Does a long‐term orientation create value? Evidence from a regression discontinuity
Bibliographic record
Abstract
Research summary : In this paper, we theorize and empirically investigate how a long‐term orientation impacts firm value. To study this relationship, we exploit exogenous changes in executives' long‐term incentives. Specifically, we examine shareholder proposals on long‐term executive compensation that pass or fail by a small margin of votes. The passage of such “close call” proposals is akin to a random assignment of long‐term incentives and hence provides a clean causal estimate. We find that the adoption of such proposals leads to (1) an increase in firm value and operating performance—suggesting that a long‐term orientation is beneficial to companies—and (2) an increase in firms' investments in long‐term strategies such as innovation and stakeholder relationships. Overall, our results are consistent with a “time‐based” agency conflict between shareholders and managers . Managerial summary : This paper shows that corporate short‐termism is hampering business success. We show clear, causal evidence that imposing long‐term incentives on executives—in the form of long‐term executive compensation—improves business performance. Long‐term executive compensation includes restricted stocks, restricted stock options, and long‐term incentive plans. Firms that adopted shareholder resolutions on long‐term compensation experienced a significant increase in their stock price. This stock price increase foreshadowed an increase in operating profits that materialized after two years. We unpack the reasons for these improvements in performance, and find that firms that adopted these shareholder resolutions made more investments in R&D and stakeholder engagement, especially pertaining to employees and the natural environment . Copyright © 2016 John Wiley & Sons, Ltd.
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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 | 0.021 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 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".