An Analytical and Empirical Measure of the Degree of Conditional Conservatism
Bibliographic record
Abstract
There is a profound gap between models of accounting conservatism and the proxies for conditional conservatism currently used by the empirical literature. Not one of the proxies employed by the empirical literature to date obtains from a rigorous definition of conditional conservatism. In contrast, this study defines conditional conservatism in terms of truncated distributions and derives analytically a nonlinear relation between revisions to returns and earnings news for the conservative firm. This nonlinear relation is shown to be mathematically equivalent to two linear relations conditioned on the firm’s degree of conservatism (DCON). From these relations, we derive a model-based proxy of the DCON at the firm-year level, which is a function of the determinants of conditional conservatism. To account for the endogeneity of the firm’s DCON and mitigate sample selection bias, the model is implemented empirically using a switching regression approach in which the switch point, namely, the DCON, is unobservable and endogenously determined. Consistent estimates of the parameters of the switching regression, including the endogenous determinants of conservatism posited by Watts (2003a, 2003b), are obtained by simultaneous maximum likelihood estimation. The results indicate that the DCON is a positive function of contractual information asymmetry and litigation risk but a negative function of taxes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".