Social Structure and Redistribution: Evidence from Age Vs Kin Based Organizations∗

Social Structure and Redistribution: Evidence from Age vs Kin Based Organizations∗ Jacob Mosconay and Awa Ambra Seckz August 9, 2021 Abstract We document that ethnic groups’ social structure shapes patterns of economic interaction and hence the impact of public policy. Our analysis focuses on age set societies, ethnic groups comprising over 130 million people in sub-Saharan Africa in which individuals are organized into social groups based on age, known as “age sets,” that take priority over kin. Ethnographic accounts suggest that in age set societies, within-cohort economic ties are strong while inter-generational family ties are compara- tively weak. First, we analyze a randomized unconditional cash transfer program in Northern Kenya and document that in age set societies, but not in kin-based societies, there are large consumption spillovers within the age cohort. Moreover, focusing on an arm of the experiment that simulates a pension program, we find that randomly increasing the income of older people improves child health and increases household education spending in kin based societies, but has no such impact in age set societies. Next, exploiting the staggered roll-out of Uganda’s social pension program, we document a similar pattern at a national scale: household exposure to the pension program has a large, positive effect on child health in kin-based societies, but no impact in age set societies. These findings high- light how local variation in social structure can lead to markedly different, yet predictable, patterns of redistribution, thereby shaping the consequences of national policies. ∗We thank Daron Acemoglu, Alberto Alesina, Emily Breza, Rema Hanna, Michael Kremer, Nathan Nunn, Ben Olken, Gautam Rao, James A. Robinson, and Robert M. Townsend for their guidance and support. We also thank seminar partici- pants at Harvard, MIT, DEVPEC, and CEPR/LEAP for their comments. yDepartment of Economics, Massachusetts Institute of Technology, 50 Memorial Drive, Cambridge, MA 02142, U.S.A. (email: [email protected]; website: http://economics.mit.edu/grad/moscona) zDepartment of Economics, Harvard University, 1805 Cambridge Street, Cambridge, MA, 02138, U.S.A. (email: [email protected]; website: https://scholar.harvard.edu/awaambraseck) 1 Introduction Informal income transfers are pervasive, particularly in low-income contexts, and the pattern and scale of these transfers shape the impact of public policy (e.g. Egger et al., 2019). When credit and insurance markets are imperfect, the ability to share risk and redistribute income is key for prevent- ing major individual hardship (e.g. Rosenzweig and Stark, 1989; Townsend, 1994, 1995). But what determines real-world patterns of financial ties and redistribution? Economic analysis has often presupposed that the same interests and incentives are paramount in all settings—for example, individual desire to minimize consumption risk—and that individuals are most connected with members of their family or village, social groups familiar in the West (e.g. Townsend, 1994; Kocherlakota, 1996). Policy analysis and design are informed by a similar set of as- sumptions, including the idea that patterns of re-distribution are driven by the same passions across contexts and can hence be expected to look similar.1 Anthropologists since Franz Boas(1887) and Robert Lowie(1917), however, have documented vast differences in the patterns and goal of economic exchange across cultures and argue that ostensibly clear-cut social groupings, like the family, vary widely in relevance. Local patterns of allegiance are persistent and not always free to adjust as individual or regional economic conditions change.2 The anthropologist’s perspective would be that patterns of exchange and their underlying causes are vastly different across contexts and can only be understood through the lens of local social structure. This paper empirically investigates the extent to which long-standing features of social organization predict the shape of modern economic networks and the spillover effects of public policy. Our focus is the stark distinction between societies organized around kin-based social groups and societies organized around age-based social groups, known as “age set societies.” In the latter, the primary social unit is the “age set,” a group of individuals who are a similar age and are initiated into adulthood at the same time. In age set societies, the strongest feelings of obligation and allegiance are typically between individuals of the same age cohort, rather than family or extended family. Ex- amples of age set organization exist around the world but are particularly common in sub-Saharan Africa, where we estimate at least 130 million people live in age set societies; it is described by Lowie (1921) as the “most important example of an alternative to kinship-based organization.”3 This unique setting, in which two markedly distinct forms of social organization co-exist in the same area, allow us to provide direct evidence of the relevance of local social structure for understanding modern redistribution and the spillover effects of public policy. Age set organization has been thoroughly studied by anthropologists. Their qualitative accounts 1For example, as discussed in more detail below, it is often presumed that pension programs will have positive spillover effects on children because, in kin-based societies, grandparents are expected to invest in grandchildren. Uganda’s Ministry of Gender, Labour, and Social Development uses this idea to justify its Senior Citizen Grant program; see here: https:// socialprotection.go.ug/senior-citizens-grants-scgs/. Spatial models often assume a parametric spatial structure to spillover effects that are the same throughout the sample (e.g. Egger et al., 2019) 2While a growing number of economic analyses directly measure local financial networks (See e.g. Ambrus et al., 2014; Breza et al., 2019; Kinnan et al., 2020), many contexts pose limits on such detailed data collection, leaving us with a limited understanding of the structure and formation of social and financial ties. We describe this work more in detail below. 3Examples include the Oromo, Zulu, Maasai, Kikuyu, Samburu, Tiv, and Iteso; while age set organization exists in West Africa (e.g. the Tiv), to our knowledge it is most prevalent in East and South Africa. The 130 million estimate combines our coding of the existence of age set organization across groups and the most recent census data from each country. 1 suggest that in age set societies, compared to kin based societies, within-cohort economic ties are strong and within-family and inter-generational ties are weak (e.g. Radcliffe-Brown, 1929; Dyson- Hudson, 1963; Morton, 1979; Handley, 2009). According to Eisenstadt(1954): “[R]elations between age mates [involve] general and permanent obligations of cooperation, solidarity, [and] help, closely resembling, in this respect, the family and kinship.” Still today, in age set societies in Kenya it is “toward age mates that men look for support rather than brothers or close patrilineal kin” (Spencer, 2014, p. 48). Age mates are initiated together at a young age and pass through different phases of life together, creating a strong within-cohort bond that persists until an individual dies.4 Anthro- pological accounts not only provide evidence that age set societies are a prevalent form of social organization throughout sub-Saharan Africa, but also that across studied examples they lead to a strikingly different structure of allegiance and pattern of resource redistribution (Morton, 1979).5 We use a wide range of ethnographic accounts to compile data on the social structure of as many ethnic groups as possible in Kenya and Uganda, the focus of our empirical analysis. Information on the presence of age set organization is not available from standard ethnographic databases for sub-Saharan Africa, including the Ethnographic Atlas. Therefore, following the canonical definition of age set organization from Radcliffe-Brown(1929), for each ethnicity we coded by hand whether the dominant social form is the age set or the kin group. By population, we were able to definitively determine the dominant form of social organization for 99% of the studied sample in Kenya and 85% of the sample in Uganda, where we find members of age set societies comprise 72% and 27% of the population respectively. Section2 and Appendix A1 describe this coding process in detail. Our main hypothesis is that age set organization affects the structure of local financial ties by en- couraging stronger within-cohort links and weaker within-family and inter-generational links. Fur- ther, we argue that this translates in different patterns of redistribution which, in turn, affect the spillover effects of national policy. The first part of our empirical analysis homes in on a randomized evaluation of Kenya’s Hunger Safety Net Program (HSNP), a large-scale cash transfer program, which to our knowledge is the only setting with randomized cash transfers and all baseline data re- quired for investigating our hypothesis.6 Sub-locations in Northern Kenya were randomly assigned to treatment and control groups. Within both treatment and control locations, individuals were se- lected to receive the cash transfer via one of several targeting mechanisms; in treatment locations delivery of the transfers began immediately while in control locations they were delayed for two years. Crucially, there are age set and kin based societies in both treatment and control sub-locations. We first exploit the randomized transfer experiment and a triple differences design to estimate within-cohort consumption spillovers, separately for members of age set and kin-based societies. In age set societies, randomly increasing the income of an individual’s cohort members via the transfer program dramatically increases consumption spending; consistent with the HSNP’s goal to reduce 4In Northern Kenya, age mates of all ages “share milk, meat, tea, sugar, etc” and “can rely on each other even if you have no money or social problems” (Handley, 2009).

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