nep-net New Economics Papers
on Network Economics
Issue of 2022‒02‒21
seventeen papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. Homophily in preferences or meetings? Identifying and estimating an iterative network formation model By Luis Antonio Fantozzi Alvarez; Cristine Campos de Xavier Pinto; Vladimir Pinheiro Ponczek
  2. In platforms we trust: misinformation on social networks in the presence of social mistrust By Charlson, G.
  3. Measuring the effect of distance on the network topology of the Global Container Shipping Network By Dimitrios Tsiotas; César Ducruet
  4. A study on bribery networks with a focus on harassment bribery and ways to control corruption By Chanchal Pramanik
  5. Network Structure and Fragmentation of the Argentinean Interbank Markets By Pedro Elosegui Author-Email: Author-Workplace-Name: Central Bank of Argentina; Federico Forte; Gabriel Montes-Rojas
  6. Reconstructing production networks using machine learning By Lafond, François; Farmer, J. Doyne; Mungo, Luca; Astudillo-Estévez, Pablo
  7. Heterogeneous Peer Effects under Endogenous Selection: An Application to Local and Migrant Children in Elementary Schools in Shanghai By Chen, Yuanyuan; Feng, Shuaizhang; Yang, Chao
  8. Combating online hate speech: The impact of legislation on Twitter By Andres, Raphaela; Slivko, Olga
  9. No inventor is an island: social connectedness and the geography of knowledge flows in the US By Diemer, Andreas; Regan, Tanner
  10. The financial network channel of monetary policy transmission: An agent-based model By Michel Alexandre; Gilberto Tadeu Lima; Luca Riccetti; Alberto Russo
  11. Bank Credit and Money Creation on Payment Networks: A Structural Analysis of Externalities and Key Players By Li, Ye; Li, Yi; Sun, Huijun
  12. The Changing Shape of the World Automobile Industry: A Multilayer Network Analysis of International Trade in Components and Parts By Margherita Russo; Fabrizio Alboni; Jorge Carreto Sangines; Manlio De Domenico; Giuseppe Mangioni; Simone Righi; Annamaria Simonazzi
  13. The component-wise egalitarian Myerson value for Network Games By Surajit Borkotokey; Sujata Goala; Niharika Kakoty; Parishmita Boruah
  14. Bayesian Estimation of Multivariate Panel Probits with Higher-order Network Interdependence and an Application to Firms' Global Market Participation in Guangdong By Badi H. Baltagi; Peter H. Egger; Michaela Kesina
  15. Sharing with Minimal Regulation? Free Riding and Neighborhood Book Exchange By Schippers, Anouk L.; Soetevent, Adriaan R.
  16. Trade persistence and trader identity - evidence from the demise of the Hanseatic League By Max Marczinek; Stephan Maurer; Ferdinand Rauch
  17. Test of Neglected Heterogeneity in Dyadic Models By Jinyong Hahn; Hyungsik Roger Moon; Ruoyao Shi

  1. By: Luis Antonio Fantozzi Alvarez (Institute of Mathematics and Statistics, University of S\~ao Paulo); Cristine Campos de Xavier Pinto (Insper Institute of Education and Research); Vladimir Pinheiro Ponczek (S\~ao Paulo School of Economics, Funda\c{c}\~ao Get\'ulio Vargas)
    Abstract: Is homophily in social and economic networks driven by a taste for homogeneity (preferences) or by a higher probability of meeting individuals with similar attributes (opportunity)? This paper studies identification and estimation of an iterative network game that distinguishes between these two mechanisms. Our approach enables us to assess the counterfactual effects across time of changing the meeting protocol between agents. As an application, we study the role of preferences and meetings in shaping classroom friendship networks in Brazil. We find that in a network structure in which homophily due to preferences is stronger than homophily due to meeting opportunities, tracking students may lead to welfare improvement, but the relative benefit of this policy diminishes over the school year.
    Date: 2022–01
  2. By: Charlson, G.
    Abstract: We examine the effect social mistrust has on the propagation of misinformation on a social network. Agents communicate with each other and observe information sources, changing their opinion with some probability determined by their social trust, which can be low or high. Low social trust agents are less likely to be convinced out of their opinion by their peers and, in line with recent empirical literature, are more likely to observe misinformative information sources. A platform facilitates the creation of a homophilic network where users are more likely to connect with agents of the same level of social trust and the same social characteristics. Networks in which worldview is relatively important in determining network structure have more pronounced echo chambers, reducing the extent to which high and low social trust agents interact. Due to the asymmetric nature of these interactions, echo chambers then decrease the probability that agents believe misinformation. At the same time, they increase polarisation, as disagreeing agents interact less frequently, leading to a trade-off which has implications for the optimal intervention of a platform wishing to reduce misinformation. We characterise this intervention by delineating the most effective change in the platform's algorithm, which for peer-to-peer connections involves reducing the extent to which relatively isolated high and low social trust agents interact with one another.
    Keywords: communication, misinformation, network design, platforms
    JEL: D82 D83 D85
    Date: 2022–01–14
  3. By: Dimitrios Tsiotas (Agricultural University of Athens); César Ducruet (EconomiX - UPN - Université Paris Nanterre - CNRS - Centre National de la Recherche Scientifique)
    Abstract: This paper examines how spatial distance affects network topology on empirical data concerning the Global Container Shipping Network (GCSN). The GCSN decomposes into 32 multiplex layers, defined at several spatial levels, by successively removing connections of smaller distances. This multilayer decomposition approach allows studying the topological properties of each layer as a function of distance. The analysis provides insights into the hierarchical structure and (importing and exporting) trade functionality of the GCSN, hub connectivity, several topological aspects, and the distinct role of China in the network's structure. It also shows that bidirectional links decrease with distance, highlighting the importance of asymmetric functionality in carriers' operations. It further configures six novel clusters of ports concerning their spatial coverage. Finally, it reveals three levels of geographical scale in the structure of GCSN (where the network topology significantly changes): the neighborhood (local connectivity); the scale of international connectivity (mesoscale or middle connectivity); and the intercontinental market (large scale connectivity). The overall approach provides a methodological framework for analyzing network topology as a function of distance, highlights the spatial dimension in complex and multilayer networks, and provides insights into the spatial structure of the GCSN, which is the most important market of the global maritime economy.
    Date: 2021–10–28
  4. By: Chanchal Pramanik
    Abstract: The paper focuses on the bribery network emphasizing harassment bribery. A bribery network ends with the police officer whose utility from the bribe is positive and the approving officer in the network. The persistent nature of corruption is due to colluding behavior of the bribery networks. The probability of detection of bribery incidents will help in improving controlling corruption in society. The asymmetric form of punishment and award equivalent to the amount of punishment to the network can enhance the probability of detection of harassment bribery $(p_{h})$ and thus increasing the probability of detection of overall bribery $(p_{h} \in p)$.
    Date: 2022–01
  5. By: Pedro Elosegui Author-Email: Author-Workplace-Name: Central Bank of Argentina; Federico Forte (BBVA Research, BBVA Argentina); Gabriel Montes-Rojas (IIEP-BAIRES-UBA, CONICET)
    Abstract: This paper studies the network structure and fragmentation of the Argentine interbank market. Both the unsecured (CALL) and the secured (REPO) markets are examined. The aim of this study is to understand their actual fragmentation, as well as its potential implications for monetary policy and financial stability. Applying network analysis, different underlying segments within the market are identified. We approximate the theoretical distribution that better fits the empirical degree distribution of the interbank loan networks. Based on standard topological metrics, it is found that, although the secured market has less participants, its nodes are more densely connected than in the unsecured market. In addition, the interrelationships in the unsecured market are less stable, as it was witnessed during the 2018 currency crisis, making its structure more volatile and vulnerable to negative shocks. The analysis identifies two "hidden" underlying sub-networks within the REPO market: one based on the transactions collateralized by Treasury bonds (REPO-T) and other based on the operations collateralized by Central Bank (CB) securities (REPO-CB). The connectivity indicators were significantly more stable in the REPO-T market than in the REPO-CB segment. The changes in monetary policy stance and monetary conditions seem to have a substantially smaller impact in former than in the latter "sub-market". Hence, the connectivity levels within the REPO-T market remain relatively unaffected by the (in some period pronounced) swings in the other segment of the market. These results have implications in terms of the interpretation of the interest rates that arise from these markets.
    Keywords: network analysis, interbank market, fragmentation, central bank, monetary policy, Argentina
    JEL: C2 C12 G21 G28
    Date: 2021–12
  6. By: Lafond, François; Farmer, J. Doyne; Mungo, Luca; Astudillo-Estévez, Pablo
    Abstract: The vulnerability of supply chains and their role in the propagation of shocks has been high- lighted multiple times in recent years, including by the recent pandemic. However, while the importance of micro data is increasingly recognised, data at the firm-to-firm level remains scarcely available. In this study, we formulate supply chain networks' reconstruction as a link prediction problem and tackle it using machine learning, specifically Gradient Boosting. We test our approach on three di↵erent supply chain datasets and show that it works very well and outperforms three benchmarks. An analysis of features' importance suggests that the key data underlying our predictions are firms' industry, location, and size. To evaluate the feasibility of reconstructing a network when no production network data is available, we attempt to predict a dataset using a model trained on another dataset, showing that the model's performance, while still better than a random predictor, deteriorates substantially.
    Keywords: Supply chains, Network reconstruction, Link prediction, Machine learning
    JEL: C53 C67 C81
    Date: 2022–01
  7. By: Chen, Yuanyuan; Feng, Shuaizhang; Yang, Chao
    Abstract: This paper develops a model that allows for heterogenous contemporaneous peer effects among different types of agents who are endogenously selected into different peer groups. Using our framework, we characterize the reduced-form coefficient in the peer effect literature and show that it is a priori ambiguous in sign. We apply our approach to migrant and local students in Shanghai, where local students all go to public schools, but migrant students are endogenously selected into either public schools or lower-quality private schools. The results suggest large contemporaneous peer effects among all student groups. We conduct policy experiments to examine the effect of transferring migrant students from private schools to public schools. We show that peer effect can be substantially more important than the school effect in accounting for the total treatment effect of moving to better schools.
    Keywords: Peer Effects,Sample Selection,Education,Migrant Children
    JEL: C31 C34 I21
    Date: 2022
  8. By: Andres, Raphaela; Slivko, Olga
    Abstract: We analyze the impact of the Network Enforcement Act, the first regulation which aims at restraining hate speech on large social media platforms. Using a difference-in- differences framework, we measure the causal impact of the German law on the prevalence of hateful content on German Twitter. We find evidence of a significant and robust decrease in the intensity and volume of hate speech in tweets tackling sensitive migration-related topics. Importantly, tweets tackling other topics as well as the tweeting style of users are not affected by the regulation, which is in line with its aim. Our results highlight that legislation for combating harmful online content can influence the prevalence of hate speech even in the presence of platform governance mechanisms.
    Keywords: Social Networks,User-Generated Content,Hate Speech,Policy Evaluation
    JEL: H41 J15 K42 L82 L86
    Date: 2021
  9. By: Diemer, Andreas; Regan, Tanner
    Abstract: Do informal social ties connecting inventors across distant places promote knowledge flows between them? To measure informal ties, we use a new and direct index of social connectedness of regions based on aggregate Facebook friendships. We use a well-established identification strategy that relies on matching inventor citations with citations from examiners. Moreover, we isolate the specific effect of informal connections, above and beyond formal professional ties (co-inventor networks) and geographic proximity. We identify a significant and robust effect of informal ties on patent citations. Further, we find that the effect of geographic proximity on knowledge flows is entirely explained by informal social ties and professional networks. We also show that the effect of informal social ties on knowledge flows is greater for new entrepreneurs or ‘garage inventors’, for older or ‘forgotten’ patents, and for flows across distant technology fields. It has also become increasingly important over the last two decades.
    Keywords: diffusion; informal networks; knowledge flows; social connectedness; PhD Studentship
    JEL: O33 R12 Z13
    Date: 2022–03–01
  10. By: Michel Alexandre (Central Bank of Brazil and Institute of Mathematics and Computer Science, University of Sao Paulo, Sao Carlos, Brazil); Gilberto Tadeu Lima (Department of Economics, University of Sao Paulo, Brazil); Luca Riccetti (Department of Economics and Law, University of Macerata, Italy); Alberto Russo (Department of Management, Università Politecnica delle Marche, Ancona, Italy and Department of Economics, Universitat Jaume I, Castellón, Spain)
    Abstract: The purpose of this paper is to contribute to a further understanding of the impact of monetary policy shocks on a financial network, which we dub the “financial network channel of monetary policy transmisión”. To this aim, we develop an agent-based model (ABM) in which banks extend loans to firms. The bank-firm credit network is endogenously time-varying as determined by plausible behavioral assumptions, with both firms and banks being always willing to close a credit deal with the network partner perceived to be less risky. We then assess through simulations how exogenous shocks to the policy interest rate affect some key topological measures of the bank-firm credit network (density, assortativity, size of largest component, and degree distribution). Our simulations show that such topological features of the bank-firm credit network are significantly affected by shocks to the policy interest rate, and this impact varies quantitatively and qualitatively with the sign, magnitude, and duration of the shocks.
    Keywords: Financial network, monetary policy shocks, agent-based modeling
    JEL: C63 E51 E52 G21
    Date: 2022
  11. By: Li, Ye (Ohio State University); Li, Yi (Board of Governors of the Federal Reserve System); Sun, Huijun (Columbia Business School)
    Abstract: This paper documents a strong connection between payment system and credit supply. The dual role of deposits as financing instruments for banks and means of payment for bank customers implies spillover effects of bank lending. After a bank finances loans with new deposits, the deposit holders' payments cause reserves and deposits to flow from the lending bank to the payees' banks. The change in liquidity conditions for both banks and their customers gives rise to two opposing forces that generate respectively strategic complementarity and strategic substitution in banks' lending decisions. We model bank lending through a linear-quadratic game on a random graph of payment flows and structurally estimate the spillover effects using Fedwire data to quantify the probability distribution of payment-flow network. Payment network externalities reduce the average level of aggregate credit supply by 9% while amplify the volatility by 20%. We identify a small subset of banks that have a disproportionately large influence on credit supply due to their special positions in the payment-flow network.
    JEL: E42 E43 E44 E51 E52 G21 G28
    Date: 2021–12
  12. By: Margherita Russo (University of Modena and Reggio Emilia); Fabrizio Alboni (University of Modena and Reggio Emilia); Jorge Carreto Sangines (Universidad Nacional Autonoma de Mexico); Manlio De Domenico (Fondazione Bruno Kessler); Giuseppe Mangioni (University of Catania); Simone Righi (Ca' Foscari University of Venice Italy); Annamaria Simonazzi (Sapienza Università di Roma, Italy)
    Abstract: In 2018, after 25 years of the North America Trade Agreement (NAFTA), the United States requested new rules which, among other requirements, increased the regional content in the production of automotive components and parts traded between the three partner countries, United States, Canada and Mexico. Signed by all three countries, the new trade agreement, USMCA, is to go into force in 2022. Nonetheless, after the 2020 Presidential election, the new treaty's future is under discussion, and its impact on the automotive industry is not entirely defined. Another significant shift in this industry - the accelerated rise of electric vehicles - also occurred in 2020: while the COVID-19 pandemic largely halted most plants in the automotive value chain all over the world, at the reopening, the tide is now running against internal combustion engine vehicles, at least in the announcements and in some large investments planned in Europe, Asia and the US. The definition of the pre-pandemic situation is a very helpful starting point for the analysis of the possible repercussions of the technological and geo-political transition, which has been accelerated by the epidemic, on geographical clusters and sectorial specializations of the main regions and countries. This paper analyzes the trade networks emerging in the past 25 years in a new analytical framework. In the economic literature on international trade, the study of the automotive global value chains has been addressed by using network analysis, focusing on the centrality of geographical regions and countries while largely overlooking the contribution of countries' bilateral trading in components and parts as structuring forces of the subnetwork of countries and their specific position in the overall trade network. The paper focuses on such subnetworks as meso-level structures emerging in trade network over the last 25 years. Using the Infomap multilayer clustering algorithm, we are able to identify clusters of countries and their specific trades in the automotive international trade network and to highlight the relative importance of each cluster, the interconnections between them, and the contribution of countries and of components and parts in the clusters. We draw the data from the UN Comtrade database of directed export and import flows of 30 automotive components and parts among 42 countries (accounting for 98% of world trade flows of those items). The paper highlights the changes that occurred over 25 years in the geography of the trade relations, in particular with regard to denser and more hierarchical network generated by Germany's trade relations within EU countries and by the US preferential trade agreements with Canada and Mexico, and the upsurge of China. With a similar overall variety of traded components and parts within the main clusters (dominated respectively by Germany, US and Japan-China), the Infomap multilayer analysis singles out which components and parts determined the relative positions of countries in the various clusters and the changes over time in the relative positions of countries and their specializations in multilateral trades. Connections between clusters increase over time, while the relative importance of the main clusters and of some individual countries change significantly. The focus on US and Mexico and on Germany and Central Eastern European countries (Czech Republic, Hungary, Poland, Slovakia) will drive the comparative analysis.
    Keywords: international trade; regional specialization; automotive components and parts; dynamics of change; Infomap multilayer analysis.
    JEL: F14 L62 D85
    Date: 2022–01–03
  13. By: Surajit Borkotokey; Sujata Goala; Niharika Kakoty; Parishmita Boruah
    Abstract: We introduce the component-wise egalitarian Myerson value for network games. This new value being a convex combination of the Myerson value and the component-wise equal division rule is a player-based allocation rule. In network games under the cooperative framework, the Myerson value is an extreme example of marginalism, while the equal division rule signifies egalitarianism. In the proposed component-wise egalitarian Myerson value, a convexity parameter combines these two attributes and determines the degree of solidarity to the players. Here, by solidarity, we mean the mutual support or compensation among the players in a network. We provide three axiomatic characterizations of the value. Further, we propose an implementation mechanism for the component-wise egalitarian Myerson value under subgame perfect Nash equilibrium.
    Date: 2022–01
  14. By: Badi H. Baltagi (Center for Policy Research, Maxwell School, Syracuse University, 426 Eggers Hall, Syracuse, NY 13244); Peter H. Egger (ETH Zürich, CEPR, CESifo, GEP); Michaela Kesina (University of Groningen)
    Abstract: This paper proposes a Bayesian estimation framework for panel-data sets with binary dependent variables where a large number of cross-sectional units is observed over a short period of time, and cross-sectional units are interdependent in more than a single network domain. The latter provides for a substantial degree of flexibility towards modelling the decay function in network neighborliness (e.g., by disentangling the importance of rings of neighbors) or towards allowing for several channels of interdependence whose relative importance is unknown ex ante. Besides the flexible parameterization of cross-sectional dependence, the approach allows for simultaneity of the equations. These features should make the approach interesting for applications in a host of contexts involving structural and reduced-form models of multivariate choice problems at micro-, meso-, and macroeconomic levels. The paper outlines the estimation approach, illustrates its suitability by simulation examples, and provides an application to study exporting and foreign ownership among potentially interdependent firms in the specialized and transport machinery sector in the province of Guangdong.
    Keywords: Network Models; Spatial Models; Higher-Order Network Interdependence; Multivariate Panel Probit; Bayesian Estimation; Firm-Level Data; Chinese Firms
    JEL: C11 C31 C35 F14 F23 L22 R10
    Date: 2022–02
  15. By: Schippers, Anouk L.; Soetevent, Adriaan R.
    Abstract: Informal peer-to-peer services to share or barter goods often succumb to free riding behavior because they lack the tools to enforce compliance and reciprocity. We collect unique quantitative data on a form of unregulated peer-to-peer in kind exchange that appears viable on an international scale: the exchange of books via little free libraries. We find surprisingly limited free riding in this market. Users return 9 books for every 10 taken. An incentivized survey identifies the presence of strong social norms and preferences for cooperation among owners and users as key behavioral primitives that can explain the observed high and stable level of reciprocal exchange. However, a small field experiment does not show that a positive supply shock of high-quality books generates a lasting impact on a library's book stock.
    Keywords: Peer-to-peer exchange,honor systems,free riding,social norms,altruistic preferences,sharing economy
    JEL: D49 H42
    Date: 2022
  16. By: Max Marczinek; Stephan Maurer; Ferdinand Rauch
    Abstract: How do trade networks persist following disruptions of political networks? We study different types of persistence following the decline of the Hanseatic League using a panel of 21,590 city-level trade flows over 190 years, covering 1,425 cities. We use the Sound Toll data, a dataset collected by the Danish crown until 1857 that registered every ship entering or leaving the Baltic Sea, forming one of the most granular and extensive trade data sets. We measure trade flows by counting the number of ships sailing on a particular route in a given year and estimate gravity equations using PPML and an appropriate set of fixed effects. Bilateral gravity estimation results show that trade among former Hansa cities only shows persistence after its dissolution in 1669 for about 30 years, but this persistence is not robust across different regression specifications. However, when we incorporate the flag under which a ship is sailing and consider trilateral trade (where an observation is a combination of origin, destination, and flag), we find that trade persistently exceeds the gravity benchmark: Hansa cities continued to trade more with each other, but only on ships that were owned in another former Hansa city and thus sailed under a Hansa flag. Similar effects are found for trade among former Hansa cities and their trading posts abroad, yet again only conditional on the ship sailing under a former Hanseatic flag. Trade flows among the same pair of origin and destination cities, but under a different flag, do not show this persistence. Our main result shows that the identity of traders persists longer and more strongly than other forms of trading relationships we can measure. Apart from these new quantitative and qualitative insights on the persistence of trade flows, our paper is also of historic interest, as it provides new and detailed information on the speed of decline of trade amongst members of the Hanseatic League.
    Date: 2022–01–26
  17. By: Jinyong Hahn (UCLA); Hyungsik Roger Moon (USC & Yonsei); Ruoyao Shi (Department of Economics, University of California Riverside)
    Abstract: We develop a Lagrange Multiplier (LM) test of neglected heterogeneity in dyadic models. The test statistic is derived by modifying Breusch and Pagan (1980)’s test. We establish the asymptotic distribution of the test statistic under the null using a novel martingale construction. We also consider the power of the LM test in generic panel models. Even though the test is motivated by random effects, we show that it has a power for detecting fixed effects as well. Finally, we examine how the estimation noise of the maximum likelihood estimator affects the asymptotic distribution of the test under the null, and show that such a noise may be ignored in large samples.
    Keywords: Lagrange Multiplier test, dyadic regression model, error component panel regression model, fixed effects, local power
    JEL: C12 C23
    Date: 2022–02

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