Determinant factors on the disclosure level of local government’s financial statements in Indonesia

  • Received November 15, 2021;
    Accepted February 9, 2022;
    Published February 16, 2022
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/pmf.11(1).2022.01
  • Article Info
    Volume 11 2022, Issue #1, pp. 1-9
  • TO CITE АНОТАЦІЯ
  • Cited by
    11 articles
  • 668 Views
  • 204 Downloads

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License

Financial reports are required from both the federal and municipal governments to demonstrate and improve governance and raise openness and accountability of government financial management. This study aims to determine how much mandatory disclosure in local government financial reports can be increased by adding variables such as the number of members in the legislative body, debt, and population, as well as a control variable – the age of the municipal authorities. The population of this study comprised all Local Government Financial Statements (LGFS) in Indonesia and the Supreme Audit Agency’s financial statement auditing requirements as of 2018. The paper adopted a purposive sample technique; 248 local governments in Indonesia were sampled. This study tested hypotheses using multiple regression analysis with the SPSS Version 25 application. The findings show that the number of members in the legislative body, debt, and total population do not affect the level of LGFS disclosure; however, the level of welfare does. This study should provide information that can help increase LGFS disclosure in a way that is valuable for local governments. Utilization of information technology in meeting social demands more efficiently and effectively is one of the strategies for local governments in carrying out financial statement disclosures.

view full abstract hide full abstract
    • Table 1. Research sample criteria
    • Table 2. Research variables measurement
    • Table 3. Descriptive statistics
    • Table 4. Regression analysis
    • Conceptualization
      Khoirul Aswar, Alvin Yoga Fanany
    • Data curation
      Khoirul Aswar, Alvin Yoga Fanany, Mahendro Sumardjo, Meilda Wiguna, Eka Hariyani
    • Resources
      Khoirul Aswar, Meilda Wiguna
    • Software
      Khoirul Aswar, Alvin Yoga Fanany
    • Writing – original draft
      Khoirul Aswar, Alvin Yoga Fanany
    • Writing – review & editing
      Khoirul Aswar
    • Formal Analysis
      Alvin Yoga Fanany, Meilda Wiguna, Eka Hariyani
    • Methodology
      Alvin Yoga Fanany, Mahendro Sumardjo
    • Supervision
      Alvin Yoga Fanany
    • Validation
      Alvin Yoga Fanany, Mahendro Sumardjo, Meilda Wiguna, Eka Hariyani
    • Investigation
      Mahendro Sumardjo
    • Project administration
      Meilda Wiguna, Eka Hariyani