Collaborative Research: The Economic Benefits of Investing into Social Relationships

人们对社交网络在经济互动中扮演的角色产生了爆炸性的兴趣。发展中经济体为深入了解社交网络如何促进经济增长提供了极好的试验台(例如，见Kremer和Miguel(2007)、Conley和Ury(2010)、Banerjee等人)。(2013)，Ambrus等人。(2014年，适用于社会学习和风险分担)。然而，网络数据收集的成本阻碍了实证研究。这里有一种自然的紧张。由于基于网络的研究问题自然是关于相互依存的，因此需要来自许多独立村庄网络的数据才能进行有效的统计分析。同时，对社交网络进行细致入微的分析需要获得网络结构的完整和全局图景；因此，需要对整个社区进行调查，这可能会很耗时。研究人员通常要么在许多网络上拥有非常不完整的网络数据，要么在少数几个网络上拥有详细的数据。我们建议使用一种方法，极大地减少网络数据的收集(我们估计成本为1/6)，以便可以轻松地跨多个社区获取完整的数据。我们将其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和Kocherlakota(2001))。随着城市化程度的提高和临时移民的增加，村民可能看不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个村庄进行全面的网络调查，然后提供收入监测服务，根据我们是否向不同的社区成员披露收入实现情况而定。我们将研究(I)当个人选择监控服务时，他们在多大程度上能够更好(或更差)地应对冲击，(Ii)风险共享网络如何随着我们的实验条件而演变，以及(Iii)风险共享网络中的哪些成员最受/最不受激励地迁移。在这个项目中，我们试图了解社交网络的监控功能如何与分担风险的能力相互作用。通过随机披露一名外来务工人员的信息？S收入给农民工？S家乡风险分担网络的成员，我们可以干净利落地研究这对风险分担网络的影响。如果信息摩擦是经典的，就像科尔和柯薛拉柯塔(2001)那样，承诺披露收入将增加保险的范围。然而，如果个人面临着时间不一致的诱惑，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村庄社交网络传递的知之甚少。个人是否觉得有必要隐瞒收入信息？关于收入冲击的知识是否会在无意中通过网络传播？因此，我们将收集的第一条数据包括一项详细的人种学调查，这将帮助我们了解隐藏/传播村民面临的收入信息的动机。上述网络数据将使用以下协议进行收集。第一天，一个小组将收集一份完整的村庄普查报告，这在许多实地开发项目中是典型的。关键的区别是，我们将在普查期间拍摄房屋和任何现有家庭成员的照片。在最小的时间成本下，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Facebook的帮助下提出文献中使用的标准网络调查问题，这使我们可以绕过纸质数据输入，最大限度地减少名称匹配错误，并相对于使用标准码本机制节省了大量时间。

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.