New avenues of research to explain the rarity of females at the top of the accountancy profession
Bibliographic record
Abstract
Abstract The rarity of females in leadership positions has been an important subject of study in economics research. The existing research on gender inequality has established that important variations exist across time and place and that these differences are partly attributable to the cultural differences regarding gender roles. The accounting research has also established that women are rarely promoted to the top of the Big Four audit firms (KPMG, Deloitte, PricewaterhouseCoopers and Ernst & Young). However, the majority of research in accountancy has focused on Anglo-Saxon contexts (the United States, the United Kingdom and Australia) or country case studies without explicitly considering the role that cultural variations may play. Because the Big Four are present in more than 140 countries, we argue that the accountancy research that attempts to explain gender disparities at the top of these organizations would benefit from considering cultural factors. Such research, however, faces a key methodological challenge—specifically, the measurement of the cultural dimensions that relate to gender. To address this challenge, we propose an emerging approach that uses the gender distinctions in language to measure cultural attitudes toward gender roles. The idea that language may capture gender roles and even influence their formation and persistence has been the focus of emerging research in linguistics and economics. To support our proposition, we follow two steps. First, we review the accounting research by performing a systematic query on the bibliographic databases of the accounting articles that study gender and language. Second, we present data regarding the diversity of the global boards of the Big Four and the diversity of the linguistic environments in which they operate. We find that half of the countries where the Big Four are present exhibit a sex-based grammatical system for their most-spoken language, while the other half of the countries do not exhibit this system. Our findings suggest that the use of language as a measure of culture is a novel approach in accounting research. We conclude by emphasizing some potential directions for future research, namely, studying the linguistic determinants of the rarity of females at the top of audit firms and exploring accountancy practices in countries with linguistically diverse environments, such as Canada or Belgium, among others. This article is published as part of a collection on the role of women in management and the workplace.
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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 | 0.020 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.002 |
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".