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on Network Economics |
| By: | John Higgins (Department of Economics, University of Wisconsin, Madison, WI 53706, USA); Tarun Sabarwal (Department of Economics, University of Kansas, Lawrence, KS 66045, USA) |
| Abstract: | We study proliferation of an action in binary action network coordination games that are generalized to include global effects. This captures important aspects of proliferation of a particular action or narrative in online social networks, providing a basis to understand their impact on societal outcomes. Our model naturally captures complementarities among starting sets, network resilience, and global effects, and highlights interdependence in channels through which contagion spreads. We present new, natural, computationally tractable, and efficient algorithms to define and compute equilibrium objects that facilitate the general study of contagion in networks and prove their theoretical properties. Our algorithms are easy to implement and help to quantify relationships previously inaccessible due to computational intractability. Using these algorithms, we study the spread of contagion in scale-free networks with 1,000 players using millions of Monte Carlo simulations. Our analysis provides quantitative and qualitative insight into the design of policies to control or spread contagion in networks. The scope of application is enlarged given the many other situations across different fields that may be modeled using this framework. |
| Keywords: | Network games, coordination games, contagion, algorithmic computation |
| JEL: | C62 C72 |
| Date: | 2022–04 |
| URL: | http://d.repec.org/n?u=RePEc:kan:wpaper:202213&r= |
| By: | Federico Huneeus; Borja Larrain; Mauricio Larrain; Mounu Prem |
| Abstract: | We use matched employer-employee data together with data on the ownership networks of Chilean firms to document a novel relationship between inequality in labor income and ownership structures. Exploiting transitions of firms in and out of networks, we show that network afiliation is associated with higher inequality along two dimensions. First, network firms pay higher average wages than stand-alone firms, increasing between-firm inequality. Second, the dispersion of wages within a network firm is higher than within a stand-alone firm, increasing within-firm inequality. The effects are driven by increases in the wages of top workers, and by the entry of new top workers. Our findings shed light on the relationship between ownership structures and the distribution of labor income in the economy. |
| Date: | 2022–03 |
| URL: | http://d.repec.org/n?u=RePEc:chb:bcchwp:945&r= |
| By: | Jochmans, Koen |
| Abstract: | This paper proposes a solution to the problem of the self-selection of peers in the linear-in-means model. We do not require to specify a model for how the selection of peers comes about. Rather, we exploit two restrictions that are inherent in many such specifications to construct conditional moment conditions. The restrictions in question are that link decisions that involve a given individual are not all independent of one another, but that they are independent of the link decisions made between other pairs of individuals that are located sufficiently far away in the network. These conditions imply that instrumental variables can be constructed from leave-own-out networks. |
| Keywords: | instrumental variable; linear-in-means model; network; self-selection |
| JEL: | C31 C36 |
| Date: | 2022–07–25 |
| URL: | http://d.repec.org/n?u=RePEc:tse:wpaper:127215&r= |
| By: | Kara Karpman (Department of Statistics and Data Science, Cornell University); Samriddha Lahiry (Department of Statistics and Data Science, Cornell University); Diganta Mukherjee (Sampling and Official Statistics Unit, Indian Statistical Institute Kolkata); Sumanta Basu (Department of Statistics and Data Science, Cornell University) |
| Abstract: | In the post-crisis era, financial regulators and policymakers are increasingly interested in data-driven tools to measure systemic risk and to identify systemically important firms. Granger Causality (GC) based techniques to build networks among financial firms using time series of their stock returns have received significant attention in recent years. Existing GC network methods model conditional means, and do not distinguish between connectivity in lower and upper tails of the return distribution - an aspect crucial for systemic risk analysis. We propose statistical methods that measure connectivity in the financial sector using system-wide tail-based analysis and is able to distinguish between connectivity in lower and upper tails of the return distribution. This is achieved using bivariate and multivariate GC analysis based on regular and Lasso penalized quantile regressions, an approach we call quantile Granger causality (QGC). By considering centrality measures of these financial networks, we can assess the build-up of systemic risk and identify risk propagation channels. We provide an asymptotic theory of QGC estimators under a quantile vector autoregressive model, and show its benefit over regular GC analysis on simulated data. We apply our method to the monthly stock returns of large U.S. firms and demonstrate that lower tail based networks can detect systemically risky periods in historical data with higher accuracy than mean-based networks. In a similar analysis of large Indian banks, we find that upper and lower tail networks convey different information and have the potential to distinguish between periods of high connectivity that are governed by positive vs negative news in the market. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2207.10705&r= |
| By: | Pongou, Roland; Sidie, Ghislain Junior; Tchuente, Guy; Tondji, Jean-Baptiste |
| Abstract: | How do pandemics affect for-profit and not-for-profit organizations differently? To address this question, we analyze optimal lockdowns in a two-sector continuous-time individual-based mean-field epidemiological model. We uncover a unique solution that depends on network structure, lockdown effectiveness, and the planner's tolerable infection incidence. Using unique data on nursing home networks in the United States, we calibrate the model and jointly quantify state-level lockdown effectiveness and preference for enforcing stringent containment strategies during the COVID-19 pandemic. We also empirically validate simulation results derived from the theoretical analyses. We find that for-profit nursing homes experience higher COVID-19 death rates than not-for-profit nursing homes. In addition, this differential health effect increases with lockdown effectiveness. |
| Keywords: | Pandemics,Profits,Social networks,Lockdown effectiveness,Nursing Homes |
| JEL: | D85 E61 H12 I18 J14 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:glodps:1131&r= |
| By: | Arun G. Chandrasekhar; Robert Townsend; Juan Pablo Xandri |
| Abstract: | Consider an economy in which agents face income risk but interact in a stochastic financial network where the randomness is dictated by both chance and choice. We study the financial centrality of an agent defined as the ex-ante marginal social value of providing a small liquid asset to that agent. We show financially central agents are not only those who are linked often, but are more likely to be linked when (i) the realized network is fragmented, (ii) income risk is high, (iii) shocks are positively correlated, (iv) attitudes toward risk are more sensitive in the aggregate, and (v) there are tail risks. We apply our framework to models of financial markets with participation shocks, supply chains subject to disruptions, and village risk-sharing networks. We also study how the stochastic financial network structure influences bargaining, thereby endogenizing Pareto weights in the planner's problem. Evidence from Thai villages is consistent with these bargaining foundations, showing that agents who are more central indeed receive greater Pareto weight. We conclude by examining the welfare consequences of targeting larger liquid assets to key traders in markets, and to the most liquidity-sensitive links in supply chains. |
| JEL: | D14 E44 G01 L14 O16 |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:30270&r= |
| By: | Bhaskarjit Sarmah; Nayana Nair; Dhagash Mehta; Stefano Pasquali |
| Abstract: | Understanding non-linear relationships among financial instruments has various applications in investment processes ranging from risk management, portfolio construction and trading strategies. Here, we focus on interconnectedness among stocks based on their correlation matrix which we represent as a network with the nodes representing individual stocks and the weighted links between pairs of nodes representing the corresponding pair-wise correlation coefficients. The traditional network science techniques, which are extensively utilized in financial literature, require handcrafted features such as centrality measures to understand such correlation networks. However, manually enlisting all such handcrafted features may quickly turn out to be a daunting task. Instead, we propose a new approach for studying nuances and relationships within the correlation network in an algorithmic way using a graph machine learning algorithm called Node2Vec. In particular, the algorithm compresses the network into a lower dimensional continuous space, called an embedding, where pairs of nodes that are identified as similar by the algorithm are placed closer to each other. By using log returns of S&P 500 stock data, we show that our proposed algorithm can learn such an embedding from its correlation network. We define various domain specific quantitative (and objective) and qualitative metrics that are inspired by metrics used in the field of Natural Language Processing (NLP) to evaluate the embeddings in order to identify the optimal one. Further, we discuss various applications of the embeddings in investment management. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2207.07183&r= |
| By: | Richard S. J. Tol |
| Abstract: | It is unclear whether the hierarchy in the economics profession is the result of the agglomeration of excellence or of nepotism. I construct the professor-student network for laureates of and candidates for the Nobel Prize in Economics. I study the effect of proximity to previous Nobelists on winning the Nobel Prize. Conditional on being Nobel-worthy, students and grandstudents of Nobel laureates are not significantly more or less likely to win. Professors of Nobel Prize winners, however, are significantly more likely to win. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2207.04441&r= |
| By: | Luca Barbaglia; Christophe Croux; Ines Wilms |
| Abstract: | Despite the increasing integration of the global economic system, anti-dumping measures are a common tool used by governments to protect their national economy. In this paper, we propose a methodology to detect cases of anti-dumping circumvention through re-routing trade via a third country. Based on the observed full network of trade flows, we propose a measure to proxy the evasion of an anti-dumping duty for a subset of trade flows directed to the European Union, and look for possible cases of circumvention of an active anti-dumping duty. Using panel regression, we are able correctly classify 86% of the trade flows, on which an investigation of anti-dumping circumvention has been opened by the European authorities. |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2207.05394&r= |
| By: | Ivan Soraperra; Joël van der Weele; 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); Shaul Shalvi |
| Abstract: | We experimentally study the social transmission of "inconvenient" information about the externalities generated by one's own decision. In the laboratory, we pair uninformed decision makers with informed senders. Compared to a setting where subjects can choose their information directly, we find that social interactions increase selfish decisions. On the supply side, senders suppress almost 30 percent of "inconvenient" information, driven by their own preferences for information and their beliefs about the decision maker's preferences. On the demand side, about one-third of decision makers avoids senders who transmit inconvenient information ("shooting the messenger"), which leads to assortative matching between information-suppressing senders and information-avoiding decision makers. Having more control over information generates opposing effects on behavior: selfish decision makers remain ignorant more often and donate less, while altruistic decision makers seek out informative senders and give more. We discuss applications to information sharing in social networks and to organizational design. |
| Keywords: | Social interactions,information avoidance,assortative matching,ethical behavior |
| Date: | 2022–07–17 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:hal-03725590&r= |
| By: | Lamiae Benhayoun-Sadafiyine (LITEM - Laboratoire en Innovation, Technologies, Economie et Management (EA 7363) - UEVE - Université d'Évry-Val-d'Essonne - Université Paris-Saclay - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris], TIM - Département Technologies, Information & Management - IMT - Institut Mines-Télécom [Paris] - IMT-BS - Institut Mines-Télécom Business School - IMT - Institut Mines-Télécom [Paris]); Marie-Anne Le Dain (G-SCOP_CC - Conception collaborative - G-SCOP - Laboratoire des sciences pour la conception, l'optimisation et la production - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes); Carine Dominguez-Péry (CERAG - Centre d'études et de recherches appliquées à la gestion - UGA - Université Grenoble Alpes); Andrew C. Lyons (University of Liverpool) |
| Abstract: | SMEs increasingly participate in collaborative innovation networks (CINs), enabling them to access valuable external knowledge from other actors while maintaining high levels of internal competencies. The SME absorbs this knowledge to achieve reciprocal learning through its contribution to the common CIN goals, and one-way learning to improve its own organization's performance. This knowledge absorption varies according to the SME's context, described with factors such as the turbulence of its external environment, the motivations to contribute to the CIN, or the cognitive distance separating it from the network actors. To better guide this knowledge absorption, this research uses a two-stage mixed method to propose a contextualized operational measure of absorptive capacity (ACAP) for an SME embedded in a CIN. A qualitative phase consisting of semi-structured interviews was implemented first and enabled characterizing the SME's ACAP through a set of practices and dimensions that it could implement. Then a quantitative phase using the partial least squares (PLS) method established a model predicting the absorption dimensions and practices that the SME should master primarily according to its context in the CIN. Hence, this study provides SMEs with an instrument to assess their strengths and weaknesses with regard to ACAP in CINs. |
| Keywords: | SME,Collaborative network,Open innovation,Absorptive capacity,Inter-organizational learning,Partial least squares |
| Date: | 2020–10 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-03144459&r= |