Collaborative Research: The Economic Benefits of Investing into Social Relationships

人们对社交网络在经济互动中所扮演的角色产生了爆炸式的兴趣。发展中经济体提供了一个很好的测试平台，可以深入了解社会网络如何促进经济发展(例如，Kremer和Miguel (2007)， Conley和Udry （2010)， Banerjee等人（2013），Ambrus等人（2014）对社会学习和风险分担的应用）。然而，实证研究一直受到网络数据收集成本的制约。这里有一种自然的张力。由于基于网络的研究问题自然是关于相互依赖的，因此需要来自许多独立村庄网络的数据进行有效的统计分析。与此同时，对社交网络进行细致入微的分析需要对网络结构有一个完整的全局图景；因此，需要对整个社区进行调查，这可能很耗时。研究人员通常要么在许多网络中拥有非常不完整的网络数据，要么在少数网络中拥有细致的数据。我们建议使用一种大大减少网络数据收集的方法（我们估计成本为1/6），以便可以轻松地在众多社区中获得完整的数据。我们将此应用于风险分担网络的研究。限制村民形成风险分担网络程度的一个主要因素是，各方可能无法相互观察。收入（Cole and Kocherlakota, 2001）。随着城市化进程的加快和临时移民的增加，村民们可能无法看到其他家庭份额的增加。收入，这可能会侵蚀风险分担网络。我们建议在100个村庄进行完整的网络调查，然后提供收入监测服务，无论我们是否向不同的社区成员透露收入实现情况。我们将研究(i)当个人选择监测服务时，他们在多大程度上能够更好（或更差）地应对冲击，（ii）风险分担网络如何响应我们的实验条件而演变，以及（iii）风险分担网络中哪些成员最有/最不愿意迁移。在这个项目中，我们试图了解社会网络的监测功能如何与风险分担能力相互作用。通过随机透露临时移民的信息？给移民成员的收入？S家村风险分担网络，我们可以清楚地研究这对风险分担网络的影响。如果信息摩擦是经典的，a la Cole和Kocherlakota(2001)，承诺披露收入会增加保险的范围。然而，如果个人面临时间不一致的诱惑，以满足从他们的社交网络转移的请求，预测就不那么明确了（Banerjee和Mullainathan(2010)）。尽管隐性收入很重要，但我们对收入信息实际上是如何通过农村社会网络传递的知之甚少。个人是否觉得有必要隐藏收入信息？关于收入冲击的知识会无意中通过网络传播吗？因此，我们将收集的第一个数据包括详细的民族志调查，这将帮助我们理解隐藏/传播村民所面临的收入信息的感知动机。上述网络数据将使用以下协议收集。在第一天，一个小组将收集完整的村庄人口普查，这是许多实地发展项目的典型做法。关键的区别在于，在人口普查期间，我们将拍摄房屋以及任何在场的家庭成员的照片。以最小的时间成本，我们能够创建一个村庄的facebook。因此，在第二天，我们的工作人员可以在这个facebook的帮助下询问文献中使用的标准网络调查问题，这使我们能够绕过纸质数据输入，最大限度地减少名称匹配错误，并且相对于使用标准代码本机制节省大量时间。

There has been an exploding interest in the role that social networks play in economic interactions. Developing economies provide an excellent testbed to gain insights about how social networks facilitate economic (see, e.g., Kremer and Miguel (2007), Conley and Udry (2010), Banerjee et al. (2013), Ambrus et al. (2014) for applications to social learning as well as risk-sharing). However, empirical research has been hamstrung by the cost of network data collection. There is a natural tension here. Because networks-based research questions are naturally about interdependence, data from many independent village networks are required for valid statistical analysis. At the same time, a nuanced analysis of social networks requires getting a complete and global picture of the network structure; thus, a survey of the entire community is needed and this can be time-consuming. Researchers typically either have very incomplete network data across many networks or meticulous data across a handful of networks. We propose using a method that reduces the collection of network data tremendously (we estimate 1/6 the cost) so that complete data can be obtained across numerous communities with ease. We apply this to the study of risk-sharing networks. A major factor that limits the extent to which villagers form risk-sharing networks is the fact that parties may not be able to observe each others? incomes (Cole and Kocherlakota (2001)). With increased urbanization and the growth of temporary migration, villagers may be unable to see increasing shares of other households? incomes, which may erode the risk-sharing network. We propose conducting complete network surveys across 100 villages, and then offering an income monitoring service, varying whether or not we reveal income realizations to various community members. We will study (i) the extent to which individuals are better (or worse) able to cope with shocks when they opt for the monitoring service, (ii) how the risk-sharing network evolves in response to our experimental conditions, and (iii) which members of a risk-sharing network are most/least incentivized to migrate.In this project, we seek to understand how the monitoring function of the social network interacts with the ability to share risk. By randomly revealing information about a temporary migrant?s income to members of the migrant?s home village risk-sharing network, we can cleanly to study how this affects the risk-sharing network. If the information friction is classical, a la Cole and Kocherlakota (2001), committing to reveal the income would increases the scope for insurance. However, if individuals face time-inconsistent temptations to honor requests for transfers from their social networks, predictions are less clear-cut (Banerjee and Mullainathan (2010)). Despite the importance of hidden income, we know very little about how information about incomes actually passes through village social networks. Do individuals feel compelled to hide income information? Does knowledge about income shocks unintentionally diffuse through the network? Thus, the first piece of data we will collect includes a detailed ethnographic survey that will help us understand the perceived incentives to hide/spread income information faced by villagers. The aforementioned network data will be collected using the following protocol. On the first day a team will collect a complete village census, as is typical in many field-based development projects. The key difference is that we will take pictures of the houses as well as any present household members during the census. For a minimal time-cost, we are able to create a facebook of the village. Thus, on the second day, our staff can ask standard networks survey questions used in the literature with the aid of this facebook, which allows us to bypass paper data-entry, minimizes name-matching errors, and saves an immense amount of time relative to using a standard codebook mechanism.