ETHNIC STATE CAPACITY AND CONTRACT
ENFORCEABILITY∗
RAUL SANCHEZ DE LA SIERRA
State capacity has recently become the workhorse of development scholarship. One reason why state capacity matters is that expanding the state legal capacity may increase trade
where social institutions cannot govern agency relations (Greif, 1993). However, the state
itself may be captured, and thus expanding the state could generate adverse results. Furthermore, the state may crowd-out pre-existing informal mechanisms of contract enforcement.
Estimating the impact of the state is challenging, because state formation is endogenous,
and because in the absence of a functioning state, there is usually no data. I create a delivery business in the Democratic Republic of the Congo, involving traders and customers
who learned to operate without the state, and randomly introduce state contracts. I find
that state contracts strongly reduce shirking. However, the results uncover an ethnic bias in
contract enforcement by the state: only some ethnic groups can draw on the state’s legal capacity, and customers anticipate this bias. Furthermore, ex-ante, I find that state contracts,
when they are enforceable, and coethnicity are substitutes to solve commitment problems
that prevent trade in the presence of agency relations. These results suggest that while
social institutions govern some agency relations, they also govern the state administration,
therefore distorting the impact of state legal capacity. JEL Codes:.
∗
1727 Cambridge street, Harvard University, Room E113, Mailbox 42, Cambridge, MA 02138. Fax: (617) 4969592. Phone: (917) 488-9151 [email protected]. Jean-Paul Zibika provided invaluable research
assistance and Gauthier Marchais provided an outstanding collaboration. I am grateful to Christopher Blattman,
Ritam Chaurey, Pierre-Andre Chiappori, Jonas Hjort, Macartan Humphreys, Supreet Kaur, Christian Mastaki,
Suresh Naidu, Bernard Salanie, and Eric Verhoogen, for invaluable contributions. This project was supported
by the Private Enterprise Development in Low-Income Countries exploratory grants (PEDL), Russell Sage Small
Grants in Behavioral Economics, the Center for the Study of Development Strategies at Columbia University.
1
1
Introduction
State capacity has recently become the workhorse of development scholarship(Besley and Persson,
2013). One way in which state capacity can create development is legal capacity. For instance, in a
stateless economy, social institutions can govern agency relations and solve commitment problems
that prevent trade. While social institutions rely on the social structure to monitor and sanction
defectors, the social structure is often fragmented. Hence, social institutions can be inefficient
(Dixit, 2003, Greif, 1993). In this case, creating or expanding the state monopoly of violence and
its legal capacity can increase trade by introducing a commitment device that social institutions
are unable to produce. However, despite its control over violence, the state is often governed by
the interests of administrators (Gonzalez de Lara, Greif, and Jha, 2008, Greif, 2007). Furthermore,
the state system of contract enforcement can undermine the conditions that sustain trade in the
absence of the state, thus crowding-out trade (Bénabou and Tirole, 2003, Bowles and PolaniaReyes, 2012, Kranton, 1996, Lowes, Nunn, Robinson, and Weigel, 2015). Expanding the state may
thus be counter-productive in areas where the state has not previously expanded.
Estimating the impact of the state is challenging. Before states expand, systematic data do
not usually exist. Furthermore, state formation and trading relations are endogenous (Gambetta,
1993, Tilly, 1990). Due to these challenges, the impact of the state remains an empirical question,
unexplored with statistical methods.
In this paper, I create delivery business involving traders and customers in Eastern Democratic
Republic of the Congo (DRC), where the economy has developed in the absence of a functionning
state since its collapse in the nineties, and where social networks and ethnicity govern economic relations (Fund For Peace, 2013, Nest, Grignon, and Kisangani, 2011). The delivery business allows
me to observe the success of economic transactions that would otherwise not occur, and introduce
state contracts using random assignment. A commitment problem is intrinsic to the relationship
between traders and customers, which allows me to compare the impact of the state and of social
institutions on the incentives of the agent, as well as on the willingness of the principal to trust
the agent and engage in trade.
In the first part of the paper, I examine how the introduction of state contracts, which are
previously not used, impacts the behavior of the agents. Traders visit 1,000 customers and offer a household consumption good at a discount (cell phone credit), with the condition that the
2
customer (the agent) promises to pay by cell phone within two days. In collaboration with a
professional lawyer and the Provincial Government, I develop a state contract that exposes a random sample of customers to legal action if they fail to pay on time. Randomly introducing state
contracts for customers whose payment data I observe allows me to examine the impact of state
contracts on the behavior of the agent. However, customers can choose to accept or reject the
offer depending on whether they are asked to sign a state contract. This creates a selection bias.
To isolate the incentive effect of state contracts, I implement a design à-la Karlan and Zinman
(2009). While traders require all customers to accept to sign a state contract before they accept
the deal, a random sample of customers who accept the deal gets away without signing a state
contract. Average payment rates are 24% for transactions not involving state contracts, but this
rate is 50% higher for customers who signed the contract.
However, I also find that only the ethnic groups that have captured the administration are
able to enforce contracts. To examine how capture of the state administration by different ethnic
groups impacts contract enforcement, I use random assignment to different ethnic compositions of
the trader customer match. Some traders and customers belong to ethnic groups which have captured the state administration (Bantu ethnic groups), while the other are excluded from the state
administration (Tutsis). I find that, on average, state contracts increase payment rates by 50%
when used by Bantu traders but have zero effect on payment rates when used by Tutsi traders.
Additional behavioral and survey evidence confirm that customers use the trader’s and customer’s
ethnicity to predict their power to enforcing the state contract, consistent with administrative
capture by Bantus. This behavior provides evidence that administrative power, in particular the
capture of the administration by ethnic groups, governs the enforceability of contracts.
While the results suggest that Bantus are able to enforce state contracts, state contracts may
be too weak to solve commitment problems ex-ante, and may crowd-out informal mechanisms of
contract enforcement. Thus, in the second part of the paper, I examine the effect of state contracts
on trade among different Bantu ethnic groups when agency relations are inherent. To observe the
behavior of the agent, I change the features of the economic activity by implementing a business
extension. I recruit new traders from the population of customers in the first part of the paper,
and deploy them to sell a household consumption good collecting payments before delivery (the
agents). In order to avoid suspicions arising from comparison between the two business activities,
I implement this activity in different areas. This leads me to offer a different consumption good
3
that matches customers’ demands (soaps), and to focus on traders of Bantu ethnic groups for
security reasons. For a randomly selected subset of customers (the principals), the traders offer a
state contract, exposing themselves to legal action if they fail to deliver the good. This allows me
to examine whether principals are more likely to engage in risky trade when state contracts are
made available. If traders are of a different ethnic group than customers and do not offer state
contracts, 40% of customers are willing to accept the trade. In the absence of state contracts,
coethnicity increases the customers’ acceptance rate by 40%. If trader and customer belong to
different ethnic groups, state contracts increase the customers’ acceptance by 40%. This behavior
provides evidence that state contracts and coethnicity are substitutes at generating trade, among
groups that can enforce contracts.
While state contracts and coethnicity increase trade, their effect may reflect that customers
have a preference for the payoff of coethnics or for traders who show contracts. I thus go a step
further and isolate the effect of state contracts and coethnicity on trade through expected behavior
from their effect through preferences. I randomly select customers to two types of sales. In the first
group, the payment is made upon delivery (sales on the spot), while in the second, the payment is
still required in anticipation of delivery within one day (sales on debit). A commitment problem
is only present in the second group of sales. State contracts and coethnicity increase trade when a
commitment problem is present, but have no effect when there is no commitment problem. This
suggests that state contracts and ethnic based social institutions are substitutes at generating
trade because they are substitutes for solving commitment problems inherent in agency relations.
Eastern DRC is a well-suited experimental ground to study the impact of state penetration
into social relations. The Democratic Republic of Congo state is considered a “failed state”, since
its collapse in the nineties.1 To cope with a predatory state and pervasive armed groups, the
economy organized around informal ethnic networks outside the reach of the state’s legal system
(Mathys, 2014). Due to the prevalence of holdup problems that arose in the absence of a functioning state, the economy developed around small-scale transactions involving minimal investments
and minimal risk taking (Geenen, 2013, Nest, Grignon, and Kisangani, 2011). However, since its
collapse in the nineties, the Congolese state has recovered state capacity in the East, including a
full-fledged coercive and judiciary system. Thus, expanding the reach of the state as a third-party
contract enforcer can unleash market forces that are currently unexploited .
1
Source: Fund For Peace (2013).
4
While a large number of studies documents how individuals and groups solve commitment
problems in transactions taking place outside of a legal framework (Alesina, Baqir, and Easterly,
1999, Greif, 1993, Habyarimana, Humphreys, Posner, and Weinstein, 2007, Hjort, 2013, Miguel
and Gugerty, 2005), this paper is the first to explore the impact of access to the state system of
contract enforcement in a real context aiming to establish causal identification.
This paper complements the literature on contract enforceability by proposing a strategy to
address existing empirical challenges. First, since the choice to use contracts is endogenous, estimating the effect of state contracts in observational studies is affected by endogeneity problems
(Fafchamps, 2000, 2006). I address this challenge by using random assignment. Second, estimating the effect of the social structure is challenging, because social interaction is endogenous
to unobservable characteristics that may predict both the formation of links and the success of
trade (Chandrasekhar, Kinnan, and Larreguy, 2015). To address this challenge, I focus real ethnic
groups, whose divisions have deep historical roots. Drawing on these pre-existing groups, I randomly match traders and customers who have not previously met, but who belong to the different
ethnic groups. This allows me to estimate the impact of ethnic based social institutions on trade,
whether they stem from networks inherited from the past, social norms, or culture, while avoiding
endogeneity issues arising in network formation and matching of partners. Third, since introducing the requirement to sign contracts can create losses to traders or businesses, researchers usually
draw on laboratory environments to establish causal identification on coethnicity or the impact
of commitment devices (Chandrasekhar, Kinnan, and Larreguy, 2015, Habyarimana, Humphreys,
Posner, and Weinstein, 2007). I complement the existing work by organizing a business with an
inherent agency relation. This allows me to introduce experimentally variations to the features
of the business and to the identity of the traders, and observe the resulting effects in real-world
transactions.
Furthermore, by measuring the impact of administrative capture on contract enforceability,
this paper takes the literature on contract enforceability a step further. In seminal work, a few
scholars have argued that the power of the administrators is a determinant of the behavior of
rulers and states (Gonzalez de Lara, Greif, and Jha, 2008, Greif, 2007). However, administrators
are also required to enforce contracts and thus maintain order necessary for trade. To date, the
administrative foundations of contract enforceability are under-explored. Exploiting the fact that
certain ethnic groups have captured the administration, while other have not, I show that even
5
contract enforceability for politically irrelevant transactions are determined by the power and (ethnic) interests of the administration. Although few scholars (Shayo and Zussman, 2011) measure
biases in the judicial system, very little research examines the impact of administrative capture
on economic organization and trade.
This paper’s contribution is also to provide empirical evidence to a theoretically ambiguous
relationship. Existing literature proposes a number of ways in which contracts and social organization can affect the patterns of trade, and their effect is potentially ambiguous. First, trading
partners who share a social structure may be able to solve commitment problems by exploiting
features of repeated interaction (Greif, 1993, Morjaria and Macchiavello, 2014, Tirole, 1996).2 If repeated interaction within groups can sustain cooperation (Alesina and Ferrara, 2004, Dixit, 2003,
Miguel and Gugerty, 2005), third-party contract enforcement can introduce outside options to
trading relationships, thus potentially undermine the conditions that sustain trade within groups
(Kranton, 1996). Since formal contracts potentially solve commitment problems, the overall effect
of contracts is an empirical question. Second, co-ethnics may be able to solve contracting problems
because of parochial altruism (Bernhard, Fischbacher, and Fehr, 2006, Bowles, 2006, Charness and
Rabin, 2002). Chen and Li (2009) draw on social psychology (Tajfel and Turner, 1979) and find
evidence in a laboratory setting that group identities are associated with strong in-group altruism. If social relations are governed by reciprocal social preferences, state penetration can impact
social preferences in unknown ways (Lowes, Nunn, Robinson, and Weigel, 2015). Third, groups
may be able to solve contracting problems by appealing to group norms of behavior, which can be
internalized (Fehr and Gaechter, 1999) or by self-enforcing equilibria (Greif, 1993, Habyarimana,
Humphreys, Posner, and Weinstein, 2007). If the strategic environment in which they operate has
asymmetric information, the introduction of contracts can potentially crowd-out social equilibria
by changing information sets (Bénabou and Tirole, 2003, Bowles and Polania-Reyes, 2012, Gneezy
and Rustichini, 2000). In this framework, the impact of contracts is also theoretically ambiguous.
This paper contributes to this literature by providing an empirical estimate of the effect of contracts on incentives to cheat and to trade, and isolating the mechanisms through which they work.
The remainder of the paper is organized as follows. Section 2 presents the context. Section 3
presents the design, econometric strategy, and results of the main field experiment, designed to
2
Greif (1993) describes how the threat of collective punishment and concern with reputation sustained trust
and trade among Maghrebi traders in eleventh - century Mediterranean trade. McMillan and Woodruff (1999)
document the extent to which former relationships predict current supply of risky trade credit among firms in
Vietnam.
6
estimate the effect of state contracts on incentives to cheat. Section 4 presents the design, econometric strategy, and results of the extension, designed to estimate the effect of state contracts on
willingness to engage in trade when commitment problems are present. Section 5 concludes.
2
Context
The Eastern Province of Sud Kivu in the Democratic Republic of the Congo is composed of Bashis,
Bahavus, Balegas, Batembos, Bafuliros, Pygmies, Tutsis, and to a lesser extent, Hutus.
The history of Tutsis offers one of the major ethnic divides in Eastern Congo. Tutsis of Eastern
Congo are historically cattle herders who migrated from Rwanda and live mostly in the highlands of
the Kivus.3 They self-identify as belonging to Banyarwandas, Banyabwishas, or Banyamulengues
depending on their location and the migration wave from which they arise.4 Tutsi populations in
Eastern Congo are marginalized, often prosecuted. Except for certain battalions of the Congolese
Army, Tutsis are largely excluded from the state administration and the related networks of
patronage. For instance, the state administration of the Province of Sud Kivu is disproportionately
composed of non-Tutsi civil servants. As a result of their vulnerability, the Tutsis of Sud-Kivu
are quasi-nomadic and alternate between urban Tutsi-dominated neighborhoods, the highlands of
Uvira and Fizi, and Rwanda, depending on the security situation.5
The other ethnic groups belong to the Bantu family and self-identify as distinct from the
Tutsis. The languages of Bashis, Bahavus, and Batembos belong to the Shi-Havu language family
and their lexical similarity is 70%.6 Bashis and Bahavus have been politically active in similar
groups and their division is not salient. Batembos, however, are a minority in Eastern Congo, and
have been a major force in local rebellions known as Mayi-Mayi groups. Batembos live mostly in
remote areas of Sud-Kivu, such as Shabunda and western Kalehe (Kalonge, Ziralo and Buloho),
3
Tutsis first recorded migration wave took place well before the colonial period, in the seventeenth century,
fleeing taxes in Rwanda. A second migration took place in 1880 (today these are called Banyamulengue - the
people of the Mulengue mountain). The Belgian colonial administration orchestrated subsequent migrations in the
1930’s and 1940’s, sending workers to coffee plantations in DRC. In the second half of the twentieth century, large
numbers of Tutsis fled conflicts in Rwanda to the Congo, culminating in the migration of two million refugees in
the 1994 Rwanda crisis - mostly Hutus. This demographic and land pressure exacerbated the ethnic tensions with
Tutsis.
4
Such groups sometimes gather a mix of Rwandophone populations: Tutsi, Hutu, and Twa groups. While
Banyarwandas are mostly Hutu, Banyamulengues are mostly Tutsi.
5
See Stearns (2011), Ngonzola-Ntalaja (2002), and Newbury (1992) for accounts of current ethnic relations in
Sud-Kivu and their historical foundations.
6
https://www.ethnologue.com/language/shr
7
and have a long history of struggle against domination from other groups, in particular from
Bahavus since the 1940’s (Mathys, 2014, Newbury, 1992). Conflicts over land and power involving
the Batembos are still relevant today. Furthermore, Batembos have been particularly affected by
recent waves of armed conflict, in particular by the FDLR and CNDP. Nevertheless, in contrast
to the Tutsi groups, Batembos’ citizenship and access to the state was never contested.7 Balegas,
mostly present in Mwenga and Shabunda, have good relationships with the Bashis and Bahavus.8
Finally, Bafuliros, mostly located in Uvira, consider themselves close related to the Bashis and
Bahavus.
In the first part of the paper, I examine contract enforceability as a function of access to the
state administration, and thus focus on the Tutsi vs. non-Tutsi as the relevant divide. Half the
traders are Bantus and the other half are Tutsis, while customers include Bantus and Tutsis.
I randomly assign traders to customers within lottery bins defined by the lowest administrative
division (urban avenue) and randomize traders to each administrative divisions. This allows me to
examine the impact of contracts on incentive to cheat, and isolate the incentive effect for traders
who have not captured the state administration, the Tutsis, from the rest of traders.
In the second part of the paper, I focus on traders and customers among groups for whom
enforceability of state contracts is credible, Bantus.9 To compare the effect of salient ethnic social
institutions to the effect of state contracts on agency relations, I examine the interactions between
Batembos and the rest of Bantus, as well as the interactions within Bantus, since Batembos are the
salient ethnic divide with deep historical roots. While Batembos have historical grievances mostly
with Bahavus, Bahavus and other Bantu groups (especially Bashis) are almost indistinguishable
to the eye of the Batembo customer. The analysis in the second part thus focuses on interactions
between Batembos and Bashi/Bahavus, although I am able to isolate a Bahavu effect among
Batembos in particular. Due to the obviously fluid meaning of ethnicity across contexts, this
paper does aim at providing an externally valid estimate of the effect of ethnicity. Instead, given
a particular social structure in which there are salient historical divisions, I examine the impact
of state contracts within and across the groups defined by the contextually relevant social divides.
7
See Mathys (2014):“Whilst in the case of the [Ba]tembo cited above this led to the emergence of local conflicts
and local contestations of belonging, this did not lead to a contestation of the ’ethnic’ (and thus ’civic’) citizenship
of these populations on the national scene.”
8
Balegas, Bashis and Bahavus regularly refer to each other as “brothers”.
9
For security reasons, it was not possible to include Tutsi in the second part of the paper.
8
3
The aministrative foundations of contract enforceability
In this section, I establish that state contracts decrease the incentives to cheat in trading relations
involving agency problems, but only for individuals whose trading partner belongs to an ethnic
group that has captured the state administration. I first present the field experiment design,
then the empirical strategy, and the results. I find that signing a state-backed contract reduces
the agents temptation to renege upon its promise, but this effect depends on ex-post bargaining
power: groups without links to the administration are unable to reduce shirking by requiring a
state contract.
3.1
The home delivery sector: customers as agents
To examine behavior in the presence of agency relations in a real context, I organized a real delivery business, in which traders and customers are residual claimants, and I observe the behavior
of traders and customers. I next describe the delivery business.
Traders sell a basic consumption good, door to door, to customers in 1,000 randomly selected
customers of ethnically diverse semi-urban neighborhoods of Bukavu. Traders deliver cell phone
credit in cards on the spot, at a discounted price below the market price. In exchange, the customers’ who accept the deal commit to pay by cellphone within two days through the organization’s
central payment system.10 While traders receive a minimal fixed compensation by the research
project which is independent on sales, traders are residual claimants on all sales and derive the
largest part of their daily income through the sale of the cell phone credit. Customers receive no
payment, except the consumer surplus inherent in the price discount of the offer.
Absent enforceable state contracts or social sanctions, it is in the customer’s best interest to
accept the purchase and renege on payment. To randomly selected customers, traders require
the customers to sign a state contract if they want to proceed with the transaction, in which
they expose themselves to legal sanctions if they were to renege payment. The state contract was
drafted by the a local lawyer, and stamped by the Ministry of the Interior. This design allows me
to examine the impact of state contracts on payment rates.11
10
I chose cell phone credit cards after implementing a market study. In the areas in which I implemented the
study, there was compelling evidence that cell phone credit was in excess demand at a reasonable price.
11
The contract reads as follows:
“ I, the undersigned... , recognize to have received ... cell phone units of the company ... from ... , for a value
of 500 Congolese Francs per unit. I hereby commit to pay ... in exchange of these cell phone units to ... in the
9
The design tackles a selection problem. Some customers anticipate they may not be able to
pay, and the requirement to sign a contract can lead them to refuse the offer. This can create
a selection bias: the customers who have accepted the offer with a requirement to sign the contract are likely different than those who accepted when the requirement to sign a contract was
absent. Comparing payment rates across the two groups would capture both selection and incentive effects. To isolate the effect of contracts on incentives to pay from their effect on selection
of customers into trade, I implement a two-step randomization, analogous to Karlan and Zinman
(2009): traders offer the deal to all customers with the requirement to sign a contract before
obtaining their approval. Among the customers who have accepted the deal, traders withdraw
the contract requirement in a random sample of customers. I thus compare the payment rates of
customers who signed a state contract and those who did not among customers who accepted the
deal. The timing of the transaction is as follows:
Step 1. As part of the sales protocol, and prior to the customers’ decision to accept the purchase, traders introduce the sale and explain the timing of the transaction. Furthermore, ex-ante,
they show and explain the state contract, and announce that the state contract is required to
proceed with the purchase. The state contract stipulates that the customer exposes himself to
legal sanctions if he fails to pay within the agreed timeline (two days). Let Bt=1 ∈ {0; 1} denote
the customer’s decision. Customers buy, Bt=1 = 1 or reject the offer, Bt=1 = 0, and the trader
records the response of the customer before proceeding to the next step.
Step 2a. Among customers who have accepted the offer, traders withdraw the requirement to
sign a contract to a random sample of customers.12 Customers that accept the offer are thus randomly assigned to two groups: those who sign the contract, F = 1, and those who do not sign the
contract, F = 0. Traders also record the final decision of the customer Bt=2 . Since withdrawing
the contract requirement is a positive transfer to the customer, Bt=2 = 0 and Bt=2 = 1 is never
observed.
Step 2b. Among customers who have rejected the offer, traders withdraw the requirement to
interval of TWO days at most. I am ready to bring this contract, if necessary, to a legal representative. I recognize
that in case of no payment, I am exposed to the prosecutions and sanctions that the Congolese law considers for
these cases. Done in... . Date ... . Signature of debtor... Signature of creditor... Signature of witness... .”
12
The script reads as follows: “I see I do not have enough contracts. It is therefore not necessary to sign
this contract and my protocols stipulates that in such cases we shall proceed with the transaction. [payment
instructions]”. Forgiving the requirement to sign a contract could induce reciprocity, and bias behavior. To avoid
this, the script specified that traders did not have sufficient contracts to pursue with the signature of the contract.
Since the traders had already committed to sell the good, the design does not generate compliance problems withdrawing the contract requirement is a net positive transfer to the customer.
10
sign a contract to a random sample of customers. Traders then allow the customers who refused
the offer initially, Bt=1 = 0, and for whom the contract requirement was withdrawn to reconsider
their decision. Traders also record the final decision of the customer Bt=2 .
Step 3. Immediately after, traders supply the cell phone credit cards to the customers who
have accepted the purchase, and provide instructions on how to implement payment by cell phone.
Traders then implement an exit survey to all customers, regardless of whether they have made the
purchase. In addition, to capture social preferences, all customers are also offered phone credit
card on the spot: in exchange for cash, the trader supplies a fixed amount of phone cards on
the spot. The remaining amount needs to be purchased on credit. Figure 1 provides a graphical
representation of the experiment.13
3.2
Theoretical framework
Let Ei,j ∈ Rn×n characterize the ethnic match between trader i and customer j, both of whom
are drawn from an n-dimensional vector of ethnicities. The variables F ∈ {0; 1}, D ∈ {0; 1}
respectively indicate whether the transaction is formalized by a contract, F = 1, and whether
the sale is on debit, D = 1. Let v(Ei,j , F ) be the private valuation of acquiring the good, and
℘ the price. The term l(Ei,j ) ∈ R denotes the expected cost of cheating arising from the legal
system. It is a function of the ethnic characteristics of the trader-customer match, since the
ability to enforce legal sanctions may depend on the ethnicity of the trader and customer. Let
P = {0; 1} indicate whether the customer implements the payment, and the term θ(Ei,j , F ) indicate the informal sanctions incurred by the buyer upon reneging payment, P = 0, if he had
finally agreed to make the payment, Bt=2 = 1. I allow θ(Ei,j , F ) to depend on Ei,j , through some
ethnic sanction technology, and F , if complementarities exist between formal and ethnic contracts. The cost θ(Ei,j , F ) could indicate utility losses stemming from internalized social norms,
such as utility losses arising from guilt, social preferences or reference points. It could also be
the product of any social sanctioning technology specific to the ethnic match. Let Bt=2 denote
whether the customer finally buys the good and 0 otherwise (henceforth B). The buyer’s utility
is: uBP = B [v(Ei,j , F ) − ℘P − (1 − P ) (θ(Ei,j , F ) + F l(Ei,j ))].
Table I maps the parameter space onto the strategy set. There are four possible strategies:
(B = 1, P = 1), (B = 1, P = 0), (B = 0, P = 0), (B = 0, P = 1). I ignore the last strategy,
13
Figure A.2 in the online appendix shows the coupon.
11
because it is never observed and because it is strictly dominated. Figure 2 provides a graphical
representation.
The terms αi ∈ {1, 2, 3, 4} denote the mass of agents in cell, defined by observable strategies.
I consider the following partition of the parameter space: cells uniquely identify the strategies
chosen by customers as a function of whether state contracts are used, under the assumption that
state contracts are enforceable. For instance, while α3 and α4 display the same behavior in the
absence of state contracts, α3 values the product enough that he is willing to accept a purchase
that requires him to sign an enforceable state contract. In contrast, α4 would not purchase the
good if he was requested to sign an enforceable state contract that would force him to pay expost.14
Assume that the state contract is sufficient to motivate payment: ℘ < l(Ei,j ). In that case, α2
and α4 reject the offer. Thus, if the traders randomly lift the requirement to sign the contract, α3
avoid making a payment. Assuming that formal contracts do not affect the private valuation of
the good or informal sanctions, I can now describe the main testable implications. The functions
v(Ei,j ) and θ(Ei,j ) then determine the mass of customers in each cell: α1 (Ei,j ), α2 (Ei,j ), α3 (Ei,j ),
α4 (Ei,j ). Table II presents the selection and incentive effects of state contracts, holding constant
the ethnicity of the match. I next discuss four empirical implications from this setup.
First, contracts have incentive effects (are enforceable) if and only if α3 (Ei,j ) > 0. Indeed
α3 (Ei,j ) is the mass of customers who prefer to forgo payment but pay if they were requested to
sign a contract.
Second, Ei,j has a total effect on the incentive effect of state contracts, through v(Ei,j ), θ(Ei,j ),
∆α3
∆α3 ∆v
∆α3 ∆θ
∆α3 ∆l
and l(Ei,j ): ∆E
=
+
+
∆v
∂E
∆θ
∆E
∆l ∆Ej . Since Tutsi traders are excluded from the
i,j
j
j
state administration but Bantus are not, ex-post, their relative power to enforce a state contract
is weaker than for Bantus, for a given a customer. Thus l(Ei,j )|Ej =T utsi < l(Ei,j )|Ej =Bantu and
∆α3 ∆l
∆α3 ∆θ
3 ∆v
α3 (Ei,j )|Ej =T utsi < α3 (Ei,j )|Ej =Bantu if and only if: ∆α
∆v ∆Ej < ∆l ∆Ej + ∆θ ∆Ej .
Third, if state contracts increase the trader’s ex-post enforcement power, by backward induc-
tion, state contracts should affect the decision of customers to accept the deal, thus potentially
generating selection. Enforceable state contracts are thus a screening device to attract customers
14
If the informal cost of reneging payment, θ, is sufficiently high relative to v, I label the customers as “honest.”
If the customer’s valuation of the good, v, is higher than the monetary cost, ℘, the customers are “peaches”
(lemons otherwise). The terminology captures the notion that trade is socially optimal only when the customer
has a sufficiently high private valuation, which is unobserved. It does not capture whether customers are of high
quality, in contrast to the standard adverse selection literature (Akerlof, 1970).
12
with a higher likelihood to pay. To see this, note that pool of customers who accept the offer
when traders do not require a signature on a state contract, α1 (Ei,j ) + α3 (Ei,j ) + α4 (Ei,j ) contains
a larger fraction of customers who will not pay than the pool who accepts the offer when traders
require state contracts, α1 (Ei,j ) + α3 (Ei,j ). Furthermore, if state contracts induce a selection of
customers, then conditioning on the sample of customers in which the trader randomly lifts the
contract requirement, payment rates must be higher among those who initially accepted the offer
when the contract was requested α1 (Ei,j ) + α3 (Ei,j ), than among those who first reject, but accept
it only when the trader (randomly) lifted the requirement to sign the contract, α4 (Ei,j ).
Fourth, if state contracts are less enforceable by Tutsi traders, then state contracts are a
weaker screening device when they are used by Tutsi traders. Indeed, customers enter into the
sale depending on θ(Ei,j ), v(Ei,j ), and l(Ei,j ). However, identifying the heterogeneous screening
effect across ethnic groups is challenging. Since formal contracts can affect θ or v, the ethnic
composition of the match has an ambiguous effect on the strengh of the screening device. The
sub-sample of sales on the spot, in which payment is immediate, allows me at least to disentangle
pure social preference channels,
∆v(Ei,j ,F )
.
∆Ej
In sales on the spot, the customer’s decision to buy
depends only on v(Ei,j ) and ℘. Therefore, if customers have a preference bias against trading with
Tutsi traders, v(Ei,j )|Ej =T utsi < v(Ei,j )|Ej =Bantu , the mass of customers in sales on the spot who
accept the sale when the trader is Tutsi must be lower than the mass of those who accept when the
trader is Bantu.15 If customers do not have a preference bias against trading with Tutsi traders,
but if Tutsis are less able to activate legal sanctions, then l|Ej =T utsi < l|Ej =Bantu (and potentially
θ|Ej =T utsi < θ|Ej =nonT utsi ). If Tutsis and Bantus are able to activate legal sanctions equally, then if
θ is lower for Tutsi traders, contracts will have a stronger screening effect for Tutsi traders – since
in the absence of contracts, there are weaker informal sanctions. If Tutsis and Bantus are able to
activate informal sanctions equally, then if l is lower for Tutsi traders, contracts will have a weaker
screening effects for Tutsi traders – contracts lead to a smaller increase in pool quality, since they
are less likely to be enforced and customers anticipate that. Therefore, in the absence of customers’
preference bias against trading with Tutsis, and in the absence of heterogeneous informal sanction
technologies within Bantus and Tutsis, l is smaller for Tutsi traders if and only if contracts have
a smaller effect on payment when requested by Tutsi traders among Bantu customers.
15
This is true even with a homogeneous distribution of preferences within ethnic group. Otherwise, this result
will depend on the mass of customers whose preference parameter is above the purchasing threshold ℘.
13
3.3
Econometric strategy
Let Bi,j ∈ {0, 1} indicate whether customer i accepts the sale offer from trader j. In sales on
credit, customers make the decision in two steps. Let the dummy B(t = 1)i,j indicate whether
the customer accepts the sale initially (as thet trader announces the state contract is requested),
and the B(t = 2)i,j indicate whether the customer accepts the sale in step 2 (after the trader
implemented the customer level randomization). Let Pi,j ∈ {0, 1} indicate whether the customer
pays. Outcome Pi,j is only observed if the customer accepted the purchase. Let Tj ∈ {0, 1} denote
whether the trader is Tutsi, Fi ∈ {0, 1} whether the trader requires a formal state contract to
customer i, and Ci ∈ {0, 1} whether the sale is credit (payment expected in the future). Each
trader team is composed of a Tutsi and a non-Tutsi trader, who work separately but in the same
administrative unit. I randomly assign teams of two traders to urban avenues. Within each
avenue, I randomly assign traders to customers. Finally, I randomly assign the instruction to lift
the contract requirement at the customer level, using randomization blocks defined by avenue ×
trader. I can thus include avenue ηa and team φe fixed effects to increase precision of the coefficient
estimate, for avenues a = 1, ..., A and teams e = 1, ..., E.
To capture the incentive effects of contracts, I run the following linear probability model:
Pi,j = c0 + c1 Fi + c2 Tj + c3 Fi Tj + ηa + φe + ei,j
(1)
where I condition the sample on Ci,j = 1 and B(t = 1)i,j = 1.16 The parameter c1 captures the
incentive effect of contracts for Bantu traders, while c1 +c3 captures the incentive effect of contracts
among Tutsi traders. Contracts are less effective on the agent’s behavior for Tutsi traders than
for Bantu traders if and only if c3 < 0. To measure the customers’ preference bias against Tutsi,
I run the following linear probability model in the sub-sample of sales on the spot:
Bi,j = b0 + b1 Tj + ηa + φe + ei,j
(2)
where I condition the sample on Ci,j = 0. The parameter b1 captures the mass of customers
who would prefer to purchase if the trader was Tutsi, but not otherwise. There is a preference
bias against Tutsi if and only if b1 < 0. The dummy B(t = 1)i,j ∈ {0, 1} indicates whether the
16
Empirically, whenever B(t = 1)i,j = 1, B(t = 2)i,j = 1.
14
customer was initially screened into the pool of buyers willing to purchase the good, despite the
initial request to sign a contract. To capture the screening effects of contracts on customer quality,
I thus run the following linear probability model in the subsample of sales on credit:
Pi,j = d0 + d1 B(t = 1)i,j + d2 Tj + d3 Tj B(t = 1)i,j + ηa + φe + ei,j
(3)
where I condition the sample on Ci,j = 1 and Fi = 0.17 Since Pi,j is only observed when B(t =
2)i,j = 1, the parameter d1 captures the selection (screening) effect of contracts for Bantu traders
– the difference in payment rates between customers who accepted head on and customers who
accepted only when the state contract was lifted – while d1 + d3 captures the selection effect for
Tutsi traders. Table III presents the testable implications.
3.4
Results: contracts are enforceable, but only by some ethnic groups
I now present the experimental results of the effect of state contracts on incentives for the agent.
I first show the main result that signing state contracts increase incentives to pay when the trader
is Bantu. I then then show that the effect of the contract on incentives to pay depends on the
ethnicity of the trader, consistent with the prior that Tutsi traders have weaker ex-post enforcement
power of state contracts: the impact on customers’ payment rates of signing state contracts on
incentives is smaller if the trader is Tutsi. Additional experimental and survey evidence supports
this interpretation.
3.4.1
Average effect of state contracts on incentives of the agent
On average, 72% of customers are willing to accept the purchase. To examine whether contracts
decrease incentives to cheat, I first condition the analysis the pool of customers who accept the
purchase. Table IV presents the result from econometric specification 1.18 Columns (1)-(4) restrict the sample to transactions in which the retailer is Tutsi. Column (1) presents the baseline
specification, without fixed effects. Column (2) includes team fixed effects, column (3) includes
in addition avenue fixed effects, column (4) includes both fixed effects, as well as household-level
17
For all three specifications, a conditional logit produces analogous results.
The sample size is smaller than 1,000. This corresponds to the conditioning on the 72% of customers accept
the transaction.
18
15
controls. As household controls I use the size of the household, as well as a dummy variable
indicating whether the customer purchased the phone credit when offered on the spot.19 Both
variables are aimed to control for unobservable characteristics correlated with purchasing power
in order to increase precision.20 The coefficient on Contract in columns (1) to (4) shows that for
Bantu traders, the requirement to sign a state contract increases the probability that the customer
implements the payment by 38%, and the coefficient statistically significant.
3.4.2
Heterogeneous effects by ethnic composition of the trader and customer match
Table IV also presents the effect of state contracts on payment when retailers are Tutsi, and the
difference in marginal effects of state contracts with transactions in which retailers are not Tutsi.
Columns (1) to (8) show that state contracts have no effect on payment rates when retailers are
Tutsi.21 The controls included in columns (5) to (8) are analogous to columns (1) to (4). Columns
(1) to (4) show that the effect of contracts is different for Tutsi and non Tutsi retailers, and that
the difference is statistically significant. The coefficient on T utsi is zero, suggesting that among
transactions where no contract is requested, Tutsi traders are equally able to extract payment
from the customers. The coefficient on Contract X T utsi is negative and statistically significant,
and suggests that the effect of state contracts on payment is 58% weaker for Tutsi retailers.
These results confirm that expanding access to state contracts can be used to discipline incentives of individuals who face a commitment problem, even in a weak state. However, signing
a state contracts does not increase payment rates to Tutsi traders. This is consistent with the
prior that the the enforceability of contracts depends on ex-post enforcement power linked to state
capture by members of the traders’ ethnic group.
Despite the results are compelling, there are still competing explanations. Tutsi retailers may
obtain customers of a different type, and these customers may just be less responsive to state
contracts. I next provide additional evidence suggesting that Tutsis have weaker ex-post formal
19
The control for purchase on the spot is not endogenous to the treatments, since it precedes the sale on credit.
The logic behind the inclusion of this control is that it is akin to a sufficient statistic for purchasing power: it
captures all variables supposed to affect the willingness to purchase, such as wealth, which are unaffected by the
treatment.
20
I am unable to match additional survey measures to the payment and purchase data.
21
Since the population of Tutsi is very small compared to the rest of ethnic groups, the population average
treatment effect should be computed using weights inversely proportional to the sampling probabilities. Since half
of the selected retailers are Tutsi, Tutsis are thus oversampled. However, the sample average treatment effect allows
me to separately estimate the marginal effect of contracts among non-Tutsi traders, among Tutsi traders, and their
difference in a transparent way.
16
contract enforcement power and that the results cannot be explained by differential selection of
customers of different types among traders of different ethnicities, that would then respond differently to state contracts.
In what follows, I show that the empirical patterns are only consistent
with the interpretation that Tutsis have weaker ex-post enforcement power of state contracts,
L(Ei,j )|Ej =T utsi < L(Ei,j )|Ej =nonT utsi .
3.4.3
Mechanism: evidence from self-reported beliefs about contract enforceability
I first describe the customers’ self-reported beliefs about the trader’s ability to enforce the state
contract. Upon completing each transaction, traders asked customers what they thought the consequences would be if the customers would renege making the payment, and traders recorded the
answers from customers. To avoid priming the customer’s answers on the trader’s ethnicity, the
question does not mention ethnicity and focuses instead on the customer’s beliefs about the corresponding trader. To avoid priming the customer’s responses to possible outcomes, the question is
open ended, and the trader records the answer by selecting in a list of possible categories available
on his tablet. I generate a vector of dummies, each indicating whether the customer self-reported
that a given outcome was the likely consequence of non-payment in the open ended question.
Table V uses a linear probability model to regress dummies indicating possible consequences,
on dummies indicating the customer and the trader’s ethnicities.22 Columns (1) to (4) report the
results on dummies indicating the answer of the customer to the following question “According
to you, what consequences will there be if you don’t pay?”. Across columns (1) to (4), I include
all customers, since I am unable to observe whether the customer was required to sign a contract
for the exit survey, and thus the likelihood of legal sanctions is an underestimate of the likelihood
when contracts are signed. Columns (1) to (4) use as dependent variables the following dummy
variables: whether the customer answered that legal sanctions would be activated, whether that
the customer answered that he would experience shame, whether the customer answered that he
would lose friends, whether the customer answered that he would suffer physical violence. Column (5) uses as dependent variable a dummy indicating whether the customer reports that legal
sanctions will be activated if he does not pay, among customers having signed the formal contract.
Traders asked the following question to customers who signed a contract: “Do you think that the
22
The ethnicity of the customer could not be matched to payment data, and hence I cannot use it in the main
results.
17
state contract you just signed can have judicial consequences if you don’t pay?.”23
Customers expect that traders will be able to enforce state contracts, but they expect Tutsi
traders to have weaker power to do so. Column (1) shows that when asked about the consequences
of reneging payment, 23% of customers report without priming that there would be legal sanctions
if they would not pay.24 The result suggests that Bantu customers are 53% less likely to expect
legal consequences if they would sign a contract when asked by a Tutsi trader, and the difference
is statistically significant. Furthermore, consistent with the prior that Bantu traders have higher
ex-post bargaining power, Bantu traders are expected to be 45% more likely to enforce state contracts among Tutsi customers than among non-Tutsi customers, and the difference is statistically
significant at the 10% level. The effect of customers’ ethnicity drops to zero when the trader is
Tutsi (Tutsi Customer + Tutsi Trader X Tutsi Customer). Columns (2) to (3) suggest that Tutsi
traders are less likely to activate shame or loss of friends among Bantu customers who would
renege payment. Furthermore, consistent with the prior that Tutsis are less able to activate the
legal system and are thus more vulnerable, Tutsi customers fear physical violence if they renege
payments, but only if they renege payment on a Bantu trader. This suggests that Bantu traders
are expected to be able to organize violence with impunity against Tutsi customers to sanction
Tutsi defectors, consistent with the view that Bantu traders have captured the administration.
Finally, column (5) indicates that 70% of Bantu customers who sign a contract believe that there
will likely be legal sanctions if they renege payment. This proportion drops by 14% when the
customer signed a contract for a Tutsi trader, and the decrease is statistically significant. Consistent with the interpretation that traders differ in ex-post contract enforcement power of state
contracts, the coefficient on T utsi Customer suggests that Tutsi customers who purchased from
a Tutsi retailer are 10% more likely to believe that contracts will lead to legal sanctions than non
Tutsi customers who purchased from a non-Tutsi retailer, although the difference is not statistically significant. Consistent with the view that Tutsi traders have weaker power to enforce state
contracts, this difference drops to zero when the trader is Tutsi, as evidenced by the coefficient on
T utsi T rader X T utsi Customer. Figure 3 provides a graphical representation of these results.
Overall, beliefs about ex-post enforcement power by Tutsi traders perfectly predicts the patterns
23
I replicated these regressions with controls for customer’s gender, age, and education and the results are
identical.
24
The low proportion reflects that column (1) includes customers who did not accept the purchase, but accepted
the survey, as well as customers who accepted the purchase, but who ultimately did not sign a state contract.
Response rates are close to 100%.
18
of customers behavior.
3.4.4
Mechanism: additional behavioral evidence
I begin by showing that customers do not have a preference bias against Tutsi traders, ruling
out preference mechanisms, where v(Ei,j )|Ej =T utsi 6= (Ei,j )|Ej =Bantu that might lead to bias if v
is correlated with L. If there is a social preference bias against Tutsis, Tutsi traders will be less
successful at generating purchase among sales on the spot since the customers will attach a weaker
social preference weight on the Tutsi trader’s payoff. Table VI presents the results from econometric specification 2. I implement a linear probability model of whether trade occurs, T radei,j ,
on whether the trader was Tutsi, T utsij , in sales on the spot. Since I randomize the request to
sign state contracts only after the sales on the spot are implemented, the variable Contract is
orthogonal to whether trade occurs.25 I include team and avenue fixed effects in all specifications.
Columns (1)-(3) show the results respectively on: Contract, T utsi, and the fully saturated model.
As expected, the coefficient on contract in column (1) is negligible. As seen in column (2), customers who receive the offer from a Tutsi trader are 4% less likely to accept the deal. There is
thus no evidence in favor of preference-based discrimination against Tutsi traders. I then turn to
the analysis of screening using the sample of sales on credit.
Having established that customers have no preference bias against Tutsi retailers, v(Ei,j )|Ej =T utsi =
(Ei,j )|Ej =Bantu , the results from econometric specification 1 are simpler to interpret. Suppose for
simplicity that the absence of mean difference in purchase rates for Tutsi and Bantu traders in
sales on the spot reflects that Tutsi traders do not induce to a mean-preserving shift in the distribution.26 If contracts are less effective for Tutsi traders, it must be that the distribution of
θ when purchasing from a Tutsi trader first order stochastically dominates the distribution of θ
when purchasing from a Bantu trader, or that the expected costs from legal sanctions L are lower
when purchasing from a Tutsi trader. Table IV showed that customers are equally likely to pay to
Tutsi and Bantu traders when customers do not sign state contracts. This suggests that the mass
of θ above the payment threshold is comparable for Tutsis and Bantus. If the mass of θ above the
payment threshold would have been lower for purchases from Tutsi traders, Tutsi traders would
have extracted lower payment rates. A lower value of legal sanctions L from Tutsi traders should
25
26
I include the variable Contract as a balance test only.
True, for instance, when v follows a homogeneous distribution.
19
also lead to a lower screening effect of contract.
I then exploit the two-step randomization in order to identify the selection effects of contracts,
for each ethnic group of traders. I focus on a sample of customers who purchased the good, but
ended up not signing a state contract as a result of the field experiment’s design. Among these
customers, some accepted the purchase already when a contract was initially requested, while
other (the “opportunists”) first rejected the purchase when the contract was initially requested,
but accepted only when the trader lifted the requirement to sign a contract. I can thus estimate
the effect of selection by comparing the behavior of customers who ultimately accepted the trade,
B(t = 2) = 1, but did not initially accept, B(t = 1) = 0, and did not sign a state contract, to the
behavior of customers who ultimately accepted the trade, B(t = 2), and also initially accepted,
B(t = 1) = 1, and did not sign any state contract.
To identify the selection effects of contracts, I begin by focusing on acceptance. I show that the
decision to ultimately accept the trade, B(t = 2), is negatively affected if traders require a state
contract, but only for Bantu traders. I use a linear probability model to regress a dummy indicating whether the customer ultimately accepts the deal, B(t = 2), on the following dummy variables:
Contract ultimately requested, Contract, whether the trader is Tutsi, T utsi, and their interaction,
Contract X T utsi. Table VII presents the results. Columns (1) to (3) report the average effects
of Contract respectively for the entire sample, transactions of Bantu traders, and transactions of
Tutsi traders. Column (4) reports the results from regressing trade on the trader’s ethnicity and
column (5) reports the fully saturated model. Column (1) shows that customers are 6% more
likely to accept to trade at this second step if the trader lifts the contract requirement, and this
difference is statistically significant – this effect is driven by the customers who initially refused,
but then accepted once the trader lifted the requirement to sign the state contract. Columns (2)
and (3) show that this effect is entirely driven by the transactions of Bantu traders, suggesting
contracts have a screening effect for Bantu traders, but not for Tutsi traders, consistent with the
prior that Tutsi traders are less able to enforce state contracts. Column (4) suggests Tutsi traders
are less likely to achieve successful purchase on average, and the difference is marginally significant.
Column (5) confirms that contracts have a screening effect only for Bantu traders. The coefficient
on Contract shows that contracts have a strong screening effect for Bantu traders: customers
are 9% more likely to accept the purchase if the contract requirement is withdrawn. However,
contracts have no effect on trade when the trader is Tutsi. Indeed, Contract + Contract X T utsi,
20
which captures the effect of contracts among the Tutsi traders, is not significantly distinct from
zero.
I then identify the selection effects of contracts by focusing on the quality of self-selected customers. Among customers who ultimately accepted but did not have to sign a contract, I compare
the payment rates of customers who initially accepted the purchase to customers who initially
rejected it. This obtains the screening effect of contracts on customer’s quality.27 Table VIII
presents the results from the estimation of a linear probability model with avenue and team fixed
effects. Columns (1) to (3) show the baseline specification, and columns (4) to (6) add avenue
and team fixed effects. Columns (1) and (4) focus on the entire sample, columns (2) and (5)
restrict the sample to sales by non-Tutsi traders, and columns (3) and (6) restrict the sample to
sales by Tutsi traders. The variable B(t = 1) indicates whether the customer accepted the initial
offer, in which signing the contract was a requirement. The coefficient on B(t = 1) indicates how
much more likely to pay are customers who were initially screened, holding constant their current
contractual conditions. Columns (1) to (3) show that there is no effect of contract requirement on
the quality of the selected customers. When I add avenue and team fixed effects, the coefficient
in column (4) is positive and significant. Columns (5) and (6) suggest that this effect is entirely
driven by Bantu sales: the coefficient in the Bantu sample is almost identical, while the coefficient
on the Tutsi sample is zero.28
Overall, the evidence presented in this section shows that state contracts are enforceable even
in the Democratic Republic of the Congo, but that contract enforceability depends on the ex-post
enforcement power determined at least in part by the ethnic proximity to the ethnic groups who
have captured the state administration – Bantus. However expanding the state penetration into
social relations may not lead to changes in the patterns of trade, unless contract enforceability is
sufficiently credible that agents are willing to take risks in agency relations when they are protected by state contracts. Furthermore, the impact of state contracts on trade will depend on
whether they substitute pre-existing social institutions that may already govern agency relations.
27
Another possibility is that some of the customers who maintained their rejection after the contract was
withdrawn but would have actually valued the transaction without contract are on average better than those who
changed their mind. In that case, it is possible that the average payment rates among the pool of customers who
change their mind is a biased estimate of the quality of customers who were screened out because of the contracts.
The bias would be negative if the customers who change their mind are worse than those who do not dare to change
their mind despite they would value the transaction.
28
These results should be interpreted with caution due to the fact that they rely on the behavior of very few
customers.
21
In the next part of the paper, I examine the impact of state contracts on the willingness to engage
in risky trade in the presence of commitment problems, and compare the effect of state contracts
to the effect of salient forms of social organization: coethnicity.
4
Do state contracts substitute for social institutions?
In this section, I establish that state contracts increase trade, and that they do so by solving
commitment problems inherent in agency relations of trade. However, state contracts do not outperform existing social institutions of ethnic groups that govern agency relations. State contracts
and ethnic-based mechanisms governing agency relations are substitutes.
4.1
The home delivery sector: customers as principals
To observe the willingness to engage in risky trade in the presence of commitment problems, I
now examine the behavior of the principal. One way to observe this behavior is for the traders to
offer a household good at a discount, with the requirement that customers must pay first, in order
for the trader to deliver the good in the future. To avoid creating confusion among customers
in the areas visited by the activity of section 3, I implement this sale in different areas. Traders
visited 1,700 randomly selected customers in semi-urban areas of Sud Kivu and sold soaps instead.
Soaps are particularly attractive items because they are relatively scarce in such areas.29 Traders
offer five soaps to each customer (whose market price is 2.5 USD) for the price of two (1 USD).
After the traders presented the offer and collected the payments, traders invited customers to
take an exit survey. The survey contains information on demographics, ethnicity, and measures
of perceptions. While customers are a different population, their behavior reflects their beliefs
on the same population as in the first part of the paper. Indeed, traders were recruited from the
same neighborhoods in which I implemented the activity in the first part of the paper. This design
allows me to have agents drawn from the same population as the customers in the first part of the
paper.
Traders offer the soaps at a discount and promise to deliver the soaps in the near future (two
days after purchase). However, traders require immediate payment. Customers either accept or
29
Soaps are of comparable value in relative terms, and even at market prices, were in excess demand in semiurban areas of Sud Kivu.
22
reject the offer. If they accept, a transaction occurs and the customer may expect the trader to
deliver the soaps within two days. This design of the sale creates a commitment problem (Greif,
1993, Williamson, 1983), that allows me to observe the behavior of the principals, the customers,
caught in agency relations with the agents, the traders – whereby the traders are drawn from
the same population as the customers and traders of experiment one. In the absence of social
or state-based mechanisms that provide traders with incentives to deliver the soaps, customers
would refuse the offer even if they would prefer to purchase the soaps at that price when no risk
is involved. Figure 4 provides a graphical representation of the experiment setup.
I randomize traders to customers. I require the trader to show and offer to sign a state contract to randomly selected customers as part of the sales protocol and before the customer makes
a decision. The state contract exposes the trader to legal sanctions if he does not deliver the
soap.30 This experiment design allows me to identify the marginal effect on trade of contracts
between coethnics and non-coethnics and their interactions in the presence of commitment problems. Furthermore, it allows me to identify how coethnicity affects trade. However, if customers
have a taste for coethnics or for trade with contracts, contracts and ethnicity may affect in the
private valuation of customers, and thus their willingness to trade.
To disentangle whether contracts and coethncity increase the customers’ beliefs about the behavior of the trader from their effect on preferences, I introduce sales in which traders provide the
soaps at the time of payment, in a random sample of customers (sales on the spot, henceforth).
This allows me to identify the effect of contracts and coethnicity for sales with commitment problems, and for sales without commitment problems. If contracts and coethnicity improve trust on
the trader, then their marginal effect on trade should be significantly larger for debit sales than for
sales on the spot. Figure 4 provides a graphical representation of the experiment setup. Table IX
shows the factorial design of the experiment.
30
A local lawyer drafted the contract, and the contract was endorsed and stamped by the Ministry of Interior
of Sud Kivu. Traders carried copies of the original contract. The contract stipulates the following:
“I, the
undersigned... , recognize to have received... Congolese Francs from... in anticipation of ... soaps of type... ,
for a value of 200 Congolese Francs per unit. I hereby commit to deliver... soaps of the type... and of value 200
Congolese Francs per unit to... in the interval of TWO days at most. I am ready to bring this contract, if necessary,
to a legal representative. I recognize that in case of no delivery, I am exposed to the prosecutions and sanctions that
the Congolese law considers for these cases. Done in... . Date... . Signature of debtor... Signature of creditor...
Signature of witness... .” Figure A.1 shows the state contract with its visible stamp of the Ministry of the Interior.
23
4.2
Theoretical framework
The customer’s utility to depends on the monetary payoff of the trader, through a separable social
preference parameter à-la Charness and Rabin (2002). The social preference weight indicates that
the customer values the trader’s payoff, but it could also indicate dis-utility from violating a social
norm of surplus-sharing with the trader. Legal and social sanctions of traders if they renege on
a promise. The term λ(Ei,j , F ) is the weight that the buyer assigns to the monetary payoff of
the trader. I allow the social preference weight to depend on the trader’s ethnic characteristic
of the match, Ei,j , and whether the sale is formalized, F . I normalize the customer’s utility of
not purchasing to 0. The dummy D indicates whether the sale is on debit. It takes value 1 if
the trader promises to deliver the soaps in the future. Customers discount the future value of
consumption by their subjective probability that the trader will deliver the good η (Ei,j , F, D) and
by their discount factor, β (Ei,j , F, D). The subjective probability of delivery and the discount
factor depend on the social (ethnic) proximity and formalization. For instance, customers may
be more impatient with delivery lags by non-coethnics traders and when the trader signed a
contract; in addition, customers may assign a different probability of delivery to coethnics and
to sales that are formalized with a contract. In sales on the spot, there is no uncertainty of
whether the trader will provide the good, η (Ei,j , F, D = 0) = 1, ∀E, F and since the delivery is
immediate β (Ei,j , F, D = 0) = 1, ∀Ei,j , F . Therefore, in what follows, for both functions, I omit
the argument D. The customer’s utility is:
UB = B(β(Ei,j , F )η (Ei,j , F ) v − ℘ + λ(Ei,j , F )℘)
When sales on the spot, η (Ei,j , F ) = 1. The buyer’s utility is: UB = B(v − ℘ + λ(Ei,j , F )(℘ −
℘a )). The marginal effect of ethnic proximity for sales on the spot, when a contract is not used
is:
∂UB
|
∂Ei,j F =0,D=0
ences,
=
∂λ(Ei,j ,F )
∂UB
|F =0,D=0 .
∂λ(Ei,j ,F )
∂Ei,j
∂λ(Ei,j ,F,C)
|F =0
∂Ei,j
When the buyer has ethnically biased social prefer-
> 0, coethnicity in sales on the spot increases the buyer valuation of the
transaction, since the trader is making positive profit. The marginal effect of contracts when a sale
is with a non-coethnic and on the spot is:
∂UB
|
∂F Ei 6=Ej ,D=0
= (℘ − ℘a )
∂λ(Ei,j ,F )
|Ei 6=Ej .
∂F
has a preference for the payoff of traders who formalize transactions,
If the customer
∂λ(Ei,j ,F,C)
|Ei 6=Ej
∂F
> 0, the
use of contracts in sales on the spot increases the buyer’s valuation of the purchase. If contracts
crowd-out ethnic social preferences −
∂λ(Ei,j ,F )
|Ei =Ej
∂F
24
>−
∂λ(Ei,j ,F )
|Ei 6=Ej ,
∂F
the effect of con-
tracts on trade is negative. Allowing some transactions to be on the spot allows to separate this
effect of contracts from their effect on expectations of trader’s delivery. I next provide propositions
to guide the empirical analysis.31
Proposition 1. Introducing sale on debit reduces the expected payoff customers derive from the
deal. This effect is a function of both the discount factor and the subjective probability that the
trader will deliver the good as promised when the trader promises to deliver the good in the future.
Proposition 2. If the time preference parameter is independent of the characteristics of the trader,
the effect of coethnicity in sales on the spot is smaller than the effect of coethnicity in sales on
debit, if and only if coethnicity increases the subjective probability of delivery, η(Ei,j , F ).
Proposition 3. If the time preference parameter is independent of whether the trader signs a
state contract, the effect of contracts among sales on the spot is smaller than the effect of contracts
among sales on debit if and only if contracts increase the subjective probability of delivery, η(Ei,j , F )
(η(Ei,j , F = 1) > η(Ei,j , F = 0), ∀Ei,j ).
Proposition 4. Contracts affect less the impact of uncertain delivery on trade among coethnics
than they affect the impact of uncertain delivery on trade among non-coethnics, if and only if:
η (Ei 6= Ej , F = 0) − η (Ei 6= Ej , F = 1) − (η (Ei = Ej , F = 0) − η (Ei = Ej , F = 1)) < 0
4.3
Econometric strategy
The outcome is whether customer i visited by trader j buys the soaps, T radei,j
∈ {0; 1}. I
refer to this event as “trade occurs.” To identify the effect of individual treatments, I compare
trading rates in each cell of Table IX. While the traders offer prices below market prices, I expect
customers to reject the offer for multiple reasons, including liquidity constraints. Since treatments
are randomized, the unobservables affecting rejection are orthogonal to the treatments, so the
sample difference of means is an unbiased estimate of the population effect of contracts.
I estimate the average treatment effect using a linear probability model in all regressions
below.32 Let Ei,j ∈ {0; 1} denote whether the interaction is coethnic, Fi,j ∈ {0; 1} whether formal
31
32
Proofs are in the online appendix
Results using conditional logit are analogous. I use linear probability model for simplicity of interpretation.
25
contracts are used, and Ci,j ∈ {0; 1} whether the sale is made on debit, all of which are randomized
at the dyad level. In addition, let Tvt be a vector of separable village and trader fixed effects:
T radei,j = a0 + a1 Ei,j + a2 Fi,j + a3 Di,j + a4 Ei,j Fi,j + a5 Ei,j Di,j + a6 Fi,j Di,j + a7 Ei,j Fi,j Di,j + Tvt + ei,j
(4)
Table X presents the testable implications.
4.4
Results: state contracts and coethnicity as substitutes
I first estimate the effect of contracts and coethnicity on trade. I then isolate their effects on trade
that stem from shifts in the principal’s expecations about the agent’s behavior.
4.4.1
Effect of contracts and coethnicity on trade in the presence of agency relations
Figure 5 presents the main result. When transactions do not use state contracts, 48% of noncoethnics trade, against 62% among coethnics and this difference is statistically significant. If,
however, the trader uses a state contract, there is no statistically distinguishable difference between
the rate of successful trades of coethnics and non-coethnics. The proportion of non-coethnics who
accepts the sale rises from 48% to 69%, while the proportion of coethnic customers who accepts the
deal remains unchanged. A linear probability model suggests that the effect of contracts among
non-coethnics is statistically significantly different from the effect of contracts among coethnics.
4.4.2
Mechanisms: contracts shift principal’s expectations about agents’ behavior
To elicit whether contracts and coethnicity affect the expectations of traders’ delivery, I introduce
sales on the spot. Some customers could have social preferences that are biased in favor of
∂λ
∂λ
coethnics ( ∂E
> 0) or in favor of traders who reveal to have state-backed contracts ( ∂F
> 0).
Preference mechanisms could lead contracts and coethnicity to increase trade. To isolate the
effects of contracts and coethnicity on expectations about the behavior of the trader, I randomly
assigned whether the sale was made on debit or on the spot.
Table XI reports the econometric results. I use a linear probability model with village fixed
effects to regress whether trade occurs in a fully saturated model. Column (1) presents the main
effect of sale on debit on trade in the whole sample. Column (2) restricts the sample to sales
26
made on the spot and reports the main effect of Contract. Column (3) restricts the sample to
sales made on debit and reports the main effect of Contract. In columns (4) and (5), I restrict
the sample similarly, but focus on the effect of Coethnicity. Column (6) presents the coefficients
in the fully saturated model and Column (7) adds household level controls to the fully saturated
model (age of customer, age of customer squared, and number of children in the household as a
proxy for household wealth).
The coefficient on Sale on Debit suggests that the proportion of customers who accepts the
trade decreases by 21% when delivery is not immediate. Columns (2) and (3) show that contracts
increase trade when delivery is in the future, but not when delivery is on the spot. The coefficient
on Contract is insignificantly different from zero for trades on the spot. However, column (3)
shows that for debit sales, the proportion of customers who accepts the trade increases by 16%
the trader signs a contract Columns (4) and (5) provide similar results for coethnicity.
Column (6) shows the fully specified model. Contract and Coethnicity increase trade, only
when trade is on debit. Indeed, the coefficients on Sale on Debit X Contract and Sale on Debit X
Coethnic are respectively .28 and .29 and are statistically significant. However, the coefficients on
Contract and Coethnic are negative and insignificant, suggesting they do not affect trade when sale
is on the spot. Finally the coefficient on the triple interaction Sale on Debit X Contract X Coethnic
is negative marginally significant, and equal to −.28. This suggests that contracts have a weaker
effect on expectations of delivery among coethnics. Furthermore, inspection of the coefficient
magnitude suggests that contracts do not change expectations of delivery among coethnics: the
magnitude is exactly the inverse of the Sale on Debit X Contract coefficient. Results are unchanged
when I add household level controls in Column (7). This section thus established that contracts
and coethnicity increase trade, because they solve trade related commitment problems.
5
Conclusion
While the impact of the state on economic development has attracted scholars of all social sciences
(Bates, 2011), there is little statistical evidence of economic activity in the absence of the state and
the impact of the state instruments of legal enforcement on economic organization. I implement
a field experiment in East Congo, where the state is relatively absent, and I am able to estimate
the impact of expanding the state.
27
My results suggest that expanding access to the state judicial system by providing contracts
has potentially large welfare gains, especially in populations where social groups are fragmented.
Furthermore, my findings suggest that the design of interventions that ignore the allocation of
material power and the political equilibrium may be misguided (Acemoglu and Robinson, 2013).
State-backed contracts activate threats of judiciary sanctions enforced by the state. However, the
state is embedded in a network of social relations and some groups have captured the state.
HARVARD UNIVERSITY
SUPPLEMENTARY MATERIAL
An online appendix for this article can be found as a separate file.
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31
Tables
Table I: Characterization of the four types of customers: customers as agents
Strategies
B = 1, P = 1
B = 0, P = 0
B = 1, P = 0
Preference ordering
1, 1 1, 0
1, 1 0, 0
0, 0 1, 0
0, 0 1, 1
1, 0 1, 1
1, 0 0, 0
1, 0 1, 1 0, 0 1, 0 0, 0 1, 1
Conditions
θ + Fl > ℘
v>℘
θ + Fl > v
℘>v
℘ > θ + Fl
v > θ + Fl
v>℘
℘ > θ + Fl
v > θ + Fl
v<℘
α1
Honest peaches
α2
Honest lemons
α3
Moral Hazards
α4
Dishonest lemons
Mass
Label
Notes: This table characterizes the strategies in the parameter space in the absence of legal contracts. The first
line presents the strategies. The second line presents the preference orderings associated to the corresponding
strategies. Line three presents the parameter relationships implied by the observable strategies. The terms in line
four, αi ∈ 1, 2, 3, 4, are the mass of agents in each of the cells defined by observable strategies. For instance, while
α3 and α4 display the same behavior when no state contract is involved, α3 values the good enough that he would
be willing to accept a deal even when he is required to sign an enforceable contract, while α4 would not. I label
each mass according to two dimensions. First, if the cost of not paying, θ, is sufficiently high, compared to the
individual’s private valuation v of consuming the good, I label them honest. This reflects that if they buy, they
would never renege payment. Second, if the individual’s private valuation of consuming the good, v, is higher than
the monetary cost, I label them peach. If the individual’s private valuation of consuming the good, v, is lower
than the monetary cost, ℘, I label them lemon. The peach/lemon terminology captures that trade is only socially
optimal when the customer has a sufficiently high private valuation, which is unobserved.
32
Table II: Screening and Incentives: customers as agents
Step 1: Selection
Who buys?
F=1
B=0
B=1
α2 + α4
α1 + α3
Step 2: Incentives
F=0
F=1
Who pays? (P = 1)
α1
α1 + α3
Notes: This table presents the selection and incentives effects when customers are agents. To disentangle contract
incentive effects from their effect on selection, prior to customer’s decision, traders request customers to sign a
contract guaranteeing that they will pay within 2 days by cellphone. Individuals in the groups α2 and α4 reject
the offer, since they don’t value the good enough so as to pay for it given the expectation of contract enforceability.
Once customers have self-selected, at the time of the transaction, the trader announces in a randomly selected
subset of customers that he cannot offer a contract because he does not have enough contracts, F = 0, while the
remaining are still required to sign the contract to proceed with the transaction. The last line shows the mass of
customers who pay. Among the customers in which the contract requirement was withdrawn at step 2 (F = 0), α3
prefers to avoid payment, while α1 still prefer to pay, despite they ended up not signing a contract.
33
Table III: Testable Implications: cutomers as agents
Hypothesis
Testable implication
Preferences-based ethnic bias
b1 < 0
Contracts have incentive effects when trader is non-Tutsi
c1 > 0
Contracts have incentive effects when trader is Tutsi
c1 + c3 > 0
Contracts have screening effects when trader is non-Tutsi
d1 > 0
Contracts have screening effects when trader is Tutsi
d1 + d3 > 0
Contracts have stronger incentive effects when trader is non-Tutsi
d1 + d3 > 0
Smaller screening effect for Tutsi traders
d3 < 0
Notes: This table presents the testable implications when customers are principals. Sales are either on credit or
on the spot. In sales on credit, the trader first provides the good, and asks the customer to pay in the future by
cellphone. In sales on the spot, the trader provides the good on the spot immediately upon receiving payment.
The left column describes the hypothesis. The right column describes the testable implication in the framework
of the econometric specification. In particular, it indicates the implied sign of the parameter in the corresponding
specification. Let Bi,j ∈ {0, 1} indicate whether the customer accepts to buy. For sales on credit, B(t = 1)i,j
indicates whether the customer accepted the sale initially (when signing the contract was requested) and B(t = 2)i,j
indicates whether the customer accepted the sale after the randomization was implemented and they were asked
to reconsider their choice. Let Pi,j ∈ {0, 1} indicate whether the customer pays for the transaction. This is only
observed if the customer accepted the purchase. Let Rj ∈ {0, 1} denote whether the trader is Tutsi, Fi,j ∈ {0, 1}
whether formal contracts are used, and Di,j ∈ {0, 1} whether the sale is made on credit. In addition, let Ta ∈ {0, 1}
be avenue fixed effects and Te ∈ {0, 1} denote team fixed effects. Trader teams of two are randomly assigned
to avenues. Within each avenue, traders are randomly assigned to customers. Finally the contract treatment is
randomly assigned within avenue for each trader. The linear probability model specifications are as follows. To
capture the incentive effects: Pi,j = c0 + c1 Fi,j + c2 Ri,j + c3 Fi,j Ri,j + Aa + Te + ei,j where I condition the sample on
Di,j = 1 and B(t = 1)i,j = 1. To capture ethnic preferences: Bi,j = b0 +b1 Ri,j +Aa +Te +ei,j where I condition the
sample on Di,j = 0. To capture the screening effects of contracts: Pi,j = d0 + d1 B(t = 1)i,j + d2 Ri,j + d3 Ri,j B(t =
1)i,j + Aa + Te + ei,j where I condition the sample on Di,j = 1 and Fi,j = 0.
34
Table IV: Effect of trader’s ethnicity on incentive effects of contracts: customers as agents
VARIABLES
Contract
Tutsi
Contract X Tutsi
Constant
35
Observations
R-squared
Team FE
Avenue FE
Household controls
(1)
Pay
(2)
Pay
0.09**
(0.04)
-0.01
(0.04)
-0.14**
(0.06)
0.24***
(0.03)
0.08*
(0.04)
0.00
(0.04)
-0.16**
(0.06)
0.41***
(0.07)
668
0.18
NO
NO
NO
(3)
Pay
(4)
Pay
(5)
Pay
0.09** 0.09**
-0.06
(0.04)
(0.04)
(0.05)
0.01
0.01
(0.04)
(0.04)
-0.16** -0.15**
(0.06)
(0.06)
0.44*
0.48** 0.24***
(0.22)
(0.23)
(0.03)
668
668
667
295
0.17
0.22
0.22
0.18
YES
YES
YES
NO
NO
YES
YES
NO
NO
NO
YES
NO
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
(6)
Pay
(7)
Pay
(8)
Pay
-0.07
(0.05)
-0.07
(0.05)
-0.06
(0.05)
0.44*** 0.44
(0.09) (0.27)
0.52*
(0.29)
295
0.13
YES
NO
NO
295
0.19
YES
YES
NO
294
0.20
YES
YES
YES
Notes: Table IV presents the effect of state contracts when traders are Tutsi, and the difference in marginal effects with transactions in which retailers are
not Tutsi. Columns (1)-(4) restrict the sample to transactions in which the retailer is Tutsi, and columns (5)-(8) present the sample average treatment effects
including all transactions. Since the population of Tutsi is very small compared to the rest of ethnic groups, the population average treatment effect should
be computed using weights inversely proportional to the sampling probabilities. Since half of the selected retailers are Tutsi, Tutsis are thus over-sampled.
However, the sample average treatment effect allows me to separately estimate the marginal effect of contracts among non-Tutsi traders, among Tutsi traders,
and their difference. Column (1) presents the baseline specification, column (2) adds team fixed effects, column (3) adds avenue fixed effects in addition to
the team fixed effects, and column (4) adds household controls. Similarly, column (5) presents the baseline specification with the fully saturated interactions
with contract and Tutsi dummy, column (6) adds team fixed effects, column (7) adds avenue fixed effects in addition to the team fixed effects, and column
(8) adds household controls.
Table V: Belief about contract enforceability: customers as agents
(1)
(2)
(3)
(4)
Legal
Shame
Loss of
Physical
sanctions
friends
violence
VARIABLES
Tutsi Trader
Tutsi Customer
Tutsi Trader X Tutsi Customer
Constant
Observations
R-squared
-0.127***
(0.0256)
0.109*
(0.0615)
-0.0985
(0.0749)
0.224***
(0.0170)
-0.0837***
(0.0231)
0.0150
(0.0553)
0.0117
(0.0674)
0.165***
(0.0153)
(5)
Legal
sanctions
yes/no
0.0872*** 0.00513 -0.108***
(0.0197)
(0.0125)
(0.0383)
-0.0192
0.0983***
0.0744
(0.0472)
(0.0301)
(0.0901)
0.0700
-0.123***
-0.0737
(0.0575)
(0.0366)
(0.110)
0.0449*** 0.0299*** 0.668***
(0.0131) (0.00834) (0.0245)
971
971
971
0.035
0.015
0.032
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
971
0.013
764
0.014
Notes: This table presents the results from a linear probability model to regress dummies indicating possible
consequences, on dummies indicating the customer and the trader’s ethnicities. Columns (1) to (4) report the
results on dummies indicating the answer of the household to the following question “According to you, what
consequences will there be if you don’t pay?” Across columns (1) to (4), I include all customers, since I am unable
to observe whether the household was required to sign a contract for the exit survey, and thus the likelihood of legal
sanctions is an underestimate of the likelihood when contracts are signed. Columns (1) to (4) use as dependent
variables the following dummy variables: whether the customer answered that legal sanctions would be activated,
whether that the customer answered that he would experience shame, whether the customer answered that he
would lose friends, whether the customer answered that he would suffer physical violence. Column (5) uses as
dependent variable a dummy indicating whether the customer reports that legal sanctions will be activated if he
does not pay, among customers having signed the formal contract. Traders asked the following question: “Do you
think that the state contract I have shown you can have judicial consequences if you don’t pay?” and positives
were coded as 1.
36
Table VI: Preference against Tutsis, sales on the spot: customers as agents
VARIABLES
Contract
(1)
Trade
-0.0328
(0.0271)
Tutsi
Contract X Tutsi
Constant
0.0962
(0.152)
(2)
Trade
(3)
Trade
-0.0514
(0.0369)
-0.0426 -0.0621
(0.0272) (0.0379)
0.0391
(0.0541)
0.101
0.127
(0.152) (0.153)
Observations
1,009
1,009
1,009
R-squared
0.201
0.202
0.203
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Notes: All regressions consider only sales on the spot, in which the trader provided the good on the spot in
exchange of immediate payment. I regress a dummy variable indicating whether the trade occurred (Trade) on the
dummy Contract, Tutsi, and their interaction. Contract indicates whether the household ultimately was requested
to sign the contract in which he commits to pay by cell phone. Randomization was implemented as randomly
withdrawing the requirement to sign the contract in some households after they had accepted the deal (the script
specifies that the trader does not have enough contracts to allow him to request a contract here). Tutsi indicates
whether the trader is Tutsi, and Contract X Tutsi is their interaction. In column (1) I regress Trade on Contract
only. Column (2) I regress Trade on Tutsi only. In column (3) I report the fully saturated regression model. All
regressions are a linear probability model with avenue and team fixed effects. Mixed ethnic two-person trader teams
were randomly allocated to avenues and households within avenues were randomly allocated to each trader. The
contract randomization was implemented within trader. Since traders’ ethnicity is fixed within trader, I do not
include traders’ fixed effects. The variable Tutsi does not decrease Trade when sales are on the spot, at conventional
levels of statistical significance. This suggests absence of preference-based ethnic bias against Tutsis.
37
Table VII: Selection effect of contracts on purchase, by traders’ ethnicity: customers as agents
VARIABLES
Contract
(1)
Trade
(2)
Trade
(3)
Trade
-0.0568**
(0.0271)
-0.0958***
(0.0355)
-0.0159
(0.0425)
Tutsi
(4)
Trade
1.181***
(0.156)
-0.0848**
(0.0365)
-0.0770**
(0.0383)
0.0622
(0.0543)
1.224***
(0.157)
960
0.191
ALL
960
0.196
ALL
-0.0463*
(0.0275)
Contract X Tutsi
Constant
Observations
R-squared
Traders
1.185***
(0.156)
1.214***
(0.196)
1.286***
(0.203)
960
525
435
0.192
0.241
0.212
ALL
Non TUTSI TUTSI
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
(5)
Trade
Notes: This table presents a linear probability model to regress a dummy indicating whether the household ultimately accepts the deal, B(t = 2), on the following dummy variables: Contract ultimately requested, Contract,
whether the trader is Tutsi, T utsi, and their interaction, Contract X T utsi. Columns (1) to (3) report the average
effects of Contract respectively for the entire sample, transactions of Bantu traders, and transactions of Tutsi
traders. Column (4) reports the results from regressing trade on the trader’s ethnicity and column (5) reports
the fully saturated model. Column (1) shows that customers are 6% more likely to accept to trade at this second
step if the trader lifts the contract requirement, and this difference is statistically significant – this effect is driven
by the customers who initially refused, but then accepted once the trader lifted the requirement to sign the state
contract. Columns (2) and (3) show that this effect is entirely driven by the transactions of Bantu traders, suggesting contracts have a screening effect for Bantu traders, but not for Tutsi traders, consistent with the prior that
Tutsi traders are less able to enforce state contracts. Column (4) suggests Tutsi traders are less likely to achieve
successful purchase on average, and the difference is marginally significant. Column (5) confirms that contracts
have a screening effect only for Bantu traders. The coefficient on Contract shows that contracts have a strong
screening effect for Bantu traders: customers are 9% more likely to accept the purchase if the contract requirement
is withdrawn. However, contracts have no effect on trade when the trader is Tutsi. Indeed, the linear combination
Contract + Contract X T utsi, which captures the effect of contracts among the Tutsi traders, is not significantly
distinct from zero.
38
Table VIII: Selection effect of contracts on customer quality, by traders’ ethnicity: customers as
agents
VARIABLES
B(t=1)
Constant
Observations
R-squared
Team FE
Avenue FE
Traders
(1)
Pay
(2)
Pay
(3)
Pay
(4)
Pay
(5)
Pay
(6)
Pay
0.246
(0.193)
0
(0.192)
0.244
(0.216)
0
(0.214)
0.248
(0.435)
0
(0.433)
0.346*
(0.181)
-0.0621
(0.207)
0.386*
(0.213)
-0.0353
(0.219)
0
(0.409)
0.429
(0.419)
355
0.005
YES
YES
ALL
201
154
355
201
0.006
0.002
0.268
0.377
YES
YES
YES
YES
YES
YES
YES
YES
NON TUTSI TUTSI
ALL
NON TUTSI
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
154
0.375
YES
YES
TUTSI
Notes: This table presents the results from the estimation of a linear probability model with avenue and team
fixed effects. Columns (1) to (3) show the baseline specification, and columns (4) to (6) add avenue and team fixed
effects. Columns (1) and (4) focus on the entire sample, columns (2) and (5) restrict the sample to sales by nonTutsi traders, and columns (3) and (6) restrict the sample to sales by Tutsi traders. The variable B(t = 1) indicates
whether the household accepted the initial offer, in which signing the contract was a requirement. The coefficient
on B(t = 1) indicates how much more likely to pay are households who were initially screened, holding constant
their current contractual conditions. Columns (1) to (3) show that there is no effect of contract requirement on
the quality of the selected customers. When I add avenue and team fixed effects, the coefficient in column (4) is
positive and significant. Columns (5) and (6) suggest that this effect is entirely driven by sales by Bantus: the
coefficient in the Bantu sample is almost identical, while the coefficient on the Tutsi sample is zero
39
Table IX: Experiment design: customers as principals
Coethnic trader non-Coethnic trader
Contract
I
II
No contract
III
IV
Sales on the Spot
Coethnic trader non-Coethnic trader
Contract
V
VI
No contract
VII
VIII
Sales on Debit
Notes: This table presents the factorial design when customers are principals. There are eight treatment cells,
according to whether the trader was coethnic, whether the trader was instructed to offer a state-backed contract to
guarantee the sale, and also whether the trade was implemented on the spot or on debit. In sales on the spot, the
trader provides the good on the spot immediately upon receiving payment. In sales on debit, the trader promises
to deliver the good in two days, in exchange of immediate payment by the household. In cells I to IV are the
households in which trade is implemented on the spot. In Cell I, the trader is coethnic and offers a contract. In cell
II, the trader is non-coethnic and offers a contract. In cell III, the trader is coethnic and does not offer a contract.
In cell IV, the trader is non-coethnic and does not offer a contract. In cells V to VIII are the households in which
trade is implemented on debit. In Cell V, the trader is coethnic and offers a contract. In cell VI, the trader is
non-coethnic and offers a contract. In cell VII, the trader is coethnic and does not offer a contract. In cell VIII,
the trader is non-coethnic and does not offer a contract.
40
Table X: Testable Implications: customers as principals
Hypothesis
Testable
implication
Households prefer to trade with traders of their ethnic group
a1 > 0
Households prefer to trade with traders that have revealed to have access to a2 < 0
state-backed contracts (when there is no risk involved)
Contract dis-taste is larger among coethnics
a4 < 0
Households value more immediate delivery than future delivery
a3 < 0
Households trust more traders of their ethnic group
a5 < 0
Households trust more traders who use contracts to back their delivery promise a6 < 0
Contracts improve trust less among coethnics
a7 < 0
Contracts reduce/increase trust among coethnics
a6 + a7 < 0
Notes: This table presents the testable implications when customers are principals. The left column describes the
hypothesis. The right column describes the testable implication in the framework of the econometric specification.
Let Ei,j ∈ {0; 1} denote whether the interaction is coethnic, Fi,j ∈ {0; 1} whether formal contracts are used,
and Ci,j ∈ {0; 1} whether the sale is made on debit. In addition, let Tv be village fixed effects. The regression
specification is: T radei,j = a0 +a1 Ei,j +a2 Fi,j +a3 Di,j +a4 Ei,j Fi,j +a5 Ei,j Di,j +a6 Fi,j Di,j +a7 Ei,j Fi,j Di,j +Tv +ei,j .
Sales are either on debit or on the spot. In sales on debit, the trader promises to deliver the good in two days, in
exchange of immediate payment by the household. In sales on the spot, the trader provides the good on the spot
immediately upon receiving payment.
41
Table XI: Effect of contracts and coethnicity on trade: customers as principals
VARIABLES
Sale on debit
(1)
Trade
(2)
Trade
(3)
Trade
(4)
Trade
(5)
Trade
-0.161***
(0.0313)
Contract
0.0591
(0.0430)
0.0953***
(0.0320)
Coethnic
-0.0897
(0.0879)
0.106**
(0.0536)
0.786***
(0.0826)
0.524***
(0.0495)
Contract X Coethnic
Sale on debit X Contract
Sale on debit X Coethnic
Sale on debit X Contract X Coethnic
Constant
42
Observations
R-squared
Trader FE
Household controls
Sample
0.752***
(0.0243)
1,308
0.069
NO
NO
ALL
0.675***
(0.0304)
0.569***
(0.0226)
(6)
Trade
(7)
Trade
(8)
Trade
-0.443***
(0.131)
-0.00975
(0.146)
-0.0913
(0.118)
0.0775
(0.154)
0.284*
(0.172)
0.293**
(0.136)
-0.281
(0.182)
0.808***
(0.113)
-0.442***
(0.135)
-0.0283
(0.151)
-0.0840
(0.120)
0.0891
(0.158)
0.301*
(0.178)
0.283**
(0.140)
-0.263
(0.188)
0.518**
(0.214)
-0.441***
(0.130)
-0.0229
(0.145)
-0.119
(0.117)
0.0937
(0.153)
0.283*
(0.171)
0.293**
(0.136)
-0.281
(0.181)
0.971***
(0.218)
450
858
450
858
1,308
1,179
1,308
0.084
0.131
0.082
0.126
0.083
0.095
0.098
NO
NO
NO
NO
NO
NO
YES
NO
NO
NO
NO
NO
YES
YES
SPOT
CREDIT
SPOT
CREDIT
ALL
ALL
ALL
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Notes: This table presents the main results when customers are principals. I implement a linear probability model of the dummy Trade (indicating whether
the trade was successful) on the following dummies: Sale on Debit, Contract, Coethnic. Sales are either on debit or on the spot. In sales on debit, the trader
promises to deliver the good in two days, in exchange of immediate payment by the household. In sales on the spot, the trader provides the good on the
spot immediately upon receiving payment. Sale on Debit is a dummy takes value 1 if the sale was on debit and 0 if the delivery was implemented on the
spot.Column (1) presents the average effect of Sale on Debit in the whole sample. Columns (2) and (3) present respectively the average effect of Contract,
in the sample of sales on the spot and the sample of sale on debit. Columns (4) and (5) present respectively the average effect of Coethnic, in the sample
of sales on the spot and the sample of sale on debit. Column (6) presents the fully saturated model. Column (7) includes household level controls (age, age
squared, and number of children) and Column (8) adds trader fixed effects. All regressions include village fixed effects.
Figures
Figure 1: Experiment design: customers as agents
ays
d P v − ℘, −℘a
l
o
eh us
Ho
ld
ac
ce
pt
s
HH
ed
st
e
u
Ho HHH
eq
t r
use
c
a
hol HH
tr n
o
d D HH v − θ − L, ℘ − ℘a
C
efec
ts
H
HH
s
y
a
v, −℘a
Co HH
ld P
ntr H
o
h
act H
use wit HH
Ho
hdr H
awnH
H
H
H
ou
se
ho
H
Ho HHH
use
hol HH
d D HH v − θ, ℘ − ℘
a
efec
ts
Sale on credit
u
Contract requested @@
@
@
@
@
ld
ho
se
ou
H
@
@
c
je
re
@
ts
@
@
@
@
Household chooses
0, 0
Randomization
Household chooses
Notes: This graph illustrates the structure of sales when customers are agents. Traders present the offer to the
customer. The trader informs the customer that he will make the good immediately available to the customer, if
the customer promises to pays by cell phone in the near future (two days). However, all customers are informed
that in order for the sale to be possible, the customer needs to sign a state-backed contract in which he commits
to pays within two days. This is the sale credit. The customer then accepts or rejects. Once the decision has been
recorded, a random sample of customers is selected in which the requirement to sign the contract is withdrawn. In
these customers, the trader announces “I do not have a sufficient number of contracts today that would allow me
to have you sign a contract. Since we have decided to make the deal, I must go ahead without having you sign a
contract”. In the remaining customers, the requirement to sign the contract is maintained. The trader then leaves
and the customer can pays or defects. The figure includes the payoffs that the trader and the customer would
obtain, with linear and separable utility.
43
Figure 2: Customers’ best responses in the parameter space: customers as agents
θ
B = 1;P = 1
u11 > u10
B=0
α1
B = 1;P = 1
α2
α3
B=0
u11 < u10
α4
B = 1;P = 0
B = 1;P = 0
u10 < u0
v
u11 < u0
u11 > u0
u10 > u0
Notes: This figure maps the the parameters to the best responses of the household. Thick lines delineate areas
where the observed strategies are different. Dotted lines delineate areas where the preference ordering changes, but
leads to no change in observed behavior whether contracts are required or not. αi ∈ 1, 2, 3, 4 are the mass of agents
in each of the cells. For instance, while α3 and α4 display the same behavior in the absence of contracts, α3 values
the good enough that he would be willing to accept a purchase requiring enforceable contract while α4 not.
44
Figure 3: Beliefs about contract enforceability: customers as agents
Notes: This figure presents the beliefs result, dis-aggregated by households’ and traders’ ethnicity. Traders are
implemented on credit. In sales on credit, traders provide the good, and ask the household to pay within two days
by cellphone. At the end of the transaction, the trader asks the household what sanctions he thinks he incurs if he
does not pay as promised. In this figure, I present the proportion of households who believe that there will be legal
sanctions if they do not pay. I separate matches by whether the household is of the majority ethnic groups and
whether the household is of the Tutsi minority. For each type of household, I include the proportion who believe
that there will be legal consequences when the trader that visited the household is not Tutsi, and when the trader
that visited the household is Tutsi. I label each of the four interactions according to the ethnicity of the trader,
followed the ethnicity of the household.
45
Figure 4: Experiment design: customers as principals
y r
e
v
i
el D
s
HH
pt
ce
c
a
DH
efH
ld
o
ectHH
h
e
s
ion H
u
o
HH
H
HH
Ho HHH
use H
hol H
d r HH
efu H
ses H
H 0, 0
−℘, ℘ − θ − F L
Sa
le
on
D
eb
it
Experiments I, II
v − ℘, ℘
Household u
@
@
@
le
Sa
@
@
on
@
@
@
e
th
s
pt
ce
ac
@
@
t
o
Sp
@
Experiment II
ld
ho
e
s
ou
H
@
@
HH
H HH
ous H
eho H
ld H
refH
usH
esHH
H
Randomization
v − ℘, ℘
Household chooses
0, 0
Trader chooses
Notes: This graph illustrates the structure when customers are principals. I randomly allocate customers to either
Sale on Credit or Sale on the Spot. In sale on credit, the trader makes the offer and requests payment immediately,
in exchange for the promise of delivering the good to the customer in the near future (in a few days). In sale on the
spot, the trader makes the offer and requests payment immediately, but makes the good available to the customer
immediately upon payment. Once the offer has been made, the customer can choose to accept it, in which case
trade occurs, or reject it. In sales on credit, if the customer rejects, the sale ends, and if the customer accepts, then
the customer makes the payment to the trader. Later, the trader may deliver the good, or may defect. In sales on
the spot, if the customer rejects, the sale ends, and if the customer accepts, then the customer makes the payment
to the trader and the trader immediately provides the good. I have included the payoffs that the trader and the
customer would obtain, assuming that the utility function is linear and separable.
46
Figure 5: Effect of contracts and coethnicity on trade: customers as principals
Notes: This figure presents the main result when customers are principals. Traders implement sales on credit. In
sales on credit, the trader promises to deliver the good in two days, in exchange of immediate payment by the
household. The vertical axis indicates the share of attempted sales that were successful. There are four columns.
The first two columns from the left indicate the share of successful sales among households that are non-coethnics
of the traders. Among these households, the first column reports the share for households in which traders did
not show a contract, and the second, the share for households in which the traders showed a contract. Columns 3
and 4 have the same interpretation, albeit for sales in which traders and households are coethnics. Red intervals
indicate the 95% confidence interval.
47
A
Mathematical Appendix
Proof of Proposition 1 To see this, note that the effect of debit on the buyer’s utility from purchase
is:
UB (E, F, D = 1) − UB (E, F, D = 0) = −v(1 − β(E, F )η(E, F ))
The effect of contracts on the buyer’s utility from purchase when delivery is in the future is:
UB (E, F = 1, D = 1)−UB (E, F = 0, D = 1) = v(η(E, F = 1)−η(E, F = 0))+(℘−℘a )(λ(E, F =
1) − λ(E, F = 0)).
Finally, the effect of coethnicity on the buyer’s utility from purchase when delivery is in the future
is: UB (E = 1, F, D = 1) − UB (E = 1, F, D = 1) = v(η(E = 1, F ) − η(E = 0, F )) + (℘ − ℘a )(λ(E =
1, F ) − λ(E = 0, F ))
Proof of Proposition 2 Assuming the discount factor is independent of the ethnic proximity
of the trader, E, and on formalization, F , the difference in the two marginal effects of contracts
implies η(E = 1, F = 0) > η(E = 0, F = 0) If trust is higher among coethnics, i.e., if η(E =
1, F ) > η(E = 0, F ), then it must be that, whenever v > 0:
∆UB
∆UB
|E=0,F =0 −
|E=1,F =0
∆D
∆D
= v (β(E = 1, F )η(E = 1, F ) − β(E = 0, F )η(E = 0, F ))
∆D (E, F ) = |
> 0
Proof of Proposition 3
∆ (E) = ∆D (E, F = 1) − ∆D (E, F = 0) = vβ(η(E, F = 0) − η(E, F = 1))
< 0
Proof of Proposition 4 ∆ (E = 0) > ∆ (E = 1) if η(E = 1, F = 0) > η(E = 0, F = 0) = 1 and
η(E = 1, F = 1) = η(E = 0, F = 1),
48
A
A.1
Online Appendix
Additional tables and figures
Figure A.1: State contract
Notes: This figure shows the state contract when customers are principals.
49
Figure A.2: Instructions for payment by cellphone: customers as agents
Notes: This figure shows the payment instructions given to the customer, when customers are the agents.
A.2
Incentive compatibility of traders
While they also receive a fixed wage, traders are residual claimants on sales, which is a central
element of external validity.33 The revenues from sales are a non-negligible part of the income of
traders. In experiment I, if all trades are successful during the selling day, each trader generates
9 USD per day of sales. In experiment II, the equivalent number was 12 USD. The figures are
reduced by 25% less than the total revenues from sales due to project reimbursements, which I
describe below. Since traders are residual claimants, their incentives pose a threat to the quality
of the research. I next describe the strategies I use to minimize this threat.
If traders accept payments and do not deliver the goods, their profits increase. To avoid traders
reneging on their delivery promises, I design a cell phone monitoring system. Traders provide
customers with a project cell phone number and instructions for how to register a complaint. In
addition, I require traders to collect the customers’ cell phone numbers during the exit survey.
I inform the traders that the supervisor will contact a random sample of respondents to check
whether the sales were implemented as planned.34 I inform traders that their salaries would be
withdrawn if they fail to deliver the soaps, which we verify with the villages in which they operate.
Finally, traders collect the GPS coordinates of every customer in both the urban and rural areas,
33
The fixed component was set after discussions with traders and surveyors, so that their profit, adjusted for
uncertainty, was equal to the market wage they would otherwise obtain as surveyors, or as school teachers.
34
I recorded no instance of fraud or cheating by traders, among all customers in which the Supervisor implemented the verification.
50
Figure A.3: Disaggregation of the main effect by ethnic sub-group: customers as principals
Notes: This figure presents the main result when customers are principals, dis-aggregated by customers’ and traders’
ethnicity. Sales are implemented on credit. In sales on credit, the trader promises to deliver the good in two days,
in exchange of immediate payment by the customer. The vertical axis indicates the share of attempted sales that
were successful. There are two groups of columns. The first group columns on the left (dark columns) indicate the
share of successful sales among customers visited by a Bashi trader. Columns are grouped in two for each ethnic
group of the customer: the first column reports the share for customers in which traders did not show a contract,
and the second, the share for customers in which the traders showed a contract. The second group columns on
the left (light columns) indicate the share of successful sales among customers visited by a Bahavu trader and its
interpretation is identical. Red intervals indicate the 95% confidence interval.
51
which decreases their incentives to shirk, especially for Experiment I in rural areas.
35
Despite
strict monitoring, the design of the experiment could affect the incentives of traders.
First, traders may be tempted to accept payments below the price set by the research project,
hence extracting strictly positive surplus from customers that would otherwise have refused the
purchase, which is a standard problem of industrial organization. To avoid this, I require traders
to pay a fixed amount that is lower than the sales price for each pack of 5 soaps they sell. The
supervisor verifies the stock of soaps and traders pay in proportion to the missing soaps. This
strategy reduces the set of prices below the recommended price at which the traders would make
positive profit. I recorded no sales at lower prices than recommended.
Second, 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 give traders enough soaps for all households that they had to visit. Also, traders
could be tempted to violate the random allocation of households and select richer households to
extract higher surplus. However, discovering the wealth distribution in the village is difficult.36
Furthermore, I inform traders that researchers use statistical techniques such as randomization to
verify implementation violations.
A.3
Sampling of customers
Traders sample randomly selected households within each village in rural areas and within an urban
neighborhood in Bukavu. In the first day in the village (or in each urban neighborhood), traders
establish a village census with assistance from village (or neighborhood) authorities. Traders based
the random selection of households and their treatment on a list of randomly selected numbers
that were previously created using a statistical package.37 Experiment I includes 2,684 households
composed of the following ethnic groups: Balegas (n=1,208), Bashi-Bahavus (n=993), Batembos
(n=188), Tutsis (n=161), Other groups (n=134) and their main economic activities are agriculture
35
More importantly, traders work in a long-term basis for various research projects for the authors and fear their
loss in reputation if caught cheating.
36
See Sanchez de la Sierra (2014) for a description of how armed groups who are settled in the village struggle
to know its wealth distribution.
37
For each village size, I generated a sequence of random numbers lower than the total number of households.
Traders then selected the households whose numbers in the census they drew coincides with the randomly selected
numbers.
52
and mining. All target customers are randomly selected males within the selected households.
53