The Urban Informal Sector and Poverty: Effects of Trade Reform and Capital Mobility in India
Bibliographic record
Abstract
Studies on formal-informal interactions in the labor markets of developing countries claim that economic reform increases the level of informal activity. Although the extent of such claims differs across countries, it is generally believed that reform is likely to depress informal wage by contracting the formal sector and driving labor onto its informal counterpart. However, available empirical evidence suggests that real wage and real fixed assets in the informal manufacturing sector have risen significantly across most states in post-liberalization India. Using this as a benchmark, we formalize a general equilibrium model of inter-sectoral capital mobility and informal wage to argue that, with limited degree of capital mobility, trade reform reduces the informal wage. This is the convetional wisdom usually obtained under a partial equilibrium framework. However, with increased mobility of capital this result is reversed. We offer detailed emmpirical evidence on the movements of real wage in the informal sector in India and how this affects poverty at the state level. The basic result on income mobility is corroborated by a primary survey in the province of West Bengal, for which we offer descriptive analysis on household income levels in the province's informal manufacturing and service sectors.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".