Wage Productivity and Wage Income Differential in Labor Market: Evidence from RMG Sector in Bangladesh
Bibliographic record
Abstract
Labor Market outcomes in Bangladesh at different stages of industrialization need not be similar as behavioral rationality of economic agents depends on the market context. This paper examines the issues related to wage income differentials in RMG sector. In fact wage determination behavior during the last two decades did not follow one of the basic premises in the theory of labor market in economics. In general, employment and wage determined by the marginal productivity of labor in value term. Labor Market outcomes in Bangladesh at different stages of industrialization need not be similar as behavioral rationality of economic agents depends on the market context. Women are increasingly becoming visible in the productive labor force in Bangladesh. The surplus labor is expected to take in the family farms in the rural areas and in other informal enterprise. During the last two decades, rapid changes had taken place in this scenario. Women’s employment in garment factories is one of these changes. The garment sector is rapidly rising in the country since the 1980’s. The paper is based survey findings conducted among the woman’s workers of RMG industries located at Dhaka and Narayangong – the second largest hub of RMG factories in Bangladesh. The study was conducted on 200 female garment workers of 20 garment industries. This paper investigates the existing labor market and status of women workers in the context of wage discrimination based on gender, obstacles of work place environment in and outside of the industries. The study also analyzes the socio economic status of the women workers in the export-oriented industries like Ready Made Garments. In terms of social and economic context, that women’s working condition is not favorable for the work place. The study shows that distribution of labor force by two main sector agriculture and non-agriculture shows that during the period of 1990-91 to 1999-2000 there has been a small decline in the share of agriculture in total employment. It should be pointed out that the decline in the share of agriculture is more or less continuous for male labor force. Women contributed to 37.9 percent and 39.3 percent of employment in the manufacturing sector in 2000 and 2003 respectively. The garment sector is dependent on female labor largely because underemployment is high among women. In the urban area, the shares of women and man skilled are 12 percent and 35 percent respectively. Empirical result also shows that both the male and female labor force participation is higher in Bangladesh compare to the South Asian Countries.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".