nep-net New Economics Papers
on Network Economics
Issue of 2022‒12‒12
eleven papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. Structural Modelling of Dynamic Networks and Identifying Maximum Likelihood By Christian Gourieroux; Joann Jasiak
  2. Are larger cities more central in urban networks: A meta-analysis By Li, Xiaomeng; Neal, Zachary P.
  3. Projecting XRP price burst by correlation tensor spectra of transaction networks By Abhijit Chakraborty; Tetsuo Hatsuda; Yuichi Ikeda
  4. Bit by Bit - Colocation and the Death of Distance in Software Developer Networks By Moritz Goldbeck
  5. Yardstick Competition in the Digital Age : Unveiling New Networks in Tax Competition By Lockwood, Ben; Porcelli, Francesco; Redoano, Michela; Schiavone, Antonio
  6. Peer Effects in Active Labour Market Policies By Ulrike Unterhofer
  7. Graphical model inference with external network data By Jack Jewson; Li Li; Laura Battaglia; Stephen Hansen; David Rossell; Piotr Zwiernik
  8. Estimating Dynamic Spillover Effects along Multiple Networks in a Linear Panel Model By Clemens Possnig; Andreea Rot\u{a}rescu; Kyungchul Song
  9. Formation of Optimal Interbank Lending Networks under Liquidity Shocks By Daniel E. Rigobon; Ronnie Sircar
  10. Female Neighbors, Test Scores, and Careers By Goulas, Sofoklis; Megalokonomou, Rigissa; Zhang, Yi
  11. Vizaj - A free online interactive software for visualizing spatial networks By Thibault Rolland; Fabrizio de Vico Fallani

  1. By: Christian Gourieroux; Joann Jasiak
    Abstract: This paper considers nonlinear dynamic models where the main parameter of interest is a nonnegative matrix characterizing the network (contagion) effects. This network matrix is usually constrained either by assuming a limited number of nonzero elements (sparsity), or by considering a reduced rank approach for nonnegative matrix factorization (NMF). We follow the latter approach and develop a new probabilistic NMF method. We introduce a new Identifying Maximum Likelihood (IML) method for consistent estimation of the identified set of admissible NMF's and derive its asymptotic distribution. Moreover, we propose a maximum likelihood estimator of the parameter matrix for a given non-negative rank, derive its asymptotic distribution and the associated efficiency bound.
    Date: 2022–11
  2. By: Li, Xiaomeng; Neal, Zachary P.
    Abstract: As cities develop more and longer-range external relations, some have challenged the long-standing notion that population size indicates a city's power in its urban system. Instead, they contend that cities' centrality within urban networks provides a better indicator of power. But are city population size and city network centrality really independent properties in practice, or do larger cities tend to be more central in urban networks? To answer this question, we conducted a systematic literature search and performed meta-analysis on 36 reported correlations between city size and degree centrality. The results show that population size and degree centrality are significantly and positively correlated for cities across various urban systems (r=0.75), but the correlation varies by network scale and type. The size-centrality association is weaker for global economic and transportation networks (r = 0.43), and stronger for non-global social and communication networks (r = 0.92). The findings suggest that while city size and centrality may become decoupled at the global scale, size and centrality are closely associated at the regional and national scales, thereby clarifying seemingly contradictory predictions in the literature regarding the association between size and centrality for cities.
    Date: 2022–07–05
  3. By: Abhijit Chakraborty; Tetsuo Hatsuda; Yuichi Ikeda
    Abstract: Cryptoassets are becoming essential in the digital economy era. XRP is one of the large market cap cryptoassets. Here, we develop a novel method of correlation tensor spectra for the dynamical XRP networks, which can provide an early indication for XRP price. A weighed directed weekly transaction network among XRP wallets is constructed by aggregating all transactions for a week. A vector for each node is then obtained by embedding the weekly network in continuous vector space. From a set of weekly snapshots of node vectors, we construct a correlation tensor. A double singular value decomposition of the correlation tensors gives its singular values. The significance of the singular values is shown by comparing with its randomize counterpart. The evolution of singular values shows a distinctive behavior. The largest singular value shows a significant negative correlation with XRP/USD price. We observe the minimum of the largest singular values at the XRP/USD price peak during the first week of January 2018. The minimum of the largest singular value during January 2018 is explained by the decomposing the correlation tensor in the signal and noise components and also by evolution of community structure.
    Date: 2022–11
  4. By: Moritz Goldbeck
    Abstract: Digital tools potentially enable remote collaboration. Analyzing how some 191 thousand software developers in the United States collaborate on the largest online open source code repository platform, I find 79.8% of users clustering in only ten economic areas. Conditional on economic-area characteristics, colocated users collaborate about nine times as much as non-colocated users. Apart from this colocation effect, distance is not significantly related to collaboration among software developers. Comparison to social networks shows the colocation effect is weaker for software developers and relative connectedness probability remains at a much higher (stable) level with increasing distance. Software developer and social networks show no significant regional overlap.
    Keywords: Geography, digitization, online, open-source, high-skilled, collaboration
    JEL: L84 O18 O30 R32
    Date: 2022
  5. By: Lockwood, Ben (University of Warwick, Department of Economics); Porcelli, Francesco (University of Bari and CAGE); Redoano, Michela (University of Warwick, Department of Economics); Schiavone, Antonio (University of Bologna, Department of Economics)
    Abstract: We exploit a data disclosure project by the Italian government (OpenCivitas) which allowed mayors to view each other’s detailed expenditure data through a dedicated website. We interpret views on the website as generating a directed network. Mayors in the network are on average younger, more educated, they are more likely to come from larger cities which more often are in the northern regions and are more likely to be affliated to traditional parties, although populist parties usually rely more on the web for communication and political activities. Using directed dyadic models we find that mayors tend to form links with mayors of similar age who manage similar-sized cities and most often in their same region. However, links are more likely to be formed when mayors don’t share the same gender, education and party affliation. Mayors in this network do not engage in yardstick competition with neighbouring municipalities while all the other mayors do, and rather compete with each other, despite the physical distance. We show that this network existed before the website opened, but we find that after data disclosure yardstick competition within the network becomes strongly driven by mayors who are up for re-election. This was not the case before data disclosure. For the other municipalities, yardstick competition between neighbours remains uncorrelated with mayors’ term limits.
    Keywords: yardstick competition ; tax competition ; network ; open data ; property tax ; municipalities ; italy JEL Codes: H11 ; H71 ; H77
    Date: 2022
  6. By: Ulrike Unterhofer
    Abstract: This paper studies peer effects in the context of public sponsored vocational training for jobseekers in Germany. Using rich administrative data, I investigate how individual labour market outcomes of program participants are affected by the peer "quality" in the course, focusing on the employability of the peers. To identify a causal effect, I exploit quasi-random variation in the peer group composition within courses offered by the same training providers over time. I find strong evidence that peer composition matters. Greater average exposure to highly-employable peers has a moderate positive impact on job stability after program participation. Peer effects on earnings are large and differ by program type. They are positive, and long-lasting in classic vocational training and negative but of short duration in retraining. Jobseekers with an individual employability below the median benefit comparatively more across all programs. Overall, the results suggest that peer effects depend on specific program features.
    Date: 2022–11
  7. By: Jack Jewson; Li Li; Laura Battaglia; Stephen Hansen; David Rossell; Piotr Zwiernik
    Abstract: A frequent challenge when using graphical models in applications is that the sample size is limited relative to the number of parameters to be learned. Our motivation stems from applications where one has external data, in the form of networks between variables, that provides valuable information to help improve inference. Specifically, we depict the relation between COVID-19 cases and social and geographical network data, and between stock market returns and economic and policy networks extracted from text data. We propose a graphical LASSO framework where likelihood penalties are guided by the external network data. We also propose a spike-and-slab prior framework that depicts how partial correlations depend on the networks, which helps interpret the fitted graphical model and its relationship to the network. We develop computational schemes and software implementations in R and probabilistic programming languages. Our applications show how incorporating network data can significantly improve interpretation, statistical accuracy, and out-of-sample prediction, in some instances using significantly sparser graphical models than would have otherwise been estimated.
    Date: 2022–11–08
  8. By: Clemens Possnig; Andreea Rot\u{a}rescu; Kyungchul Song
    Abstract: Spillover of economic outcomes often arises over multiple networks, and distinguishing their separate roles is important in empirical research. For example, the direction of spillover between two groups (such as banks and industrial sectors linked in a bipartite graph) has important economic implications, and a researcher may want to learn which direction is supported in the data. For this, we need to have an empirical methodology that allows for both directions of spillover simultaneously. In this paper, we develop a dynamic linear panel model and asymptotic inference with large $n$ and small $T$, where both directions of spillover are accommodated through multiple networks. Using the methodology developed here, we perform an empirical study of spillovers between bank weakness and zombie-firm congestion in industrial sectors, using firm-bank matched data from Spain between 2005 and 2012. Overall, we find that there is positive spillover in both directions between banks and sectors.
    Date: 2022–11
  9. By: Daniel E. Rigobon; Ronnie Sircar
    Abstract: We formulate a model of the banking system in which banks control both their supply of liquidity, through cash holdings, and their exposures to risky interbank loans. The value of interbank loans jumps when banks suffer liquidity shortages, which can be caused by the arrival of large enough liquidity shocks. In two distinct settings, we compute the unique optimal allocations of capital. In the first, banks seek only to maximize their own utility -- in a decentralized manner. Second, a central planner aims to maximize the sum of all banks' utilities. Both of the resulting financial networks exhibit a `core-periphery' structure. However, the optimal allocations differ -- decentralized banks are more susceptible to liquidity shortages, while the planner ensures that banks with more debt hold greater liquidity. We characterize the behavior of the planner's optimal allocation as the size of the system grows. Surprisingly, the `price of anarchy' is of constant order. Finally, we derive capitalization requirements that cause the decentralized system to achieve the planner's level of risk. In doing so, we find that systemically important banks must face the greatest losses when they suffer liquidity crises -- ensuring that they are incentivized to avoid such crises.
    Date: 2022–11
  10. By: Goulas, Sofoklis (Stanford University); Megalokonomou, Rigissa (University of Queensland); Zhang, Yi (University of Queensland)
    Abstract: How much does your neighbor impact your test scores and career? In this paper, we examine how an observable characteristic of same-age neighbors—their gender—affects a variety of high school and university outcomes. We exploit randomness in the gender composition of local cohorts at birth from one year to the next. In a setting in which school assignment is based on proximity to residential address, we define as neighbors all same-cohort peers who attend neighboring schools. Using new administrative data for the universe of students in consecutive cohorts in Greece, we find that a higher share of female neighbors improves both male and female students' high school and university outcomes. We also find that female students are more likely to enroll in STEM degrees and target more lucrative occupations when they are exposed to a higher share of female neighbors. We collect rich qualitative geographic data on communal spaces (e.g., churches, libraries, parks, Scouts and sports fields) to understand whether access to spaces of social interaction drives neighbor effects. We find that communal facilities amplify neighbor effects among females.
    Keywords: neighbor gender peer effects, cohort-to-cohort random variation, birth gender composition, geodata, STEM university degrees
    JEL: J16 J24 I24 I26
    Date: 2022–11
  11. By: Thibault Rolland (ARAMIS - Algorithms, models and methods for images and signals of the human brain - SU - Sorbonne Université - Inria de Paris - Inria - Institut National de Recherche en Informatique et en Automatique - ICM - Institut du Cerveau = Paris Brain Institute - AP-HP - Assistance publique - Hôpitaux de Paris (AP-HP) - INSERM - Institut National de la Santé et de la Recherche Médicale - CHU Pitié-Salpêtrière [AP-HP] - AP-HP - Assistance publique - Hôpitaux de Paris (AP-HP) - SU - Sorbonne Université - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique); Fabrizio de Vico Fallani (ARAMIS - Algorithms, models and methods for images and signals of the human brain - SU - Sorbonne Université - Inria de Paris - Inria - Institut National de Recherche en Informatique et en Automatique - ICM - Institut du Cerveau = Paris Brain Institute - AP-HP - Assistance publique - Hôpitaux de Paris (AP-HP) - INSERM - Institut National de la Santé et de la Recherche Médicale - CHU Pitié-Salpêtrière [AP-HP] - AP-HP - Assistance publique - Hôpitaux de Paris (AP-HP) - SU - Sorbonne Université - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique, ICM - Institut du Cerveau = Paris Brain Institute - AP-HP - Assistance publique - Hôpitaux de Paris (AP-HP) - INSERM - Institut National de la Santé et de la Recherche Médicale - CHU Pitié-Salpêtrière [AP-HP] - AP-HP - Assistance publique - Hôpitaux de Paris (AP-HP) - SU - Sorbonne Université - SU - Sorbonne Université - CNRS - Centre National de la Recherche Scientifique)
    Abstract: In many fields of science and technology we are confronted with complex networks. Making sense of these networks often require the ability to visualize and explore their intermingled structure consisting of nodes and links. To facilitate the identification of significant connectivity patterns, many methods have been developed based on the rearrangement of the nodes so as to avoid link criss-cross. However, real networks are often embedded in a geometrical space and the nodes code for an intrinsic physical feature of the system that one might want to preserve. For these spatial networks, it is therefore crucial to find alternative strategies operating on the links and not on the nodes. Here, we introduce Vizaj a javascript web application to render spatial networks based on optimized geometrical criteria that reshape the link profiles. While optimized for 3D networks, Vizaj can also be used for 2D networks and offers the possibility to interactively customize the visualization via several controlling parameters, including network filtering and the effect of internode distance on the link trajectories. Vizaj is further equipped with additional options allowing to improve the final aesthetics, such as the color/size of both nodes and links, zooming/rotating/translating, and superimposing external objects. Vizaj is an open-source software which can be freely downloaded and updated via a github repository. Here, we provide a detailed description of its main features and algorithms together with a guide on how to use it. Finally, we validate its potential on several synthetic and real spatial networks from infrastructural to biological systems. We hope that Vizaj will help scientists and practitioners to make sense of complex networks and provide aesthetic while informative visualizations.
    Keywords: Complex systems,Physical networks,Dataviz,Software,Art
    Date: 2022–11–03

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