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on Network Economics |
| By: | Kavitha Nambiar (Ph.D Student, Madras School of Economics); Ekta Selarka ((Corresponding author), Madras School of Economics, Gandhi Mandapam Road, Behind Government Data Centre, Kotturpuram, Chennai, 600025) |
| Abstract: | This study examines the relationship between network centrality of women directors in Indian boards on their corporate social responsibility (CSR). While existing research primarily focuses on board gender diversity, we argue that the ability of women directors to affect firm decisions also depends on how well connected these directors are. Using the enforcement of mandatory CSR as a natural experiment on a sample of non-financial firms listed on the National Stock Exchange (NSE) in India during 2016-2023 we find that firms with higher women directors centrality exhibit higher CSR spending, stronger compliance with the CSR mandate and a greater likelihood of spending above their industry peers. The effects were stronger among firms that engage in CSR consistently and was also robust across alternative measures of network centrality and alternative specifications to address the endogeneity. Our findings contributed to the literature on gender diversity and CSR, by indicating that the network centrality constitute an important mechanism through which women directors influence CSR outcomes. |
| Keywords: | Women directors, Board networks, Corporate social responsibility, Board diversity |
| JEL: | G34 M14 D85 J16 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:mad:wpaper:2026-296 |
| By: | Feinstein, Zachary; Sojmark, Andreas |
| Abstract: | We introduce a dynamic and stochastic interbank model with an endogenous notion of distress contagion, arising from rational worries about future defaults and ensuing losses. This entails a mark-to-market valuation adjustment for interbank claims, leading to a forward-backward approach to the equilibrium dynamics whereby future default probabilities are needed to determine today's balance sheets. Distinct from earlier models, the resulting distress contagion acts, endogenously, as a stochastic volatility term that exhibits clustering and down-market spikes. Furthermore, by incorporating multiple maturities, we provide a novel framework for constructing systemic interbank term structures, reflecting the intertemporal risk of contagion. We present the analysis in two parts: first, the simpler single maturity setting that extends the classical interbank network literature and, then, the multiple maturity setting for which we can examine how systemic risk materializes in the shape of the resulting term structures. |
| Keywords: | systemic risk; distress contagion; dynamic network model; multiple maturities; valuation adjustment; volatility effects; term structure; yield curves |
| JEL: | C1 |
| Date: | 2026–02–23 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:137337 |
| By: | Xu, Lei (Loughborough University); Zhu, Yu (University of Dundee) |
| Abstract: | We apply a novel approach to estimate the effects of exposure to peers with different attributes by using the predetermined leave-own-out attributes of all classmates in randomly assigned classes. This strategy allows a behavioural interpretation of the peer effect over and above the pure mechanical channel. We find that being exposed to peer groups with attributes conducive to academic achievements, induced by random variations in the shares of classmates with college-educated parents, increases exam scores. We show that estimates based on the commonly used leave-own-out measures are highly sensitive to sample selection bias arising from non-random tracking in the sample. We show that estimates based on the commonly used leave-one-out measures are highly sensitive to non-random tracking in the sample. |
| Keywords: | parental education, random assignment, China |
| JEL: | I20 I24 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18416 |
| By: | Bing Han; Haoyang Liu; Pengfei Sui |
| Abstract: | Using new data on social interactions and individual trading records in the Bitcoin market, we show that investor sentiment spreads across social connections. Investors systematically revise their beliefs about Bitcoin prices in the direction of average peer sentiment—even though that sentiment does not predict future prices. We document specific patterns in the diffusion of beliefs across networks, including evidence consistent with confirmation bias. Moreover, this social-sentiment contagion influences both individual trading decisions and overall market dynamics. Our novel measure of contagion intensity significantly forecasts Bitcoin volatility, trading volume and market crashes. |
| Keywords: | social interactions; belief updating; sentiment contagion; bitcoin; bubbles |
| JEL: | G11 G12 G41 G53 |
| Date: | 2026–03–02 |
| URL: | https://d.repec.org/n?u=RePEc:fip:feddwp:102864 |
| By: | Pablo Aguilar (BANCO DE ESPAÑA AND ECB); Rubén Domínguez-Díaz (BANCO DE ESPAÑA); José-Elías Gallegos (BANCO DE ESPAÑA); Javier Quintana (BANCO DE ESPAÑA) |
| Abstract: | We analyze how production networks transmit foreign price shocks and reshape monetary policy trade-offs in an open-economy New Keynesian model with domestic and international input–output linkages. Analytically, we show that closing the output gap does not generally stabilize domestic inflation, as sector-level terms-of-trade movements and trade imbalances become additional drivers of inflation dynamics. Quantitatively, we study an international energy price shock in a model calibrated to major euro area countries and their trade partners. We find that production networks significantly amplify the cumulative headline inflation response and substantially worsen monetary policy trade-offs, as measured by the sacrifice ratio. |
| Keywords: | open economy, production networks, New Keynesian, monetary policy |
| JEL: | E31 E32 E52 E70 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:bde:wpaper:2607 |
| By: | Vipin P. Veetil |
| Abstract: | This paper develops a simple model of the world supply chain to estimate the effects of sanctions that restrict the flow of inputs from one country to another. Such restrictions operate through changes in the weights of the global production network: the sanctioning country ceases supplying certain inputs to the target country and reallocates its production to other destinations. Using the OECD Inter-Country Input--Output tables, we calibrate the model to assess the vulnerability of the Indian economy. We consider two classes of counterfactuals: restrictions on a single sector of a foreign country supplying India, and restrictions on all sectors of a foreign country supplying India. We then rank foreign countries and foreign country-sectors by the risk that their supply restrictions pose to economic activity in India. Our results show that India's greatest country-level vulnerability is to Saudi Arabia, followed by the United Arab Emirates, China, Singapore, the United States, and Russia. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.12128 |
| By: | C\'eline Pagnier; Tord Gunnar Holen; Thomas Haugen de Lange; Patrick Levin; Steffen J. S. Bakker; Peter Sch\"utz |
| Abstract: | Decarbonizing long-haul freight requires large-scale deployment of high-power charging infrastructure. This paper studies a multi-period charging station location problem that determines where and when to deploy charging capacity for battery-electric heavy-duty vehicles under uncertain future demand and local grid capacity availability. The problem is formulated as a two-stage stochastic mixed-integer program that maximizes covered electric freight flow. Feasible truck routes are generated a priori using a resource-constrained label-setting algorithm that enforces range limitations and driving-break regulations. To solve large-scale instances, an integer L-shaped decomposition method embedded in a branch-and-cut framework and accelerated by a deterministic warm start is implemented. Computational experiments are conducted on a nationwide Norwegian case study based on real candidate locations provided by a charging station operator. The approach solves instances intractable for a monolithic formulation and achieves near-optimal solutions within practical runtimes. For larger networks, the value of the stochastic solution is substantial, highlighting the importance of explicitly modeling uncertainty in long-term infrastructure planning. Optimal investments prioritize major freight corridors in early periods and subsequently reinforce and expand the network. Grid capacity constraints discourage large, concentrated stations and shift deployments toward more distributed layouts. Covered demand increases rapidly at low budget levels but exhibits diminishing returns as the network approaches saturation. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.01782 |
| By: | Jing Liu; Maria Grith; Xiaowen Dong; Mihai Cucuringu |
| Abstract: | This paper studies cross-market return predictability through a machine learning framework that preserves economic structure. Exploiting the non-overlapping trading hours of the U.S. and Chinese equity markets, we construct a directed bipartite graph that captures time-ordered predictive linkages between stocks across markets. Edges are selected via rolling-window hypothesis testing, and the resulting graph serves as a sparse, economically interpretable feature-selection layer for downstream machine learning models. We apply a range of regularized and ensemble methods to forecast open-to-close returns using lagged foreign-market information. Our results reveal a pronounced directional asymmetry: U.S. previous-close-to-close returns contain substantial predictive information for Chinese intraday returns, whereas the reverse effect is limited. This informational asymmetry translates into economically meaningful performance differences and highlights how structured machine learning frameworks can uncover cross-market dependencies while maintaining interpretability. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.10559 |