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on Network Economics |
| By: | Xinyue Li (Harvard University); Armando Miano (University of Naples Federico II, CSEF, and CEPR); Sophia Mo (Harvard University) |
| Abstract: | We investigate how coworkers shape job mobility decisions by influencing workersÕ perceptions of their outside options. Using novel survey data from a representative sample of U.S. wage and salaried workers, we identify two distinct channels through which current and former coworkers affect mobility. First, having more current coworkers with prior experience in an industry enhances both the accuracy of workersÕ wage beliefs and their perceived probability of receiving a job offer from that industry. Second, having more past coworkers currently employed in a sector raises the perceived likelihood of receiving an offer from that sector. At the firm level, personal connections increase the perceived probability of receiving an offer from that specific firm, as shown in a survey experiment eliciting subjective job-offer probabilities. We incorporate these findings into a job choice model featuring coworker-based learning and referral effects. Relative to standard models that assume perfect information about wages and job opportunities, our framework demonstrates that coworker networks facilitate labor reallocation and mitigate the welfare losses associated with information frictions. |
| Keywords: | Job Mobility, Job Search, Coworker Networks, Industries, Survey, Subjective Expectations. |
| JEL: | J01 J62 D91 D83 E71 |
| Date: | 2025–12–11 |
| URL: | https://d.repec.org/n?u=RePEc:sef:csefwp:768 |
| By: | Yuichi IKEDA; Hideaki AOYAMA; Tetsuo HATSUDA; Tomoyuki SHIRAI; Taro HASUI; Yoshimasa HIDAKA; Krongtum SANKAEWTONG; Hiroshi IYETOMI; Yuta YARAI; Abhijit CHAKRABORTY; Yasushi NAKAYAMA; Akihiro FUJIHARA; Pierluigi CESANA; Wataru SOUMA |
| Abstract: | This study proposes an artificial intelligence framework to detect price surges in crypto assets by leveraging network features extracted from transaction data. Motivated by the challenges in Anti-Money Laundering, Countering the Financing of Terrorism, and Counter-Proliferation Financing, we focus on structural features within crypto asset networks that may precede extreme market events. Building on theories from complex network analysis and rate-induced tipping, we characterize early warning signals. Granger causality is applied for feature selection, identifying network dynamics that causally precede price movements. To quantify surge likelihood, we employ a Boltzmann machine as a generative model to derive nonlinear indicators that are sensitive to critical shifts in transactional topology. Furthermore, we develop a method to trace back and identify individual nodes that contribute significantly to price surges. The findings have practical implications for investors, risk management officers, regulatory supervision by financial authorities, and the evaluation of systemic risk. This framework presents a novel approach to integrating explainable AI, financial network theory, and regulatory objectives in crypto asset markets. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:eti:dpaper:25113 |
| By: | Katharina Ledebur (Supply Chain Intelligence Institute Austria); Ladislav Bartuska (Supply Chain Intelligence Institute Austria); Klaus Friesenbichler; Peter Klimek (Supply Chain Intelligence Institute Austria) |
| Abstract: | The automotive industry is undergoing a profound transformation, driven by the electrification of powertrains, the rise of software-defined vehicles, and the adoption of circular economy concepts. These trends are increasingly blurring the boundaries between the automotive sector and other industries. The pace of adaptation to electrification varies considerably between regions and firms. Unlike internal combustion engine (ICE) production, where mechanical capabilities dominated, competitiveness in electric vehicle (EV) production increasingly depends on expertise in electronics, batteries, and software. This study investigates whether and how firms' ability to leverage cross-industry diversification contributes to their competitive advantage in this evolving landscape. We develop a country-level product space covering all industries, and an industry-specific product space covering over 900 automotive components. This allows us to identify clusters of parts which are exported together, revealing shared manufacturing capabilities. Closeness centrality in the country-level product space, rather than simple proximity, is a strong predictor of where new comparative advantages are likely to emerge. First, we examine this relationship across all industrial sectors to establish general patterns of path dependency, diversification and capability formation. Then, we focus specifically on the electric vehicle (EV) transition. It is argued that new strengths in vehicles and aluminum products in the EU will generate 5 and 4.6 times more EV-specific strengths, respectively, than other EV-relevant sectors over the next decade. In contrast, these sectors are expected to generate only 1.6 and 4.5 new strengths, respectively, in already diversified China. A different pattern emerges when these country-level results are compared to the firm-level product space. Countries such as South Korea, China, the USA and Canada show the greatest potential for diversification into EV-related products. Established producers in the EU are likely to come under pressure. These findings suggest that the success of the automotive transformation will depend on the ability of regions to mobilize existing industrial capabilities, particularly in related sectors such as machinery and electronic equipment. |
| Keywords: | diversification, car industry, automotive, electric cars, supply chains, network, product space, regions, firms, transition, complexity |
| Date: | 2025–12–15 |
| URL: | https://d.repec.org/n?u=RePEc:wfo:wpaper:y:2025:i:717 |