MétaCan
Menu
Back to cohort
Record W3216341042 · doi:10.1287/mnsc.2022.4412

Female Directors and Firm Value: New Evidence from Directors’ Deaths

2022· article· en· W3216341042 on OpenAlexfundno aff
Thomas Schmid, Daniel Urban

Bibliographic record

VenueManagement Science · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicGender Diversity and Inequality
Canadian institutionsnot available
FundersUniversität MannheimLondon School of Economics and Political ScienceUniversity of EdinburghGöteborgs UniversitetKU LeuvenYork University
KeywordsStock marketGlobeEnterprise valueStock (firearms)BusinessDemographic economicsValue (mathematics)Market valueCentralityCapital marketMonetary economicsAccountingLabour economicsEconomicsFinancePsychologyGeography

Abstract

fetched live from OpenAlex

This paper examines how female directors (FDs) affect firm value in the absence of mandatory gender quotas. Using a newly collected data set on director deaths around the globe, we find that stock prices decrease approximately 2% more when an FD passes away, compared with a male director. What explains this negative capital market reaction? We find evidence that finding successors for deceased FDs is challenging for firms: Succession delays are longer, and although firms try to replace FDs with women, two-thirds of their successors are male. Furthermore, their successors tend to be younger, less experienced, and more often externally hired. Stock prices decline less if more potential female successors exist in a country, the firm is larger, or FDs other than the deceased woman were on the board. Because observable characteristics such as age, tenure, education, and network centrality cannot explain the negative stock market reaction, unobserved differences across genders that lead to a lower fit of male successors to the existing board are the most likely explanation for the firm value loss after the death of an FD. This paper was accepted by David Simchi-Levi, finance. Supplemental Material: Data are available at https://doi.org/10.1287/mnsc.2022.4412 .

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.156
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.133
GPT teacher head0.320
Teacher spread0.187 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueManagement ScienceSame topicGender Diversity and InequalityFrench-language works237,207