{"@context":"http://schema.org","@type":"Dataset","@id":"https://doi.org/10.34820/FK2/06LQEJ","identifier":"https://doi.org/10.34820/FK2/06LQEJ","name":"Transaction Network Structural Shift under Crisis: Macro and Micro Perspectives","creator":[{"name":"Andry Alamsyah","affiliation":"Telkom University"},{"name":"Dian Puteri Ramadhani","affiliation":"Telkom Universitas"},{"name":"Farida Titik Kristanti","affiliation":"Telkom Universitas"},{"name":"Khairunnisa Khairunnisa","affiliation":"Telkom Universitas"}],"author":[{"name":"Andry Alamsyah","affiliation":"Telkom University"},{"name":"Dian Puteri Ramadhani","affiliation":"Telkom Universitas"},{"name":"Farida Titik Kristanti","affiliation":"Telkom Universitas"},{"name":"Khairunnisa Khairunnisa","affiliation":"Telkom Universitas"}],"datePublished":"2022-03-04","dateModified":"2022-03-04","version":"1","description":["In 2008, the Lehman Brothers’ bankruptcy, accumulated from the global financial crisis, proved a unique role of the highly interconnected financial entities. Shocks in a bank might trigger loss, induce spillovers, provoke a contagion shock spreading to other entities, trigger the whole banking system to collapse, and ultimately unsettle the worldwide economy. Therefore, evaluating financial stability through a system-wide network approach provides more adequate knowledge than evaluating a bank as an individual. In this approach, individual banks and their transaction activities are modeled into a transaction network, forming a network topology. Financial shocks are generally detected through various macro procedures, such as outstanding external debt and uncontrolled transaction deficits. This study proposes financial shock detection from a macro and micro perspective by exploring the effect of disruption on transaction network structure. We investigate the most changing triadic motif as a crisis predictor from a micro perspective due to the crisis period. The case study is the transaction network structural shift under the 2008 crisis in Indonesia, where the observations were performed from the pre-crisis to the post-crisis period. We discovered a motif with the significant changes as the underlying financial crisis predictor. This scenario provides support for the financial system’s stability control"],"keywords":["Business and Management","transaction network; network structure; network topology; network motifs; financial crisis"],"citation":[{"@type":"CreativeWork","text":"Alamsyah, A.; Ramadhani, D.P.; Kristanti, F.T.; Khairunnisa, K. Transaction Network Structural Shift under Crisis: Macro and Micro Perspectives. Economies 2022, 10, 56. https://doi.org/10.3390/economies10030056","@id":"https://www.mdpi.com/2227-7099/10/3/56","identifier":"https://www.mdpi.com/2227-7099/10/3/56"}],"license":{"@type":"Dataset","text":"CC0","url":"https://creativecommons.org/publicdomain/zero/1.0/"},"includedInDataCatalog":{"@type":"DataCatalog","name":"Root","url":"https://dataverse.telkomuniversity.ac.id"},"publisher":{"@type":"Organization","name":"Root"},"provider":{"@type":"Organization","name":"Root"},"distribution":[{"@type":"DataDownload","name":"economies-10-00056.pdf","fileFormat":"application/pdf","contentSize":1965048,"@id":"https://doi.org/10.34820/FK2/06LQEJ/VDLKAG","identifier":"https://doi.org/10.34820/FK2/06LQEJ/VDLKAG","contentUrl":"https://dataverse.telkomuniversity.ac.id/api/access/datafile/4180"}]}