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on Network Economics |
| By: | Lorenzo Ductor (Department of Economic Theory and Economic History, University of Granada.); Sanjeev Goyal (Christ's College and Faculty of Economics, Cambridge.); Anja Prummer (Johannes Kepler University Linz, Queen Mary University London.) |
| Abstract: | We connect gender disparities in research output and collaboration patterns in economics. We first document large gender gaps in research output. These gaps persist across 50 years despite a significant increase in the fraction of women in economics during that time. We further show that output differences are closely related to differences in the co-authorship networks of men and women; women have fewer collaborators, collaborate more often with the same co-authors, and a higher fraction of their co-authors collaborate with each other. Taking into account co-authorship networks reduces the gender output gap by 18%. |
| Keywords: | Gender Inequality, Co-authorship, Networks, Homophily. |
| JEL: | D8 D85 J7 J16 O30 |
| Date: | 2023–04–13 |
| URL: | http://d.repec.org/n?u=RePEc:gra:wpaper:23/01&r=net |
| By: | Anwesha Sengupta; Shashankaditya Upadhyay; Indranil Mukherjee; Prasanta K. Panigrahi |
| Abstract: | Market competition has a role which is directly or indirectly associated with influential effects of individual sectors on other sectors of the economy. The present work studies the relative position of a product in the market through the identification of influential spreaders and its corresponding effect on the other sectors of the market using complex network analysis during the pre-, in-, and post-crisis induced lockdown periods using daily data of NSE from December, 2019 to June, 2021. The existing approaches using different centrality measures failed to distinguish between the positive and negative influences of the different sectors in the market which act as spreaders. To obviate this problem, this paper presents an effective measure called LIEST (Local Influential Effects for Specific Target) that can examine the positive and negative influences separately with respect to any crisis period. LIEST considers the combined impact of all possible nodes which are at most three steps away from the specific targets for the networks. The essence of non-linearity in the network dynamics without considering single node effect becomes visible particularly in the proposed network. |
| Date: | 2022–10 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.05432&r=net |
| By: | Feinstein, Zachary; Hałaj, Grzegorz |
| Abstract: | Interconnectedness is an inherent feature of the modern financial system. While it con-tributes to efficiency of financial services, it also creates structural vulnerabilities: pernicious shock transmission and amplification impacting banks’ capitalization. This has recently been seen during the Global Financial Crisis. Post-crisis reforms addressed many of the causes of this event, but contagion effects may not be fully eliminated. One reason for this may be related to financial institutions’ incentives and strategic behaviours. We propose a model to study contagion effects in a banking system capturing network effects of direct exposures and indirect effects of market behaviour that may impact asset valuation. By doing so, we can embed a well-established fire-sale channel into our model. Unlike in related literature, we relax the assumption that there is an exogenous pecking order of how banks would sell their assets. Instead, banks act rationally in our model; they optimally construct a portfolio subject to budget constraints so as to raise cash to satisfy creditors (interbank and external). We assume that the guiding principle for banks is to maximize risk-adjusted returns gener-ated by their balance sheets. We parameterize the theoretical model with publicly available data for a representative sample of European banks; this allows us to run simulations of bank valuations and asset prices under a set of stress scenarios. JEL Classification: C62, C63, G11, G21 |
| Keywords: | fire sales, interbank contagion, optimal portfolio, systemic risk |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20232806&r=net |
| By: | Rasoul Amirzadeh; Asef Nazari; Dhananjay Thiruvady; Mong Shan Ee |
| Abstract: | The growth of market capitalisation and the number of altcoins (cryptocurrencies other than Bitcoin) provide investment opportunities and complicate the prediction of their price movements. A significant challenge in this volatile and relatively immature market is the problem of predicting cryptocurrency prices which needs to identify the factors influencing these prices. The focus of this study is to investigate the factors influencing altcoin prices, and these factors have been investigated from a causal analysis perspective using Bayesian networks. In particular, studying the nature of interactions between five leading altcoins, traditional financial assets including gold, oil, and S\&P 500, and social media is the research question. To provide an answer to the question, we create causal networks which are built from the historic price data of five traditional financial assets, social media data, and price data of altcoins. The ensuing networks are used for causal reasoning and diagnosis, and the results indicate that social media (in particular Twitter data in this study) is the most significant influencing factor of the prices of altcoins. Furthermore, it is not possible to generalise the coins' reactions against the changes in the factors. Consequently, the coins need to be studied separately for a particular price movement investigation. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.16148&r=net |
| By: | Rui Yao; Shlomo Bekhor |
| Abstract: | This paper proposes a general equilibrium model for multi-passenger ridesharing systems, in which interactions between ridesharing drivers, passengers, platforms, and transportation networks are endogenously captured. Stable matching is modeled as an equilibrium problem in which no ridesharing driver or passenger can reduce ridesharing disutility by unilaterally switching to another matching sequence. This paper is one of the first studies that explicitly integrates the ridesharing platform multi-passenger matching problem into the model. By integrating matching sequence with hyper-network, ridesharing-passenger transfers are avoided in a multi-passenger ridesharing system. Moreover, the matching stability between the ridesharing drivers and passengers is extended to address the multi-OD multi-passenger case in terms of matching sequence. The paper provides a proof for the existence of the proposed general equilibrium. A sequence-bush algorithm is developed for solving the multi-passenger ridesharing equilibrium problem. This algorithm is capable to handle complex ridesharing constraints implicitly. Results illustrate that the proposed sequence-bush algorithm outperforms general-purpose solver, and provides insights into the equilibrium of the joint stable matching and route choice problem. Numerical experiments indicate that ridesharing trips are typically longer than average trip lengths. Sensitivity analysis suggests that a properly designed ridesharing unit price is necessary to achieve network benefits, and travelers with relatively lower values of time are more likely to participate in ridesharing. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.16595&r=net |
| By: | Alberto Baccini; Cristina Re |
| Abstract: | Members of editorial boards play the role of gatekeepers of science because. This paper analyses the national distribution of editorial boards members of economics journal, their affiliation, and their gender. It studies also the interlocking editorship network generated by the presence of a same person on the editorial board of more than one journal. The analysis is based on a unique database comprising all the 1, 516 journals indexed in the database EconLit with an active editorial board in 2019. For each journal, we manually collected the names of the board members along with their affiliation, obtaining a database containing more than 44, 000 members from more than 6, 000 institutions and 142 countries. These data allow to investigate the phenomenon of gatekeeping in contemporary economics on an unprecedented large scale. The obtained results highlight some common issues concerning the editorial gatekeeping, leading to the conclusion that in Economics the academic publishing environment is governed by an \'elite composed mainly of men affiliated with United States \'elite universities. Homophily in terms of geographic, institutional and gender distribution is higher in the most prestigious journal and among Editors-in-Chief. Finally, it appears that `strategic decisions' in the selection of board members reproduce this homophily. |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2304.04242&r=net |
| By: | Zhizhong Tan; Min Hu; Yixuan Wang; Lu Wei; Bin Liu |
| Abstract: | It is a challenging problem to predict trends of futures prices with traditional econometric models as one needs to consider not only futures' historical data but also correlations among different futures. Spatial-temporal graph neural networks (STGNNs) have great advantages in dealing with such kind of spatial-temporal data. However, we cannot directly apply STGNNs to high-frequency future data because future investors have to consider both the long-term and short-term characteristics when doing decision-making. To capture both the long-term and short-term features, we exploit more label information by designing four heterogeneous tasks: price regression, price moving average regression, price gap regression (within a short interval), and change-point detection, which involve both long-term and short-term scenes. To make full use of these labels, we train our model in a continual manner. Traditional continual GNNs define the gradient of prices as the parameter important to overcome catastrophic forgetting (CF). Unfortunately, the losses of the four heterogeneous tasks lie in different spaces. Hence it is improper to calculate the parameter importance with their losses. We propose to calculate parameter importance with mutual information between original observations and the extracted features. The empirical results based on 49 commodity futures demonstrate that our model has higher prediction performance on capturing long-term or short-term dynamic change. |
| Date: | 2023–03 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2303.16532&r=net |
| By: | Larosa, Francesca; Mysiak, Jaroslav; Molinari, Marco; Varelas, Panagiotis; Akay, Haluk; McDowall, Will; Spadaru, Catalina; Fuso-Nerini, Francesco; Vinuesa, Ricardo |
| Abstract: | Innovation is a key component to equip our society with tools to adapt to new climatic conditions. The development of research-action interfaces shifts useful ideas into operationalized knowledge allowing innovation to flourish. In this paper we quantify the existing gap between climate research and innovation action in Europe using a novel framework that combines artificial intelligence (AI) methods and network science. We compute the distance between key topics of research interest from peer review publications and core issues tackled by innovation projects funded by the most recent European framework programmes. Our findings reveal significant differences exist between and within the two layers. Economic incentives, agricultural and industrial processes are differently connected to adaptation and mitigation priorities. We also find a loose research-action connection in bioproducts, biotechnologies and risk assessment practices, where applications are still too few compared to the research insights. Our analysis supports policy-makers to measure and track how research funding result in innovation action, and to adjust decisions if stated priorities are not achieved. |
| Keywords: | climate innovation; natural language processing; knwoledge production |
| JEL: | H54 O32 O33 O38 |
| Date: | 2023 |
| URL: | http://d.repec.org/n?u=RePEc:pra:mprapa:116771&r=net |
| By: | Nicolás Ajzenman (McGill University); Bruno Ferman (São Paulo School of Economics - FGV); Pedro C. Sant’Anna (São Paulo School of Economics - FGV) |
| Abstract: | We study the interplay between political and other social identities in the formation of social ties in a setting of intense affective polarization. We created fictional accounts on Twitter that signaled their political preference for one of the two leading candidates in the Brazilian 2022 Presidential election, their preference for a Brazilian football club, or both. We interpret preference for a football club as an affective dimension of identity. The bots randomly followed Twitter accounts with congruent and incongruent identities across these two dimensions, and we computed the proportion of follow-backs and blocks they received. Both dimensions of identity are relevant in forming ties, but the effect of sharing a political identity is significantly greater. Moreover, affective identity becomes substantially less relevant when information about political identity is available, indicating that political identity can overshadow other dimensions of identity. Still, shared affective identity has a positive effect in fostering ties even among politically opposite individuals. This result suggests that shared identities such as preference for a football club have the potential to reduce politically induced societal divides, despite the evidence that affective polarization may diminish this effect. |
| Keywords: | Social Identity; Affective Polarization; Brazilian Elections; Social Media. |
| JEL: | D72 D91 C93 Z20 |
| Date: | 2023–04 |
| URL: | http://d.repec.org/n?u=RePEc:aoz:wpaper:231&r=net |