during the pastweek and another 25% reported earning less than 500 Kenyan Shillings/7500 Tanzanian. Thus,while each token was worth a relatively low sum in absolute terms, the amount at stake ineach game relative to weekly incomes was quite meaningful.

4Only the researchers knew how the numbers drawn corresponded to groups and rolesso that all game partners remained anonymous. See Section E of the appendix for moreinformation about random assignment to groups and treatments.

5In experimental sessions with equal proportions of Kuria and Luo participants, all fourexperimental conditions (ABC, AB, AC, and BC) were equally likely. The more unbalancedthe ethnic composition of the experimental session, however, the more likely the ABC condi-tion became relative to the other three conditions. On average, our 20 sessions were 55% oneethnic group and 45% the other, but imbalance was as large as 71%-29% in one session. In

8

To minimize the possibility of spillovers between games, we designed the treatments such

that each participant made only one decision across both games (and, thus, each participant

was only called into a separate room once). This was done by assigning Player A in DG3

the role of Player B in RIG3, assigning Player B in DG3 the role of Player C in RIG3, and

assigning Player C in DG3 the role of Player A in RIG3 (see Figures A.4 and A.5). Since only

Players A and C in DG3 and Player C in RIG3 make experiment decisions (see Figure 1),

each participant only made one experiment decision for both games. And because subjects

were not informed about the game outcomes until the end of the experiment, participants’

decisions in one game could not have been conditioned on outcomes from the other game.

Subjects did not know the identity of any player during the game and, in keeping with this

design feature, made game decisions in a separate room from other participants. To avoid

revealing our interest in ethnicity, participants were not given the precise ethnic affiliation

of their partners but instead the names of the villages they were from. Because villages are

highly homogeneous in this region, home village conveyed a strong signal of partners’ ethnic

affiliations without cuing participants in to the intention of our group treatments.6 Like the

nationalism “treatment,” however, information about a partner’s village is a also bundled

treatment, conveying information about the likely ethnicity of that partner along with other

information related to village affiliation. However, we recruited from 12 different villages and

the effects of shared ethnicity among group members is averaged over lots of different village

pairings. In addition, we include village fixed effects for each decision maker in order to

this most extreme case, assignment to the ABC condition became 50% more likely than eachof the other three conditions. While such imbalance reduces observations in some treatmentgroups, and thus statistical power, it should not introduce bias in differences across treatmentssince individual characteristics are still orthogonal to treatment assignment.

694% of Kenyan participants and 93% of Tanzanian participants belonged to the ethnicgroup majority in their village. We exclude participants who report being from an ethnicgroup other than the Kuria or Luo (n = 24), and we recode treatment assignment for Kuriaand Luo ethnic minorities (n = 12). For example, a Kuria from a Luo village assigned to roleA in a group considered homogenous (ABC) based on village majorities will perceive her owntreatment as BC instead of ABC. Player C in this group, however, perceives his treatment asABC because he will infer (incorrectly in this rare case) that Player A is Luo given that she isfrom a Luo majority village. We also report the results when these participants are excluded(Tables A.5, A.6, and A.7 of the appendix).

9

capture village level differences in cooperation, sanctioning, and inequality aversion.

After making her decision, each participant completed a brief questionnaire (see Section

F of the appendix). After all decisions were made, participants were individually informed of

their game outcome and given their earnings in cash based on true game decisions. Survey

responses, as well as the experimental decisions, were collected using hand-held mobile devices

equipped with Open Data Kit (ODK).

Results

Sharing was common in the DG3 in both Kenya and Tanzania. The median allocation Player

A gave to Player B equaled almost three tokens in the overall sample. Costly sanctioning in

the DG3 was also common: roughly 56% of Players C in the DG3 spent at least one token to

punish Player A (reducing Player A’s payoff by 3 tokens), 18% spent two or more tokens, and

2% spent 3 tokens. Table 1 reports average levels of cooperative sharing and costly sanctioning

overall, as well as broken down by country and coethnicity treatment.

To determine if coethnicity influenced sharing in the DG3, we estimate a simple linear

model in which A’s transfer to B serves as the dependent variable and binary indicators

of treatment group status serve as the independent variables (with ABC as the omitted

category).7 After estimating that parsimonious model, we add covariates that condition

treatment estimates on Player A’s age, gender, income, education, and village. Estimated

coefficients from these models appear in Table 2, with Models 1-2 reporting estimates for

the Kenyan subsample and Models 3-4 reporting estimates for the Tanzanian subsample.

In Models 1 and 3, which solely contain treatment indicators, we cannot reject the null

hypothesis at the 95% confidence level that the coefficient estimates associated with our

treatment indicators equal zero. Including covariates in the models increases the variation in

sharing that we can explain, but the coefficient estimates for our treatment indicators remain

small relative to their standard errors, save for the coefficient estimate associated with the

AC indicator in Model 2, which is estimated on data from the Kenyan sample. However,

7The results are similar when estimated using an ordered probit model (Table A.2).

10

the parameterization of the model reported in Table 2 only presents comparisons between

the ABC condition and each of the other conditions. Substantively meaningful differences in

sharing might exist, however, when taking into account other comparisons across treatment

groups. As a result, we conduct a series of pair-wise comparisons presented in Table 3. We

present both unadjusted p values and p values adjusted for multiple comparisons using the

Duncan method (Duncan 1955).8

The pairwise comparisons performed in Table 3 indicate significant differences across treat-

ment groups, but only with 90% confidence. In Kenya, participants in AB and AC groups

share less than their peers in ABC groups (Table 3, Rows 1 and 2) and participants in AC

groups share less than BC groups (Table 3, Row 6). This pattern suggests that the presence of

coethnicity between players B and C, which occurs in both BC and ABC groups, yields higher

levels of sharing (almost a full token more, on average). The pattern of play in Kenya is thus

inconsistent with past political science research showing that cooperation is induced by the

threat of sanctioning from an ingroup member (e.g., Miguel and Gugerty 2005; Habyarimana

et al. 2009), and is instead in line with the findings from Bernhard et al. (2006), which show

that pro-social sharing peaks when the potential punisher comes from the same group as the

individual who receives the gains from sharing. Moreover, these results support our general

expectation that ethnicity would influence behavior in Kenya but not Tanzania; we find no

differences in levels of sharing across treatment groups in Tanzania. Thus, albeit with lower

confidence (partially due to the limited statistical power of our tests, see Appendix Section

C), we find evidence consistent with the proposition that the ethnic composition of a group

influences adherence to pro-social sharing norms.

8Our statistical power to detect treatment effects is quite limited by the small samplesizes within each of four treatment groups across the two countries (see Section C of theappendix), making the risk of a Type II error already quite high. Because multiple comparisonadjustments reduce the risk of Type I errors at the expense of increasing the chance of TypeII errors, we utilize the Duncan adjustment, which is less conservative than many alternativemethods of multiple comparisons adjustment.

11

Tab

le1:

Des

crip

tive

Sta

tist

ics

ofE

xp

erim

ent

Ou

tcom

es

Ken

yaT

anza

nia

Fu

llABCDG

,AB

DG

,ACDG

,BCDG

,ABCDG

,AB

DG

,ACDG

,BCDG

,S

am

ple

ABCRIGBCRIGAB

RIG

ACRIGABCRIGBCRIGAB

RIG

ACRIG

Dic

tato

rG

am

e:

Tok

ens

Kep

tby

A7.

326.8

37.5

07.8

66.6

17.4

17.6

17.1

37.4

5(1.9

0)

(1.6

3)(1.8

4)(1.7

1)(1.9

1)(2.1

4)(1.7

5)(2.0

0)(2.0

6)

Toke

ns

Tra

nsf

erre

dto

B2.

683.1

72.5

02.1

43.3

92.6

02.3

92.8

72.5

5(1.8

9)

(1.6

3)(1.8

4)(1.7

1)(1.9

1)(2.1

4)(1.7

5)(2.0

0)(2.0

6)

Toke

ns

Pai

dby

Cto

Red

uce

A0.7

60.6

51.0

81.0

00.7

90.4

80.7

30.5

30.7

6(0.8

0)

(0.8

0)(0.9

1)(0.8

0)(0.8

9)(0.6

5)(0.7

0)(0.7

4)(0.7

8)

Ran

dom

Incom

eG

am

e:

Toke

ns

All

oca

ted

toA

5.4

96.8

65.0

07.5

36.4

75.8

95.0

62.9

54.6

8(3.2

2)(2.8

0)(3.7

0)(2.3

2)(3.6

0)(2.7

5)(3.4

0)(2.6

9)(2.7

0)

Tok

ens

All

oca

ted

toB

4.51

3.1

45.0

02.4

73.5

34.1

14.9

47.0

55.3

2(3.2

2)(2.8

0)(3.7

0)(2.3

2)(3.6

0)(2.7

5)(3.4

0)(2.6

9)(2.7

0)

Tok

ens

Paid

by

Cto

Red

uce

A0.3

20.3

80.4

70.5

30.2

70.6

0.44

0.17

0.1

00.1

8(0.5

2)(0.5

9)(0.5

1)(0.8

0)(0.4

6)(0.5

1)(0.3

9)(0.3

1)(0.3

9)

Toke

ns

Pai

dby

Cto

Red

uce

B0.

270.3

30.1

90.

120.0

70.2

20.1

80.6

70.2

7(0.5

6)(0.7

3)(0.5

1)(0.3

3)(0.2

6)(0.5

1)(0.3

9)(0.8

0)(0.4

6)

Nu

m.

of

Gro

up

s171

2426

2118

2718

1522

Note

:M

eans

wit

hst

andard

dev

iati

ons

inpare

nth

esis

.ABC

repre

sents

trea

tmen

tgro

ups

inw

hic

hP

layer

sA

,B

,and

Care

all

from

the

sam

etr

ibe,

AB

repre

sents

trea

tmen

tgro

ups

inw

hic

honly

Pla

yer

sA

and

Bare

from

the

sam

etr

ibe,

AC

repre

sents

trea

tmen

tgro

ups

inw

hic

honly

Pla

yer

sA

and

Care

from

the

sam

etr

ibe,

andBC

repre

sents

trea

tmen

tgro

ups

inw

hic

honly

Pla

yer

sB

and

Care

from

the

sam

etr

ibe.

12

Table 2: Cooperative Sharing Across Coethnicity Treatments

Kenya Tanzania

(1) (2) (3) (4)

AB −0.667 −0.904∗ −0.204 −0.001(0.501) (0.503) (0.612) (0.589)

AC −1.024∗ −1.054∗∗ 0.274 0.190(0.529) (0.518) (0.648) (0.648)

BC 0.222 −0.071 −0.047 0.326(0.552) (0.538) (0.578) (0.589)

Age −0.290 0.420∗∗

(0.214) (0.207)

Male −0.536 −0.146(0.391) (0.478)

Education Level 0.002 0.026(0.183) (0.409)

Income Level −0.025 0.108∗

(0.054) (0.063)

Constant 3.167∗∗∗ 3.860∗∗∗ 2.593∗∗∗ 1.403(0.362) (0.788) (0.387) (1.117)

Village Fixed Effects No Yes No Yes

R2 0.07 0.29 0.01 0.18Observations 89 89 82 82

OLS estimates with standard errors in parentheses. ABC treatment group omitted.

The dependent variable is tokens transferred from Player A to Player B.

Village fixed effects are based on A’s village.∗p < 0.10, ∗∗p < 0.05, ∗∗∗p < 0.01

13

Table 3: Differences in Cooperative Sharing in DG3 by Coethnicity Treatment

Kenya Tanzania

Difference Unadj. Adj. Difference Unadj. Adj.(Std. Err.) p p (Std. Err.) p p

AB vs. ABC −0.90 0.08 0.08 −0.00 1.00 1.00(0.50) (0.59)

AC vs. ABC −1.05 0.05 0.06 0.19 0.77 0.77(0.52) (0.65)

BC vs. ABC −0.07 0.90 0.91 0.33 0.58 0.58(0.54) (0.59)

AC vs. AB −0.15 0.78 0.78 0.19 0.79 0.81(0.53) (0.70)

BC vs. AB 0.83 0.12 0.14 0.33 0.61 0.61(0.53) (0.64)

BC vs. AC 0.98 0.08 0.08 0.14 0.85 0.86(0.55) (0.71)

Note: Based on Models 2 and 4 of Table 2. Differences reflect the first treatment minus the secondtreatment. Adjusted p values are adjusted using the Duncan method. ABC represents treatment groups inwhich Players A, B, and C are all from the same tribe, AB represents treatment groups in which only PlayersA and B are from the same tribe, AC represents treatment groups in which only Players A and C are fromthe same tribe, and BC represents treatment groups in which only Players B and C are from the same tribe.

14

Next, we evaluate the impact of the coethnicity treatments on Player C’s willingness to

sanction non-cooperation at a personal cost. OLS coefficient estimates with and without the

full set of controls are given in Table 4, again with the ABC treatment group as the omitted

category.9 In both countries, norm violations – as measured by the amount kept by Player A

– positively affect costly sanctioning and are, by far, the strongest predictors of sanctioning.

Moreover, the coefficient estimate associated with the amount Player A kept takes a greater

value in Kenya than Tanzania (z = 4.63, p < 0.01; Table 4, Models 1 and 3).

In terms of the effect of coethnicity, the treatment indicators included in the models of

Table 4 only focus comparisons on a treatment group’s effect relative to the ABC condition. As

a result, we use estimates from Models 2 and 4 of Table 4 to execute all pairwise comparisons

across treatment conditions and we present those results in Table 5. Again, if we lower our

confidence to the 90% level and focus primarily on unadjusted p values, we note modest effects

of coethnicity. In the Kenyan subsample, while controlling for A’s decision in the first stage,

sanctioning by C is slightly greater in BC groups than in ABC groups or AC groups, although

these effects are estimated with considerable imprecision. Thus, A players in Kenya seem

to have correctly anticipated leniency from coethnics in the AC condition and retribution

from non-coethnics in the BC condition, but incorrectly anticipated punishment in the ABC

condition, which was instead treated much more like AC than BC. These patterns are again

most consistent with Bernhard et al.’s (2006) finding that costly sanctioning is greatest in

BC, and inconsistent with the general expectation in political science that sanctioning should

be greatest in homogenous groups (ABC). In Tanzania, we see the same general pattern of

sanctioning across coethnicity treatments (BC≥AB>ABC=AC), but the differences across

treatments are not statistically significant by any conventional standard.


15

Table 4: Costly Sanctioning Across Coethnicity Treatments

Kenya Tanzania

(1) (2) (3) (4)

Amount Kept (A) 0.360∗∗∗ 0.391∗∗∗ 0.113∗∗∗ 0.106∗∗

(0.035) (0.038) (0.041) (0.041)

AB 0.153 0.123 0.185 0.226(0.160) (0.164) (0.227) (0.232)

AC 0.005 −0.023 0.023 −0.094(0.165) (0.175) (0.226) (0.227)

BC 0.284 0.338∗ 0.239 0.283(0.188) (0.192) (0.196) (0.199)

Age 0.014 0.013(0.061) (0.079)

Male −0.207 0.281∗

(0.130) (0.162)

Education Level −0.035 0.016(0.067) (0.100)

Income Level 0.013 0.037(0.018) (0.025)

Constant −1.840∗∗∗ −2.298∗∗∗ −0.335 −0.672(0.267) (0.420) (0.325) (0.488)


R2 0.58 0.65 0.12 0.23Observations 88 88 80 80


The dependent variable is tokens spent by C to reduce income of A.


16

Table 5: Differences in Costly Sanctioning in DG3 by Coethnicity Treatment

Kenya Tanzania

Difference Unadj. Adj. Difference Unadj. Adj.(Std. Err.) p p (Std. Err.) p p

AB vs. ABC 0.12 0.45 0.48 0.23 0.33 0.38(0.16) (0.23)

AC vs. ABC −0.02 0.90 0.90 −0.09 0.68 0.68(0.17) (0.23)

BC vs. ABC 0.34 0.08 0.11 0.28 0.16 0.19(0.19) (0.20)

AC vs. AB −0.15 0.38 0.38 −0.32 0.22 0.25(0.17) (0.26)

BC vs. AB 0.21 0.28 0.28 0.06 0.81 0.81(0.20) (0.23)

BC vs. AC 0.36 0.07 0.09 0.38 0.11 0.11(0.20) (0.23)

Note: Based on Models 2 and 4 of Table 4. Differences reflect the first treatment minus the secondtreatment. Adjusted p values are adjusted using the Duncan method. ABC represents treatment groups inwhich Players A, B, and C are all from the same tribe, AB represents treatment groups in which only PlayersA and B are from the same tribe, AC represents treatment groups in which only Players A and C are fromthe same tribe, and BC represents treatment groups in which only Players B and C are from the same tribe.

17

Sanctioning behavior in the DG3 is thus consistent with past research showing that costly

punishment is more severe for non-cooperation by a noncoethnic affecting a coethnic, at least

in Kenya. However, what appears to be punishment might instead be motivated by a desire

to rectify inequality generated by norm violations, rather than punishment of norm violations

per se. Within the DG3 game alone, these two very different motivations would produce

observationally equivalent behavior. We thus compare these results to the rate of income

adjustment by Player C in the RIG3 game, where inequality between Player A and Player B

was randomly determined.

It is clear from Figure 4 that participants were willing to alter incomes, even at a cost

to themselves, in order to rectify randomly generated income. The figure shows that both

the willingness to alter incomes and the degree of alteration increased with greater inequality.

However, Figure 4 also shows that some participants (n = 16) altered incomes to increase

inequality rather than decrease it, by reducing the income of the player that received equal

to or less than her partner. Such spiteful behavior was not observed even once in the DG3,

and is quite surprising given that such income alteration was costly to player C. There was no

differences in the rate of spiteful behavior across the two countries (t = 1.04, p > 0.10), but

most spiteful income reduction (69%) was targeted at non-coethnics. Because the primary use

of the RIG3 is to determine whether the patterns of income reduction in the DG3 game are

potentially due to inequality aversion, we focus here on income adjustment patterns excluding

the 16 participants who increased inequality.10

We replicate our analysis of sanctioning behavior in the DG3 – conditional on coethnicity

treatments – for income adjustment in the RIG3. Patterns of coethnicity are again captured

by dummy variables for AB, AC, and BC conditions, with ABC as the omitted category.

Unlike in the DG3, Player C in the RIG3 was able to alter the income of either Player A or

Player B: we thus model the reduction of Player A’s randomly generated income separately

from reduction of Player B’s income. The degree of inequality randomly generated is captured

10Results with spiteful participants included in the analysis are reported in Table A.8 ofthe appendix; including those participants does not alter the substantive interpretation of theresults.

18

Figure 4: Player C’s Income Adjustment as a Function of Randomized Inequality

0.2

.4.6

.8Av

erag

e To

kens

Pai

d fo

r Inc

ome

Adju

stm

ent

0 1 2 3 4 5 6 7 8 9 10Random Allocation to Player A

Reduced A Reduced B

by a variable indicating A or B’s allocation. OLS coefficient estimates with and without the

full set of controls are reported separately for Kenya (Models 1-4) and Tanzania (Models 5-8)

in Table 6, and pairwise comparisons across treatment groups are presented in Table 7.11

The results in Table 6 show that in both Kenya and Tanzania the degree of income

inequality between Players A and B in the RIG3 is the strongest predictor of income alteration,

just as it was in the DG3. Furthermore, the ethnic composition of a participant’s group has

no consistent effect on income reduction across models. In models estimated on the Kenyan

sample, the coefficients associated with the coethnicity treatment groups appear small relative

to their standard errors, thus preventing us from rejecting the null hypothesis that those

coefficients equal zero (Table 7). There are similarly no significant effects in the adjustment

of B’s income in Tanzania. The only statistically significant patterns we observe are in the

adjustment of A’s income in Tanzania, which was significantly higher in ABC groups than

either BC or AC groups (Table 7). Given that this pattern of behavior was not apparent

in adjusting Player A’s income, and that it is not consistent with behavior in the DG3, we


19

cautiously interpret these findings as spurious. In short, we find very little evidence that

income reduction in the RIG3 varies with the ethnic composition of a participant’s group in

either country.

The amount by which individuals punished or reduced incomes, due to inequitable alloca-

tions, varied across the DG3 and RIG3, respectively. Upon viewing the coefficient estimates

associated with the variables relating to the amount Player A kept or the amount randomly

allocated, one can note that the amount of costly income reduction in the RIG3 per unit

of inequality appeared less than the amount of costly punishment per unit of inequality in

the DG3. To understand such differences and to assess if these two games captured differ-

ent motivations for income adjustment, we examine whether or not feelings of anger toward

human-produced inequality and a taste for retributive violence (collected during the attitu-

dinal survey after game play) correlate with income alteration. Following Fehr and Gachter

(2002), we asked participants to “imagine that in Activity 1 (the DG3), an individual gave

you 1 token and kept 9” and, then, we asked whether or not the participants would feel “not

at all angry,” “a little angry,” “angry,” “quite angry,” or “very angry.” We also asked par-

ticipants to express, on a 5-point scale, the degree to which they agreed that “in order for

justice to be served, violence should be repaid with violence.” Both anger at failing to share

(t=7.05, p<0.01) and a taste for retribution (t=6.58, p<0.01) were significantly stronger in

Kenya than in Tanzania. When comparing answers to these questions to real game behavior,

we find that decisions about how much to spend to sanction others in the DG3 is positively

correlated with both self-reported anger at non-cooperation by Player C (ρ = 0.20, p < 0.01)

and support for retribution (ρ = 0.22, p < 0.01). In contrast, there is no correlation between

income alteration in RIG3 and feelings of anger toward human-produced inequality (ρ=0.06,

p = 0.42) or taste for retribution (ρ=0.05, p = 0.54). These correlations are consistent with

the view that the costly punishment of norm violations (DG3) does not solely result from

aversion toward the material inequality resulting from norm violations.

20

Tab

le6:

Cos

tly

Inco

me

Ad

just

men

tA

cros

sC

oet

hnic

Tre

atm

ents

Ken

yaT

an

zan

ia

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Am

ou

nt

All

oca

ted

(A)

0.0

85∗∗∗

0.0

67∗∗∗

0.05

5∗∗∗

0.0

57∗∗∗

(0.0

22)

(0.0

24)

(0.0

14)

(0.0

15)

Am

ount

All

oca

ted

(B)

0.08

1∗∗∗

0.07

1∗∗∗

0.1

00∗∗∗

0.0

98∗∗∗

(0.0

16)

(0.0

18)

(0.0

19)

(0.0

20)

AB

0.127

0.15

6−

0.1

10−

0.14

4−

0.18

4−

0.19

70.

090

0.04

7(0.1

91)

(0.1

99)

(0.1

38)

(0.1

46)

(0.1

23)

(0.1

26)

(0.1

64)

(0.1

71)

AC

−0.

136

−0.1

36−

0.1

75−

0.10

9−

0.19

4∗−

0.20

5∗

−0.1

77−

0.1

69(0.1

92)

(0.2

05)

(0.1

39)

(0.1

50)

(0.1

13)

(0.1

13)

(0.1

50)

(0.1

54)

BC

0.085

−0.1

07−

0.1

010.

038

−0.

235∗∗

−0.

260∗∗

−0.1

58−

0.1

96(0.1

71)

(0.1

93)

(0.1

24)

(0.1

41)

(0.1

17)

(0.1

15)

(0.1

56)

(0.1

56)

Age

0.0

17−

0.0

300.0

75∗

−0.0

27(0.0

70)

(0.0

51)

(0.0

39)

(0.0

53)

Male

−0.0

680.2

48∗

0.10

80.0

27(0.1

72)

(0.1

25)

(0.0

93)

(0.1

26)

Ed

uca

tion

Lev

el0.

136∗

−0.1

30∗∗

−0.

048

−0.2

09∗∗

(0.0

77)

(0.0

56)

(0.0

63)

(0.0

85)

Inco

me

Lev

el0.

011

−0.0

09−

0.02

5∗−

0.0

07(0.0

25)

(0.0

18)

(0.0

13)

(0.0

17)

Con

stan

t−

0.16

6−

0.3

98−

0.0

460.

463∗

0.09

30.

062

−0.

174

0.21

0(0.1

83)

(0.3

62)

(0.1

02)

(0.2

38)

(0.1

12)

(0.2

25)

(0.1

23)

(0.3

04)

Vil

lage

Fix

edE

ffec

tsN

oY

esN

oY

esN

oY

esN

oY

es

R2

0.24

0.39

0.33

0.45

0.28

0.40

0.35

0.44

Ob

serv

atio

ns

6868

6868

7776

7776

OL

Ses

tim

ate

sw

ith

standard

erro

rsin

pare

nth

eses

.A

BC

trea

tmen

tgro

up

om

itte

d.

The

dep

enden

tva

riable

isto

ken

ssp

ent

by

Cto

reduce

inco

me

of

A(M

odel

s1-2

,5-6

)or

B(M

odel

s3-4

,7-8

)

Villa

ge

fixed

effec

tsare

base

don

C’s

villa

ge.

∗p<

0.1

0,∗∗p<

0.0

5,∗∗∗p<

0.0

1

21

Tab

le7:

Diff

eren

ces

inIn

com

eR

edu

ctio

nin

RIG

3by

Coet

hn

icit

yT

reat

men

t

Ken

yaT

an

zan

ia

Red

uct

ion

of

AR

edu

ctio

nof

BR

edu

ctio

nof

AR

edu

ctio

nof

B

Diff

eren

ceU

nad

j.A

dj.

Diff

eren

ceU

nad

j.A

dj.

Diff

eren

ceU

nad

j.A

dj.

Diff

eren

ceU

nad

j.A

dj.

(Std

.E

rr.)

pp

(Std

.E

rr.)

pp

(Std

.E

rr.)

pp

(Std

.E

rr.)

pp

AB

vs.

AB

C0.1

60.

440.4

4−

0.1

40.

330.3

3−

0.2

00.

120.

140.

050.

780.7

8(0.2

0)

(0.1

5)(0.1

3)(0.1

7)

AC

vs.

AB

C−

0.14

0.51

0.5

4−

0.1

10.

470.5

0−

0.2

10.

070.

10−

0.1

70.

280.2

8(0.2

1)

(0.1

5)(0.1

1)(0.1

5)

BC

vs.

AB

C−

0.11

0.58

0.5

80.

040.

790.7

9−

0.2

60.

030.

03−

0.2

00.

210.2

4(0.1

9)

(0.1

4)(0.1

1)(0.1

6)

AC

vs.

AB

−0.

290.

20

0.2

00.

030.

830.8

3−

0.0

10.

950.

95−

0.2

20.

240.2

6(0.2

3)

(0.1

7)(0.1

3)(0.1

8)

BC

vs.

AB

−0.

260.

24

0.2

70.

180.

270.2

9−

0.0

60.

630.

63−

0.2

40.

180.1

8(0.2

2)

(0.1

6)(0.1

3)(0.1

8)

BC

vs.

AC

0.03

0.89

0.9

00.

150.

330.3

8−

0.0

60.

670.

69−

0.0

30.

880.8

9(0.2

1)

(0.1

5)(0.1

3)(0.1

7)

Note

:B

ase

don

Model

s2,

4,

6and

8of

Table

6.

Diff

eren

ces

reflec

tth

efirs

ttr

eatm

ent

min

us

the

seco

nd

trea

tmen

t.A

dju

sted

pva

lues

are

ad-

just

edusi

ng

the

Dunca

nm

ethod.ABC

repre

sents

trea

tmen

tgro

ups

inw

hic

hP

layer

sA

,B

,and

Care

all

from

the

sam

etr

ibe,

AB

repre

sents

trea

tmen

tgro

ups

inw

hic

honly

Pla

yer

sA

and

Bare

from

the

sam

etr

ibe,

AC

repre

sents

trea

tmen

tgro

ups

inw

hic

honly

Pla

yer

sA

and

Care

from

the

sam

etr

ibe,

andBC

repre

sents

trea

tmen

tgro

ups

inw

hic

honly

Pla

yer

sB

and

Care

from

the

sam

etr

ibe.

22

Conclusion

This study examined how the experimental manipulation of a group’s ethnic composition

influenced sharing and costly income alteration in norm-laden (DG3) versus norm-free (RIG3)

contexts across Kenya and Tanzania. Across both games and in both countries, we found some

evidence that the coethnicity of group members influenced game play. We find evidence that

cooperative sharing and third-party punishment are conditioned by coethnicity, but theses

effects are only observed in Kenya. In Kenya, we find that cooperative sharing is greater when

the potential victim of non-cooperation and the third-party punisher are coethnics, regardless

of the dictator’s own ethnic affiliation. Punishment was indeed harsher for non-coethnics

who failed to share with the punisher’s coethnic, but not for coethnics who failed to share

in homogenous groups. Thus, in Kenya, we find evidence of parochialism (Bernhard et al.

2006), characterized by greater punishment of outgroup members who fail to share with in-

group members, and leniency towards selfish in-group members. However, these results derive

from estimates that exhibit considerable imprecision and we can reject the null hypotheses

with which they correspond only at confidence levels below the conventional 95% level.

Noting the uncertainty of our estimates, the patterns of ethnic-based sharing and sanc-

tioning we observe are at odds with prominent theories of coethnic cooperation in political

science. In particular, Habyarimana et al.’s (2009) coethnicity experiments in Uganda, Miguel

and Gugerty’s (2005) research on diverse communities in Kenya, and Fearon and Laitin’s

(1996) in-group policing model all anticipate that sanctioning will be concentrated on in-

group members, since its provision is costly and its benefits are shared among the whole

group. In contrast, our findings from Kenya are more consistent with punishment being used

to protect in-group members especially against out-group members. Economists and psy-

chologists have documented similar behavior in different contexts (Bernhard et al. 2006;

Baumgartner et al. 2012), which suggests that theories that assume costly punishment will

be reserved for coethnic non-cooperation should be amended to account for mounting evidence

to the contrary.

Consistent with our expectation that ethnic affiliation would shape cooperation and sanc-

23

tioning in Kenya but not Tanzania, we find no statistically significant effects of coethnicity on

behavior in Tanzania as compared to the weak evidence of ethnic effects in Kenya. We antic-

ipated this pattern due to stark differences in the degree of pan-ethnic national identification

in Tanzania compared to Kenya. However, like many studies that utilize African borders to

generate variation in an independent variable of interest (McCauley and Posner 2015), we are

unable to definitively attribute our findings to differences in nationalism alone. The location of

our study – very near the international border and with the same two ethno-linguistic groups

– holds constant geographic characteristics and local ethnic considerations that are likely to

be similar on each side of the border. Nevertheless, there are other important differences

between Kenya and Tanzania in the post-independence period in general, such as the degree

of political competitiveness and the nature of their economies, as well as differences in how

the locality on each side of the border is situated within the larger national context, includ-

ing distance to the capital and the relative sizes of the two ethnic groups. Such differences

are likely responsible for variation in play across the two countries, including higher rates of

punishment, anger at non-cooperation, and support for retribution in Kenya. In addition, the

modest difference we see in the role of ethnicity in shaping game play across these national

contexts could also result from a combination of these various factors.

We also note that the influence of coethnicity and national context appears limited in

comparison to the robust and noteworthy effects of norm violations and inequality. The most

robust predictor of costly sanctioning in our DG3 study is the amount that dictators allocated

to their game partner, which itself appears to have resulted primarily from factors beyond

the ethnic makeup of the group. Similarly, in the RIG3, randomly-generated inequality serves

as the best predictor of costly income reduction. In sum, our findings suggest that research

studying the role of ethnic affiliations and national context may prove relevant at explaining

some of the variation in the social behaviors we study, but factors shared across ethnic and

national contexts appear to be the primary drivers of both costly sanctioning and the costly

rectification of inequality.

24

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27

Online Appendix

A Descriptive Statistics 2

B Additional Analyses and Robustness Checks 4

B.1 Ordered Probit Regressions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

B.2 Excluding All Ethnic Minorities . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

B.3 Including Spiteful Income Adjustment . . . . . . . . . . . . . . . . . . . . . . . 10

C Statistical Power 12

D Participant Recruitment 15

E Experiment Procedures 16

E.1 Common Group Instructions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

E.2 Dictator Game Interview for Player A . . . . . . . . . . . . . . . . . . . . . . . 23

E.3 Dictator Game Interview for Player C . . . . . . . . . . . . . . . . . . . . . . . 24

E.4 Random Income Activity Interview for Player C . . . . . . . . . . . . . . . . . 26

F Exit Survey 28

F.1 Instructions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

F.2 Exit Survey Questionnaire . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

1

A Descriptive Statistics

Table A.1 provides descriptive statistics of participant background for the full sample and by treatment

group. Of the 501 individuals that that are included in the analyses, 53% were male, the mean age

bracket was 30-39 years of age, the mean level of educational attainment was primary school, and the

mean income bracket was KES 500-749 in Kenya and TZ 5,000-7,499 in Tanzania. The 501 individuals

participating in the experiment were randomly assigned to one of the three roles for each of the

experimental games.

Table A.1: Descriptive Statistics of Participants

Kenya Tanzania

Full ABCDG, ABDG, ACDG, BCDG, ABCDG, ABDG, ACDG, BCDG,Sample ABCRIG BCRIG ABRIG ACRIG ABCRIG BCRIG ABRIG ACRIG

Age 2.06 1.97 1.86 1.74 1.70 2.32 2.38 2.34 2.16(1.11) (1.07) (1.09) (0.98) (1.02) (1.09) (1.14) (1.21) (1.08)

Education Level 1.98 1.54 2.12 2.05 1.89 2.04 1.96 1.96 2.22(0.98) (1.13) (1.19) (1.13) (1.15) (0.69) (0.64) (0.73) (0.82)

Income Level 3.50 3.08 3.75 4.33 4.34 3.63 2.84 4.00 2.33(3.48) (3.22) (3.37) (3.39) (4.19) (3.60) (3.07) (3.58) (3.17)

Male 0.53 0.47 0.49 0.62 0.45 0.54 0.58 0.60 0.52(0.50) (0.50) (0.50) (0.49) (0.50) (0.50) (0.50) (0.49) (0.50)

Religiosity 1.18 1.18 1.28 1.49 1.28 1.13 0.94 1.10 1.00(0.93) (0.84) (0.79) (1.16) (1.12) (0.95) (0.87) (0.86) (0.77)

Luo 0.47 0.40 0.50 0.57 0.51 0.38 0.52 0.48 0.48(0.50) (0.49) (0.50) (0.50) (0.51) (0.49) (0.50) (0.50) (0.50)

Ethnic Minority 0.02 0.00 0.00 0.03 0.04 0.01 0.04 0.04 0.03(0.15) (0.00) (0.00) (0.18) (0.20) (0.11) (0.20) (0.20) (0.17)

Num. of Subjects 500 72 72 61 47 79 50 50 69

Note: Means with standard deviations in parenthesis. ABC represents treatment groups in which PlayersA, B, and C are all from the same tribe, AB represents treatment groups in which only Players A and Bare from the same tribe, AC represents treatment groups in which only Players A and C are from the sametribe, and BC represents treatment groups in which only Players B and C are from the same tribe. Age isa categorical variable representing a participants’ age (0 if between 18-29 years old, 2 if between 30-39 yearsold, 3 if between 40-49 years old, and 5 if over 50 years old). Education Level is a categorical variable thatrepresents participants’ highest level of education (0 if no formal schooling, 1 if some primary, 2 if primary, 3if some secondary, 4 if secondary, and 5 if post-secondary). Income Level is a categorical variable representingparticipants’ income in the previous week (0 if no income, 1 if KES 1-99/TZS 1-2,499, 2 if KES 100-249/TZS2,500-4,999, 3 if KES 250-499/TZS 5,000-7,499, 4 if KES 500-749/TZS 7,500-9,999, 5 if KES 750-999/TZS10,000-12,499, 6 if KES 1,000-1,249/TZS 12,500-14,999, 7 if KES 1,250-1,499/TZS 15,000-17,499, 8 if KES1,500-1,749/TZS 17,500-19,999, 9 if KES 1,750-1,999/TZS 20,000-29,999, 10 if KES 2,000-2,999/TZS 30,000+,and 11 if KES 3,000+). Male is a dummy indicating gender. Religiosity signifies the number of times aparticipant attended a religious service in the past week. Luo is a dummy indicating Luo ethnicity. EthnicMinority is a dummy indicating Luo living in Kuria-majority villages and Kuria living in Luo-majority villages.

2

Fig

ure

A.1

:C

ON

SO

RT

Dia

gram

Recruited

(n=672)

Excluded

(n=114):

a)Did

notshow

up(n=76)

b)Show

eduptoolate

(n=9)

c)Leftearly(n=3)

d)Could

notform

group(n=26)

Randomized

(n=558)

ABCD

G

ABCRIG

ABD

G

BCRIG

ACD

G

ABRIG

BCD

G

ACRIG

Assigned

(n=164)

Assigned

(n=138)

Assigned

(n=128)

Assigned

(n=128)

Excluded

(n=15):

a)Poor

comprehen

sion(n=8)

b)Ethnic

minority(n=5)

c)Spiteful(n=2)

Excluded

(n=22):

a)Poor

comprehen

sion(n=10)

b)Ethnic

minority(n=6)

c)Spiteful(n=6)

Excluded

(n=20):

a)Poor

comprehen

sion(n=9)

b)Ethnic

minority(n=7)

c)Spiteful(n=4)

Excluded

(n=16):

a)Poor

comprehen

sion(n=6)

b)Ethnic

minority(n=6)

c)Spiteful(n=4)

Analyzed(n=151):

AD

G:n=51

CD

G:n=51

CRIG:n=47

Analyzed(n=122):

AD

G:n=44

CD

G:n=40

CRIG:n=32

Analyzed(n=112):

AD

G:n=36

CD

G:n=38

CRIG:n=34

Analyzed(n=116):

AD

G:n=40

CD

G:n=39

CRIG:n=33

3

B Additional Analyses and Robustness Checks

B.1 Ordered Probit Regressions

Table A.2: Cooperative Sharing Across Coethnicity Treatments (Ordered Probit)

Kenya Tanzania

(1) (2) (3) (4)

AB −0.435 −0.729∗∗ −0.030 0.093(0.302) (0.338) (0.326) (0.333)

AC −0.611∗ −0.752∗∗ 0.128 0.099(0.319) (0.342) (0.346) (0.368)

BC 0.214 −0.014 −0.020 0.255(0.343) (0.360) (0.310) (0.341)

Age −0.225 0.205∗

(0.143) (0.118)

Male −0.383 −0.113(0.256) (0.273)

Education Level 0.001 −0.085(0.117) (0.241)

Income Level −0.024 0.073∗∗

(0.035) (0.037)

Cut 1 −1.321∗∗∗ −2.210∗∗∗ −0.684∗∗∗ −0.346(0.260) (0.563) (0.228) (0.639)

Cut 2 −0.860∗∗∗ −1.627∗∗∗ −0.333 0.053(0.239) (0.535) (0.225) (0.643)

Cut 3 −0.283 −0.936∗ 0.011 0.447(0.227) (0.518) (0.223) (0.650)

Cut 4 0.070 −0.530 0.197 0.651(0.229) (0.519) (0.223) (0.653)

Cut 5 0.335 −0.234 0.632∗∗∗ 1.126∗

(0.231) (0.523) (0.226) (0.654)


Observations 89 89 82 82

Ordered probit estimates with standard errors in parentheses. ABC treatment group omitted.



4

Table A.3: Costly Sanctioning Across Coethnicity Treatments (Ordered Probit)

Kenya Tanzania

(1) (2) (3) (4)

Amount Kept (A) 0.360∗∗∗ 0.391∗∗∗ 0.199∗∗∗ 0.210∗∗∗

(0.035) (0.038) (0.071) (0.075)

AB 0.153 0.123 0.367 0.462(0.160) (0.164) (0.381) (0.407)

AC 0.005 −0.023 0.047 −0.134(0.165) (0.175) (0.399) (0.413)

BC 0.284 0.338∗ 0.444 0.565(0.188) (0.192) (0.337) (0.359)

Age 0.014 0.024(0.061) (0.138)

Male −0.207 0.468(0.130) (0.291)

Education Level −0.035 0.060(0.067) (0.184)

Income Level 0.013 0.072(0.018) (0.044)

Constant −1.840∗∗∗ −2.298∗∗∗

(0.267) (0.420)

Cut 1 1.741∗∗∗ 2.653∗∗∗

(0.589) (0.940)

Cut 2 2.900∗∗∗ 3.929∗∗∗

(0.629) (0.978)


R2 0.58 0.65Observations 88 88 80 80

Ordered probit estimates with standard errors in parentheses. ABC treatment group omitted.


Village fixed effects are based on C’s village.∗p < 0.10, ∗∗p < 0.05, ∗∗∗p < 0.01

5

Tab

leA

.4:

Cost

lyIn

com

eA

dju

stm

ent

Acr

oss

Coet

hn

icT

reat

men

ts(O

rder

edP

rob

it)

Ken

yaT

an

zan

ia

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Am

ou

nt

All

oca

ted

(A)

0.3

74∗∗∗

0.4

23∗∗∗

0.3

10∗∗∗

0.4

66∗∗∗

(0.1

09)

(0.1

31)

(0.0

97)

(0.1

67)

Am

ount

All

oca

ted

(B)

0.42

3∗∗∗

1.11

4∗∗

0.48

4∗∗∗

0.53

0∗∗∗

(0.1

39)

(0.4

55)

(0.1

19)

(0.1

39)

AB

0.249

0.59

3−

3.7

72−

2.39

0−

0.7

65−

1.2

67−

0.1

90−

0.57

7(0.4

46)

(0.5

49)

(465.7

63)

(132

3.48

4)(0.6

54)

(0.9

28)

(0.5

14)

(0.6

13)

AC

−0.

556

−0.7

29−

1.2

072.

980

−0.6

38−

0.7

15−

0.9

06−

1.30

2∗

(0.5

37)

(0.6

67)

(0.8

47)

(5.2

77)

(0.4

83)

(0.6

07)

(0.6

29)

(0.7

45)

BC

0.246

0.16

4−

0.7

2611.0

45−

1.0

60∗

−1.7

60∗

−0.9

91−

1.71

9∗∗

(0.4

36)

(0.5

29)

(0.6

86)

(10.

104)

(0.5

62)

(0.9

13)

(0.6

28)

(0.8

61)

Age

0.0

890.

344

0.50

4∗

0.16

3(0.2

00)

(0.9

13)

(0.2

68)

(0.2

15)

Male

−0.3

9013.6

130.

708

0.1

95(0.5

21)

(9.5

98)

(0.5

49)

(0.4

81)

Ed

uca

tion

Lev

el0.

350

−3.

813

−0.0

94−

0.99

4∗∗

(0.2

31)

(4.0

58)

(0.4

57)

(0.4

63)

Inco

me

Lev

el0.

118

−0.

349

−0.0

69−

0.05

8(0.0

81)

(0.5

88)

(0.0

79)

(0.0

69)

Cu

t1

3.20

5∗∗∗

4.7

58∗∗∗

3.2

52∗∗∗

7.29

22.

169∗∗∗

4.3

62∗∗

3.53

4∗∗∗

2.15

7(0.9

24)

(1.4

08)

(1.0

38)

(6.7

49)

(0.7

10)

(2.0

81)

(0.8

65)

(1.4

76)

Cu

t2

4.709∗∗∗

6.7

04∗∗∗

4.1

03∗∗∗

11.3

085.2

11∗∗∗

4.06

0∗∗∗

(1.0

02)

(1.5

58)

(1.1

34)

(7.6

61)

(0.9

84)

(1.5

37)

Cu

t3

5.87

1∗∗∗

4.95

6∗∗∗

(1.0

74)

(1.5

49)

Vil

lage

Fix

edE

ffec

tsN

oY

esN

oY

esN

oY

esN

oY

es

R2

Ob

serv

atio

ns

6868

6868

7776

7776

Ord

ered

pro

bit

esti

mate

sw

ith

standard

erro

rsin

pare

nth

eses

.A

BC

trea

tmen

tgro

up

om

itte

d.

The

dep

enden

tva

riable

isto

ken

ssp

ent

by

Cto

reduce

inco

me

of

A(M

odel

s1-2

,5-6

)or

B(M

odel

s3-4

,7-8

)

Villa

ge

fixed

effec

tsare

base

don

C’s

villa

ge.

∗p<

0.1

0,∗∗p<

0.0

5,∗∗∗p<

0.0

1

6

B.2 Excluding All Ethnic Minorities

Table A.5: Cooperative Sharing Across Coethnicity Treatments (No Ethnic Minorities)

Kenya Tanzania

(1) (2) (3) (4)

AB −0.667 −0.931∗ −0.343 −0.210(0.502) (0.506) (0.639) (0.608)

AC −1.024∗ −1.056∗∗ 0.193 0.155(0.530) (0.520) (0.667) (0.653)

BC 0.127 −0.150 −0.047 0.310(0.562) (0.549) (0.581) (0.584)

Age −0.278 0.512∗∗

(0.216) (0.213)

Male −0.519 −0.348(0.393) (0.489)

Education Level 0.004 0.038(0.183) (0.406)

Income Level −0.027 0.110∗

(0.054) (0.062)

Constant 3.167∗∗∗ 3.851∗∗∗ 2.593∗∗∗ 1.297(0.362) (0.790) (0.390) (1.109)


R2 0.07 0.29 0.01 0.21Observations 88 88 79 79




7

Table A.6: Costly Sanctioning Across Coethnicity Treatments (No Ethnic Minorities)

Kenya Tanzania

(1) (2) (3) (4)

Amount Kept (A) 0.367∗∗∗ 0.401∗∗∗ 0.113∗∗∗ 0.105∗∗

(0.034) (0.037) (0.041) (0.041)

AB 0.149 0.107 0.186 0.224(0.155) (0.158) (0.228) (0.233)

AC 0.063 0.049 0.023 −0.095(0.161) (0.171) (0.227) (0.229)

BC 0.287 0.353∗ 0.236 0.275(0.181) (0.186) (0.199) (0.202)

Age 0.041 0.012(0.060) (0.080)

Male −0.286∗∗ 0.288∗

(0.129) (0.165)

Education Level 0.024 0.012(0.068) (0.101)

Income Level 0.010 0.036(0.018) (0.025)

Constant −1.885∗∗∗ −2.540∗∗∗ −0.333 −0.659(0.258) (0.417) (0.328) (0.493)


R2 0.61 0.67 0.12 0.23Observations 87 87 79 79




8

Tab

leA

.7:

Cos

tly

Inco

me

Ad

just

men

tA

cros

sC

oet

hnic

Tre

atm

ents

(No

Eth

nic

Min

orit

ies)

Ken

yaT

an

zan

ia

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Am

ou

nt

All

oca

ted

(A)

0.0

92∗∗∗

0.0

75∗∗∗

0.05

5∗∗∗

0.0

56∗∗∗

(0.0

23)

(0.0

24)

(0.0

15)

(0.0

15)

Am

ount

All

oca

ted

(B)

0.09

1∗∗∗

0.07

8∗∗∗

0.0

96∗∗∗

0.0

97∗∗∗

(0.0

16)

(0.0

17)

(0.0

20)

(0.0

20)

AB

0.125

0.17

1−

0.1

35−

0.17

1−

0.18

5−

0.20

60.

084

0.02

6(0.1

92)

(0.1

99)

(0.1

36)

(0.1

42)

(0.1

30)

(0.1

35)

(0.1

71)

(0.1

81)

AC

−0.

124

−0.1

16−

0.2

18−

0.13

2−

0.20

1∗−

0.20

9∗

−0.1

61−

0.1

67(0.1

94)

(0.2

05)

(0.1

38)

(0.1

46)

(0.1

17)

(0.1

16)

(0.1

53)

(0.1

56)

BC

0.144

−0.0

42−

0.1

60−

0.00

2−

0.24

3∗−

0.25

7∗∗

−0.1

78−

0.1

98(0.1

77)

(0.1

96)

(0.1

26)

(0.1

40)

(0.1

23)

(0.1

21)

(0.1

61)

(0.1

62)

Age

−0.0

10−

0.00

10.0

81∗

−0.0

49(0.0

72)

(0.0

51)

(0.0

42)

(0.0

56)

Male

−0.0

730.2

26∗

0.10

70.0

46(0.1

72)

(0.1

23)

(0.0

97)

(0.1

30)

Ed

uca

tion

Lev

el0.

130∗

−0.1

31∗∗

−0.

056

−0.1

89∗∗

(0.0

77)

(0.0

55)

(0.0

67)

(0.0

91)

Inco

me

Lev

el0.

010

−0.0

10−

0.02

5∗−

0.0

14(0.0

25)

(0.0

17)

(0.0

14)

(0.0

19)

Con

stan

t−

0.22

9−

0.4

12−

0.0

400.

443∗

0.10

40.

073

−0.

170

0.22

8(0.1

96)

(0.3

61)

(0.1

01)

(0.2

33)

(0.1

19)

(0.2

34)

(0.1

23)

(0.3

13)

Vil

lage

Fix

edE

ffec

tsN

oY

esN

oY

esN

oY

esN

oY

es

R2

0.25

0.41

0.37

0.50

0.27

0.40

0.34

0.42

Ob

serv

atio

ns

6666

6666

7473

7473

OL

Ses

tim

ate

sw

ith

standard

erro

rsin

pare

nth

eses

.A

BC

trea

tmen

tgro

up

om

itte

d.

The

dep

enden

tva

riable

isto

ken

ssp

ent

by

Cto

reduce

inco

me

of

A(M

odel

s1-2

,5-6

)or

B(M

odel

s3-4

,7-8

)

Villa

ge

fixed

effec

tsare

base

don

C’s

villa

ge.

∗p<

0.1

0,∗∗p<

0.0

5,∗∗∗p<

0.0

1

9

B.3 Including Spiteful Income Adjustment

10

Tab

leA

.8:

Cos

tly

Inco

me

Ad

just

men

tA

cros

sC

oet

hn

icT

reat

men

ts(w

ith

Sp

itef

ul

Pu

nis

her

s)

Ken

yaTanzania

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Am

ount

Alloca

ted

(A)

0.0

68∗∗∗

0.0

52∗∗

0.0

42∗∗∗

0.0

43∗∗∗

(0.0

21)

(0.0

24)

(0.0

15)

(0.0

15)

Am

ount

Alloca

ted

(B)

0.0

69∗∗∗

0.0

66∗∗∗

0.0

83∗∗∗

0.0

83∗∗∗

(0.0

18)

(0.0

21)

(0.0

19)

(0.0

20)

AB

0.0

99

0.1

46

−0.0

93

−0.1

67

−0.2

35∗

−0.2

45∗

0.2

23

0.2

06

(0.1

88)

(0.2

01)

(0.1

57)

(0.1

70)

(0.1

23)

(0.1

27)

(0.1

58)

(0.1

66)

AC

−0.0

55

−0.0

25

−0.2

70∗

−0.2

85

−0.2

13∗

−0.2

45∗∗

−0.0

64

−0.0

81

(0.1

94)

(0.2

09)

(0.1

62)

(0.1

77)

(0.1

17)

(0.1

20)

(0.1

50)

(0.1

56)

BC

0.1

81

0.0

83

−0.2

27

−0.2

21

−0.2

40∗

−0.2

60∗∗

−0.0

73

−0.1

14

(0.1

73)

(0.1

94)

(0.1

44)

(0.1

65)

(0.1

22)

(0.1

22)

(0.1

57)

(0.1

58)

Age

0.0

06

−0.0

57

0.0

55

−0.0

16

(0.0

71)

(0.0

60)

(0.0

41)

(0.0

54)

Male

0.0

26

0.1

77

0.1

42

−0.0

63

(0.1

72)

(0.1

46)

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11

C Statistical Power

The experiment reported in the main text involves four treatment conditions implemented across two

national contexts. By spreading the study sample across these factors, our investigation allocates a

relatively small number of observations to each treatment condition in each national context. These

allocations of experiment participants raise concerns about statistical power.

To understand the relevance of these concerns, we performed post hoc power calculations for a

range of possible effect sizes, taking as given the sample sizes used in the various pairwise compar-

isons reported in the main text. Figures A.2 and A.3 display these calculations for our Kenyan and

Tanzanian samples, respectively. Each figure consists of six panels representing each of the six pair-

wise comparisons that can be made between our treatment conditions. At the top of each panel, the

relevant pairwise comparison is listed along with the sample size for each treatment condition. In

the plotting field of each panel, we display the statistical power of a two-sample t-test with unequal

sample sizes (viz. those sample sizes displayed in the title of each panel) for a range of hypothetical,

standardized effect sizes. The hypothetical effect sizes (in standard deviation units) range from those

deemed very small (d = 0.25) to very large (d = 0.8) by past researchers (Cohen 1988). We do not

use the estimated effect sizes from our present investigation because we acknowledge that they might

themselves result from inadequate power. Instead, we draw on hypothetical effects sizes in order to

learn how large an effect would need to be in order for us to detect it, given our sample sizes.

As Figures A.2 and A.3 indicate, for small effect sizes we are grossly under-powered and we do

not reach conventional levels of power (power=0.8) even for effects that researchers would deem large.

This dearth of power appears evident across all panels, thus suggesting that we remain under-powered

for all comparisons of our focal treatment conditions.

12

Figure A.2: Power Calculations for Treatment Comparisons in Kenya

0.3 0.4 0.5 0.6 0.7 0.8

0.2

0.3

0.4

0.5

0.6

0.7

0.8

ABC (n=24) and AB (n=26)

Hypothetical Effect Size

Pow

er

0.3 0.4 0.5 0.6 0.7 0.8

0.2

0.3

0.4

0.5

0.6

0.7

ABC (n=24) and AC (n=21)


Pow

er

0.3 0.4 0.5 0.6 0.7 0.8

0.1

0.2

0.3

0.4

0.5

0.6

0.7

ABC (n=24) and BC (n=18)


Pow

er

0.3 0.4 0.5 0.6 0.7 0.8

0.2

0.3

0.4

0.5

0.6

0.7

AB (n=26) and AC (n=21)


Pow

er

0.3 0.4 0.5 0.6 0.7 0.8

0.2

0.3

0.4

0.5

0.6

0.7

AB (n=26) and BC (n=18)


Pow

er

0.3 0.4 0.5 0.6 0.7 0.8

0.1

0.2

0.3

0.4

0.5

0.6

0.7

AC (n=21) and BC (n=18)


Pow

er

Note: The panels display the power of a two-sample t-test for varying effect sizes (in standard deviationunits), given the actual, unequal sample sizes for experiments conducted in Kenya.

13

Figure A.3: Power Calculations for Treatment Comparisons in Tanzania

0.3 0.4 0.5 0.6 0.7 0.8

0.2

0.3

0.4

0.5

0.6

0.7

ABC (n=27) and AB (n=18)


Pow

er

0.3 0.4 0.5 0.6 0.7 0.8

0.1

0.2

0.3

0.4

0.5

0.6

0.7

ABC (n=27) and AC (n=15)


Pow

er

0.3 0.4 0.5 0.6 0.7 0.8

0.2

0.3

0.4

0.5

0.6

0.7

0.8

ABC (n=27) and BC (n=22)


Pow

er

0.3 0.4 0.5 0.6 0.7 0.8

0.1

0.2

0.3

0.4

0.5

0.6

AB (n=18) and AC (n=15)


Pow

er

0.3 0.4 0.5 0.6 0.7 0.8

0.1

0.2

0.3

0.4

0.5

0.6

0.7

AB (n=18) and BC (n=22)


Pow

er

0.3 0.4 0.5 0.6 0.7 0.8

0.1

0.2

0.3

0.4

0.5

0.6

AB (n=15) and BC (n=22)


Pow

er

Note: The panels display the power of a two-sample t-test for varying effect sizes (in standard deviationunits), given the actual, unequal sample sizes for experiments conducted in Tanzania.

14

D Participant Recruitment

Before the experiment began, research teams made visits to the target villages to seek permission

from local authorities to recruit participants. Participants were recruited from 12 different villages

in southwest Kenya and northwest Tanzania – where the ancestral homelands of the Kuria and Luo

meet. This included 8 small villages near Karamu, Kenya (4 Kuria villages and 4 Luo villages) and 4

small villages near Somba Nyasoko, Tanzania (2 Kuria villages and 2 Luo villages). The RAs recruited

participants by taking a random walk from a pre-designated part of the village (typically the center)

and recruiting a random household member from every Xth home, determined by a dice roll. When

recruiting, RAs alternated between gender, and selected individuals randomly using a dice roll. These

procedures involved the following script (spoken in Swahili or participant’s mother tongue):

Hello, my name is , and we would like to recruit a member of your household

to participate in a research project involving [Kenyatta University (Kenya) / the Shirati

Health, Education, and Development Foundation (Tanzania)], and Stanford University

in the United States. If your household agrees to participate, you will be given [KES

300 / TZS 5000] cash for your time and matched with other participants from the area

to complete a series of activities. The experiment will be held next weekend here in

at 8am, and it should take no more than four or five hours total. Would

somebody here be interested in participating?

Please know that participation is completely voluntary, and you may withdraw from the

research at any time. But if you show up for the experiment you will receive [KES 300 /

TZS 5000] and a chance to win more money depending on the decisions made by you and

others in the experiment. May I schedule you for the experiment?

Great, from this household, I need to select a [male / female]. Can we gather all the

[males / females] of the house?

[SELECT BY ROLLING DICE AND RECORD RESPONDENT INFO]

You are now registered for the experiment. It will be held at . Please know

that other people from this village are being recruited to attend sessions on different days,

so you should pay attention to the day that you are invited, which is written here on the

15

experiment info sheet. Please try to arrive 15 minutes early – if you are late, we cannot

give you any money.

E Experiment Procedures

All experiment instructions given to a group were in Swahili. During one one one interactions between

the RA and a participant, either Swahili or the participant’s home language (Kuria or Luo).

Each experiment session in Kenya and Tanzania involved 30 participants at the most. When

participants arrived to the experiment site they were randomly assigned a participant ID that ranged

from 1-30 by drawing a number out of a hat. These IDs were then used to randomly assign participants

to groups of three. Figure A.4 shows the forms used to connect randomly assigned numbers to specific

groups and roles within each game for that group. We relied variation in the ethnic composition of

these randomly assigned groups to generate variation in the treatment conditions. In experimental

sessions with equal proportions of Kuria and Luo participants, all four experimental conditions (ABC,

AB, AC, and BC) were equally likely. The more unbalanced the ethnic composition of the experimental

session, however, the more likely the ABC condition became relative to the other three conditions. On

average, our 20 sessions were 55% one ethnic group and 45% the other, but imbalance was as large

as 71% one ethnic group in one session. In this most extreme case, assignment to the ABC condition

became 50% more likely than each of the other three conditions. This helps account for the greater

number of ABC groups (n = 55) than AB (n = 45), AC (n = 44), and BC (n = 42) in the DG3 game.

Participants that could not be assigned to a group were sent away with the promised show-up fee.

Each participant played one dictator game and one random-income game. To avoid the possibility

of spillovers across games we took two precautions. First, results from the experimental games were not

given to participants until after the experiment was over. Second, player assignments were designed

so that each participant made only one experiment decision (despite playing two games). This was

accomplished by assigning player B of the dictator game to the role of player C in the random-income

game, and assigning players A and C of the dictator game to the role of players A and B in the

random-income game. All experiment decisions and survey responses were recorded on mobile phones

equipped with Open Data Kit (ODK).

16

Figure A.4: Form which mapped numbers 1-30 drawn from a hat to particular groups androles for the dictator game with third party punishment.

PLA

YE

R A

SS

IGN

ME

NT

KE

Y –

Dic

tato

r (A

ctiv

ity 1

) C

ount

ry:

Ken

ya /

Tanz

ania

D

ate:

___

____

__

Village'

ID'

Interview2'

Game2'

Role2'

Grou

p2'

Allocatio

n2'

Redu

ction2

'Pa

yout2'

!1!

YES!

Dictator!

A!1!

!!

!!

2!!

Dictator!

B!1!

!!

!!

3!YES!

Dictator!

C!1!

!!

!!

4!YES!

Dictator!

A!2!

!!

!!

5!!

Dictator!

B!2!

!!

!!

6!YES!

Dictator!

C!2!

!!

!!

7!YES!

Dictator!

A!3!

!!

!!

8!!

Dictator!

B!3!

!!

!!

9!YES!

Dictator!

C!3!

!!

!!

10!

YES!

Dictator!

A!4!

!!

!!

11!

!Dictator!

B!4!

!!!!

!!

12!

YES!

Dictator!

C!4!

!!!!

!!

13!

YES!

Dictator!

A!5!

!!

!!

14!

!Dictator!

B!5!

!!!!

!!

15!

YES!

Dictator!

C!5!

!!!!

!!

16!

YES!

Dictator!

A!6!

!!

!!

17!

!Dictator!

B!6!

!!!!

!!

18!

YES!

Dictator!

C!6!

!!!!

!!

19!

YES!

Dictator!

A!7!

!!

!!

20!

!Dictator!

B!7!

!!!!

!!

21!

YES!

Dictator!

C!7!

!!!!

!!

22!

YES!

Dictator!

A!8!

!!

!!

23!

!Dictator!

B!8!

!!!!

!!

24!

YES!

Dictator!

C!8!

!!!!

!!

25!

YES!

Dictator!

A!9!

!!

!!

26!

!Dictator!

B!9!

!!!!

!!

27!

YES!

Dictator!

C!9!

!!!!

!!

28!

YES!

Dictator!

A!10!

!!

!!

29!

!Dictator!

B!10!

!!!!

!!

30!

YES!

Dictator!

C!10!

!!!!

!! R

A N

ame:

___

____

____

____

___!

17

Figure A.5: Form which mapped numbers 1-30 drawn from a hat to particular groups androles for the random income game with third party income adjustment.

PLA

YER

ASS

IGN

MEN

T K

EY –

RIG

(Act

ivity

2)

Cou

ntry

: K

enya

/ Ta

nzan

ia

D

ate:

___

____

__

Village'

ID'

Interview2'

Game2'

Role2'

Grou

p2'

Allocatio

n2'

Redu

ction2

'Pa

yout2'

!1!

!RIG!

B!1!

!!

!!

2!YES!

RIG!

C!1!

!!

!!

3!!

RIG!

A!1!

!!

!!

4!!

RIG!

B!2!

!!

!!

5!YES!

RIG!

C!2!

!!

!!

6!!

RIG!

A!2!

!!

!!

7!!

RIG!

B!3!

!!

!!

8!YES!

RIG!

C!3!

!!

!!

9!!

RIG!

A!3!

!!

!!

10!

!RIG!

B!4!

!!

!!

11!

YES!

RIG!

C!4!

!!!!

!!

12!

!RIG!

A!4!

!!!

!!

13!

!RIG!

B!5!

!!

!!

14!

YES!

RIG!

C!5!

!!!!

!!

15!

!RIG!

A!5!

!!!

!!

16!

!RIG!

B!6!

!!

!!

17!

YES!

RIG!

C!6!

!!!!

!!

18!

!RIG!

A!6!

!!!

!!

19!

!RIG!

B!7!

!!

!!

20!

YES!

RIG!

C!7!

!!!!

!!

21!

!RIG!

A!7!

!!!

!!

22!

!RIG!

B!8!

!!

!!

23!

YES!

RIG!

C!8!

!!!!

!!

24!

!RIG!

A!8!

!!!

!!

25!

!RIG!

B!9!

!!

!!

26!

YES!

RIG!

C!9!

!!!!

!!

27!

!RIG!

A!9!

!!!

!!

28!

!RIG!

B!10!

!!

!!

29!

YES!

RIG!

C!10!

!!!!

!!

30!

!RIG!

A!10!

!!!

!! R

A N

ame:

___

____

____

____

____

____

____

____

___

18

E.1 Common Group Instructions

Once all participants were present and assigned IDs, the RAs started the experiment by obtaining oral

consent and giving the experiment introduction to participants collectively:

Thank you for signing up for this experiment, and for contributing your time and effort

to this research. Today you will be matched with other participants from the area and

asked to complete a series of activities. The purpose of this experiment is to study how

people spend money. Please know that your participation here is completely voluntary

and that you are free to withdraw from this experiment at any time without any penalty.

As promised, you will receive [KES 300 (Kenya) / TZS 5000] cash for showing up today.

But if you complete the experiment, you will have a chance of winning more depending

on the decisions made by you and others in the experiment.

Do you agree to participate?

After obtaining oral consent:

Great, let’s get started. Today’s experiment may take up to 4 hours so if you think you

will not be able to stay that long let me know now. Before we begin I want to make

some general comments about what we are doing here today and explain the rules that we

must follow. We will be performing some experiments in which you can get some money.

Whatever money you will get in the experiments will be yours to keep and take home.

Maybe you won’t get any money from the experiment, but you will receive the promised

KES 300 (Kenya)/ TZS 5000 (Tanzania) for showing up today. This money is not part

of the experiment, it is yours to keep. Assistant 1 and I will be supplying the money,

but you should understand that this is not our own money. It is money given to us by a

University in the United States to use for research.

Before we proceed any further, let me stress something that is very important. You were

invited here without understanding very much about what we are planning to do today.

If at anytime you find that this is something that you do not wish to participate in for

any reason, you are free to leave. You may leave at anytime whether we have started the

experiment or not.

I will now explain the experiment to you. Afterwards each of you will come into the

adjacent room one-at-a-time with me and carry out the experiment. It is important that

19

you listen as carefully as possible, because only people who understand the experiment

will actually be able to participate. We will run through some examples here while we

are all together. You cannot ask questions or talk while here in the group. This is very

important. Please be sure that you obey this rule because it is possible for one person to

spoil the experiment for everyone. If one person talks about the experiment while sitting

in the group, we will not be able to carry out the experiment today. Do not worry if you

do not completely understand the experiment as we go through the examples here in the

group. Each of you will have a chance to ask questions in private to be sure that you

understand what you have to do.

Also, your decisions in the experiment will determine how many shillings you receive at

the end of the experiment. In the experiment we will use tokens, not shillings. Here are

100 (Kenya) / 2000 (Tanzania) shillings. (Currency is shown to participants.) Here are

ten tokens (Poker chips are shown to subjects.) Every token is worth 10 / 200 shillings.

(Subjects are shown 1 token and 10 / 200 shillings.) In the experiment we will only use

tokens, but at the end of the experiment each token will be exchanged for shillings. If you

obtain 0 tokens, you will get 0 shillings; if you obtain 1 token, you will obtain 10 / 200

shillings; if you obtain 2 tokens, you will obtain 20 / 400 shillings; if you obtain 3 tokens,

you will obtain 30 / 600 shillings; if you obtain 4 tokens, you will obtain 40 / 800 shillings;

if you obtain 5 tokens, you will obtain 50 / 1000 shillings; if you obtain 6 tokens, you will

obtain 60 / 1200 shillings; if you obtain 7 tokens, you will obtain 70 / 1400 shillings; if

you obtain 8 tokens, you will obtain 80 / 1600 shillings; if you obtain 9 tokens, you will

obtain 90 / 1800 shillings; if you obtain 10 tokens, you will obtain 100 / 2000 shillings.

After the introduction, RAs explained what the dictator and random-income games would entail,

starting with the dictator game (activity 1):

We will now explain the experiment. Please listen closely as you will need to remember

these instructions in order to complete the experiment and earn additional shillings.

You will all play two types of games: Activity 1, and Activity 2. Both activities are

similar and involve three people – Person A, Person B, and Person C. Each of you have

been assigned a random number and this determines whether you will be playing the role

of A, B, or C in these activities. The number was determined by chance, and each of you

had an equal chance of being assigned to each role. During the experiment, none of you

20

will know exactly with whom you are interacting, only which village the other person is

from. I cannot tell you the identity of the people you are matched with, so please do not

ask. I will, however, tell you that today there are people from 4 villages: W, X, Y, Z.

Here are 100 / 2000 shillings. (Currency is shown to participants.) Here are ten tokens

(Poker chips are shown to subjects.) Again, remember, every token is worth 10 / 200

shillings. (Subjects are shown 1 token and 10 / 200 shillings.) Here are 10 tokens.

In Activity 1, Person A must decide how many of these ten tokens to give to Person B

and how many to keep. Person B takes home whatever Person A gives to him, but Person

A has to wait until Person C has made a decision before finding out what he is going to

take home. Person C is given 5 tokens. Person C can do two things with his 5 tokens.

1. Person C can reduce the income of Person A. For the cost of 1 token, Person C can

reduce by 3 tokens the amount of money Person A gets to keep.

2. Person C can pay nothing, keep the 5 tokens and leave Person A with the tokens he

or she wanted to keep for him or herself untouched.

All of these decisions will be made anonymously in the adjacent room. We will call each

of you in order of your participant ID. When it’s your turn to do the experiment, you

can come inside the adjacent room. I will tell you whether you are Person A, Person

B, or Person C, explain the experiment again, and ask you to work through a couple of

examples to be sure that you understand. After you have completed the experiment, you

can come back out to this room and wait for others to complete the experiment. Please

know that it is perfectly acceptable to keep all the money given to you in the activity.

Some people must take home the money because they have a sick child or must pay for

their children’s school fees. It is also ok to use the money to pay for food and other bills.

The random-income game (activity 2) was then described:

Now there is also a second activity. In Activity 2, there are still three players, A, B, C.

Rules are similar to Activity 1, in that Persons A and B receive some amount of money,

and Person C gets a chance to modify the income of Person A or B. However, in this

activity, the amount of money that Person A and B receive is determined randomly by

this wheel (see Figure A.6). Each wheel is divided into 12 sections. Each section has a

value in it from 0 to 10 and one re-spin. Each wheel will be spun once for each group and

21

this will determine the incomes of A and B in the activity. The likelihood of the marker

landing on any given number is the same for all numbers. The number that the marker

lands on indicates the number of tokens given to Person A. For example, if the marker

lands on 6, then Person A is given 6 tokens. Person B gets the remaining tokens; that is,

Person B gets 10 minus the number of tokens shown on the wheel.

Figure A.6: Wheel used to generate random division of 10 tokens in the RIG3.

Before Person A and Person B are given these tokens, however, Person C must make a

decision. Person C is given 5 tokens. Person C can do three things with his 5 tokens.


reduce by 3 tokens the amount of money obtained by Person A.

2. Person C can reduce the income of Person B. For the cost of 1 token, Person C can

reduce by 3 tokens the amount of money obtained by Person B.

3. Person C can pay nothing, keep the 5 tokens and leave the money of Person A and

Person B untouched.

Again, this will be done anonymously in the adjacent room. We will call each of you in

22

order of your participant ID. When it’s your turn to do the experiment, you can come

inside the adjacent room. I will tell you whether you are Person A, Person B, or Person

C, explain the experiment again, and ask you to work through a couple of examples to be

sure that you understand. After you have completed the experiment, you can come back

out to this room and wait for everybody else to complete the experiment.

When you have finished there, you have to wait until everybody has performed the exper-

iment. Remember that you are not allowed to come and talk to the people still waiting

to carry out the experiment. When everyone has finished the experiment, I will again call

you in one-at-a-time and pay you your experiment winnings. Again, please know that it

is perfectly acceptable to keep all the money given to you in the experiment.

Are there any questions?

After the common group instructions were given, RAs took participants to a private room one by

one to conduct the games.

E.2 Dictator Game Interview for Player A

Player A of the dictator game received the following instructions:

Hello. I will now interview you to play Activity 1. You’ve been selected to play the role

of A. As I have told you, there are three persons in this experiment – Person A, Person B,

and Person C. None of you will know exactly with whom you are interacting, only which

village they are from. Only I know who will be interacting with whom; I will never tell

anyone else. Now you yourself are Person A, and you are playing with a B from village

and a C from village .

[SHOW PROPS HERE DISPLAYING A, B, C, THEIR VILLAGES, AND ARROWS

INDICATING THE DIRECTION OF TRANSFER]

Here are 10 tokens. You must decide how much of this money you want to give to Person

B from village and how much you want to keep for yourself. Person B

takes home whatever you give him, but you will have to wait until Person C from village

has decided what he wants to do before finding out how much money you

23

can take home. Person C from village will be given 5 tokens. Person C can

do two things with his 5 tokens.


reduce by 3 tokens the amount of money Person A wants to keep.

2. Person C can pay nothing, keep the 5 tokens and leave the money Person A wanted

to keep for himself untouched.

Now I would like to ask you some questions to make sure you understand the activity:

1. You are A, and imagine that you allocate 1 token to person B. How many tokens do

you have at that point? [Answer: 9 tokens]

2. Ok, so you have 9 tokens. Say that C reduces your income by 6. How many tokens

do you have at that point? [Answer: 3 token]

3. How many tokens does B have at that point? [Answer: 1 token]

4. How many tokens did C have to pay to reduce your income by 6? [Answer: 2 tokens]

[FURTHER EXAMPLES AND TEST QUESTIONS, IF NEEDED]

Now it is your turn to play. Here are 10 tokens. You can now decide how much of this

money you want to give Person B and how much money you want to keep for yourself.

Please divide this money into two piles and put the amount that you wish to give to

Person B from village in the B cup and the amount that you wish to keep

for yourself in your A cup, and remember it is ok to keep all the tokens for yourself.

Okay, we will split the money as you decided between yourself and Player B. To see how

much you can take home of the money you have kept for yourself, we first have to find

out what Person C decides to do. We will tell you how much you can take home at the

end of the experiment, after everybody has played. In the meantime, please do not talk

to anybody else about the activities you played.

E.3 Dictator Game Interview for Player C

Player C of the dictator activity received the following instructions:

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Hello. I will now interview you to play Activity 1. You’ve been selected to play the role of

C. As I have told you, there are three persons in this experiment ? Person A, Person B,

and Person C. None of you will know exactly with whom you are interacting, only their

village. Only I know who is to interact with whom and I will never tell anyone else. Now

you yourself are Person C, and you are playing with an A from village and

a B from village . [Show props here displaying A, B, C, their villages, and

arrows indicating direction of transfers?]

Person A was given 10 tokens, worth 100 / 2000 shillings. Person A told me how much

of these 10 tokens he wants to give to Person B from village , and how much

he wants to keep for himself to take home. Now I will give you 5 tokens. With these 5

tokens you can do one of two things:

1. You can reduce the income of Person A. For the cost of 1 token, you can reduce by

3 tokens the amount of money Person A wants to keep. The most you can reduce

Person A’s money to is zero tokens.

2. You can do nothing, keep the 5 tokens and leave the money Person A wanted to

keep for himself untouched.

Now I’d like to ask you some questions to make sure you understand the activity:

1. Imagine that person A allocates 1 token to person B. How many tokens does A have

at that point? [Answer: 9 tokens]

2. Ok, so A has 9 tokens, and you are C. Say that you reduce A’s income by 6. How

many tokens does A have at that point? [Answer: 3 token]


4. How many tokens did you have to pay to reduce A’s income by 6? [Answer: 2 tokens]


Now its your turn to play. The allocation that Person A from village has

made to Person B from village is written on the paper in front of me. It

says that A kept and gave to B. Would like to reduce Person

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A’s income? If yes, please tell me how many tokens you’d like to reduce A’s income by.

Remember you must pay 1 token for every 3 tokens by which you reduce A’s income, and

its ok to keep all 5 tokens for yourself.

[DIVIDE The 5 TOKENS VISUALLY TO CONFIRM RESPONSE]

Okay, we will reduce A’s income by the amount you wish. We will tell you how much you

can take home at the end of the experiment, after everybody has played the two activities.

In the meantime, please do not talk to anybody else about the activities you played.?

E.4 Random Income Activity Interview for Player C

The following instructions were given to player C of the random-income game:

Hello. I will now interview you to play Activity 2. You’ve been selected to play the role of

C. As I have told you, there are three persons in this experiment ? Person A, Person B,

and Person C. None of you will know exactly with whom you are interacting, only their

village. Only I know who is to interact with whom and I will never tell anyone else. Now

you yourself are Person C, and you are playing with an A from village and

a B from village .

[SHOW PROPS HERE DISPLAYING A, B, C, THEIR VILLAGES, AND ARROWS

INDICATING THE DIRECTION OF TRANSFER]

In a moment I will spin a wheel and it will determine how much Person A and B receive

in this activity. Person A from will receive the amount of tokens equal to the value on

which the marker landed. Person B will receive 10 minus that number of tokens. But

first, I will give you 5 tokens. With these 5 tokens you can do three things:

1. You can reduce the income of Person A. For the cost of 1 token, you can reduce by

3 tokens the amount of money obtained by Person A. You can reduce the amount

of money Person A wants to at most zero tokens.

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2. You can reduce the income of Person B. For the cost of 1 token, you can reduce by

3 tokens the amount of money obtained by Person B. You can reduce the amount of

money Person B wants to at most zero tokens.

3. You can pay nothing, keep the 5 tokens and leave the money of Person A and Person

B untouched.

Now I’d like to ask you some questions to make sure you understand the activity:

Imagine that I spin this wheel and person A gets 9 tokens and person B gets 1 token. Say

that you reduce A’s income by 3 tokens.

1. How many tokens does A have at that point? [Answer: 6 tokens]


3. How many tokens did you have to pay to reduce A’s income by 3? [Answer: 1 token]

4. If you keep all 5 tokens, how many tokens do A and B have at that point? [Answer:

9 tokens for A and 1 token for B]


Now I will spin this wheel to determine Person A and B’s incomes. [Spin Wheel] The

wheel marker landed on . This means that Person A currently will receive

tokens and Person B will receive tokens. Now, do you want

to pay to reduce one of the players’ incomes? If yes, whose income, and by how many

tokens? Remember you must pay 1 token for every 3 tokens by which you reduce A or

B’s income, and it is ok to keep all 5 tokens for yourself.

[DIVIDE The 5 TOKENS VISUALLY TO CONFIRM RESPONSE]

Okay, we will reduce A’s and B’s incomes by the amount you wish. We will tell you how

much you can take home at the end of the experiment, after everybody has completed the

experiment. In the meantime, please do not talk to anybody else about the activities you

played.

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F Exit Survey

F.1 Instructions

After completing the experimental games, the RAs thanked participants for their time and informed

them that they would be called back into the adjacent room one at a time to take the exit survey and

receive their winnings.

The exit survey was administered using the following script:

The experiment is now complete. Thank you again for your participation. Your group

responses have been recorded, and in just a moment I will pay you your winnings from

both activities.

Before I pay you, however, I’d like to ask you a few questions about your background, your

interests, and what you thought about the experiment. Your responses to these questions

will not affect your winnings so please answer the questions honestly. Your responses will

remain completely confidential, and they will be used only by researchers to study how

people make decisions involving money.

After finishing the exit survey, participants were given their experiment winnings and asked not

to speak with other participants until everybody has been interviewed and paid out.

F.2 Exit Survey Questionnaire

1. Participant ID.

2. Experiment Session ID / Date / Country.

3. Participant Gender.

4. What is your age?

(a) 18-29

(b) 30-39

(c) 40-49

(d) 50-59

(e) 60+

5. Have you ever lived outside your current district for more than 6 months? Yes or No

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6. What is your highest level of education?

(a) None

(b) Some primary

(c) Finished primary

(d) Some secondary

(e) Finished secondary

(f) Post-secondary school (university)

7. Which languages can you speak well enough to have a conversation?

(do not read list, check all those named)

(a) English

(b) Kuria

(c) Luo

(d) Swahili

(e) Other

8. What is your tribe?

(a) Kuria

(b) Luo

(c) Other

9. In the past week, how many times did you attend a religious service, if at all?

10. Did you earn any cash income in the past week? Yes or No

If yes, how much?

11. In the past week, how many days did you read the newspaper, if at all?

12. Imagine that in Activity 1 of this experiment, an individual gave you 1 token and kept 9. Please

indicate your feelings toward this person.

(a) Not at all angry

(b) A little angry

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(c) Angry

(d) Quite angry

(e) Very angry

13. Think about all Tanzanians [Kenyans]. Which of the following statements is closest to your

view?

(a) I see myself as quite similar to most Tanzanias [Kenyans].

(b) I see myself as quite different from most Tanzanians [Kenyans].

14. Think about all Tanzanians [Kenyans]. Which of the following statements is closest to your

view?

(a) Because there is a lot of cultural variety in Tanzania, there is very little that makes us the

same.

(b) Even though there is a lot of cultural variety in Tanzania, we are more the same than we

are different.

15. Think about all Luos [Kurias]. Which of the following statements is closest to your view?

(a) I see myself as quite similar to most Luos [Kurias].

(b) I see myself as quite different from most Luos [Kurias].

16. Do you personally know anybody that has married a member of a different tribe?

(a) Yes

(b) No

17. Let us suppose that you had to choose between being a [national group] and being a [ethnic

group. Which of the following statements best expresses your feelings?

(a) You feel only [national].

(b) You feel more [national] than [ethnic].

(c) You feel equally [national] and [ethnic].

(d) You feel more [ethnic] than [national].

(e) You feel only [ethnic].

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18. How much do you agree with the following statement: In order for justice to be served, violence

should be repaid with violence.

(a) Disagree strongly

(b) Disagree somewhat

(c) Neither agree nor disagree

(d) Agree somewhat

(e) Agree strongly

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Nationalism and Social Sanctioning Across Ethnic Lines€¦ · fects sanctioning in Kenya { namely increased punishment of outgroup violations against ingroup members { is at odds

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