Behavioral Economics of Accounting: A Review of Archival Research on Individual Decision Makers*
Bibliographic record
Abstract
ABSTRACT This paper develops a unified framework to synthesize the growing stream of positive research on the role of individual decision makers in shaping observed accounting phenomena. This line of research recognizes two central ideas in behavioral economics. First, individual behavior depends not only on economic incentives and accessible information but also on individual preferences, abilities, experiences, and other characteristics. Second, the constraints that structure human interactions encompass both formal institutions (e.g., rules, laws, constitutions) and informal institutions (e.g., norms, conventions, rituals). Our review covers a broad set of individuals who are of interest in accounting research: managers, directors, audit partners, analysts, standard setters, politicians, judges, journalists, loan officers, financial advisors, and investors. We aim to understand the systematic effects of individual characteristics on a wide spectrum of accounting phenomena, including financial reporting, disclosure, tax planning, auditing, and corporate social responsibility. We highlight the importance of personal characteristics not only for an individual's own behavior but also for others' perceptions. Our review mainly focuses on archival research in accounting and provides some thoughts about opportunities for archival empiricists going forward. We also, when feasible, highlight opportunities for future field, survey, and experimental research. A central takeaway from our review is that individual‐level factors significantly improve our ability to explain and predict accounting phenomena beyond firm‐, industry‐, and market‐level factors.
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 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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".