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on Network Economics |
| By: | Michel Alexandre; Thiago Christiano Silva; Francisco Aparecido Rodrigues |
| Abstract: | In this study, we propose a method for the identification of influential edges in financial networks. In our approach, the critical edges are those whose removal would cause a large impact on the systemic risk of the financial network. We apply this framework to a thorough Brazilian data set to identify critical bank-firm edges. In our data set, banks and firms are connected through two financial networks: the interbank network and the bank-firm loan network. We found at least 18% of the edges are critical, in the sense they have a significant impact on the systemic risk of the network. We then employed machine learning (ML) techniques to predict the critical status and – for a large level of the initial shock – the sign of the impact of bank-firm edges on the systemic risk. The level of accuracy obtained in these prediction exercises was very high (above 90%). Posterior analysis through Shapley values shows: i) the PageRank of the edge’s destination node (the firm) is the main driver of the critical status of the edges; and ii) the sign of the edges’ impact depends on the degree of the edge’s origin node (the bank). |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:bcb:wpaper:594 |
| By: | Thiago Christiano Silva; Paulo Victor Berri Wilhelm; Diego Raphael Amancio |
| Abstract: | This study examines the effects of deglobalization trends on international trade networks and their role in improving forecasts for economic growth. Using section-level trade data from more than 200 countries from 2010 to 2022, we identify significant shifts in the network topology driven by rising trade policy uncertainty. Our analysis highlights key global players through centrality rankings, with the United States, China, and Germany maintaining consistent dominance. Using a horse race of supervised regressors, we find that network topology descriptors evaluated from section-specific trade networks substantially enhance the quality of a country's economic growth forecast. We also find that non-linear models, such as Random Forest, eXtreme Gradient Boosting, and Light Gradient Boosting Machine, outperform traditional linear models used in the economics literature. Using SHapley Additive exPlanations values to interpret these non-linear model's predictions, we find that about half of the most important features originate from the network descriptors, underscoring their vital role in refining forecasts. Moreover, this study emphasizes the significance of recent economic performance, population growth, and the primary sector's influence in shaping economic growth predictions, offering novel insights into the intricacies of economic growth forecasting. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:bcb:wpaper:597 |
| By: | Thiago Christiano Silva; Carlos Eduardo de Almeida |
| Abstract: | The COVID-19 pandemic has profoundly disrupted global supply chains, necessitating a reconfiguration of traditional networks. This study investigates the impact of the pandemic on Brazil's supply chain by using a massive firm-to-firm payment dataset composed of identified fast payments, invoices, and wire transfers. Our analysis gauges the heterogeneous impacts across industries and reveals a marked shift towards a more diversified supply chain network following the COVID-19 outbreak. As firms redirected their connections away from heavily impacted urban centers toward inland cities, a more intricate and geographically dispersed network emerged, characterized by less negative assortativity, increased density, and reduced inequality among municipalities. The diversification allowed firms to mitigate the pandemic's effects, underscoring the adaptability and potential soundness of a more decentralized supply chain structure. The findings provide insights for public policymaking and can guide targeted industrial policy design and financial risk mitigation strategies in the face of future disruptions. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:bcb:wpaper:595 |
| By: | L. Sanna Stephan |
| Abstract: | Dyadic network formation models have wide applicability in economic research, yet are difficult to estimate in the presence of individual specific effects and in the absence of distributional assumptions regarding the model noise component. The availability of (continuously distributed) individual or link characteristics generally facilitates estimation. Yet, while data on social networks has recently become more abundant, the characteristics of the entities involved in the link may not be measured. Adapting the procedure of \citet{KS}, I propose to use network data alone in a semiparametric estimation of the individual fixed effect coefficients, which carry the interpretation of the individual relative popularity. This entails the possibility to anticipate how a new-coming individual will connect in a pre-existing group. The estimator, needed for its fast convergence, fails to implement the monotonicity assumption regarding the model noise component, thereby potentially reversing the order if the fixed effect coefficients. This and other numerical issues can be conveniently tackled by my novel, data-driven way of normalising the fixed effects, which proves to outperform a conventional standardisation in many cases. I demonstrate that the normalised coefficients converge both at the same rate and to the same limiting distribution as if the true error distribution was known. The cost of semiparametric estimation is thus purely computational, while the potential benefits are large whenever the errors have a strongly convex or strongly concave distribution. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.04552 |
| By: | Ixandra Achitouv |
| Abstract: | Financial stock returns correlations have been studied in the prism of random matrix theory, to distinguish the signal from the "noise". Eigenvalues of the matrix that are above the rescaled Marchenko Pastur distribution can be interpreted as collective modes behavior while the modes under are usually considered as noise. In this analysis we use complex network analysis to simulate the "noise" and the "market" component of the return correlations, by introducing some meaningful correlations in simulated geometric Brownian motion for the stocks. We find that the returns correlation matrix is dominated by stocks with high eigenvector centrality and clustering found in the network. We then use simulated "market" random walks to build an optimal portfolio and find that the overall return performs better than using the historical mean-variance data, up to 50% on short time scale. |
| Date: | 2024–07 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2407.20380 |
| By: | Neugart, Michael; Zaharieva, Anna |
| Abstract: | Empirical studies show that women have lower chances of reaching top management positions, known as the glass‐ceiling effect. To study women's careers, we develop a search and matching model where job ladders consist of three hierarchical levels and workers can progress in the career by means of internal promotions or by transitioning to another firm. Both, formal applications and referral hiring via endogenous social networks can be used for moving between firms. We show that when female workers are minority in the labor market and social link formation is gender‐biased (homophilous), there are too few female contacts in the social networks of their male colleagues. This disadvantage implies that female workers are referred less often and, thereby, become underrepresented in top‐level management positions of firms relative to their fraction in the market. Our main theoretical results are consistent with the empirical evidence based on the German Socio‐Economic Panel. |
| Date: | 2024–08–19 |
| URL: | https://d.repec.org/n?u=RePEc:dar:wpaper:149297 |
| By: | Rose, C.;; Williams J.;; Bretteville-Jensen, , A.L.; |
| Abstract: | We study peer effects in recovery from substance use disorders. We focus on peers who share an inpatient treatment episode and who reside in the same county, reflecting the salience of geographic proximity for peer influence in risky behaviors, and examine peer effects on posttreatment mortality. We access linked administrative data on death for the universe of individuals who are admitted to inpatient treatment for a substance use disorder in Norway in 2009-2010. The impact of peers is identified using variation in the timing of admissions into treatment, which institutional factors ensure is conditionally exogenous. Patients exposed to a greater share of peers from their home-county have a lower mortality risk. A standard deviation increase in the share of home -county peers reduces mortality by 36% relative to the mean, with one additional peer leading to a 5% reduction. The peer-induced reduction in mortality is concentrated amongst individuals admitted for treatment for a drug use disorder (as opposed to an alcohol use disorder). This is driven by peers who themselves receive treatment for a drug use disorder, and is consistent with peer effects working through two potential channels; reduced illicit drug use and safer illicit drug use. Examining hospital episodes for intoxication and (nonfatal) overdose indicates a limited role for safer drug use, suggesting that peers primarily reduce mortality by reducing drug use. We conclude that peers from inpatient treatment episodes can be instrumental in supporting recovery outside of clinical settings. |
| Keywords: | peer effects; substance use treatment; mortality; |
| JEL: | I12 D85 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:yor:hectdg:24/18 |
| By: | Matthew Greenwood-Nimmo; Evzen Kocenda; Viet Hoang Nguyen |
| Abstract: | Connectedness quantifies the extent of interlinkages within economies or markets based on a network approach. Connectedness is measured by the Diebold-Yilmaz spillover index, and abrupt increases in this measure are thought to result from major events. However, formal statistical evidence of events causing such increases is scant. We develop a bootstrap-based technique to evaluate the probability that the value of the spillover index changes at a statistically significant level following an exogenously deï¬ ned event. We further show how our procedure can detect the dates of unknown events endogenously. The results of a simulation exercise support the effectiveness of our method. We revisit the original dataset from Diebold and Yilmaz’s seminal work and obtain statistical support that the spillover index increases quickly in the wake of adverse shocks. Our methodology accounts for small sample bias and is robust with respect to modifications of the pre-event period and forecast horizon. |
| Keywords: | connectedness, spillover index, adverse shocks, impactful events, financial contagion, bootstrap-after-bootstrap procedure |
| JEL: | C32 C58 G15 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2024-51 |