nep-net New Economics Papers
on Network Economics
Issue of 2021‒12‒20
seven papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. Clubs and Networks By Sihua Ding; Marcin Dziubiński; Sanjeev Goyal
  2. Dynamic Network Quantile Regression Model By Xiu Xu; Weining Wang; Yongcheol Shin; Chaowen Zheng
  3. Network regression and supervised centrality estimation By Junhui Cai; Dan Yang; Wu Zhu; Haipeng Shen; Linda Zhao
  4. The Dynamics of French Universities in Patent Collaboration Networks By Isabel Cavalli; Charlie Joyez
  5. Portfolio optimization with idiosyncratic and systemic risks for financial networks By Yajie Yang; Longfeng Zhao; Lin Chen; Chao Wang; Jihui Han
  6. Isolated States of America: The Impact of State Borders on Mobility and Regional Labor Market Adjustments By Riley Wilson
  7. Skewness and Time-Varying Second Moments in a Nonlinear Production Network: Theory and Evidence By Ian Dew-Becker; Alireza Tahbaz-Salehi; Andrea Vedolin

  1. By: Sihua Ding; Marcin Dziubiński; Sanjeev Goyal (Division of Social Science)
    Abstract: A recurring theme in the study of society is the concentration of influence and power that is driven through unequal membership of groups and associations. In some instances these bodies constitute a small world while in others they are fragmented into distinct cliques. This paper presents a new model of clubs and networks to understand the sources of individual marginalization and the origins of different club networks. In our model, individuals seek to become members of clubs while clubs wish to have members. Club value is increasing in its size and in the strength of ties with other clubs. We show that a stable membership proï¬ le exhibits marginalization of individuals and that this is generally not welfare maximizing. Our second result shows that if returns from strength of ties are convex (concave) then stable memberships support fragmented networks with strong ties (small worlds held together by weak ties). We illustrate the value of these theoretical results through case studies of inter-locking directorates, boards of editors of journals, and defence and R&D alliances.
    Date: 2021–12
  2. By: Xiu Xu; Weining Wang; Yongcheol Shin; Chaowen Zheng
    Abstract: We propose a dynamic network quantile regression model to investigate the quantile connectedness using a predetermined network information. We extend the existing network quantile autoregression model of Zhu et al. (2019b) by explicitly allowing the contemporaneous network effects and controlling for the common factors across quantiles. To cope with the endogeneity issue due to simultaneous network spillovers, we adopt the instrumental variable quantile regression (IVQR) estimation and derive the consistency and asymptotic normality of the IVQR estimator using the near epoch dependence property of the network process. Via Monte Carlo simulations, we confirm the satisfactory performance of the IVQR estimator across different quantiles under the different network structures. Finally, we demonstrate the usefulness of our proposed approach with an application to the dataset on the stocks traded in NYSE and NASDAQ in 2016.
    Date: 2021–11
  3. By: Junhui Cai; Dan Yang; Wu Zhu; Haipeng Shen; Linda Zhao
    Abstract: The centrality in a network is a popular metric for agents' network positions and is often used in regression models to model the network effect on an outcome variable of interest. In empirical studies, researchers often adopt a two-stage procedure to first estimate the centrality and then infer the network effect using the estimated centrality. Despite its prevalent adoption, this two-stage procedure lacks theoretical backing and can fail in both estimation and inference. We, therefore, propose a unified framework, under which we prove the shortcomings of the two-stage in centrality estimation and the undesirable consequences in the regression. We then propose a novel supervised network centrality estimation (SuperCENT) methodology that simultaneously yields superior estimations of the centrality and the network effect and provides valid and narrower confidence intervals than those from the two-stage. We showcase the superiority of SuperCENT in predicting the currency risk premium based on the global trade network.
    Date: 2021–11
  4. By: Isabel Cavalli (Université Côte d'Azur, France; CNRS, GREDEG; Institute of Economics, Scuola Superiore Sant'Anna, Italy); Charlie Joyez (Université Côte d'Azur, France; CNRS, GREDEG)
    Abstract: Innovation is a dynamic process whose complexity lies in networks among heterogeneous actors, with collaboration often ending in patent co-ownership. Governments introduced many policies to redefine the role of universities in research collaboration once acknowledging their value in scientific knowledge. This paper explores how patent co-ownership evolved in France after decisive policy interventions (1999, 2006, 2007). Using French copatent data (1978-2018), we first employ Network Analysis to capture the evolution of centrality of French Universities. We then apply a Dif-in-Dif, incorporating a Propensity Score Matching (PSM), to investigate the potential causal relationship between policy interventions and the evolution of universities' centrality, contrasting with with French Public Research Organizations as well as German and Italian universities. Our results point to the increasing centrality gained by French universities in patenting co-ownership over the years and its essential role, as an innovator actor, in the French innovation system. Although the Innovation Act (1999) positively impacted their centrality, the impact of 2006-on legislation is either null or even negative, offsetting the initial trend.
    Keywords: Innovation dynamics, Universities, Collaborative Patents, Network centrality, treatment effect
    JEL: C54 D85 O32 O33 O34 O38
    Date: 2021–12
  5. By: Yajie Yang; Longfeng Zhao; Lin Chen; Chao Wang; Jihui Han
    Abstract: In this study, we propose a new multi-objective portfolio optimization with idiosyncratic and systemic risks for financial networks. The two risks are measured by the idiosyncratic variance and the network clustering coefficient derived from the asset correlation networks, respectively. We construct three types of financial networks in which nodes indicate assets and edges are based on three correlation measures. Starting from the multi-objective model, we formulate and solve the asset allocation problem. We find that the optimal portfolios obtained through the multi-objective with networked approach have a significant over-performance in terms of return measures in an out-of-sample framework. This is further supported by the less drawdown during the periods of the stock market fluctuating downward. According to analyzing different datasets, we also show that improvements made to portfolio strategies are robust.
    Date: 2021–11
  6. By: Riley Wilson (Brigham Young University)
    Abstract: I document a new empirical pattern of internal mobility in the United States. Namely, county-to-county migration and commuting drop off discretely at state borders. People are three times as likely to move to a county 15 miles away, but in the same state, than to move to an equally distant county in a different state. These gaps remain even among neighboring counties or counties in the same commuting zone. This pattern is not explained by differences in county characteristics, is not driven by any particular demographic group, and is not explained by pecuniary costs such as differences in state occupational licensing, taxes, or transfer program generosity. However, county-to-county social connectedness (as measured by the number of Facebook linkages) follows a similar pattern. Although the patterns in social networks would be consistent with information frictions, nonpecuniary psychic costs, or behavioral biases such as a sate identity or home bias, the data suggest that state identity and home bias play an outsized role. This empirical pattern has real economic impacts. Building on existing methods, I show that employment in border counties adjusts more slowly after local economic shocks relative to interior counties. These counties also exhibit less in-migration and in-commuting, suggesting the lack of mobility leads to slower labor market adjustment.
    Keywords: Internal migration, commuting, social networks, border discontinuities
    JEL: J6 R1
    Date: 2021–12
  7. By: Ian Dew-Becker; Alireza Tahbaz-Salehi; Andrea Vedolin
    Abstract: This paper studies asymmetry in economic activity over the business cycle. It develops a tractable multisector model of the economy in which complementarity across inputs causes aggregate activity to be left skewed with countercyclical volatility. We then examine implications of the model regarding the time-series skewness of activity at the sector level, cyclicality of dispersion and skewness across sectors, and the conditional covariances of sector growth rates, finding support for each in the data. The empirical skewness of employment growth, industrial production growth, and stock returns increases with the level of aggregation, which is consistent with the model's implication that it is the nonlinearity in the production structure of the economy that generates the skewness. Other prominent models of asymmetry are not able to simultaneously match the range of empirical facts that the production network model can.
    JEL: E10 E23 E32 E44
    Date: 2021–11

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