Collaborative research: Informal networks and economic development

经济学文献表明，在任何互助安排中都存在执行问题。然而，几乎没有经验证据表明这些问题对经济发展的影响。这项研究将研究非正式网络对经济发展的影响以及经济发展对网络的影响。这项研究项目将使用两个独特的数据集来建模和量化非正式网络在发展中经济体中的作用，这两个数据集描述了过去30年印度城市和农村亚种姓群体之间的家庭行为。具体地说，这项研究将考察劳动力市场网络如何影响印度孟买的教育决策，在那里，由于20世纪90年代的S领导的印度经济改革，英语学校的回报率大幅上升，以及印度绿色革命带来的经济增长如何影响农村网络的表现和生存能力。这项研究将确定(I)网络在多大程度上减少了信贷、保险和劳动力市场的缺陷，并改变了经济发展的动态；(Ii)使网络崩溃并危及社会保险的因素；以及(Iii)网络对经济流动性和技术采用的限制。本研究将使用次种姓数据来构建网络的边界，使用地理编码的家庭和村庄数据，以更好地评估当地事件的空间影响。亚种姓数据使得能够以比以前的工作更精确的方式识别网络效应。亚种姓成员资格还允许调查不同市场，包括劳动力和信贷市场的网络之间的业绩差异。这项研究还研究了网络的成本-对不同网络成员之间互动的限制

The economics literature indicates the existence of enforcement problems in any mutual assistance arrangement. However, there is little empirical evidence on the consequences of such problems on economic development. This research will study the effects of informal networks on economic development and the effects of economic development on networks.This research project will model and quantify the role of informal networks in developing economies using two unique data sets describing household behavior across and within sub-caste groups in urban and rural India over the past three decades. Specifically, particular, the research will examine how labor-market networks shape schooling decisions in Mumbai, India where the returns to English language schooling have risen substantially due to the Indian economic reforms of the 1990's, and how rural network performance and viability, have been affected by the economic growth induced by the Indian green revolution. The research will identify (i) the extent to which networks reduce imperfections in credit, insurance and labor markets and change the dynamics of economic development; (ii) factors that break down networks thus and jeopardize social insurance; and (iii) constraints imposed by networks on economic mobility and technology adoption.This research will use sub-caste data to construct the boundaries of networks using geo-coded household and village data to better assess the spatial impacts of local events. Sub-caste data enables the identification of network effects in a much more precise way than in prior work. Sub-caste membership also permits the investigation of performance differences among networks in different markets, including labor and credit markets. The research also studies the costs of networks--- restrictions on interactions between individual members of different networks