New CEOs and Old SG&A: Managing Inherited Intangible Assets
Bibliographic record
Abstract
ABSTRACT We consider the role of inherited intangible assets in the performance of newly appointed CEOs. Using selling, general, and administrative expenses (inclusive of research and development expenses) to measure investments in intangible assets we find that, on average, CEOs manage inherited intangible assets less well than their own investments in intangibles. Turnovers involving outside successors drive this result. Industry-year and firm fixed effects help rule out explanations that industry conditions or heterogeneity across firms explain our results. The results are robust to controls for the outgoing CEO’s performance and continue to hold on a subsample of firms conducting restructuring activities coincident with CEO turnover. Higher SG&A spending in the final two years of outgoing CEOs’ tenures reduces the likelihood that boards select outside replacements following voluntary turnovers, suggesting boards are aware of the problems of managing inherited intangible assets. Data Availability: The data underlying this article are subject to third-party restrictions. The data that support the findings of this study are available from Standard & Poor’s via its Compustat and Execucomp data products. Restrictions apply to the availability of these data, which were used under license for this study. The forced turnover data come from two sources. Some were provided by Dirk Jenter. We also obtained forced turnover data at https://doi.org/10.5281/zenodo.4543893. Hand-collected data from annual reports are available upon request. JEL Classifications: M40; J24; J41; J63.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".