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on Network Economics |
| By: | Raj Chetty; Matthew O. Jackson; Theresa Kuchler; Johannes Stroebel; Nathaniel Hendren; Robert B. Fluegge; Sara Gong; Federico González; Armelle Grondin; Matthew Jacob; Drew Johnston; Martin Koenen; Eduardo Laguna-Muggenburg; Florian Mudekereza; Tom Rutter; Nicolaj Thor; Wilbur Townsend; Ruby Zhang; Mike Bailey; Pablo Barberá; Monica Bhole; Nils Wernerfelt |
| Abstract: | In this paper—the first in a series of two papers that use data on 21 billion friendships from Facebook to study social capital—we measure and analyze three types of social capital by ZIP code in the United States: (i) connectedness between different types of people, such as those with low vs. high socioeconomic status (SES); (ii) social cohesion, such as the extent of cliques in friendship networks; and (iii) civic engagement, such as rates of volunteering. These measures vary substantially across areas, but are not highly correlated with each other. We demonstrate the importance of distinguishing these forms of social capital by analyzing their associations with economic mobility across areas. The fraction of high-SES friends among low-SES individuals—which we term economic connectedness—is among the strongest predictors of upward income mobility identified to date, whereas other social capital measures are not strongly associated with economic mobility. If children with low-SES parents were to grow up in counties with economic connectedness comparable to that of the average child with high-SES parents, their incomes in adulthood would increase by 20% on average. Differences in economic connectedness can explain well-known relationships between upward income mobility and racial segregation, poverty rates, and inequality. To support further research and policy interventions, we publicly release privacy-protected statistics on social capital by ZIP code at www.socialcapital.org. |
| JEL: | R0 |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:30313&r= |
| By: | Liza Charroin (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique); Bernard Fortin (ULaval - Université Laval [Québec]); Marie Claire Villeval (GATE Lyon Saint-Étienne - Groupe d'analyse et de théorie économique - ENS Lyon - École normale supérieure - Lyon - UL2 - Université Lumière - Lyon 2 - UCBL - Université Claude Bernard Lyon 1 - Université de Lyon - UJM - Université Jean Monnet [Saint-Étienne] - Université de Lyon - CNRS - Centre National de la Recherche Scientifique, IZA - Forschungsinstitut zur Zukunft der Arbeit - Institute of Labor Economics) |
| Abstract: | If individuals tend to behave like their reference group, is it because of peer effects, selfselection, or both? Using a peer effect model allowing for conformity and link formation, we designed a real-effort laboratory experiment in which individuals could misreport their performance and select their peers. Our results reveal both a preference for conformity and homophilous link formation, but only among individuals cheating in isolation. This suggests that such link formation was not motivated by a taste for similarity but by acquiring self-serving information. Importantly, we reject the presence of a self-selection bias in the peer effect estimates by showing that the size of peer effects is similar when identical peers were randomly assigned and when individuals selected them. |
| Keywords: | Peer effects,Self-selection,Homophily,Dishonesty,Experiment |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03712450&r= |
| By: | Shraddha Pathak; Ankur A. Kulkarni |
| Abstract: | During an epidemic, the information available to individuals in the society deeply influences their belief of the epidemic spread, and consequently the preventive measures they take to stay safe from the infection. In this paper, we develop a scalable framework for ascertaining the optimal information disclosure a government must make to individuals in a networked society for the purpose of epidemic containment. This problem of information design problem is complicated by the heterogeneous nature of the society, the positive externalities faced by individuals, and the variety in the public response to such disclosures. We use a networked public goods model to capture the underlying societal structure. Our first main result is a structural decomposition of the government's objectives into two independent components -- a component dependent on the utility function of individuals, and another dependent on properties of the underlying network. Since the network dependent term in this decomposition is unaffected by the signals sent by the government, this characterization simplifies the problem of finding the optimal information disclosure policies. We find explicit conditions, in terms of the risk aversion and prudence, under which no disclosure, full disclosure, exaggeration and downplay are the optimal policies. The structural decomposition results are also helpful in studying other forms of interventions like incentive design and network design. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2207.11578&r= |
| By: | Kara Karpman; Sumanta Basu; David Easley |
| Abstract: | Financial networks are typically estimated by applying standard time series analyses to price-based economic variables collected at low-frequency (e.g., daily or monthly stock returns or realized volatility). These networks are used for risk monitoring and for studying information flows in financial markets. High-frequency intraday trade data sets may provide additional insights into network linkages by leveraging high-resolution information. However, such data sets pose significant modeling challenges due to their asynchronous nature, nonlinear dynamics, and nonstationarity. To tackle these challenges, we estimate financial networks using random forests. The edges in our network are determined by using microstructure measures of one firm to forecast the sign of the change in a market measure (either realized volatility or returns kurtosis) of another firm. We first investigate the evolution of network connectivity in the period leading up to the U.S. financial crisis of 2007-09. We find that the networks have the highest density in 2007, with high degree connectivity associated with Lehman Brothers in 2006. A second analysis into the nature of linkages among firms suggests that larger firms tend to offer better predictive power than smaller firms, a finding qualitatively consistent with prior works in the market microstructure literature. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.03568&r= |
| By: | Gianmarco Ricciardi; Guido Montagna; Guido Caldarelli; Giulio Cimini |
| Abstract: | Modelling systems with networks has been a powerful approach to tame the complexity of several phenomena. Unfortunately, such an approach is often made difficult by the large number of variables to take into consideration. Methods of dimensional reduction are useful tools to rescale a complex dynamical network down to a low-dimensional effective system and thus to capture the global features of the dynamics. Here we study the application of the degree-weighted and spectral reduction methods to an important class of dynamical processes on networks: the propagation of credit shocks within an interbank network, modelled according to the DebtRank algorithm. In particular we introduce an effective version of the dynamics, characterised by functions with continuous derivatives that can be handled by the dimensional reduction. We test the reduction methods against the full dynamical system in different interbank market settings: homogeneous and heterogeneous networks generated from state-of-the-art reconstruction methods as well as networks derived from empirical e-MID data. Our results indicate that, for proper choices of the bank default probability, reduction methods are able to provide reliable estimates of systemic risk in the market, with the spectral reduction better handling heterogeneous networks. Finally we provide new physical insights on the nature and working principles of dimensional reduction methods. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2207.11491&r= |
| By: | Natkamon Tovanich; R\'emy Cazabet |
| Abstract: | Bitcoin is the first and highest valued cryptocurrency that stores transactions in a publicly distributed ledger called the blockchain. Understanding the activity and behavior of Bitcoin actors is a crucial research topic as they are pseudonymous in the transaction network. In this article, we propose a method based on taint analysis to extract taint flows --dynamic networks representing the sequence of Bitcoins transferred from an initial source to other actors until dissolution. Then, we apply graph embedding methods to characterize taint flows. We evaluate our embedding method with taint flows from top mining pools and show that it can classify mining pools with high accuracy. We also found that taint flows from the same period show high similarity. Our work proves that tracing the money flows can be a promising approach to classifying source actors and characterizing different money flow patterns |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2207.07315&r= |
| By: | Mohit Arora; Deepankar Basu |
| Abstract: | Recent developments in the theory of production networks offer interesting applications and revival of input-output analysis. Some recent papers have studied the propagation of a temporary, negative shock through an input-output network. Such analyses of shock propagation relies on eigendecomposition of relevant input-output matrices. It is well known that only diagonalizable matrices can be eigendecomposed; those that are not diagonalizable, are known as defective matrices. In this paper, we provide necessary and sufficient conditions for diagonalizability of any square matrix using its rank and eigenvalues. To apply our results, we offer examples of input-output tables from India in the 1950s that were not diagonalizable and were hence, defective. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.00226&r= |
| By: | Antonio Cabrales; Esther Hauk |
| Abstract: | In this paper we model the interaction between leaders, their followers and crowd followers in a coordination game with local interaction. The steady states of a dynamic best-response process can feature a coexistence of Pareto dominant and risk dominant actions in the population. The existence of leaders and their followers, plus the local interaction, which leads to clustering, is crucial for the survival of the Pareto dominant actions. The evolution of leader and crowd followership shows that leader followership can also be locally stable around Pareto dominant leaders. The paper answers the questions (i) which Leader should be removed and (ii) how to optimally place leaders in the network to enhance payoff dominant play. |
| Keywords: | coordination games, leaders, followers, evolution |
| JEL: | C70 D85 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_9845&r= |
| By: | Krishnadas M.; K. P. Harikrishnan; G. Ambika |
| Abstract: | The financial markets are understood as complex dynamical systems whose dynamics is analysed mostly using nonstationary and brief data sets that usually come from stock markets. For such data sets, a reliable method of analysis is based on recurrence plots and recurrence networks, constructed from the data sets over the period of study. In this study, we do a comprehensive analysis of the complexity of the underlying dynamics of 26 markets around the globe using recurrence based measures. We also examine trends in the nature of transitions as revealed from these measures by the sliding window analysis along the time series during the global financial crisis of 2008 and compare that with changes during the most recent pandemic related lock down. We show that the measures derived from recurrence patterns can be used to capture the nature of transitions in stock market dynamics. Our study reveals that the changes around 2008 indicate stochasticity driven transition, which is different from the transition during the pandemic. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.03456&r= |
| By: | Marcos Fernandes |
| Abstract: | I propose a theoretical social learning model to investigate how confirmation bias affects opinions when agents exchange information over a social network. For that, besides exchanging opinions with friends, individuals observe a public sequence of potentially ambiguous signals and they interpret it according to a rule that accounts for confirmation bias. I first show that, regardless the level of ambiguity and both in the case of a single individual or of a networked society, only two types of opinions might be formed and both are biased. One opinion type, however, is necessarily less biased than the other depending on the state of the world. The size of both biases depends on the ambiguity level and the relative magnitude of the state and confirmation biases. In this context, long-run learning is not attained even when individuals interpret ambiguity impartially. Finally, since it is not trivial to ascertain analytically the probability of emergence of the less biased consensus when individuals are connected through a social network and have different priors, I use simulations to analyze its determinants. Three main results derived from this exercise are that, in expected terms, i) some network topologies are more conducive to consensus efficiency, ii) some degree of partisanship enhances consensus efficiency even under confirmation bias and iii) open-mindedness, i.e. when partisans agree to exchange opinions with other partisans with polar opposite beliefs, might harm efficiency in some cases. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2207.12594&r= |
| By: | Victor Sellemi |
| Abstract: | Economic models with input-output networks assume that firm or sector (unit) growth is driven by a weighted sum of trade partners' growth and an independently-drawn idiosyncratic shock. I show that the idiosyncratic risk assumption in a broad class of network models implicitly generates restrictions on the network weights which are unrealistic. When allowing for correlated shocks, units are exposed to an additional risk term which captures the ability to substitute away from supply and demand shocks propagating through the network. I provide empirical evidence that changes in substitutability between trade partners are inversely related to changes in the panel of realized industry variance. Moreover, I find that supply-side (demand-side) substitutability is closely related to technological (product) dispersion of a unit's suppliers (customers). To synthesize these results, I propose a production-based asset pricing model in which supply chain substitutability is a function of dispersion in product/technology space and correlation in supply and demand shocks is driven by shared customers and suppliers between firms. The model predicts that assets which are positively exposed to average propagation of upstream and downstream shocks are useful hedges and thus earn lower average risk premia. Consistently, I find that estimated upstream (downstream) propagation factors earn return spreads of -11.4% (-4.2%) and are negatively associated with aggregate consumption, output, and dividend growth. |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2208.01467&r= |
| By: | Michael B. Devereux (Vancouver school of economics, University of British Columbia, NBER and CEPR.); Karine Gente (Aix-Marseille Universite, CNRS, AMSE, Marseille, France.); Changhua Yu (China Center for Economic Research, National School of Development, Peking University, China.) |
| Abstract: | This paper analyzes the impact of fiscal spending shocks in a dynamic, multi-country model with international production networks. We first derive a decomposition of the effects of a fiscal spending shock on the GDP of any country. This decomposition defines the response as the sum of a Direct, Income, and Price effect. The Direct Effect depends only on structural parameters and is independent of assumptions about monetary policy, wage setting, or capital mobility, while the Price Effect is zero in the aggregate across countries. We apply this decomposition to an analysis of fiscal spillovers in the Eurozone, using the production network structure from the World Input Output Database (WIOD). We find that fiscal spillovers from Germany and some other large Eurozone countries may be large, and within the range of empirical estimates. Without international production network linkages, spillovers would be only a third as large as predicted by the baseline model. Finally, we explore the diffusion of identified government spending shocks at the sectoral level, both within and across countries, using an empirical measure of the response, based on the theoretical decomposition. The empirical estimates are strongly consistent with the theoretical model. |
| Keywords: | production network, fiscal policy, spillovers, Eurozone, nominal rigidities |
| JEL: | E23 E62 F20 F42 H50 |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:aim:wpaimx:2216&r= |