UNORGANIZED SECTOR IN INDIA: EMPLOYMENT ELASTICITY AND WAGE-PRODUCTIVITY NEXUS
Bibliographic record
Abstract
In India the formal, or organized, sector is not able to generate employment opportunities for the unskilled or semi-skilled workers on a large scale, forcing them to get residually absorbed in the unorganized sector. At the same time, the unorganized sector is believed to have work consignments from the organized sector and this ancillarization process is contributing to employment creation. In the backdrop of these views the present study, using the unit level data of the National Sample Survey (NSS, 2010-11), makes an attempt to estimate the employment elasticity and wage-productivity nexus in the unorganized sector. Although the employment function estimated in the paper suggests employment can be raised through wage reduction, it can affect the wellbeing of the workers because the wage rate in the unorganized sector is already very low. Further, subcontracting or ancillarization does not seem to be contributing to employment generation in unorganized manufacturing or trade related activities. However, in the services sector it shows a positive impact. The equation representing determinants of wages shows units with assets are better-off compared to those that do not have them. This has an important policy implication, suggesting that through asset creation, government may bring in improvements in livelihood of the unorganized sector enterprises.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".