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(IN)FORMAL CONTRACT ENFORCEABILITY RA ´ UL S ´ ANCHEZ DE LA SIERRA * November 17, 2017 Abstract Through their command over the threat of force, states enforce contracts, which is often assumed to be welfare enhancing. However, non-state social organization can provide even better contract enforcement, particularly in the presence of weak or captured states. In this paper, I experimentally test two propositions about the relative efficacy of state and social organization in enforcing contracts. The first is that when the state administration is cap- tured by one social group, contract enforcement is biased in favor of that group, which has implications for trade. The second is that the effect of expanding contract enforcement by the state depends on how it interacts with social organization, which already governs many agency relations outside the state and can be complemented or instead crowded-out. I use experimental variation to test these propositions, randomizing moral hazard and contract form in household sales across trader-customer pairs. First, I find that in the absence of state enforced contracts, a specific form of social organization (co-ethnicity) increases trade and mitigates moral hazard, largely through the threat of lynching and social exclusion. Sec- ond, I find that state-enforced contracts have only mild crowding-out effects and also increase trade and mitigate moral hazard, but this effect is only found in the groups that control the local judiciary. This suggests that formal institutions may have limited efficacy relative to informal institutions in fragmented polities. The results are consistent with a view of a weak state not as lacking enforcement potential, but in which social organization governs many agency relations, including the state administration itself. JEL classification: D02, D23, H11, J41, N47, P48 Keywords: Contracts, Property rights, Ethnicity, State * The work of Avner Greif, as well as long conversations with him, greatly inspired this paper. I am thankful to Christopher Blattman, Pierre-Andre Chiappori, Donald Davis, Macartan Humphreys, Suresh Naidu, Bernard Salani´ e, and Eric Verhoogen for guidance. I am grateful to Philippe Aghion, Charles Angelucci, Natalie Bau, Roland B´ enabou, Matilde Bombardini, Guillermo Caruana, Benjamin Enke, James Fearon, Avner Greif, Jonas Hjort, Supreet Kaur, Gauthier Marchais, Monica Martinez Bravo, Joel Mokyr, Eduardo Montero, Nathan Nunn, Torsten Persson, Bernard Salani´ e, Mariano S´ anchez Talanquer, Shanker Satyanath, Monica Singhal, Franziska Schwingeler, Guido Tabellini, Francesco Trebbi, Hans-Joachim Voth, Noam Yuchtman, Fabrizio Zilibotti, and seminar participants at the Canadian Institute For Advanced Research Zurich meeting 2017 of Organizations Institutions and Growth, Columbia, NYU, the Harvard Development Faculty retreat, CEMFI, NEUDC 2014, the Norwegian School of Economics, the University of British Columbia, and to the students of the UC Berkeley PHDBA279C graduate course on Political Economy for invaluable comments. Aimable Amani Lameke, Jean- Paul Zibika, provided excellent management support, and Carlos Schmidt Padilla provided outstanding research assistance. This project was supported by Private Enterprise Development in Low-Income Countries exploratory grants (PEDL), Russell Sage Small Grants in Behavioral Economics, and the Center for the Study of Development Strategies at Columbia University. Email: [email protected]. 1

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(IN)FORMAL CONTRACT ENFORCEABILITY

RAUL SANCHEZ DE LA SIERRA∗

November 17, 2017

Abstract

Through their command over the threat of force, states enforce contracts, which is oftenassumed to be welfare enhancing. However, non-state social organization can provide evenbetter contract enforcement, particularly in the presence of weak or captured states. In thispaper, I experimentally test two propositions about the relative efficacy of state and socialorganization in enforcing contracts. The first is that when the state administration is cap-tured by one social group, contract enforcement is biased in favor of that group, which hasimplications for trade. The second is that the effect of expanding contract enforcement bythe state depends on how it interacts with social organization, which already governs manyagency relations outside the state and can be complemented or instead crowded-out. I useexperimental variation to test these propositions, randomizing moral hazard and contractform in household sales across trader-customer pairs. First, I find that in the absence ofstate enforced contracts, a specific form of social organization (co-ethnicity) increases tradeand mitigates moral hazard, largely through the threat of lynching and social exclusion. Sec-ond, I find that state-enforced contracts have only mild crowding-out effects and also increasetrade and mitigate moral hazard, but this effect is only found in the groups that control thelocal judiciary. This suggests that formal institutions may have limited efficacy relative toinformal institutions in fragmented polities. The results are consistent with a view of a weakstate not as lacking enforcement potential, but in which social organization governs manyagency relations, including the state administration itself.

JEL classification: D02, D23, H11, J41, N47, P48Keywords: Contracts, Property rights, Ethnicity, State

∗The work of Avner Greif, as well as long conversations with him, greatly inspired this paper. I am thankfulto Christopher Blattman, Pierre-Andre Chiappori, Donald Davis, Macartan Humphreys, Suresh Naidu, BernardSalanie, and Eric Verhoogen for guidance. I am grateful to Philippe Aghion, Charles Angelucci, Natalie Bau,Roland Benabou, Matilde Bombardini, Guillermo Caruana, Benjamin Enke, James Fearon, Avner Greif, JonasHjort, Supreet Kaur, Gauthier Marchais, Monica Martinez Bravo, Joel Mokyr, Eduardo Montero, Nathan Nunn,Torsten Persson, Bernard Salanie, Mariano Sanchez Talanquer, Shanker Satyanath, Monica Singhal, FranziskaSchwingeler, Guido Tabellini, Francesco Trebbi, Hans-Joachim Voth, Noam Yuchtman, Fabrizio Zilibotti, andseminar participants at the Canadian Institute For Advanced Research Zurich meeting 2017 of OrganizationsInstitutions and Growth, Columbia, NYU, the Harvard Development Faculty retreat, CEMFI, NEUDC 2014, theNorwegian School of Economics, the University of British Columbia, and to the students of the UC BerkeleyPHDBA279C graduate course on Political Economy for invaluable comments. Aimable Amani Lameke, Jean-Paul Zibika, provided excellent management support, and Carlos Schmidt Padilla provided outstanding researchassistance. This project was supported by Private Enterprise Development in Low-Income Countries exploratorygrants (PEDL), Russell Sage Small Grants in Behavioral Economics, and the Center for the Study of DevelopmentStrategies at Columbia University. Email: [email protected].

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1 Introduction

Consider a typical economic exchange relation: an agency relationship, with an inherent commit-

ment problem—a merchant and an agent, who can sell the merchant’s good. Trade is mutually

beneficial, but the agent has an incentive to defect, hence trade often collapses. Without state

enforcement of contracts, trade may be sustained through “social organization,” which can gov-

ern the agent’s incentives to defect (Greif, 1993). Through their command over force, states can

threaten the agents who defect, which is often assumed to be more efficient. However, the view of

the state as an autonomous bureaucracy that can be leveraged to solve moral hazard is often not

warranted: the state itself is often governed by social organization, which can bias enforcement.

Furthermore, when social organization governs trade without state enforcement, expanding state

enforcement can complement it, or crowd it out. Yet, the effect of state contract enforcement on

economic exchange, let alone how it is shaped by social organization, is not well understood.

In this paper, I propose and experimentally test a framework that accounts for social organi-

zation to study the effect of introducing the state into agency relationships—relationships with

an inherent commitment problem. First, in contrast to views of the state as an autonomous

bureaucracy, I allow social groups to control the administration. This introduces a bias in the en-

forceability of state contracts in favor of such groups, which trading partners anticipate when they

decide to whether trade and whether to defect. Second, the effect of the state depends on whether

contract enforcement by the state, substitutes, complements, or crowds-out the mechanisms that

govern agency relations in the absence of state enforced contracts (Benabou and Tirole, 2003,

Bowles and Polania-Reyes, 2012). A challenge to examine the effect of state contract enforcement,

however, is that social organization and the state are endogenous.

As a foundation for this paper, I create a home delivery sale with an inherent commitment

problem that can lead mutually beneficial trade to breakdown. The sales take place in Sud Kivu,

Democratic Republic of Congo (DRC), where relationships are mostly governed informally. This

allows me to experimentally vary moral hazard and contract type across trader-customer pairs.

To tailor the study design to the context, I first describe the relevant social organization. First,

the so-called “autochtonous” (native) groups have captured the provincial administration, which

2

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they use as a vehicle for their interests (henceforth, state group). The other groups (the Tutsi) are

largely prevented from accessing positions in the provincial administration (henceforth, nonstate

groups). I conduct a survey of the administration, which confirms this pattern. Second, important

social (ethnic) divisions between the state groups exist which, despite the recovery by the state of

many of its functions, have crystallized into a culture of informal ethnic governance of trade. This

paper does not aim to establish that ethnicity matters. Instead, it uses it as a well-documented

form of social organization to examine formal and informal contract enforceability.

I organize the paper in three parts. In the first part, I examine the principal’s decision to

trade in trader-customer pairs belonging to multiple state groups, as a function of moral hazard,

pair composition, and contract type. In the second part, I confirm that the agents’ decisions to

defect are consistent with the principal’s implied beliefs. In the third part, I introduce principals

of nonstate groups to measure the implication of administrative capture on moral hazard.

In the first part of the paper, I examine the decision to trade when there is an inherent com-

mitment problem. I recruit traders of different state groups to administer home sales in 1,860

households, also of different state groups. Traders offer to deliver a household good at reduced

price in a given time frame. In exchange, customers must pay immediately (Sales on Debit).

This generates an agency relationship, since the trader (the agent) has an incentive to defect by

reneging to deliver the good, which the customer (the principal) anticipates.

I use the sales introduced in this first part to quantify the effect of social organization of the dif-

ferent state groups (henceforth, co-ethnicity) on trade. To ensure that co-ethnic trader-customer

matches are not systematically different, I randomly assign traders to customers. I find that when

trader and customer are from the same group, customers are 22% more likely to buy. To iden-

tify whether such effect reflects homophilic tastes for in-groups, or instead, the customers’ belief

that shared ethnicity curbs traders’ defection, I experimentally eliminate trader moral hazard in a

subset of sales (Sales on the Spot). The findings from this design rule out homophilic tastes, and

establish that co-ethnicity increases trade only by inducing customers to expect lower likelihood

of defection. Furthermore, customers matched to an in-group trader are more likely to report that

they expect the trader to be lynched and to lose relationships if he defects. They are also more

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willing to pay so that the trader is caught. This suggests that social organization governs social

sanctions and in-group norms of reciprocity and revenge (Fehr and Gaechter, 1999).

Using these sales, I then introduce contract enforcement by the state. In randomly selected

customer-trader pairs, the trader signs a contract, which endows the customer with the right to

take the trader to court if the trader defects (legal contracts). I find that trade is 20% more likely

to occur in pairs where the trader signs a legal contract. Furthermore, legal contracts increase

trade only in pairs in which there is trader moral hazard, thus showing that state contract enforce-

ment, even in a weak state, increases trade because customers expect the trader who has signed

a legal contract to be less likely to defect. Legal contracts leave customer priors about the trader

type unaffected, which allows me to rule out that legal contracts simply signal a better trader

type. Instead, I find that customers in pairs where the trader signs a legal contract are more likely

to expect that the trader’s incentives will be constrained by the judiciary.

The setup of this first part finally allows me to examine how legal contracts interact with so-

cial organization. First, legal contracts and co-ethnicity are equally effective to solve commitment

problems and increase trade, but legal contracts are mildly less effective among co-ethnics. Second,

customers whose trader signs a legal contract are significantly less likely to expect that the trader

will be socially marginalized if he defects—which is also the social sanction that better predicts

the customer’s decision to trade. This effect, which counteracts the incentive effect of introducing

the state, is consistent with crowding-out (Bowles and Polania-Reyes, 2012): by signing a legal

contract, the trader signals that he has no expectations that the decision to buy, and thus the

relationship, is governed by social organization. Yet, despite this crowding-out, the incentive effect

of legal contracts dominates its effect on social sanctions for the decision to trade.

In the second part of the paper, I examine whether the agents’ decision to defect is consis-

tent with the implied beliefs of customers of the first part, again among state groups. I sample

971 households from the population where I recruited the traders of the first part, and observe

their decision to defect as customers to a new sale (now agents). Recruiting customers from this

population allows me to measure the decision to defect by the population of the traders of the

first part, without exposing the agents to risk, since I control the decision to take customers to

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court, but cannot control the customers’ decision. This new sale introduces customer moral haz-

ard. The customer obtains the good with no delay, and agrees to pay using a cell phone system,

which gives him the opportunity to defect (Sales on Credit). I find that customer defection in

co-ethnic customer-trader pairs is 14 percentage points smaller than in non-coethnic pairs, and the

customer’s concern for lynching and social marginalization is significantly higher. Furthermore,

consistent with potential defectors anticipating the incentive effects of co-ethnicity, co-ethnicity

deters customer opt-in to Sales on Credit, but not to Sales on the Spot.

The design of the sale in the second part of the paper allows me to separate the incentive effect

of legal contracts on defection from their effect on customers selection (screening)—which will

arise if customers who might defect anticipate that legal contracts make defection costly. In a first

step, traders require the customer to sign the contract before the customer decides whether to buy

the good. In a second step, a random subset of customers ultimately does not sign the contract

(a-la Karlan and Zinman (2009)). Legal contracts reduce defection by 14 percentage points, which

identifies their incentive effect among customers who opt-in. Using an additional experimental

variation, I find that legal contracts also deter a significant mass of customers from opting-in to

the sale, establishing that customers who might defect anticipate the incentive effect of legal con-

tracts. Furthermore, customers who sign a contract are significantly more likely to expect being

brought to court if they defect. As before, legal contracts and co-ethnicity are substitutes, and

the effect of legal contracts on defection is weaker among co-ethnic pairs.

In the third part of the paper, I examine the implication of capture of the administration

for the governance of agency relations. Alongside the Sales on Credit traders, I introduce an

equal number of traders of non-state groups and randomly match them to customers. In contrast

to customers visited by traders of state groups, I find that for nonstate traders, legal contracts

leave customer defection and opt-in unaffected, and that customers who sign a legal contract are

no more likely to expect judicial consequences. Legal contracts reduce defection only when they

protect the property of individuals whose groups control the administration. This is consistent with

a model in which administrators use state contract enforcement for in-group redistribution.

This paper examines whether the state can redress the inefficiencies of social organization, a

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natural but unexplored question after Greif (1993). The paper explores two new areas of research.

First, this paper demonstrates that state capture can have asymetric implications for contract

enforceability of different groups, and thus moral hazard. In principle, a ruler has incentives

to have contracts enforced, because he is a residual claimant on successful transactions through

the taxes he collects (Besley and Persson, 2009, Olson, 1993). However, contracts are enforced

by administrators, whose incentives are often misaligned with the ruler’s (Glaeser, Ponzetto and

Shleifer, 2016, Greif, 2007, Tirole, 1986). Rather than being an autonomous bureaucracy, the

state is a network of individuals, who are themselves part of the relevant social organization. Yet,

despite evidence that the judiciary can be biased (Abrams, Bertrand and Mullainathan, 2012,

Shayo and Zussman, 2011), most literature on state capture and formal institutions has focused

on political and economic institutions (Acemoglu and Johnson, 2005). Social organization, by

governing the incentives of administrators, can use the judiciary like other informal instruments

to redistribute resources between groups. A captured judiciary, through the channel of contract

enforcement, has implications for the distribution of benefits from formalization, the patterns of

trade, and can be used to reinforce existing inequalities. This has implications for the view of the

state as an autonomous bureaucracy summarized by views of the state as “state capacity.”

Second, while legal contracts introduce the power of the state as a third-party enforcer, they

also introduce explicit incentives into informal agreements, thus they have the potential to crowd-

out the very social organization that governs agency relations. Yet, we know little about how

the state might crowd-out governance of agency relations. A growing literature examines the ef-

fect of extrinsic incentives on crowding-out of socially efficient behaviors (Gneezy and Rustichini,

2000), when behavior is motivated by concerns for self-image (Benabou and Tirole, 2003) or social

preferences (Bowles and Polania-Reyes, 2012). However, introducing a third-party enforcer (the

judiciary) also introduces incentives. I find that legal contracts crowd-out the threat of social

marginalization, sending a cue that the relationship is seen as less personal.

In passing, the paper also makes methodological contributions to established fields.

First, after Greif (1993), many authors have been able to empirically confirm that social or-

ganization governs trade and credit (Banerjee and Munshi, 2004, Chandrasekhar, Kinnan and

6

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Larreguy, 2014, Fafchamps, 2000, Fisman, Paravisini and Vig, 2017, McMillan and Woodruff,

1999). A challenge to study the role of social organization, however, is that transactions form

endogenously, and that the the mechanisms through which social organization governs trade are

difficult to isolate. Random assignment in this study allows me to quantify the effect of a specific

form of social organization, and, furthermore, isolate the mechanisms through which it operates.

Second, several studies have examined the economic effects of social divisions (Dixit, 2003,

Greif, 1993).1 Social divisions can be costly (Alesina and La Ferrara, 2004, Alesina, Baqir and

Easterly, 1999, Alesina, Michalopoulos and Papaioannou, 2016, Hjort, 2013, Miguel and Gugerty,

2005), especially when they carry cultural divisions (Desmet, Ortuno Ortın and Wacziarg, 2017).

However, the mechanisms that lead social divisions to be costly in the real world are relatively un-

known. I find no evidence for taste-based discrimination, but social divisions govern moral hazard

and reduce defection. Furthermore, complementing studies that examine mechanisms in the lab

(Habyarimana et al., 2007, Lowes et al., 2017), this paper examines typical trade transactions.

Sections 2 and 3 present the context and the framework. Section 4 examines the decision to

trade. Section 5 examines the decision to defect. Section 6 examines the implication of enforce-

ability bias in favor of state groups. Section 7 presents a validation exercise. Section 8 concludes.

2 Context

2.1 Institutional framework

This paper does not aim to establish that ethnicity matters. This has already been established

(Alesina and La Ferrara, 2004, Hjort, 2013). Rather than aiming to produce a measure of the

effect of ethnicity, which is context-specific, I use it instead as a well documented form of social

organization in this context to to examine how formal and informal contract enforceability com-

pare and interact. I next describe such context.

The context offers an opportunity to examine state expansion through legal contracts as a

1Greif (1993) describes how, when the scale of trade expanded, the Maghrebis slowly lost their control overlong distance trade. Dixit (2003) shows how distance and outside options undermine informal contracts.

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form of institutional innovation. After the collapse of the state that began in the 1970’s, informal

networks became central to organize economic exchange (Nest, Grignon and Kisangani, 2011).

Yet, while the government has recovered the functions of the state administration since the end

of the Second Congo War in 2004 (Sanchez de la Sierra, 2015), the reliance on social groups for

the governance of economic exchange has crystallized into a culture of economic relations based

on trusting co-ethnics and where ethnicity is a salient feature of social interaction (Lowes et al.,

2015). The state has regained some of its functions, but culture of economic exchange remains

informal, thus some authors advocate for formalization (Geenen, 2013).

The population of Sud Kivu is composed of salient social groups. On the one hand are the

groups of direct Bantu descent, whose origins date back to the “Bantu expansion” (500 BC). On

the other hand are the pastoralist groups, who migrated significantly later (the Tutsi). As a result

of their historical exposure to the Bantu after their migration to the area, the Tutsi also share

Bantu genetic heritage, but unlike the “original” Bantu, they have a distinct Nilo-Saharan genetic

descent which makes their distinction salient.2 This distinction underpins frequent discrimination

by the so-called Bantu “autochthons”(Jackson, 2006). Table I lists the groups as a function of

this distinction. Figures A.1 and A.2 in the online appendix show their historical boundaries.3

The D.R.C. is a highly decentralized unitary state, where Provinces have their own govern-

ment, Ministries and legislature. Only the first set of groups has access to the administration of

the state in Sud Kivu. Access to the provincial administration operates in “mafia-type networks,

ethnic circles and other forms of patron-client relationships” which exclude the Tutsi group (Tre-

fon, 2009). The capture of the administration by the “autochthon” groups at the provincial level,

through well-documented nepotist networks, makes it a vehicle for the interests of “autochthons”

2Unlike in Rwanda, where Tutsi and other groups (in particular, the Rwandan Bahutu) have historically mixed,the Tutsi communities of Sud Kivu (Banyamulenge) have been relatively closed-knit.That Tutsi only accept cattle asdowry limited possibilities for inter-marriage with other ethnic groups. Bashi, Bahavu, Balega, Batembo, Bafuliro,and in a lesser prevalence, Bahutu, Babembe, Babuyus Babwaris, Bamasanze, Barundis, and Baviras compose theuncontested Bantu ethnic groups of Eastern Congo. The Tutsi groups of Eastern Congo are broken down intoBanyarwanda, Banyabwisha, or Bayamulengue. In Sud Kivu, the Banyamulenge are the predominant Tutsi group,which likely first arose from a wave of migration from Rwanda 500 years ago. The century of migration is debated,but there is consensus that the largest group migrated in the 19th century (Verweijen and Vlassenroot, 2015).

3See Luis, J., Rowold, D., Regueiro, M., Caeiro, B., Cinniolu, C., Roseman, C. (2004), Newbury (1992),Ngonzola-Ntalaja (2002), Stearns (2011) for accounts of current ethnic relations in Sud-Kivu.

8

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(Geenen, 2013, Geenen and Claessens, 2013, Hesselbein, 2007).4 Due to their control over the

provincial administration, I henceforth refer to the the set of ethnic groups that compose the

so-called “autochthons” as state groups, in contrast to the Tutsi, which I refer to as nonstate

groups. While the state groups have strong claims to citizenship and the state, in contrast, the

nonstate groups (the Tutsi) are excluded from the favors of the provincial administration. The

Tutsi association with the Rwandan government during the Second Congo War and in rebellions

against the “autochthons” (for instance, in the so-called Simba rebellion) lead to their isolation

from the state, which was exacerbated by their poor political organization (Court, 2013, Jackson,

2007, Rukundwa, 2004, Verweijen and Vlassenroot, 2015, Vlassenroot, 2002)5 As a result, state

and nonstate groups differ in their ability to activate state protection and the judiciary.6

Despite their privileged access to the state administration, however, the different ethnic groups

that compose the state groups frequently rely on their (salient) ethnicity for informal agreements.

For example, conflicts over land and political power involving the Batembo against other groups,

such as the Bahavu, all of which are part of the state groups have been frequent dating back

to the 1940’s (Mathys, 2014, Newbury, 1992).7 As a result, while they all have access to the

administration’s recruitment networks in contrast to nonstate groups, the different state groups

can easily recognize one another (which the empirical section directly tests), reinforcing a culture

of economic exchange that is informal, and based on ethnicity.

4The Congolese “state agents and citizens seem to have reached a complex but workable form of accommo-dation” and there is varying degrees of interaction between the state and it citizens (Trefon, 2009). Despite such“accommodation,” Trefon (2009) notes: “A recent survey [...] included the question: ‘if the state were a person,what would you do to him?’ ‘Kill him’ was the unequivocal reaction of most respondents.”

5See Commission de l’immigration et du statut du refugie au Canada (2013): “According to MRG, ‘prejudiceagainst Banyamulenge interests remains entrenched in Kinshasa, including within the administration.”’ Tutsi areexcluded from the provincial civil administration and its patronage networks. At certain periods, the Tutsi hadinfluence in other provincial administrations.

6See, for instance, Trefon (2009): “Administrative personnel in Congo [...] pragmatically address their ownneeds and expectations before those of the services they are supposed to provide [...] The only certainty is thatpersonal opportunism governs their actions.”

7See Mathys (2014): “Whilst in the case of the [Ba]tembo [...] this led to the emergence of local conflicts andlocal contestations of belonging, this did not lead to a contestation of the ‘ethnic’ (and thus ‘civic’) citizenship ofthese populations on the national scene.”

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2.2 Social capture of the Sud Kivu administration: a survey

While most transactions are still informal, Section D.1 and Figure A.3 in the online appendix

show that legal contracts are nonetheless widely used in Sud Kivu. Expanding their use does not

introduce a foreign concept, but instead uses a known instrument for which citizens have formed

a habit not to use. When a contract is breached, one of the parties can bring the case to local

courts, which are under the provincial Ministry of Justice, but also to any administration that is

relevant to the nature of the contract breach.8 I next characterize the main findings of a survey

of the administration I conducted to examine patterns of administrative bias.9

First, composition. State groups dominate the positions of the provincial administration.

Figure 1 presents the distribution of staff in such Ministries of the provincial government. Only

5 of 78 positions are filled nonstate groups.10 Second, discretionary hiring practices. Hiring is

always at the discretion of provincial Ministers and Governor. Family ties, party affiliation, and

ethnicity are the main reasons why administrators obtained their jobs. The Governor consults the

traditional chiefs of ethnic homelands (“Bami”) to select the Ministers, who then have leverage

over the staffing inside the Ministry. Third, patronage. The composition of the administration

is the result patronage and not just discretionary appointments. In order to obtain profitable

positions in the provincial government, one has to be of the state groups.11 There is an implicit

agreement for the exchange of favors, which bureaucrats refer to as the “Agreements of La Roche,”

which stipulates that each ethnic homeland gets one provincial Ministry in exchange for political

favors. Fourth, discrimination. Nonstate groups are discriminated from positions in the provincial

administration. The Ministers do not trust Tutsi to work in the agencies.12

8For employment, the case goes to the “inspection du travail,” for land, to the department of cadastre.9I hired surveyors to conduct a survey in the administration. Table B.6 describes the variables.

10Of these individuals, four occupy positions in the Ministry of Transport, a not strategic agency. The governorof the Province, his Executive Secretary, Executive Vice-Secretary, Director of Cabinet and Vice-Director of Cabinetare Bashi, while the Vice-Governor is a Babembe. None of ten Provincial Ministers are from state groups (twoBashi, one Bahavu, two Balega, one Bafuliru, one Batembo, one Babembe), while only one is a nonstate group(Ministry of Planning). Furthermore, ethnicities of the Directors of Cabinet of the six most important Ministries,the most strategic positions below the Minister, are all from state groups: six Bashis, one Balega.

11For instance, one answer provided was: “One has to be Bashi to obtain the good positions. The others receiveno consideration.” Similarly for other “autochthonous” groups: “At this level, I can say yes. I am [Ba]Havu, it is anopportunity for me to place two of my brothers. As we are one with the people of Kalehe [predomintanly Bahavu],I had a recommendation from the MUHA [Bahavu mutuality] and I took one of them.” Author’s translation.

12Ministry of the Interior: “You have to put someone you trust in here. The Governor does not trust Tutsi.”

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3 Framework

In period 1, the principal chooses whether to take a costly action B(uy) ∈ {0; 1}. In period 2, the

agent observes B, and chooses whether to defect, D ∈ {0; 1}. If B = 0, both payoffs are zero. If

B = 1, the payoff of the agent is:

ua(D|Ea, Ep, F, p, c) = p− c−D (θ(Ep, Ea, F ) + L(Ep, Ea)F − c)

where θ(Ep, Ea, F ) ∈ R indicates social sanctions. These depend on the ethnic group of the

principal Ep, the ethnic group of the agent, Ea, and whether a legal contract has been signed,

F ∈ {0; 1} (for formalization), for instance, if legal contracts crowd-out social sanctions. Social

sanctions can include internalized motives such as reciprocal social preferences (from the agent

or from the principal, who can desire punishing the agent if he agreed to the sale) or guilt and

can be complemented or crowded out by the legal system, are private information for the agent.

Social sanctions have a common knowledge cumulative density function (c.d.f.) Fθ(Ep,Ea,F ). The

term L(Ep, Ea) ∈ R indicates the legal cost of defection. Finally p ∈ R indicates the sale price

and c ∈R the marginal cost of not defecting. Co-ethnics have better enforcement technology than

non-coethnics if Fθ(Ep,Ea=Ep,F ) = FCθ first order stochastically dominates Fθ(Ep,Ea 6=Ep,F ) = FNC

θ . An

agent facing social sanctions θ(Ep, Ea, F ) chooses not to defect if and only if: c < θ(Ep, Ea, F ) +

L(Ea, Ep)F . The payoff of the principal is:

uP (B|Ea, Ep, F, p, c) = B(β(1− Fθ(Ep,Ea,F )(c− L(Ea, Ep)F )

)v − p+ λ(Ep, Ea, F )

)where v ∈ R is the principal’s private valuation of the good, and λ(Ep, Ea, F ) ∈ R is his social

utility from accepting the sale—this includes the principal’s intrinsic motivation to help a trader

achieve its goal.13 v is i.i.d. across principals and has c.d.f. Fv. It follows that:

Prob (B = 1) = 1− Fv(

p− λβ(1− Fθ)

)13In general, this is allowed to depend on p− c. There is no loss of generality, given that the price and marginal

cost are invariant.

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Assuming away social organization implies that the effect of introducing a contract is trivially

1− Fv( pβ ) if and only if c < L (strong enforceability) and is zero otherwise (weak enforceability).

Strong enforceability might not be warranted, hence the first objective of the empirical section is

to examine whether L is large enough to motivate the introduction of state contracts, even if the

state is considered weak. For this reason, I first focus on state groups.

Allowing social organization to govern informal agency relations creates testable implications

for its mechanisms. First, the effect of co-ethnicity on trade among other forms of social organi-

zation depends on the relative effect of social sanctions and preferences:

∆Prob(Buy)C−NC = Fv

(p− λNC

β(1− FNCθ (c))

)−Fv

(p− λC

β(1− FCθ (c))

)> 0 ⇐⇒ 1− FC

θ (c)

1− FNCθ (c)

>p− λC

p− λNC

Shutting down co-ethnic preferences, co-ethnics will trade more, if FOSD holds, that is, if FCθ (θ) <

FNCθ (θ) ∀ θ. Allowing positive co-ethnic intrinsic preferences, λNC < λC , strengthens this result.

However, if co-ethnics have an intrinsic preference to trade with non-coethnics, this result is no

loner warranted. Second, in the absence of commitment problems (such as sales in the spot),

this condition boils down to λC > λNC . I can thus isolate the preferences channel by examining

sales where there is no commitment problem. If Prob(Buy)C−NC = 0 in sales on the spot, then

∆Prob(Buy)C−NC > 0 ⇐⇒ FCθ (c) < FNC

θ (c), thus sales with commitment problem pin down

co-ethnic social sanctions. Furthermore, even if λC > λNC and thus co-ethnic social sanctions

cannot be quantified, sales with commitment problems and sales on the spot can be used to prove

their existence: Prob(Buy)C−NC |commitment > Prob(Buy)C−NC |nocommitment ⇐⇒ FCθ (c) <

FNCθ (c).

Allowing social organization to govern informal agency relations, which can interact with ex-

plicit incentives, the effect of introducing contracts is no longer trivial and depends on how social

sanctions and social preferences respond to contracts. Let F iθ(F=j) , i ∈ {C,NC}, j ∈ {0; 1} be

allowed to depend on whether a contract is used and whether principal and agent are co-ethnics:

∆Prob(Buy)F−I > 0 ⇐⇒1− F i

θ(F=1)(c− L)

1− F iθ(F=0)(c)

>p− λi(F = 1)

p− λi(F = 0)

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First, ∆Prob(Buy)F−I > 0 if legal contracts do not crowd-out intrinsic motivation for the sale,

λi(F = 0) ≤ λi(F = 1) and if contracts do not crowd-out social sanctions, F iθ(F=1)(θ) ≤ F i

θ(F=0)(θ).

If any of these two conditions fails, more conditions are needed. If the intrinsic preference effect

is shown to be zero among sales on the spot, then ∆Prob(Buy)F−I > 0 ⇐⇒ F iθ(F=0)(c) >

F iθ(F=1)(c − L). Even in that case, ∆Prob(Buy)F−I > 0 is not warranted, since if F i

θ(F=0)(θ) <

F iθ(F=1)(θ) ∀θ (FOSD), which can occur if legal contracts crowd-out social sanctions, ∃L > 0

such that ∀L < L, F iθ(F=0)(c) < F i

θ(F=1)(c − L). Thus, to pin down whether stronger incentive

effects hide crowding-out, I measure social sanctions in addition to observing the decision to trade.

Second, if co-ethnicity crowds-out legal sanctions, L(C) < L(NC), the effect of co-ethnicity on

social sanctions is dampened by crowding-out of legal sanctions. Last, if L(C) < L(NC), then the

incentive effect of introducing legal contracts among co-ethnics will be reduced. Furthermore, if

legal contracts crowd-out social sanctions among co-ethnics but do not among non-coethnics (or

less so), FCθ(F=1) > FC

θ(F=0) and FNCθ(F=0) = FNC

θ(F=1), then everything else being equal, the effect of

legal contracts will be smaller among co-ethnics. Similarly, if legal contracts crowd-out intrinsic

motivation more for co-ethnics, λC(F = 0)−λC(F = 1) > λNC(F = 0)−λNC(F = 1), then absent

social sanctions crowding-in, legal contracts will have weaker effects on co-ethnics. Contracts are

likely to crowd-out social sanctions and intrinsic motivation, which are stronger among co-ethnics.

However, co-ethnics might be better able to coordinate so that legal sanctions are applied. Thus,

the interaction between co-ethnicity and legal contracts is an empirical question.

In the last part, I examine the implications of a weak state for L. As the administration is

controlled by state groups, nonstate groups might not be able to achieve enforceability: L(Ep ∈

ENS, Ea) = 0 < L(Ep ∈ ES, Ea) = L, where ENS and ES are the sets of nonstate and state groups.

In Section E, I explicitly model this assumption, extending the framework of Besley and Persson

(2009) to allow enforcement to be set by an administrator. The administrator’s choice depends on

his private benefit and the payoffs of the two groups. In a weak state, the administrator’s marginal

cost of deviating from the target rate of enforcement set by the ruler is finite, thus enforcement is

weaker when it harms the administrator’s group. This asymmetry distorts the patterns of trade,

making it easier for “state” groups to use contracts as a creditor.

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4 The decision to trade, among state groups

In this section, I describe the agency relations, and examine the effect of co-ethnicity among state

groups. I compare the effect and mechanism of legal contracts to those of of co-ethnicity.

4.1 Baseline design: measuring agency relations between state groups

In order to observe behavior in agency relations, I recruit traders of multiple state groups to sell

discounted household goods to 1,860 state households.14 The traders visit households door-to-

door, and offer a reduced price household good, which they promise to deliver within three days.

In exchange, the customer must pay the discounted price on the spot (henceforth, sale on Debit).

This induces a commitment problem on the side of the trader: while exchange is mutually benefi-

cial, the customer anticipates that the trader might have incentives to defect.15 Once the customer

decides whether to buy the good, the trader then records the customer’s choice, and administers

an exit survey. Traders are residual claimants.16 There was no reputation to build from, except

that the sale looked like a typical ambulant sale that is common in the area. Less than 5% of

customers found the transaction unusual.17 The upper arm of Figure A.4 in the online appendix,

provides a graphical representation of the experimental design.

This design creates a commitment problem that allows me to observe the behavior of the

principal, the customer, in an agency relation with the agent, the trader. Absent enforceable

contracts, social sanctions, or social preferences, the customer would anticipate that the trader’s

best response is to sell and then renege on the promise. However, many reasons, such as social

preferences or social sanctions, can induce the customer to expect that the trader will deliver.18

14I create an organization for that purpose, which recruited like local firms. The organization communicatedjob openings through social networks, and interviewed by potential applicants. All traders were required to have aUniversity degree. The activity was authorized by the Provincial Ministry of the Interior.

15The traders explain that they collect the cash first as commitment to then obtain wholesale prices, and thatthey are part of a new organization testing sales to generate profit.

16Traders receive a fixed wage of 10 USD per day and keep the revenues from the sales, net of a marginal cost.17Traders offered standard packages of five soaps of a well-known local brand (whose market price is .5 USD

per soap) for the price of two soaps. This ensures that moral hazard on the trader’s side can only be on delivery,not on soap quality. Before choosing to sell soaps, I conducted a market study to identify what goods would be inrelative scarcity, and would not be unusual to sell. A pilot with phone credit cards generated the same patterns.

18For instance, when asked whether they think the trader will deliver and why, many customers mention thatthe trader’s “consciousness” is what will ensure that he delivers. I ensured that traders did not defect. No customer

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4.2 Effect of co-ethnicity on agency relations between state groups

In this section, I examine the main effect and channels of co-ethnicity on trade.

4.2.1 Effect of co-ethnicity on trade

To measure the effect of co-ethnicity on agency relations, I randomly assign households, within

ethnic group of the households (e) and town/neighborhood (v), to one of the following condi-

tions: not targeted, targeted by trader 1 only, targeted by trader 2 only.19 This ensures that the

characteristics of traders and customers are orthogonal to unobserved characteristics that might

otherwise lead matches to form. I can estimate the impact of co-ethnicity, Ei,j ∈ {0, 1}, on

whether customer i visited by trader j buys the good, Bi ∈ {0; 1}. I implement the following

linear probability model, which includes randomization block fixed effects in all specifications, ηve:

Bi = a0 + a1Ei,j + ηve + ei,j (1)

If a1 > 0, then co-ethnicity increases trade. Figure 2, left side of panel A, presents the

regression results. The light gray bar indicates the level of the constant, a0, which is the proportion

of customers who buy the good when visited by a non-coethnic trader. The the dark gray bar

presents the same proportion for customers visited by their co-ethnic traders. The red line shows

the 90% interval of the coefficient on co-ethnicity, a1. The proportion of customers who buy

the good is significantly higher if the trader that faces the commitment problem belongs to the

same ethnic group than the customer, 51% to 63% (both from state groups). Co-ethnicity, thus,

increases trade in the absence if the state.

had heard of the sales before .19The potential customers are identified through three-steps clustered sampling. In the first step, I randomly

select sixty small towns/neighborhoods in the proximity of the city of Bukavu. I deploy six teams of traders of mixedethnic groups, each team to a sequence of unique town/neighborhoods. In each town/neighborhood, the assignedteam of traders conducts a census of all households. In the second step, the traders randomly select thirty-twohouseholds from such census, following pre-determined randomization protocols that create household ethnic groupblocks. Each trader draws sequences of households within each block to maximize variance in co-ethnicity. In thethird step, in each household, the trader randomly selects one adult. Based on a pre-selected random sequence,a trader “dictionary” assigns households to traders randomly. The traders’ compose the majority of the stategroups of Sud Kivu. The customers ethnic groups vary, which ensures variation in co-ethnicity. The trader teamsare composed of Batembo, Bashi, Bafuliiro, Bahavu, and Balega traders. The remainder (Bahutu, Banyamulenge,Babembe, Banyiundu and Pigmeas) are located in areas difficult to reach and thus I did not include them.

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4.2.2 Mechanisms: homophilic tastes or expectations of trader defection?

The result of the previous section can accommodate two interpretations. On the one hand, cus-

tomers might be more willing to buy from a co-ethnic trader because they have more trust in

co-ethnics — expectations. On the other hand, they may just have a taste for purchasing from

co-ethnics. Only the first interpretation implies that co-ethnicity governs agency relations.

To disentangle expectations from tastes, I also introduce sales in which there are no commit-

ment problems, as discussed in Section 3. In such sales (Sales on the Spot), the trader provides

the household good a the same time that he receives the payment from the customer, and thus,

has no opportunity to defect. The effect of co-ethnicity among Sales on the Spot thus captures

the customer’s taste for trading with co-ethnics, which allows me to rule out co-ethnic homophilic

preferences if co-ethnicity has no effect in these sales. The effect of co-ethnicity in Sales on Debit

captures, in addition, the effect of co-ethnicity on expectations that the trader will deliver. As

shown in in Section 3, comparing the effect of co-ethnicity among Sales on the Spot to its effect

among Sales on Debit allows me to identify the effect of co-ethnicity on the customer’s expecta-

tions that the trader defects. Figure A.4 in the online appendix presents the experimental design.

As before, I estimate the impact of co-ethnicity on whether customer i visited by trader j

decides to buy, Bi ∈ {0; 1}. Let Ei,j ∈ {0, 1} denote whether customer i and trader j are co-

ethnics, and now Di ∈ {0, 1} whether customer i is assigned to Sale on Debit, both of which are

randomized at the customer level within village/neighborhood blocks. I implement the following

linear probability model.20

Bi = a0 + a1Ei,j + a3Di, + a5Ei,jDi + ηve + ei,j (2)

If and only if defection is not perfectly governed by social organization then a3 ≤ 0. Co-ethnicity

solves commitment problems in transactions not governed by legal contracts if and only if a5 > 0.

Figure 2, Panel A, presents the results from both types of sales. While co-ethnicity significantly

increases purchase for Sales on Debit, its effect among Sales on the Spot is insignificant (and

20Results using conditional logit are identical.

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negative). This suggests that co-ethnicity increases trade only by shifting customers’ expectations

that the trader will defect. Thus, co-ethnicity increases trade because it governs agency relations.

Table II reports the results from econometric specification 2. Column (1) uses a dummy

variable for whether the sale is On Debit or instead On the Spot. Among all sales, introducing

the possibility that the trader can defect significantly reduces the percentage of sales that are

successful from 64% to 52%. This supports the validity of the design of Sales on the Spot and

shows that traders in many transactions are unable to solve their commitment problems.

Columns (2) and (3) separately identify the marginal effect of co-ethnicity, respectively among

Sales on the Spot and among Sales on Debit. In Sales on the Spot, the coefficient on “Co-ethnic”

is indistinguishable from zero — and again, is even negative. In contrast, in Sales on Debit, co-

ethnicity increases the probability that the customer will purchase the good from 49% to 59%.

This effect fully cancels out the commitment problem estimated in Column (1).

I estimate the difference in the effects of co-ethnicity for the two types of sales in Column

(4), which presents the results of the fully saturated model of Specification 2. The coefficient on

Sale on Debit measures the extent of the commitment problem in non-coethnic sales, and is larger

than the same coefficient in Column (1), for all sales. The coefficient on Sale on debit X Coethnic

captures the differential effect of co-ethnicity on the probability to purchase among Sale on Debit,

compared to its effect among Sale on the Spot. The coefficient is large (15%), and statistically

significant. The decrease in trade introduced by the trader’s opportunity to defect among non-

coethnics (line 1) turns zero among co-ethnics, confirming that co-ethnicity reduces commitment

problems. Columns (5) - (7) use actual co-ethnicity as an instrument for the customer’s perceived

co-ethnicity, in a two-stages least square framework. Table B.2 in the online appendix presents the

first stage as well as the psychological mechanisms of perceived co-ethnicity.21 The 2SLS results

are stronger in magnitude, suggesting that the main effect under-estimates the effect of perceived

co-ethnicity. This confirms that co-ethnicity increases trade because it governs agency relations.

21Column (1) of Table B.2 presents the first stage, which supports that co-ethnicity is salient and that actualco-ethnicity is a valid instrument. Columns (2) to (7) use the 2SLS specification, and show that interacting witha co-ethnic increases the salience of co-ethnicity, both in terms of how the other is perceived but also in howthe customer perceives himself. This result is consistent with the interpretation that being subject to a co-ethnicinteraction triggers parochial moral values (Enke, 2017).

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4.2.3 Mechanisms: why does co-ethnicity change expectations of defection?

The results suggest that customers expect co-ethnics to be less likely to defect. How does co-

ethnicity achieve this shift in expectations? In this section, I examine what social sanctions are

activated by co-ethnicity, and which are likely to play a role at explaining the main effect.22 In

addition, I also measure the customer’s internalized preference for sanctioning a defecting trader

even when enforcement is privately costly (Fehr and Gaechter, 1999).23

Figure 2, Panel B presents the results from an OLS regression of dummies for different social

sanctions (lynching, loss of friendships, shame, and whether the customer would be willing to pay

to ensure that the trader, if he defects, is caught) on co-ethnicity.24 The gray bar indicates the

proportion for non-coethnic interactions, and the dark gray bar indicates the level for co-ethnic

interactions. The most frequent social sanction is shame and loss of friendships. Co-ethnics who

defect are disproportionately subject to the threat of lynching and the loss of friends. This suggests

that that co-ethnics are more easy to find, consistent with Habyarimana et al. (2007), or simply

that co-ethnics are better able to coordinate their members on sanctions (similar to Miguel and

Gugerty (2005)). Furthermore, co-ethnic customers are significantly more willing to pay to ensure

that the trader is caught, suggesting the presence of strong in-group norms and reciprocity. It

suggests that in-groups have a governance advantage by activating sentiments of revenge-taking

upon violation of a norm. This observation is consistent with the interpretation that co-ethnic

interactions trigger parochial moral values (Enke, 2017). Thus, customers who buy from a co-

ethnic have better expectations about the co-ethnic trader, in part because the trader faces higher

risk of lynching, of losing valuable relationships, and because the customer anticipates stronger

in-group norms of reciprocity and revenge taking.

22First, thieves are often lynched publicly. Second, defectors are sometimes ostracized and lose future busi-ness opportunities. Third, many individuals feel shame, which might capture emotional reactions or simply aninternalized fear of opportunities, as in Bursztyn, Fujiwara and Pallais (2017).

23Traders ask customers in the exit survey how much they would want to pay, if the trader defects, in orderto catch him. Since this offers a hypothetical service, it is also an incentive compatible question. The answercaptures the preference for enforcing a punishment for violating a norm, which might arise through reciprocalsocial preferences (Charness and Rabin, 2002), thus revenge.

24The figure presents the proportion of customers, among those who accepted the purchase in Sales on Debit,who answer the following question of the exit survey (non prompted) “If the trader does not deliver, what conse-quences do you think are likely?” All of these possible consequences are codified using a predetermined list in thetablet and are non-prompted. The answers are not prompted.

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4.3 Formalized agency relations: introducing legal contracts

In the previous section, I have shown that agency relations are partly governed by co-ethnicity.

In this section, I examine whether introducing legal contracts mitigates commitment problems.

4.3.1 Effect of legal contracts on trade

I introduce sales in which, prior to the transaction, the trader explains that he will sign a legal

contract. The contract allows the customer to bring the trader to court if the trader defects. If

the customer accepts the sale, upon signing the contract, the trader hands a signed copy to the

customer and collects the payment.25 Then, when the trader delivers the good, the customers sign

a receipt. I randomly assign households to a legal contract within trader (t) and neighborhood (n)

blocks. I can thus measure the effect of the legal contract on purchase. I estimate econometrically

the impact of legal contracts on whether customer i, visited by trader j, decides to buy, Bi ∈ {0, 1}.

I now introduce Fi ∈ {0, 1}, indicating whether the transaction was formalized by a legal contract.

I implement the following linear probability model among Sales on Debit:

Bi = a0 + a2Fi,j + ηtn + ei,j (3)

Figure 3, Panel A, reports the main result. The figure shows that contracts increase sales

among Sales on Debit from 50% to 65%, an effect that is comparable to the effect of co-ethnicity.

4.3.2 Mechanisms: tastes for contracts or expectation of trader defection?

To isolate the effect of the legal contract on the customer expectations about the trader’s behavior,

I compare, as before, Sales on Debit to Sales on the Spot. I implement the following linear

probability model:

Bi = a0 + a2Fi,j + a3Di,j + a4Di,Fi,j + ηtn + ei,j (4)

25The contracts (Figures A.5, A.6) are drafted by a lawyer and sealed by the Provincial Ministry of the Interior.

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Figure 3 reports the results from Specification 4 for Sales on the Spot and Sales on Debit.

The effect of contracts is larger among Sales on Debit, suggesting contracts solve a commitment

problem. I examine this difference in Table II. Columns (8) and (9) respectively identify the effect

of legal contracts in Sales on the Spot and in Sales on Debit. The coefficient on “Contract” is

insignificant in Sales on the Spot. In contrast, among Sales on Debit, signing a legal contract

increases the probability that the customer will purchase the good from 53% to 66%, statistically

significant at the 1% level. The coefficient precisely cancels out the effect of Sales on Debit —

shown in column (1), 12%. This suggests that legal contracts suppress the commitment prob-

lem inherent in Sales on Debit. I implement Specification 4 in Column (10). The coefficient

on Sales on Debit, -17%, measures the effect of introducing the trader’s opportunity to defect

among sales without a legal contract. The coefficient on Sale on Debit X Contract, captures

the differential effect of legal contracts on the probability to purchase among Sales on Debit,

compared to its effect among Sales on the Spot. The coefficient, 6%, is marginally significant.

Thus, Sales on Debit decrease buying in 15% when a contract is not signed, against 9% when

a contract is signed. Table B.1 adds customer controls, which sharply strengthens the effect of

contracts through Sales on Debit.

4.3.3 Mechanisms: why do contracts shift expectations? Incentives vs signaling

The legal contract might simply lead the customer to update his belief about the trader type. Even

if legal contracts had no incentive effects, this would induce the customer to have more trust. To

rule out signaling, I examine whether the legal contract changes the customer’s priors about the

background of the trader. Table B.4 regresses dummies of customers’ self-reported posterior beliefs

about the trader on whether legal contract. Columns (1) to (6) show that legal contracts leave

customers’ priors unaffected. Columns (7) to (9) then include the posterior beliefs as controls in

the main specification. If they mediate the relationship between contract and willingness to buy

(a signaling mechanism), adding them as controls should weaken the coefficient on Contract. Yet,

the effect of contract is identical with and without such controls, showing that signaling cannot

explain the effect of legal contracts on the customers’ expectations of defection.

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4.3.4 Mechanisms: why do contracts shift expectations? Incentive channels

Having ruled out that legal contracts induce customers to think that defection is less likely because

they signal that the trader is of a different type, I now examine whether customers anticipate that

legal contracts are enforceable, and thus whether customers expect legal contracts to have incentive

effects. To measure this effect, I use customers’ beliefs to examine customers expect legal contracts

to constrain the incentives of traders. Figure 3, Panel B, examines the effect of legal contracts on

the customers’ beliefs about legal sanctions. Signing a legal contract significantly increases the

proportion of customers who expect that the trader risks judicial sanctions, from 13% to 21%.

4.4 Crowding-out or crowding-in?

In this section, I examine whether choosing to use legal contracts displaces social sanctions, damp-

ening the incentive effects of contracts—crowding-out—or whether instead it crowds-them in.

Crowding-out can occur because legal contracts introduce incentives to relationships that might

be motivated by intrinsic motives, reciprocal social preferences, concerns for public and even self-

image. By offering to use a legal contract, the trader signals that he expects the customer to have

low trust, and cancels the ability of the customer to use his decision to buy as a signal of his

motives. This can lead legal contracts to crowd-out other motives of behavior than purely the in-

terest of consuming the good (Benabou and Tirole, 2003, Bowles and Polania-Reyes, 2012, Gneezy

and Rustichini, 2000), even if the overall effect of legal contracts is to increase trade — which can

occur when incentives dominate the crowding-out effects. Alternatively, by making an agreement

explicit and reducing uncertainty, legal contracts may allow for better coordinating informal so-

cial sanctions, rather than activating the legal system alone— crowding-in. In that latter case,

legal contracts might interact with social organization positively. Understanding the mechanisms

through which legal contracts introduce incentives in a weak state is particularly important for

the design of policies such as formalization (Glaeser, Ponzetto and Shleifer, 2016). Finally, reverse

crowding-out might also occur if social organization (co-ethnicity) crowds-out the reliance on the

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legal system.26 This is especially likely to occur if use contextual cues to infer about the type of

relationship in which they operate: one formal, the other informal.

I first examine whether the effect of legal contracts on incentives outweigh their potential

crowding-out effects. If the crowding-out effect dominates, then the effect of legal contracts may

be smaller among co-ethnics, whose social sanctions may be crowded-out. On the other hand, if

co-ethnics are better able to monitor defectors and bring defectors to justice, or if legal contracts

improve enforcement among co-ethnics, then legal contracts may have stronger effects among co-

ethnics. The difference in the effect of contracts for co-ethnics and non-coethnics, thus, is an

empirical question. Figure 4, Panel A, shows that the effect of legal contracts is large for both

co-ethnics and non-coethnics — legal contracts increase baseline sales rates in 26%. Table III

presents the fully saturated model. Column 1 regresses, among sales on debit, whether the cus-

tomer buys on Contract, Coethnic and Contract X Coethnic. While the coefficients on Contract

and Coethnic are large and statistically significant, their interaction is negative but insignificant.

However, this may mask that the mechanisms of legal contracts differ for co-ethnics.

I thus examine the effect of legal contracts on social sanctions. Figure 4, Panel B, shows the le-

gal contracts crowd-out social sanctions. The probability that the customer thinks the trader risks

losing valuable relationships drops from 15% to 7% when the trader signs a contract. This suggests

that legal contracts act as a cue for a “formal” interaction. Table III measures the role played by

expected social sanctions. First, column (2) includes as regressors the main social sanctions. It

shows that expected legal sanctions (the court) and the loss of friendships are strongly associated

with the customer’s decision to buy the good. Thus, the social sanctions that legal contracts

displace, and the legal sanctions that they activate, judicial sanctions, are relevant for trade.27

Second, columns (3) through (12) regress respectively, among non-coethnics (NC) and among co-

ethnics (C): lynching (columns 3 and 4), loss of friends (columns 5 and 6), shame (columns 7 and

26The term crowding-out is also used when incentives attract individuals of a specific type, for instance whentypes and the response to incentives are correlated in the population, such as in Dal Bo, Finan and Rossi (2013). Ifind no evidence that customers’ priors about the trader’s type are affected, and thus that customers are updatingtheir beliefs about the type of trader they face. I focus in this paper on classical crowding-out, which focuses onwithin individual effects.

27Of course, customers who believe the loss of friendships is likely might also have a tendency to accept the salefor other reasons.

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8), the courts (columns 9 and 10), and whether the customer is willing to pay in order to catch the

trader (columns 11 and 12) on Contract. Column (5) shows that legal contracts crowd-out the

threat of relationship loss mostly among non-coethnics. While co-ethnics are significantly more

likely to expect lynching, this expectation is unaffected by signing a legal contract.

These results suggest individuals use cues to identify the type of relationship, and that legal

contracts act as a cue. However, the incentive effects of co-ethnicity and legal contracts are strong

enough to compensate for the displacement they generate on some dimensions of sanctions.

The next section considers the behavior of the agent—the decision to defect—in order to demon-

strate that, consistent with the customers’ beliefs in this first part of the paper, legal contracts

(and co-ethnicity) have incentive effects and actually decrease defection of the same individuals.

5 The decision to defect, among state groups

In this second part of the paper, I examine whether the agents’ decision to defect is consistent

with the implied beliefs of customers of the first part.

5.1 Effect of co-ethnicity and legal contracts on defection: design

I sample 971 households from the population where I recruited the traders of the first part, and

observe their decision to defect as customers to a new sale. Recruiting customers from this

population allows me to measure the decision to defect by the population of the traders of the

first part, without exposing them to risk. In this new sale, traders provide the good to customers

with no delay. In exchange, the new customers agree to pay using a cell phone system, which

gives them the opportunity to defect (Sales on Credit).28 In this new type of sales (henceforth,

Sale On Credit), the trader offers to supply a household good immediately. In exchange, willing

28I focus on the behavior of new customers for three additional reasons. First, ethical considerations. Allowingtraders to defect would expose traders to risk of retaliation, while vice-versa, I hide the identity of customerswho defect and thus prevent harm on study participants. Second, observing the traders’ defection instead wouldintroduce interpretation challenges: traders have an incentive to signal to the organization that they are trustworthy;while it is natural in Eastern Congo to be visited by a trader in the household, it is unnatural to observe the behaviorof individuals in a newly created position. Third, logistical challenges. I would have to hire 971 traders in Bukavu(and word of this kind of mass employment would certainly spread, undermining the interpretation of behavior).

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customers commit to pay within three days. To prevent priming the customer, the customer pays

through a cell phone system.29 Traders administer the sales door to door to 971 randomly selected

households in ethnically diverse semi-urban neighborhoods of Bukavu. I use clustered sampling to

select customers.30 Once with the customer, each trader proceeds to the Sale On Credit, which

is composed of two steps (Step 1 and Step 2). Then, the trader administers an exit survey to

the customer. Sales On Credit allow me to observe which customers accept to purchase the good

and, among customers who have purchased the good, which customers pay. Absent enforceable

contracts, social sanctions, or internalized norms, the customer’s best choice is to purchase and

then defect.31 As before, the sales consisted of one-shot interactions with traders who are strangers,

a typical activity in Eastern Congo. The new customers are sampled from the same population

as the traders, almost no customer had any connection with any trader, nor did they know the

trader. Only 5% were aware of having any form of social tie with the trader.

It would be natural to instruct traders to request (randomly selected) customers to sign the

contract before they decide whether to purchase the good, leaving other customers free of such

requirement. However, this would induce customers who are asked to sign a contract and those that

do not to opt-out of the sale differentially, leading the composition of customers who accepted the

sale and signed a contract to be different than those for whom a legal contract was not requested.

The design explicitly tackles this in order to separately identify selection from incentives. The

29Payment by cell phone is widespread in Eastern Congo, and allows me to monitor payments. The traderexplains that the cell phone system lowers transaction costs.

30I randomly assign a sequence of households to each of the two traders of a team, who operate autonomously.The traders then randomly select one adult in the household among those who are willing to participate. Forthe sampling frame, I mapped all neighborhoods of popular neighborhoods of Bukavu in September 2013, incollaboration with the local administrative authorities. This yielded a comprehensive census. Unlike in Saleson Debit, Co-ethnicity was not randomized within blocks of household ethnic groups to reduce spillovers thatmight arise from implementation delay arising from census-taking in more populated areas, and thus there are norandomization blocks.

31As before, sellers receive a daily wage of 10 USD, and are residual claimants on sales. Traders derive the largestpart of their daily income from sales, indirectly given to them using anonymity by the organization. Daily revenuesfrom sales can oscillate between 0 if the trader is unsuccessful, and 50 USD if the trader is always successful.This ensures that the traders have the right incentives. Traders are not hired as surveyors, but instead as traders.Similarly, the PI had no contact with any of the traders, trained by the supervisors and monitored by the manager.In this second part of the paper, sales consist mostly of standard cell phone cards. Given the slightly higher levelof income of the target population—the population from where traders were originally recruited—I conducted amarket study to compare soaps to cell phone credit. While cell phone credit were in relative scarcity and in highdemand, the same was not true of soaps. In addition, the discount offered for sales on credit was slightly higher,proportional to the average wealth increase of the population.

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upper arm of Figure 5 presents the design. Traders introduce the sale on credit and explain to

all customers that, were they to accept the deal, they would have to sign a legal contract (Step

1). The legal contract stipulates that the customer exposes himself to legal sanctions if he fails to

pay within two days.32 The customer is then invited to decide whether to buy the good, which I

denote Bt=1 = 1, or instead reject the offer, which I denote Bt=1 = 0, where t = 1 denotes Step 1.

Once the customer has made his decision and the trader has recorded it (Step 2), the trader

randomly assigns the customers who have accepted the purchase to the experimental conditions:

customers who sign the contract, F = 1, and customers who do not, F = 0.33 The trader explains

to customers assigned to “no contract” that he does not have other contracts than the model, and

in this case, their “sales protocol specifies that they should proceed with the transaction” making

it clear that the decision is independent of the trader’s intention.34

This generates two groups. Group I are those who initially accepted (Bt=1 = 1) and ultimately

sign a legal contract, and Group II are those who initially accepted and ultimately do not sign

a legal contract. To measure the incentive effect of legal contracts, I can simply compare the

customers’ decision to pay between the two groups — they undergo the same selection process,

but are subject to different contractual conditions. Immediately after, the trader supplies the good

to the customer, if the customer accepted the offer in Step 2.

To prevent information spillovers, most likely to contaminate the sample within avenues, the

activity was implemented at the highest pace possible logistically (one avenue per team per day).

32A local lawyer drafted the formal contract and the Ministry of the Interior certified it. The Ministry of theInterior stamped all contracts with the seal of the Ministry to certify their legal validity. The legal contract isidentical to the legal contract introduced in the previous section, but with the agency relation reversed and thenature of the promise (paying by phone) adjusted to the experiment design.

33Lifting the legal contract requirement, a positive transfer to the customer, was a better suited strategy than thealternative of requiring to sign a contract without anticipating, which would lead to selective attrition. To assignhouseholds to contract treatment status, I randomly generated a treatment roster using a statistical package. Basedon the treatment roster, traders knew whether they had to use the contract or not. Traders were instructed thatthey could not find out about the specific treatment status of a household prior to offering the sale.

34This protocol ensured that the customer does not interpret this outcome as leniency from the trader. Failingto control the customers’ beliefs could trigger gift-exchange and reciprocity mechanisms, or could be perceived asodd and could lead the control customers (no contract) to reduce their tendency to pay, which would bias theestimate of the incentive effect. Training focus groups as well as daily debriefs suggest that customers did not findthis unusual and that the explanation given was believable to all customers. Given the diversity of small businessactivities in Eastern Congo and the low levels of education which make mistakes part of daily life, the lifting didnot generate feelings of oddness. Furthermore, it is unlikely that it would have led the customer to discredit thelegal contract, which was stamped by the Ministry of the Interior, the highest authority in the Province.

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The exit survey suggests spillovers did not occur. Only 5% of customers had heard about the sale

taking place, one customer had heard that the traders use legal contracts. Furthermore, 6% of

customers found the sale to be strange, at identical rates whether they signed a contract.35

5.2 Effect of co-ethnicity on defection, and mechanisms

I first confirm that co-ethnicity is salient as before. Like Table B.2, Table B.3 supports that

perceived co-ethnicity reflects actual co-ethnicity, and that ethnicity is salient. The first stage is

strong, and the magnitude is identical to the first stage shown in the first experiment in Table B.2,

suggesting that customers can identify whether traders are co-ethnic or not. As before, co-ethnic

social interaction increases the salience of ethnicity as a cue to identify the visitor and oneself, and

decreases customer’s anxiety, thus confirming co-ethnicity is a relevant aspect of social interaction.

I then examine the effect of co-ethnicity on defection. Figure 6 Panel A presents the results

of Specification 5 using co-ethnicity as explanatory variable. Since traders are randomized to

households and households and traders are of multiple state groups, this estimates the effect of co-

ethnicity. Consistent with the beliefs of the customers of the first part of the paper, co-ethnicity

reduces defection: the proportion of customers who buy increases from 22% to 34%, and the

increase is statistically significant. Figure 6, Panel B, presents the answers to the question of the

exit survey, also implemented in these Sales on Credit, albeit in a reverse manner: “If you do not

pay, what consequences do you think are likely?” The figure is consistent with the customer beliefs

of the first part of the paper, when asked, as principals, to guess the social sanctions that applied

to their agent, the trader. Shame and loss of relationships appear to be the most prevalent social

sanctions. Co-ethnics are much more likely to expect being lynched if they do not pay, and twice

as likely to expect losing relationships (marginally significant). Table V, Column (1) presents the

result from econometric specification 5. Consistent with the first part of the paper, customers

(now the agents) are 14% less likely to defect if the trader is a co-ethnic.

3531 of those who signed against 34 of those who did not.

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5.3 Effect of legal contracts on defection, and mechanisms

I then examine whether legal contracts have incentive effects. Figure 7, Panel A, presents the

results from Specification 5 using legal contracts as a regressor, applied to customers who initially

accept to buy. Improving over the design for ethnicity, the design allows me to identify the incentive

effect of contracts, controlling for their effect on selection of customers (since all customers first

agree to sign a contract). Customers who sign a legal contract are 50% more likely to pay, which

identifies the incentive effect of legal contracts among state groups. Furthermore, Figure 7, Panel

B presents the results on customer expectations. Consistent with the beliefs of customers in first

part of the paper, customers who sign a legal contract in this part are much more likely to expect

that legal sanctions will be activated against them— the proportion rises from 16% to 30%.

The design of this section shuts down most mechanisms of crowding-out, since all customers see

the intention of the trader to request a legal contract. I nonetheless examine how legal contracts

interact with co-ethnicity, since the two might be complements if co-ethnics are better able to

enforce contracts, or substitutes, if signing contracts are a cue for the type of relationship. Table V,

Column (1), shows that co-ethnicity and legal contracts are perfect substitutes, although co-

ethnicity halves the effect of contracts (not significant).

5.4 Measuring incentives and selection: estimation framework

An additional variation in the design allows me to measure the selection effect of contracts—screening.

Figure 5, bottom arm, presents the variation graphically. The bottom arm presents the customers

who rejected the offer initially when the trader required them to sign a contract (those who chose

Bt=1 = 0). As with customers who initially accept the deal, the trader announces, in a randomly

selected subset of these customers who reject, that he no longer has enough contracts. The trader

indicates that according to the protocol, they can reconsider their decision. The trader gives the

customer some time to reflect and to reconsider his decision, leading to the decision to buy at

step 2. Unlike for the customers who initially accept, for these customers, Bt=2 might differ from

Bt=1 since it might now be worth to accept the sale without a legal contract, even though it was

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not when a legal contract was requested. Customers who refused at t = 1 but accept after the

contract requirement has been lifted are labeled Group III. Like Group II, they have promised to

pay for the good and have not signed any legal contract, but unlike Group II, they had initially

been deterred by the requirement of the contract.36

In Appendix E, I present a theoretical framework for estimation, from which I derive the

following results. Table IV, Panel A, maps the parameter space onto the strategy set. The cus-

tomer has three possible choices: {B = 1, P = 1}, {B = 1, P = 0}, {B = 0, P = 0}. The

terms αk ∈ {1, 2, 3, 4, 5} are a partition the space of customers, uniquely identifying their optimal

choices. For α1, “honest peaches,” purchasing, net of the price, is more attractive than not pur-

chasing, and their high informal costs of defection induce them to purchase the good and always

pay. Individuals α2, “honest lemons,” prefer not to purchase, since they would prefer to pay than

defect, which gives them lower utility than not purchasing. Individuals α3, α4, and α5 prefer to

accept the offer and renege payment in the absence of legal contracts. The α3, moral hazards, are

willing to accept the purchase even if they have to sign a legal contract, but would renege payment

if and only if they were not compelled to do so by a legal contract. State institutions can generate

large welfare gains precisely if α3 is large, for whom trade is beneficial but who would defect in the

absence of legal contracts. In contrast, the α4 would not purchase the good if they were required to

sign a legal contract, but would do so if they were not compelled to do so. Finally, α5 purchase the

good and renege payment even if a legal contract is used. Table IV, Panel B, shows the partition

of customers into different choices as a function of the experimental conditions.

Proposition 1. Incentives. Contracts have incentive effects if α3 > 0. Furthermore, the difference

in payment rates between customers who sign a contract and customers who do not, P (P = 1|F =

1, B = 1)− P (P = 1|F = 0, B = 1), equals α3

α1+α3+α5, the proportion of “compliers” among those

who accept the sale (moral hazards). The proportion of “state indifferent” types among those who

36This additional design variation can appear more unusual than the first, and I thus only use the behaviorof these customers for additional screening analysis, on which the main analysis does not hinge. For instance, byreconsidering their decision but accepting when the contract requirement is lifted, the customers might reveal tothe trader that they had no intention to pay. This can induce bias if customers are sufficiently sophisticated, andif customers care about what the trader thinks — which they might not. If there is any concern for their image, orfor self-image, some of the customers who would like to accept the deal when the contract is lifted, after they hadrejected, might refuse to do so. This particular mechanism would lead me to under-estimate the size of the pool ofthese customers.

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accept the sale, α5

α1+α3+α5is equal to P (P = 0|F = 1, B = 1) (P (P = 0|Coethnic, B = 1)),

the proportion of customers who do not pay, among those who buy and sign a contract. Finally,

among customers who purchase, the proportion of “honest peaches” is given by α1

α1+α3+α5, is equal

to P (P = 1|F = 0, B = 1), those who pay in the absence of legal contracts. Scaling down

αk

α1+α3+α5, k ∈ {1, 3, 5} by P (B = 1) obtains the population proportions αk, k ∈ {1, 3, 5}.

Proposition 2. Screening. Contracts have screening effects if α4 > 0. Furthermore, among

customers who initially reject the offer when a contract is required, the proportion of those who

reconsider their decision when the requirement is lifted, P (B(t = 2) = 1|F = 0, B(t = 1) = 0),

equals α4

α2+α4, the proportion of dishonest lemons among those who reject the offer initially. The

proportion of honest peaches among those who initially reject, α2

α2+α4is equal to P (B(t = 2) =

0|F = 0, B(t = 1) = 0). Scaling down αk

α2+α4, k ∈ {2, 4} by 1 − P (B = 1) obtains the population

proportions αk, k ∈ {2, 4}.

To estimate the incentive effects of legal contracts and of co-ethnicity, I run the following linear

probability model on the sample the customers who initially accepted, Bij(t = 1) = 1:

Pi = c0 + c1Fi + ηt + ei,j (5)

where Bij(t = 1) ∈ {0, 1} indicates whether customer i initially accepts the sale offer from trader

j, Pi ∈ {0, 1} indicates whether the customer pays, only observed if the customer accepts the

purchase, and ηt are randomization block fixed effects. The term Fi ∈ {0, 1} indicates whether

the customer i signs the contract. To estimate the effect of co-ethnicity instead of the effect

of signing a legal contract, I use instead the dummy Coehtnic, while I condition the sample

to Fi = 0. As shown in Proposition 1, the parameter c1 estimates P (P = 1|F = 1, B(t =

1) = 1) − P (P = 1|F = 0, B(t = 1) = 1) = α3

α1+α3+α5, the share of moral hazards induced to

pay among the customers who accept the sale, and is positive if legal contracts are enforceable.

The parameter c0 estimates the proportion of “honest peaches,” α1

α1+α3+α5, and 1 − (c0 + c1)

estimates the proportion of state indifferent, α5

α1+α3+α5. Furthermore, since the mass of customers

who decline the offer initially is α2 + α4, I can back out the values of αk ∈ {1, 3, 5} as follows:

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αk = αk

α1+α3+α5∗(1−(α2 +α4)) = αk

α1+α3+α5∗(P (B(t = 1) = 1)). P (B(t = 1) = 1) can be estimated

from the proportion of customers who accept the sale at t = 1. Of all customers, 72% are willing

to take on the offer at t = 1, suggesting that α2 + α4 = 28%.

Table V shows the incentive results econometrically. The coefficient on Contract, c1 shown in

column (3) indicates the effect of legal contracts on payment among sales where the trader belongs

to a state group, on average. It shows that the requirement to sign a formal contract increases

the probability that the customer implements the payment by 11 percentage points, equivalent to

an increase of payment rates in 55% from its baseline rate under the No Contract baseline. Thus,

when visited by a state trader, the share of moral hazards in the population is α3 = α3

α1+α3+α5∗(1−

(α2 +α4)) = .11 ∗ .72 = 8%. This implies that legal contracts have incentive effects. Furthermore,

the share of “honest peaches” is given by α1 = c0 ∗ (1− (α2 + α4)) = .20 ∗ .72 = 14%. Finally, the

proportion of state indifferent types is: α5 = (1 − (c0 + c1)) ∗ (1 − (α2 + α4)) = .69 ∗ .72 = 49%.

Thus, the contribution of the α3 to the overall mass of customers for whom legal contracts has

incentive effects is 8%, while it is 0% for the α5, by definition, and is unclear for the α1, for

whom it could be as high as 14% if they were all subject to incentive effects. Considering the 72%

of customers who buy, the contribution of this mass to the overall mass of customers for whom

legal contracts have incentive effects lies in the interval [8%, 22%]. I narrow the interval using the

screening results. To back out α2 and α4, I run the following linear probability model:

Bij(t = 2) = b0 + b1Fi + ηt + ei,j (6)

It follows from Proposition 2 that E(b1) = P (B(t = 2) = 1|F = 0)−P (B(t = 2) = 1|F = 1) = α4,

which is positive if and only if legal contracts have incentive effects. Furthermore, 1− b0 estimates

the proportion of “honest lemons,” α2. Table VI presents the result from specification 6. In

Columns (1) through (6), I regress a dummy indicating whether the customer ultimately accepts

the deal, B(t = 2) on whether the contract was ultimately signed (requested) Contract, a dummy

for co-ethnicity Coethnic, and their product. Column (3) shows that the mass of customers who

initially refuse, and then accept when the requirement is lifted, P (B = 1|F = 0, B1 = 0) = b0 =

α4

α4+α2is equal to 12%, thus α4 = .12 ∗ .28 = 3.4%. Furthermore, P (B = 0|F = 0, B1 = 1) =

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1− b0 = α2

α4+α2= 88%, thus α2 = .88 ∗ .28 = 24.7%. Since for all α4, by definition, legal contracts

have incentive effects, the proportion of customers for whom signing a contract activates incentives

to pay that are otherwise absent is now at least 8% + 3.4% = 11.4%. Legal contracts leave the

behavior of α1 and α2 unaffected, which is governed by informal sanctions already. However, some

of these customers might also find legal contracts to be binding even if they were not already

compelled through social sanctions. Assuming that such proportion is identical across honest and

dishonest types (θ ⊥ l), this would imply that overall, legal contracts have incentive effects for

11.4% ∗ 1α3+α4+α5

= 19%. Thus, legal contracts have incentive effects for a mass in [19%, 22%].

While the experiment is not designed to do so for obvious logistical reasons, Columns (1)-

(4) also suggest that co-ethnicity has anticipated incentive effects, and thus generates a similar

screening effect: while co-ethnics are no less likely to buy in Sales on the Spot, they are less likely

to buy when they anticipate that there is a risk that they might defect, suggesting that they

anticipate stronger sanctions if they defect against a co-ethnic.37

6 Contract enforceability, for whom? Nonstate groups

Legal contracts have incentive effects, but Eastern Congo is a weak state. This explains why in

the previous section, I found that legal contracts have incentive effects for a mass of customers

between 19% and 22%. More importantly, the state, far from being an autonomous bureaucracy,

is captured by the state groups. The previous results, thus, will not hold for nonstate groups if

nonstate groups are known to be less able to enforce contracts that secure their property.

6.1 Legal contracts for nonstate groups: estimation and results

I examine how the control of the provincial administration by state groups affects agency relations

by biasing contract enforceability. I compare the incentive effect of legal contracts when they

secure the property of a state trader to their effects when they secure the property of a nonstate

37Columns (1)-(4) suggest that on average, co-ethnicity decreases the willingness to purchase. This might reflecttastes for/against co-ethnics. Table B.9 uses Sales on the Spot in this population to show that if anything, there isa positive, but marginally significant, taste for trading with co-ethnics when there is no risk of defecting involved.

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trader. Since I randomly assign customers to each of the team’s traders, and since trader teams

always have one state and one nonstate trader, I can estimate the effect of legal contracts for

nonstate traders separately.38

Proposition 3. Ethnic contract enforceability. The mass of moral hazards, as well as the mass

of state indifferent types, is smaller for nonstate traders than for state traders, respectively,

α3

α3+α4+α5|Ea∈ENS <

α3

α3+α4+α5|Ea∈ES and α5

α3+α4+α5|Ea∈ENS <

α5

α3+α4+α5|Ea∈ES , if and only

if the legal sanctions a nonstate trader can activate are smaller, lij|Ea∈ENS < lij|Ea∈ES .

I implement the following specification:

Pi = c0 + c1Fi + c2Tj + c3FiNSj + ηt + ei,j (7)

customers who have accepted the sale at step 1. The term NSj ∈ {0, 1} denotes whether the

trader is nonstate (Tutsi), and Fi indicates whether customer i signs a contract. The parameter

c1 captures the incentive effect of contracts requested by state traders, α3

α3+α4+α5|NS=0 while c1 + c3

captures the incentive effect of contracts requested by nonstate traders, α3

α3+α4+α5|NS=1. A negative

value for c3 indicates contracts are less effective on the agent’s behavior for nonstate traders.

Having introduced nonstate groups, I first confirm that state vs nonstate group is a salient

distinction. Table B.5 presents the effect of interacting with a trader of a nonstate group on

perceptions. Column (1) shows the first stage is significantly stronger than for any ethnicity dis-

tinction among state groups, and supports that belonging to a nonstate group is salient. Columns

(2) to (7) show that interacting with a trader of a nonstate group increases the salience of ethnicity,

reduces the salience of the nation. Finally, column (8) shows that interacting with a nonstate also

induces more anxiety, supporting that nonstate group is salient.39

Figure 8, Panel A, presents the results from specification 7: signing a legal contract is ineffec-

tive if the trader is of a nonstate group. Panel B runs a linear probability model of whether the

38This balance in the number of traders of state and nonstate groups and block randomization by traders’belonging to a state group serves as an unwritten pre-analysis plan.

39Table B.3 does the same for co-ethnic and non-coethnic interactions among different state groups. The resultsshow that the division between state groups and nonstate groups is stronger than the average distinction betweenstate groups, and are consistent with Section 4.2.2 (Table B.2).

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customer expects legal sanctions to be triggered on whether the customer signed a legal contract.

Panel B confirms that customers expect legal contracts to be ineffective if the contracts secure the

property of an individual of a nonstate group. Indeed, customers anticipate that the judiciary will

be ineffective to enforce a legal contract that benefits a member of a nonstate group.

Table V presents the econometric results. Column (2) conditions the sample to transactions

where the trader is from a nonstate group, and columns (3) to (9) present the results for the

fully saturated model introduced in specification 7, with different specifications for robustness as

follows. Column (3) presents the baseline fully saturated model with randomization block fixed ef-

fects, used to estimate the effect of the trader being of a nonstate group, while column (4) excludes

them. Column (5) adds controls for observable customer characteristics.40 Column (6) includes a

control for the negotiated price, which is left open for bargaining for the trader so that the sale is

incentive compatible. One concern is that ethnicity, socially constructed, might not be identifiable

in brief interactions (Brubaker and Cooper, 2000). Thus, Column (7) uses instead a “state group”

degree variable in [0, 1]. Such variable is constructed using the proportion of customers who, in

the exit survey, correctly guess that the trader is from a state group when asked to guess the

ethnic group of the trader. Finally, column (8) uses the customer self-reported guess about the

trader’s ethnicity in the exit survey as the treatment variable. This varies from customer to cus-

tomer. Since the guessed ethnicity is endogenous to unobserved customer characteristics, column

(8) implements a 2SLS, where the customer’s perception that the trader belongs to a state group

is instrumented by the dummy for whether the trader actually belongs to a state group. Column

(9) does the same and adds customer-level controls. The coefficient on Contract in column (2)

shows that, for traders who are of a nonstate group, customer payment is unaffected by whether

the customer signs a legal contract. The coefficient on Contract X Nonstate trader in columns (3)

to (9) is negative, confirming that the effect of legal contracts is zero when it is used to protect the

property of a nonstate trader. In contrast, the coefficient on Contract is positive and significant.

It follows that lij|NS=1 = 0 < lij|NS=0. There are no “moral hazards,” α3, for nonstate traders.

40The controls are age, years of education, whether the customer and the trader have any social tie, which is trueonly in 1% of cases, whether the customer and the trader know people in common, gender, whether the customerhas heard of the sale before, which is true only in 8% of cases, sector of occupation, customer ethnicity, and whetherthe customer buys when the sale is offered on the spot.

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I next present the results on screening. Table VI presents the results for both groups of traders.

Columns (3), (5), and (6) report respectively the effect of Contract on purchase for state traders,

for nonstate traders, and the fully saturated model including their interaction. Column (5) shows

that, unlike for state traders, contracts have zero screening effect on the selection of customers

for a nonstate trader. Thus, α4|NS=1 = 0, consistent with lij|NS=0 > lij|NS=1 = 0. In Columns

(7) to (10), I regress whether a customer pays on whether the customer had initially accepted

in step 1, B(t = 1), thus estimating the quality difference of customers of Group II and III.41

The coefficient on B(t = 1) estimates the the difference in payment rates between customers who

selected in, despite the requirement to sign a contract, and those that did not, but ultimately

accepted when the requirement was lifted. While, for state traders, non-coethnic customers who

initially refuse the sale but then accept if the contract is lifted are less likely to pay, the same is

not true for nonstate traders (or for state traders who are co-ethnics). This result suggests that

legal contracts are effective devices to screen for better customers, only when they are used to

protect the property of an individual belonging to a state group.

Section D presents extensive robustness checks aimed at ruling out that trader-specific imbal-

ances are driving the results. First, I conducted a separate representative survey on 500 households

randomly sampled from the exact same avenues. The survey allows me to elicit the average house-

hold conscious and unconscious attitudes towards each individual trader. I achieve this by having

a separate team of surveyors showing the individual photo of each of twenty traders on a tablet.

Second, I also conducted a survey on each individual trader to gather hard information on each

trader. The results are robust to trader-specific controls for variables gathered in these two sur-

veys. In addition, I rerun the contract effect for each individual trader and plot the trader-level

effects (Figure A.7), I rerun the contract effect for each trader ethnic group (Figure A.8), and I

conduct randomization inference where I simulate 10,000 ethnic group assignments to the traders

and estimate a counter-factual distribution of Contract*Nonstate coefficients under the null hy-

pothesis of no effect (Figure A.9). The results cannot be driven by trader-specific imbalances.

This suggests individuals anticipate the judiciary to be only effective to represent state groups.

41Formally, the quality improvement due to screening is equally to the difference in the proportion of customerswho pay between Group II and Group III, that is: α1

α1+α3+α5− α1

α1+α3+α4+α5.

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7 Validation using external data

In this section, I use external data I collected as additional validation of the results.

7.1 Are the sales unusual?

Figure A.3, Panel A, shows whether survey respondents consider a home sale on credit to be

normal. First, sale on credit is widespread in Eastern Congo. Column 1 shows the proportion of

respondents who answer yes to the question “Is sale on credit usual for you?” 60% answered yes.

Second, ambulant home delivery sellers are also a very common phenomenon in Eastern Congo.

Households were asked to estimate how many times per year they see an ambulant seller per

year. The column shows almost 80% of respondents report to see one more than once per month.

Column 2 shows that the mean answer to the question is 142. Ambulant sales are commonplace.

Third, legal contracts are usual for 65% of respondents, and 40% frequently use contracts.

7.2 Self-reported beliefs about contract enforceability

Customers of the experiment reported to expect the legal contracts to trigger judicial action. The

extension survey also suggests households expect contracts to be enforceable. Figure A.3, Panel

B, presents the answers to the question “Do you think that a legal contract that this one would

trigger consequences upon breach,” 80% of households answered yes, consistent with the view

portrayed in Section 2 that the Congolese state has recovered its ability to perform some functions

despite a persistent culture of self-reliance. Furthermore, when asked “If this contract is breached,

which consequences are the most likely to occur?” 50% of households reported judicial sanctions.

Column 3 indicates that such households expect such sanctions to be legal. Column 4 shows that

less than 5% think that breach of this contract would trigger violence. Finally, Column 5 shows

that 35% of households believe that customers would lose reputation.

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7.3 Can customers distinguish between state and non-state groups?

Tables B.2 to B.5 show that 75% of customers guess whether a trader is from a state group or

not, and 45% of customers guess the exact ethnic group among the state group. The following

open ended answer supports that customers correctly anticipate the importance of ethnic ties to

the administration: “Tutsi are afraid of the justice system. They avoid taking any debt to avoid

activating legal threats.” Table B.7, line 1, presents the proportion of households in the extension

survey who were able to accurately guess whether the trader is a state or a nonstate group by

guessing their precise ethic group simply from their photo. Column 1 indicates the sample mean

for state traders, and Column 2 for nonstate traders. 88% of the 500 households interviewed in

the follow-up survey guess correctly that the trader is from a state group, 67% when shown the

photo of a trader from a nonstate group.

8 Conclusion

Consider again the typical economic exchange relation between the merchant and his agent, who

has incentives to defect. I experimentally tested two propositions about the relative efficacy of the

state and social organization to prevent trade to collapse between the merchant and the agent.

First, when the merchant has ethnic ties to the groups who control the state administration, and

who are responsible to enforce contracts, he can credibly threaten to use the judiciary to prose-

cute an agent who defects. This implies that the agents can credibly commit not to defect against

this merchant, which sustains trade. However, when the merchant has no ethnic ties to the state

administration, the merchant is cursed because he is unable to use the state to curb the agent’s

incentives to defect, the agent cannot credibly commit not to defect, and thus the merchant is

unable to engage in mutually beneficial trade. This effect is specific to the merchant: in con-

trast to the merchant, the agent can always match with another merchant who has ties with the

administration, and credibly commit. This asymmetry distorts the patterns of trade that ensue

formalization, and has distributional consequences: the state only offers trade opportunities to

merchants from the groups who have ethnic ties with the administration, amplifying inequalities

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that exist in the absence of the state. Second, in the absence of state contract enforcement, the

agency relationship between the merchant and the agent can be governed by social organization,

which is only mildly crowded out if the merchant proposes to use legal contract. Agents who

share ethnic ties with their agents are much less likely to defect, largely due to the agent’s fear

of lynching and social marginalization, and thus more likely to make a credible promise to the

merchant that induces trade with him. The results are consistent with the view of a “weak state”

in which society has developed non-state mechanisms to govern exchange, and where the interests

of social groups permeate the administration of the state, even when the state can implement

policy. A strong social organization limits the efficacy of formal institutions relative to informal

institutions who have developed outside the state, and extended their tentacles inside the state.

THE HARVARD ACADEMY, HARVARD UNIVERSITY

HAAS SCHOOL OF BUSINESS, UNIVERSITY OF CALIFORNIA, BERKELEY

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TablesTable I: Ethnic Groups of Sud Kivu, Democratic Republic of the Congo

STATE NONSTATE(Tutsi)

Batembo BanyamulengueBashi BanyabuishaBafuliiru BanyarwandaBanyiunduBahavuBalegaBabembeBahutus

Notes: This table presents the main ethnic groups of Sud Kivu. Tutsi of Sud Kivu arrived mostly in waves ofmigration 500 years ago. While the exact year for the first migration wave is largely contested among historians,there is consensus that the largest wave of migration occurred in the 19th century (Verweijen and Vlassenroot,2015). A major group among these came to be named Banyamulenge (“the people Mulenge,” a city of Sud Kivu, inSwahili). While the origin of the Tutsi of Sud Kivu is contested, in particular whether they have Nilotic origins (theirDNA shows significantly more Nilo-Saharian linkages), Congolese frequently distinguish the “autochtonous” (native)groups, which emerged from the Bantu great migration, from the Tutsi, who arise from much later migrations.Because Tutsi are often seen by the other groups as “invaders,” many themselves “Banyamulenge” to hide theirforeign origin. As a result, while divisions among the so-called “autochtonous” ethnic groups are a relevant formof social organization, the Tutsi are systematically excluded nowadays from the provincial administration. Thisunderscores the following nomenclature in the rest of the paper: on the one hand, I refer to the “autochtonous,”non-Tutsi groups as “state” groupes due to their control over the state at the provincial level through networks ofpatronage, and I refer to the Tutsi groups as “nonstate” ethnic groups due to their exclusion from control of thestate at the provincial level.

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Table II: The decision to trade — State groups

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)VARIABLES Buy Buy Buy Buy Buy Buy Buy Buy Buy Buy

Sale on debit -0.12*** -0.15*** -0.21*** -0.17***(0.03) (0.03) (0.06) (0.04)

Coethnic -0.04 0.10** -0.07 -0.23 0.15* -0.23*(0.08) (0.05) (0.06) (0.20) (0.10) (0.18)

Sale on Debit X Coethnic 0.15** 0.32**(0.07) (0.16)

Contract 0.05 0.12*** 0.05*(0.04) (0.03) (0.04)

Sale on Debit X Contract 0.08*(0.05)

Constant 0.70*** 0.67*** 0.57*** 0.71*** 1.23*** 1.00*** 1.21*** 0.64*** 0.53*** 0.68***(0.02) (0.02) (0.02) (0.03) (0.45) (0.43) (0.45) (0.03) (0.02) (0.03)

Observations 1,854 593 1,180 1,773 392 845 1,237 622 1,232 1,854R-squared 0.15 0.25 0.19 0.14 0.29 0.23 0.16 0.40 0.09 0.29Constraints . SPOT DEBIT . SPOT DEBIT . SPOT DEBIT .Ethnic groups of traders STATE STATE STATE STATE STATE STATE STATE STATE STATE STATESpecification OLS OLS OLS OLS 2SLS 2SLS 2SLS OLS OLS OLSMeasure of coethnicity Actual Actual Actual Actual Perceived Perceived Perceived Actual Actual Actual

Notes: This table presents the results from econometric specifications 1, 2, and 4 introduced in Section 4 among all the state groups. In all columns, the de-pendent variable indicates whether trade successfully occurs (Buy=1). Column (1) uses the entire sample, and regresses Buy on a dummy for Sales on Debit(when such dummy takes value zero, then these are Sales on the Spot). Columns (2) and (3) respectively restrict the sample to Sales on the Spot, andto Sales on Debit, and regresses Buy on a dummy indicating whether the customer and the trader are of the same state group (Coethnic). Column (4)includes the following regressors: Coethnic, Sales on Debit, and their product in order to estimate their interaction. Columns (5) - (7) use actual coethnicitybetween the trader and the customer as an instrument for the customer self-reported perception of whether he and the trader are coethnics in a two-stagesleast square framework. Customer self-reported perception was gathered in the exit survey that followed the sale, in which the trader asks the customerto guess the ethnicity of the trader. Table B.2 presents the first stage as well as the saliency of ethnicity as a function of perceived co-ethnicity. Thefirst stage is strong, supporting that co-ethnicity is salient and that actual co-ethnicity can be a valid instrument for perceived co-ethnicity. Columns (8) -(10) replicate the structure of columns (2) to (4), but instead of using Coethnic as a regressor, they include the results from regressing Buy on Contractin Sales on the Spot (column 8), the results from regressing Buy on Contract in Sales on Debit (column 9), and on Coethnic, Sales on Debit, andtheir product (column 10) in order to estimate their interaction. All regressions include randomization block fixed effects. All regressions include *,**,***,respectively as labels for 1%, 5%, and 10% significance level in the one-sided tests for the null hypotheses of whether legal contracts (co-ethnicity) have nopositive effect. Table B.1 presents the same results adding customer controls to each column. Adding controls strengthens the results.

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Table III: The decision to trade — State groups — Crowding-out or Crowding-in?

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)Friend Friend

VARIABLES Buy Buy Lynching Lynching loss loss Shame Shame Court Court Punish Punish

Contract 0.14*** -0.00 0.02 -0.07*** -0.04 0.03 -0.07 0.04 0.07* 0.06* 0.03(0.03) (0.01) (0.04) (0.03) (0.05) (0.04) (0.07) (0.03) (0.05) (0.04) (0.07)

Coethnic 0.11**(0.06)

Coethnic X Contract -0.02(0.07)

Lynching -0.10(0.11)

Friendhsip loss 0.13**(0.06)

Shame -0.03(0.04)

The court 0.11**(0.05)

Constant 0.50*** 0.60*** 0.01* 0.05** 0.15*** 0.12*** 0.34*** 0.34*** 0.17*** 0.07** 0.18*** 0.20***(0.02) (0.03) (0.01) (0.03) (0.02) (0.04) (0.03) (0.06) (0.03) (0.04) (0.03) (0.05)

Observations 1,180 1,046 468 152 468 152 468 152 468 152 466 151R-squared 0.20 0.23 0.02 0.05 0.14 0.09 0.10 0.15 0.13 0.06 0.18 0.13Constraints . . NC C NC C NC C NC C NC CEthnic groups of traders STATE STATE STATE STATE STATE STATE STATE STATE STATE STATE STATE STATESpecification OLS OLS OLS OLS OLS OLS OLS OLS OLS OLS OLS OLSMeasure of coethnicity Actual Actual Actual Actual Actual Actual Actual Actual Actual Actual Actual Actual

Notes: This table examines the interaction between legal contracts and social organization. Columns (1) and (2) implement a linear probability model withdependent variable whether the customer buys. Column (1) includes Contract, Coethnic, and Contract X Coethnic as regressors. Column (2) includesinstead as regressors the main mechanisms through which the trader behavior is disciplined according to the customer. Columns (3) through (12) regressrespectively whether the customer expects: lynching (columns 3 and 4), loss of friends (columns 5 and 6), shame (columns 7 and 8), the courts (columns9 and 10), and whether the customer is willing to pay in order to punish the trader in case of defection (columns 11 and 12). I regress each outcome onContract, among non-coethnics (NC) and among co-ethnics (C). All regressions include randomization block fixed effects and restrict the analysis to Saleson Debit, in which the customer is the principal and the trader is the agent, with an inherent commitment problem on the side of the trader. All regressionsinclude randomization block fixed effects. All regressions include *,**,***, respectively as labels for 1%, 5%, and 10% significance level in the one-sided testsfor the null hypotheses of whether legal contracts (co-ethnicity) have no positive effect. Results are identical with and without controls.

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Table IV: The decision to defect — Customer types and selection

PANEL A: Partition of the parameter space into buyer types

No Contract B = 1, B = 0, B = 1, B = 1, B = 1,Strategies P = 1 P = 0 P = 0 P = 0 P = 0

Informal 1, 1 � 1, 0 0, 0 � 1, 0 1, 0 � 1, 1Preference 1, 1 � 0, 0 0, 0 � 1, 1 1, 0 � 0, 0Ordering 1, 1 � 0, 0 0, 0 � 1, 1

Conditions θ1−λ > ℘ θ

1−λ > ℘ θ1−λ < ℘ θ

1−λ < ℘

v1−λ > ℘ v

1−λ < ℘ v1−λ > ℘ v

1−λ < ℘

θ+l1−λ ≥ ℘ θ+l

1−λ < ℘ θ+l1−λ < ℘

Mass α1 α2 α3 α4 α5

Label Honest Honest Dishonest Dishonest Statepeaches lemons peaches lemons indifferent

PANEL B: Selection of buyer types into purchase, by experimental conditions

Step 1 (F=1)B = 0 B = 1

Selection: Who buys? α2 + α4 α1 + α3 + α5

Steps 2a, 2b F=0 F=1 F=0 F=1B = 0 B = 1 B = 0 B = 1 B = 1

Selection: Who buys? α2 α4 α2 + α4 α1 + α3 + α5 α1 + α3 + α5

Step 3P = 1 P = 1 P = 1

Incentives: Who pays? α1 α1 + α3

Notes: This table presents the selection and incentive effects in Sales on Credit, in which the trader is the principaland the customer is the agent, who can defect. Traders request customers to sign a contract. Customers α2 and α4

reject the offer. Once customers have decided whether to purchase the good, the trader announces to a randomlyselected subset of customers that he can no longer request a contract, F = 0, but the sale proceeds anyway.Customers α3, who had accepted the sale, avoid making a payment if the trader lifts the contract requirement. α4

opt out whenever requested to sign a contract, but come back in whenever the contract requirement is lifted. α2

always opt out, since they do not value the good enough, and would always prefer to pay than not to pay due tohigh cost of informal sanctions. α1 always opt in, since they value the good high enough that, even if they arealways induced to pay even in the absence of a formal contract, it is always worth accepting the good. α5 areunaffected by social sanctions or legal contracts: they always buy and defect.

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Table V: The decision to defect — State groups, and Nonstate groups

(1) (2) (3) (4) (5) (6) (7) (8) (9)VARIABLES Pay Pay Pay Pay Pay Pay Pay Pay Pay

Contract 0.14** -0.03 0.11** 0.12*** 0.13*** 0.11*** 0.12*** 0.14** 0.18***(0.06) (0.06) (0.05) (0.05) (0.05) (0.05) (0.05) (0.06) (0.06)

Coethnic Trader 0.14**(0.07)

Contract X Coethnic Trader -0.07(0.10)

Nonstate Trader 0.09** 0.06 0.06 0.03 0.07 0.08 0.10*(0.06) (0.05) (0.06) (0.06) (0.07) (0.07) (0.07)

Contract X Nonstate Trader -0.14** -0.14** -0.16** -0.14** -0.19** -0.23** -0.27***(0.08) (0.08) (0.08) (0.08) (0.10) (0.11) (0.11)

Negotiated price 0.12***(0.05)

Constant 0.16*** 0.26*** 0.16*** 0.18*** 0.00 -0.03 0.19*** 0.36*** 0.04(0.04) (0.04) (0.04) (0.04) (0.47) (0.10) (0.04) (0.07) (0.46)

Observations 297 198 515 515 508 513 515 495 488R-squared 0.03 0.00 0.02 0.12 0.18 0.13 0.12 0.13 0.20Ethnic groups of traders STATE NONSTATE All All All All All All AllRandomization block fixed effects NO NO NO YES YES YES YES YES YESCustomer controls NO NO NO NO YES NO NO NO YESSpecification OLS OLS OLS OLS OLS OLS OLS 2SLS 2SLSMeasure of traders’ ethnicity Actual Actual Actual Actual Actual Actual Degree Perceived Perceived

Notes: Columns (1) to (9) regress whether the customer pays on properties of the sale (specification 5) for Sales on Credit - trader provides the good first, and customer can paywithin three days, which gives the customer the opportunity to defect. Column (1) presents the baseline specification for sales implemented by state traders, using Coethnicity, whethera contract was signed by the customer, and their interaction as regressors. Unlike in Sales on Debit, Co-ethnicity was not randomized within blocks of household ethnicities to reducespillovers that might arise from implementation delay arising from census-taking in more populated areas, and thus there are no randomization blocks. Column (2) does the same forsales implemented by nonstate traders, but excludes Coethnicity since nonstate traders are all Tutsi almost no household is Tutsi. Columns (3)-(8) include as regressors whether thecustomer signs a contract (Contract), whether the trader belongs to a nonstate group (Nonstate Trader), and their product (Contract X NonstateTrader), aimed at measuring theirinteraction effect. Column (3) presents the baseline saturated model, and Column (4) does the same including randomization block fixed effects. Column (5) includes the followingcustomer-level controls: age, years of education, whether the customer and the trader have any social tie, whether the customer and the trader know people in common, gender, whetherthe customer has heard of the sale before (true only in 8% of cases), occupational category, ethnicity, and whether the customer has previously accepted the sale on the spot. Includinga subset of these controls (such as age and occupation as in Table II) does not change the result. In this second experiment, the trader and the customer were allowed to bargain fora price. Different prices can lead to different incentives to defect. Thus, column (6) controls for the negotiated price between the trader and the customer, which leaves the resultunchanged. Columns (7) and (8) include for the customers’ beliefs about the traders’ ethnicities. These are measured in the exit survey following the sale, in which the trader asks thecustomer to guess what the trader’s ethnicity is. In column (7), I use as a regressor, instead of the trader’s ethnicity, the proportion of customers who perceive this trader to be of astate group, or instead a nonstate group (thus a trader-level variable between 0 and 1). I use such degree measure of ethnicity, labeled Nonstate Trader (degree), as the regressor forthe trader’s ethnicity instead of the dummy. In Column (8), I use the trader actual social identity (state or nonstate group) as an instrument for the individual customer’s beliefs aboutthe ethnicity of the trader visiting him in a two-stage least squares framework. This allows me to measure the local average treatment effect of beliefs about the trader ethnicity forthe subset of customers who are perceptive to the actual ethnicity of the trader. Column (9) does the same but adds customer controls. All regressions include *,**,***, respectivelyas labels for 1%, 5%, and 10% significance level in the one-sided tests for the null hypotheses of whether legal contracts (co-ethnicity) have no positive effect and whether such effect isnot smaller for nonstate traders.

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Table VI: The decision to buy and to defect when the customer is the agent — State groups, and Nonstate groups

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)Mass: Mass: Mass: Mass: Mass: Mass: Quality: Quality: Quality: Quality:

VARIABLES Buy(t=2) Buy(t=2) Buy(t=2) Buy(t=2) Buy(t=2) Buy(t=2) Pay Pay Pay Pay

Contract -0.04 -0.12*** -0.15*** -0.04 -0.12***(0.06) (0.04) (0.06) (0.05) (0.04)

Coethnic Trader 0.11* -0.11** -0.18***(0.07) (0.05) (0.07)

Contract X Coethnic Trader -0.01 0.12*(0.10) (0.09)

Nonstate Trader -0.10**(0.04)

Contract X Nonstate Trader 0.09*(0.06)

Reject offer (t=1) -0.10 -0.30** 0.09 -0.12(0.14) (0.17) (0.27) (0.41)

Constant 0.33*** 0.75*** 0.78*** 0.83*** 0.68*** 0.78*** 0.21*** 0.16*** 0.29*** 0.24***(0.04) (0.03) (0.03) (0.04) (0.04) (0.03) (0.03) (0.03) (0.06) (0.04)

Observations 419 441 508 419 328 836 198 113 57 117R-squared 0.05 0.08 0.09 0.09 0.04 0.06 0.21 0.26 0.24 0.21Traders STATE STATE STATE STATE NONSTATE ALL STATE STATE STATE NONSTATEConstraints SPOT CREDIT CREDIT CREDIT CREDIT CREDIT CREDIT CREDIT CREDIT CREDITReject sale at t=1 X X X X X X . . . .Buy t=2 . . . . . . X X X XContract . . . . . . X X X X

Notes: This table estimates the preference mechanisms and the screening effect for legal contracts and co-ethnicity. Column (1) regresses whether the customer buys in Sales on theSpot in the second experiment on Contract, Coethnic Trader, and their interaction. None of them has a significant effect on sales. Columns (1)-(5) estimate the mass of customerswho legal contracts and co-ethnicity screen out in Sales on Credit. The columns regress the decision to accept the sale at t=2 (Buy (t=2) on Co-ethnicity (column 2), legal contract(column 3), and their interaction (column 4) among state groups. Then, column (5) uses as regression only Contract (since nonstate groups are all coethnics). Column (6) pools alltraders and runs purchase on Contract, Nonstate trader, an their interaction. Columns (7) to (10) then estimate the quality difference of this mass of customers. To do so, they restrictthe analysis to customers who accept to purchase the good at t=2 and who face the same contractual conditions/incentives (they do not sign a legal contract). Some of these acceptedat t=1 despite the requirement to sign a legal contract, while some only accepted once such requirement was lifted. While these two groups of customers face the same incentives, theywere nonetheless selected through different channels, and these columns thus estimate the selection effect of legal contracts. To compare the likelihood that customers pay dependingon whether they initially rejected the offer (Reject sale at t=1) when they were first introduced the sale with a requirement that they have to sign a legal contract, the columns regresswhether the customer pays on whether the customer rejected the offer at t=1, for customers who accept the offer at t=2 and who do not sign a legal contract. Column (7) estimatessuch quality difference for customers visited by any trader of a state group, column (8) does so for traders of a state group that are not coethnic with the customer, and column (9) forstate trader-customer interactions that are coethnic. Column (10) does so for nonstate traders. Among traders of state groups, legal contracts and co-ethnicity screen out customers.Among nonstate traders, legal contracts have no screening value (column 5). Furthermore, legal contracts have screening value for co-ethnics, suggesting that they co-ethnicity reinforcesthe effect of contracts. All regressions include randomization blocks fixed effects. All regressions include *,**,***, respectively as labels for 1%, 5%, and 10% significance level in theone-sided tests for the null hypotheses of whether legal contracts (co-ethnicity) have no positive effect. Only 5% of customers who reject at t=1 reconsider their decision.

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FiguresFigure 1: Control of the provincial administration by the state groups

0

10

20

30

40

50

60

70

Ministry ofInterior

Ministry ofJustice

Ministry ofEducation

Ministry ofAgriculture

Ministry ofEconomy

Ministry ofTransport

Total

Non-Tutsi (state ethnicities) Tutsi (nonstate ethnicities)

Notes: This figure presents the mapping exercise of the provincial administration of Sud Kivu (DRC). The 78 staffmembers that compose the administration are mostly of non-Tutsi ethnic groups (so-called “autochthons”). TheTutsi ethnic groups (which are mostly Banyamulenge in Sud Kivu) are mostly absent from the positions in theprovincial administration. In the rest of the paper, I refer to non-Tutsi as state groups due to their disproportionatecontrol of the provincial administration, while I refer to the Tutsi ethnic groups as the nonstate groups.

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Figure 2: The decision to trade — Co-ethnicity

PANEL A: Effect of co-ethnicity on trade—expectations of defection or homophilic preferences?

PANEL B: Effect of co-ethnicity on expected sanctions for defecting

Notes: PANEL A presents the results of regressing whether the customer accepts the sale on whether the trader and customer areco-ethnics. It examines the effect of co-ethnicity among Sales on Debit and Sales on the Spot. The right side of the figure presentsthe results for transactions where there is no agency relation (Sales on the Spot), and the left side of the figure presents the results fortransactions where there is an agency relation (Sales on Debit). PANEL B examines the effect co-ethnicity on the informal sanctionsthat the customer expects the trader to be subject to. The question used to measure beliefs is taken from the exit survey, implementedat the end of the transaction: “If I do not deliver the good as promised, what do you think is likely to happen to me?” The questionwas non-prompted—customers were not given options to respond but responded in an open-ended question and the trader selectedamong existing categories or filled manually when the category did not exist. The option to answer “no consequence” was alwaysoffered. Light gray indicates non-coethnic interactions, dark gray co-ethnic interactions. In this first experiment, both customers andtraders belong to the state groups.

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Figure 3: The decision to trade — Legal contracts

PANEL A: Effect of legal contracts on trade—expectations of defection or taste for contracts?

PANEL B: Effect of legal contracts on expected legal sanctions if the trader defects

Notes: PANEL A presents the results of regressing whether the customer accepts the sale on whether the trader signs a legal contract.It examines the effect of legal contracts among Sales on Debit and Sales on the Spot. The right side of the figure presents the resultsfor transactions where there is no agency relation (Sales on the Spot), and the left side of the figure presents the results for transactionswhere there is an agency relation (Sales on Debit). PANEL B examines the effect legal contracts on the customer’s belief that, upondefecting, the trader would face judicial consequences. The question used to measure beliefs is taken from the exit survey, implementedat the end of the transaction: “If I do not deliver the good as promised, what do you think is likely to happen to me?” The question wasnon-prompted—customers were not given options to respond but responded in an open-ended question and the trader selected amongexisting categories or filled manually when the category did not exist. The option to answer “no consequence” was always offered. Inboth Panel A and Panel B, light gray indicates informal interactions, dark gray interactions where a legal contract is used. In this firstexperiment, both customers and traders belong to the state groups.

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Figure 4: The decision to trade among state groups — Crowding-out or crowding-in?

PANEL A: Effect of legal contracts—non-coethnics and co-ethnics—Sales on Debit

PANEL B: Effect of legal contracts on informal sanctions—Sales on Debit

Notes: PANEL A presents the results of regressing whether the customer accepts the sale on whether the trader signs a legal contractamong non-coethnics and among co-ethnics, in Sales on Debit. PANEL B examines the effect legal contracts on the belief that, upondefecting, the trader would face social sanctions. The question used to measure beliefs is taken from the exit survey, implemented atthe end of the transaction: “If I do not deliver the good as promised, what do you think is likely to happen to me?” The question wasnon-prompted—customers were not given options to respond but responded in an open-ended question and the trader selected amongexisting categories or filled manually when the category did not exist. In both Panel A and Panel B, light gray indicates informalinteractions, dark gray interactions where a legal contract is used. In this first experiment, both customers and traders belong to thestate groups. 52

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Figure 5: The decision to defect among state groups — design

Notes: This figure presents the design of the Sale on Credit. The customer chooss to acquire the good, and thenwhether to defect by reneging payment (in this case, the customer is the agent, and the trader is the principal).The upper leg presents the experimental conditions for customers who initially accept the purchase when the legalcontract is required, while the bottom leg presents the experimental conditions for customers who initially rejectthe purchase. The trader then lifts the requirement to sign a legal contract to a randomly selected subset ofcustomers—among those who accepted and those who did not. The proportion of customers who pay as promisedin Group II compared to the same proportion in Group I estimates the incentive effect of legal contracts amongaccepting customers. The proportion of customers in Group III — those that accept the purchase when there isno contract requirement, but don’t otherwise — compared to the proportion in Group II estimates the selectioneffects of contracts (screening). While both Group II and Group III identical incentive contracts (they have notsigned a contract), those in Group II were selected screening out customers who initially did not want the sale ifa legal contract was required. If legal contracts have anticipated incentive effects, the proportion of Group II islarger than Group III, as “bad” customers (or customers who fear the risk of defection) opt out whenever they arerequired to sign a legal contract. In this second experiment, customers belong to the state groups.

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Figure 6: The decision to defect — Co-ethnicity

PANEL A: Effect of co-ethnicity on customer defection

PANEL B: Effect of co-ethnicity on customer anticipated social sanctions

Notes: This figure presents the results of the Sales on Credit. The customer is allowed to defect on payment (agent) and the traderdelivers the good first (principal). PANEL A compares the payment rates for customers visited by a co-ethnic trader of a state groupto those that are visited by a non-coethnic trader of a state group — the figure restricts the payment rates to customers who acceptedthe sale and thus promised to pay. PANEL B compares the beliefs about social sanctions for customers who are co-ethnic with thetrader and and customers who are not, when visited by a trader of a state group. The proportion is taken from the exit survey answersto the following question of the exit survey (non prompted) “If you do not pay, what consequences do you think are likely?”. Tradersand customers belong to different state groups. The exit survey for this experiment does not contain information on whether thecustomer is willing to pay to catch the trader.

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Figure 7: The decision to defect — Legal contracts

PANEL A: Effect of legal contracts on customer defection, state principal

PANEL B: Effect of legal contracts on customer anticipated legal sanctions, state principal

Notes: This figure presents the results of the Sales on Credit. The customer is allowed to defect on payment (agent) and the traderdelivers the good first (principal). PANEL A compares the payment rates for customers visited by a trader of a state group and whosign a legal contract to those that are visited by a trader of a state group and do not sign a legal contract — the figure restricts thepayment rates to customers who accepted the sale and thus promised to pay. PANEL B compares the beliefs about legal sanctionsfor customers who signed a legal contract and customers who did not, when visited by a trader of a state group. The proportion istaken from the exit survey answers to the following question of the exit survey (non prompted) “If you do not pay, what consequencesdo you think are likely?”. Traders and customers belong to different state groups.

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Figure 8: Contract enforceability, for whom? Nonstate traders

PANEL A: Effect of legal contracts on customer defection, nonstate principal

PANEL B: Effect of legal contracts on customer anticipated legal sanctions, nonstate principal

Notes: This figure presents the behavioral results of the Sales on Credit when traders and customers belong to a nonstate group. Thecustomer is allowed to defect on payment (agent) and the trader delivers the good first (principal), introduced in the second experiment,where customers are agents. PANEL A shows the payment rates for customers visited by a nonstate trader, comparing the paymentrates of those who signed a legal contract to those that did not — the figure restricts the payment rates to customers who acceptedthe sale and thus promised to pay. PANEL B compares the beliefs about legal sanctions for customers who signed a legal contractand customers who did not, when visited by a trader of a nonstate group. The proportion is taken from the exit survey answers to thefollowing question of the exit survey (non prompted) “If you do not pay, what consequences do you think are likely?”.

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A Appendix Figures

Figure A.1: Bantus in the African continent

Notes: This figure shows the historical homeland of the families of ethnic groups in Africa. While Tutsi carry Bantugenetic descendance, they arise from a later migration wave than the “Bantu expansion” and from a different area,which explains that unlike other Bantu groups, the Tutsi have substantial genetic markers of Nilotic descent. Thefact that Tutsi of Eastern Congo have maintained close kin for centuries makes them very easy to distinguish forthe average Congolese, as Table B.5 shows. Source: Blench and MacDonald (2000).

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Figure A.2: Map of ethnic groups in Kivu

Notes: This figure presents the colonial map of the different ethnic groups of the Kivu. Due to lack of data at thetime, it still ignores a number of large groups, such as the Batembo.

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B Appendix Tables

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Figure A.3: Validation using beliefs in separate survey

Notes: Extension surveys were implemented in a separate sample of 500 households, drawn from the same popu-lation, in order to measure beliefs, in order to avoid priming the subjects of the Sale on Credit experiment beforethey make their decision to pay. The figure presents the proportion of households who believe that the propertiesof the transaction are usual, and their beliefs about the detailed implications of signing a legal contract.

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Figure A.4: Decision to trade among state groups — Experiment design

Notes: This figure presents the design of the experiment in the first part of the paper. The customer (in this case, theprincipal) decides whether to buy a good, or not, anticipating that the trader might (in this case, the agent) defecton his promise of delivering the good. The upper arm presents the Sales on Debit, while the bottom arm presentsthe Sales on the Spot, where the trader delivers the good with no delay (and thus no risk of defection). Traders ofmultiple state groups are randomly assigned to households of multiple state groups, hence this tree is implementedin two samples: co-ethnic trader/customer interactions, and non-coethnic trader/customer interactions.

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Figure A.5: Legal contract used in Sales on Debi, soaps

Notes: This figure presents the contract used for Sales on Credit for soaps.

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Figure A.6: Legal contract used in Sales on Debit, phone cards

Notes: This figure presents the contract used for Sales on Debit for phone units.

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Figure A.7: Decision to defect — Effect of legal contracts, by trader

Notes: This figure presents the distribution of the effect of legal contracts, measured separately for each trader, oncustomer probability of payment in Sales on Credit. The effects are estimated by regressing in a linear probabilitymodel specification the customers’ decision to pay on trader dummies and the product of Contract and traderdummies. The bars represent the size of the coefficient on the product for each trader and the lines represent theconfidence intervals.

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Figure A.8: Decision to defect — Effect of legal contracts, by trader’s ethnicity

Notes: This figure presents the distribution of the effect of legal contracts, measured separately for each trader’sethnicity, on customer probability of payment in Sales on Credit. The effects are estimated by regressing in a linearprobability model specification the customers’ decision to pay on trader ethnic group dummies and the product ofContract and trader ethnic group dummies. The bars represent the size of the coefficient on the product for eachtrader ethnic group and the lines represent the confidence intervals.

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Figure A.9: Decision to defect — Effect of legal contracts for state vs nonstate traders, random-ization inference

Notes: This figure presents the distribution of estimated effects using simulated random assignments of (state vsnonstate) ethnicity to traders (randomization inference). I generate 10,000 simulated different assignments of the”Nonstate trader” dummy across the traders. For each simulated assignment, I implement the main specificationfor Sale on Credit, and store the coefficient on Contract X Nonstate trader. The bars represent the density ofcoefficients thus estimated for different bin levels, and the vertical line locates the coefficient estimated with thereal ethnicity. The mass to the left of the vertical line measures the p-value of the test of the null hypothesis thatthe effect of contracts is different for traders of nonstate groups, using as the counterfactual distribution for thecoefficients the one obtained through simulation, and thus free of assumptions about standard errors but containingthe information of the level at which the treatment is assigned and using the variance structure in the data.

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Table B.1: Decision to trade among state groups — Robustness

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)VARIABLES Buy Buy Buy Buy Buy Buy Buy Buy Buy Buy

Sale on debit -0.12*** -0.16*** -0.22*** -0.18***(0.03) (0.03) (0.06) (0.04)

Coethnic -0.09 0.10** -0.10* -0.29* 0.18** -0.24*(0.08) (0.05) (0.06) (0.20) (0.10) (0.18)

Sale on Debit X Coethnic 0.17*** 0.33**(0.07) (0.16)

Contract 0.03 0.14*** 0.04(0.04) (0.03) (0.04)

Sale on Debit X Contract 0.10**(0.05)

Constant 0.64*** 0.79*** 0.49*** 0.67*** 1.39*** 0.95** 1.20*** 0.77*** 0.48*** 0.63***(0.14) (0.30) (0.15) (0.14) (0.43) (0.44) (0.46) (0.30) (0.15) (0.14)

Observations 1,801 582 1,150 1,732 388 830 1,218 610 1,191 1,801R-squared 0.18 0.31 0.22 0.17 0.38 0.26 0.20 0.45 0.42 0.31Constraints . SPOT DEBIT . SPOT DEBIT . SPOT DEBIT .Ethnic groups of traders STATE STATE STATE STATE STATE STATE STATE STATE STATE STATECustomer controls YES YES YES YES YES YES YES YES YES YESSpecification OLS OLS OLS OLS 2SLS 2SLS 2SLS OLS OLS OLSMeasure of coethnicity Actual Actual Actual Actual Perceived Perceived Perceived Actual Actual Actual

Notes: This table presents the results from econometric specifications 1, 2, and 4 introduced in Section 4, using customer-level controls in all specifications(age, occupational category, and whether the customer knows the trader). In all columns, the dependent variable indicates whether trade successfullyoccurs (Buy=1). Column (1) uses the entire sample, and regresses Buy on a dummy for Sales on Debit (when such dummy takes value zero, then theseare Sales on the Spot). Columns (2) and (3) respectively restrict the sample to Sales on the Spot, and to Sales on Debit, and regresses Buy on adummy indicating whether the customer and the trader are of the same state group (Coethnic). Column (4) includes the following regressors: Coethnic,Sales on Debit, and their product in order to estimate their interaction. Columns (5) - (7) use actual coethnicity between the trader and the customeras an instrument for the customer self-reported perception of whether he and the trader are coethnics in a two-stages least square framework. Customerself-reported perception was gathered in the exit survey that followed the sale, in which the trader asks the customer to guess the ethnicity of the trader.Table B.2 presents the first stage as well as the saliency of ethnicity as a function of perceived co-ethnicity. The first stage is strong, supporting thatco-ethnicity is salient and that actual co-ethnicity can be a valid instrument for perceived co-ethnicity. Columns (8) - (10) replicate the structure of columns(2) to (4), but instead of using Coethnic as a regressor, they include the results from regressing Buy on Contract in Sales on the Spot (column 8), theresults from regressing Buy on Contract in Sales on Debit (column 9), and on Coethnic, Sales on Debit, and their product (column 10) in order to estimatetheir interaction. All regressions include randomization block fixed effects. All regressions include *,**,***, respectively as labels for 1%, 5%, and 10%significance level in the one-sided tests for the null hypotheses of whether legal contracts (co-ethnicity) have no positive effect. Table II presents the sameresults without customer controls.

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Table B.2: Decision to trade among state groups — Saliency of ethnicity

(1) (2) (3) (4) (5) (6) (7)Customer Customer Customer Customer Customer Customer

Trader perceives perceives perceives perceives perceives perceivesseen as trader as trader as trader by self as self as self by

VARIABLES Coethnic Congolese Ethnic residence Congolese Ethnic residence

Coethnic 0.47***(0.04)

Trader seen as Coethnic -0.26*** 0.42*** -0.13* -0.49*** 0.37*** 0.14**(0.10) (0.08) (0.08) (0.10) (0.08) (0.08)

Constant 0.27** 0.03 0.99*** 0.00 0.21 0.84** -0.03(0.12) (0.46) (0.38) (0.37) (0.45) (0.36) (0.34)

Observations 1,242 1,242 1,242 1,242 1,242 1,242 1,242R-squared 0.51 0.20 0.18 0.23 0.20 0.18 0.26Specification OLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLSRandomization blocks fixed effects YES YES YES YES YES YES YESCustomer controls YES YES YES YES YES YES YES

Notes: This table presents the results of regressing customers’ perceptions on a dummy indicating whether the trader and the customer are coethnic. At theend of each sale, the trader administers a short exit survey to the customer, capturing his beliefs. Column (1) uses the customer’s belief about the trader’sethnicity, which varies at the customer*trader level. The column presents the simple OLS regression of a dummy indicating whether the customer perceivesthe trader to be a co-ethnc, using the customer’s self-reported perception about the ethnicity of the trader. Columns (2) to (4) examine the customer’sself-reported markers of the perceived identity of the trader. In the exit survey, customers are asked to rank three dimensions of identity in order of howstrongly they feel they characterize the trader: the trader’s ethnicity, the trader’s nationality (Congolese), and the trader’s place of residence. Each ofcolumns (2) to (4) then uses as a regressor a dummy that takes value 1 if the corresponding marker of identity was selected as the most important for thattrader, respectively: Congolese, ethnicity, residence. The results using multinomial logit and ordered logit are identical. Columns (5) to (7) implement theidentical specification for markers of the customer’s identity - thus using the information from the answer to the exit survey question: “Rank these threedimensions of identity in order of how strongly the represent yourself.” All regressions present the results of a one-sided test, and include randomizationblock fixed effects and the following controls: age and occupation. All regressions include randomization block fixed effects. All regressions include *,**,***,respectively as labels for 1%, 5%, and 10% significance level in the one-sided tests for the null hypotheses of whether co-ethnicity has no positive effect.Results are unchanged without controls.

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Table B.3: Decision to defect among state groups — Saliency of ethnicity

(1) (2) (3) (4) (5) (6) (7) (8)Customer Customer Customer Customer Customer Customer Customerperceives perceives perceives perceives perceives perceives perceivestrader as trader as trader as trader by self as self as self by

VARIABLES Coethnic Congolese Ethnic residence Congolese Ethnic residence Anxiety

Coethnic Trader 0.43***(0.04)

Trader seen as Coethnic -0.44*** 0.45*** 0.00 -0.15 0.25** -0.05 -0.17(0.13) (0.12) (0.09) (0.12) (0.11) (0.09) (0.19)

Constant 0.81*** 1.66*** -0.95*** 0.26 1.07*** -0.05 -0.19 1.26***(0.21) (0.33) (0.31) (0.23) (0.31) (0.28) (0.22) (0.47)

Observations 611 611 611 611 611 611 611 611R-squared 0.35 0.07 0.10 0.08 0.17 0.16 0.10 0.17Ethnic groups of traders STATE STATE STATE STATE STATE STATE STATE STATERandomization blocks fixed effects YES YES YES YES YES YES YES YESCustomer controls YES YES YES YES YES YES YES YESSpecification OLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLSMeasure of traders’ ethnicity Actual Perceived Perceived Perceived Perceived Perceived Perceived Perceived

Notes: This table presents the results of regressing customers’ perceptions on a dummy indicating whether the trader is of a nonstate group (Tutsi). At the end of each sale, the traderadministers a short exit survey to the customer, capturing his beliefs. Column (1) uses the customer’s belief about the trader’s ethnicity, which varies at the customer*trader level. Thecolumn presents the simple OLS regression of a dummy indicating whether the customer perceives the trader to be of a nonstate group (Tutsi) on the trader’s actual ethnicity. Columns(2) to (4) examine the customer’s self-reported markers of the perceived identity of the trader. In the exit survey, customers are asked to rank three dimensions of identity in orderof how strongly they feel they characterize the trader: the trader’s ethnicity, the trader’s nationality (Congolese), and the trader’s place of residence. Each of columns (2) to (4) thenuses as a regressor a dummy that takes value 1 if the corresponding marker of identity was selected as the most important for that trader, respectively: Congolese, ethnicity, residence.The results using multinomial logit and ordered logit are identical. Columns (5) to (7) implement the identical specification for markers of the customer’s identity - thus using theinformation from the answer to the exit survey question: “Rank these three dimensions of identity in order of how strongly the represent yourself.” Finally, column (8) examines theeffect of the social identity of the assigned trader on customer’s measures of anxiety. To measure the customer’s anxiety, I use two standard scales used in psychology. The first of suchscales indicates how satisfied the customer is with his own life in a scale from zero to four, and the second indicates how satisfied the customer is with his own choices, in a scale fromzero to four. Both scales produce identical results, and the results are equivalent whether I use ordered logit, multinomial logit, or OLS using the trader’s ethnicity as an independentvariable. Thus, column (8) uses as a regressor the average ranking reported on the two scales. I did not gather measurements of anxiety for the customers of the first experiment.All regressions present the results of a one-sided test, and include randomization block fixed effects and the following controls to increase statistical power due to the high variance incustomers’ perceptions: age, years of education, gender, all collected in the exit survey. In addition, unlike the first experiment, I collected a set of trader-level controls in an extensionsurvey administered to a separate random sample of households, measuring their perceptions and implicit bias about the traders, as seen in individual photos on a table. Since these arelikely to affect how the customers perceived the traders, I include such controls in the regressions of this table: perceived trader’s reliability, perceived trader’s friendliness, perceivedtrader’s trust, which are instead collected in an extension survey administered to a separate sample using photos of the traders. Results are qualitatively unchanged without controls.All regressions include randomization block fixed effects. All regressions include *,**,***, respectively as labels for 1%, 5%, and 10% significance level in the one-sided tests for thenull hypothesis of whether co-ethnicity has no positive effect. The first stage in column (1) is almost identical to the first stage in Table B.2, which is noteworthy given that these aredifferent samples, and the nature of the interaction is different (Sale on Credit vs Sale on Debit).

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Table B.4: The decision to trade among state groups — Posterior beliefs

(1) (2) (3) (4) (5) (6) (7) (8) (9)Trader Trader Trader Trader Trader Traderseen as seen as seen as seen as seen as seen as

backed by backed by backed by backed by backed byVARIABLES University Firm Nonprofit State Foreigners Coethnic Buy Buy Buy

Contract 0.02 0.01 -0.00 -0.03** -0.00 -0.03* 0.13*** 0.13*** 0.14***(0.02) (0.02) (0.02) (0.01) (0.01) (0.02) (0.03) (0.03) (0.03)

Trader seen as backed by firm -0.05 -0.03(0.04) (0.04)

Trader seen as backed by nonprofit 0.02 0.01(0.04) (0.04)

Trader seen as backed by state -0.10** -0.10**(0.06) (0.06)

Trader seen as backed by foreigners 0.08 0.06(0.09) (0.09)

Constant 0.36*** 0.24*** 0.32*** 0.13*** 0.04*** 0.33*** 0.53*** 0.54*** 0.50***(0.01) (0.01) (0.01) (0.01) (0.01) (0.01) (0.02) (0.03) (0.15)

Observations 1,898 1,898 1,898 1,898 1,898 1,262 1,232 1,231 1,190R-squared 0.37 0.42 0.42 0.43 0.29 0.63 0.39 0.40 0.42Constraints . . . . . . DEBIT DEBIT DEBITEthnic groups of traders STATE STATE STATE STATE STATE STATE STATE STATE STATECustomer controls NO NO NO NO NO NO NO NO YESSpecification OLS OLS OLS OLS OLS OLS OLS OLS OLS

Notes: This table examines whether the main effect of legal contracts on willingness to trade can be explained by the signaling value of having access to legalcontracts. Columns (1) to (6) respectively regress the following six dummies indicating various aspects of the customer posterior beliefs about the identityof the trader on the contract dummy: whether the customer perceives the trader as backed by a local University (as part of market research), backed bya firm (as part of casual sales), backed by a nonprofit (as part of market research), backed by the state, backed by foreigners, and whether the customerperceives the trader to be co-ethnic. Having examined whether contracts shift these prior beliefs into different posterior beliefs about the trader, columns(7) to (9) regress whether the customer accepts the sale on the Contract dummy and as well as all (endogenous) posterior beliefs. Column (7) replicates thebaseline regression, Column (8) adds the posterior beliefs as controls, and Column (9) also adds the customer level controls (age, occupational category, andwhether the customer and the trader have someone in common). All regressions include randomization block fixed effects. All regressions include *,**,***,respectively as labels for 1%, 5%, and 10% significance level in the one-sided tests for the null hypothesis of whether Contract has no positive effect.

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Table B.5: Decision to defect, state and nonstate groups — Saliency of ethnicity

(1) (2) (3) (4) (5) (6) (7) (8)Customer Customer Customer Customer Customer Customer Customerperceives perceives perceives perceives perceives perceives perceivestrader as trader as trader as trader by self as self as self by

VARIABLES Nonstate Congolese Ethnic residence Congolese Ethnic residence Anxiety

Nonstate Trader 0.73***(0.03)

Trader seen as Nonstate -0.50*** 0.52*** -0.05 -0.12* 0.13** -0.03 0.53***(0.09) (0.08) (0.06) (0.08) (0.07) (0.06) (0.13)

Constant 0.15 1.12*** -0.48** 0.35** 1.01*** 0.21 -0.31* 1.25***(0.14) (0.27) (0.27) (0.19) (0.26) (0.23) (0.19) (0.40)

Observations 798 702 702 702 702 702 702 702R-squared 0.67 0.09 0.06 0.07 0.18 0.17 0.10 0.14Ethnic groups of traders ALL ALL ALL ALL ALL ALL ALL ALLRandomization blocks fixed effects YES YES YES YES YES YES YES YESCustomer controls YES YES YES YES YES YES YES YESSpecification OLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLS 2SLSMeasure of traders’ ethnicity Actual Perceived Perceived Perceived Perceived Perceived Perceived Perceived

Notes: This table presents the results of regressing customers’ perceptions on a dummy indicating whether the trader is of a nonstate group (Tutsi). At the end of each sale, the traderadministers a short exit survey to the customer, capturing his beliefs. Column (1) uses the customer’s belief about the trader’s ethnicity, which varies at the customer*trader level.The column presents the simple OLS regression of a dummy indicating whether the customer perceives the trader to be of a nonstate group (Tutsi) on the trader’s actual ethnicity.Columns (2) to (4) examine the customer’s self-reported markers of the perceived identity of the trader. In the exit survey, customers are asked to rank three dimensions of identityin order of how strongly they feel they characterize the trader: the trader’s ethnicity, the trader’s nationality (Congolese), and the trader’s place of residence. Each of columns (2) to(4) then uses as a regressor a dummy that takes value 1 if the corresponding marker of identity was selected as the most important for that trader, respectively: Congolese, ethnicity,residence. The results using multinomial logit and ordered logit are identical. Columns (5) to (7) implement the identical specification for markers of the customer’s identity - thususing the information from the answer to the exit survey question: “Rank these three dimensions of identity in order of how strongly the represent yourself.” Finally, column (8)examines the effect of the social identity of the assigned trader on customer’s measures of anxiety. To measure the customer’s anxiety, I use two standard scales used in psychology.The first of such scales indicates how satisfied the customer is with his own life in a scale from zero to four, and the second indicates how satisfied the customer is with his own choices,in a scale from zero to four. Both scales produce identical results, and the results are equivalent whether I use ordered logit, multinomial logit, or OLS using the trader’s ethnicity asan independent variable. Thus, column (8) uses as a regressor the average ranking reported on the two scales. I did not gather measurements of anxiety for the customers of the firstexperiment. All regressions present the results of a one-sided test, and include randomization block fixed effects and the following controls to increase statistical power due to the highvariance in customers’ perceptions: age, years of education, gender, all collected in the exit survey. In addition, unlike the first experiment, I collected a set of trader-level controls inan extension survey administered to a separate random sample of households, measuring their perceptions and implicit bias about the traders, as seen in individual photos on a table.I include such controls in the regressions of this table: perceived trader’s reliability, perceived trader’s friendliness, perceived trader’s trust, which are instead collected in an extensionsurvey administered to a separate sample using photos of the traders. Results are qualitatively unchanged without controls. All regressions include *,**,***, respectively as labels for1%, 5%, and 10% significance level in the one-sided tests for the null hypothesis of whether co-ethnicity has no positive effect.

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Table B.6: Variables collected in the survey of the provincial administration

PANEL A PANEL B

Individual variables Institutional variables

For all active staff in Province

First, Last According to the law, how does one obtain a position inthis Ministry?

Exact position According to the custom, how does one obtain a positionin this Ministry?

Location of the position According to the custom, are there different ways toobtain a position that depend on the ethnic group?

Date since occupies this position The key strategic decisions of this Ministry, are theyfilled by individuals of a particular ethnic group? De-scribe.

Date since civil servant

How did you obtain this position?

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Table B.7: Balance between state and nonstate traders

State Nonstate Difference(p-value)

HOUSEHOLD SURVEY. . . .Ethnicity guessed correctly 0.88 0.67 -0.21∗∗∗

. . . (0.00)Trader seen as friendly 0.61 0.55 -0.06. . . (0.29)Trader would trust you 0.42 0.37 -0.05. . . (0.19)You would trust the trader 0.41 0.38 -0.03. . . (0.39)Unconscious Bias (I.A.T.) 0.02 0.04 0.02. . . (0.64)TRADER SURVEY. . . .Assets index (p.c.) 0.07 -0.10 -0.17. . . (0.86)Unemployed 0.18 0.38 -0.19. . . (0.35)Years of Education 16.82 16.75 -0.07. . . (0.88)Used contracts before 0.27 0.25 0.02. . . (0.91)

Notes: This table presents the balance on observable characteristics between traders of a state ethnicity (non-Tutsi),and traders of a nonstate ethnicity (Tutsi). The table uses the extension surveys collected after the experiment ina separate random sample of 500 househols, drawn from the same population of households as the customers in theSale on Credit. The upper part of the table presents the variables collected in the household survey. These includeself-reported answers to survey questions containing the trader photo for each individual trader (conscious), as wellas Implicit Association Tests administered in each household for each individual trader (unconscious). The lowerpart contains the variables from the trader survey, administered directly on traders.

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Table B.8: Decision to defect, state and nonstate groups — Robustness

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)VARIABLES Pay Pay Pay Pay Pay Pay Pay Pay Pay Pay

Contract 0.15*** 0.11* 0.11** 0.09* 0.17*** 0.11** 0.05 0.04 0.11** 0.12***(0.05) (0.07) (0.06) (0.06) (0.06) (0.06) (0.10) (0.09) (0.05) (0.05)

Nonstate Trader 0.08* 0.04 0.04 0.04 -0.00 0.04 0.04 0.00 0.04 0.04(0.06) (0.06) (0.06) (0.06) (0.06) (0.06) (0.06) (0.06) (0.06) (0.06)

Contract X Nonstate Trader -0.18*** -0.11* -0.11* -0.12* -0.09 -0.11* -0.12* -0.10 -0.12* -0.11*(0.08) (0.08) (0.08) (0.08) (0.08) (0.08) (0.08) (0.08) (0.08) (0.08)

Contract X Control 0.04 0.05 0.10* -0.10* 0.05 0.10 0.11 0.07 0.03(0.08) (0.08) (0.08) (0.08) (0.08) (0.10) (0.09) (0.08) (0.16)

Control 0.01 -0.00 0.02 0.13** -0.02 -0.13* -0.15** -0.00 0.30*(0.07) (0.07) (0.06) (0.06) (0.06) (0.10) (0.07) (0.07) (0.19)

Constant -5.81 -3.78 -3.62 -3.57 -4.12 -3.71 -4.21 -4.43 -3.98 -4.41(5.29) (5.27) (5.28) (5.24) (5.27) (5.27) (5.26) (5.29) (5.26) (5.27)

Observations 509 495 495 495 495 495 495 495 495 495R-squared 0.21 0.22 0.22 0.23 0.23 0.22 0.22 0.23 0.22 0.23Ethnic groups of traders ALL ALL ALL ALL ALL ALL ALL ALL ALL ALLRandomization block fxed effects YES YES YES YES YES YES YES YES YES YESCustomer controls YES YES YES YES YES YES YES YES YES YESSpecification OLS OLS OLS OLS OLS OLS OLS OLS OLS OLSMeasure of traders’ ethnicity Actual Actual Actual Actual Actual Actual Actual Actual Actual ActualControl . Reliable Likable Trusting IAT Assets U. degree Employed Experience Femalecontract + contract X CONTROL 0.154 0.160 0.190 0.0700 0.157 0.152 0.156 0.186 0.153t-stat 2.649 2.605 3.054 1.020 2.576 2.924 3.059 2.393 1.001p-val 0.00836 0.00949 0.00240 0.308 0.0103 0.00364 0.00235 0.0171 0.317

Notes: This table presents the results from Specification 7, but includes trader-level controls gathered in the household follow-up surveys for robustness of the nonstate trader ethnicityeffect to trader level characteristics. Column 1 replicates the main econometric specification. Columns 2-10 include controls for trader-level variables collected in the follow-up surveyadministered in a separate random sample of 500 households drawn from the same population. The controls are trader-level averages of how households perceive the trader. Sincethe result that legal contracts are less effective for Tutsi traders stems from within-trader variation (across legal contract treatment conditions), I also include the interaction betweenthe dummy Contract and the corresponding control in each column. If trader level observables that correlate with ethnicity explain the effect of the contract, including the regressorContractXControl would render the coefficient on ContractXTutsi insignificant. Furthermore, by including the regressor ContractXControl, I can examine whether the effect of thelegal contracts on payment rates to non-Tutsi traders stems from a particular subgroup defined by the corresponding control. For that reason, in each column, I report the F test forwhether the sum of the coefficient on Contract and the coefficient on ContractXControl is significantly different from zero. The coefficient on Contract estimates the effect of using alegal contract on payment for non-Tutsi traders who are satisfy Control = 0, while the sum of the coefficient on Contract and the coefficient on ContractXControl estimates the effectof using a legal contract on payment for non-Tutsi traders who are defined by Control = 1. Columns 2-5 respectively use as Control the following trader-level dummies, indicatinghow the households perceive the trader, as seen in the photo of the trader shown in the tablet: as reliable, likable, trusting, and the score obtained by the photo of such trader inimplicit association tests implemented with each of the 500 households. Columns 6-10 include instead trader-level observables captured in the post-experimental trader survey: anindex of assets, constructed with principal components of the trader’s indicators of wealth (cattle, family size, income, asset ownership, as described in detail in the text), whether thetrader holds a university degree, whether the trader has an alternative employment after the sales, whether the trader had previously used legal contracts in their professional activities,and whether the trader is female (only one is). All regressions include randomization block fixed effects. All regressions include *,**,***, respectively as labels for 1%, 5%, and 10%significance level in the one-sided tests.

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C Experimental protocols

C.1 Incentive compatibility of traders

When traders are the agents, if traders accept payments and do not deliver the goods, their profitsincrease. To avoid traders reneging on their delivery promises, I design a cell phone monitoringsystem. Traders provide customers with a project cell phone number and instructions for how toregister a complaint. In addition, I require traders to collect the customers’ cell phone numbersduring the exit survey. I inform the traders that the supervisor will contact a random sampleof respondents to check whether the sales were implemented as planned.42 I inform traders thattheir salaries would be withdrawn if they fail to deliver the soaps. Finally, traders collect the GPScoordinates of every customer in both the urban and rural areas. More importantly, traders workin a long-term basis for various research projects for the authors and fear huge losses in reputationif caught cheating.

Traders may be tempted to accept payments below the price set by the research project, henceextracting strictly positive surplus from customers that would otherwise have refused the purchase.To avoid this, I require traders to pay a fixed amount that is lower than the sales price for eachpack of 5 soaps they sell. The supervisor verifies the stock of soaps and traders pay in proportionto the missing soaps. This strategy reduces the set of prices below the recommended price at whichthe traders would make positive profit. I recorded no sales at lower prices than recommended.

Traders may be tempted to sell above the price set by the project to extract additional surplus.I allow traders to sell above the price set by the project if customers agree to pay the higher price.To reduce the risk that traders would reallocate soaps to customers offering higher prices, I givetraders enough soaps for all households that they had to visit. Also, traders could be tempted toviolate the random allocation of households and select richer households to extract higher surplus.However, discovering the wealth distribution in the village is difficult.43 Furthermore, I informtraders that researchers use statistical techniques such as randomization to verify implementationviolations. Traders felt under very strict monitoring, especially by the data collection equipmentthey had at hand (tablets, gps devices).

C.2 Scripts

Introduction of the Sale on DebitThe sale is introduced as follows: “I am selling these goods on a piece-rate as part of the first stepsof a new firm. If you accept, you will have two days to pay by phone, and will have to sign thiscontract.” The description of the Sale on Credit is identical, but reversed.

Legal Contract, Sale on DebitThe contract reads as follows: “I, the undersigned... , recognize to have received ... cell phoneunits of the company ... from ... , for a value of 500 Congolese Francs per unit. I hereby committo pay ... in exchange of these cell phone units to ... in the interval of TWO days at most. Iam ready to bring this contract, if necessary, to a legal representative. I recognize that in case of

42I recorded no instance of fraud or cheating by traders, among all customers in which the Supervisor imple-mented the verification.

43See Sanchez de la Sierra (2015) for a description of how armed groups who are settled in the village struggleto discover the wealth distribution.

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Table B.9: Decision to defect — Ruling out homophilic preferences

Panel A: State vs Nonstate traders(1) (2) (3) (4) (5) (6)Buy Buy Buy Buy Buy Buy

VARIABLES Spot Spot Spot t=1 t=1 t=1

Nonstate Trader -0.07* 0.00 -0.20* -0.04 -0.07 -0.18(0.03) (0.12) (0.12) (0.03) (0.11) (0.12)

Constant 0.35*** 7.91* 5.58 0.70*** -13.55*** -11.24***(0.02) (4.54) (4.08) (0.02) (4.35) (4.01)

Observations 836 699 699 832 695 695R-squared 0.03 0.07 0.20 0.05 0.13 0.22Ethnic groups of traders ALL ALL ALL ALL ALL ALLRandomization block fxed effects YES YES YES YES YES YESCustomer controls NO YES YES NO YES YESTrader controls NO YES YES NO YES YESSpecification OLS OLS 2SLS OLS OLS 2SLSMeasure of traders’ ethnicity Actual Actual Perceived Actual Actual Perceived

Panel B: Coethnicity among state traders(1) (2) (3) (4) (5) (6)Buy Buy Buy Buy Buy Buy

VARIABLES Spot Spot Spot t=1 t=1 t=1

Coethnic Trader 0.08* 0.06 0.25** -0.08* -0.07 -0.18(0.05) (0.05) (0.13) (0.05) (0.05) (0.13)

Constant 0.32*** 8.93* 10.76** 0.73*** -14.53*** -15.39***(0.03) (4.57) (4.60) (0.03) (4.45) (4.62)

Observations 734 659 608 733 658 607R-squared 0.01 0.08 0.10 0.00 0.10 0.08Ethnic groups of traders STATE STATE STATE STATE STATE STATERandomization block fxed effects YES YES YES YES YES YESCustomer controls NO YES YES NO YES YESTrader controls NO YES YES NO YES YESSpecification OLS OLS 2SLS OLS OLS 2SLSMeasure of traders’ ethnicity Actual Actual Perceived Actual Actual Perceived

Notes: This table presents the results where I regress a dummy variable indicating whether the trade occurred(Buy) on a dummy indicating the ethnicity of the trader (Pane A: Nonstate trader; Panel B: Coethnic). Columns(1) to (3) regress whether the customer buys the good in Sales on the Spot, and Columns (4) to (6) regress whetherthe customer buys the good in Sales on the Spot. Column (1) implements the basic OLS specification with nocontrols, and column (2) adds trader level and customer level contrls. Column (3) uses actual trader’s ethnicity asinstrument for customer level perceived ethnicity in order to estimate the effect of beliefs about trader’s ethnicityon whether the customer buys the good. Columns (4) to (6) are identical to columns (1) to (6) but use insteadas a regressor whether the customer buys the good in the Sale on Credit at t=1. All regressions include *,**,***,respectively as labels for 1%, 5%, and 10% significance level in the one-sided tests for the null hypotheses ofwhether legal contracts have no positive effect and whether such effect is not smaller for nonstate traders. Allcolumns include randomization block fixed effects.

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no payment, I am exposed to the prosecutions and sanctions that the Congolese law considers forthese cases. Done in... . Date ... . Signature of debtor... Signature of creditor... Signature ofwitness... .” The legal contract for the Sale on Credit is identical, but reversed

Script for withdrawing contracts, Sale on CreditThe script reads as follows: “I see I do not have enough contracts. It is therefore not neces-sary to sign this contract and my protocol stipulates that in such cases we shall proceed with thetransaction.”

D Robustness

D.1 Robustness to alternative interpretations

In this section, I address remaining challenges to the validity of the estimates.

D.1.1 Incidental imbalance in trader-level unobservables

Unlike the analysis in part I, where I can introduce trader fixed effects, a concerning threat tovalidity is that trader-specific characteristics that affect customers’ observed behavior in responseto the perceived ethnicity of the trader could correlate with trader ethnicity.44 This is particularlyimportant if, even if Tutsi and non-Tutsi might be balanced on relevant characteristics in thepopulation, sampling error in the recruitment of traders creates incidental differences.

I proceed in three steps. First, I re-estimate the effect of legal contracts in various ways. Sincethe legal contract treatment is orthogonal to the traders’ identities by design, I can estimate theaverage treatment effect of the contract by each trader. Figure A.7 presents the average treatmenteffects, re-estimated by trader. The figure ranks the treatment effects by their magnitude. Legalcontracts have an effect systematically only for state traders (if at all, one nonstate outlier goesagainst the main result). Thus, the main effect is unlikely to be driven by strange trader outliers.In addition, I further examine whether the effect of contracts among state traders is driven by aparticular ethnicity. Figure A.8 decomposes the effect of legal contracts by the ethnicity of traders.The magnitudes show that legal contracts have an effect in most ethnicities that compose the stategroups — although low statistical power reduces the ability to separately identify the effect for allgroups. Finally, to correctly account for the standard errors when the number of traders is low, Iconduct an exercise of randomization inference. I simulate 10,000 placebo ethnicity assignmentsto the traders, and estimate the effect of nonstate trader on the effect of the legal contract foreach placebo assignment. Figure A.9 presents the ditsribution of all estimated placebo treatmenteffects, which as expected is symmetric and centered at zero — it is the counterfactual distribu-tion of the treatment effect on the interaction under the null hypothesis of no effect. The red lineindicates the coeffcient estimated with the real traders’ ethnicity. The mass of placebo treatmenteffects that are lower than the real effect produces the correct p-value: 1.4%. Thus, the main

44For instance, suppose that the population of Congolese Tutsi is systematically poorer. Customers mightcondition their decision whether to pay on the observed wealth of the trader, and not on his ethnicity. In this case,customers would take legal contracts less seriously when requested by a Tutsi trader, but for a different reason thanethnic capture of the state. If Tutsi are systematically less able to enforce contracts because they are poorer, thisalso is, by definition, an ethnic effect.

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effect of contracts cannot be explained by particular individuals, or particular non-Tutsi groupsthat might have been incidentally sampled due to the small sample size of traders.

Second, I examine the balance on traders’ observables, by whether they are from a state group.To do so, I first describe the data used for this exercise. In addition to the exit surveys I describedin Sections 4 and 5, I implement, months after the ending of the activity, a survey to the traders,and a survey of 500 households, randomly sampled from the same sampling frame as customersof the experiment — henceforth, both surveys are labeled as extension surveys. I gather socio-economic data about each trader through trader interviews administered by a separate team ofsurveyors.45 Furthermore, I implement a survey on a separate sample of households which I sam-ple following the identical sampling method, from the same population. This extension surveyallows me to measure beliefs about contracts, and perceptions of individual traders in the generalpopulation. Using a tablet, a surveyor presents sequentially the photos of the traders that imple-mented the sale, and for each photo, asks a series of questions about the person on the photo. Ialso examine unconscious attitudes towards each of the trader that took part of the sales, usingImplicit Associations Tests (IAT). This is especially important if unconscious biases led the cus-tomers of the experiment to take different decisions that they are unable to report consciously.For instance, if a trader has a negative bias against a set of individual traders, and that this biascorrelates with the ethnicity of traders, it might be that the nonstate traders I selected inducedifferent unconscious associations for other reasons than ethnicity.46 Finally, the survey also asksabout their beliefs on some properties of the sale that could be perceived to be unusual.

Table B.7 presents the balance of traders on observable characteristics. Column 1 indicatesthe sample mean for the trader-specific observables for state traders, and Column 2 for nonstatetraders. Column 3 shows the difference in between state and nonstate traders and the p-value.47

The first result from Table B.7 is that traders are indistinguishable for the average household,except for their ethnicity. Lines 2 to 4 present the proportion of households who, after being shown

45I observe their wealth, age, occupation, gender, education, ethnic group, as well as their own perceptions aboutlegal contracts. To measure wealth, I collected the following variables: Number of daughters and sons his fatherhad in total, Number of women his father had in total, Number of cows owned, Number of porks owned, number ofhouses owned, Congolese Francs owned, number of cars owned, number of bikes owned, number of sons ever had,number of daughters ever had, number of wives ever had, value of capital owned, field ownership, land area.

46IAT’s were developed in psychology (Bluemke and Friese, 2008, Greenwald, McGhee and Schwartz, 1998,Nosek, Greenwald and Banaji, 2006) and were recently used in economics (Lowes et al., 2017, 2015). In the versionof IAT I administered here on a tablet, the left of the screen shows a smiling (sad) face, and the right a sad (smiling)face. Subjects are then presented sequentially with ten faces at the center of the screen, and have to sort suchfaces (smiling or sad) to the left or right of the screen, so that smiling faces are sorted to the side where there isa smiling face, and vice-versa. Sorting is intuitive and fast. In the next round, a photo of the face of trader j isdisplayed on one side, below one of the side faces, and the task is repeated. In a third sequence, the face of traderj is presented on the opposite side and the task is repeated. If subjects are asked to sort smiles to smiles in thepresence of trader j’s face next to the smiling face on the side, this would be much less intuitive and would takelonger if the subject has a negative association towards the trader. In contrast, if the photo of trader j would bedisplayed below a sad face, sorting sad faces that appear in the center towards the sad face on the side would bemore intuitive if subjects negatively associate with trader j. I construct a z-score, standardized by the mean andstandard deviation based on the time of response, and the frequency of mistakes. This pattern is systematicallypresent for images that clearly generate negative associations (such as spiders, snakes): subjects take much longerand make more mistakes when they have to associate smiling faces to the side of the smiling face if a “bad” imageis displayed below the smiling face on the side. In contrast, subjects will make less mistakes if the “bad” imageis displayed below a sad face. Sorting sad faces to sad faces is easier because “bad” images are associated withsad faces. Nosek, Greenwald and Banaji (2006) propose this difference in delay as a measure of system 1, implicitattitudes towards a person, before they are rationalized by system 2 and without the subject’s awareness of them.

47When comparing dummies, I implement one sample tests of proportions, and the results are identical.

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the photo of each trader, answer yes to the following questions, respectively: “Does this personseem friendly to you?”, “Would this person trust you?”, and “Would you trust this person?”. Forall of these questions, there is no significant difference for answers given about state and nonstatetraders. Households find most traders friendly, but not many trustworthy.

The second result from Table B.7 is that, except for their ethnicity, traders are also indistin-guishable for the econometrician, who can use the information collected in the trader survey thatthe households might not directly observe. Lines 5-8 present the results. In line 5, I use the traderspecific asset index, which I have constructed using principal components on all self-reported mea-sures of wealth.48 There is no statistically significant difference between trader groups. Line 6shows that the proportion of individuals unemployed is higher among nonstate traders (38%),the proportion is statistically indistinguishable from the proportion of unemployed among statetraders (18%). Similarly for years of education (line 7).

A remaining possible concern is indeed that if nonstate traders can be expected to be less ableto enforce legal contracts, it is also plausible that they may also be less likely to use contracts. Thiscould induce households to consider the transaction as more unusual if a nonstate trader shows alegal contract, than if a state trader shows a legal contract, which could incidentally manipulatethe customers’ posterior beliefs about the authenticity of the contracts — on top of the hetero-geneity in their priors about the enforceability of the contract. Similarly, if nonstate traders areless familiar with the use of contract, they might send unconscious signals to the buyer about theirinexperience with legal contracts, which might incidentally shift households’ prior about contractenforceability.49 Line 8 of Table B.7 is that households in the extension survey do not perceive thetraders to be not differentially familiar with legal contracts: 27% of state traders in the study hadpreviously used legal contracts, against 25% nonstate, statistically indistinguishable. It is thusimplausible that unobserved characteristics account for the Tutsi effect in the main regression.

Third, I examine whether the main result is robust to including trader-level controls. Ta-ble B.8, which replicates the main result adding trader-level controls, is structured as follows.Column 1 replicates the main econometric specification. Columns 2-10 include controls for trader-level variables. Since the result that legal contracts are less effective for nonstate traders stemsfrom within-trader variation (across legal contract treatment conditions), I also include the inter-action between the dummy Contract and the corresponding trader-level control in each column.If trader level observables that correlate with ethnicity explain the effect of the contract, includingthe regressor Contract X Control would render the coefficient on Contract X Nonstatetrader in-significant. Furthermore, by including the regressor Contract X Control, I can examine whetherthe effect of the legal contracts on payment rates to non-Tutsi traders stems from a particularsubgroup defined by the corresponding control. For that reason, in each column, I report the Ftest for whether the sum of the coefficient on Contract and the coefficient on Contract X Controlis significantly different from zero. The coefficient on Contract estimates the effect of using alegal contract on payment for State traders who are satisfy Control = 0, while the sum of thecoefficient on Contract and the coefficient on Contract X Control estimates the effect of using alegal contract on payment for State traders who are defined by Control = 1.50

48I conducted principal component analysis of all the wealth variables I collected (progeniture of their father,number of wives of their father, cattle owned today, savings in Congolese Francs, number of cars and bikes ownedtoday, their own progeniture today, their own number of wives today), and predicted an index for each trader.

49For instance, there is evidence that humans are able to accurately predict intention based on unconscious bodyexpressions of intention. See Pesquita, Chapman and Enns (2016).

50The effect of using a legal contract for the average State trader can be calculated as follows. Let sA be theproportion of State traders who are defined by Control = 1 and βContract and βContractXControl be respectively

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Columns 2-10 use respectively the following Controls: whether the households perceive thetrader as reliable, likable, trusting, the trader average I.A.T. score, the trader asset score, whetherthe trader has a university degree, whether the trader is employed, whether the trader has expe-rience with using contracts before, and whether the trader is female (only one is).51

Controlling for trader specific variables does not affect the main result. First, the coefficient onContract X Nonstatetrader is unchanged and still significant, suggesting that this effect cannotbe explained by the variables described above. Second, the sum of coefficients on Contract andContract X Control is sometimes positive and statistically significant. In these two columns,the coefficient on Contract is positive and significant and the coefficient on Contract X Controlis qualitatively zero. Third, among transactions where legal contracts are not used, traders whogenerate more positive unconscious biases in the I.A.T., traders without university degree, traderswho are unemployed, and the female trader, are more successful in obtaining payment from cus-tomers. This provides additional confidence in the trader-level variables.

These results establish that state and nonstate traders are not statistically distinguishable onconscious and unconscious outcomes collected in follow-up surveys from households and traders.Furthermore, when trader-level observables are used as controls in the main specification, the re-sults are unchanged, suggesting that the effect of legal contracts estimated in Table V are unlikelyto be explained by omitted variables. Section ?? in the appendix discusses remaining limitations.

D.1.2 Imbalance in types of customers enrolled by nonstate traders

A remaining threat to validity is that nonstate traders might successfully enroll customers whoworry less about the consequences of violating a legal contract. Table B.9, Panel A, presentsthe results from econometric specification 7. Columns (1)-(3) show the effect of the trader beingfrom a nonstate group on sale among Sales on the Spot. Column (1) presents the baselinespecification, Column (2) adds customer and trader controls, and Column (3) implements the2SLS, where perceived ethnicity is instrumented by actual ethnicity. Columns (4) to (6) do thesame for Sales on Credit. Overall, there is weak evidence that nonstate traders are less likely torecruit customers among Sales on the Spot, and there is no effect among Sales on Credit. Thissuggests that differential enrollment by nonstate traders, together with heterogeneous treatmenteffects among differentially enrolled customers, is unlikely account for the effect of legal contractson defection when used by a state group. Panel B examines differential enrollment among co-ethnic vs non-coethnic state groups. Similarly, and as found in the first experiment, there is noevidence of co-ethnic social preferences among state groups.

the coefficients on Contract and ContractXControl. ∂pay∂Contract |Control=1UControl=0 = sA

∂pay∂Contract |Control=1 +

(1 − sA) ∂pay∂Contract |Control=0 = βContract + sAβContractXControl. Since we know from the main specification

that ∂pay∂Contract |Control=1UControl=0 > 0, I focus show here only the result of the test of whether βContract +

βContractXControl ≤ 0, which estimates the effect of the legal contract for traders defined by Control = 0. Ifthe sum of the two coefficients is significantly different from zero, legal contracts works for State traders, which weknow from the main regression.

51I use dummies for simplicity of interpretation of the coefficients, but the specification that use continuousproportions instead yield qualitatively identical results. To construct trader-level dummies, I compute the propor-tion of “yes” answers among the 500 households for each of the corresponding survey questions, and then assign adummy with value 1 to the traders whose proportion of “yes” is above the median, and zero otherwise.

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E Mathematical appendix

In this section, I present the argument, using a simplified version of Besley and Persson (2009).

E.1 Setup

The economy is composed of two groups, G ∈ {A,B}. Each group is respectively the shares βB

and βP of the entire population. There is one period.Individuals of groups G ∈ {A,B} are endowed with initial wealth wA and wB, and have

linear preferences in consumption. Individuals can borrow bG ∈ {A,B} and lend lG ∈ {A,B}in competitive capital markets, and invest IG of their wealth into their own projects. A fractionσ of individuals in each group has access to projects with return rI = rH , while the rest of thegroup has access to projects with return rI = rL, where rH > rL. The interest rate that clears themarket is rM .

In order to be able to borrow, individuals need to put a share of their wealth, cG, as collateral.The borrower can keep a share of the collateral ex-post. This imperfect protection of the creditor’sright is represented by the share, pG. A better enforcement of property rights is associated witha higher pG,∀G. The level of property rights protection is limited by an implementation capacityconstraint by the state: pG ∈ [0, P ], with P ≤ 1. If lenders of both groups invest a fixed share oftheir wealth l, the capital market equilibrium is pinned down by the following equality:

σAβApAwA + σBβBpBwB = l[(1− σA)βAwA + (1− σB)βBwB] (8)

The first term is the demand for credit. Clearly, high return individuals invest all of their wealth,demand up to σGβGpGwG in credit, and low return individuals lend all their wealth up to l.Competitive markets imply that price equals to marginal cost, hence rM = rL. Group G averageindividual’s indirect utility is: vG = (1− tG)(rH + (1 + pG)(rH − rL)− rL)wG

Private agents operate in an institutional framework that is chosen by a ruler, and influencedby an administrator. The ruler can tax realized income of both groups separately, tA and tB, andcan choose a desired level of property rights protection, pG ∈ {A,B}. The administrator choosespA(pA) and pB(pB) after loans have been made at the time when creditors attempt to enforce theircontracts. Effective contract enforcements, pG(pG), G ∈ {A,B}, are modeled below.

There are limits to the policies that the ruler and administrators can choose. When facinga tax, individuals can go informal. If individuals go informal, the friction of hiding the incomeimplies a decrease in the return, so that an individual who goes informal earns (1 − T ) of thenormal returns. It is straightforward to show that the state can only tax up to T beyond whichpoint the group whose income tax is higher than T goes informal. Similarly, the ruler can choosethe desired level of creditor property rights protection, pGs , up to the maximum that he can enforce,P . P is thus the upper bound of desired and implementable contract enforcement.

The ruler’s objective function may be biased towards one of the two groups. To simplifyexposition, assume that the ruler maximizes ρA(1− tAs )Y A

s + ρB(1− tBs )Y Bs , where ρG, G ∈ {A,B}

is the weight that the ruler assigns to each population group in his objective function. I assumeρA > ρB. Similarly, the administrator may also be captured by social groups. The administratormaximizes ρaA(1 − tA)Y A + ρaB(1 − tB)Y B −

∑G∈{A,B}

γ2(pG − pG)2 after loans have been made,

where γ captures the corruptibility of the administrator — high values indicate that it is costlyfor the administrator to deviate from policy. Thus, the fact that the administrator optimizes after

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investments are made introduces the standard holdup problem into the administration.

E.2 Timing

Step 1: The ruler chooses tA, tB, pA, pB to maximize:

ρA(1− tA)Y A + ρB(1− tB)βBYB (9)

subject to: tAβAY A + tBβBY B = 0, pG ≤ P ,tG ≤ T , where Y G indicates the output (income) bygroup G, and ρG ∈ {A,B} is the weight that the ruler puts on the welfare of group i (ρA+ρB = 1).Since the individuals with high return projects invest all their wealth and obtain in addition pGwG

in borrowing, and have to repay at interest rate rL, and since households with low return make rLon their borrowed and borrowed wealth, group G’s output is Y G = wG[σG(1 + pGs )(rH − rL) + rL].

It is straightforward to see that since increasing pG relaxes the budget constraint of the ruler(through Y G) and the objective function is increasing in pG, the optimal level of property rightsprotection is the maximum allowed by the institutional constraint pG ≤ P , thus pG = P . Preciselybecause the ruler is residual claimant through taxation, he internalizes the effect of improving eco-nomic activity through protection of creditor’s rights. Furthermore, all of the tax extraction occursin order to redistribute as much as possible to group A. In that case group B is taxed at full ca-

pacity, tB = T and all the tax revenues are used for the group’s A consumption: tB = −TβBY B

βAY A .

Step 2: Private agents choose bG, IG, cG, dG, nG, lG and markets clear. Private agents maxi-mize:

uG = (1− tG)(rIIG − rMbG + rM l

G) + (tG − T )bG + rM(bG − pGcGwG)dG

subject to an incentive compatibility constraint (they can borrow until they have incentives todefault), bG ≤ pGcGwG and a budget constraint IG + lG ≤ wG + bG. From this and the incentivecompatibility constraint, it is straightforward to derive aggregate demand and supply for credit.

Step 3: After loans have been made and investments have yielded returns, individuals canchoose whether default and lose their collateral pGwG, or instead return the amount owed, bGrM .The administrator thus chooses the effective enforceability of contracts pA, pB to maximize: ρaA(1−tAs )Y A

s + ρaB(1− tBs )Y Bs −

∑G∈{A,B}

γ2(pi− pG), anticipating that individuals in the last step (after

investments yield) decide whether to default or not, and their payoffs are of the form:

uGend = (1− tG)(rM lG − rMbG) + rM(bG − pGcGwG)dG

The administrator cannot influence the payment of the debt if individuals do not default, but onlythe share of the collateral to be expropriated if individuals default.

E.3 Equilibrium

From this setup, it is straightforward to characterize equilibrium. High return individuals investall their wealth in their projects, put all their collateral to borrow to pGwG, and do not lend. Lowreturn individuals lend up to share l, constrained. Markets clear, and the market return is rL.

The optimal desired policy by the ruler is straightforward. Since there is no public goods

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motive, and since ρA > ρB, the ruler taxes as much as possible from B in order to redistribute it

to A, from whom he raises a negative tax. The optimal tax rates are: tA = −TβBY B

βAY A and tB = T .Since collateral protection increases output, through the increase in lending, and since the

ruler taxes the output of group B and values the output of group A, the optimal desired collateralprotection is the maximum allowed by its legal capacity, ie: pG = P , ∀i = A,B.

Finally, the administrator, however, does not internalize the effect of protecting property rightson taxation, nor does he internalize the fact that by potentially choosing a lower protection ofcollateral of group A, he is thereby undermining group A’s ability to borrow. The administrator,who chooses in the last step, replicates the standard holdup problem, because his preferencesbetter reflect group A, and is tempted in the last step to protect collateral at a level that benefitsgroup A. His chosen level of collateral protection is:

pA = P − σβArLwAγ

(1− βA)(ρaA − ρaB)

andpB = P

For the share of group A’s collateral that the administrator chooses to enforce, there are twosteps. First, some of the redistribution occurs to creditors of group A, and is thus neutral forthe administrator’s objective function. Second, a share βB of loans in the hands of group Amembers has to be transformed into payments to group B lenders. The administrator would liketo prevent such redistribution from happening, and this force thus tends to decrease the effectiveenforcement, pA. For the protection of property rights over the collateral posted by group B, theopposite forces are at play: the administrator would like to have as high as possible rate of propertyrights protection, since that means a transfer to A. However, the administrator is constrained bylegal capacity, and cannot increase pB beyond P.

The distortion introduced by the administrator creates a wedge ∆W between the welfare thatwould be attained by the ruler if he was able to implement his desired policy, and the case inwhich he faces an administrator:

∆W = −σβA(1− βA)rLw2A

γ(ρaA − ρaB)(1− t∗A)(rH − rL)2

The strategic manipulation of contract enforceability in the last period thus introduces a holdupproblem that reduces welfare. Trying to improve the indirect utility of individuals of group A,the administrator makes it harder for them to attract borrowing, reducing group A’s outputand welfare. Such welfare loss is larger the larger is the return from the foregone economicopportunities (rH−rL), the wealthier is group A, wA (because the foregone opportunities in termsof posted collateral are larger), the higher the share of high return individuals σ, and the higherthe corruptibility of the administrator (inversely related to γ). In the utilitarian social plannerbenchmark, t∗A = 0, and thus |∆W | > |∆UtilitarianW |, since t∗A < 0 in the case of biased ruler. It isstraightforward to see why this is the case: the administrator maximizes the net payoff of groupA, discounted by taxation. Since every dollar that group A can keep is multiplied by a negativetax rate, this increases the distortion generated by the administrator.

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