AN EXPLORATION OF AGENCY PROBLEMS IN THE FAMILY FIRM: THE CASE OF DISTRIBUTING EQUAL SHARES TO CHILDREN1
Bibliographic record
Abstract
We show that agency problems exist in the family firm although ownership and management are not separated. This is counter to the common belief in the finance literature that agency costs do not exist in the owner-managed firm. Altruism may exacerbate the shirking problem but mitigates an adverse selection problem. Agency problems are used by financial economists to explain capital structure (e.g., Jensen & Meckling, 1976), managerial incentives (e.g., Grossman & Hart, 1982), roles of majority and minority shareholders (e.g., Holderness & Sheehan, 1988), and other decision issues in firms where ownership and management are separated. It is generally assumed, however, that agency problems are negligible or nonexistent in the owner-managed firm because ownership and management are not separated (e.g., Jensen & Meckling, 1976; Ang & Cole, 2000). This may not be true because in the family firm where the parent and the children co-own the business and, together, manage the business, there could still be shirking and adverse selection problems. Family firms are ubiquitous in the global economy. For instance, a study by Deloitte & Touche (1999) suggests that Canadian family firms provide as many as 4.7 million full time jobs and 1.3 million part-time jobs. Yet agency problems in family firms have not been studied by finance researchers. This exploratory study is a step in that direction.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".