Public data openness and stock price crash risk: evidence from a quasi-natural experiment of government data platforms
Public data serves as a fundamental pillar in the advancement of the digital economy. Its importance for unlocking the value associated with information asymmetry has attracted substantial attention in both practice and theory. We leverage a quasi-natural experiment from China’s local public data op...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co. Ltd.
2025-09-01
|
| Series: | Data Science and Management |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666764924000638 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849228294498549760 |
|---|---|
| author | Yongkang Lin Linlin Zheng Qiming Zhong |
| author_facet | Yongkang Lin Linlin Zheng Qiming Zhong |
| author_sort | Yongkang Lin |
| collection | DOAJ |
| description | Public data serves as a fundamental pillar in the advancement of the digital economy. Its importance for unlocking the value associated with information asymmetry has attracted substantial attention in both practice and theory. We leverage a quasi-natural experiment from China’s local public data openness platforms. Employing data for A-share listed firms from 2009 to 2021, we use a time-varying difference-in-differences model to systematically examine how public data openness affects corporate stock price crash risk. The results demonstrate that public data openness significantly reduces the accumulation of corporate stock price crash risk. This effect is primarily attributed to lower production of inappropriate information and enhanced information disclosure quality. Further analysis indicates that a supportive institutional environment amplifies the risk-reducing effect of public data openness. This effect is particularly pronounced in firms with strained government-market relationships, non-state ownership, and minimal agency conflicts. These insights highlight the potential that public data openness has for improving information efficiency and facilitating a transition toward digital governance. |
| format | Article |
| id | doaj-art-5a8eddeba72549bdb2b143cc61d0d523 |
| institution | Kabale University |
| issn | 2666-7649 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | KeAi Communications Co. Ltd. |
| record_format | Article |
| series | Data Science and Management |
| spelling | doaj-art-5a8eddeba72549bdb2b143cc61d0d5232025-08-23T04:49:18ZengKeAi Communications Co. Ltd.Data Science and Management2666-76492025-09-018328429510.1016/j.dsm.2024.11.002Public data openness and stock price crash risk: evidence from a quasi-natural experiment of government data platformsYongkang Lin0Linlin Zheng1Qiming Zhong2School of Insurance, Shandong University of Finance and Economics, Jinan, 250014, ChinaSchool of Finance, Zhongnan University of Economics and Law, Wuhan, 430073, ChinaSchool of Finance, Shanghai University of Finance and Economics, Shanghai, 200433, China; Corresponding author.Public data serves as a fundamental pillar in the advancement of the digital economy. Its importance for unlocking the value associated with information asymmetry has attracted substantial attention in both practice and theory. We leverage a quasi-natural experiment from China’s local public data openness platforms. Employing data for A-share listed firms from 2009 to 2021, we use a time-varying difference-in-differences model to systematically examine how public data openness affects corporate stock price crash risk. The results demonstrate that public data openness significantly reduces the accumulation of corporate stock price crash risk. This effect is primarily attributed to lower production of inappropriate information and enhanced information disclosure quality. Further analysis indicates that a supportive institutional environment amplifies the risk-reducing effect of public data openness. This effect is particularly pronounced in firms with strained government-market relationships, non-state ownership, and minimal agency conflicts. These insights highlight the potential that public data openness has for improving information efficiency and facilitating a transition toward digital governance.http://www.sciencedirect.com/science/article/pii/S2666764924000638Public data opennessInformation productionInformation disclosureCrash riskDigital government |
| spellingShingle | Yongkang Lin Linlin Zheng Qiming Zhong Public data openness and stock price crash risk: evidence from a quasi-natural experiment of government data platforms Data Science and Management Public data openness Information production Information disclosure Crash risk Digital government |
| title | Public data openness and stock price crash risk: evidence from a quasi-natural experiment of government data platforms |
| title_full | Public data openness and stock price crash risk: evidence from a quasi-natural experiment of government data platforms |
| title_fullStr | Public data openness and stock price crash risk: evidence from a quasi-natural experiment of government data platforms |
| title_full_unstemmed | Public data openness and stock price crash risk: evidence from a quasi-natural experiment of government data platforms |
| title_short | Public data openness and stock price crash risk: evidence from a quasi-natural experiment of government data platforms |
| title_sort | public data openness and stock price crash risk evidence from a quasi natural experiment of government data platforms |
| topic | Public data openness Information production Information disclosure Crash risk Digital government |
| url | http://www.sciencedirect.com/science/article/pii/S2666764924000638 |
| work_keys_str_mv | AT yongkanglin publicdataopennessandstockpricecrashriskevidencefromaquasinaturalexperimentofgovernmentdataplatforms AT linlinzheng publicdataopennessandstockpricecrashriskevidencefromaquasinaturalexperimentofgovernmentdataplatforms AT qimingzhong publicdataopennessandstockpricecrashriskevidencefromaquasinaturalexperimentofgovernmentdataplatforms |