Measuring the Gender Gap in Unpaid Care Work in Colombia: A Decomposition Analysis
Bibliographic record
Abstract
ABSTRACT Care work plays a vital role in developing, maintaining and enhancing human capabilities, yielding substantial benefits for both individuals and society. Among the various forms of care work, childcare stands out as one of its most significant determinants. In Colombia, women living in households with dependents—particularly those with children under the age of five—devote considerably more time to care‐related activities than men, which limits the opportunities of women for leisure and participation in the labour market. This study aims to identify and measure gender gaps in unpaid care work, with a specific focus on households with children under 5 years of age. Using data from the 2020/2021 Colombian National Time Use Survey (NTUS), gender disparities in the allocation of time across different types of care‐related activities are analysed, including indirect care work, direct care work and exclusive care work for young children. The Oaxaca‐Blinder decomposition method was employed to assess the gender gap in unpaid care work and the relative contributions of socioeconomic and cultural factors. The results reveal a statistically significant gender gap in unpaid care time. More than half of the gender gap (52% indirect care, 60% direct care and 73% in exclusive care of children under five) can be attributed to observable characteristics such as education, marital status and adherence to traditional gender roles. Furthermore, the findings confirm the persistent gender gap in unpaid care work within Colombian households and highlight the influence of socioeconomic and cultural factors on how care responsibilities are distributed. These results underscore the importance of public policies and educational initiatives aimed at challenging traditional gender roles and promoting more equitable sharing of care‐related duties between men and women—especially in households with young children.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".