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on Network Economics |
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Issue of 2018‒05‒21
five papers chosen by Pedro CL Souza Pontifícia Universidade Católica do Rio de Janeiro |
| By: | Andrew B. Bernard; Andreas Moxnes |
| Abstract: | Trade occurs between firms both across borders and within countries, and the vast majority of trade transactions includes at least one large firm with many trading partners. This paper reviews the literature on firm-to-firm connections in trade. A growing body of evidence coming from domestic and international transaction data has established empirical regularities which have inspired the development of new theories emphasizing firm heterogeneity among both buyers and suppliers in production networks. Theoretical work has considered both static and dynamic matching environments in a framework of many-to-many matching. The literature on trade and production networks is at an early stage, and there are a large number of unanswered empirical and theoretical questions. |
| JEL: | F10 F12 F14 L11 L21 |
| Date: | 2018–04 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:24556&r=net |
| By: | Kotowski, Maciej H. (Harvard University); Leister, C. Matthew (Monash University) |
| Abstract: | We study an economy where intermediaries facilitate exchange between a supplier and consumers. The set of feasible transactions is characterized by a network. Free entry helps form the network. There is under-entry of intermediaries in equilibrium due to complementarities among agents in distant parts of the economy. When intermediaries are speculators, equilibrium networks exhibit an asymmetric structure that amplifies certain traders’ importance. An extension of the model allows for disintermediation. Generally, free-entry and competition may fail to purge redundant intermediaries from the market. However, avoidance of a reseller’s curse deters superfluous speculators. |
| JEL: | D44 D85 L14 |
| Date: | 2018–01 |
| URL: | http://d.repec.org/n?u=RePEc:ecl:harjfk:rwp18-001&r=net |
| By: | Ashani Amarasinghe; Roland Hodler; Paul A. Raschky; Yves Zenou |
| Abstract: | The aggregate economic impact of any developmental project depends on its effects within the chosen administrative region as well as its economic spillovers into other regions. However, little is known about how these spillovers propagate through geographic, ethnic and road networks. In this paper, we analyze both theoretically and empirically the role of these networks in the spatial diffusion of local economic shocks. We develop a network model that shows how a district’s level of prosperity is related to its position in the network. The network model’s first-order conditions are used to derive an econometric model of spatial spillovers that we estimate using a panel of 5,944 districts from 53 African countries over the period 1997-2013. To identify the causal effect of spatial diffusion, we exploit cross-sectional variation in the location of mineral mines and exogenous time variation in world mineral prices. Our results show that road and ethnic connectivity are particularly important factors for diffusing economic spillovers over longer distances. We then use the estimated parameters from the econometric model to calculate the key player centralities, which determine which districts are key in propagating local economic shocks across Africa. We further show how counterfactual exercises based on these estimates and the underlying network structure can inform us about the potential gains from policies that increase economic activity in specific districts or improve road connectivity between districts. |
| Keywords: | economic development, networks, spatial spillovers, key player centrality, natural resources, transportation, Africa |
| JEL: | O13 O55 R12 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_7001&r=net |
| By: | Christoph Aymanns; Co-Pierre Georg; Benjamin Golub; |
| Abstract: | Banks provide intermediation of two economically coupled assets, each traded on an OTC market—e.g., secured debt and the underlying collateral. We model banks’ decisions to provide liquidity as a game of strategic complements on two coupled trading networks:incentives to be active in one network are increasing in its neighbors’ activity in both networks. When an exogenous shock renders some banks inactive, other banks follow in an illiquidity spiral across the two networks. Liquidity can be improved if one of the two OTC markets is replaced by an exchange. For a class of market structures associated with random graphs, liquidity changes discontinuously in the size of an exogenous shock, in contrast to contagion on one network. |
| Keywords: | market liquidity, funding liquidity, over-the-counter markets |
| JEL: | G21 G23 D85 |
| Date: | 2017–12 |
| URL: | http://d.repec.org/n?u=RePEc:usg:sfwpfi:2018:10&r=net |
| By: | Christoph Aymanns; Jakob Foerster; Co-Pierre Georg |
| Abstract: | We model the spread of news as a social learning game on a network. Agents can either endorse or oppose a claim made in a piece of news, which itself may be either true or false. Agents base their decision on a private signal and their neighbors’ past actions. Given these inputs, agents follow strategies derived via multi-agent deep reinforcement learning and receive utility from acting in accordance with the veracity of claims. Our framework yields strategies with agent utility close to a theoretical, Bayes optimal benchmark, while remaining flexible to model re-specification. Optimized strategies allow agents to correctly identifymostfalseclaims, whenallagentsreceiveunbiasedprivatesignals. However, anadversary’s attempt to spread fake news by targeting a subset of agents with a biased private signal can be successful. Even more so when the adversary has information about agents’ network position or private signal. When agents are aware of the presence of an adversary they re-optimize their strategies in the training stage and the adversary’s attack is less effective. Hence, exposing agents to the possibility of fake news can be an effective way to curtail the spread of fake news in social networks. Our results also highlight that information about the users’ private beliefs and their social network structure can be extremely valuable to adversaries and should be well protected. |
| Keywords: | social learning, networks, multi-agent deep reinforcement learning |
| Date: | 2017–08 |
| URL: | http://d.repec.org/n?u=RePEc:usg:sfwpfi:2018:04&r=net |