The impact of childcare centres' closures due to COVID-19 on women's labour supply
Bibliographic record
Abstract
Purpose This paper evaluates the short-term impact of childcare centres' closures, due to COVID-19 restrictions, on Brazilian mothers' labour force participation and employment rates. Design/methodology/approach Formal education is non-mandatory according to Brazilian law until the age of four, allowing the identification of children that attend childcare centres and of those that do not attend. Using data from the Brazilian Household Survey, PNAD Contínua/IBGE, the authors construct a two-period panel with women sampled in the second quarter of 2019 and 2020. The authors apply propensity score matching and differences-in-differences methods to control selection into treatment. Findings The results show a negative impact in terms of employment for mothers whose children attended a childcare centre before the COVID-19 pandemic. But there was no impact in terms of labour force participation rates. Investigating heterogeneous effects associated with childcare centres' closures, the authors find that women with fewer years of schooling, with children aged two or three years old and located in urban areas, suffered greater penalties in the labour market due to the closure of childcare centres. Originality/value Few studies could distinguish the pandemic effects directly associated with childcare centres' closures. The paper is the first to analyse the Brazilian case, undertaking an original approach to handle the problem of selection bias. The results help identify the most vulnerable groups of women in the labour market, shedding light on the importance of childcare centres on women's labour supply and of compensating mechanisms to serve as protection during the crisis. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2022-0748 .
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".