ter the baseline. As a result of these changes, the reciprocity of ultra-poor households’ transfer arrangements with higher socio-economic classes increased significantly. In particular, the reciprocity of ultra-poor households’ transfer links with middle class households increased by 9ppt (19% relative to control) and by 12ppt (44%) with upper class households.

Finally, I find that the program reduced the likelihood that ultra-poor households receive transfers from their employers. Ultra-poor households in treatment communities were 4ppt less likely to report an employer as a source of transfers - a large effect considering that in the control group only 9% of ultra-poor households reported an employer as a source of transfers. To understand whether this effect was driven by a general shift away from wage-labor I restrict the sample to ultra-poor households who were engaged in wage-employment at baseline as well as at the two followup surveys; and I find that they were also less likely to receive transfers from their employers. While this analysis has a caveat (since it is based on a selected subsample), it nevertheless suggests that the effect is not only due to a shift away from wage employment. Overall, the findings suggest that there may be a causal relationship between poverty and alternative forms of reciprocity in informal insurance networks: poor households may reciprocate transfers from wealthier members of the community by selling their labor cheaply to them; while less poor households may reciprocate with transfers of their own.

The paper contributes to the literature on informal insurance in village economies in a number of ways. First, it is related to the literature on risk-sharing through reciprocal transfers (Townsend 1994, Udry 1994). This literature has demonstrated that risksharing networks are likely to be concentrated along dimensions of proximity such as kinship, caste or geographic proximity.<sup>2</sup> What has been less studied is whether poverty has a causal effect on who agents are matched with in risk-sharing networks. Previous empirical studies have exploited cross-sectional variation or lab experiments to shed light on this.<sup>3</sup> This paper contributes to the literature by showing that an ex-

> 2Theoretical reasons for this have been studied by La Ferrara (2003), Genicot and Ray (2003), Bramoullé and Kranton (2007), Bloch, Genicot and Ray (2008) and Ambrus, Mobius and Szeidl (2014). Empirically, this point has been demonstrated by Fafchamps and Lund (2003), La Ferrara (2003), Dercon et al. (2006), Fafchamps and Gubert (2007), Attanasio et al. (2012), Barr et al. (2012), Munshi and Rosenzweig (2016), Angelucci et al. (forthcoming), Chandrasekhar et al. (forthcoming).

> 3Jalan and Ravallion (1999) show that poorer households in rural China are less well insured against fluctuations in their income. Fafchamps and Gubert (2007) show that the wealth difference between two households is negatively correlated with having a risk-sharing connection in the Phillipines, while De Weerdt (2004) finds the opposite relationship in Tanzania. Schechter and Yuskavage (2011a, 2011b) highlight that the failure to distinguish between reciprocated and unreciprocated links could be one reason for these conflicting findings. They show, using survey data form Paraguay, that reciprocated links are more likely to exist between households who have similar wealth or live in close proximity, while unreciprocated transfers links are more likely between households of different wealth or education levels. Attanasio et al. (2012) show experimentally that among close friends and relatives, individuals with

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