nep-net New Economics Papers
on Network Economics
Issue of 2021‒01‒25
thirteen papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. The Social Side of Early Human Capital Formation: Using a Field Experiment to Estimate the Causal Impact of Neighborhoods By John A. List; Fatemeh Momeni; Yves Zenou
  2. Dynamic Network Prediction By Ravi Goyal; Victor De Gruttola
  3. COVID-19 spreading in financial networks: A semiparametric matrix regression model By Billio Monica; Casarin Roberto; Costola Michele; Iacopini Matteo
  4. The structure of multiplex networks predicts play in economic games and real-world cooperation By Curtis Atkisson; Monique Borgerhoff Mulder
  5. Broker Network Connectivity and the Cross-Section of Expected Stock Returns By Tinic, Murat; Sensoy, Ahmet; Demir, Muge; Nguyen, Duc Khuong
  6. Identifying the Latent Space Geometry of Network Models through Analysis of Curvature By Shane Lubold; Arun G. Chandrasekhar; Tyler H. McCormick
  7. Multi-Scale Games: Representing and Solving Games on Networks with Group Structure By Kun Jin; Yevgeniy Vorobeychik; Mingyan Liu
  8. Peer Effects of Corporate Disclosure in Pandemic Era By Fujitani, Ryosuke; Kim, Hyonok; Yamada, Kazuo
  9. How do countries specialize in food production? A complex-network analysis of the global agricultural product space By Mercedes Campi; Marco Dueñas; Giorgio Fagiolo
  10. Quantifying the importance of firms by means of reputation and network control By Yan Zhang; Frank Schweitzer
  11. Spatial and Spatio-temporal Error Correction, Networks and Common Correlated Effects By Arnab Bhattacharjee; Jan Ditzen; Sean Holly
  12. Ordinal status games on networks By Kukushkin, Nikolai S.
  13. Fast and accurate variational inference for large Bayesian VARs with stochastic volatility By Joshua C.C. Chan; Xuewen Yu

  1. By: John A. List; Fatemeh Momeni; Yves Zenou
    Abstract: The behavioral revolution within economics has been largely driven by psychological insights, with the sister sciences playing a lesser role. This study leverages insights from sociology to explore the role of neighborhoods on human capital formation at an early age. We do so by estimating the spillover effects from a large-scale early childhood intervention on the educational attainment of over 2,000 disadvantaged children in the United States. We document large spillover effects on both treatment and control children who live near treated children. Interestingly, the spillover effects are localized, decreasing with the spatial distance to treated neighbors. Perhaps our most novel insight is the underlying mechanisms at work: the spillover effect on non-cognitive scores operate through the child's social network while parental investment is an important channel through which cognitive spillover effects operate. Overall, our results reveal the importance of public programs and neighborhoods on human capital formation at an early age, highlighting that human capital accumulation is fundamentally a social activity.
    JEL: C93 I21 I24 I26 I28 R1
    Date: 2020–12
  2. By: Ravi Goyal; Victor De Gruttola
    Abstract: The authors present a statistical framework for generating predicted dynamic networks based on the observed evolution of social relationships in a population.
    Keywords: dynamic network congruence , class model , prediction , co-sponsorship network
  3. By: Billio Monica; Casarin Roberto; Costola Michele; Iacopini Matteo
    Abstract: Network models represent a useful tool to describe the complex set of financial relationships among heterogeneous firms in the system. In this paper, we propose a new semiparametric model for temporal multilayer causal networks with both intra- and inter-layer connectivity. A Bayesian model with a hierarchical mixture prior distribution is assumed to capture heterogeneity in the response of the network edges to a set of risk factors including the European COVID-19 cases. We measure the financial connectedness arising from the interactions between two layers defined by stock returns and volatilities. In the empirical analysis, we study the topology of the network before and after the spreading of the COVID-19 disease.
    Date: 2021–01
  4. By: Curtis Atkisson; Monique Borgerhoff Mulder
    Abstract: Explaining why humans cooperate in anonymous contexts is a major goal of human behavioral ecology, cultural evolution, and related fields. What predicts cooperation in anonymous contexts is inconsistent across populations, levels of analysis, and games. For instance, market integration is a key predictor across ethnolinguistic groups but has inconsistent predictive power at the individual level. We adapt an idea from 19th-century sociology: people in societies with greater overlap in ties across domains among community members (Durkheim's "mechanical" solidarity) will cooperate more with their network partners and less in anonymous contexts than people in societies with less overlap ("organic" solidarity). This hypothesis, which can be tested at the individual and community level, assumes that these two types of societies differ in the importance of keeping existing relationships as opposed to recruiting new partners. Using multiplex networks, we test this idea by comparing cooperative tendencies in both anonymous experimental games and real-life communal labor tasks across 9 Makushi villages in Guyana that vary in the degree of within-village overlap. Average overlap in a village predicts both real-world cooperative and anonymous interactions in the predicted direction; individual overlap also has effects in the expected direction. These results reveal a consistent patterning of cooperative tendencies at both individual and local levels and contribute to the debate over the emergence of norms for cooperation among humans. Multiplex overlap can help us understand inconsistencies in previous studies of cooperation in anonymous contexts and is an unexplored dimension with explanatory power at multiple levels of analysis.
    Date: 2020–12
  5. By: Tinic, Murat; Sensoy, Ahmet; Demir, Muge; Nguyen, Duc Khuong
    Abstract: We examine the relationship between broker network connectivity and stock returns in an order-driven market. Considering all stocks traded in Borsa Istanbul between January 2006 and November 2015, we estimate the monthly density, reciprocity and average weighted clustering coefficient as proxies for the broker network connectivity. Our firm-level cross-sectional regressions indicate a negative and significant predictive relationship between connectivity and one-month ahead stock returns. Our analyses also show that stocks in the lowest connectivity quintile earn 1.0% - 1.6% monthly return premiums. The connectivity premium is stronger in terms of both economic and statistical significance for small size stocks.
    Keywords: Stock market; trading networks; broker networks, network connectivity, pricing factors.
    JEL: G1 G12
    Date: 2020–11
  6. By: Shane Lubold; Arun G. Chandrasekhar; Tyler H. McCormick
    Abstract: Statistically modeling networks, across numerous disciplines and contexts, is fundamentally challenging because of (often high-order) dependence between connections. A common approach assigns each person in the graph to a position on a low-dimensional manifold. Distance between individuals in this (latent) space is inversely proportional to the likelihood of forming a connection. The choice of the latent geometry (the manifold class, dimension, and curvature) has consequential impacts on the substantive conclusions of the model. More positive curvature in the manifold, for example, encourages more and tighter communities; negative curvature induces repulsion among nodes. Currently, however, the choice of the latent geometry is an a priori modeling assumption and there is limited guidance about how to make these choices in a data-driven way. In this work, we present a method to consistently estimate the manifold type, dimension, and curvature from an empirically relevant class of latent spaces: simply connected, complete Riemannian manifolds of constant curvature. Our core insight comes by representing the graph as a noisy distance matrix based on the ties between cliques. Leveraging results from statistical geometry, we develop hypothesis tests to determine whether the observed distances could plausibly be embedded isometrically in each of the candidate geometries. We explore the accuracy of our approach with simulations and then apply our approach to data-sets from economics and sociology as well as neuroscience.
    JEL: C01 C12 C4 C52 C6 D85 L14
    Date: 2020–12
  7. By: Kun Jin; Yevgeniy Vorobeychik; Mingyan Liu
    Abstract: Network games provide a natural machinery to compactly represent strategic interactions among agents whose payoffs exhibit sparsity in their dependence on the actions of others. Besides encoding interaction sparsity, however, real networks often exhibit a multi-scale structure, in which agents can be grouped into communities, those communities further grouped, and so on, and where interactions among such groups may also exhibit sparsity. We present a general model of multi-scale network games that encodes such multi-level structure. We then develop several algorithmic approaches that leverage this multi-scale structure, and derive sufficient conditions for convergence of these to a Nash equilibrium. Our numerical experiments demonstrate that the proposed approaches enable orders of magnitude improvements in scalability when computing Nash equilibria in such games. For example, we can solve previously intractable instances involving up to 1 million agents in under 15 minutes.
    Date: 2021–01
  8. By: Fujitani, Ryosuke; Kim, Hyonok; Yamada, Kazuo
    Abstract: We show that a peer firm’s management forecast provides information for other firms in the same industry. Specifically, we show that a firm’s management forecast is positively associated with the stock return of other firms in the same industry. Furthermore, we show that such peer effect is observed when peer firms are the first disclosure company in the industry. We also find that the peer effect is more pronounced among firms with higher information asymmetry. Finally, we find that the peer effect is observed only in 2020 and not in other years between 2001 and 2019. Overall, the analysis provides strong evidence of peer effects under the COVID-19 pandemic period. This paper suggests that management forecast of peer firm plays a vital role as useful information set for investors that have limited access to public information due to the global pandemic.
    Keywords: information spillover, COVID-19 pandemic, management forecast
    JEL: M4 G14
    Date: 2021–01
  9. By: Mercedes Campi (Instituto Interdisciplinario de Economía Política de Buenos Aires - UBA - CONICET); Marco Dueñas (Universidad de Bogotá Jorge Tadeo Lozano); Giorgio Fagiolo (Istituto di Economia, Scuola Superiore Sant’Anna)
    Abstract: In the last years, there has been a growing interest in studying the global food system as a complex evolving network. Much of the literature has been focusing on the way countries are interconnected in the food system through international-trade linkages, and what consequences this may have in terms of food security and sustainability. Little attention has been instead devoted to understanding how countries, given their capabilities, specialize in agricultural production and to the determinants of country specialization patterns. In this paper, we start addressing this issue using FAO production data for the period 1993-2013. We characterize the food production space as a time-sequence of bipartite networks, connecting countries to the agricultural products they produce, and we identify properties and determinants underlying their evolution. We find that the agricultural product space is a very dense network, which however displays well-defined and stable communities of countries and products, despite the unprecedented pressure that food systems have been undergoing in recent years. We also find that the observed community structures are not only shaped by agro-ecological conditions but also by economic, socio-political, and technological factors. Finally, we discuss the implications that such findings may have on our understanding of the complex relationships involving country production capabilities, their specialization patterns, food security, and the nutrition content of the domestic part of their food supply.
    Keywords: Food systems, Food production, Specialization, Bipartite networks, Community structure detection, Hypergeometric filtering
    JEL: Q10 Q18 F63
  10. By: Yan Zhang; Frank Schweitzer
    Abstract: The reputation of firms is largely channeled through their ownership structure. We use this relation to determine reputation spillovers between transnational companies and their participated companies in an ownership network core of 1318 firms. We then apply concepts of network controllability to identify minimum sets of driver nodes (MDS) of 314 firms in this network. The importance of these driver nodes is classified regarding their control contribution, their operating revenue, and their reputation. The latter two are also taken as proxies for the access costs when utilizing firms as driver nodes. Using an enrichment analysis, we find that firms with high reputation maintain the controllability of the network, but rarely become top drivers, whereas firms with medium reputation most likely become top driver nodes. We further show that MDSs with lower access costs can be used to control the reputation dynamics in the whole network.
    Date: 2021–01
  11. By: Arnab Bhattacharjee (Heriot-Watt University and National Institute of Economic & Social Research, UK); Jan Ditzen (Free University of Bozen-Bolzano, Italy, and Center for Energy Economics Research and Policy (CEERP), Heriot-Watt University, Edinburgh, UK); Sean Holly (Faculty of Economics, University of Cambridge, UK)
    Abstract: We provide a way to represent spatial and temporal equilibria in terms of error correction models in a panel setting. This requires potentially two different processes for spatial or network dynamics, both of which can be expressed in terms of spatial weights matrices. The first captures strong cross-sectional dependence, so that a spatial difference, suitably defined, is weakly cross-section dependent (granular) but can be nonstationary. The second is a conventional weights matrix that captures short-run spatio-temporal dynamics as stationary and granular processes. In large samples, cross-section averages serve the first purpose and we propose the mean group, common corrrelated effects estimator together with multiple testing of cross-correlations to provide the short-run spatial weights. We apply this model to the 324 local authorities of England, and show that our approach is useful for modelling weak and strong cross-section dependence, together with partial adjustments to two long-run equilibrium relationships and short-run spatio-temporal dynamics, and provides exciting new insights.
    Keywords: Spatio-temporal dynamics; Error Correction Models; Weak and strong cross sectional dependence
    JEL: C21 C22 C23 R3
    Date: 2021–01
  12. By: Kukushkin, Nikolai S.
    Abstract: We consider a modification of ordinal status games of Haagsma and von Mouche (2010). A number of agents make scalar choices, e.g., their levels of conspicuous consumption. The wellbeing of each agent is affected by her choice in three ways: internal satisfaction, expenses, and social status determined by comparisons with the choices of others. In contrast to the original model, as well as its modifications considered so far, we allow for some players not caring about comparisons with some others. Assuming that the status of each player may only be "high" or "low," the existence of a strong Nash equilibrium is shown; for a particular subclass of such games, the convergence of Cournot tatonnement is established. If an intermediate status is possible, then even Nash equilibrium may fail to exist in very simple examples.
    Keywords: status game; strong equilibrium; Nash equilibrium; Cournot tatonnement
    JEL: C72
    Date: 2020–12–14
  13. By: Joshua C.C. Chan; Xuewen Yu
    Abstract: We propose a new variational approximation of the joint posterior distribution of the log-volatility in the context of large Bayesian VARs. In contrast to existing approaches that are based on local approximations, the new proposal provides a global approximation that takes into account the entire support of the joint distribution. In a Monte Carlo study we show that the new global approximation is over an order of magnitude more accurate than existing alternatives. We illustrate the proposed methodology with an application of a 96-variable VAR with stochastic volatility to measure global bank network connectedness. Our measure is able to detect the drastic increase in global bank network connectedness much earlier than rolling-window estimates from a homoscedastic VAR.
    Keywords: large vector autoregression, stochastic volatility, Variational Bayes, volatility network, connectedness
    JEL: C11 C32 C55 G21
    Date: 2020–12

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