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on Network Economics |
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Issue of 2018‒10‒22
three papers chosen by Pedro CL Souza Pontifícia Universidade Católica do Rio de Janeiro |
| By: | Shweta Gaonkar (Johns Hopkins Carey Business School, 100 International Dr, Baltimore, MD 21202 - USA); Angelo Mele (Johns Hopkins Carey Business School, 100 International Dr, Baltimore, MD 21202 - USA) |
| Abstract: | We develop and estimate a structural model of strategic network formation to study the determinants of firms collaborations for patenting new technology in the medical device industry. Our aim is to bridge the strategy literature on interorganizational networks and the economic literature on structural estimation of network models. In our model, firms have payoffs that depend on linking costs and benefits, as well as externalities from common partners and popular partners. Firms are characterized by observed and unobserved characteristics, that affect both their opportunity and their willingness to form links. The equilibrium networks are sparse and match the aggregate clustering levels observed in the data. We use the network of patent collaborations among medical device firms, to estimate the structural parameters using a Bayesian approach. Our results show that firms tend to partner domestically and collaborate with companies in similar markets, perhaps due to technological complementarities or regulation effects. Unobserved heterogeneity matters: we find that firms' payoffs vary by type. Finally we show that the estimated model including unobserved heterogeneity provides a better fit of crucial features of the data. |
| Keywords: | firm networks, strategic alliances, exponential random graphs, weak dependence, homophily, clustering, sparse networks |
| JEL: | C13 C31 L14 D85 |
| Date: | 2018–09 |
| URL: | http://d.repec.org/n?u=RePEc:net:wpaper:1807&r=net |
| By: | Hsieh, Chih-Sheng (Chinese University of Hong Kong); Konig, Michael D. (Centre for Economic Policy Research; Swiss Economic Institute; VU Amsterdam); Liu, Xiaodong (University of Colorado Boulder); Zimmermann, Christian (Federal Reserve Bank of St. Louis) |
| Abstract: | We study the impact of research collaborations in coauthorship networks on research output and how optimal funding can maximize it. Through the links in the collaboration network, researchers create spillovers not only to their direct coauthors but also to researchers indirectly linked to them. We characterize the equilibrium when agents collaborate in multiple and possibly overlapping projects. We bring our model to the data by analyzing the coauthorship network of economists registered in the RePEc Author Service. We rank the authors and research institutions according to their contribution to the aggregate research output and thus provide a novel ranking measure that explicitly takes into account the spillover effect generated in the coauthorship network. Moreover, we analyze funding instruments for individual researchers as well as research institutions and compare them with the economics funding program of the National Science Foundation. Our results indicate that, because current funding schemes do not take into account the availability of coauthorship network data, they are ill-designed to take advantage of the spillover effects generated in scientific knowledge production networks. |
| Keywords: | coauthor networks; scientific collaboration; spillovers; key player; research funding; economics of science |
| JEL: | C72 D43 D85 L14 Z13 |
| Date: | 2018–10–09 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedlwp:2018-028&r=net |
| By: | Jingbo Cui; GianCarlo Moschini (Center for Agricultural and Rural Development (CARD)) |
| Abstract: | This paper examines the role of a firm’s internal network in determining plant shutdown decisions in response to environmental regulations. Using unique plant-level data for U.S. manufacturing industries from 1990 to 2008, we find evidence that, in response to increasingly stringent environmental regulations at the county level, multi-plant firms do exercise their greater flexibility in adjusting production, relative to single-plant firms. Specifically, in regulated counties, the likelihood of a plant shutting down is higher for multi-plant firms. Moreover, we measure the firm internal network effect at the local, neighborhood, and the wider-area levels, as defined by the number of affiliated plants clustered in different regional levels. Their effects on plant closure decisions for dirty subsidiaries vary with the network level. We further decompose the neighborhood network into those in regulated and unregulated neighborhood counties, and examine how these network metrics are associated with closure decisions of dirty plants affiliated with multi-plant firms. The presence of more sibling plants residing in neighboring counties that are free from regulatory controls are associated with a higher closure probability of dirty plants in a regulated county. |
| Date: | 2018–10 |
| URL: | http://d.repec.org/n?u=RePEc:ias:cpaper:18-wp585&r=net |