Firm Ownership and Enterprise Risk Management Implementation: Evidence from the Nordic Region
Bibliographic record
Abstract
The purpose of this paper is to investigate whether firm ownership characteristics can explain demand for Enterprise Risk Management (ERM) implementation. Specifically, we examine the relationship between the presence of large shareholders, multiple blockholders and a dual-class share structure, and ERM implementation. To our knowledge we provide the first evidence on the effect of multiple blockholders and dual-class share structures on the implementation of ERM. ERM best practices can be considered as governance tools, used to monitor managerial discretion in risk management, ultimately reducing the agency cost of risk management. Accordingly, we analyze the demand for ERM in certain governance (e.g., ownership) settings. We use quantitative methods in our study: survey and regressions (tobit and logit models). Ownership data is hand-collected while ERM data comes from a survey conducted in the Nordic region. We find that ERM is implemented less frequently in firms where there are multiple blockholders, and where large controlling owners hold dual-class shares. These findings indicate that there is less demand for ERM’s monitoring role in firms that are associated with high agency costs. Given the increasing use of dual-class share structures, we believe further examination of ownership characteristics and corporate risk management is warranted.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".