Overloaded and overwhelmed: Weakened partner aspirations of women public accountants during the <scp>COVID</scp>‐19 pandemic
Bibliographic record
Abstract
Abstract Despite years of initiatives to improve gender equity in public accounting firms, women continue to be underrepresented at the partner level. Supporting women's aspirations to become partner is important to ensure there are more women in the pipeline of potential partners. However, we argue that challenges during the COVID‐19 pandemic are likely to have negative downstream effects on accounting firms' efforts to improve female partner representation. Therefore, using a survey of 192 certified public accountants (CPAs), we develop and test a theoretical model that examines changes in women's partner aspirations during the COVID‐19 pandemic. Using the conservation of resources (COR) theory, we predict that women experienced disproportionately higher role overload during the pandemic compared to men. We also rely upon COR theory to predict that higher levels of role overload will be associated with weakened partner‐track motivations (i.e., less value placed on the advantages of the partner level and lower willingness to make the sacrifices necessary to pursue the partner level) and weakened partner aspirations. Consequently, we expect that women public accountants experienced weakened partner aspirations through higher role overload during the pandemic. Results support our predictions as we find that women experienced higher levels of role overload, which are associated with weakened partner‐track motivations, which in turn are associated with weakened partner aspirations. In sum, our results suggest that the COVID‐19 pandemic exacerbated pre‐pandemic gender equity challenges within public accounting firms. Fortunately, we also find that higher levels of supervisor support and coworker support help limit role overload and mitigate declines in partner aspirations. We discuss several insights that firms can use to mitigate post‐pandemic declines in women's partner aspirations.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".