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Record W1576243653

The impact of internet use on individual earnings in Latin America

2010· preprint· en· W1576243653 on OpenAlexfundno aff
Lucas Navarro

Bibliographic record

VenueEconstor (Econstor) · 2010
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsThe InternetEarningsLatin AmericansMatching (statistics)BusinessInternet accessDemographic economicsPanel dataDeveloping countryWork (physics)EconomicsEconomic growthPolitical scienceFinanceEngineeringComputer scienceEconometrics
DOInot available

Abstract

fetched live from OpenAlex

This paper uses matching techniques to examine the impact of internet use on individual earnings in six Latin American countries using recent household surveys data. Given their different internet use patterns and their implications, the analysis is done for salaried and self-employed workers separately. While salaried worker users mainly access the internet at work, self employed users access the internet mainly at other places. Therefore, the returns to internet use for salaried workers may be associated not only to individual but also to workplace characteristics. Results indicate a large effect of internet use on earnings for both groups of workers in most of the countries studied. These returns are high compared with estimates for industrialized countries. This could be explained by the much lower prevalence of internet use in the region for the international standards. Additionally, given that the estimations rely on cross-section data, they may not fully control for individuals' characteristics before internet adoption. This calls for the need of panel-data on new ICTs diffusion in the region.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, 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 score0.015
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.003
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 head0.030
GPT teacher head0.255
Teacher spread0.224 · 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

Citations11
Published2010
Admission routes1
Has abstractyes

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