nep-net New Economics Papers
on Network Economics
Issue of 2021‒07‒19
sixteen papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. Effects of Social Networks on Job Attainment and Match Quality: Evidence from the China Labor-Force Dynamics Survey By Nie, Peng; Yan, Weibo
  2. Counterparty Choice, Bank Interconnectedness, and Systemic Risk By Andrew Ellul; Dasol Kim
  3. What Does Network Analysis Teach Us about International Environmental Cooperation? By Stefano Carattini; Sam Fankhauser; Jianjian Gao; Caterina Gennaioli; Pietro Panzarasa
  4. Interactions Between Global Value Chains and Foreign Direct Investment: A Network Approach By Amat Adarov
  5. Who should get vaccinated? Individualized allocation of vaccines over SIR network By Toru Kitagawa; Guanyi Wang
  6. Complex networks of stakeholders and corporate political strategy By Michel Ferrary
  7. The Information and Communication Technology Cluster in the Global Value Chain Network By Amat Adarov
  8. Collaborative Insurance Sustainability and Network Structure By Arthur Charpentier; Lariosse Kouakou; Matthias L\"owe; Philipp Ratz; Franck Vermet
  9. Financial Markets and the Phase Transition between Water and Steam By Christof Schmidhuber
  10. Does Money Strengthen Our Social Ties? Longitudinal Evidence of Lottery Winners By Costa-Font, Joan; Powdthavee, Nattavudh
  11. The global migration network of sex-workers By Luis E C Rocha; Petter Holme; Claudio D G Linhares
  12. Bitcoin's Crypto Flow Newtork By Yoshi Fujiwara; Rubaiyat Islam
  13. Gravity models of networks: integrating maximum-entropy and econometric approaches By Marzio Di Vece; Diego Garlaschelli; Tiziano Squartini
  14. A Neural Frequency-Severity Model and Its Application to Insurance Claims By Dong-Young Lim
  15. Network manipulation algorithm By David M\"uller; Vladimir Shikhman
  16. Network and Panel Quantile Effects Via Distribution Regression By Victor Chernozhukov; Ivan Fernandez-Val; Martin Weidner

  1. By: Nie, Peng (Xi’an Jiaotong University); Yan, Weibo (Zhongnan University of Economics and Law)
    Abstract: Using nationally representative data from the 2012 and 2014 China Labor-force Dynamics Survey, this paper investigates the effects of network types (kinship/non-kinship) and network resources (information/influence) on job attainment and match quality in China. We find a wage premium obtained through both kinship and non- kinship networks but shorter job duration only in jobs obtained through non-kinship networks. In regards to the different types of networks, resources embedded in the networks are not important. This conundrum can be reconciled if we take the structure of the network and the type of work unit into account. Kinship networks are more pervasive in the public sector, with better earnings and stable job positions. Non-kinship networks bring about a wage premium but lead to job dissatisfaction, especially in regards to promotion opportunities. This paper highlights the structure of the job market when studying networks and sheds new light on the types of networks that really matter in job attainment and those that result in the possible loss of match quality.
    Keywords: network types, network resources, job attainment, match quality
    JEL: J30 J31 J64
    Date: 2021–06
  2. By: Andrew Ellul (Indiana University, Office of Financial Research, Centre for Economic Policy Research, Center for Studies of Economics and Finance, European Corporate Governance Institute); Dasol Kim (Office of Financial Research)
    Abstract: We provide evidence on how banks form network connections and endogenous risk-taking in their non-bank counterparty choices in the OTC derivatives markets. We use confidential regulatory data from the Capital Assessment and Stress Testing reports that provide counterparty-level data across a wide range of OTC markets for the most systemically important U.S. banks. We show that banks are more likely toeither establish or maintain a relationship, and increase their exposures within an existing relationship, with non-bank counterparties that are already heavily connected and exposed to other banks. Banks in such densely-connected networks are more likely to connect with riskier counterparties for their most material exposures. The effects are strongest in the case of (non-bank) financial counterparties. These findings suggest moral hazard behavior in counterparty choices. Finally, we demonstrate that these exposures are strongly linked to systemic risk. Overall, the results suggest a network formation process that amplifies risk propagation through non-bank linkages in opaque financial markets.
    Keywords: counterparty risk, financial networks, bank interconnectedness, over-the-counter markets, derivatives
    Date: 2021–07–12
  3. By: Stefano Carattini; Sam Fankhauser; Jianjian Gao; Caterina Gennaioli; Pietro Panzarasa
    Abstract: Over the past 70 years, the number of international environmental agreements (IEAs) has increased substantially, highlighting their prominent role in environmental governance. This paper applies the toolkit of network analysis to identify the network properties of international environmental cooperation based on 546 IEAs signed between 1948 and 2015. We identify four stylised facts that offer topological corroboration for some key themes in the IEA literature. First, we find that a statistically significant cooperation network did not emerge until the early 1970, but since then the network has grown continuously in strength, resulting in higher connectivity and intensity of cooperation between signatory countries. Second, over time the network has become closer, denser and more cohesive, allowing more effective policy coordination and knowledge diffusion. Third, the network, while global, has a noticeable European imprint: initially the United Kingdom and more recently France and Germany have been the most strategic players to broker environmental cooperation. Fourth, international environmental coordination started with the management of fisheries and the sea, but is now most intense on waste and hazardous substances. The network of air and atmosphere treaties is weaker on a number of metrics and lacks the hierarchical structure found in other networks. It is the only network whose topological properties are shaped significantly by UN-sponsored treaties.
    Keywords: environmental cooperation, international environmental agreements, global governance, network analysis
    JEL: F53 H87 Q58
    Date: 2021
  4. By: Amat Adarov (The Vienna Institute for International Economic Studies, wiiw)
    Abstract: The world economy is increasingly shaped by cross-border production and investment activity. The paper uses complex network analysis along with panel data econometric techniques to study the structure and interactions between the networks of global value chains (GVC) and foreign direct investment (FDI). The analysis reveals that both FDI and GVC networks have a distinct core-periphery structure dominated by a relatively small number of countries with the USA constituting the global hub interlinked with regional European and Asian clusters, which, in turn, are centered around regional hub countries like China and Germany. Simultaneous equation model regressions using three-stage least squares suggest that FDI centrality facilitates GVC centrality of countries. However, FDI centrality is driven to a large extent by the FDI statutory restrictions and tax offshore regulations, rather than GVC connectivity.
    Keywords: global value chains; foreign direct investment; network analysis; cross-border connectivity; simultaneous equation model
    JEL: F10 F14 F15 F21
    Date: 2021–07
  5. By: Toru Kitagawa (Institute for Fiscal Studies and cemmap and University College London); Guanyi Wang (Institute for Fiscal Studies)
    Abstract: How to allocate vaccines over heterogeneous individuals is one of the important policy decisions in pandemic times. This paper develops a procedure to estimate an individualized vaccine allocation policy under limited supply, exploiting social network data containing individual demographic characteristics and health status. We model the spillover effects of vaccination based on a Heterogeneous-Interacted-SIR network model and estimate an individualized vaccine allocation policy by maximizing an estimated social welfare (public health) criterion incorporating these spillovers. While this optimization problem is generally an NP-hard integer optimization problem, we show that the SIR structure leads to a submodular objective function, and provide a computationally attractive greedy algorithm for approximating a solution that has a theoretical performance guarantee. Moreover, we characterise a finite sample welfare regret bound and examine how its uniform convergence rate depends on the complexity and riskiness of the social network. In the simulation, we illustrate the importance of considering spillovers by comparing our method with targeting without network information.
    Date: 2020–12–14
  6. By: Michel Ferrary (SKEMA Business School)
    Abstract: This article makes a theoretical contribution by applying two concepts from complex network theory to stakeholder management and corporate political strategy: systemic shocks and small-world networks. Shocks may be random or intentionally caused by a firm. The nature of a shock determines the urgency of the situation faced by a firm and the legitimacy of managerial decisions. A small-world network is a set of dense clusters loosely connected with one another. This study characterizes the structure of the stakeholders' network in which the firm is embedded. A firm may be highly or loosely embedded in a given cluster. Embeddedness relates both to the firm's resource dependence and its quest for legitimacy. Combining the nature of the shock and the degree of embeddedness offers a conceptual framework to explore corporate political strategy aimed at managing stakeholders. When a firm that is loosely embedded in a cluster of stakeholders faces a random shock, it chooses a reactive corporate political strategy. A firm that is highly embedded in a cluster and facing a random shock favours an accommodative corporate political strategy. A firm loosely embedded in a cluster in which it intentionally causes a shock chooses a proactive corporate political strategy. A firm highly embedded in a cluster in which it intentionally provokes a shock adopts a defensive corporate political strategy. Four examples of industrial downsizing understood as systemic shocks illustrate this conceptual framework.
    Keywords: stakeholder theory,corporate political strategy,complex network theory,industrial downsizing
    Date: 2020–05–12
  7. By: Amat Adarov (The Vienna Institute for International Economic Studies, wiiw)
    Abstract: Global value chains (GVCs) are among the critical factors shaping the world economy nowadays. Within cross-border production networks an increasingly important role has been played by the information and communication technology (ICT) sectors. Based on the multi-country input-output database recently developed by the Vienna Institute for International Economic Studies, covering the period 2005-2018, this policy brief examines the structure and the dynamics of global value chains associated with the ICT sectors. To this end we use complex network analysis techniques to characterise the overall topology of the international ICT cluster in the GVC network, identify the key countries and sectors therein from the perspective of their connectivity. The analysis shows that the ICT GVC network is dominated by the mutual value-added trade linkages between China, South Korea and Taiwan in the Computers and electronics manufacturing sector. These sectors are heavily interlinked via backward and forward GVC linkages with a large number of ICT and non-ICT sectors, many of which are located in the USA, China and Germany. In the recent decade, there has been a major shift in terms of importance to the GVC network from ICT manufacturing towards ICT services, especially prominent for the ICT services sector in Ireland, which has become among the most interconnected sectors in the global ICT cluster.
    Keywords: global value chains; ICT sector; network analysis; digitalisation
    JEL: F10 F14 F15
    Date: 2021–07
  8. By: Arthur Charpentier; Lariosse Kouakou; Matthias L\"owe; Philipp Ratz; Franck Vermet
    Abstract: The peer-to-peer (P2P) economy has been growing with the advent of the Internet, with well known brands such as Uber or Airbnb being examples thereof. In the insurance sector the approach is still in its infancy, but some companies have started to explore P2P-based collaborative insurance products (eg. Lemonade in the U.S. or Inspeer in France). The actuarial literature only recently started to consider those risk sharing mechanisms, as in Denuit and Robert (2021) or Feng et al. (2021). In this paper, describe and analyse such a P2P product, with some reciprocal risk sharing contracts. Here, we consider the case where policyholders still have an insurance contract, but the first self-insurance layer, below the deductible, can be shared with friends. We study the impact of the shape of the network (through the distribution of degrees) on the risk reduction. We consider also some optimal setting of the reciprocal commitments, and discuss the introduction of contracts with friends of friends to mitigate some possible drawbacks of having people without enough connections to exchange risks.
    Date: 2021–07
  9. By: Christof Schmidhuber
    Abstract: We present a lattice gas model of financial markets to explain previous empirical observations of the interplay of trends and reversion. The shares of an asset are modeled by gas molecules that are distributed across a hidden social network of investors. Neighbors in the network tend to align their positions due to herding behavior. The model is equivalent to the Ising model on this network, with the magnetization in the role of the deviation of the asset price from its value. For N independent assets, it generalizes to an O(N) vector model. In efficient markets, the system is driven to its critical temperature. There, it is characterized by long-range correlations and universal critical exponents, in analogy with the second-order phase transition between water and steam. Using the renormalization group, we show that these critical exponents imply predictions for the auto-correlations of financial market returns. For a simple network topology, consistency with observation implies a fractal dimension of the network of 3.3 and a correlation time of 10 years. While the simple model agrees well with market data on long time scales, it cannot explain the observed market trends over time horizons from one month to one year. In a next step, the approach should therefore be extended to other models of critical dynamics and to general network topologies. It opens the door for indirectly measuring universal properties of the hidden social network of investors from the observable interplay of trends and reversion.
    Date: 2021–07
  10. By: Costa-Font, Joan (London School of Economics); Powdthavee, Nattavudh (University of Warwick)
    Abstract: We study the effect of lottery wins on social ties and support network in the United Kingdom. On average, we find that winning more in the lottery increases the probability of meeting friends on most days, which is consistent with the complementary effect of income on social ties. The opposite is true with regards to social ties held for more instrumental reasons such as talking to neighbors. Winning more in the lottery also lessens an individual support network consistently with a substitution for instrumental social ties. However, further robustness checks reveal that the average lottery effects are driven by the few outliers of very large wins in the sample, thus suggesting that small to medium-sized wins (
    Keywords: income, lottery, socialization effect, unearned income, friendships, neighborhood, social ties
    JEL: Z1
    Date: 2021–06
  11. By: Luis E C Rocha; Petter Holme; Claudio D G Linhares
    Abstract: Differences in the social and economic environment across countries encourage humans to migrate in search of better living conditions, including job opportunities, higher salaries, security and welfare. Quantifying global migration is, however, challenging because of poor recording, privacy issues and residence status. This is particularly critical for some classes of migrants involved in stigmatised, unregulated or illegal activities. Escorting services or high-end prostitution are well-paid activities that attract workers all around the world. In this paper, we study international migration patterns of sex-workers by using network methods. Using an extensive international online advertisement directory of escorting services and information about individual escorts, we reconstruct a migrant flow network where nodes represent either origin or destination countries. The links represent the direct routes between two countries. The migration network of sex-workers shows different structural patterns than the migration of the general population. The network contains a strong core where mutual migration is often observed between a group of high-income European countries, yet Europe is split into different network communities with specific ties to non-European countries. We find non-reciprocal relations between countries, with some of them mostly offering while others attract workers. The GDP per capita is a good indicator of country attractiveness for incoming workers and service rates but is unrelated to the probability of emigration. The median financial gain of migrating, in comparison to working at the home country, is 15.9%. Only sex-workers coming from 77% of the countries have financial gains with migration and average gains decrease with the GDPc of the country of origin. Our results shows that high-end sex-worker migration is regulated by economic, geographic and cultural aspects.
    Date: 2021–07
  12. By: Yoshi Fujiwara; Rubaiyat Islam
    Abstract: How crypto flows among Bitcoin users is an important question for understanding the structure and dynamics of the cryptoasset at a global scale. We compiled all the blockchain data of Bitcoin from its genesis to the year 2020, identified users from anonymous addresses of wallets, and constructed monthly snapshots of networks by focusing on regular users as big players. We apply the methods of bow-tie structure and Hodge decomposition in order to locate the users in the upstream, downstream, and core of the entire crypto flow. Additionally, we reveal principal components hidden in the flow by using non-negative matrix factorization, which we interpret as a probabilistic model. We show that the model is equivalent to a probabilistic latent semantic analysis in natural language processing, enabling us to estimate the number of such hidden components. Moreover, we find that the bow-tie structure and the principal components are quite stable among those big players. This study can be a solid basis on which one can further investigate the temporal change of crypto flow, entry and exit of big players, and so forth.
    Date: 2021–06
  13. By: Marzio Di Vece; Diego Garlaschelli; Tiziano Squartini
    Abstract: The World Trade Web (WTW) is the network of international trade relationships among world countries. Characterizing both the local link weights (observed trade volumes) and the global network structure (large-scale topology) of the WTW via a single model is still an open issue. While the traditional Gravity Model (GM) successfully replicates the observed trade volumes by employing macroeconomic properties such as GDP and geographic distance, it, unfortunately, predicts a fully connected network, thus returning a completely unrealistic topology of the WTW. To overcome this problem, two different classes of models have been introduced in econometrics and statistical physics. Econometric approaches interpret the traditional GM as the expected value of a probability distribution that can be chosen arbitrarily and tested against alternative distributions. Statistical physics approaches construct maximum-entropy probability distributions of (weighted) graphs from a chosen set of measurable structural constraints and test distributions resulting from different constraints. Here we compare and integrate the two approaches by considering a class of maximum-entropy models that can incorporate macroeconomic properties used in standard econometric models. We find that the integrated approach achieves a better performance than the purely econometric one. These results suggest that the maximum-entropy construction can serve as a viable econometric framework wherein extensive and intensive margins can be separately controlled for, by combining topological constraints and dyadic macroeconomic variables.
    Date: 2021–07
  14. By: Dong-Young Lim
    Abstract: This paper proposes a flexible and analytically tractable class of frequency-severity models based on neural networks to parsimoniously capture important empirical observations. In the proposed two-part model, mean functions of frequency and severity distributions are characterized by neural networks to incorporate the non-linearity of input variables. Furthermore, it is assumed that the mean function of the severity distribution is an affine function of the frequency variable to account for a potential linkage between frequency and severity. We provide explicit closed-form formulas for the mean and variance of the aggregate loss within our modelling framework. Components of the proposed model including parameters of neural networks and distribution parameters can be estimated by minimizing the associated negative log-likelihood functionals with neural network architectures. Furthermore, we leverage the Shapely value and recent developments in machine learning to interpret the outputs of the model. Applications to a synthetic dataset and insurance claims data illustrate that our method outperforms the existing methods in terms of interpretability and predictive accuracy.
    Date: 2021–06
  15. By: David M\"uller; Vladimir Shikhman
    Abstract: In this paper, we present a network manipulation algorithm based on an alternating minimization scheme from (Nesterov 2020). In our context, the latter mimics the natural behavior of agents and organizations operating on a network. By selecting starting distributions, the organizations determine the short-term dynamics of the network. While choosing an organization in accordance with their manipulation goals, agents are prone to errors. This rational inattentive behavior leads to discrete choice probabilities. We extend the analysis of our algorithm to the inexact case, where the corresponding subproblems can only be solved with numerical inaccuracies. The parameters reflecting the imperfect behavior of agents and the credibility of organizations, as well as the condition number of the network transition matrix have a significant impact on the convergence of our algorithm. Namely, they turn out not only to improve the rate of convergence, but also to reduce the accumulated errors. From the mathematical perspective, this is due to the induced strong convexity of an appropriate potential function.
    Date: 2021–07
  16. By: Victor Chernozhukov (Institute for Fiscal Studies and MIT); Ivan Fernandez-Val (Institute for Fiscal Studies and Boston University); Martin Weidner (Institute for Fiscal Studies and cemmap and UCL)
    Abstract: This paper provides a method to construct simultaneous confidence bands for quantile functions and quantile effects in nonlinear network and panel models with unobserved two-way effects, strictly exogenous covariates, and possibly discrete outcome variables. The method is based upon projection of simultaneous confidence bands for distribution functions constructed from fixed effects distribution regression estimators. These fixed effects estimators are debiased to deal with the incidental parameter problem. Under asymptotic sequences where both dimensions of the data set grow at the same rate, the confidence bands for the quantile functions and effects have correct joint coverage in large samples. An empirical application to gravity models of trade illustrates the applicability of the methods to network data.
    Date: 2020–06–15

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