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on Network Economics |
| By: | Yulia Evsyukova; Felix Rusche; Wladislaw Mill |
| Abstract: | We assess the impact of discrimination on Black individuals’ job networks across the U.S. using a two-stage field experiment with 400+ fictitious LinkedIn profiles. In the first stage, we vary race via AI-generated images only and find that Black profiles’ connection requests are 13 percent less likely to be accepted. Based on users’ CVs, we find widespread discrimination across social groups. In the second stage, we exogenously endow Black and White profiles with the same networks and ask connected users for career advice. We find no evidence of direct discrimination in information provision. However, when taking into account differences in the composition and size of networks, Black profiles receive substantially fewer replies. Our findings suggest that gatekeeping is a key driver of Black-White disparities. |
| Keywords: | discrimination, job networks, labor markets, field experiment |
| JEL: | J71 J15 C93 J46 D85 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11433 |
| By: | INOUE Hiroyasu; TODO Yasuyuki |
| Abstract: | We constructed an establishment-level production network where each establishment inputs and outputs multiple products, using data that includes the firm-level production network and all establishments within those firms. The network represents the manufacturing sector with 183, 951 establishments across 157, 537 firms and 919, 982 inter-establishment linkages. A probabilistic model of supply chain disruptions was applied to this network. The key findings are as follows: (1) The establishment-level network exhibits greater shock propagation compared to the firm-level network. (2) Incorporating actual product information leads to a larger impact on propagation compared to using industry-level information. (3) Regional shock simulations reveal that while the firm-level network shows greater shock propagation when the shock originates in Tokyo, no such difference is observed in the establishment-level network. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:eti:dpaper:24076 |
| By: | Sophia Baum; Moritz Laber; Martin Bruckner; Liuhuaying Yang; Stefan Thurner; Peter Klimek |
| Abstract: | Global food production and trade networks are highly dynamic, especially in response to shortages when countries adjust their supply strategies. In this study, we examine adjustments across 123 agri-food products from 192 countries resulting in 23616 individual scenarios of food shortage, and calibrate a multi-layer network model to understand the propagation of the shocks. We analyze shock mitigation actions, such as increasing imports, boosting production, or substituting food items. Our findings indicate that these lead to spillover effects potentially exacerbating food inequality: an Indian rice shock resulted in a 5.8 % increase in rice losses in countries with a low Human Development Index (HDI) and a 14.2 % decrease in those with a high HDI. Considering multiple interacting shocks leads to super-additive losses of up to 12 % of the total available food volume across the global food production network. This framework allows us to identify combinations of shocks that pose substantial systemic risks and reduce the resilience of the global food supply. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.03502 |
| By: | Sabrina Di Addario; Zhexin Feng; Michel Serafinelli |
| Abstract: | This paper presents direct evidence on how firms’ innovation is affected by access to knowledgeable labor through co-worker network connections. We use a unique dataset that matches patent data to administrative employer–employee records from "Third Italy"—a region with many successful industrial clusters. Establishment closures displacing inventors generate supply shocks of knowledgeable labor to firms that employ the inventors’ previous co-workers. We estimate event-study models where the treatment is the displacement of a "connected" inventor (i.e., a previous coworker of a current employee of the focal firm). We show that the displacement of a connected inventor significantly increases connected inventors’ hiring. Moreover, the improved access to knowledgeable workers raises firms innovative activity. We provide evidence supporting the main hypothesized channel of knowledge transfer through firm-to-firm labor mobility by estimating IV specifications where we use the displacement of a connected inventor as an instrument to hire a connected inventor. Overall, estimates indicate that firms exploit displacements to recruit connected inventors and the improved capacity to employ knowledgeable labor within the network increases innovation. |
| Keywords: | social connections, firm-to-firm labor mobility, patents, establishment closure |
| JEL: | J60 O30 J23 |
| Date: | 2024 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_11432 |
| By: | Kamil Fortuna; Janusz Szwabi\'nski |
| Abstract: | The fragility of financial systems was starkly demonstrated in early 2023 through a cascade of major bank failures in the United States, including the second, third, and fourth largest collapses in the US history. The highly interdependent financial networks and the associated high systemic risk have been deemed the cause of the crashes. The goal of this paper is to enhance existing systemic risk analysis frameworks by incorporating essential debt valuation factors. Our results demonstrate that these additional elements substantially influence the outcomes of risk assessment. Notably, by modeling the dynamic relationship between interest rates and banks' credibility, our framework can detect potential cascading failures that standard approaches might miss. The proposed risk assessment methodology can help regulatory bodies prevent future failures, while also allowing companies to more accurately predict turmoil periods and strengthen their survivability during such events. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.10386 |
| By: | Hugo Schnoering; Michalis Vazirgiannis |
| Abstract: | Bitcoin, launched in 2008 by Satoshi Nakamoto, established a new digital economy where value can be stored and transferred in a fully decentralized manner - alleviating the need for a central authority. This paper introduces a large scale dataset in the form of a transactions graph representing transactions between Bitcoin users along with a set of tasks and baselines. The graph includes 252 million nodes and 785 million edges, covering a time span of nearly 13 years of and 670 million transactions. Each node and edge is timestamped. As for supervised tasks we provide two labeled sets i. a 33, 000 nodes based on entity type and ii. nearly 100, 000 Bitcoin addresses labeled with an entity name and an entity type. This is the largest publicly available data set of bitcoin transactions designed to facilitate advanced research and exploration in this domain, overcoming the limitations of existing datasets. Various graph neural network models are trained to predict node labels, establishing a baseline for future research. In addition, several use cases are presented to demonstrate the dataset's applicability beyond Bitcoin analysis. Finally, all data and source code is made publicly available to enable reproducibility of the results. |
| Date: | 2024–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2411.10325 |