Telework in the Brazilian Context: Social and Economic Factors Under a Machine Learning Approach

Telework has expanded rapidly, yet its determinants and temporal dynamics remain insufficiently documented in developing countries. This study examines the evolution of telework in Brazil from 2022 to 2025 using machine learning models applied to nationally representative microdata from the Continuous National Household Sample Survey, based on approximately 210,000 households per reference period. A standardized pipeline was implemented across four time windows, including preprocessing, missing-data handling, class balancing via random under-sampling, feature encoding and normalization, and stratified data splitting with 5-fold cross-validation. Nine classification algorithms were evaluated and hyperparameter-tuned using ANOVA racing, with model performance assessed primarily through the ROC AUC metric. The results indicate consistently high discriminative performance across all analyzed periods (ROC AUC > 0.80). The temporal evaluation further reveals overlapping confidence intervals among the predictive models, indicating statistically comparable performance over time and no evidence of a universally dominant algorithm. Variable-importance analyses show that the set of the eight most relevant predictors remained stable, although their relative rankings changed, with gender increasing in importance in the most recent periods. Overall, telework in Brazil is jointly shaped by sociodemographic and occupational factors, highlighting its selective nature and the relevance of temporal monitoring to inform research and policy.