Earnings management in times of crisis: A political cost hypothesis - Harina Paramastri; Sari Atmini; Aulia Fuad Rahman (Universitas Muhammadiyah Yogyakarta, Indonesia, 2023)
Research aims: The COVID-19 pandemic disrupted multiple industries, including chemistry, pharmaceuticals, telecommunications, and healthcare. This study, thus, explores how these sectors managed their earnings concerning political factors during the pandemic. Since these sectors play a critical role in maintaining business stability during this period, the authors hypothesize that some companies within them may have manipulated their profits.Design/Methodology/Approach: This study used quantitative methods to analyze a sample of companies in the chemical, pharmaceutical, telecommunications, and healthcare sectors listed on the Indonesia Stock Exchange from 2018 to 2022. Out of 60 companies, 20 met the criteria. The authors then tested the political costs hypothesis using Kotari et al.'s (2005) calculation method and a discrimination test for discretionary accrual values.Research findings: The findings reveal that some companies practiced earnings management with varying degrees across sectors. Notably, not all companies employed revenue reduction as a manipulation tactic. Significant differences were observed between the pre-pandemic and pandemic periods. It is worth highlighting that not all COVID-19 beneficiaries resorted to earnings management to access government incentives or facilities.Theoretical contribution/Originality: The findings of this research offer empirical evidence from both a theoretical standpoint and an analysis of the financial status of the involved companies.Practical/Policy Implication: This study aims to investigate if companies manipulated earnings during the COVID-19 pandemic by intentionally reducing their profits when their sectors were thriving due to the pandemic.Research Limitation: This research is limited by its use of quarterly data, incomplete data for some companies, and a reliance on existing data rather than current information collection.