Across the Pond: How <scp>US</scp> Firms' Boards of Directors Adapted to the Passage of the General Data Protection Regulation†
Bibliographic record
Abstract
ABSTRACT One of the prime responsibilities of the board of directors is to understand and oversee its firm's risk profile. We exploit a recent European Union (EU) regulation, the General Data Protection Regulation (GDPR), as a quasi‐exogenous shock to the cyber risk landscape to assess whether boards of US firms changed their focus and governance structures to deal with this new challenge. The GDPR encompasses a sweeping set of regulations aimed at protecting EU citizens from unwanted uses of their personal Internet data. Although an EU regulation, the GDPR applies to all US public firms with at least one EU user. Adopting a difference‐in‐differences methodology, we use firms that already fall under a US data privacy regulation as a control group and find that boards of treated US firms, on average, increase their focus on cyber risk, add more directors with cyber/IT expertise, and more frequently assign cyber risk oversight to the board or to a board committee. In cross‐sectional tests, we show that these changes are positively associated with a firm's ex ante cyber risk, but are unrelated to whether a firm had a large EU presence, suggesting a more global reaction to the GDPR. In addition, we examine some of the consequences of these board changes. We find boards that promptly responded by changing their board focus, expertise, and monitoring assignment of cyber risk around the passage of GDPR had fewer future cyberattacks/data breaches and less related media attention. Our findings suggest that, on average, American corporate boards promptly responded to changes in the cyber risk environment in ways that reduced their firms' overall future cyber risk. Our results have implications for the efficacy and flexibility of US corporate boards to respond to unexpected changes in risk.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".