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Kaivan Munshi Publications

Publish Date
Journal of Development Economics

We provide evidence of the role of community networks in emergence of Indian entrepreneurship in early stages of cotton and jute textile industries in the late 19th and early 20th century respectively, overcoming lack of market institutions and government support. From business registers, we construct a yearly panel dataset of entrepreneurs in these two industries. We find no evidence that entry was related to prior upstream trading experience or price shocks. Firm directors exhibited a high degree of clustering of entrepreneurs by community. Consistent with a model of network-based dynamics, the stock of incumbent entrepreneurs of different communities diverged non-linearly, controlling for year and community fixed effects.


There are many economic environments in which an object is offered sequentially to prospective buyers. It is often observed that once the object for sale is turned down by one or more agents, those that follow do the same. One explanation that has been proposed for this phenomenon is that agents making choices further down the line rationally ignore their own assessment of the object’s quality and herd behind their predecessors. Our research adds a new dimension to the canonical herding model by allowing agents to di er in their ability to assess the quality of the offered object. We develop novel tests of herding based on this ability heterogeneity and also examine its efficiency consequences, applied to organ transplantation in the U.K. We nd that herding is common but that the information lost due to herding does not substantially increase false discards of good organs or false acceptances of bad organs. Our counter-factual analysis indicates that this is due (in part) to the high degree of heterogeneity in ability across transplant centers. In other settings, such as the U.S., where organ transplantation is organized very differently and the ability distribution will not be the same, the inefficiencies due to herding might well be substantial.