Barriers to workplace economic mobility of women in Sri Lanka; Insights from World Bank gender statistics
Bibliographic record
Abstract
Background: Women as an indispensable part of the global economy record only a quarter or less of labor participation in South Asian region to the labor market. Thus, this study aimed to investigate the barriers impacting the economic mobility of women in Sri Lanka. Method: This study employed the quantitative research approach based on secondary data from World Bank Gender Statistics (1971-2023). Descriptive and trend analysis were used to determine the gender gap through the ratio of women to men in the labor force as well as a detailed analysis of women in labor force participation. Results: It was found that there is a persistent gender gap as the ratio of women to men labor force participation declined from 53.2% in 1991 to 44.6% in 2023, while the female labor force participation rate fluctuated between 32% and 35%. Key findings from the secondary qualitative data revealed that women in Sri Lanka face systemic barriers such as restrictions on night work, industrial employment, unequal remuneration, disparities in pension benefits at retirement age, and inability to administrate 100 %t of maternity leave by the government. Notably, Sri Lankan laws lacked gender-based credit access and employment discrimination provisions. Especially, there was no law on sexual harassment in employment and no criminal penalties or civil remedies existed for sexual harassment in employment until the introduction of section 345 of the Penal Code (Amendment) Act No.22 of 1995 against sexual harassment. Conclusions: The study underscores the need for gender-inclusive policies and legal reforms from time to time for both the corporate and government sectors to address institutional barriers to enhance women's economic mobility in Sri Lanka.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".