Directed Generosity in Social and Economic Networks

Directed Generosity in Social and Economic Networks ^∗

Ben D’Exelle Arno Riedl November 19, 2008

Abstract

We explore, first, if network structure and network embeddedness can account for the variation observed in individual generosity, and what characteristics of the network are im- portant (if any). Second, if the influence of network structure and position is invariant across networks of different social and economic contents, and what different network characteristics are important for networks of different contents. Third, if there is explanatory room left for observable individual characteristics.

Our results show that networks matter for giving behavior, hence, it matters which net- works one is looking at when relating behavior to network structures and an individual’s position in a network. Regarding observable individual characteristics only gender has ex- planatory power. Interestingly, and in contrast to many other studies, women are less gener- ous than men, especially when the recipient is male. A final finding is that certain individual characteristics are important determinants of link formation in all elicited networks. This suggests that the influence of observable characteristics on generosity runs indirectly through the network effects.

∗

Ben D’Exelle: University of Antwerp, IOB, Stadscampus, Lange Sint Annastraat 7, 2000 Antwerp, Belgium, ben.dexelle@ua.ac.be; Arno Riedl: CESifo, IZA, and Maastricht University, Department of Economics (AE1), P.O. Box 616, 6200 MD Maastricht, the Netherlands, a.riedl@algec.unimaas.nl. This paper is part of the research project Experimental analysis of the formation, dynamics and economic consequences of social networks financed by the Oesterreichische Nationalbank (project number 11429). It has also benefited from support provided by VLIR-UOS and IOB-UA. The authors would like to thank Guy Delmelle, Ligia G´ omez, Miguel Alem´ an, Francisco P´erez, Selmira Flores and Alfredo Ru´ız for interesting methodological discussions; Tania Paz Mena, Leonardo Matute, Francisco Paiz Salgado, Edna Garc´ıa Flores, F´ atima Guevara, Silvia Martinez Arr´ oliga and Will Tellez for support in the field work; Vanessa Castrillo and Jazmina Andino for their help in the search for sufficient coins of money; Elizabeth Campos and Manuel Bermudez of the Fondo de Desarrollo Local for offering a safe in one of their local banking offices; the local support of community leader Francisco Varela; colleagues at MU and IOB-UA for comments on the experimental design; participants of the sixth workshop on ‘Dynamic Networks’

at Utrecht School of Economics, ESA 2007 (Rome), ECINEQ 2007 (Berlin), CIDIN (Radboud University) and

the Young Talent Day of the Maastricht Graduate School of Governance for comments on earlier versions of this

paper. We also benefited from comments of Rebecca Blank (Brookings Institution) and Michael Woolcock (Brooks

World Poverty Institute).

1 Introduction

Generosity is a widespread, economically and socially relevant, phenomenon. However, not all people exhibit generous behavior and those who give vary substantially in their generosity. This variation in giving remains a puzzle because observable individual characteristics are largely uncorrelated with it. In this paper we study the effects of embeddedness in real social and eco- nomic networks on generosity, and show how different network structures and network positions contribute to the observed heterogeneity in giving to members in the network.

In controlled experiments it is repeatedly observed that people are willing to give up money even if the recipient is an anonymous stranger (Camerer, 2003, chapter 2). This habit of giving, which is observed across nations and cultures (Henrich et al., 2005) is not only confined to experimental environments. For instance, only about half a year after the hurricanes Katrina and Rita hit the U.S. Gulf Coast the Center on Philanthropy at Indiana University reports private cash donations for disaster relief of more than USD 3.35 billion. ¹ Generally, generous behavior in the form of charity giving is an important economic factor. According to Andreoni (2008), in 2005 charitable giving by private individuals totaled about USD 200 billion in the United States.

Less pronounced but qualitatively similar figures are found in other countries around the world (Andreoni, 2006). Generosity is also widespread in every day life as, e.g., neighborhood support or volunteering, and is generally perceived as a desirable habit in society.

However, giving behavior varies substantially among individuals. In dictator game experi- ments any sharing between 0 and 50 (sometimes even 100) percent of the allocator’s endowment is observed (cf. Camerer, 2003 for an overview; Fisman et al., 2007; List, 2007; Whitt and Wilson, 2007; Bardsley, 2008; Carpenter et al., 2008; Konow et al., 2008 for recent evidence). Field evi- dence on charity giving also shows a large variety in the amounts given even when controlling for income (Andreoni, 2006). Many attempts have been made to explain variations in giving behav- ior by resorting to demographic and socio-economic variables. Although some of the observed variance can indeed be related to such measurable individual characteristics, the explanatory power of these characteristics appears to be weak. ² For instance, in their cross-cultural study

1

Total cash donations as of February 20, 2006. Information retrieved from www.philanthropy.iupui.edu on September 23, 2008 (1:14 PM). Similarly, total private U.S. donations for relief efforts in Southeast Asia following the tsunami of December 2004 amounted to more than USD1.6 billion (www.philanthropy.iupui.edu).

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In the field next to income only age has been regularly found to be positively correlated with generosity

(Andreoni, 2006). Also in the experimental literature only age turns out to have a clear (positive) effect on gen-

erosity (e.g., Krause and Harbaugh, 2000; Bellemare et al., 2008; Carpenter et al., 2008). Regarding gender the ev-

idence appears somewhat mixed. Croson and Gneezy (2008) conclude in their survey that women tend to be more

generous, but are also more sensitive to context. Some studies also found that pro-social behavior can be affected

by ethnicity (Glaeser et al., 2000; Eckel and Grossman, 2001; Fershtman and Gneezy, 2001; Whitt and Wilson,

Henrich et al. (2001) have to conclude that “individual-level economic and demographic variables do not explain behavior either within or across groups” (p. 74).

Although it is now widely recognized among economists that social and economic networks play an important role in economic life, ³ in the past surprisingly little attention has been given to the question how the embeddedness into real social and economic networks contributes to heterogeneity in giving behavior. A notable exception is Glaeser et al. (2000), who investigate the effect of social closeness between individuals on trust and trustworthiness, but not on generosity. ⁴ An important objective of our study is, therefore, to examine in what way the structure of and the position in a real social and economic network influences a person’s generosity.

When looking at social and economic networks it is important to recognize that people are simultaneously embedded in networks of different contents that are not necessarily (completely) overlapping. ⁵ Friends, family members, neighbors, colleagues at the workplace, etc. are gener- ally not all the same people and the ties with them are likely to differ. Different networks are important for different activities, may exhibit different structures and, therefore, have different effects on behavior. For instance, Coleman (1988, 1990) argues that networks that are locally dense and have high clustering are beneficial for overcoming free-riding in collective action prob- lems. Hence, such network structures may also foster generous behavior within the network.

Friendship networks are often of this type. On the other hand, according to Burt (1992, 2005) agents with a central position in a network are favored as they have more power and, hence, are able to pursue their own goals better. Whether this leads to more or less generosity remains to be seen. Since it is conceivable that networks with different contents exhibit different structures, it follows that their effects on giving behavior may also differ. Surprisingly, Podolny and Baron

2007). For an overview, see Camerer (2003, chapter 2.3). In a recent innovative study, Cesarini et al. (2009) suggest that part of the observed variation is due to genetic differences.

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Network effects have been shown to be important among others for welfare culture (Bertrand et al., 2000), migration and labor markets (Rees, 1966; Granovetter, 1973, 1995; Montgomery, 1991; Ioannides and Loury, 2004;

Munshi, 2003), mutual insurance (Fafchamps and Lund, 2003; De Weerdt and Dercon, 2006), informal credit mar- kets (McMillan and Woodruff, 1999; Karlan, 2007), and international trade (Casella and Rauch, 2002).

4

Charness and Gneezy (2008) find that decreasing social distance by revealing the name of the recipient in- creases dictator game giving and Bohnet and Frey (1999) report that decreasing social distance in the form of identification by looking at each other positively influences contributions to public goods. Hoffman et al. (1996) in their seminal work have shown that the social distance between experiment subjects and experimenter also influences giving behavior in dictator games. These findings are recently challenged by Dufwenberg and Muren (2006), who question that less anonymity equals less social distance.

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One may call networks of different contents, ‘types of networks’. However, often network types are also used as a synonym for network architectures. Therefore, to avoid confusion we will use the terms ‘contents’ and

‘dimensions’ when we refer to networks of different contents.

(1997) seem to be the only authors who have taken up this issue. ⁶ Hence, little is known about the differential behavioral effects of networks with different contents. Our study contributes to this knowledge.

In this paper we explore, first, if network structure and network embeddedness can account for the variation observed in individual generosity, and what characteristics of the network are important (if any). Second, we study, if the influence of network structure and position indeed varies across networks of different social and economic contents, and what different network characteristics are important for the different dimensions of networks. Third, we investigate, if there is explanatory room left for observable individual characteristics. Finally, we also explore if individual background characteristics influence network formation itself. Finding such an effect would indicate that these characteristics affect generosity indirectly via the channel of network formation.

To address these issues we elicited (almost) completely real networks of different contents (friendship, kinship, neighborhood, economic relations and more) among household heads in a rural village in Nicaragua by means of a network survey. ⁷ In addition, we gathered important individual background characteristics as sex, age, wealth, education etc. To measure generosity and relate it to the networks we organized a series of dictator game experiments with household heads. Each ‘dictator’ was matched with known participants from the village as well as a stranger from another village. The latter gives an indication of the dictator’s general inclination towards generosity independent of the network structure.

There are a few very recent papers that report on studies related to ours. These studies differ in set-up, subject pool, and research questions, however. Goeree et al. (2007) and Leider et al.

(2008) also combine network elicitation with controlled experiments. The latter elicited the friendship networks among students residing in two dormitories at Harvard University and let them play variants of the dictator game and a helping-game. The former authors also elicited friendship networks and investigate dictator game giving among teenagers in an all-girls school.

Bra˜ nas-Garza et al. (2006) conduct a similar study with Spanish students.

Our study differs in several important aspects from this literature. First, we conducted our experiment not at a school or university but in the field where heterogeneity of participants tends to be much larger. This increases the chance to detect the influence of observable individual char-

6

In his seminal contribution Burt (1992) maps different contents of social relations of managers but ignores it in his empirical analysis for reasons of tractability. Podolny and Baron (1997) criticize this and show that the implications of network structures are not independent of link content. (We thank Saurabh Arora for providing this information in a personal communication.)

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We address the advantages and possible disadvantages of conducting our research in such a village below in

Section 2.

acteristics on generosity. Second, we elicited social and economic networks of different contents (e.g., friendship, family, social support, economic exchange, neighborhood), which allows us to separately analyze the effect of these different networks. Importantly, as the mentioned studies, we also elicited friendship networks which makes it possible to check if the friendship network effects found among students and pupils carry over to friendship networks outside schools or colleges. Third, we conducted our study in a rural village in a developing country where other cultural and social forces than among Western student populations are likely to be important (Henrich et al., 2005; see also, Ensminger, 2004). If we nevertheless find that the same network variables are important determinants of generous behavior this would give strong support for the hypothesis that network effects are universally applicable.

Indeed, regarding the network effects of friendship ties we confirm the main result of Goeree et al. (2007) and Leider et al. (2008) that social distance matters for generosity also in our very different subject pool. Direct friends are treated significantly more generous then people at larger geodesic differences, including complete strangers. This result also holds for general networks (i.e., the union of all specific networks) and extended family networks. Importantly, however, the network effect on giving is not invariant across other networks with different con- tents. Furthermore, for generosity not only social distance but also network positions and local network structure matter. In the friendship network generosity decreases with the connectedness of dictators’ and receivers’ indirect friends, indicating that structural power - in the sense of Burt - correlates positively with generosity. In support networks social distance is not important for generosity but the density of a dictator’s local network correlates significantly positively with giving behavior. This result supports Coleman’s (1990) idea that locally dense networks favor pro-social behavior. In networks defined by social public activities generosity towards a recipient negatively correlates with the recipient’s local centrality, that is, her importance in such activi- ties. In extended family networks, social distance is an important determinant of giving behavior.

On top of this, generosity also decreases with the size of the local network of the dictator and the recipient. Interestingly, in the network defined by joint economic relations neither the position in nor the structure of the network exhibit any significant effect on generosity.

Together, our results show that networks matter for giving behavior and that network effects

clearly depend on the contents of networks. Regarding observable individual characteristics we

find that after controlling for network effects only gender has explanatory power. Interestingly,

and in contrast to many other studies, women are less generous than men, especially when

the recipient is male. A final finding is that certain individual characteristics are important

determinants of link formation in the elicited networks. This suggests that the influence of

observable individual characteristics on generosity runs indirectly through the network effects.

The rest of the paper is organized as follows. In Section 2 we describe our research design, in particular the network elicitation procedure and the procedures regarding the dictator game experiment. In Section 3 first some descriptive statistics are presented followed by regression results explaining dictator giving with network effects and observable individual characteristics.

The section closes with an analysis of the determinants of link formation in the different networks.

Section 4 summarizes and concludes. Appendix A, contains the instructions for the experiment, the post-experimental questionnaire, and the description of network elicitation.

2 Research Design

We collected data from household head(s) of an entire village in rural Nicaragua. Our research design is composed of three components. First, a questionnaire study for gathering individual background characteristics was organized. Second, a network survey for eliciting social and eco- nomic ties was appended to this questionnaire. Third, dictator game experiments between were conducted immediately after the survey work was finished. After the experiment the participants were asked to answer some post-experimental questions. The study was carried out in April, 2007 and completed within five days. In the following we further describe the main features of our research design. For details we refer the reader to Appendix A.

2.1 Background Characteristcs and Network Elicitation

With the household survey we gathered data on economic assets and activities (e.g., possession of land or cattle), family composition, education, age, sex, etc. The household survey was conducted in a standard manner, by visiting the households in the village and interviewing the household head(s). For network elicitation we adapted a method successfully used by economic anthropologists and sociologists for mapping bounded networks. ⁸ In brief, to elicit the ties of an interviewee we used a staple of small cards representing all households in the village. Each card held the names of the household heads. For each of the cards the interviewee was asked whether he or she knows the household and whether he or she had a “social relation of any kind”

with one of its members. If the answer was affirmative we asked for details on the content of the relation. This enabled us to capture individual ties on multiple dimensions. In particular,

8

Bounded networks are networks with - as the name suggests - clearly defined boundaries, such as networks

within villages and organizations, for which all members are surveyed. In the sociological and anthropological

literature bounded networks are sometimes also called complete networks. For a description of the method see,

e.g., the Documents section of Jean Ensminger’s and Joseph Henrich’s Roots of Sociality project web-site at

http://www.hss.caltech.edu/roots-of-sociality/phase-ii/docs.

next to the general (social) relations, we elicited network data for friendships, extended families, support, neighbors, social public activities, and economic/business relations. ⁹

2.2 Experiment

Each participant in the role of dictator was explained that he or she would sequentially receive six small cylinder-boxes each containing 20 coins of one C´ ordoba, c$ (the Nicaraguan currency), which he or she could share with one other person, the recipient. For each dictator the first re- cipient was an unknown person from another village in the region. The five subsequent recipients were randomly selected village members. The random selection involved the dictator drawing cards out of a bag containing all 123 household heads. The name of a recipient was drawn only after the dictator had finished the previous distribution decision. Dictators were informed of the procedures before they made any decision and, hence, knew that their maximum possible earnings would be c$ 120,- (USD 6.70 at the time of the experiment), which corresponded to more than a two days average income in Nicaragua. ¹⁰ . Our research strategy was to conduct the experiment with all households. However, to minimize contagion and spill-over effects only one household head per household was allowed to participate as a dictator. In case of a two-headed household it was randomly determined who was asked to participate. We did not exclude par- ticipation of the other household head in the role of receiver, but ensured that heads of the same household were not matched as a dictator-receiver pair.

In most experimental studies conducted in small-scale societies participants gather at one public spot (e.g., Cardenas et al., 2000; Henrich et al., 2004). However, there are a couple of potential problems coming along with such a set-up. First, self-selection is impossible to avoid, because some people are more inclined to participate in public events than others. Second, during such gatherings communication and mutual influence among participants is hard to control.

Therefore, we decided to organize our experiment in a decentralized way. We employed five Nicaraguan research assistants, each of which visited participants at his or her home to conduct the experiment.

To minimize behavioral biases due to potential post-experimental side payments or social

9

Leider et al. (2008) used an incentivized coordination game procedure for eliciting their friendship networks among Harvard students. There are two reasons why we did not adopt their elicitation method. First, it would have become too complicated for our often illiterate subjects, who are not used at all to abstract exercises. Second, practically it is only applicable for the elicitation of one network content. One of our main interests, however, lies in capturing multiple dimensions of networks.

10

We also considered to pay out only one randomly chosen decision, but decided against it because the (expla-

nation and implementation of a) relatively abstract randomization device would have been very time consuming

and may have also raised suspicion in our subjects who are not used to experiments.

pressure we implemented a one-way anonymity design. Each dictator was clearly made aware that, although he or she knows the identity of the recipient, the recipient does not know and also does not get to know who has distributed the money. ¹¹ To minimize influence on dictators’

decisions potentially exerted by the assistant’s presence the experimental procedures included the following three elements. First, decisions were made in full privacy. As a rule participants went inside their house or to a separate room when making a decision. If this was not possible the assistant turned his or her back when the dictator was handling the coins. The dictators were also instructed not to make any comments about their decisions. Second, after having taken the coins they wanted to keep dictators had to fill up the box with metal rings. This ensured that the weight of the box remained constant irrespective of the amount of coins taken out. Third, after each decision the box was sealed with tape. The decisions were recorded by the assistants’

supervisor (one of the experimenters) who did not have any interaction with the participants.

The dictators were aware of these procedural details before they made any decisions.

An important aspect in experiments is that participants trust the researchers. This is espe- cially true in the field and when participants are not used to experiments. To build trust with local people, we first conducted the household and network survey. This ensured that when con- ducting the experiment the research assistants were already known to the local people. Another important element was the support of the well-respected local community leaders, who presented our team to each household and asked people to cooperate. After having finished the surveys, which took four days, we immediately organized the experiment. By conducting the whole ex- periment in only one day we minimized possible contagion and spill-over effects. Indeed, from the debriefing we know that 94.5 percent of the participants did not talk about the experiment with other village members who had already participated before. In addition, the research assis- tants were asked to make a subjective evaluation about the participant’s dedication, trust and understanding of the experiment. We did not notice any problems that could have affected the dictators’ decisions.

3 Results

In this section we start out with reporting important descriptive summary statistics about the experiment site and participants. We present socio-economic data and discuss important network

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When delivering the money to recipients we did neither reveal the identity of the dictators, nor did we inform

them about how many dictators had participated. Dictators and recipients did also not learn anything about

others earnings. All this was known by the dictators before they made their decisions. In this way, the influence

of anticipated post-experimental side payments on dictators’ decisions was minimized. It also eliminated the

potential influence of anticipated repercussions or social pressure after the experiment.

measures of elicited networks of different contents. Thereafter, we bring together the individual and network data with giving behavior in the experiment and present regression analyzes. Finally, we investigate how individual characteristics influence network formation and, hence, indirectly giving behavior.

3.1 Socio-economic Background

The investigated village is located in a rural area in the Northern part of the Pacific region of Nicaragua, close to the border with Honduras. The difficult agro-ecological conditions (dry season, irregular rainfall, low fertility of soils, etc.) make agricultural activities little profitable.

Cattle-breeding is one of the most lucrative economic activities in the region, as it is both an income source and an important savings instrument that enables local people to bridge the long and harsh dry season. The possession of cattle is therefore an important wealth indicator.

Table 1: Summary statistics of important socio-economic characteristics

Households mean/percentage st.dev. obs. percentage of all

Land (percentage of owners) 35 - 58 87.9

Land (average in ha.) 8.22 (17.33) 58 87.9

Cattle (percentage of owners) 47 - 58 87.9

Cattle (average in # of animals) 3.55 (9.44) 58 87.9

Household heads mean/percentage st.dev. obs.

Sex (percentage male) 50 - 107 87.0

Age 45.86 (14.55) 107 87.0

Years of education 4.19 (3.60) 107 87.0

Years of residence 33.22 (15.63) 100 81.3

Visits to urban center 2.07 (3.36) 100 81.3

Table 1 shows some important descriptive statistics regarding the households and the house-

hold heads in the village. In total there are 66 households in the studied village, of which 9 are

single-headed and 57 two-headed, thus 123 household heads in total. We succeeded to gather data

of 58 households (87.9 percent). The table shows quite some heterogeneity among households as

well as household heads. Only 35 percent of all households own any land and land possession in

terms of hectares is very unequally distributed. The standard deviation is more than two times

larger than the mean of 8.22 hectares. For cattle possession the figures are similar. Only 47 per-

cent of all households possess any cattle and the average number of cattle per household is 3.55

with a standard deviation of 9.44. The unequal distribution of land and cattle implies a large

variation in wealth among households. The variation in important individual characteristics of

household heads is also considerable. About 50 percent of all interviewed household heads are

female and the age varies between 21 and 86 with an average of 46 years. The household heads reside in the village between only half a year and 70 years with an average of 33 years. The average education level of the household heads, measured in the number of years of schooling, is lower than 5 years with also quite some variation. Another potentially important characteristic is the frequency of contact with the urban center. It varies between 0 and 26 visits in the most recent month before the survey. In summary, we have considerable variation in socio-economic characteristics giving it good preconditions for explaining possible variations in generosity in our experiment.

3.2 Social and Economic Networks

An important characteristic of interactions between people is that they take place on several dimensions. People are related not only via kinship and friendship but are also connected be- cause they support each other, engage in economic transactions or because they are neighbors.

Naturally, these networks of different contents will partly overlap but partly also differ. Business partners are not necessarily friends and friends are not necessarily neighbors and so on. We also expect that the structures of these networks differ and that network positions of agents vary across different network contents. In consequence, we expect that the network effects on giving behavior will differ across networks with different contents. For instance, one may hypothesize that friendship networks are more important than economic relations, as the latter are based on a market-logic where there is probably little room for generosity.

We succeeded in gathering network data for 100 of the in total 123 household heads (81.3 percent). ¹² When eliciting the networks we first asked the interviewees about having any social relation with the person in question. In this way we gathered data of general relations, which are relations aggregating all types of social and economic interactions. Next to this network we elicited social and economic networks of six different contents (dimensions). First, friendship relations are relations where a person calls another one a friend. Second, family relations are kinship relations with parents, brothers, sisters or children. We extended these family relations by also including relations of godparenthood, which are important in Nicaraguan rural life. ¹³ This defines our network of extended family relations. Third, support relations are relations where any type of help, e.g., small amounts of food, cash, transport manpower, is given to a needy person in

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This ratio is slightly higher than those of Goeree et al. (2007) (77 percent) and Leider et al. (2008) (71 percent).

Of the missing 23 household heads 21 were not present in the village during our presence; only two household heads refused to participate.

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In Nicaragua, making someone a godparent of ones children is conceived as making someone part of the family

(Merrill, 1993).

at least one direction. Fourth, neighbor relations are relations between two persons who consider themselves as neighbors. Fifth, relations through social public activities are ties brought about by activities related to religion, political parties, the village school, sport, cooperative organization, development projects or the village committee. ¹⁴ Sixth, economic relations are relations that result from an exchange of land or labor, a commercial activity, a service provision or a lending activity.

Table 2: Networks of different contents and their characteristics

Reciprocation

Density

Centrality

Clustering

General relation 0.302 0.354 0.441 0.554 (0)

Friendship relation 0.115 0.186 0.010 0.384 (0)

Extended family relation 0.423 0.033 0.083 0.377 (3)

Support relation 0.021 0.032 0.217 0.181 (2)

Neighbor relation 0.145 0.052 0.164 0.372 (3)

Social public activities 0.171 0.039 0.277 0.351 (23)

Economic relation 0.088 0.038 0.487 0.397 (13)

Note:

all two-sided links as fraction of all one-sided links, intra-household relations be- tween household heads ignored;

OR-networks, intra-household relations (not) counted as valid links in general, friendship, extended family, and support relations (neighbor, social public activities, and economic relations);

actual links as fraction of all possible links;

Freeman’s graph centralization measure;

overall graph clustering coefficient without isolated nodes, number of isolated nodes in parentheses.

Table 2 lists the elicited networks with different contents together with some important net- work characteristics. The second column shows the frequency of reciprocated ties, that is the links where both nodes named each other as a fraction of all links where at least one mentioned the other. The rate of reciprocation for general relations is with 30.2 percent similar to the re- ciprocation rate of 36.7 percent reported by Leider et al. (2008) who elicited friendship networks only. The rate of reciprocated ties decreases for the more specific network contents. This is not surprising because there exists a trade-off between capturing networks with multiple contents and reciprocated ties. Different people likely put different emphasis on different dimensions of rela- tions, lowering the rate of reciprocated ties when asked to describe their specific relations. As the reciprocation rates in Table 2 show, extended family relations seem to be considered important by most people. On the other hand, reciprocation is very low with support relations. This can be explained by the asymmetric nature of such relations. Support givers and support receivers are unlikely to put the same emphasis on this type of social relation. ¹⁵ A similar argument holds

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The village committee is a group of elected community members that represents the community to the mu- nicipal and central government.

15

That this is indeed the case is supported by the following observation. Considering all one- and two-sided

for economic relations.

In processing the network data we proceeded in the following way. First, we only took links between household heads into account, as only 1 percent of all identified links were links with members other than household heads. Second, intra-household relations between household heads are considered as valid for general relations, friendship relations, extended family relations, and support relations but not for the other elicited relations. ¹⁶ Third, we symmetrized the resulting adjacency matrix. That is, for each dyad, a relation is assumed to exist if at least one node mentions the relation. In this way we obtained the so-called OR-networks which we will also use in the following analyzes. ¹⁷

In the following we discuss the standard network measures density, centrality and clustering for the different elicited networks (see columns 3-5 in Table 2). Network density is measured as the sum of actual ties divided by the number of all possible ties. It is not surprising that density is highest for general relations because they aggregate relations of all contents. Among the specific networks friendship networks are relatively dense, whereas the other networks have relatively low densities. Hence, friendship relations are more frequent than other relations and, we will show below, in many cases having a specific relation other than friendship often implies a friendship relation but not vice versa.

Network centrality measures how unequally links are distributed among network nodes. For instance, a star network where all people are linked to exactly one and the same person has highest centrality, whereas a complete network where everybody has a link with everybody else has lowest centrality. We measure centrality with Freeman’s graph centralization measure (Freeman, 1977, 1979) which assumes the value zero for the complete network and the value one

support relations, we find that nodes who posses cattle, i.e. potential support givers, mention their relation significantly less often than nodes without cattle, i.e., potential support receivers (50.5 percent versus 66.2 percent;

p = 0.005 two-sided χ

-square test). A similar result holds when comparing nodes with and without land (45.1 percent versus 65.8 percent; p < 0.001 two-sided χ

-square test).

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Household members are usually not only family, but also tend to be friends and support each other. However, members of the same household are never neighbors to each other and it is highly unlikely that they have business- like economic relations or that they are related via social public activities. All reported results hold also if we add intra-household head relations to the latter three networks.

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The alternative would have been to use so-called AND-networks where links are taken to be valid only if

both sides of a dyad mention the relation. There are at least two arguments in favor of the use of OR-networks,

both related to the danger to miss out actually existing links when using AND-networks. First, those who have

many links are more likely to forget to mention a link than those who have only a few links. Second, people put

perhaps different emphasis on relations of different content, which may make people missing out relations of certain

contents. See also the discussion in Leider et al. (2008) who also use OR-networks in their analysis of friendship

relations.

for a star-shaped network. ¹⁸

As Table 2 shows, next to general relations, centrality is clearly highest in the economic network followed by the network through social public activities and the support network. The relatively high centrality in economic relations is likely to reflect the unequal distribution of wealth in the village. ¹⁹ Friendship and neighbor relations are low in centrality and, hence, relatively equally distributed.

For our purpose, an important characteristic of networks is the degree of clustering. Coleman (1990) argues in favor of the importance of clustering to sustain pro-social behavior. Considering generosity as a pro-social trait we expect that high clustering also favors generous behavior. We measure clustering with a clustering coefficient that is equal to the average of the densities of the local networks of all nodes, save isolates, with the density of a node equal to the number of ties divided by the maximally possible number of ties given the size of a node’s network. Formally, the clustering coefficient is given by (1/n) ^{P n} _i=1 d i , where n is the number of nodes and d i the (local) density of node i. The local density of a node i is given by d i = t i (s i !)/(2!(s i − 2)!), where s i is equal to the number of direct links of node i (i.e., the size of i’s network) and t i equal to the number of direct links among these nodes directly linked to i. Among the specific networks clustering is highest for friendship, family, neighbor, and economic relations. The high clustering in the economic network is particularly interesting in combination with the high centrality and low density. It implies that for economic relations there are only a few nodes important at the village level. Those nodes tend to be embedded in a dense net of local links, giving them a powerful brokerage position in the sense of Burt (1992). Friendship networks are characterized by high density, high clustering and low centrality and, hence, are - in the sense of Coleman (1990) - prototype for sustaining generosity. We expect to see these differences reflected when relating network structures to giving-behavior in the dictator games. Figure 1 visualizes the differences between the friendship network and the economic network. ²⁰

The discussed network measures already indicate that networks tend to be very different for different network contents. Another way of inferring differences and similarities between networks of different contents is by looking at their overlap. Table 3 shows the percentage of such an overlap at the dyad level. In the table inclusion runs from the row relations to the column relations. For instance, only 6.38 percent of the friendship ties are also support ties, but

18

Freeman’s graph centralization measure is a degree centrality measure and can be best understood as expressing the degree of inequality or variance in a network as a percentage of that of a star network of the same size.

19

On average, household heads owning cattle indeed have more direct economic links (6.06) than those owning no cattle (3.80). The difference is marginally statistically significant (p = 0.075, one-sided t-test). In Section 3.5 we show that the formation of economic links is strongly correlated with cattle ownership, thus wealth.

20

A visualization of all elicited networks can be found in Appendix B.

(a) Friendship network (b) Economic network Note: Household heads of the same household are placed next to each other.

Figure 1: Friendship network and economic network 42.93 percent of the support ties are also friendship ties.

Table 3: Overlap of networks of different contents (in percent)

Friendship Support Neighbor Activities Economic Ext. family

Friendship 100 6.38 12.67 9.44 8.93 2.74

Support 42.93 100 25.54 10.33 15.76 5.43

Neighbor 40.15 12.02 100 5.12 5.37 1.79

Social public activities 40.34 6.55 6.90 100 5.17 1.38

Economic 36.75 10.25 7.42 5.30 100 2.83

Extended family 17.99 5.29 3.70 2.12 4.23 100

Note: Possible intra-household links between household heads ignored.

Generally, for any network content a dyadic relationship often implies a friendship relation

(column ‘Friendship’ in Table 3). There is also much overlap between support and neighbor

relations, as more than a quarter of support relations is between neighbors. Finally, a special

category are the extended family relations who show little overlap with other relations. This is

likely due to the specific nature of family relations who are - except for godparenthood - not

chosen but biologically determined. In summary, although the networks of different contents

show some overlap they are clearly different as measured by the standard network measures

density, centrality and clustering.

3.3 Dictator Games

In total 57 household heads participated as dictators in the experiment ²¹ Figure 2a shows the distribution of coins given to village recipients taking all 285 decisions into account. It shows a dominant mode at the equal split of 10 coins and a second (much smaller) mode at the selfish decision of 0 coins. What is intriguing is the relatively large share of decisions where recipients received more than 50 percent of the endowment of 20 coins. The average share left to recipients is with 48 percent (9.6 coins) also higher than what is mostly observed in laboratory dictator games. The relatively small size of the community where our experiment was conducted together with the fact that dictators got to know the names of the recipients when making their decisions may account for this ‘super fair’ behavior. Indeed, Bohnet and Frey (1999) showed that revealing the identity of the recipient significantly increases dictator giving and Henrich et al. (2005) also observe ‘super fair’ proposals in ultimatum game experiments in small-scale societies.

mean = 9.6 st.dev. = 4.58

0 5 10 15 20 25 30 35

Percent

0 5 10 15 20

coins to village recipient .

(a) Village recipient

mean = 0.4 st.dev. = 4.63

0 5 10 15 20 25 30 35

Percent

−10 −5 0 5 10

coins to village recipient − coins to stranger Note: bars to the extreme left and right summarize observation smaller −10 and larger +10

(b) Difference with stranger

Figure 2: Dictator game giving

Next to the five distribution decisions regarding different village recipients our dictators also had to make a decision regarding a stranger in another village. We adopt the interpretation of Leider et al. (2008) and consider dictator giving to the stranger as ‘baseline’ or ‘general’ generos- ity. Interestingly, the mean number of coins left for the stranger is with 9.2 coins (46 percent) only little (and statistically insignificantly) less than the average left for village recipients. Im- portantly, however, this does not imply that village recipients and strangers always received the same or similar amounts. Quite to the contrary. Figure 2b shows the distribution of differences in coins left to village recipients and strangers. Although, there is a mode at zero, the large

21

We targeted at all 66 households but, in order to avoid contagion and spill-over effects, also wanted to finish

the whole experiment in one day. Some household heads were not present at the day of the experiment, which is

the reason for the nine missing data.

majority of choices (78.2 percent) gives village recipients and strangers different amounts. ²² The distribution shows an intriguing symmetry indicating that village members receive as often less than strangers as they receive more. Thus, social proximity in terms of being in the same net- work does not necessarily imply better treatment than any stranger would receive. ²³ It also indicates that social ties are not necessarily positive but may have negative load as identified by van Dijk et al. (2002).

3.4 Explaining Patterns of Generosity - Individual and Network Effects Although, the correlation between giving to the stranger and average giving (per dictator) to village recipients is positive and significant (Pearson correlation coefficient = 0.674, p < 0.001, two-sided test), the large variation in giving to these distinct groups of recipients indicates that baseline generosity alone is insufficient in explaining generosity directed to the network members.

In this section we examine if observable individual characteristics and network structures and positions can account for the observed pattern in generosity directed towards network village members.

In Section 3.2 we showed that structures of networks of different contents only partly overlap and exhibit considerable structural differences. Since we expect differential influences of the different network contents on directed generosity we investigate the individual and network effects on dictator giving separately for the various elicited social and economic networks. Table 4 gives a first flavor of the possible similarities and differences of network effects on dictator giving. For all elicited networks, the table shows descriptive statistics of dictator giving as a function of geodesic distance. It is clear from the table that dictator giving across networks and distances differs.

There are, however, also some similarities across network contents. For all networks (except neighbors) dictator giving to recipients at distance 1 is more generous than giving to strangers, indicating that generosity towards direct relations is larger than general generosity. In addition, in most networks there is also a social distance effect in that direct relations receive more than indirect relations and relations at even larger social distances. This effect is most pronounced for friendship, family, and support relations but absent in neighbor and economic networks.

Beyond distance 2 there seems to be no clear monotonic relationship between generosity and

22

In fact, there is also quite some variation in choices within the same dictator. Only six dictators gave the same amount to all village members. Five of them chose the equal split and one left 8 coins to each recipient. Only four of them gave also the same amount to the stranger.

23

Note, that this observation also casts new light on the idea of and evidence on in-group favoritism (see, e.g.,

Efferson et al., 2008; Chen and Li, 2008). Our data indicate that favoritism is not per-se towards own group

members but only toward socially close members in the own group.

social distance, except for the support network. ²⁴

Table 4: Dictator giving and social distance in different networks

Geodesic (social) distance of

Network content Stranger 1 2 3 4 ≥5

General 9.18

10.07 9.29 9.00

(5.43)

(4.40) (4.70) - [57]

[114] [170] [1]

Friendship 9.18 10.80 9.26 9.83

(5.43) 4.80 4.52 4.30 [57] [54] [207] [24]

Support 9.18 11.00 9.90 9.78 9.66 8.66

(5.43) (5.72) (5.25) (4.84) (4.24) (3.59)

[57] [12] [48] [95] [74] [56]

Neighbor

9.18 9.15 10.20 9.44 9.87 5.80

(5.43) (3.98) (4.58) (4.44) (4.66) (5.53)

[57] [20] [80] [112] [63] [10]

Social public activities

9.18 10.35 10.24 8.72 10.05 9.64 (5.43) (4.69) (4.52) (4.83) (3.99) (4.42)

[57] [17] [84] [89] [20] [75]

Economic

9.18 9.67 9.98 9.08 9.40 9.59

(5.43) (5.33) (4.65) (4.74) (2.95) (4.54)

[57] [15] [121] [83] [20] [46]

Extended family 9.18 11.14 9.07 9.00 9.75 9.80

(5.43) (3.93) (5.38) (3.85) (4.71) (4.65)

[57] [7] [29] [52] [65] [132]

Note:

Average number of coins left to stranger recipient and recipients at different distances, respectively;

standard deviation in parentheses;

number of observations in brackets;

possible intra-household links between household heads ignored.

The standard deviations depicted in Table 4 reveal quite some variation in giving behavior for each social distance and each network content. In the following we present regression analyzes to pin down determinants of the pattern of directed generosity in the various networks. In particular, we estimate regression models that incorporate observable individual characteristics of dictators and recipients, the social distances between them, dictators’ general generosity, and (local) network characteristics. We estimate the following Ordinary Least Squares (OLS)

24

Leider et al. (2008) report a similar non-monotonicity for their friendship networks in the non-anonymous

treatment.

model: ²⁵

Y ij = α + βX ij + e ij , (1)

where Y ij denotes the amount of coins dictator i gave to recipient j, α is the intercept, X ij

denotes the vector of explanatory variables more specifically discussed below, and e _ij is an error term. In the experiment, each dictator made giving decisions regarding five different village recipients. Hence, some of our observations may not be assumed to be statistically independent, implying that E [e ij , e ik ] 6= 0 for all k. Further, different dictators may have been asked to make a distribution decision with the same recipient, implying that E [e ij , e _kj ] 6= 0 for all k. To correct standard errors for these dependencies we apply clustering on both dimensions separately (for a technical discussion of this issue, see e.g., Cameron et al., 2006).

Our research strategy in estimating individual and network related determinants of dic- tator giving is the following. In a first model we include only individual characteris- tics which are likely to influence dictator-giving. For both the dictator and the recipi- ent, we control for gender, age and wealth, because previous studies have found some evi- dence that these traits influence giving behavior, or pro-social behavior in general (see, e.g., Eckel and Grossman, 1998; Andreoni and Vesterlund, 2001; Croson and Gneezy, 2008, on gen- der, List, 2004; Egas and Riedl, 2008, on age, and Eckel and Grossman, 1996; Bra˜ nas-Garza, 2006; Cappelen et al., 2008, on wealth.

In our regression model we take what female dictators give to male recipients as the benchmark. The variables MD FR, MD MR, and FD FR are dummy variables for the dicta- tor/recipient combinations male/female, male/male, and female/female, respectively. Regarding age we control for the absolute age of the dictator (AGE D) and the absolute difference in age between the dictator and the recipient (AGE DIFF). To control for the influence of wealth we use dummy variables indicating whether the households the dictator and the recipient belong to own cattle or not. The possession of cattle is a good proxy for wealth because cattle are an important economic asset in the region where the village is located. In the regressions we take the case where both the dictator and the recipient household own cattle as the benchmark.

The variables CD nCR, nCD CR, and nCD nCR are dummy variables for the dictator/recipient combinations where the dictator household owns cattle but the recipient household does not, the dictator household does not own cattle but the recipient household does, and both house- holds do not own cattle, respectively. ²⁶ In addition, we also control for the education level of

25

We also estimated a random-effects (at dictators’ level) tobit regression model. Due to the limited censoring (see Figure 2a) outcomes do not signifcantly differ from the reported results.

26

We use a dummy variable here because more than half of the village population does not have any cattle at

all.

the dictator (EDUC D) and the absolute difference in education between dictator and recipient (EDUC DIFF), both measured in years of schooling; the dictator’s years of residence in the village (RESID D) and absolute difference of years of residence between the dictator and the recipient (RESID DIFF); and the dictator’s frequency of contact with the nearest urban center (CONT D), measured as the number of visits in the most recent month before the interview, and its difference between dictator and recipient (CONT DIFF).

In a second model, we additionally control for the amount of coins left to the stranger (GIVE STRANGER). In this way we can isolate the effect of general generosity on directed generosity. In a third model, we examine the role of social distance between dictator and recipi- ents as an explanatory variable by adding a variable (DISTANT) that takes value 1 if the social (geodesic) difference is larger than 1.

Finally, in our fourth model, we add more sophisticated network variables which we expect to influence directed generosity. Figure 3 illustrates the investigated network variables. First, we control for dictators’ and recipients’ embeddedness in the network by adding variables measuring the size of the dictator(recipient)-network, i.e. the number of direct links dictators (NETSIZE D) and recipients (NETSIZE R) have. Second, according to Coleman (1990) local density facilitates pro-social behavior. As generosity is one form of pro-social behavior we examine this hypothesis by adding a variable consisting of the number of direct links among the nodes directly linked to the dictator (INNER D) and recipient (INNER R), respectively. When controlling for the local network size these variables measure local density. Third, Burt (1992, 2005) argues that an agent can pursue his or her self-interest better if he or she is connected to many nodes that have only a few links themselves. Burt calls such an agent ‘broker’ and argues that he or she has power because agents with few links are more dependent on the broker then agents who have many links themselves. To examine if there is an influence of network brokerage on generosity in the network we add the variables OUTER D and OUTER R for the dictator and recipient, respectively. These variables measures the total number of ties nodes have outside the network of the dictator (recipient) they are directly linked with.

Table 5 shows the regression results of all four models for the network of general relations.

The results for model 1 where only observable individual characteristics are taken into account show a strong and surprising effect of gender. In contrast to other studies that found a gender effect (e.g., Eckel and Grossman, 1998; Croson and Gneezy, 2008; Konow et al., 2008), in our subject pool female dictators tend to give less than male dictators. ²⁷ In particular, male and female recipients receive about 2.7 and 2.2 coins more from male dictators than from female

27

Andreoni and Vesterlund (2001) find that female dictators are less generous than male dictators when giving

is cheap. For cost parameters similar to ours they find that women are more generous.

NETSIZE_i=3 i

INNER_i=2 OUTER_i=2

Figure 3: Illustration of the regression variables NETSIZE i, INNER i and OUTER i (i=D,R) dictators. At the same time female dictators also tend to give more to female recipients than to male recipients, indicating that not only the dictator’s gender is important but also the gender of the recipient. This gender effect is robust across model specifications and even tends to become stronger with more explanatory variables added (see Table 5 models 2-4).

Age, education and duration of residence in the village do not exert any significant influence on dictator giving. The frequency of contact with the urban center increases dictator giving, which is consistent with the finding of Henrich et al. (2005) that stronger market integration tends to foster pro-sociality. Interestingly, if dictator and recipient differ in the frequency of contact with the urban center dictator giving decreases. A look at models 2-4 in Table 5, however, shows that the effect is not robust to adding other explanatory variables. The wealth variables show interesting and intuitive effects. Dictators without cattle give about 2 coins less to recipients than dictators who own cattle do. However, also these wealth effects are not robust when controlling for general generosity and network effects.

In model 2 we add the coins given to the stranger (GIVE STRANGER) as an explanatory variable. It turns out that there is a strong but imperfect correlation between general and directed generosity. Dictators who give one coin more to the stranger tend to give a little less than half a coin more to recipients in the network. The results for this variable in models 3-4 also shows that this is a robust effect. Furthermore, adding general generosity as an explanatory variable shows that of the observable characteristics only gender and to lesser extent being poor (i.e., owing no cattle) still have explanatory power.

In models 3 and 4 we examine the explanatory power of network variables. In model 3 the

social (geodesic) distance between dictator and recipient is added as an explanatory variable.

Table 5: Determinants of directed generosity in the general relations network

Dep. variable: coins given to village recipient

Socio-econ. char. with stranger with distance with network variables

Model 1 Model 2 Model 3 Model 4

MD FR 2.198

(1.200) 2.293

(0.836) 2.320

(0.845) 2.785

(1.152) MD MR 2.730

(1.225) 2.738

(0.897) 2.518

(0.920) 2.868

(1.102) FD FR 1.072

(0.619) 1.293

(0.619) 1.255

(0.632) 1.505

(0.871)

AGE D 0.058 (0.062) 0.053 (0.041) 0.052 (0.041) 0.050 (0.040)

AGE DIFF −0.025 (0.023) 0.001 (0.022) 0.002 (0.021) 0.008 (0.020)

EDUC D 0.016 (0.209) 0.020 (0.134) 0.015 (0.132) 0.030 (0.140)

EDUC DIFF 0.056 (0.087) −0.032 (0.078) −0.036 (0.078) −0.049 (0.086) RESID D −0.067 (0.043) −0.040 (0.029) −0.043 (0.029) −0.038 (0.028) RESID DIFF 0.008 (0.025) −0.008 (0.021) −0.003 (0.022) −0.006 (0.021)

CONT D 0.370

(0.181) 0.128 (0.135) 0.124 (0.132) 0.109 (0.138)

CONT DIFF −0.144

(0.036) −0.071 (0.049) −0.074 (0.050) −0.065 (0.052) CD nCR −0.483 (0.553) −0.415 (0.468) −0.326 (0.453) −0.239 (0.486) nCD CR −2.428

(0.988) −1.158

(0.650) −1.131

(0.659) −0.998 (0.691) nCD nCR −2.189

(0.927) −0.782 (0.750) −0.817 (0.754) −0.831 (0.760)

DISTANT −0.834

(0.435) −0.942

(0.479)

NETSIZE D 0.053 (0.182)

INNER D −0.003 (0.009)

OUTER D −0.003 (0.003)

NETSIZE R −0.058 (0.069)

INNER R 0.003 (0.003)

OUTER R 0.002 (0.001)

GIVE STRANGER 0.477

(0.083) 0.478

(0.084) 0.474

(0.084) decision −0.160 (0.149) −0.118 (0.141) −0.110 (0.139) −0.118 (0.142) constant 9.235

(2.845) 3.785 (2.437) 4.358

(2.271) 6.169

(3.062)

R

0.1605 0.4294 0.4365 0.4494

F-statistics 3.85 12.20 11.76 10.14

No. of obs. 273 273 273 273

Note: Robust standard errors (in parentheses) are obtained by means of two-way clustering at dictator and recipient level; :

,

,

indicate two-sided significance levels at 1, 5, and 10 percent, respectively;

the variable ‘decision’ measures the number in the sequence of dictator decisions made and controls for a possible sequence effect and shows if dictators tend to increase or decrease the amounts given to recipients later in the list.

The result depicted in Table 5 shows that recipients who are further away than one link indeed

receive significantly less than recipients who are directly related to the dictator. This result

is reinforced when we add the other network variables as explanatory variables. The network

variables themselves do not significantly influence giving behavior, however (Table 5, model 4).

This insignificance of the network variables is likely due to their little variation in the general relations network which is very dense and where almost all people are not more than two steps apart (cf. Table 4).

One of our main interests in this paper is to examine whether networks of different contents exhibit different or the same network effects on giving behavior, if there are any effects at all.

Therefore, in the following we present the estimation results for each specific network separately and focus on the full fledged model (model 4). Table 6 shows the regression results for the friendship, support, social public activities, and economic network. The results shown in the table reveal three things. First, gender and general generosity are important explanatory vari- ables for directed generosity independent of the network content. Second, observable individual characteristics other than gender have no or only little explanatory power in any specific network.

Third, network structures and position play a significant role in determining giving behavior but the effects clearly differ across networks of different contents.

More specifically, in each of the investigated specific networks female dictators give around 3 coins (15 percent of the endowment) less to male recipients than male dictators do (cf. MD MR).

Although, female dictators tend to give more to female recipients than to male recipients it is the male dictators who appear more generous also towards female recipients (compare MD FR with FD FR). Regarding the other observable individual characteristics AGE has a slight positive effect in friendship networks, the difference in contact to the urban center (CONT DIFF) a slight negative effect in support networks, and being poor (nCD CR) is correlated with less directed generosity in networks of social public activities. Overall and in line with other studies (e.g., Camerer, 2003) the power of these characteristics in explaining variations in generosity appears very limited. ²⁸ In all specific networks general generosity is a strong although imperfect predictor of directed generosity. As in the network of general relations, in each specific network an additional coin given to the stranger increases the generosity towards the network recipient by about half a coin.

In friendship networks social distance significantly affects directed generosity. Recipients who are not direct friends receive one and half coins less than direct friends. In addition we find a negative effect of the number of indirect links the dictator as well as the recipient have outside their respective local networks. A larger variable OUTER i means that the nodes directly linked to node i have more links outside the local network of i. When controlling for the network size of i this implies that node i has less power in the sense of Burt (1992, 2005). Thus, the negative

28

A log-likelihood ratio test, comparing the model with only individual characteristics as explanatory variables

with the reported full model shows that the fit of the latter is significantly better (p < 0.0001) for all investigated

network dimensions.

Table 6: Determinants of directed generosity in networks of different contents

Dep. variable: coins given to village recipient

Friendship Support Soc. pub. activities Economic MD FR 2.622

(1.317) 2.497

(0.811) 2.946

(0.941) 1.693

(0.925) MD MR 3.252

(1.235) 3.014

(0.923) 3.320

(0.962) 2.344

(0.889) FD FR 0.828 (0.839) 1.220

(0.612) 1.496

(0.599) 1.190

(0.645)

AGE D 0.066

(0.038) 0.058 (0.043) 0.052 (0.042) 0.049 (0.044)

AGE DIFF 0.012 (0.021) 0.001 (0.022) −0.003 (0.021) 0.003 (0.020)

EDUC D 0.051 (0.118) 0.037 (0.149) 0.023 (0.148) −0.050 (0.143)

EDUC DIFF −0.052 (0.095) −0.027 (0.081) −0.028 (0.084) 0.012 (0.086) RESID D −0.046 (0.028) −0.039 (0.028) −0.033 (0.028) −0.038 (0.029) RESID DIFF −0.012 (0.021) −0.013 (0.022) 0.002 (0.020) 0.001 (0.024)

CONT D 0.082 (0.131) 0.152 (0.134) 0.110 (0.159) 0.118 (0.141)

CONT DIFF −0.073 (0.046) −0.077

(0.047) −0.045 (0.046) −0.057 (0.047) CD nCR −0.246 (0.522) −0.514 (0.480) −0.312 (0.486) −0.326 (0.520) nCD CR −0.747 (0.684) −1.025 (0.622) −1.463

(0.707) −0.887 (0.678) nCD nCR −0.540 (0.709) −0.745 (0.709) −1.031 (0.834) −0.539 (0.752) DISTANT −1.467

(0.650) −1.098 (0.957) 0.315 (1.154) 0.549 (0.746) NETSIZE D 0.123 (0.128) −0.328

(0.178) 0.309

(0.185) 0.353 (0.240) INNER D −0.007 (0.014) 0.392

(0.189) −0.050 (0.054) −0.193 (0.167) OUTER D −0.009

(0.005) 0.007 (0.027) −0.031 (0.019) −0.009 (0.008) NETSIZE R −0.011 (0.054) −0.047 (0.132) −0.104

(0.051) −0.062 (0.147)

INNER R −0.001 (0.007) 0.078 (0.106) 0.021 (0.021) 0.055 (0.095)

OUTER R −0.001

(0.000) −0.009 (0.022) 0.015

(0.008) 0.000 (0.005) GIVE STRANGER 0.470

(0.082) 0.447

(0.081) 0.501

(0.079) 0.466

(0.085) decision −0.126 (0.149) −0.113 (0.140) −0.112 (0.135) −0.097 (0.156) constant 6.918

(2.425) 5.500

(2.565) 2.599 (3.021) 3.235 (2.676)

R

0.4653 0.4579 0.4627 0.4604

F-statistics 11.25 12.97 9.84 10.19

No. of obs. 273 273 273 273

Note: Robust standard errors (in parentheses) are obtained by means of two-way clustering at dictator and recipient level; :

,

,

indicate two-sided significance levels at 1, 5, and 10 percent, respectively;

the variable ‘decision’ measures the number in the sequence of dictator decisions made and controls for a possible sequence effect and shows if dictators tend to increase or decrease the amounts given to recipients later in the list.

coefficients indicate that structurally more powerful dictators tend to be more generous and that

structurally more powerful recipients also tend to receive more than less powerful ones. While

the latter can be interpreted as opportunistic behavior towards more powerful recipient-friends

the former is a bit of a puzzle. One plausible, albeit speculative, explanation is that in small-scale

societies power tends to be connected to clientelism, which can only be maintained if resources

are also transferred to the dependent client (Wolf, 1977). ²⁹ If our participants bring this logic to the experiment, structurally powerful dictators may more strongly feel the responsibility to share with others than less powerful dictators do.

In contrast to the friendship network, in the support network social distance does not play a significant role in explaining dictator giving. Instead the structural network variables NET- SIZE D and INNER D turn out to be statistically significant. Together, the negative coefficient of NETSIZE D and the positive coefficient INNER D imply that the density of the dictator’s local network has a significantly positive effect on dictator giving. This result is consistent with Coleman (1990)’s idea that network clustering is fostering pro-social behavior.

In the network created through social public activities the network size of the dictator has a positive influence on dictator giving, indicating that in these networks dictators who are better connected are more generous. However, the influence of the structural properties of the recipi- ents is particularly interesting. The significantly negative coefficient of NETSIZE R shows that dictators give more to recipients who have only a few direct links. At the same time, the posi- tively significant coefficient of OUTER R indicates that recipients with less power in the sense of Burt (1992) also receive more from dictators. Together this shows that recipients who are in a structurally weaker position in this network are treated more generous by dictators, especially if those are structurally strong.

Finally, in the network of economic relations neither social distance nor any of the variables related to structural network properties are of statistically significant influence on directed gen- erosity. Obviouly, economic relations in contrast to other types of relations leave little room for generosity. This seems not being too surprising because business relations follow a distinct logic than the logic of friendship and support relations.

In comparison to the other networks the network of extended family relations is special because it is partly chosen but partly also biologically determined. We therefore analyzed it separately.

Additionally, family networks are high in clustering and have few outer links by the very definition of kinship relations we also apply different structural network variables. Table 7 shows the regression results. Social distance has a very strong effect in such relations. Directly related family members receive much more than family members that are only indirectly related. A result in line with kinship favoritism and also confirming that the generosity we measure is indeed directed. However, interestingly, not only kinship relatedness is important but also structural

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At first sight it may seem peculiar to talk about clientelism in friendship networks, but often the difference

between friendship and clientelism is blurred. An interesting historical example for this is provided in Cole (2007)

where it is shown how the friendship between Maffeo Barberini and Michelangelo Buonarroti the Younger slowly

developed into a patron-client relation.