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Record W4281383576 · doi:10.1007/s11187-022-00633-6

Bribery, on-the-job training, and firm performance

2022· article· en· W4281383576 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSmall Business Economics · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicCorruption and Economic Development
Canadian institutionsUniversity of Guelph
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEndogeneityBusinessTraining (meteorology)On-the-job trainingSample (material)EntrepreneurshipInvestment (military)Demographic economicsLabour economicsEconomicsFinanceEconometricsEconomic growth

Abstract

fetched live from OpenAlex

Abstract The previous literature has extensively examined the effect of firm-level bribery on firm performance but not through on-the-job training. This paper investigates the impact of paying bribes on the firm’s investment decisions in on-the-job training and offers mediating implications of corruption on firm performance. We empirically examine the relationship between bribery and on-the-job training using firm-level data from the World Bank Enterprise Surveys consisting of a sample of 94 developing countries with 20,601 firms. The findings show that bribery and on-the-job training intensity affects real annual sales growth rates negatively and positively, respectively. Furthermore, firms exposed to more bribery reduce their on-the-job training intensity. The results are robust to the different classifications of the firm’s size, different subsamples, and controls for the endogeneity of the on-the-job training and bribery.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.920
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.072
GPT teacher head0.235
Teacher spread0.163 · 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