Diffusion of Rival Information in the Field

Diffusion of Rival Information in the Field *
Inês Vilela†
November 11, 2019
Latest version here.
Website: sites.google.com/view/inesvilela
Abstract
Information about rival opportunities becomes less valuable as more individuals learn
about them. When policy interventions are perceived as rival, secrecy may lower dissem-
ination and uptake. I conducted a lab-in-the-field to test how rivalry affects the pattern of
diffusion of information. The lab games began with 100 initial seeders randomly assigned
to play one of two games: (a) a rival game in which players received a share of a monetary
prize and (b) a non-rival game in which they received a fixed amount. The games followed a
snowball structure in which the initial players could invite others in the village to participate
in the next round of the game. The initial seeders in the rival game were 18 percentage points
less likely to share invitations to the game than those in the non-rival game, which resulted in
a lower diffusion into the wider community. I find no effects regarding sharing with people
who were less central and therefore had fewer friends with whom to share in the next rounds.
In addition to the lab-in-the-field, I conducted experiments to evaluate how the same seeders
shared invitations to two policy trainings on two distinct agricultural practices. The practices
varied in terms of the level of perceived rivalry as measured by a willingness-to-pay game.
The pattern of invitations seen in this experiment indicates a different strategy for dealing with
rivalry. Initial seeders did not discriminate in terms of the number of people invited. Instead,
seeders invited less-central people to participate in the rival policy compared to the non-rival
one - people who were less likely to diffuse the knowledge in the network. The results show
that when policy interventions are perceived as rival, policy implementers should be careful
about how it will affect its diffusion process.
Keywords: diffusion, social networks, rival goods, field experiment.
*I would like to thank Pedro Vicente for his comments and support. This project would not have been possible
without the amazing fieldwork assistance from Lucio Raul, Ana Costa, and Imamo Mussa. I would like to acknowledge
the support provided by the Aga Khan Development Network in Pemba, Mozambique, and I am particularly grateful
to Graham Sherbut. I thank the participants of Nova SBE Research Workshop and Berkeley Development Lunch for
their helpful comments. Ethics approval was secured from Universidade Nova de Lisboa. I wish to acknowledge
financial support from the Fundaçao da Ciencia e Tecnologia and the Luso-American Foundation. All errors are my
responsibility.
†
Nova SBE, Universidade Nova de Lisboa and NOVAFRICA (e-mail: ines.vilela.2013@novasbe.pt).
1

1 Introduction
In rural communities in developing countries information spreads mostly by word-of-mouth com-
munication. Due to the limited outreach of traditional media, villagers rely on neighbors and local
leaders fleaders to learn about new technologies. Aware of the importance of learning from others,
the designers of many development projects implement them by informing only a small group of
village members, who become the seeders of the information. It is assumed that they will diffuse
this knowledge through the village chatting networks.1 This methodology of seeding information
to only a few relies on the assumption that seeders benefit from informing others and that informa-
tion will flow in the village. This may not be a plausible assumption, however, if the information
concerns a rival good and seeders have an interest in excluding others from benefiting from it.
A piece of information is rival if, for example, it relates to limited funding opportunities or pro-
grams for the distribution of agriculture inputs or in any other case in which there is competition
to acquire the good. When in possession of rival information, seeders are careful about whom
they inform, since they have a preference for the information not to spread outside their circle of
close friends. This paper seeks to understand how information about a rival opportunity diffuses
in communities and what sets this diffusion process apart from the process seen when the good is
non-rival.
Individuals share information with others for two main reasons. One is purely altruistic: people
care for their friends and want them to learn the same information they have learned. The other
relates to the expectation of reciprocity in the future: people share with others hoping that in the
future those others will reciprocate and share something back. These incentives for sharing are
weighted against the costs of sharing, such as the time spent informing someone and the cost
of traveling to meet with this person. When the information concerns a rival opportunity, other
costs arise. By informing each additional person, the seeder suffers a loss in the utility she herself
derives from possessing the information, since the marginal utility of a piece of rival information
decreases as the number of people informed increases. Additionally, there is always the chance
that the person who receives the information will also decide to share it with others, which would
lead to an additional utility loss for the initial seeder. It is this risk associated with letting others
into the secret that makes seeders choose strategically the types of friends with whom they share
the rival information rather than simply decreasing the number of people they inform.
This paper reports the results of a lab-in-the-field conducted in one rural village in Cabo Delgado,
Mozambique. The community being studied is large, with approximately 1,000 households, and
the main occupation is small-scale agriculture. Since the village is far from any large town or
transportation routes, the community is isolated from the outside world. This isolation facilitates
1
Policy makers may chose to seed only to a sub-group because it is impossible to broadcast the information to the
entire community, or prohibitively expensive.
2

the mapping of the social interactions in the village. After collecting the complete social network
of the community, I randomly selected a group of 100 initial seeders, stratified by their importance
in the network. Each seeder was randomly assigned to play one of two games: a rival game
and a non-rival game. In the rival game, each seeder was to receive a share of a prize of 200
USD, which would be divided equally among all participants at the end of the rival game (the
initial 50 seeders plus those they would invite for the second round, plus all the others who would
subsequently be invited). In the non-rival game, each player was to receive a coupon worth 1 USD.
The amount of the coupon was fixed and independent of the number of people playing at the end
of the game. In both games, seeders had a single decision to make: whom to invite to join the
game they were playing. The number of invitations was limited to four, and players could decide
not to invite anyone at all. The people invited by the seeders played the same game in a second
round of the lab, and they faced the same decision as the players in the first round. Overall, the
game had five rounds. The snowball design of the game was intended to simulate in a sequential
way the diffusion of information throughout the village. It also allowed observation of sharing
at specific moments of the diffusion process. To ensure that the payoffs in the two games were
similar in expected terms, I simulated diffusion of the two games in the empirical network using
a susceptible-infected model2 with different probabilities of sharing. The simulations allowed me
to define the different parameters of the game (amounts, number of rounds, maximum number of
invitations) so that the expected payoffs would be comparable.
n addition to conducting the lab-in-the-field, I observed the diffusion of invitations to two agri-
cultural training sessions with different levels of rivalry: a training about subsidized agricultural
insurance (non-rival) and a training about opportunities to sell agricultural goods to a market out-
side the village (rival). The invitations to the two training sessions were randomly distributed
to the same 100 seeders from the lab games. The seeders could share the invitations following
the same rules as in the monetary lab games. The training sessions were delivered by a local
non-governmental organization that specializes in agricultural development. Qualitative data from
a comparable set of farmers outside the study population confirmed that the insurance was per-
ceived as a social good, that is, that individuals would benefit from sharing information about
the existence of the insurance with their social network. The qualitative data furthermore showed
that farmers believed that additional farmers receiving the markets training would increase com-
petition, increasing the likelihood of others making contacts with buyers and negotiating better
prices. I elicited each farmer’s individual beliefs regarding the rivalry of the two trainings through
a set of incentivized willingness-to-pay measures. Farmers were asked to make different bids to
participate in the trainings under different scenarios regarding the number of other participants.
The results show that while the bids for insurance did not change, statistically, with the number
2
Susceptible-Infected models are a standard type of models for diffusion and are based on the medical literature that
models the spread of infections. See Jackson (2010) for a review of diffusion models.
3

of participants, the bids for the access-to-markets training decreased drastically as the number of
participants increased.
The results of the two experiments show that the initial seeders shared the rival opportunity less
often than the non-rival one, both in the lab games and in the policy experiments. Seeders in the
rival game were 18 percentage points less likely to invite another person than those in the non-rival
game. This negative effect was also observed in the intensive margin of invitations. Looking at the
overall diffusion of the game in the last round, I observe that rivalry not only led to fewer people
being invited in the first round but also to a lower level of overall diffusion by the end of the game.
The invitations from a seeder in the rival game snowballed to nine fewer people being informed
at the end of round five, compared to an average of 15 invitations snowballed from each seeder
in the non-rival game. In terms of the importance, in a network sense, of the people invited, I do
not find a large difference across rival and non-rival invitees. Looking at overall diffusion at the
end of the games, players of the rival game had on average 0.9 more connections than those in
the non-rival game. This represents a 13 percentage point increase compared with the non-rival
benchmark. If, instead, I consider other measures of importance, such as eigenvector centrality,
which captures power-centrality, the result is no longer statistically significant. These two sets of
results show that the strategy for dealing with rivalry in the purely monetary game was to adjust
the number of people invited.
The results from the policy invitations reveal a different strategy for dealing with rivalry. In the
policy experiment, players adjusted to rivalry by inviting people with a low potential to inform
others. The rival and non-rival policies returned similar results in terms of the total number of
people invited to the trainings. But the people who received the invitation for the rival policy
are less central in the network. This might seem contradictory to the standard network literature,
which asserts that central people diffuse information at a higher rate. What happened in our
experiment was that initial seeders shared the non-rival policy with very central people, who are
a small group in the village, so that multiple invitations from different seeders resulted in the
same central person being invited to the second round. Although the individual rate of invitation
for the non-rival policy was larger than for the rival policy in rounds 2 to 5, at the end of the
rounds, the number of unique people who had been invited to the two policy trainings was not
statistically different. The experimental design allowed investigation, also, into how rivalry affects
diffusion when the seeding points are the most central people in the network. I find that when
the information is rival, targeting central seeders might not be the best strategy for maximizing
diffusion.
This paper contributes to a small but growing literature on the diffusion of rival information. Lee
et al. (2010) are the first to propose a theoretical model for the diffusion of rival information. They
analyze a game in which players play sequentially and decide whether or not to share a rival piece
4

of information. Players have purely altruistic motivations for sharing, so their utility increases
when their friends are informed. In this model, rival information is clustered around circles of
trust, and players avoid generating new links outside their circle in order to show commitment
to the secret. Using a different theoretical game in which diffusion occurs at random times and
players are not altruistic, Immorlica et al. (2014) show that under certain conditions a piece of rival
information spreads through a network. This might happen when people interact a lot or when they
interact very little. The current paper draws inspiration from both models - the sequential game
from the first and the non-altruistic motivations from the second - and extends the design to allow
the researcher to discriminate sharing among different types of friends. This approach aligns with
the idea that although people’s knowledge of the complete network is limited, they have partial
knowledge of the most influential people (Banerjee et al., 2019) and the people who are closer to
them in a network sense (Breza et al., 2018). To the best of my knowledge, Banerjee et al. (2012)
present the only other empirical evidence on the diffusion of rival information.3 For that study,
the authors randomly invited people to participate in a lab game with a limited number of spots
and asked them to tell others. The authors find that despite the competitiveness, having a friend
who had received an invitation increased the probability of participating in the lab, even if it was a
friend of a friend. This paper improves on their work by setting a research a design that allows for
(a) the explicit comparison between rival and non-rival sharing, (b) the observation of diffusion at
each period in a game with a snowball structure, and (c) simulations of sharing that ensure that the
payoffs of both games are the same in expected value.
There is a vast literature investigating the importance of peer-to-peer communication for knowl-
edge sharing and technology adoption. A large part of this work has been done in agriculture,
an area that puzzles researchers due to the low adoption of profitable technologies. Work in this
field initially focused on the role of peer-effects (Foster and Rosenzweig, 1995; Besley and Case,
1994) and later considered the explicit social network of farmers in the learning process, examples
include Bandiera and Rasul (2006), Conley and Udry (2010), Carter et al. (2014) and Magnan
et al. (2015).4 There is a growing literature on the importance of social learning in the adoption of
other types of technology and behavior in developing countries. It is worth mentioning the work
on the adoption of new financial technology (Banerjee et al., 2013; Comola and Prina, 2015; Cai
et al., 2015), health behavior (Kremer and Miguel, 2007; Oster and Thornton, 2012; Miller and
Mobarak, 2014) and political behavior (Fafchamps and Vicente, 2013; Dellavigna et al., 2016;
Fafchamps et al., 2018; Ferrali et al., 2019). Finally, there is also evidence on the role of so-
cial learning in changing education outcomes (examples include Bobonis and Finan, 2009 and
Fafchamps and Mo, 2018).
3
In a recent project with firm owners in non-rural Ghana, Hardy and McCasland (2016) find that the sharing of a
new technology increases when the firm owner is no longer part of a lottery to receive a contract.
4
A complete review of the literature on social learning in agriculture can be found in Magruder (2018) and De Janvry
et al. (2017).
5

In addition to contributing to the investigation of information sharing in the agricultural context,
this paper also contributes to the recent literature using lab games that take advantage of the net-
work structure to investigate information diffusion. Some of these experiments use the real social
networks of participants, as in the study by Mobius et al. (2015) that looks at how college students
use their social network to collect information about a treasure-hunt type of task. Other studies
use the lab as a way to generate artificial networks in order to have a better identification strategy;
see, for example, Batista et al. (2018) who conduct a text message experiment in Mozambique
and Caria and Fafchamps (2019), who invite participants to a lab in India to test different link
formation strategies.
Lastly, this paper also contributes to the literature on the selection of the optimal entry points
for increasing diffusion and adoption of a new technology. This is particularly relevant from
a policy perspective. Banerjee et al. (2013) show that targeting more central individuals in a
network improves overall adoption of a micro finance product, even in cases in which the initial
seeders do not adopt it. Beaman et al. (2018) show that farmers need to learn from multiple
sources before adopting a new agricultural technology. Their results are consistent with a model
of complex contagion (Centola and Macy, 2007). In a different paper on agriculture diffusion in
Uganda, Bandiera et al. (2018) find that overall diffusion is higher when the seeded farmer and
a counter-factual farmer are not competitors in the community. BenYishay et al. (2016) show
that women in Malawi are not as successful as men at teaching or convincing others to adopt a
new agriculture technology, although they retain information better and experience higher farm
yields than men. BenYishay and Mobarak (2018) show how small incentives can affect farmers’
adoption and communication of a new agricultural technology.
The remainder of the paper is organized as follows. Section 2 provides context on the lab-in-the-
field and policy experiments. In Section 3, I describe the experimental design, the data collection,
and the reduced-form specifications and hypotheses. The results are discussed in Section 4 and
Section 5 concludes.
2 Context
Mozambique is one of the poorest countries in the world and also one of the most dependent on
foreign aid. In 2018 the country had the seventh-lowest GDP per capita in the world 5 and was
highly dependent on foreign aid and development assistance6. In 2010, substantial natural gas
reserves were discovered in the north of the country. This has drawn considerable attention to the
5
World Bank data for 2018. Source: World Bank data: GDP per capita, PPP.
6
Over the last ten years, Mozambique ranked 15th in terms of the amount of aid received, source: World Bank data:
net official development assistance and official aid received.
6

country and changed its economic prospects.7
At the end of this research project, in 2017, the gas production had yet to begin. Seventy percent of
the adult population works in agriculture and a large majority is engaged in subsistence farming.
The country has the eleventh-lowest consumption of fertilizer in the world8 and the yield in cereal
production is around half of the average for sub-Saharan Africa.9 Since the uptake of agricultural
technology is low and agriculture is the predominant activity of the poor in Mozambique, the
policy experiment in this paper focuses on agricultural innovations in rural areas, where almost all
households practice some subsistence agriculture and therefore simple agriculture innovations can
be spread widely among the population.
The experiment was conducted in the northern province of Cabo Delgado. Although this is the
richest province in Mozambique in terms of natural resources10, Cabo Delgado remains primar-
ily rural and has the lowest human development index in the country (INE, 2015; Global Data
Lab, 2016). In this project, I collaborated with a local non-governmental organization, the Provin-
cial Farmers Organization (Uni˜ao Provincial de Camponeses de Cabo Delgado - UPC), a long-
established and well-known organization whose main purpose is to provide training to small farm-
ers in the region. For the experiment, we focused on a single village, Nancaramo, in the district of
Metuge, where UPC was conducting various training activities. Figure 1 shows the location of the
village.
The selected village is large by rural standards in Mozambique, with 1227 households. Table
A1 shows descriptive statistics at the village level using data from the census conducted at the
beginning of this project. Half of the household heads have never attended school (neither formal
education or madrassas) and 80 percent are Muslim. The average monthly income of the household
head is around 17 USD. The vast majority of households own land, which is mostly devoted to
agricultural production. Despite the importance of agriculture, there is a low rate of adoption of
technologies to increase agricultural yield such as improved seeds and fertilizers. Only 10 percent
of farmers used improved seeds in the previous agricultural season, and even fewer (3 percent)
used fertilizers.
7
See for example Financial Times, Mozambique to become a gas supplier to world.
8
In 2016, the average fertilizer consumption in Mozambique was 3.7 kilograms per hectare of arable land. Source:
World Bank data: fertilizer consumption.
9
In 2017 the average yield of cereal production in the country was 872 compared with an average for all sub-Saharan
Africa countries of 1,496. Source: World Bank data: cereal yield.
10
The natural gas discovered in 2010 is in Cabo Delgado’s shore. Also, the province has the largest known deposit
of rubies in the world as well as large deposits of graphite. Both rubies and graphite are already in the extraction phase.
7

3 Experimental Design
The main objective of this project is to understand how people share rival and non-rival informa-
tion. To meet this objective, I designed a lab-in-the-ﬁeld game with the following structure:
First round of the game:
1. One hundred households are randomly selected from all households in the village. These
are the initial seeders of the game.
2. The initial seeders are randomly assigned to one of two groups:
• The non-rival group. Each of the 50 seeders receives a coupon for 1 USD. Every
player who has a coupon at the end of the game will receive 1 USD.
• The rival group. The seeders in this group receive a share of a prize of 200 USD.
At the end of the game, the prize will be divided equally among all players who own
shares.
3. Players in both groups have the same decision to make. They are allowed to invite up to
four people in the village to participate in the same game they are playing.
• Non-rival seeders can invite up to four people to receive one 1 USD coupon.
• Rival seeders can invite up to four people to receive a share of the same 200 USD
prize.
4. 4. People invited by the seeders in each game play the same game in the next round.
Rounds 2 to 5:
1. Players in the rival and the non-rival groups are no longer randomly selected.
• Participants in the non-rival game are those invited in the previous round to play.
Those who have already played the non-rival game, or whose household members
have already played, are excluded.
• Players in the rival game are those invited in the previous round, with the same rules
of non-repetition as in the non-rival game.
2. The non-rival and rival games are conducted as in the initial round.
3. The decisions to be made by players in the non-rival and rival groups are the same as in the
initial round.
8

4. People invited by a player to join one of the games will play that game in the next round.
The game ends after the players in the fifth round decide who to invite. At this point the research
team pays the respective payoffs of the game to the participants.
3.1 Discussion of the design
Selection of initial seeders. The 100 initial seeders are randomly selected from a census that is
conducted at the beginning of the project. The census is designed to collect a complete list of
the households, along with basic demographic characteristics, GPS coordinates, and social links
(see Section 3.4 for more details). This information allows me to stratify the seeders by their
importance in the village. Section B of the Appendix, provides a detailed overview of the process
of selecting the initial seeders.
Randomization into game types. The assignment of players to the rival and non-rival games is
made using a simple randomization process. Each one of the 100 initial seeders receives a random
number between 0 and 1, and the highest 50 numbers are assigned to one of the games. The
remainder are assigned to the other.
Choice of parameters. The parameters of the game are designed so that the expected value of the
monetary incentives in the rival and non-rival games is the same. To define the number of rounds,
the limit on the number of people invited, and the monetary incentives, I simulate the process of
sharing the game in the village. This is done using information about the network as a whole and
about the identity of the initial seeders.
The simulation is based on a standard susceptible infected removed (SIR) model of contagion,
which I apply to the structure of the real network of the village. In the SIR model, nodes infect
one another with a positive probability q; for the simulations, I set the probabilities of sharing the
rival game (qr) and sharing the non-rival game (qnr) such that qr < qnr. The intervals defined
for these two parameters are based on previous literature that estimates sharing probabilities via
structural models. Each game is seeded to the group of 50 seeders in the first moment. Then each
seeder seeds to his neighbors with probability qr and qnr, respectively. In the second moment, the
people who have received invitations seed to their neighbors with the same probability as before.
The process is repeated until the fifth moment of the game. The algorithm allows for each node to
play only once and not to seed to the node that made the seed in the first place, meaning the seed
progresses forward in the game. Each game is simulated 100 times.
I consider the average results of the simulations to set the parameters of the games such that
the payoffs of the rival and the non-rival game are the same in expected terms. Section C of the
Appendix shows a detailed description of the simulation exercise and how the different parameters
9

are decided.
Players information set. Participants know all the rules of the game, the round in which they
are playing, and how many rounds remain. Players who are not seeders know the identity of the
player who invited them. All players know that the people they invite will know the identity of the
one who has invited them.
Side payments. One concern is that players might sell the vouchers. In such a case, the game
would not capture the sharing of information within the village but rather something more similar
to trade. To avoid this possibility, players who want to invite others are required to name them
immediately to the enumerator. They are not allowed to keep the vouchers or shares and decide
later to whom they will give them.
One play per household. Each game can only be played once within each household. That
is, the members of a single household can play, at most, the rival game once and the non-rival
game once. This restriction is based on two main motivations. First, it mimics the process of
sharing information. In the case of information-sharing, once a family is informed, the value of
the information has been received. If the family is offered the same information again, the value of
being informed a second time is negligible. A second reason for the restriction is to avoid sharing
the game purely for its monetary value. This is more likely in the non-rival game, because players
accrue no cost by sharing. Here it could happen that, for example, player 1 would invite player
2, who would then invite player 1, who would then invite player 2, and so on. By restricting
participation to one turn in each game per household, this type of behavior is prevented.
3.2 Within-subject design
After the fifth round of the game is over and payoff payments are made, I implement a second
session of the games. The initial seeders who played the rival game in the first session play the
non-rival game in the second session, while those who played the non-rival game in the first session
now play the rival game. All the rules of the game are the same as in the first session. Having
a second session improves the statistical power of the design. However, it also entails that all
within-subject regressions must have controls for the order in which the games are played. Figure
2 displays the order of the treatments across sessions played by the initial seeders.
3.3 Policy Experiment
An advantage of the game design is that the incentives are clear. The lab game has strict rules, and
what is being shared-money-is well defined. However, after observing how people share rival and
non-rival goods in this purely monetary setting, one might ask: What happens with information
10

about other types of rival goods? What happens when there is not such a rigid structure of rivalry
as that imposed by the game design, and when decisions are closer to the actual choices facing
households in rural areas of developing countries?
To answer these questions, I run a policy experiment at the end of session 2 of the lab game. The
experiment involves sharing invitations to two different agricultural interventions in the village.
The same group of 100 initial seeders is randomized into receiving an invitation to one of two
training sessions. To allow for comparability with the lab game, I ask the seeders to invite up to
four people, each of whom will then be allowed to invite an additional four. This process repeats
up to the fifth round, as in the lab games. All other rules discussed in section 3.1 apply to the policy
experiment. Figure 3 shows the randomization of policies across the initial seeders, conditional
on the previous lab games.
The topics for the training sessions are chosen so that one is closer to a rival good and the other
to a non-rival good. The rival policy is a UPC-implemented training program called “Access to
Markets for Small Farmers.” This is a one-day event, in which a member of the NGO meets with
the farmers invited for the training. The facilitator provides information about buyers in the region,
how to receive technical assistance from UPC in the following months, what to produce, and how
much to produce. During the training, a group of farmers is identified to form a sales commission.
Members of this group will receive assistance from the NGO in the months after the meeting. This
is the standard procedure in the region for promoting links between farmers and the market. The
non-rival policy consists of the dissemination of agricultural insurance in the village. Insurance is
almost nonexistent in Cabo Delgado. A UPC initiative to pilot an index-based insurance product
is presented in a one-day meeting in which a UPC member and a member of our team explain the
product. The farmers who participate in the meeting learn about the prerequisites for the insurance
in terms of the size of plots and about the costs of the insurance. A large part of the training session
is devoted to discussing the criteria for declaring a bad agricultural season and the amount to be
received by insured farmers. The two policies target the same economic activity (agriculture) so
that the possible beneficiaries of the non-rival and rival policies are the same.
Agriculture is chosen because almost everyone in the village engages in farming. Although the
decision to work with agricultural interventions is straightforward, decisions about particular rival
and non-rival policies are not. Before implementation, UPC and our team conducted two focus
groups with small farmers in a village in the neighborhood of the research site. During these talks,
we discussed different agriculture policies and tried to assess how the farmers perceived them. The
access-to-markets training seemed to be a good candidate for a rival good.11 Farmers mentioned
the worry that not everyone would have the opportunity to be part of the commission and that it
11
Policies concerning distribution of seeds also seemed to be perceived by farmers as rival. Farmers are used to these
type of initiatives.
11

was important to decide who should be in the meeting. Several people mentioned incentives to
keep the connection to markets to themselves in order to be a preferential seller in the village.
Additionally, some farmers mentioned the role of organizing and leading other farmers in the
process. Based on the talks, it seemed that farmers perceived the access-to-markets training as
rival.
For the non-rival training, I consider agricultural insurance because the direct benefit of being
insured does not depend on others. Anyone who owns a small farming plot can buy insurance
and be protected against a bad agricultural season. The insurance is indexed to a weather index
and is independent on what other farmers do. In the focus groups, the research team also asked
farmers about agricultural insurance. Farmers were not familiar with this type of insurance, so
some explanation was required. During the discussion, one farmer mentioned that if his friends
were insured, they might not ask him for help in case of a bad crop. Overall, there was a general
opinion that insurance would be good for everyone in the village to have, although some farmers
were worried about the price being too high. The feedback about the insurance product seemed to
define it as a non-rival good.
To measure how farmers perceived access to markets and the insurance in terms of rivalry, I asked
them to participate in a willingness-to-pay (WTP) game. The WTP game occurred at the beginning
of the project, before the lab games and the policy experiment. In this game, participants received
approximately 2 USD with which to bid on different training sessions, using the strategy method.12
The training sessions varied in terms of topics and numbers of participants. There were five
different types of training, reflecting the types of activities UPC conducts in the region: agriculture
inputs; agriculture insurance; new planting technology (planting pits); funding opportunities for
small farmers; and access to markets (sales commission). Each of the training sessions could have
10, 25, 50, 100, or 500 participants. Players bid to participate in all of the 25 combinations of
policies and number of participants.
This design allows measurement of how people value the information and skills across these stan-
dard agriculture interventions. Additionally, and more importantly for this project, it allows mea-
surement of how the valuation of each policy changes as the number of participants increases.
Figure 4 shows the WTP for the different combinations of policy and number of participants. For
the funding and access-to-markets training, we see a negative relation between the number of peo-
ple willing to pay and the number of participants, which suggests that both policies are perceived
as rival. The insurance policy, in contrast, does not have a strong variation in WTP when the num-
ber of participants increases. We observe a slightly positive correlation, which is a sign of being
perceived as a public good, although the difference is not statistically significant.
12
With this method, players make conditional decisions for every type of training. Only after collecting the bid for
each possible training, players learn which training will be implemented.
12

3.4 Experiment timeline and data collection
In this section, I first detail the timeline of the different moments of the project and then explain
the data collection in detail.
May - Jun ’16 • Village census. Each of the 1227 households in the village is interviewed to
obtain to have the complete mapping of all members of all households and
their basic demographic characteristics.
Oct - Dec ’16 • Network survey. Each household chief is interviewed about the links the
household it has with other people inside the village, along several network
dimensions. From this data, I construct the complete network of the village.
May ’17 • Lab game - session 1. See sections 3 and 3.1 for details.
Sep ’17 • Lab game - session 2. See Section 3.2 for details.
Nov ’17 • Policy experiment. For more details see section 3.3.
Dec ’17 • Policy training sessions and endline survey. Farmers invited in the policy
experiment to the access-to-markets or the agricultural insurance training par-
ticipate in the training. Afterwards, they reply to a short endline survey.
The data sources mentioned above and used during the project are now discussed in detail.
Village Census. This was the first activity conducted in the village, and the objective was to map
each household and each person living in each household. During the census, each household was
marked with a unique identifier written in chalk on the house door. The survey questions in the
census solicited information about basic demographic characteristics of all household members,
household assets, agriculture practices, and the social capital of the head of household (measured
through participation in village organizations and meetings). For every house, GPS coordinates
were also collected. Following the census, I compiled a map of the entire village on which each
household was identified, with information about each individual who lives there. This listing was
crucial for identifying people and their families in the next moment of data collection, when I
mapped the links between people.
Network survey. For the network survey, every head of household was asked about different
dimensions of her network. The network dimensions collected closely follow Banerjee et al.
(2013). The survey asked about people whom the head of household goes to church with, asks for
advice about personal issues, talks with about agriculture, has a farming plot near, visits in his or
her free time, asks for help in a medical emergency, asks for goods (salt or wood), asks for money,
listens to the radio with, or talks with about politics, as well as family members in the village. In
the survey, respondents also identified the households in the village that they believed to be poorer
and richer.
13

From the network data, I plotted the network of the entire village. Each household was a node,
and two nodes were linked if at least one member in one of the households reported having a link
along one of the network questions. This meant that I ignored possible misreporting errors, which
might arise when person A names person B as a friend but B does not name A as a friend. This
issue, first discussed by Comola and Fafchamps (2017), is not relevant to the main results of this
paper, since the randomization of players ensures that any error in misreporting is similar across
groups. Unless mentioned otherwise, the network was represented as an undirected graph.
In the analysis of the results in Section 4, I sometimes consider each dimension of the network
separately (looking only at the family network or the talking-about-agriculture network, for exam-
ple). On other occasions, all the dimensions are merged and I consider there to be a link if there
exists a connection across at least one of the dimensions, following the most common practice
in the empirical networks literature. Figure 5 shows a representation of all the dimensions of the
network.
Endline Survey. In December, UPC conducted the two training sessions in the village. The
sessions occurred on subsequent days, with the access-to-markets training taking place one day
before the agricultural insurance training. Only farmers invited to the specific training could at-
tend. At the end of each day, participants replied to the endline survey. The questions related to
knowledge about the contents of the meeting, willingness to continue to participate in the training
(self-reported intention to adopt), and whether participants would invite the same people again.
3.5 Estimation strategy
This section describes the main econometric specifications used in the paper. I begin the analysis
of the results by estimating the effect of rivalry in the monetary lab game. This effect is estimated
through the specification below, which pools together individuals’ decisions in the two sessions of
the lab game.
Yis = α + β1 Rivalis + γ Orderi + δ Xis + is (1)
where Yi is an outcome of interest and i and s are the identifiers for household and session,
respectively. Rivalis is a dummy variable with value 1 for the rival lab game and 0 for the non-
rival game. Orderi is a control for the game design: playing rival in the first session and non-rival
in the second session, or vice-versa. Xis is a vector of the demographic characteristics of the
player, which includes: gender, education level, position in the household, and whether she was
born in the village. The error term, is is clustered at the household level.13
13
I use household controls instead of household fixed effects because in an extended version of this equation I
will want to add the effect of interacting the treatment with household characteristics. Which would be impossible to
14

To evaluate the effects of rivalry in the lab games and in the policy experiment, I estimate an
extended version of the previous specification:
Yip = α + β1 Rivalip + β2 Lab gameip + β3 Rivalip ∗ Lab gameip
+ γ Orderi + δ Xip + ip
(2)
where Yip is an outcome of interest, i is the household indicator, and p is the play indicator for
the first session of the lab, second session of the lab, or the policy experiment. β1 Rivalip is a
dummy variable that takes value 1 for the rival lab game or the access-to-markets training and 0
for the non-rival lab or the agricultural insurance training. Lab gameip is a dummy variable with
value 1 for either session of the lab game and 0 for the policy experiment. Rivalip ∗Lab gameip is
the interaction term between the two previous dummies, and its coefficient captures the marginal
effect of playing rival in the lab. Orderi is a categorical variable for the order of the game, and
Xip is a vector with the same demographic characteristics as in equation 1. The error term, ip, is
clustered at the household level.
All equations are estimated by ordinary least squares (OLS) for easiness of interpretation.
3.6 Hypotheses
In this section, I detail the main hypotheses tested in this study, beginning with the two that relate
to the effect of rivalry on the number and type of people with whom the information is shared.
The hypothesis regarding the effect of rivalry on the number of people informed follows directly
from previous theoretical work on the diffusion of rival information. Immorlica et al. (2014) model
diffusion of rival information assuming that individuals have incentives to share with friends based
on altruistic motives as well as expectations of future reciprocity but have a preference that the in-
formation should not spread much further than their immediate neighborhoods, since the utility
gained from the information decays as more people are informed. The authors establish condi-
tions in which sharing of rival information reaches equilibrium. One is when there are very few
interactions between individuals, so that informed people can share, knowing it is very unlikely
that their friends will have the opportunity to share with others. In an opposite scenario, in which
interactions are very common, informed individuals share the information because it is likely that
their friends will in any case be informed by someone else, and they prefer to be the first to in-
form them. Using a sequential model of diffusion, Lee et al. (2010) show that, in the presence
of rival information, diffusion might be targeted towards those whom people trust not to share
the information further and that individuals will refuse to link with others in the network out of
commitment to the secrecy of their group. Hence, we expect that rival information to be diffused
estimate with the fixed effects. Nevertheless, the main conclusions do not change if I consider fixed effects alternatively.
15

by informed people ,although at a lower rate than a similar piece of information that is non-rival.
Hypothesis 1: Faced with a piece of rival information, individuals are less likely to share it than
to share a similar piece of information that is non-rival. In terms of the empirical strategy testing,
Hypothesis 1 translates into β1 > 0 in specification 1 and β1 + β3 > 0 in equation 2, when the
outcome of interest is the number of people invited.
The second hypothesis concerns the types of people with whom rival information is shared. Since
it is difficult to measure which people an individual trusts with rival information, we approximate
this relation in terms of the people with whom one has more interactions and closer connections.
Additionally, I consider the hypothesis that informed individuals will issue invitations strategically
based on the centrality of invitees. This idea was initially presented in Banerjee et al. (2012),which
observes how people in Indian villages share information about a lab game in which the number
of slots is limited; in this study, the authors find some evidence of strategic invitations.
Hypothesis 2: Individuals are less likely to share a piece of rival information with people who are
very central in the network than they are to share a similar piece of information that is non-rival
with people who are very central in the network. This translates into testing: β1 > 0 in specifica-
tion 1 and β1+β3 > 0 in equation 2 when the outcome of interest is the centrality of people invited.
Although it is not the main objective of this study, the design allows us to assess the efficiency of
seeding techniques that target people who are more central in the network. It is possible that these
people will decide not to share a piece of information about a rival opportunity since, because their
friends are well connected in the network, it might be riskier to share the information with them.
Hypothesis 3: Targeting initial seeders with higher centrality when diffusing rival information
leads to a lower level of diffusion than targeting if less central people.
4 Econometric results
4.1 Balance
Before presenting the main results, I demonstrate that the randomization was successful. In Table
1, column 1 shows the averages for different characteristics in the non-rival group and the respec-
tive standard deviations. Column 2 presents the difference between the rival and the non-rival
16

groups, with standard errors in brackets. Of all tests performed, only differences in schooling
across the two groups is significant. Although this difference does not invalidate the randomiza-
tion, I control for schooling in the appropriate regressions.
Table 1 also provides an overview of the descriptive characteristics of the initial seeders. They are
on average 41 years old, the majority are female, and only 23 percent were born in the village. The
literacy level is very low in the selected seeders, reflecting low schooling rates nationally. Seventy
percent of the seeders are Muslim. A seeder earns on average 19.5 USD per month. Looking at
the household-level characteristics, on average each household has four members and spends 30
USD per month. All initial seeders own land, which is important for the policy experiment, since
it relates to agriculture practices. Looking at the basic network characteristics, Table 1 shows that
seeders have on average 9.5 friends and have an eigenvector centrality score higher than the village
average. The fact that the seeders sample is more central than the village average is expected since
the sampling process for seeders was designed to over-sample more connected people.
4.2 Behavior in the money lab game
This section focuses on the behavior of the initial seeders in the purely monetary lab presented
in section 3 and considers data pooled from both sessiona 1 and 2 of the lab games. Table 2
shows the effects of playing the rival treatment versus the non-rival treatment on different outcome
variables. All columns report the results of regressions following equation 1. Rival is a dummy
variable that takes value 1 if seeders are playing the rival game and 0 if it is the non-rival game.
The demographic controls included are education, gender, and dummies for being the head of
household and for being born in the village. Regressions also have controls for the order in which
the two treatments were played.
The first set of columns, 1 to 4, report on how the decisions of seeders affected who was invited for
the second round. We can see these as the effects of rivalry on immediate sharing decisions. The
dependent variable in column 1 is a dummy variable that takes value 1 if the initial seeder invited
at least one person for the second round and 0 if the seeder decided to invite no one. Column 2
reports the intensive margin of the invitations: the number of people each seeder invited. Columns
3 and 4 report on the relative importance in the village network of the people whom the seeders
invited. Column 3 shows the results on the average degree of the people invited for round 2. The
degree is defined as the number of links a household has across any of the dimensions of the
network, as explained in detail in Section 3.4. This variable captures how connected in the village
each household is. Next, I look at a different way to measure network importance: eigenvector
centrality. This measures power and prestige in the network, assuming that more central people
are those who are friends with better-connected people.14 Eigenvector centrality is calculated by
14
The idea behind power and prestige centrality is that it does not matter whom one’s friends are but rather with
17

defining a non-negative vector C with scores for each node that solves: C = agC, where g is the
adjacency matrix. The eigenvector scores are those associated with the highest eigenvalue and are
between 0 and 1.15
Players invited others less often in the rival game than in the non-rival game but do not seem
to have targeted people according to their centrality in the network. In the rival game, seeders
were 18 percentage points less likely to invite at least one person and on average invited 0.5 fewer
people. Since players in the non-rival game invited on average 1.9 people, this effect represents a
30 percent reduction in the number of people invited. When I rerun column 2 to include only those
who invited at least one person, the effect is still statistically significant, although smaller, meaning
that even those who did issue invitations named fewer people in the rival game. Looking at the
centrality measures in columns 3 and 4, we find no evidence of targeting more central people in the
network as measured either by degree or eigenvector centrality. It is interesting to note that seeders
invited people who are similar to themselves in terms of centrality. Seeders have around 8.7 links,
and the mean in the control group is 8.4. This occurs because there is positive assortativity in the
network (people with high degree centrality are linked with people with high degree centrality),
which is a common property of social networks (Newman, 2003). The same pattern of results is
observed for eigenvector centrality in column 4.
Columns 5 to 7 report outcome variables measured at the end of all rounds of the game. It tracks
for each initial seeder who was invited in all subsequent rounds as a result of the people she
initially invited. The outcome variable in column 5 is the total number of people who were invited
as a result of the invitations made by the seeder. It takes value 0 if the seeder did not invite anyone
for round 2. Otherwise, it is the number of people she invited for round 2 plus the number of
people those invitees went on to invite for round 3, plus the number of people those invitees went
on to invite for round 4, and so on until the last round. From the total sum I exclude cases in which
the same person was invited more than once. Columns 6 and 7 report on the average degree and
eigenvector centrality for the total number of people invited that is measured in column 5. These
three regressions capture the effect of rivalry on overall diffusion throughout the network.
The rival treatment leads to lower overall diffusion and to more central people receiving invitations
to play the game. Column 5 shows that playing the rival game leads to nine fewer people being
invited by the end of the last round. This represents a 59 percent reduction compared with the
non-rival game. The people who are invited by the end of the game as the result of initial rival
invitations have on average 0.9 fewer links than those invited in the non-rival game (column 6).
Although there is not a statistically significant difference in the second measure of centrality in
whom one’s friends are friends.
15
There are different measures for centrality. For simplicity, I present only the results for eigenvector centrality since
it is the more generally accepted measure in the network literature. If I consider instead the diffusion centrality measure,
the results do not change significantly. This is due to the high correlation between the measures in our sample, which
is commonly seen in other studies as well (see Banerjee et al. (2019) for a recent example).
18

column 7, the point estimate is also positive.
4.3 Rivalry in the lab and in the policy experiment
In this section, I pool the data from the lab game and the policy experiment described in Section3.3
to see how rivalry affects immediate sharing and overall diffusion when the good being shared is
purely monetary or relates to agriculture information. Table 3 presents the results of regressing
equation 2 on the same outcome variables discussed in Section 4.2. Rival (β1) is a dummy variable
that equals 1 for behavior in the rival money lab game or the access-to-markets policy experiment
and takes value 0 for the non-rival money lab or the agricultural insurance policy. Lab game (β2)
is also a dummy and takes value 1 for behavior in the monetary lab game and 0 for the policy
experiment. The third coefficient reported, Rival x Lab game (β3) is the interaction term. The
lower panel of the table includes the p-value for the two-way test of β1 + β3 = 0, which captures
the total effect of rivalry.
Looking at both the lab and the policy, there is a clear negative effect of rivalry on the number of
people invited. This effect is evident in columns 1 and 2 from the point estimates of β1 and β3
and is seen more clearly when one considers the estimates for the total effect of rivalry, which are
statistically significant. There is also evidence that seeders shared more often in the lab game than
in the policy experiment, independently of the treatment assignment (β2 and β3). In terms of the
centrality of the people invited, we do not find evidence that, overall, rivalry affects the targeting
of more central people. The point estimates in columns 3 and 4 of testing β1 + β3 = 0 are close to
zero and statistically not different from zero. If we look only at the effect of rivalry in the policy
experiment (β1), we find that the people invited for the rival policy are less central than in the
non-rival experiment. Note that although both estimates are negative, only the one in column 4 is
statistically significant.
When sharing policy information, rivalry causes people to target people who are less central.
This result persists throughout the diffusion process and is also observed if we look at the overall
diffusion depicted in columns 5 to 7. Column 5 shows the total number of people invited as a result
of the initial invitations of the seeders. Overall, rivalry has a negative effect on total diffusion
(β1 +β3 = 0) but this effect is not present if we consider only the policy experiment. The estimate
of the Rival coefficient is positive (although not significant). From column 5, it is also evident that
there is a sharp difference in diffusion between the money lab game and the policy experiment,
which is consistent with the results in columns 1 and 2. Columns 6 and 7 show evidence that in the
sharing of policy information rivalry leads to less important people being informed, while when
sharing money the opposite happens.
19

4.4 Who are the people invited?
This section looks at the characteristics of the people invited in the money lab and the policy
experiment. In the first part of this section, I focus on the types of links the initial seeders had
with the people they invited for the second round. The second part of this section shows results on
how the wealth of their neighbors influenced the sharing behavior of the initial seeders and led to
poorer or richer people being informed at the end of the diffusion process.
4.4.1 Sharing across the different network dimensions
Table 4 presents the results of estimating equation 2. The outcome variables measure the type
of network dimension that links the initial seeder to the person she invites to play in the second
round. The table shows only effects on direct sharing outcomes and does not look at overall
diffusion measured at the end of the game. All outcome variables are constructed in the same
way as in the previous tables. They are dummy variables that have value 1 if at least one of the
people invited by the initial seeder is someone with whom she has a link along a specific network
dimension. The different dimensions are the ones collected during the network survey described
in Section 3.4. Here I treat links as weak and undirected, meaning that a connection exists if at
least one person named the other in that specific network question.
Columns 1 and 2 show sharing along chitchat dimensions: asking for and offering personal advice
and talking about politics and current events. From the lower panel, we see that in the non-rival
policy experiment, 15 percent of seeders shared with at least one person in the advice network
and 10 percent shared along the talking-about-politics network. There is no statistically significant
difference when we compare this result to that of the rival policy experiment or the lab experiment.
Although some of the people invited for the games and experiments were people with whom the
seeder shared a chitchat link, I find no evidence that having a chitchat link explains the differences
in sharing rival and non-rival information, in this setting.
Next, I consider stronger links connecting the seeders and their invitees. In column 3 the dependent
variable takes value 1 if at least one person invited is a family member of the seeder16 and column
4 considers links based on whom one would ask for help in a medical emergency. From the bottom
panel, we see that around a quarter of seeders invited at least one person with these links in the non-
rival policy. We do not find the same pattern in the invitations for the rival policy. Seeders were 14
percentage points less likely to invite at least one family member to attend the access-to-markets
training and 20 percentage points less likely to invite someone in their health emergency network.
Although the seeders invited a similar number of people for the rival and non-rival policies, they
16
Note that close family from the same household is not considered here, because seeders can only invite people
from outside their household.
20

invited more people with whom they have stronger links to participate in the insurance policy than
in the access to markets. The opposite seems to have occurred in the lab experiment. The rival
money game was shared more often across strong links, as evident in the third line of columns 3
and 4 (although only the last has statistical significance).
The agricultural dimension of the network is particularly important for our analysis for two rea-
sons. First, agriculture is the main source of income for these households. Second, the policy
interventions relate to agricultural innovations. In columns 5 and 6, I present two ways of mea-
suring agricultural links. Column 5 shows results for a dummy that takes value 1 if at least one
person invited is someone with whom the seeder talks about agriculture. Column 6 presents, in-
stead, people who have a plot neighboring the plots of the seeder. From the estimation of β2, we
see that seeders were more likely to share with their agricultural network the money lab game
than the agricultural policy experiments. Looking at the overall effect of rivalry across the lab and
policy experiments, we find a significant negative effect on sharing the rival good with agricultural
connections. This effect is evident if we look at the links with agricultural neighbors in column 6
(β1 + β3 = 0).
Players may use the experiments to help repay previous loans or to show gratitude toward people
whom they regularly ask for material help. Column 7 shows the results for inviting at least one
person from whom the seeder would request a small amount of money, excluding moneylenders.17
In column 8, the dependent variable concerns those from whom seeders might borrow household
goods, such as coal and salt. From the lower panel in column 7, we see that around 17 percent of
seeders did invite at least one person in the borrow-money network in the non-rival policy. This
sharing behavior was much less common when seeders were inviting for the rival policy: only
in less than two percent of cases did the seeder invite someone for the rival policy experiment
from whom she borrows money. This effect is similar in magnitude to the one found in the strong
personal network dimensions in columns 3 and 4. It might be the case that in column 7 we are also
measuring a strong personal link, since in this village (as in many other rural places in developing
countries) people ask for small loans from close family members and friends. Interestingly, we do
not find a similar pattern when we look, in column 8, at whom people ask for household goods.
Overall, there was a negative and significant effect when sharing the rival invitation. Additionally,
seeders shared the money lab game across this dimension of the network more often than in the
policy experiment.
Finally, I check whether the experiments were used by seeders to create new links in the village.
The outcome variable in column 9 takes value 1 if at least one of the people the seeder invited
is someone with whom she had no prior link. For this, I consider all the network dimensions
17
There are very few cases of naming money lenders and the results are not significantly changed if these are also
included.
21

collected and reported in Section 3.4. Since I had collected a very comprehensive list of possible
network dimensions, it is plausible to say that by defining this variable I can capture either no
social link or a very weak link. From the bottom panel, we see that in 15 percent of cases seeders
did invite someone with whom they do not have a previous network link. Although we do not find
differences across rival and non-rival sharing, there is suggestive evidence that link formation was
more likely in the monetary lab game than in the lab experiment.
4.4.2 Sharing with rich or poor connections
In the community-driven development literature there is evidence that local leaders and other
agents target richer villagers to be the beneficiaries of development policies (Bardhan and Mookher-
jee (2006) and Basurto et al. (2017), for example). In the experimental literature, we find evidence
of situations in which agents have a strong aversion to inequality (Fehr and Schmidt (1999) and
Fehr et al. (2007)). It is unclear, therefore, whether richer or poorer connections will be targeted in
our experiment; however, this result might well have consequences in terms of welfare distribution
at the village level. In this section, I examine whether the initial seeders targeted poorer or richer
people in the lab and the policy experiment.
Table 5 shows the results of the estimation of equation 2, in which the outcome variables are
different measures of the wealth of the people invited by the seeders. The first three columns
report the average wealth of the people directly invited by the initial seeders for the second round.
Column 1 shows the average monthly expenditure of the invited households in meticais. Column
2 shows the average monthly income of household heads in meticais. The dependent variable in
column 3 is the average of an index of the assets the invited households own.18 Columns 4 to 6
report on the same outcome variables, now measured at the level of all the people who have been
invited by the end of the last round as a result of the invitations of each seeder, meaning that these
three columns capture the wealth of the same people I consider in the last three columns of Tables
2 and 3.
From the analysis of columns 1 to 3, we find no strong evidence of seeders targeting invitations
according to income. In column 3, we find a significant positive overall effect of rivalry (β1 +β3 =
0), although it is very small. This is very different from the strong effect we find when we consider
the results on overall diffusion in column 5. The estimate for β1 shows that people who are invited
for the rival policy earn half the income of those invited for the non-rival policy, while the marginal
effect of the rival game in the lab is positive. This result could be a consequence of less important
people being the targeted in the rival policy and more central people being informed in the rival
lab, which could happen if more central people are also on average wealthier. So, could it be that
18
The index takes values between 0 and 1 and considers owning the following household items: bike, cellphone,
solar panel, motorbike, bed, television and watch.
22

seeders decided whom to invite based on wealth and not centrality? In Appendix D I investigate
this possibility and conclude that this was not the case. First, I include the interactions with wealth
as a control and rerun Table 3. Second, I look at invitations across the households the seeders
had identified as poorer and richer than their own to see whether there was a conscious motivation
related to wealth and find no statistically significant results.
4.5 Targeting central seeders to diffuse rival information
There is a growing interest in the development literature in ways to identify the best seeders in a
network. Many policy interventions depend on mouth-to-mouth dissemination, since it is impos-
sible (or prohibitively expensive) to inform everyone in a community. But collecting the complete
network data to identify the most central nodes is expensive. Instead, researchers have been look-
ing at different methods for identifying the best diffusion points. Randomly selecting among
friends-of-friends (Kim et al., 2015), identifying the sources of gossip (Banerjee et al., 2019), and
directly asking a random sample of who would be the best diffusers (Banerjee et al., 2018) are
methods that seem to work. Research by Beaman et al. (2018) in Malawi shows large gains from
targeting seeders based on diffusion theory compared to traditional methods. So, overall, there is
a consensus about the advantages of targeting based on centrality.19
In this section, I investigate how targeting based on network centrality affects diffusion when
the information is rival compared to when it is non-rival. Note that the evidence referred to above
relates to information that is closer to a non-rival good: health information, access to microfinance,
and a new planting technology. Hence, this exercise allows us to see how the previous results
translate to goods that have a rival component. To identify the differential effect of targeting in the
rival versus the non-rival case, I estimate the following equation by OLS
Yip = α + β1 Rivalip + β2 Labip + β3 Rivalip ∗ Labi + β4 Top Centralityi
+ β5 Rivalip ∗ Top Centralityi + β6 Labip ∗ Top Centralityi
+ β7 Rivalip ∗ Labip ∗ Top Centralityi + γ Orderi + δ Xip + ip
(3)
where Rivalip, Labip, Orderi and Xip are the same variables as in equation 2 and Top Centralityi
is a dummy variable that takes value 1 if the seeder centrality measure belongs to the top decile of
the distribution of centrality in the village. Centrality is calculated using either the degree or the
eigenvector centrality, and i is the the error term clustered at the household level.
Table 6 presents the results of estimating equation 3 using eigenvector centrality to calculate the
Top Centralityi variable.20 The outcome variables considered are the same as in Table 3. The
19
Akbarpour et al. (2018) is a noteworthy exception. They show theoretically and by simulations that in certain
networks a small number of extra seeders outperforms targeting.
20
Table E6 in the Appendix shows the results if degree centrality is considered instead. The main difference in the
23

first four columns report on the number of people invited by the seeders to play in the second round
and their centrality. In the last three columns, the outcome variables reflect the overall diffusion at
the end of the final round of the game or the experiment.
From the estimation of coefficient β5 + β7 = 0 in column 5, we see that targeting central nodes
to diffuse rival information leads to fewer people being informed overall. This is evidence that
the beneficial effects of targeting initial seeders on the diffusion of rival information are not the
same as those found in previous literature, where information with more non-rival characteristics
has been studied. From the last column, it is also evident that targeting reinforces the effects on
the type of people who receive the information. In the case of the policy information, targeting
the rival type leads to even less central people being informed, while targeting in the lab game
increases the average centrality of people informed in the rival case.
5 Concluding remarks
In this study, I conducted a lab-in-the-field that explores the diffusion of rival and non-rival in-
formation in one rural village in northern Mozambique. Learning in rural villages in developing
countries takes place mostly by word-of-mouth communication, which makes understanding pat-
terns of diffusion of information particularly important. In this project, I focus on one particular
characteristic of information: rivalry, meaning that the benefit to the individual of a piece of in-
formation decreases as more people are informed. After collecting the complete network of the
village, 100 households were randomly allocated into participating in one of two versions of the
game: (a) a non-rival game in which participants received one voucher worth a fixed amount (1
USD) and (b) a rival game in which received a share of a large prize (200 USD), which would,
later on, be divided equally among everyone who owned a share. Each participant in both the rival
and non-rival version had only one decision to make: she could invite up to four people in the
village to participate in the game. People invited by the seeders then had the opportunity to issue
invitations in a second round of the game. Overall the game had five rounds. In addition to the lab
game, I analyzed patterns of invitation in two policy experiments in the village. The good in one
of the experiments (agricultural insurance) was perceived as non-rival and that in the other (access
to markets) as rival.
The results of the lab show a large decrease in sharing the game when it is rival: in the rival game,
players were almost 20 percentage points less likely than in the non-rival game to share with
anyone at all. What is more, among those who did share with at least one person, players in the
rival game shared with fewer people than those in the non-rival game. Considering the complete
results if the targeting is based in degree is that there is no effect of rivalry on the total number of people invited in the
policy experiment.
24

set of people who had played by the end of all the rounds, I find that when information is rival
the diffusion is concentrated toward more central individuals. Finally, there is some indicative
evidence that targeting initial seeders who are more central in the network is not as efficient as
earlier research suggests based on cases in which the rival component is not as salient. Since
central people are connected with people who are very well connected in the village, they seem
to be particularly reluctant to share rival information. This leads to a lower overall diffusion
compared to the scenario in which the initial seeders are less central.
This study draws attention to the effects on diffusion of information being perceived as rival by
seeders. The vast majority of policy interventions in developing countries follow the seeding
model for diffusion of information. When it is impossible (or prohibitively expensive) to inform
everyone in a community, institutions inform a subset of individuals and expect them to share the
information with others. This paper shows how rivalry can significantly reduce the number of
people who come to be informed and how it might lead to information being concentrated in the
hands of the most important people in the village. Policymakers should therefore be careful when
implementing policies that could be perceived as rival by the beneficiaries. Possible solutions
might be to broadcast information, provide incentives for sharing, and target initial seeders who
are less central in the network. But further work is needed to evaluate these possible alternatives.
25

References
Akbarpour, M., S. Malladi, and A. Saberi (2018). Just a few seeds more: Value of targeting for
diffusion in networks. Stanford, Working Paper. 23
Ambrus, A., M. Mobius, and A. Szeidl (2014). Consumption Risk-Sharing in Social Networks.
American Economic Review 104(1), 149–182. 1
Bandiera, O., R. Burgess, E. Deserranno, R. Morel, I. Rasul, and M. Sulaiman (2018). Social ties
and the delivery of development programs. Working Paper. 6
Bandiera, O. and I. Rasul (2006). Social networks and technology adoption in northern mozam-
bique. The Economic Journal 116(514), 869–902. 5
Banerjee, A., E. Breza, A. G. Chandrasekhar, E. Duflo, and M. Jackson (2012). Come play with
me: Experimental evidence of information diffusion about rival goods. Working paper. 5, 16, 3
Banerjee, A., A. G. Chandrasekhar, E. Duflo, and M. O. Jackson (2013). The diffusion of micro-
finance. Science 341(6144), 1236498. 5, 6, 4
Banerjee, A., A. G. Chandrasekhar, E. Duflo, and M. O. Jackson (2019). Using Gossips to Spread
Information: Theory and Evidence from Two Randomized Controlled Trials. The Review of
Economic Studies. 5, 18, 23
Banerjee, A. V., E. Breza, A. G. Chandrasekhar, and B. Golub (2018). When less is more: Exper-
imental evidence on information delivery during india’s demonetization. Working paper Avail-
able at SSRN 3163930. 23
Bardhan, P. and D. Mookherjee (2006). Pro-poor targeting and accountability of local governments
in west bengal. Journal of development Economics 79(2), 303–327. 22
Basurto, P. M., P. Dupas, and J. Robinson (2017). Decentralization and efficiency of subsidy
targeting: Evidence from chiefs in rural malawi. NBER Working Paper. 22
Batista, C., M. Fafchamps, and P. C. Vicente (2018). Keep it simple: A field experiment on
information sharing in social networks. NBER Working Paper. 6
Beaman, L., A. BenYishay, J. Magruder, and A. M. Mobarak (2018). Can Network Theory-based
Targeting Increase Technology Adoption? NBER Working Paper (24912). 6, 23
BenYishay, A., M. Jones, F. Kondylis, and A. Mushfiq (2016). Are gender differences in perfor-
mance innate or socially mediated? Journal of Development Economics (forthcoming). 6
BenYishay, A. and A. M. Mobarak (2018). Social Learning and Incentives for Experimentation
and Communication. The Review of Economic Studies 86(3), 976–1009. 6
26

Besley, T. and A. Case (1994). Diffusion as a Learning Process: Evidence from HYV Cotton.
(174). 5
Bobonis, G. J. and F. Finan (2009). Neighborhood peer effects in secondary school enrollment
decisions. The Review of Economics and Statistics 91(4), 695–716. 5
Breza, E., A. G. Chandrasekhar, and A. Tahbaz-Salehi (2018). Seeing the forest for the trees? an
investigation of network knowledge. NBER Working Paper. 5
Cai, J., A. De Janvry, and E. Sadoulet (2015). Social networks and the decision to insure. American
Economic Journal: Applied Economics 7(2), 81–108. 5
Caria, A. S. and M. Fafchamps (2019). Can people form links to efﬁciently access information?
Economic Journal (forthcoming). 6
Carter, M. R., R. Laajaj, and D. Yang (2014). Subsidies and the persistence of technology adop-
tion: Field experimental evidence from mozambique. Technical report, National Bureau of
Economic Research. 5
Centola, D. and M. Macy (2007). Complex contagions and the weakness of long ties. American
journal of Sociology 113(3), 702–734. 6
Chandrasekhar, A. and R. Lewis (2011). Econometrics of sampled networks. MIT, Working Paper.
1
Comola, M. and M. Fafchamps (2017). The missing transfers: Estimating misreporting in dyadic
data. Economic Development and Cultural Change 65(3), 549–582. 14
Comola, M. and S. Prina (2015). Treatment effect accounting for network changes: Evidence
from a randomized intervention. Available at SSRN 2250748. 5
Conley, T. G. and C. R. Udry (2010). Learning about a new technology: Pineapple in ghana.
American economic review 100(1), 35–69. 5
De Janvry, A., E. Sadoulet, and T. Suri (2017). Field experiments in developing country agricul-
ture. In Handbook of Economic Field Experiments, Volume 2, pp. 427–466. Elsevier. 5
Dellavigna, S., J. A. List, U. Malmendier, and G. Rao (2016). Voting to Tell Others. The Review
of Economic Studies 84(1), 143–181. 5
Fafchamps, M. and F. Gubert (2007). The formation of risk sharing networks. Journal of Devel-
opment Economics 83(2), 326–350. 1
Fafchamps, M. and D. Mo (2018). Peer effects in computer assisted learning: evidence from a
randomized experiment. Experimental Economics 21(2), 355–382. 5
27

Fafchamps, M., A. Vaz, and P. C. Vicente (2018). Voting and peer effects: Experimental evidence
from mozambique. Stanford, Working Paper. 5
Fafchamps, M. and P. C. Vicente (2013). Political violence and social networks: Experimental
evidence from a nigerian election. Journal of Development Economics 101, 27–48. 5
Fehr, E., A. Klein, and K. M. Schmidt (2007). Fairness and contract design. Econometrica 75(1),
121–154. 22
Fehr, E. and K. M. Schmidt (1999). A theory of fairness, competition, and cooperation. The
Quarterly Journal of Economics 114(3), 817–868. 22
Ferrali, R., G. Grossman, M. R. Platas, and J. Rodden (2019). It takes a village: Peer effects and
externalities in technology adoption. American Journal of Political Science (forthcoming). 5
Foster, A. D. and M. R. Rosenzweig (1995). Learning by doing and learning from others: Human
capital and technical change in agriculture. Journal of political Economy 103(6), 1176–1209. 5
Global Data Lab (2016). Subnational human development index (SHDI). Technical report, Insti-
tute for Management Research, Radboud University. 7
Goyal, S. (2012). Connections: an introduction to the economics of networks. Princeton University
Press. 3
Hardy, M. and J. McCasland (2016). It takes two: Experimental evidence on the determinants of
technology diffusion. University of British Columbia, Working Paper. 5
Immorlica, N., B. Lucier, and E. Sadler (2014). Sharing rival information. Working paper. 5, 15
INE (2015). Estatisticas e Indicadores Sociais. Instituto Nacional de Estat´ıstica. 7
Jackson, M. O. (2010). Social and economic networks. Princeton university press. 3
Kermack, W. O. and A. G. McKendrick (1927). A contribution to the mathematical theory of
epidemics. Proceedings of the royal society of london. Series A, Containing papers of a mathe-
matical and physical character 115(772), 700–721. 2
Kim, D. A., A. R. Hwong, D. Stafford, D. A. Hughes, A. J. O’Malley, J. H. Fowler, and N. A.
Christakis (2015). Social network targeting to maximise population behaviour change: a cluster
randomised controlled trial. The Lancet 386(9989), 145–153. 23
Kremer, M. and E. Miguel (2007). The illusion of sustainability. The Quarterly journal of eco-
nomics 122(3), 1007–1065. 5
28

Kretzschmar, M. and M. Morris (1996). Measures of concurrency in networks and the spread of
infectious disease. Mathematical biosciences 133(2), 165–195. 3
Lee, S., P. Persson, and N. Roketskiy (2010). Circles of trust. Columbia University, Working
Paper. 4, 15
Magnan, N., D. J. Spielman, T. J. Lybbert, and K. Gulati (2015). Leveling with friends: Social
networks and indian farmers’ demand for a technology with heterogeneous beneﬁts. Journal of
Development Economics 116, 223–251. 5
Magruder, J. R. (2018). An assessment of experimental evidence on agricultural technology adop-
tion in developing countries. Annual Review of Resource Economics 10, 299–316. 5
Miller, G. and A. M. Mobarak (2014). Learning about new technologies through social networks:
experimental evidence on nontraditional stoves in bangladesh. Marketing Science 34(4), 480–
499. 5
Mobius, M., T. Phan, and A. Szeidl (2015). Treasure hunt: Social learning in the ﬁeld. NBER
Working Paper. 6
Newman, M. E. (2003). The structure and function of complex networks. SIAM review 45(2),
167–256. 18
Oster, E. and R. Thornton (2012). Determinants of technology adoption: Peer effects in menstrual
cup take-up. Journal of the European Economic Association 10(6), 1263–1293. 5
Rapoport, A. (1953a). Spread of information through a population with socio-structural bias: I.
assumption of transitivity. The bulletin of mathematical biophysics 15(4), 523–533. 3
Rapoport, A. (1953b). Spread of information through a population with socio-structural bias: Ii.
various models with partial transitivity. The bulletin of mathematical biophysics 15(4), 535–546.
3
Rapoport, A. (1954). Spread of information through a population with socio-structural bias: Iii.
suggested experimental procedures. The bulletin of mathematical biophysics 16(1), 75–81. 3
29

Table 1: Demographic characteristics of the initial seeders across treatment groups.
Non-rival (control) Rival
(1) (2)
mean diff.
[std.dev.] (std.err.)
Individual-level
Age 41.273 5.114
[17.911] (3.324)
Female 0.615 0.129
[0.506] (0.099)
Born in the village 0.231 0.064
[0.439] (0.084)
No schooling 0.615 0.207∗∗
[0.506] (0.098)
Muslim 0.692 0.074
[0.48] (0.077)
Catholic 0.231 -0.055
[0.439] (0.071)
Monthly income (in USD) 19.581 5.440
[29.128] (20.46)
Household-level
Household size 4.000 -0.559
[1.581] (0.432)
Monthly expenditure (in USD) 30.041 4.092
(15.231] (5.503)
Owns no land 0.000 .
[0.000] ()
Uses improved seeds 0.000 -0.022
[0.000] (0.064)
Uses fertilizer 0.000 0.000
[0.000] (0.044)
Network characteristics
Degree 9.462 0.093
[7.412] (0.927)
Eigenvector centrality 0.176 -0.005
[0.228] (0.03)
Note: Non-rival is the group of initial seeders that receive the non-rival treatment in the first session of the lab game. Rival are the ones
are randomized into the rival treatment in the same session. Degree is the number of links a player has across any network dimensions.
Eigenvector centrality measures power and prestige. Central people are defined as people who are friends with more central people.
And it is calculated by defining a non-negative vector C with scores for each node that solves: C=agC, where g is the adjacency matrix.
The eigenvector scores will be the ones associated with the highest eigenvalue, and are between 0 and 1. ∗∗∗ p<0.01, ∗∗ p<0.05, ∗
p<0.1.
30

Figure 1: Location of the village
Note. Location of research site in the Cabo Delgado province, Mozambique.
Figure 2: Order of the treatment allocation to the initial seeder in each session of the game.
31

Figure 3: Order of the treatment allocation to the initial seeder in the policy experiment.
Figure 4: Willingness-to-pay for different policy interventions with low, medium and high level
of participants.
Note. Data from the willingness to pay game played by the 100 initial seeders. The y-axis shows the amount in bid for each
training, from 0 to 100. In the x-axis there is the number of participants that each training will have: 10 participants (low), 100
participants (medium) and 500 participants (high).
32

Figure 5: Graph of the social connections in the village, merging all network dimensions.
Note. The graph represents the undirected and unweighted links (in red) between all nodes (blue points) in the social network of the
village. Each node represents a household. A link exists between two nodes when at least one of the nodes mentioned the other in
one of the network questions.
33

Table2:Sharingandoveralldiffusionofinitialseedersinthelabgame.
Dep.Var.DirectlyinvitedbytheseedersTotalinvitedasresultofseedersinitialinvitations
Invites(dummy)Num.invitedAveragedegreeAveragecentralityTotalinvitedTotalaveragedegreeTotalaverage
centrality
(1)(2)(3)(4)(5)(6)(7)
Rival−0.184∗∗∗−0.577∗∗∗0.097−0.001−9.022∗∗∗0.890∗∗0.018
(0.056)(0.153)(0.596)(0.018)(1.871)(0.443)(0.013)
Observations198198156156198156156
R20.1710.1560.1700.0930.1700.1140.111
Mean(non-rivalgroup)0.8811.9118.3700.14915.4167.1110.110
Note:Thistableshowsresultsoftheestimationofequation1byOLS.Rivalisadummythattakesvalueoneiftheplayerisplayingtherivallabgame.Allregressionsincludecontrolsfortheorderofthe
gameandindividualdemographiccontrols(education,genderanddummiesforbeingthehouseholdchiefandbeingnativetothevillage).Columns(1)-(4)correspondtotheplayer’sdecisionstoinvitefor
thesecondroundofthelabgame.Thedependentvariableincolumn(1)isadummythattakesvalue1whentheplayerdecidedtoinvitesomeoneforthisround.Column(2)showsthenumberofpeople
instead.Columns(3)-(4)reportonnetworkcharacteristicsofthepeopleinvitedforthenextround.Column(3)considerstheaveragedegreeofthepeopletheplayerdecidedtoinvite.Degreeisthenumber
oflinksaplayerhasacrossanynetworkdimensions.Column(4)showstheaverageoftheeigenvectorcentralityofthepeopleinvitedbyeachplayer.Eigenvectorcentralitymeasurespowerandprestige
centralities.Centralpeoplearedeﬁnedaspeoplewhoarefriendswithmorecentralpeople.Anditiscalculatedbydeﬁninganon-negativevectorCwithscoresforeachnodethatsolves:C=agC,where
gistheadjacencymatrix.Theeigenvectorscoreswillbetheonesassociatedwiththehighesteigenvalue,andarebetween0and1.Columns(5)-(7)presentsimilaroutcomevariablesconsideringthetotal
peopleinvitedtoplaythegame(fromthesecondtothelastround)asaresultoftheinitialinvitationsbyeachseeder.Column(5)isthetotalnumberofpeopleinvitedasaresultofthechaininvitationby
eachseeder.Andcolumns(6)and(7)reportthemeasuresofdegreeandeigenvectorcentralityforthepeopleincolumn(5).Standarderrorsareclusteredatthehouseholdlevelandshowninparenthesis.∗∗∗
p<0.01,∗∗p<0.05,∗p<0.1.
34

Table3:Sharingandoveralldiffusionofinitialseedersinthelabgameandpolicyexperiment.
Invites(dummy)Num.invitedAveragedegreeAveragecentralityTotalinvitedTotalaveragedegreeTotalaverage
centrality
(1)(2)(3)(4)(5)(6)(7)
(β1)Rival−0.118−0.132−1.535−0.111∗∗∗1.506−2.053∗∗−0.102∗∗∗
(0.110)(0.248)(1.119)(0.037)(2.050)(0.934)(0.037)
(β2)Labgame0.263∗∗∗1.061∗∗∗−2.217∗∗∗−0.089∗∗∗13.309∗∗∗−2.928∗∗∗−0.113∗∗∗
(0.079)(0.203)(0.732)(0.030)(2.114)(0.747)(0.033)
(β3)RivalxLabgame−0.067−0.4481.7070.112∗∗∗−10.523∗∗∗3.059∗∗∗0.123∗∗∗
(0.128)(0.301)(1.255)(0.043)(2.981)(1.064)(0.041)
β1+β3=0(p-value)0.0010.0000.7680.9480.0000.0200.119
Observations296296211211296211211
R20.1360.2020.1950.1390.2320.1610.163
Mean(non-rivalpolicy)0.5830.83310.7800.2382.9799.8310.213
Note:Thistableshowstheresultsoftheestimationofequation2byOLS.Rivalisadummythattakesvalueoneiftheplayerisplayingtherivallabgame.Labgametakesvalueoneifitisaplayinthelab
game,andzeroifitisthepolicyexperiment.RivalxLabgameistheinteractionbetweenthetwopreviousdummies.Allregressionsincludecontrolsfortheorderofthegameandindividualdemographic
controls(education,genderanddummiesforbeingthehouseholdchiefandbeingnativetothevillage).Columns(1)-(4)correspondtotheplayer’sdecisionstoinviteforthesecondroundofthelabgame.
Thedependentvariableincolumn(1)isadummythattakesvalue1whentheplayerdecidedtoinvitesomeoneforthisround.Column(2)showsthenumberofpeopleinstead.Columns(3)-(4)reporton
networkcharacteristicsofthepeopleinvitedforthenextround.Column(3)considerstheaveragedegreeofthepeopletheplayerdecidedtoinvite.Degreeisthenumberoflinksaplayerhasacrossany
networkdimensions.Column(4)showstheaverageoftheeigenvectorcentralityofthepeopleinvitedbyeachplayer.Eigenvectorcentralitymeasurespowerandprestigecentralities.Centralpeopleare
deﬁnedaspeoplewhoarefriendswithmorecentralpeople.Anditiscalculatedbydeﬁninganon-negativevectorCwithscoresforeachnodethatsolves:C=agC,wheregistheadjacencymatrix.The
eigenvectorscoreswillbetheonesassociatedwiththehighesteigenvalue,andarebetween0and1.Columns(5)-(7)presentsimilaroutcomevariablesconsideringthetotalpeopleinvitedtoplaythegame
(fromthesecondtothelastround)asaresultoftheinitialinvitationsbyeachseeder.Column(5)isthetotalnumberofpeopleinvitedasaresultofthechaininvitationbyeachseeder.Andcolumns(6)and
(7)reportthemeasuresofdegreeandeigenvectorcentralityforthepeopleincolumn(5).Standarderrorsareclusteredatthehouseholdlevelandshowninparenthesis.∗∗∗p<0.01,∗∗p<0.05,∗p<0.1.
35

Table4:Directsharinginthedifferentnetworkdimensionsinthelabgameandpolicyexperiments.
AskforadviceTalksabout
politics
FamilyHelpinhealth
sos
Talksabout
agriculture
Neighboring
plot
BorrowmoneyBorrowgoodsNoconnection
(1)(2)(3)(4)(5)(6)(7)(8)(9)
(β1)Rival−0.050−0.037−0.142∗−0.203∗∗−0.003−0.063−0.159∗∗−0.0230.024
(0.068)(0.064)(0.083)(0.082)(0.081)(0.056)(0.069)(0.084)(0.099)
(β2)Labgame0.0350.0440.0260.0170.097∗0.117∗∗∗0.0770.155∗∗0.122
(0.049)(0.049)(0.062)(0.053)(0.057)(0.041)(0.054)(0.064)(0.078)
(β3)RivalxLabgame0.0190.0130.1400.168∗−0.049−0.0510.098−0.077−0.072
(0.079)(0.082)(0.100)(0.088)(0.085)(0.072)(0.084)(0.090)(0.115)
β1+β3=00.4180.5530.9750.5020.240.0110.1750.0440.372
Observations296296296296296296296296296
R20.1100.1640.1220.1200.1020.0890.0940.1030.053
Mean(non-rivalpolicy)0.1460.1040.2500.2920.1250.1040.1670.2290.146
controls(education,genderanddummiesforbeingthehouseholdchiefandbeingnativetothevillage).Thedependentvariablesinallcolumnsaredummiesthattakevalueoneifatleastoneofthepeoplethe
initialseederinvitesforthesecondroundisinaspeciﬁcnetworkdimension.Incolumn1thedummytakesvalue1ifatleastoneofthepeopledirectlyinvitedissomeonetheseederasksorgivesadviceon
personalissues.Column2concernspeoplewithwhomtheseedertalksaboutpolitics.Incolumn(3)Iconsideradummythattakesvalue1ifatleastoneofthepersonsinvitedforthenextroundisafamily
memberoftheplayer.Column(4)issomeoneplayerswouldaskforhelpinamedicalemergency.Columns(5)considerspeoplewithwhomtheplayertalksaboutagriculture.Andcolumn(6)indicates
thosewhohaveanagriculturalplotclosetotheinitialseeder.Incolumn(7)Iconsiderpeopletheplayerusuallyasksforsmallamountsofcash,excludingmoneylender.Andcolumn(8)isforpeoplewho
seedersaskforcookinggoodssuchassaltorcoal.Inthelastcolumn,thedependentvariabletakesvalueoneifatleastonepersoninvitedisnotinanyofthedimensionsofthenetworkwecollected,and
zeroifitis.Standarderrorsareclusteredatthehouseholdlevelandshowninparenthesis.∗∗∗p<0.01,∗∗p<0.05,∗p<0.1.
36

Table5:Sharingandoveralldiffusionofinitialseedersinthelabgameandpolicyexperiments-wealthoutcomes
ExpenditureIncomeAssetsExpenditureIncomeAssets
(1)(2)(3)(4)(5)(6)
(β1)Rival−225.307−120.7920.041−150.647−770.621∗∗−0.030
(495.358)(460.527)(0.040)(384.895)(330.990)(0.032)
(β2)Labgame−272.937−146.388−0.017−461.709∗−610.456∗∗−0.043∗
(353.134)(281.905)(0.032)(240.269)(250.723)(0.025)
(β3)RivalxLabgame130.558−78.580−0.005233.801675.525∗0.042
(547.917)(505.182)(0.048)(403.529)(347.346)(0.038)
β1+β3=0(p-value)0.6180.2720.0820.5870.4960.48
Observations211211208211211210
R20.0800.1730.1590.0510.1470.068
Mean(non-rivalpolicy)2,346.51,344.60.2982,376.41,598.80.314
controls(education,genderanddummiesforbeingthehouseholdchiefandbeingnativetothevillage).Column1showstheaveragemonthlyexpenditureoftheinvitedhouseholdsinmeticais.Column2
showstheaveragemonthlyincomeofhouseholdheadsinmeticais.Thedependentvariableincolumn3istheaverageofanindexoftheassetstheinvitedhouseholdsown.Theindextakesvaluesbetween0
and1andconsidersowningthefollowinghouseholditems:bike,cellphone,solarpanel,motorbike,bed,televisionandwatch.Columns4to6showtheexactsamevariablesbutmeasuredatthelevelofall
peoplewhoareinvitedbytheendofthelastroundasaresultfromtheinvitationsofeachseeder.Standarderrorsareclusteredatthehouseholdlevelandshowninparenthesis.∗∗∗p<0.01,∗∗p<0.05,∗
p<0.1.
37

Table6:Sharingandoveralldiffusionofinitialseedersinthelabgameandpolicyexperiments-theroleofinitialtargeting-topdecile.
Invites(dummy)Num.invitedAveragedegreeAveragecentralityTotalinvitedTotalaverage
degree
Totalaverage
centrality
(1)(2)(3)(4)(5)(6)(7)
(β5)RivalxTopcentrality−0.332−0.843∗0.929−0.153−2.645−3.425−0.229∗∗
(0.258)(0.443)(4.822)(0.120)(2.687)(2.739)(0.091)
(β6)TopcentralityxLabgame−0.115−0.117−0.390−0.0575.835−0.950−0.111
(0.150)(0.447)(1.667)(0.078)(4.522)(1.675)(0.079)
(β7)TopcentralityxLabxRival0.1880.3450.4570.220−5.0004.3400.273∗∗
(0.288)(0.574)(5.335)(0.150)(5.498)(2.925)(0.107)
β5+β7=0(p-value)0.3120.2430.480.3140.1020.5360.39
Observations296296211211296211211
R20.1540.2220.2510.3020.2520.2280.322
Mean(non-rival,topcentral,policy)0.7501.25012.9260.3744.25011.7110.341
Note:Thistableshowstheresultsoftheestimationofequation3byOLS.RivalxTopdegreeistheinteractiontermbetweenadummythattakesvalueoneiftheplayerisplayingtherivallabgameanda
dummyforbeinghavingadegreeinthetopdecileofthedistributionoftheeigenvectorcentralityinthevillage.TopdegreexLabgameistheinteractiontermbetweenthesamedummyfordegreeinthetop
quartileandadummytakesvalueoneifitisaplayinthelabgame,andzeroifitisthepolicyexperiment.TopdegreexLabxRivalistheinteractiontermbetweenthetwopreviousdummies.Allregressions
includecontrolsfortheorderofthegameandindividualdemographiccontrols(education,genderanddummiesforbeingthehouseholdchiefandbeingnativetothevillage).Columns(1)-(4)correspondto
theplayer’sdecisionstoinviteforthesecondroundofthelabgame.Thedependentvariableincolumn(1)isadummythattakesvalue1whentheplayerdecidedtoinvitesomeoneforthisround.Column
(2)showsthenumberofpeopleinstead.Columns(3)-(4)reportonnetworkcharacteristicsofthepeopleinvitedforthenextround.Column(3)considerstheaveragedegreeofthepeopletheplayerdecided
toinvite.Degreeisthenumberoflinksaplayerhasacrossanynetworkdimensions.Column(4)showstheaverageoftheeigenvectorcentralityofthepeopleinvitedbyeachplayer.Eigenvectorcentrality
measurespowerandprestigecentralities.Centralpeoplearedeﬁnedaspeoplewhoarefriendswithmorecentralpeople.Anditiscalculatedbydeﬁninganon-negativevectorCwithscoresforeachnodethat
solves:C=agC,wheregistheadjacencymatrix.Theeigenvectorscoreswillbetheonesassociatedwiththehighesteigenvalue,andarebetween0and1.Columns(5)-(7)presentsimilaroutcomevariables
consideringthetotalpeopleinvitedtoplaythegame(fromthesecondtothelastround)asaresultoftheinitialinvitationsbyeachseeder.Column(5)isthetotalnumberofpeopleinvitedasaresultofthe
chaininvitationbyeachseeder.Andcolumns(6)and(7)reportthemeasuresofdegreeandeigenvectorcentralityforthepeopleincolumn(5).Standarderrorsareclusteredatthehouseholdlevelandshown
inparenthesis.∗∗∗p<0.01,∗∗p<0.05,∗p<0.1.
38

Supplementary Appendix
A Descriptive statistics of the population
Table A1: Descriptive statistics of the village.
Average St. Dev.
Age 39.64 16.27
Female 0.64 0.48
No schooling 0.52 0.50
Born in the village 0.24 0.43
Muslim 0.79 0.40
Catholic 0.17 0.38
Household size 4.46 2.26
Monthly income (in USD) 16.86 46.22
Owns no land 0.09 0.28
Uses improved seeds 0.10 0.31
Uses fertilizer 0.03 0.18
The data was collected from a census conducted at the start of this project.
B Selection of initial seeders
The experimental game was initiated through 100 initial seeders. To ensure variation in the types
of people seeding the game, I selected a group of 100 nodes using five different criteria (20 from
each). These were chosen to reflect standard methods used by policy makers to select recipients
of information about development programs.
When choosing to target a limited number of households in a village, a standard approach is to
randomly select a group of representative households. This can be done using a random walk
sampling technique, in which enumerators walk away from the center of the enumeration area and
select households using a predefined sampling interval. Twenty of our initial nodes were chosen
using this technique.2 When the geographic location of households is considered to play a role for
the program (for example, if it is an agriculture program in which plot characteristics matter, or in a
case in which social ties matter and researchers proxy those with geographic distance) another pos-
sible method is to perform a geographic randomization of the village. Since there is evidence that
geographic proximity is correlated with social proximity 3 I use a spatial randomization method
(grid-randomization) to select 20 additional households.
The remaining 60 seeders were not selected with a representative objective in mind but rather with
2
Five enumerators selected 4 households each, using a sampling interval of 45 houses.
3
Empirical evidence includes: Fafchamps and Gubert (2007), Ambrus et al. (2014) and Chandrasekhar and Lewis
(2011).
1

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