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on Network Economics |
| By: | Stark, Oded (University of Bonn); Bielawski, Jakub (University of Krakow); Falniowski, Fryderyk (University of Krakow) |
| Abstract: | We present a new index for measuring income inequality in networks. The index is based on income comparisons made by the members of a network who are linked with each other by direct social connections. To model the comparisons, we compose a measure of relative deprivation for networks. We base our new index on this measure. The index takes the form of a ratio: the network's aggregate level of relative deprivation divided by the aggregate level of the relative deprivation of a hypothetical network in which one member of the network receives all the income, and it is with this member that the other members of the network compare their incomes. We discuss the merits of this representation. We inquire how changes in the composition of a network affect the index. In addition, we show how the index accommodates specific network characteristics. |
| Keywords: | income inequality in networks, relative deprivation in networks, an index of income inequality in networks, compositional changes of networks |
| JEL: | D31 D63 I31 L14 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp16666&r=net |
| By: | Aarush Pratik Sheth; Jonah Riley Weinbaum; Kevin Javier Zvonarek |
| Abstract: | As is widely known, the stock market is a complex system in which a multitude of factors influence the performance of individual stocks and the market as a whole. One method for comprehending -- and potentially predicting -- stock market behavior is through network analysis, which can offer insights into the relationships between stocks and the overall market structure. In this paper, we seek to address the question: Can network analysis of the stock market, specifically in observation of the lead-lag effect, provide valuable insights for investors and market analysts? |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.10084&r=net |
| By: | Jalal Etesami; Ali Habibnia; Negar Kiyavash |
| Abstract: | We propose a nonparametric and time-varying directed information graph (TV-DIG) framework to estimate the evolving causal structure in time series networks, thereby addressing the limitations of traditional econometric models in capturing high-dimensional, nonlinear, and time-varying interconnections among series. This framework employs an information-theoretic measure rooted in a generalized version of Granger-causality, which is applicable to both linear and nonlinear dynamics. Our framework offers advancements in measuring systemic risk and establishes meaningful connections with established econometric models, including vector autoregression and switching models. We evaluate the efficacy of our proposed model through simulation experiments and empirical analysis, reporting promising results in recovering simulated time-varying networks with nonlinear and multivariate structures. We apply this framework to identify and monitor the evolution of interconnectedness and systemic risk among major assets and industrial sectors within the financial network. We focus on cryptocurrencies' potential systemic risks to financial stability, including spillover effects on other sectors during crises like the COVID-19 pandemic and the Federal Reserve's 2020 emergency response. Our findings reveals significant, previously underrecognized pre-2020 influences of cryptocurrencies on certain financial sectors, highlighting their potential systemic risks and offering a systematic approach in tracking evolving cross-sector interactions within financial networks. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.16707&r=net |
| By: | Miguel Calvin; Pilar Rey del Castillo |
| Abstract: | The problem of finding the factors influencing voting behavior is of crucial interest in political science and is frequently analyzed in books and articles. But there are not so many studies whose supporting information comes from official registers. This work uses official vote records in Spain matched to other files containing the values of some determinants of voting behavior at a previously unexplored level of disaggregation. The statistical relationships among the participation, the vote for parties and some socio-economic variables are analyzed by means of Gaussian Bayesian Networks. These networks, developed by the machine learning community, are built from data including only the dependencies among the variables needed to explain the data by maximizing the likelihood of the underlying probabilistic Gaussian model. The results are simple, sparse, and non-redundant graph representations encoding the complex structure of the data. The generated structure of dependencies confirms many previously studied influences, but it can also discover unreported ones such as the proportion of foreign population on all vote variables. |
| Keywords: | Bayesian networks, Gaussian distributions, voting behaviour, elections, voter turnout, political participation |
| JEL: | C46 D31 D72 D91 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_10855&r=net |
| By: | Busch, Christian; Mudida, Robert |
| Abstract: | Research Summary In socially contested settings, it is often difficult to connect with (diverse) others, and it is unclear how entrepreneurs in these contexts may develop the social ties that previous research has shown to be valuable. We studied this subject matter in Kenya, an ethnically fractionalized society that recently experienced the decentralization of government, which required entrepreneurs to deal with both in-group and out-group ethnicities. We conducted an inductive case study of four Nairobi-based companies and captured the creative tactics that they used to transcend ethnic homophily (by defocusing from ethnicity and reframing the in-group) while also asserting ethnic homophily (by signaling tribal affiliation and leveraging others' ethnicity). We contribute to a deeper understanding of how and why entrepreneurs in socially contested settings develop social ties. Managerial Summary Entrepreneurs in socially contested settings rely on social networks to access resources and opportunities. However, it is unclear how entrepreneurs in these settings develop and use these networks. We studied this question in an ethnically fractionalized setting that recently experienced the decentralization of government: Kenya. Entrepreneurs who previously provided information technology (IT) services to the central government had to deal with both own-tribe and other-tribe contacts to receive new contracts. We studied four Nairobi-based IT firms that operated across a variety of counties and analyzed the creative tactics that entrepreneurs in this context use to cross ethnic divides while also working with own-tribe contacts. This contributes to our collective understanding of how and why entrepreneurs in socially contested settings develop diverse social ties to access resources and opportunities. |
| Keywords: | case study; emerging economies; networks; resource acquisition; Sub-Saharan Africa |
| JEL: | J1 L81 |
| Date: | 2023–12–08 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:121150&r=net |