nep-net New Economics Papers
on Network Economics
Issue of 2023‒07‒31
sixteen papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. Social networks and organizational helping behavior: Experimental evidence from the helping game By Erkut, Hande; Reuben, Ernesto
  2. Impacts and Distribution of Premiums from Temporal Social Networks across Generations By Yoshitaka Ogisu
  3. Mitigating ripple effect in supply networks: the effect of trust and topology on resilience By Iftikhar, Ilaria Giannoccaro & Anas
  4. The Matching Function: A Unified Look into the Black Box By Georgios Angelis; Yann Bramoullé
  5. A Dynamic Analysis of Criminal Networks By Luca Colombo; Paola Labrecciosa; Agnieszka Rusinowska
  6. Communication and the emergence of a unidimensional world By Philippos Louis; Orestis Troumpounis; Nikolas Tsakas
  7. The rise of the chaebol: A bibliometric analysis of business groups in South Korea By Artur F. Tomeczek
  8. Identifying Socially Disruptive Policies By Eric Auerbach; Yong Cai
  9. Reaching out to socially distant trainees. Experimental evidence from variations on the standard farmer trainer system. By Olivia Bertelli; Fatou Fall
  10. Adjustments of Multinational’s Production Activities in Response to the US-Sino Trade War : Evidence from Japanese affiliate-level data By LIANG, Licheng; MATSUURA, Toshiyuki
  11. Higher-order Graph Attention Network for Stock Selection with Joint Analysis By Yang Qiao; Yiping Xia; Xiang Li; Zheng Li; Yan Ge
  12. Kin in the game: How family ties help firms overcome campaign finance regulation By Balán, Pablo; Dodyk, Juan; Puente, Ignacio
  13. Coevolution of cognition and cooperation in structured populations under reinforcement learning By Ennio Bilancini; Leonardo Boncinelli; Rossana Mastrandrea
  14. Till Death Do Us Part: Relationship shocks, supply chain organization and firm performance By Timothy DESTEFANO; ITO Keiko; Richard KNELLER; Jonathan TIMMIS
  15. Stock Price Prediction using Dynamic Neural Networks By David Noel
  16. Opinion dynamics in communities with major influencers and implicit social influence via mean-field approximation By Delia Coculescu; M\'ed\'eric Motte; Huy\^en Pham

  1. By: Erkut, Hande; Reuben, Ernesto
    Abstract: This paper studies the causal impact of social ties and network structure on helping behavior in organizations. We introduce and experimentally study a game called the 'helping game, ' where individuals unilaterally decide whether to incur a cost to help other team members when helping is a rivalrous good. We find that social ties have a strong positive effect on helping behavior. Individuals are more likely to help those with whom they are connected, but the likelihood of helping decreases as the social distance between individuals increases. Additionally, individuals who are randomly assigned to be more central in the network are more likely to help others.
    Keywords: helping, social ties, social networks, communication, organizations
    JEL: D23 D91
    Date: 2023
  2. By: Yoshitaka Ogisu (Graduate School of Economics, Kobe University and Junir Research Fellow, Research Institute for Economics & Business Administration (RIEB), Kobe University, JAPAN)
    Abstract: Social networks certainly play an important role in labor market outcomes. In particular, the structures affect inter-group inequality via referral hiring. Through the network effects, while workers surely get premiums from the group to which they belong, they may get premiums or penalties from other groups than their own. Young workers do not obtain sufficient network premiums since referrals cannot be used well due to the higher unemployment rates of their friends. As time goes by, the network structure of each generation of course changes. In other words, not only premiums from their own network group but also those from the other network groups, or the spillovers from other generations, change over time. However, these changes in intra- and inter-group network effects have been rather overlooked so far. In this paper, we compute the network premiums for each generation in a search and matching model, and clarify which generation benefits the most from time-varying networks called temporal networks. New connections are generated proportional to the number of friends of each worker over time, while the existing connections are broken at a constant rate. Under this setting, workers get premiums or penalties depending on their network structures. On average, workers receive premiums from the overall network effects although they incur penalties from their network structures in wage and unemployment rates.
    Keywords: Referral hiring; Temporal network; Network structure; Intergenerational inequality
    JEL: E24 J31 J64
    Date: 2023–06
  3. By: Iftikhar, Ilaria Giannoccaro & Anas
    Abstract: The ripple effect refers to disruption propagation across the supply network affecting its global performance. To cope with it, supply networks should be resilient. This study investigates the drivers of supply network resilience, viewed as adaptive capacity to disruptions, focusing on trust and investigating the moderating role of network topology on the relationship between trust and resilience. We first develop an NK agent-based model of the supply network to simulate resilient performance. Then, a simulation analysis is carried out, to assess the effect of trust on the resilience of supply networks displaying different complex topologies. Our results confirm that trust positively affects supply network resilience; however, across the different topologies, the beneficial effect of trust varies. In particular, we find that trust is beneficial at most for the following topologies: local, small-world, block-diagonal, and random. For centralised, diagonal, and hierarchical topologies improving trust increases resilience at a moderate level. We also find that, as the frequency of disruptions rises, the positive effect of trust on resilience decreases. Managerial implications of the main findings are finally discussed
    Date: 2023–06–26
  4. By: Georgios Angelis (Bocconi University [Milan, Italy], IGIER); Yann Bramoullé (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)
    Abstract: In this paper, we use tools from network theory to trace the properties of the matching function to the structure of granular connections between applicants and firms. We link seemingly disparate parts of the literature and recover existing functional forms as special cases. Our overarching message is that structure counts. For rich structures, captured by non-random networks, the matching function depends on whole sets rather than just the sizes of the two sides of the market. For less rich-random network-structures it depends on the sizes of the two sides and a few structural parameters. Structures characterized by greater asymmetries reduce the matching function's efficacy, while denser structures can have ambiguous effects on it. For the special case of the Erdös-Rényi network, we show that the way the network varies with the sizes of the two sides of the market determines if the matching function exhibits constant returns to scale, or even if it is of a specific functional form, such as CES.
    Keywords: Bocconi University, IGIER, Bramoullé
    Date: 2023–06
  5. By: Luca Colombo (Rennes School of Business, Rennes); Paola Labrecciosa (ESSCA School of Management (Paris Campus)); Agnieszka Rusinowska (CNRS, Paris School of Economics, Centre d'Economie de la Sorbonne)
    Abstract: We take a novel approach based on differential games to the study of criminal networks. We extend the static crime network game (Ballester et al., 2006, 2010) to a dynamic setting where criminal activities negatively impact the accumulation of total wealth in the economy. We derive a Markov Perfect Equilibrium (MPE), which is unique within the class of strategies considered, and show that, unlike in the static crime network game, the vector of equilibrium crime efforts is not necessarily proportional to the vector of Bonacich centralities. Next, we conduct a comparative dynamic analysis with respect to the network size, the network density, and the marginal expected punishment, finding results in contrast with those arising in the static crime network game. We also shed light on a novel issue in the network theory literature, i.e., the existence of a voracity effect. Finally, we study the problem of identifying the optimal target in the population of criminals when the planner's objective is to minimize aggregate crime at each point in time. Our analysis shows that the key player in the dynamic and the static setting may differ, and that the key player in the dynamic setting may change over time
    Keywords: differential games; Markov Perfect Equilibrium; criminal networks; Bonacich centrality; key player
    JEL: C73 D85 K42
    Date: 2022–02
  6. By: Philippos Louis; Orestis Troumpounis; Nikolas Tsakas
    Abstract: While individuals hold, exchange, and update opinions over multiple issues, opinions are often correlated and a unidimensional spectrum is enough to summarize them. But when should one expect opinions to be unidimensional? And how important is the underlying structure of communication? Our experimental results: i) validate the crisp predictions by DeMarzo et al. (2003) when individuals update their opinions on a fixed network always trusting the same neighbors, ii) jointly with simulations indicate the prevalence of unidimensionality as an expected outcome even when communication is less structured with individuals’ network possibly varying over time, and iii) highlight the importance of the communication structure in predicting whether individuals hold relatively moderate or extreme opinions.
    Keywords: opinion dynamics; information aggregation; persuasion bias; social networks
    JEL: D83 D85
    Date: 2023–05–18
  7. By: Artur F. Tomeczek
    Abstract: South Korea has become one of the most important economies in Asia. The largest Korean multinational firms are affiliated with influential family-owned business groups known as the chaebol. Despite the surging academic popularity of the chaebol, there is a considerable knowledge gap in the bibliometric analysis of business groups in Korea. In an attempt to fill this gap, the article aims to provide a systematic review of the chaebol and the role that business groups have played in the economy of Korea. Three distinct bibliometric networks are analyzed, namely the scientific collaboration network, bibliographic coupling network, and keyword co-occurrence network.
    Date: 2023–06
  8. By: Eric Auerbach; Yong Cai
    Abstract: Social disruption occurs when a policy creates or destroys many network connections between agents. It is a costly side effect of many interventions and so a growing empirical literature recommends measuring and accounting for social disruption when evaluating the welfare impact of a policy. However, there is currently little work characterizing what can actually be learned about social disruption from data in practice. In this paper, we consider the problem of identifying social disruption in a research design that is popular in the literature. We provide two sets of identification results. First, we show that social disruption is not generally point identified, but informative bounds can be constructed using the eigenvalues of the network adjacency matrices observed by the researcher. Second, we show that point identification follows from a theoretically motivated monotonicity condition, and we derive a closed form representation. We apply our methods in two empirical illustrations and find large policy effects that otherwise might be missed by alternatives in the literature.
    Date: 2023–06
  9. By: Olivia Bertelli (DIAL, LEDa, IRD, Université Paris-Dauphine, Université PSL); Fatou Fall (DIAL, LEDa, IRD, Université Paris-Dauphine, Université PSL)
    Abstract: The farmer trainer (FT) model has gained momentum as a cost-effective alternative to traditional agricultural extension systems. However, there may be friction in the transmission of information, whereby farmers closer to the FT may benefit more than socially distant farmers. This study explores whether variations on the standard FT model facilitate the diffusion of information outside the FT’s pre-existing social network. A sample of voluntary farmer trainers in rural Uganda was randomly assigned to receive either (i) vouchers for accessing professional extension agents, (ii) a signpost advertising the trainer services, or (iii) further training to learn to tailor training to trainee needs. The results show that the FTs assigned these treatment variations trained more farmers, a larger proportion of whom were in the FT’s own close circle. The FTs who received vouchers, however, were the only ones to reach out to more socially distant farmers and were also those who gave the most training sessions. We show that these effects are independent of any FT prominence in the village. Nevertheless, further evidence suggests exercising caution regarding the presence of friction in the transmission of knowledge, since knowledge and technology adoption appear to increase only among farmers closely connected to the FT.
    Keywords: Agricultural extension service, Social network, Dairy farming, Sub-Saharan Africa, Uganda
    JEL: O13 Q16
    Date: 2023–06
  10. By: LIANG, Licheng; MATSUURA, Toshiyuki
    Abstract: Using factual affiliate-level data of Japan’s multinational firms from 2017 through 2019, this study investigates the impact of a trade shock (the 2018 US-Sino trade war in this case) on multinational firms’ overseas production activities. Focusing on Japanese affiliates in the Association of Southeast Asian Nations (ASEAN) countries, we find evidence of a potential production shift from China to the ASEAN member countries. According to our empirical results, in response to the trade war, those affiliates in the ASEAN with vertically integrated Chinese siblings belonging to the same multinational parent’s value chains may increase their export to North America and see a growth in total sales. Fast substitution of export and production occurs through the production network within Japanese multinationals when a part of which is negatively affected by the trade shock. In addition, this group of affiliates are also likely to increase both the share and value of local procurement. The study highlights the positive role of setting up a diversified production network for multinationals.
    Keywords: trade shock, multinational enterprise (MNE), affiliates
    JEL: F13 F14 F23
    Date: 2023–07
  11. By: Yang Qiao; Yiping Xia; Xiang Li; Zheng Li; Yan Ge
    Abstract: Stock selection is important for investors to construct profitable portfolios. Graph neural networks (GNNs) are increasingly attracting researchers for stock prediction due to their strong ability of relation modelling and generalisation. However, the existing GNN methods only focus on simple pairwise stock relation and do not capture complex higher-order structures modelling relations more than two nodes. In addition, they only consider factors of technical analysis and overlook factors of fundamental analysis that can affect the stock trend significantly. Motivated by them, we propose higher-order graph attention network with joint analysis (H-GAT). H-GAT is able to capture higher-order structures and jointly incorporate factors of fundamental analysis with factors of technical analysis. Specifically, the sequential layer of H-GAT take both types of factors as the input of a long-short term memory model. The relation embedding layer of H-GAT constructs a higher-order graph and learn node embedding with GAT. We then predict the ranks of stock return. Extensive experiments demonstrate the superiority of our H-GAT method on the profitability test and Sharp ratio over both NSDAQ and NYSE datasets
    Date: 2023–06
  12. By: Balán, Pablo; Dodyk, Juan; Puente, Ignacio
    Abstract: Can campaign finance regulation curb the political influence of economic actors? In this article, we identify a new factor that may hinder its effectiveness-the social structure of organizations. We argue that such regulation creates cooperation dilemmas in firms' leadership and propose that a specific feature of organizations-family ties-help solve such problems. We evaluate this argument by studying a Supreme Court ban on corporate contributions in Brazil. Using a difference-in-differences design and data on family ties in Brazilian public companies, we show that, following the ban, members of firms' controlling families substitute individual for corporate contributions. Furthermore, we document the presence of peer effects in the contribution behavior of family members, suggesting that family ties transmit influence. These bifurcated effects illustrate how organizational structure can be a source of de facto power by limiting the effectiveness of programmatic reforms, and thus contain a cautionary tale for policymakers.
    Date: 2023
  13. By: Ennio Bilancini; Leonardo Boncinelli; Rossana Mastrandrea
    Abstract: We study the evolution of behavior under reinforcement learning in a Prisoner's Dilemma where agents interact in a regular network and can learn about whether they play one-shot or repeatedly by incurring a cost of deliberation. With respect to other behavioral rules used in the literature, (i) we confirm the existence of a threshold value of the probability of repeated interaction, switching the emergent behavior from intuitive defector to dual-process cooperator; (ii) we find a different role of the node degree, with smaller degrees reducing the evolutionary success of dual-process cooperators; (iii) we observe a higher frequency of deliberation.
    Date: 2023–06
  14. By: Timothy DESTEFANO; ITO Keiko; Richard KNELLER; Jonathan TIMMIS
    Abstract: Within modern economies firms are embedded in often complex supply chains, creating strong interdependencies between firms. But what happens when these supply chains are disrupted, what changes does this bring about? We answer these questions, focusing on what happens when connections between companies exogenously break because of the unexpected death of the CEO within one of the firms. We rely on detailed data from the TSR which provides firm-level measures of start and exit dates of CEOs along with buyer-supplier linkages. This data is matched to detailed statistics on Japanese firms which enables us to identify the effects of such leadership changes on supplier networks and subsequent performance. We find that such deaths promote the churning of suppliers but not of customers of the firm and therefore that these shocks propagate towards upstream firms through the supply chain. There is also evidence that this affects the short-term performance of indirectly affected firms as the shock propagates backwards along the supply chain.
    Date: 2023–07
  15. By: David Noel
    Abstract: This paper will analyze and implement a time series dynamic neural network to predict daily closing stock prices. Neural networks possess unsurpassed abilities in identifying underlying patterns in chaotic, non-linear, and seemingly random data, thus providing a mechanism to predict stock price movements much more precisely than many current techniques. Contemporary methods for stock analysis, including fundamental, technical, and regression techniques, are conversed and paralleled with the performance of neural networks. Also, the Efficient Market Hypothesis (EMH) is presented and contrasted with Chaos theory using neural networks. This paper will refute the EMH and support Chaos theory. Finally, recommendations for using neural networks in stock price prediction will be presented.
    Date: 2023–06
  16. By: Delia Coculescu; M\'ed\'eric Motte; Huy\^en Pham
    Abstract: We study binary opinion formation in a large population where individuals are influenced by the opinions of other individuals. The population is characterised by the existence of (i) communities where individuals share some similar features, (ii) opinion leaders that may trigger unpredictable opinion shifts in the short term (iii) some degree of incomplete information in the observation of the individual or public opinion processes. In this setting, we study three different approximate mechanisms: common sampling approximation, independent sampling approximation, and, what will be our main focus in this paper, McKean-Vlasov (or mean-field) approximation. We show that all three approximations perform well in terms of different metrics that we introduce for measuring population level and individual level errors. In the presence of a common noise represented by the major influencers opinions processes, and despite the absence of idiosyncratic noises, we derive a propagation of chaos type result. For the particular case of a linear model and particular specifications of the major influencers opinion dynamics, we provide additional analysis, including long term behavior and fluctuations of the public opinion. The theoretical results are complemented by some concrete examples and numerical analysis, illustrating the formation of echo-chambers, the propagation of chaos, and phenomena such as snowball effect and social inertia.42 pages
    Date: 2023–06

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