1 Introduction

Many people have non-selfish preferences over the distribution of economic resources. These preferences are often synonymously called social preferences, other-regarding preferences, or distributional preferences (Fehr & Schmidt, Reference Fehr and Schmidt1999; Bolton & Ockenfels, Reference Bolton and Ockenfels2000; Charness & Rabin, Reference Charness and Rabin2002; Camerer, Reference Camerer2003; Almås et al., Reference Almås, Cappelen, Sørensen and Tungodden2010). Their existence and their specific nature are very important for economic behavior and outcomes, such as, among many others, cooperation (Boyd & Richerson, Reference Boyd and Richerson2005; Fischbacher & Gachter, Reference Fischbacher and Gachter2010), productivity (Carpenter & Seki, Reference Carpenter and Seki2011; Bandiera et al., Reference Bandiera, Barankay and Rasul2005; Dohmen & Falk, Reference Dohmen and Falk2011), political preferences (Fisman et al., Reference Fisman, Jakiela and Kariv2017; Kerschbamer & Müller, Reference Kerschbamer and Müller2020), and well-being (Becker et al., Reference Becker, Deckers, Dohmen, Falk and Kosse2012).Footnote ¹ Recent studies have documented the evolution of these distributional attitudes in adolescence, from more malevolent at young ages to more benevolent when growing older. They have also stressed the large degree of individual heterogeneity of distributional preferences (Fehr et al., Reference Fehr, Glätzle-Rützler and Sutter2013; Almås et al., Reference Almås, Cappelen, Sørensen and Tungodden2010; Martinsson et al., Reference Martinsson, Nordblom, Rützler and Sutter2011; Sutter et al., Reference Sutter, Feri, Glätzle-Rützler, Kocher, Martinsson and Nordblom2018).

There are far fewer studies on the effects of the social environment and peers on distributional preferences(Charness & Kuhn, Reference Charness and Kuhn2007; Gächter et al., Reference Gächter, Nosenzo and Sefton2013; Fatas et al., Reference Fatas, Heap and Arjona2018; Bicchieri et al., Reference Bicchieri, Dimant, Gächter and Nosenzo2019). In particular, we know little about the early-life-peer influence on the emergence of distributional preferences and whether network members share distributional preferences (Hugh-Jones & Ooi, Reference Hugh-Jones and Ooi2017). To fully understand how distributional preferences are shaped in adolescence, it is important to take the close social environment and its potential influence into account. Adolescent peer networks could be important in explaining adult inter-individual heterogeneity in distributional preferences, selection into friendship/professional networks, labor market status and political views later in life, on top of potential biological determinants (Balafoutas et al., Reference Balafoutas, Kerschbamer and Sutter2012; Fisman et al., Reference Fisman, Jakiela and Kariv2017; Kocher et al., Reference Kocher, Pogrebna and Sutter2013).

Preferences could be correlated between members of social units, such as a child’s school or group of friends, beyond what is expected by the population preference distribution. Peer correlation in preferences can arise from selection into social networks whose members have similar preferences as one’s own, and through preference transmission. Besides composition, an adolescent’s position within the social network could itself be related to specific distributional preferences transmitted through various mechanisms. The potential impact of peer networks that are based on other-regarding attitudes goes beyond differential evolution of these preferences. If children are surrounded by like-minded peers, cognitive and non-cognitive abilities could also develop on different trajectories as a result of differences in cooperation and support within the network (Cunha et al., Reference Cunha, Heckman and Schennach2010; Thöni & Gächter, Reference Thöni and Gächter2015).

This paper investigates the distributional (“social”) preferences of children at primary schools in urban Tanzania and the role of peers in shaping these distributional preferences. We conduct a lab-in-the-field experiment and analyze to what extent distributional preferences of children are related to those of their peers at school, and what roles peer networks, school performance, and popularity play in explaining distributional preferences. The experiment involves choices between pairs of allocations that vary as to how much to allocate to oneself and to an anonymous passive agent (Kerschbamer, Reference Kerschbamer2015). The variation in inequality in agents’ payoffs across allocations in the choice sets allows us to classify children into four broad distributional preference types: efficiency-loving, inequality-loving, inequality-averse, and spiteful. To study the prevalence and relationships of these types in peer networks, we ask children to name and rank their three best friends. We also use survey data on background characteristics and administrative data on school grades to investigate their relationship with distributional preferences and peers.

The four distributional preference types that are used here capture a large set of potential distributional preferences under very mild assumptions see (Kerschbamer, Reference Kerschbamer2015). Efficiency-loving preferences pertain to utility functions that put emphasis on the maximum of the sum of payoffs (also called “surplus maximizing motives”). Inequality-averse preferences put disutility on inequality, whereas inequality-loving preferences put positive utility on inequality. Finally, spiteful preferences capture a disutility that is increasing in the payoffs of others (also called “competitive preferences”).

Our findings show that the majority of children exhibit choices consistent with inequality-averse (30.7%) and spiteful (42.3%) preferences. This pattern stems from a reluctance to accept disadvantageous allocations for oneself, even if they are Pareto improving. Peers’ preference types are also correlated. Even after controlling for a range of observable characteristics, we find that, if two children at the same school report a friendship link, they are 1.7% points (0.05 SD, mean = 0.33) more likely to exhibit the same preference type than if they do not. Thus, conditional on reporting a friendship link, distributional preference types of children are strongly related. This peer correlation in types is mainly driven by inequality-loving and spiteful types. Having a friend of the inequality-loving or spiteful type increases the likelihood of a child being of the same type by 6.7% points (0.2 SD) and 3.5% points (0.1 SD), respectively.

The similarity in distributional preference types in peer networks differs by gender as well, with boys driving the overall peer correlations and showing stronger correlation coefficients for spitefulness and girls sharing inequality-loving preferences.

Finally, our analysis shows that, besides network composition, the importance of the role of peers in explaining distributional preferences is linked to the position within the network. Worse relative performance in school relates positively to spiteful attitudes. The spiteful preference type is also more common when a child is central or popular within their peer networks. This suggests an importance of both social hierarchies and relative economic (human capital) position.

Our contribution in this paper is threefold. First, we investigate the role peer networks play in shaping children’s distributional preferences. Hugh-Jones and Ooi (Reference Hugh-Jones and Ooi2017) study transmission of fairness preferences in teen friendship networks and show that observing others’ choices affects adolescents’ fairness norms. We build on Hugh-Jones and Ooi (Reference Hugh-Jones and Ooi2017) and contribute to a better understanding of the evolution of preferences with age, as well as their impact on (economic) outcomes.

Second, we investigate the relationship between social hierarchies in networks and social preferences at a young age. An individual’s relative position within the social network may itself be related to distributional attitudes. We complement the view that parents’ socioeconomic status relates to the child’s social preferences (Benenson et al., Reference Benenson, Pascoe and Radmore2007; Deckers et al., Reference Deckers, Falk, Kosse, Pinger and Schildberg-Hörisch2021) by exploring the structure of the child’s own social network and its relationship to distributional preferences. If children who are disadvantaged in terms of school performance or who are less popular among peers adopt antisocial attitudes toward peers, such attitudes could be reinforced and persistently shape outcomes of future interactions. Alternatively, in line with Girard et al. (Reference Girard, Hett and Schunk2015), social structure and centrality in the social network can originate from individual preferences of children.

Third, the documentation of nuanced measures of distributional preferences at a young age in a developing country context complements a series of studies that examine distributional preferences of children in high-income contexts (Martinsson et al., Reference Martinsson, Nordblom, Rützler and Sutter2011; Fehr et al., Reference Fehr, Glätzle-Rützler and Sutter2013; Almås et al., Reference Almås, Cappelen, Sørensen and Tungodden2010; Hugh-Jones & Ooi, Reference Hugh-Jones and Ooi2017; Sutter et al., Reference Sutter, Feri, Glätzle-Rützler, Kocher, Martinsson and Nordblom2018). Distributional preferences in a setting with scarce financial resources, ethnic and religious diversity, and the absence of a welfare state, like urban Tanzania, may be of particular interest. Additionally, in an environment with high overall gender inequality, gender-specific preference formation at a young age may play an important role in explaining persistent outcome differences between males and females.Footnote ² We therefore complement previous studies on overall and gender-specific distributional preferences of children (Benenson et al., Reference Benenson, Pascoe and Radmore2007; Almås et al., Reference Almås, Cappelen, Sørensen and Tungodden2010; Martinsson et al., Reference Martinsson, Nordblom, Rützler and Sutter2011; Fehr et al., Reference Fehr, Glätzle-Rützler and Sutter2013; Sutter et al., Reference Sutter, Feri, Glätzle-Rützler, Kocher, Martinsson and Nordblom2018; Deckers et al., Reference Deckers, Falk, Kosse, Pinger and Schildberg-Hörisch2021).Footnote ³

Combining distributional preferences and social networks might ultimately provide a workable theory of reference groups. Standard models of distributional preferences remain silent on how reference groups are formed. Our results are a first step, and they show that empirical inference on reference group (network) formation is not easy, but that it can be achieved in an environment in which there is enough control. Schools are almost perfect laboratories in this sense, allowing us not only to study the emergence of distributional preferences, but also to learn more about general aspects of network formation along distributional preferences.

The rest of the paper is structured as follows. In Sect. 2 we present our theoretical framework. Section 3 discusses the sample and data, Sect. 4 describes the experimental design in more detail, Sects. 5–6 present our results, and Sect. 7 concludes the paper.

2 Theoretical framework

In this section, we provide a theoretical mapping for our experimental design to motivate why we might observe that pairs of children who report a friendship link have a higher probability of exhibiting the same preference type than other children who attend the same school. We lay out a simple extension of the workhorse model for intergenerational transmission of preferences by Bisin and Verdier (Reference Bisin and Verdier2001), where horizontal preference adoption may differ between the general population and the close social environment. Consider a child i with distributional preference type $t_i$ , where $t=\{1...K\}$ , and a friend d with type $t_d$ . With some probability $q\left(t_d\right)$ , the two children reveal the same preference type due to the distribution of types in the reference population. This likelihood depends on the fraction of that specific type in the reference population of the child, in our case, the school. With an additional probability $p\left(t_d\right)$ , the child exhibits the same type as the friend due to reasons unrelated to the overall type distribution at the school:

(1) $\begin{array}{c}{t}_i=(p+q\left(t_d\right))·t_d+(1-p-q\left(t_d\right))·t_k\end{array}$

with $t_k\ne t_d$ .

Our interest here is to estimate p, the correlation coefficient between the preference types of children and their friends jointly with and independently from the share of types in the reference population q(t).Footnote ⁴ Empirically this is achieved by sampling the peer networks of the entire reference population at the friendship dyad level. A positive p suggests that correlation in preferences between friends goes beyond q(t). Notice that it is possible that the correlation varies by preference type: $p\left(t\right)\ne p$ for all t. This means that peer correlation may be preference type specific.Footnote ⁵ Different mechanisms may explain a peer correlation ( $p>0$ ). Children may select their friends by matching on observable and unobservable characteristics, in particular their distributional preferences (ex ante similarity), i.e., they choose to form friendships with other students who have similar distributional attitudes. In a school environment, children do interact frequently and are thus able to learn about the attitudes of others. Children might also be influenced by the attitudes of their peers, such that distributional preferences could be transmitted through friends (ex post similarity). Preference transmission refers to any influence on the preference ex post to the formation of a friendship link and comprises unconscious assimilation, conscious imitation and directed socialization efforts by friends. Peer correlation in distributional preferences can therefore be decomposed into selection and preference transmission. However, disentangling transmission from selection in a sample of adolescents is empirically challenging. When participants are old enough so that one can elicit their distributional preferences meaningfully, they are likely to have grown up within the same local social and economic context, including sharing pre- or primary school classes, where transmission could take place. Generally, at this young age, it is unlikely to be possible to exploit or generate random variation in peer networks, and thus to exclude selection.Footnote ⁶

3 Sample and data

We elicited distributional preferences of students through a lab-in-the-field experiment at public primary schools in Dar es Salaam city, the commercial capital of Tanzania. The experiment was conducted in schools in the Ilala District at the beginning of the new school year in early 2018. In collaboration with the District Educational Office, we randomly chose 3 out of 112 schools for participation.Footnote ⁷ The experimental sessions took place on a single day per school during lecture hours. All present standard 6 (out of 7) students (age 12–13) participated.Footnote ⁸ The total sample contains 650 students, representing more than 90% of eligible students. In contrast to experiments in previous studies conducted with children after school hours, we had very little to no attrition and no selection effects into the experiment.

At the beginning of each session, students were randomly allocated to classrooms by drawing numbers. After a short survey on background characteristics and the students’ friend networks, pen-and-paper choice list experiments for distributional preferences and a money-earlier-or-later experiment were conducted.Footnote ⁹ The preference experiments took place in random order and were accompanied by randomly rotating teams of enumerators.Footnote ¹⁰ Students could earn money from experimental payoffs. At the end of the session, either the distributional or the time preference experiment was randomly chosen for payout, which led to guaranteed earnings between TZS 3,000 (US$1.35) and 8,000 (US$3.59), a significant amount of pocket money for these students, particularly given the low opportunity costs.Footnote ¹¹

Table 1 Summary statistics

Background characteristics	Mean	SD
Age of child	12.67	(1.070)
Female	0.523	(0.500)
Household size	5.379	(2.006)
Number of children in hh	2.649	(1.323)
Muslim	0.602	(0.490)
School grade	458.1	(123.1)
Rank in school	0.496	(0.287)
Peer networks
Number of total friends	4.667	(1.646)
Number of out-degree friends	3.000	(0.000)
Number of in-degree friends	2.801	(2.012)
Number of reciprocal friends	1.145	(1.002)
Observations	650

This table reports summary statistics of the experimental sample. School grade and rank come from the results of the national exam for grade 5, taken one month before the study. The school grade represents the grade point sum for all ten subjects: Swahili, English, mathematics, science, geography, civic education, history, art/handicraft, communication/informatics/ICT, and physical education. Rank in school is the ranking of a student of grade 6 at a given school divided by the number of grade 6 students at that school. Out-degree denotes the number of friendships reported by a student. In-degree denotes the number of friendship ties directed toward a student (i.e., reported by peers). Reciprocal friends imply that two students independently listed each other as friends

In the survey, students were asked to list and rank their three best friends within their cohort at the school. Using this information, we can construct the self-reported social networks of students. Within this network structure, various centrality measures, such as degree or eigenvector centrality, can be defined according to standard measures.Footnote ¹²

Table 1 presents descriptive statistics of student and network characteristics for the experimental sample. Approximately half of the participants are female, and a large proportion are Muslim, with the remaining 39.8% mostly of Christian faith. Reassuringly, the mean normalized student rank based on the overall grade by school is 0.5, which suggests we did not oversample students with good or bad grades. Social networks in the sample consist of on average of 4.7 peers, and an average student is named 2.8 times by friends. The friendship measures are bounded by the fact that only three friends per student were elicited. High standard deviations in these variables suggest that there is large heterogeneity in popularity across students.

4 Experimental design and definitions

The experimental design to elicit distributional preferences is based on Kerschbamer (Reference Kerschbamer2015).Footnote ¹³,Footnote ¹⁴ The exact design of the experiments and the empirical strategy were registered as a preanalysis plan prior to the fieldwork.Footnote ¹⁵ Students were asked to make ten binary choices between two payoff allocations. Each allocation consists of a payoff for the decision-maker (the active agent) and a randomly matched anonymous person (the passive agent).Footnote ¹⁶ One of the two allocations in each choice situation always gives equal payoffs to both agents (symmetric allocation). The other allocation is asymmetric, with higher payoffs for the active agent in half of the choices (advantageous block) and lower in the other half (disadvantageous block). The symmetric allocation is the same in all ten choices, while the asymmetric allocation in both blocks increases in the payoff for the decision-maker (the active agent). The changes in the asymmetric payoffs represent a change in the cost of giving to (taking from) the passive agent.

Table 2 shows the ten-item choice list design. The constant symmetric (egalitarian) allocation (right) is fixed at TZS 2,500 for both agents for the ten choices. In the five rows of the disadvantageous inequality block (DIB), the decision-maker faces lower payoffs than the passive agent (TZS 4,000) in the asymmetric allocation (left). Over the five choices, the payoff to the active agent increases monotonically from TZS 2,000 to 3,000. In the five rows of the advantageous inequality block (AIB), the decision-maker faces greater payoffs than the passive agent (TZS 1,000) in the asymmetric allocation (left). Over the five choices, the payoff to the active agent increases monotonically from TZS 2,000 to 3,000, as in the DIB.

Table 2 Choice list

	Left		Choice		Right
Disadvantageous inequality block (DIB)
	You get	Passive agent gets			You get	Passive agent gets
1	2,000	4,000	$◯$	$◯$	2,500	2,500
2	2,400	4,000	$◯$	$◯$	2,500	2,500
3	2,500	4,000	$◯$	$◯$	2,500	2,500
4	2,600	4,000	$◯$	$◯$	2,500	2,500
5	3,000	4,000	$◯$	$◯$	2,500	2,500
Advantageous inequality block (AIB)
6	2,000	1,000	$◯$	$◯$	2,500	2,500
7	2,400	1,000	$◯$	$◯$	2,500	2,500
8	2,500	1,000	$◯$	$◯$	2,500	2,500
9	2,600	1,000	$◯$	$◯$	2,500	2,500
10	3,000	1,000	$◯$	$◯$	2,500	2,500

This table presents the choice list provided to subjects (for the actual version used in the experiment, see Figure A.2 in section 3 of the appendix). In each of 10 rows, subjects are asked to choose between two pairs of allocations (left or right). These pairs denote payoffs to the subject and to an anonymous passive agent from the same school. Payoffs are in Tanzanian shillings (TZS), US$1=TZS 2230

Since the payoff to the decision-maker on the left side increases from row to row, a rational participant should only switch from right to left and only once per block. A rational participant can also always choose left or right. The pattern of choices in the blocks determines the classification of distributional preferences. In particular, the choices reveal benevolence or malevolence toward the passive agent in the disadvantageous and advantageous domains.

Benevolence means that the decision-maker is giving up his or her own payoff to increase the passive agent’s payoff. For example, already choosing left at row 1 in the DIB reveals that the decision-maker is willing to pay at least TZS 500 to increase the passive agent’s payoff by 1,500 compared with the symmetric allocation. In the AIB, switching from left to right at row 9, or 10 also implies benevolence.

Malevolence means that the decision-maker is willing to give up his or her own payoff to decrease the passive agent’s payoff. For example, never switching implies a willingness to pay at least TZS 500 to decrease the passive agent’s payoff by TZS 1,500. Switching to the left in the DIB at row 4 or 5 reveals malevolence. In the AIB, switching to the left at row 6, 7, or 8 also implies malevolence.

The definitions of benevolence and malevolence in the two domains lump together strict and weak forms. A weakly benevolent decision-maker increases the passive agent’s payoff by choosing left at row 3 at no cost, while a weakly malevolent individual renounces doing so by choosing left at row 8.

Table 3 Revealed willingness-to-pay and distributional preference types

Subject chooses left	WTP	WTP sign	Revealed attitude
for first time in row	w
Disadvantageous inequality block (DIB)
1	$0.33\le w<\infty$	>0	Benevolent
2	$0.06\le w<0.33$	>0	Benevolent
3	$0\le w<0.06$	>0	Benevolent
4	$-0.06\le w<0$	<0	Malevolent
5	$-0.33\le w<-0.06$	<0	Malevolent
Never	$-\infty <w<-0.33$	<0	Malevolent

Subject chooses left	WTP	WTP sign	Revealed attitude
for first time in row
Advantageous inequality block (AIB)
6	$-\infty <w<-0.33$	>0	Malevolent
7	$-0.33\le w<-0.06$	>0	Malevolent
8	$-0.06\le w<0$	>0	Malevolent
9	$0\le w<0.06$	<0	Benevolent
10	$0.06\le w<0.33$	<0	Benevolent
Never	$0.33\le w<\infty$	<0	Benevolent

DIB	AIB		Revealed preference type
Preference types
Benevolent	Benevolent		Efficiency-loving (EL)
Benevolent	Malevolent		Inequality-loving (IL)
Malevolent	Benevolent		Inequality-averse (IA)
Malevolent	Malevolent		Spiteful (SF)

This table shows how a choice sequence translates into the active agent’s willingness to pay (WTP) to increase/decrease the passive agent’s payoff by TZS 1

Table 3 clarifies how a choice sequence translates into the active agent’s willingness to pay (WTP) to increase/decrease the passive agent’s payoff by TZS 1. The choice list structure of the experiment only allows us to identify WTP intervals, which is sufficient to determine the signs of the WTP. Benevolence and malevolence are used to categorize subjects into four major distributional preference types. An individual who makes benevolent choices in both domains is labeled as “efficiency-loving” (EL) –that is, the decision-maker maximizes total payoffs. A subject who chooses to switch to the asymmetric allocation early in both domains reveals a preference for inequality; thus the label “inequality-loving” is used (IL). In contrast, switching to the asymmetric allocation late or never in both domains means that we classify the individual as “inequality-averse” (IA). A subject with malevolent choices in both domains is assigned to the “spiteful” preference type (SF).

At the beginning of the experiment, the instructions of the experiment and an example choice list to illustrate the choices were read to all participants.Footnote ¹⁷ In particular, subjects were informed that the passive person was a randomly chosen participant in the same session. Subsequently, students’ remaining questions were answered personally by the team of enumerators.

It was made clear that, if a student drew the distributional preference experiment for payout at the end of the session, one of the ten items on the choice list would be randomly chosen and realized. Due to random matching of active and passive agents, apart from actively choosing allocations, each child was guaranteed to be a passive agent for some other student. The passive payoff from the randomly matched participant was added to the active payoff of the decision-maker, and this was made clear in the instructions.

Fig. 1 Distribution of distributional preferences by gender. Note: Distributional preferences based on willingness to pay (WTP) to increase the passive agent’s payoff in disadvantageous (DIB, y-axis) and advantageous (AIB, x-axis) domains. Left: boys (293 observations). Right: girls (319 observations).

6 Additional results

While the main focus of this paper is to study the role of the close social environment of peers in understanding distributional preferences of children, our study additionally represents the first attempt to experimentally elicit these attitudes with the given method in a developing country context. Not surprisingly, we find that the country context also matters for other-regarding preferences in adolescence. As mentioned earlier, the shares of revealed preference types in our sample of 12- to 13-year-old Tanzanian children resemble the distribution of 8- to 9-year-olds in the sample of Austrian students studied by Fehr et al. (Reference Fehr, Glätzle-Rützler and Sutter2013), who also use simple allocation experiments; see footnote 3 for details. The gender gap in children’s distributional preferences is identical to the shares of preference types among 8- to 9-year-olds in that study. Thus, it appears that a 2- to 3-year delay exists in the evolution of distributional preferences, though individuals could be on different paths altogether. Interestingly, this delay corresponds to the deficits in human capital formation in Sub-Saharan Africa compared with developed countries. Bold et al. (Reference Bold, Filmer, Molina and Svensson2018) find that, after 3.5 years of school, primary schoolchildren in Kenya and Mozambique have gathered knowledge of only 1.5 years of effective learning. If economic underdevelopment is related to a low rate and slow formation of benevolent other-regarding preferences, cooperation and growth could be further affected negatively-a hypothesis, which, we believe is important to test in future research.

It is worth mentioning that the broad and close social environment may interact in determining preference formation at a young age. For example, peer networks in low-income, poverty-prone contexts could have stronger influences on economic behavior, given their role for providing crucial insurance and support in the context of a lack of efficient formal institutions, even at a young age.

This potential preference gap between low- and high-income contexts seems to persist over time. Results for comparable adults sampled in our low-income setting also differ significantly from distribution of types in developed countries (see Figure A.1 in section 2 of the appendix for the distribution of preferences in the adult sample). For example, a study in Austria by Balafoutas et al. (Reference Balafoutas, Kerschbamer, Kocher and Sutter2014), using the same design as our study, shows up to twice as many efficiency-loving types and a significantly lower occurrence of inequality-averse attitudes among adults. In fact, the distribution of adult preference types in our sample is strikingly close to the findings of Fehr et al. (Reference Fehr, Glätzle-Rützler and Sutter2013) for 14- to 17-year-old high school students in a high-income setting. We believe that this observation warrants future research as well.