Examining the Decision to Opt In versus Opt Out of Section 107 of the JOBS Act of 2012: Determinants and Consequences
Bibliographic record
Abstract
The Jumpstart Our Business Startups Act of 2012 (hereafter, JOBS Act) creates a new category of firms, referred to as “Emerging Growth Companies” (hereafter, EGCs). Section 107 of the JOBS Act, titled “Opt-In Right for EGCs,” gives EGCs the choice to take advantage of an extended transition period for complying with new or revised accounting standards. In other words, an EGC can choose to delay the adoption of new or revised accounting standards until those standards would otherwise apply to private companies. Using a logistic regression approach with hand-collected data, we examine the underlying firm characteristics associated with EGCs’ choice of opting in or out of the accounting standards exemption, as provided by Section 107 of the JOBS Act. Using additional ordinary least square regression analyses, we further examine whether the choice of opting in or out is associated with earnings management and financial statement restatement behavior. Our results suggest that EGC firms designated as “smaller reporting companies” are more likely to choose to delay the adoption of a new or revised accounting standard (i.e., opt in). Our findings also show that EGCs that employ Big 4 auditors are more likely to opt out. We further find that EGCs that choose to opt out are less likely to engage in earnings management behavior, proxied by the absolute value of abnormal accruals, and are less likely to restate their financial statements. Taken together, our findings suggest that EGCs that choose to opt out of Section 107 produce higher quality financial statements.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".