Aggregate-level accounting information contains GDP incremental information: the case of China
dc.contributor.author | Sun, Mu | |
dc.contributor.author | Del Campo Campos, Cristina | |
dc.contributor.author | Chamizo González, Julián | |
dc.contributor.author | Urquía Grande, María Elena | |
dc.date.accessioned | 2025-03-17T09:24:30Z | |
dc.date.available | 2025-03-17T09:24:30Z | |
dc.date.issued | 2024-10 | |
dc.description.abstract | Existing research wants to find the relationship between accounting earnings and country-level GDP usually using the aggregate earnings indicator based on accounting earnings data. However, the research on the relationship between GDP and accounting information often lacks a theoretical basis. The gaps in accounting standards in different countries leads to differences in the accounting of items in the financial statements, making in-depth discussions on this topic necessary. Therefore, the present study takes China's current accounting standards and conducts in-depth research and verification of its data availability, international comparability, and adaptability of existing research models. First, we checked the most critical existing research model on aggregate earnings and did not find strong evidence to support that existing research. Secondly, from the empirical point of view it uses the four elements of the earnings-based GDP model as independent variables to find the relationship between company-level earnings and country-level GDP. The results found that income contains the most incremental information on GDP, that depreciation of fixed assets has a strong positive effect, and that changes in tax policy leads to unexpected effects on GDP. Furthermore, this study found no evidence to support incremental GDP information contained in employee salaries item. The present research complements the theoretical basis for forecasting GDP from aggregate-level accounting information data and contributes to future research in the proposed new model. | |
dc.description.department | Depto. de Economía Financiera y Actuarial y Estadística | |
dc.description.faculty | Fac. de Ciencias Económicas y Empresariales | |
dc.description.refereed | TRUE | |
dc.description.status | pub | |
dc.identifier.citation | Sun, M., del Campo, C., Chamizo-González, J., & Urquía-Grande, E. (2024). Aggregate-Level Accounting Information Contains GDP Incremental Information: The Case of China. Pakistan Journal of Life and Social Sciences, 22(2), 7639-7652. | |
dc.identifier.doi | 10.57239/PJLSS-2024-22.2.00576 | |
dc.identifier.essn | 2221-7630 | |
dc.identifier.issn | 1727-4915 | |
dc.identifier.officialurl | https://pjlss.edu.pk/pdf_files/2024_2/7639-7652.pdf | |
dc.identifier.uri | https://hdl.handle.net/20.500.14352/118803 | |
dc.issue.number | 2 | |
dc.journal.title | Pakistan Journal of Life and Social Sciences | |
dc.language.iso | eng | |
dc.page.final | 7652 | |
dc.page.initial | 7639 | |
dc.publisher | The Elite Scientific Forum | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | en |
dc.rights.accessRights | open access | |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.subject.keyword | Aggregate-level Accounting Information | |
dc.subject.keyword | Aggregate Earnings | |
dc.subject.keyword | GDP | |
dc.subject.keyword | Economic Growth | |
dc.subject.keyword | Accounting Earnings Usefulness | |
dc.subject.ucm | Contabilidad (Economía) | |
dc.subject.ucm | Indicadores económicos | |
dc.subject.ucm | Macroeconomía | |
dc.subject.ucm | Microeconomía | |
dc.subject.unesco | 5303.01 Contabilidad Financiera | |
dc.subject.unesco | 5307.07 Previsión Económica | |
dc.title | Aggregate-level accounting information contains GDP incremental information: the case of China | |
dc.type | journal article | |
dc.type.hasVersion | VoR | |
dc.volume.number | 22 | |
dspace.entity.type | Publication | |
relation.isAuthorOfPublication | 6c714f02-4316-4960-ac6a-4cbbf3e7f233 | |
relation.isAuthorOfPublication | 6dab246f-23d5-4793-b89c-fb43c168ff43 | |
relation.isAuthorOfPublication.latestForDiscovery | 6c714f02-4316-4960-ac6a-4cbbf3e7f233 |
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