Trends in Earning Management (EM) Practices: A Cross Country and Industrial Analysis
Bibliographic record
Abstract
This paper analyzes the trends in earnings management (EM) practices of 500 non-financial firms across 8 major industries operating in both developing and developed economies for the period 2008-2017. It significantly contributes to the literature by identifying the country-specific, industry-specific, and over the time trends in accrual and real earnings management practices prevailing in the non-financial sector of both developed and developing economies of the world. EM trends are analyzed across countries, across industries, and over the sample period. The sample consists of 500 non-financial firms from 8 different industries of 22 developed and developing economies for the period 2007-2018. Accrual and real earnings management are alternatively used in the non-financial sector across the world. This study uses the Modified Jones Model (Dechow et al., 1996) and the Roychowdhury model (2006) to estimate accrual and real earnings management. The analysis of variance (ANOVA) is used to examine the mean differences between countries and industries as well as over the time differences. The country-wise analysis concludes that Pakistan is on the top of the list in managing earnings followed by Canada, particularly in accrual-based earnings management. It may be attributed to poor accounting standards and regulatory frameworks. The industry-wise analysis shows that the mining industry, characterized by high dependency on capital markets and the uncertainty associated with the prospects of mineral reserves, is highly involved in EM. Finally, the year-wise analysis determines that the year 2009 was highly escorted with accrual and real earnings management practices, which may be the result of the global financial crisis of 2007-2008. The findings imply that the policymakers of developing economies should strongly emphasize improving the general macroeconomic environment of the economy by controlling inflation, improving law and order conditions, and ensuring political stability. Additionally, the findings have potential implications for corporate managers while formulating strategies to restrict them from EM that may diminish both firm value and the economy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".