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Record W4402894173 · doi:10.1080/00036846.2024.2399162

Returns to workplace connectivity: evidence from Brazil

2024· article· en· W4402894173 on OpenAlexaff
Marcelo Arbex, Ricardo da Silva Freguglia, Rafael Rafael Siano

Bibliographic record

VenueApplied Economics · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsUniversity of Windsor
FundersFundação de Amparo à Pesquisa do Estado de Minas GeraisConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsEconomicsEconometricsFinancial economics

Abstract

fetched live from OpenAlex

We empirically investigate the quantitative effect of workplace connectivity on workers’ wages. Connectivity measures a worker’s rank within the workplace’s connection hierarchy, considering the number of co-workers in the same occupation and firm (workplace) and the relative tenure of immediate co-workers. We construct a connectivity index as the ratio of a worker’s tenure-weighted connections to the total number of connections in the workplace, using a worker’s job tenure and the connections formed over time to obtain a relative measurement of their connectivity. Using a unique dataset from the Brazilian Annual Social Information Report (RAIS), we estimate the (fixed effect) wage equation, controlling for work and firm heterogeneity. Our results show positive and statistically significant returns to connectivity. The effect is particularly pronounced for workers in administrative services and industrial goods production, those at the bottom of the connectivity distribution, and those in medium-sized firms. We also find that a worker’s connectivity is more relevant for wages than their tenure.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.018

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 head0.040
GPT teacher head0.241
Teacher spread0.202 · 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 designTheoretical or conceptual
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
Published2024
Admission routes1
Has abstractyes

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