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Record W2066501867 · doi:10.1506/kc7w-c1vn-y5d4-nav4

Earnings Management in Response to the Introduction of the Australian Gold Tax*

2003· article· en· W2066501867 on OpenAlexvenueaboutno aff
Reza Monem

Bibliographic record

VenueContemporary Accounting Research · 2003
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsEarnings managementScrutinyEarningsProsperityIncentiveSample (material)AuditGold miningEconomicsAccountingAccrualBusinessDemographic economicsPolitical scienceEconomic growthMarket economy

Abstract

fetched live from OpenAlex

Abstract Earnings from gold mining in Australia remained tax‐exempt for almost seven decades until January 1, 1991. In the early 1980s, rapid economic prosperity induced by escalated gold prices brought the Australian gold‐mining industry under intense political scrutiny. Using a variant of the modified Jones model, this paper provides evidence of significant downward earnings management by Australian gold‐mining firms, which is consistent with their attempts to mitigate political costs during the period from June 1985 to May 1988. In contrast, test of earnings management over a similar period in a control sample of Canadian gold‐mining firms produced insignificant results. Further, empirical results are robust to several sensitivity tests performed. During the period from June 1988 to December 1990, the Australian firms were found to have engaged in economic earnings management. This is consistent with the sample firms' incentive of maximizing economic earnings immediately prior to the introduction of income tax on gold mining. The findings of this study help to understand the impact of earnings management on the efficient resource allocation in an economy. They also contribute toward understanding the linkage between regulation of accounting for special purposes and general‐purpose financial reporting.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.146
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.035
GPT teacher head0.289
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations86
Published2003
Admission routes2
Has abstractyes

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