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on Network Economics |
| By: | I. S. Buhai; M. J. van der Leij |
| Abstract: | We develop a network model of occupational segregation between social groups divided along gender or racial dimensions, generated by the existence of positive assortative matching among individuals from the same group. If referrals are important for job search, then expected homophily in the structure of job contact networks induces different career choices for individuals from different social groups. This further translates into stable occupational segregation equilibria in the labor market. We derive conditions for wage and unemployment inequality in the segregation equilibria and characterize both the first and the second best social welfare optima. We find that utilitarian socially optimal policies always involve segregation, but that integration policies are justifiable by additional distributional concerns. Our analysis suggests that social interaction through homophilous job referral networks is an important channel for the propagation and persistence of gender and racial inequalities in the labour market, complementary to classical theories such as taste or statistical discrimination. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.09293&r=all |
| By: | Hledik, Juraj; Rastelli, Riccardo |
| Abstract: | We design a statistical model for measuring the homogeneity of a financial network that evolves over time. Our model focuses on the level of diversification of financial institutions; that is, whether they are more inclined to distribute their assets equally among partners, or if they rather concentrate their commitments towards a limited number of institutions. Crucially, a Markov property is introduced to capture time dependencies and to make our measures comparable across time. We apply the model on an original dataset of Austrian interbank exposures. The temporal span encompasses the onset and development of the financial crisis in 2008 as well as the beginnings of the European sovereign debt crisis in 2011. Our analysis highlights an overall increasing trend for network homogeneity, whereby core banks have a tendency to distribute their market exposures more equally across their partners. JEL Classification: X00, X01, X02, X03 |
| Keywords: | Austrian interbank market, Bayesian inference, dynamic networks, latent variable models, systemic risk |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:srk:srkwps:2020109&r=all |
| By: | Richard Heuver; Ron Berndsen |
| Abstract: | The Liquidity Coverage Ratio (LCR) requirement of the Basel III framework is aimed at making banks more resilient against liquidity shocks and indicates the extent to which a bank is able to meet its payment obligations over a 30-day stress period. Notwithstanding the fact that it forms an important addition to the available information for regulators, it presents information on the status of a single bank on a monthly reporting basis. In this paper we generate an LCR-like statistic on a daily basis and simulate liquidity failure of each of the systemically important banks, using historical payments data from TARGET2. The aim of the paper is to uncover paths of contagion. The trigger is a bank with a deteriorating LCR and the knock-on effect is modelled as the impact on the LCR of other banks. We generate then the cascade of contagion, which in general consists of multiple paths, trying to answer the question to what extent the financial network further deteriorates. In doing so we provide paths of contagion which give a sense of potential systemic risk present in the network. We find that the majority of damage is caused by a small group of large banks. Furthermore we find groups of banks that are very vulnerable to shocks, regardless of the size or location of the disruption. Our model reveals that the shortfall of liquidity at the stressed bank is a more important driver than the addition of liquidity at the other banks. A version of the contagion network based on a 14-day period reveals a monthly pattern, which is in line with other literature in which window dressing is addressed. The data used in this paper are available to supervisors, central banks and resolution authorities, therefore making it possible to anticipate contagion of failing liquidity coverage within their payment network on a daily basis. |
| Keywords: | Liquidity Coverage; Basel III; payment systems; graph theory; simulation modeling |
| JEL: | E58 G21 E42 C63 |
| Date: | 2020–03 |
| URL: | http://d.repec.org/n?u=RePEc:dnb:dnbwpp:678&r=all |
| By: | Sharat Ganapati; Woan Foong Wong; Oren Ziv |
| Abstract: | Entrepôts are hubs that facilitate trade between various origins and destinations. We study the role these hubs, and the networks they form, play in international trade. Using novel data, we trace the paths of containerized goods entering the United States. We show that the majority of trade is indirect and sent through a small number of entrepôts, resulting in lower transport costs through scale economies by using larger ships. We build a model of endogenous entrepôt formation incorporating route choice by exporters within a Ricardian setting. We use the model to estimate trade costs on each shipping leg and develop a geography-based instrument to estimate a leg-level scale elasticity. Counterfactuals opening the Arctic Passage and Brexit quantify the effects of both network spillovers and scale economies. We find that spillovers from the transportation network doubles baseline welfare gains, with scale economies further tripling them. |
| Keywords: | trade costs, scale, hubs, transport costs, transportation networks, international trade, shipping |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_8199&r=all |
| By: | Luigi Bellomarini; Marco Benedetti; Andrea Gentili; Rosario Laurendi; Davide Magnanimi; Antonio Muci; Emanuel Sallinger |
| Abstract: | In the COVID-19 outbreak, governments have applied progressive restrictions to production activities, permitting only those that are considered strategic or that provide essential services. This is particularly apparent in countries that have been stricken hard by the virus, with Italy being a major example. Yet we know that companies are not just isolated entities: They organize themselves into intricate shareholding structures --- forming company networks --- distributing decision power and dividends in sophisticated schemes for various purposes. One tool from the Artificial Intelligence (AI) toolbox that is particularly effective to perform reasoning tasks on domains characterized by many entities highly interconnected with one another is Knowledge Graphs (KG). In this work, we present a visionary opinion and report on ongoing work about the application of Automated Reasoning and Knowledge Graph technology to address the impact of the COVID-19 outbreak on the network of Italian companies and support the application of legal instruments for the protection of strategic companies from takeovers. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.10119&r=all |
| By: | Peng Yue (ECUST); Qing Cai (Zhicang Tech); Wanfeng Yan (Zhicang Tech); Wei-Xing Zhou (ECUST) |
| Abstract: | Transfer entropy measures the strength and direction of information flow between different time series. We study the information flow networks of the Chinese stock market and identify important sectors and information flow paths. This paper uses the daily closing price data of the 28 level-1 sectors from Shenyin \& Wanguo Securities ranging from 2000 to 2017 to study the information transmission between different sectors. We construct information flow networks with the sectors as the nodes and the transfer entropy between them as the corresponding edges. Then we adopt the maximum spanning arborescence (MSA) to extracting important information flows and the hierarchical structure of the networks. We find that, during the whole sample period, the \textit{composite} sector is an information source of the whole stock market, while the \textit{non-bank financial} sector is the information sink. We also find that the \textit{non-bank finance}, \textit{bank}, \textit{computer}, \textit{media}, \textit{real estate}, \textit{medical biology} and \textit{non-ferrous metals} sectors appear as high-degree root nodes in the outgoing and incoming information flow MSAs. Especially, the \textit{non-bank finance} and \textit{bank} sectors have significantly high degrees after 2008 in the outgoing information flow networks. We uncover how stock market turmoils affect the structure of the MSAs. Finally, we reveal the specificity of information source and sink sectors and make a conclusion that the root node sector as the information sink of the incoming information flow networks. Overall, our analyses show that the structure of information flow networks changes with time and the market exhibits a sector rotation phenomenon. Our work has important implications for market participants and policy makers in managing market risks and controlling the contagion of risks. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.08759&r=all |
| By: | Chiara Perillo (University of Zurich, Department of Banking and Finance, Zurich, Switzerland); Stefano Battiston (University of Zurich, Department of Banking and Finance, Zurich, Switzerland) |
| Abstract: | The long-lasting socio-economic impact of the global financial crisis has questioned the adequacy of traditional tools in explaining periods of financial distress, as well as the adequacy of the existing policy response. In particular, the effect of complex interconnections among financial institutions on financial stability has been widely recognized. A recent debate focused on the effects of unconventional policies aimed at achieving both price and financial stability. In particular, Quantitative Easing (QE, i.e., the large-scale asset purchase programme conducted by a central bank upon the creation of new money) has been recently implemented by the European Central Bank (ECB). In this context, two questions deserve more attention in the literature. First, to what extent, by injecting liquidity, the QE may alter the bank-firm lending level and stimulate the real economy. Second, to what extent the QE may also alter the pattern of intra-financial exposures among financial actors (including banks, investment funds, insurance corporations, and pension funds) and what are the implications in terms of financial stability. Here, we address these two questions by developing a methodology to map the macro-network of financial exposures among institutional sectors across financial instruments (e.g., equity, bonds, and loans) and we illustrate our approach on recently available data (i.e., data on loans and private and public securities purchased within the QE). We then test the effect of the implementation of ECB's QE on the time evolution of the financial linkages in the macro-network of the euro area, as well as the effect on macroeconomic variables, such as output and prices. |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2004.09418&r=all |
| By: | Theresa Kuchler; Dominic Russel; Johannes Stroebel |
| Abstract: | We use anonymized and aggregated data from Facebook to show that areas with stronger social ties to two early COVID-19 "hotspots" (Westchester County, NY, in the U.S. and Lodi province in Italy) generally have more confirmed COVID-19 cases as of March 30, 2020. These relationships hold after controlling for geographic distance to the hotspots as well as for the income and population density of the regions. These results suggest that data from online social networks may prove useful to epidemiologists and others hoping to forecast the spread of communicable diseases such as COVID-19. |
| JEL: | I0 R0 |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:26990&r=all |
| By: | Jonas Striaukas; Martin Schumacher; Harald Binder; Matthias Weber |
| Abstract: | Often, variables are linked to each other via a network. When such a network structure is known, this knowledge can be incorporated into regularized regression settings via a network penalty term. However, when the type of interaction via the network is unknown (that is, whether connections are of an activating or a repressing type), the connection signs have to be estimated simultaneously with the covariate coefficients. This can be done with an algorithm iterating a connection sign estimation step and a covariate coefficient estimation step. We develop such an algorithm and show detailed simulation results and an application forecasting event times. The algorithm performs well in a variety of settings. We also briefly describe the R-package that we developed for this purpose, which is publicly available. |
| Keywords: | Network regression, network penalty, connection sign estimation, regularized regression |
| JEL: | C13 C52 C53 C55 |
| Date: | 2020–01 |
| URL: | http://d.repec.org/n?u=RePEc:usg:sfwpfi:2020:01&r=all |
| By: | Harald Fadinger; Christian Ghiglino; Mariya Teteryatnikova |
| Abstract: | We study the importance of input-output (IO) linkages and sectoral productivity (TFP) levels in determining cross-country income differences. Using data on IO tables and sectoral TFP levels for 38 countries, we uncover important differences in the interaction of IO structure with sectoral TFP levels across countries: while highly connected sectors are more productive than the typical sector in poor countries, the opposite is true in rich ones. To assess the quantitative role of linkages and sectoral TFP differences in cross-country income differences, we decompose cross-country variation in real GDP per worker using a multi-sector general equilibrium model. We find that these features explain between 8 and 10 percent of cross-country income variation. |
| Keywords: | input-output structure, productivity, cross-country income differences, development accounting |
| JEL: | O11 O14 O47 C67 D85 |
| Date: | 2020–04 |
| URL: | http://d.repec.org/n?u=RePEc:bon:boncrc:crctr224_2020_168&r=all |
| By: | Jiang, Jiaming; Goel, Rajeev K.; Zhang, Xingyuan |
| Abstract: | Whereas technical standards and Standard Setting Organizations (SSOs) are omnipresent and essential to mass production and mass communications, relatively little is formally known about the propensity of firms to belong to certain SSOs. This paper uses a social network analysis technique to empirically analyze the behavior of market participants and their propensities to belong to SSOs. We concentrate our study on standard setting organizations features and their intellectual property rights (IPR) policies such as licensing rules, disclosure requirements, as well as the features of the decision process of standards. Using data on more than 1060 member firms as participants in 28 SSOs, we are able to uniquely graph the membership of firms in SSOs by highlighting some important characteristics. Finally, a multinomial logit regression analysis studies the propensities of firms to belong to four SSOs and member firms' network communities. |
| Keywords: | standard setting organizations,network analysis,intellectual property rights policies,patents,market concentration |
| JEL: | L14 O3 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:ifwkwp:2153&r=all |