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Record W3183400880 · doi:10.1086/719277

The Dynamics and Spillovers of Management Interventions: Evidence from the Training within Industry Program

2022· article· en· W3183400880 on OpenAlexaff
Nicola Bianchi, Michela Giorcelli

Bibliographic record

VenueJournal of Political Economy · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicItaly: Economic History and Contemporary Issues
Canadian institutionsKellogg's (Canada)
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentUniversity of California, San DiegoBooth School of Business, University of ChicagoErasmus Universiteit RotterdamPontifícia Universidade Católica do Rio de JaneiroKU LeuvenLeonard N. Stern School of Business, New York UniversityYale University
KeywordsSpillover effectTraining (meteorology)BusinessPsychological interventionGovernment (linguistics)Production (economics)Panel dataIndustrial organizationEconomicsMicroeconomicsPsychologyEconometrics

Abstract

fetched live from OpenAlex

This paper examines the long-term and spillover effects of management interventions on firm performance. Under the Training Within Industry (TWI) program, the U.S. government provided management training to firms involved in war production between 1940 and 1945. Using a newly collected panel dataset on all 11,575 U.S. firms that applied to the program, we find that the TWI training had positive and long-lasting effects on firm performance and the adoption of beneficial managerial practices. Moreover, it generated complementarities among different types of training and had positive spillover effects on the supply chain of trained firms.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.080
GPT teacher head0.278
Teacher spread0.198 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2022
Admission routes1
Has abstractyes

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