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Record W3124124996

The Urban Informal Sector and Poverty: Effects of Trade Reform and Capital Mobility in India

2007· preprint· en· W3124124996 on OpenAlexfundno aff
Sugata Marjit, Saibal Kar

Bibliographic record

VenueRePEc: Research Papers in Economics · 2007
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Policy
Canadian institutionsnot available
FundersInternational Development Research CentreIndian Statistical InstituteWorld Bank Group
KeywordsInformal sectorEconomicsPovertyWageLabour economicsLabor mobilityCapital (architecture)Developing countryEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.489
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.017
GPT teacher head0.261
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2007
Admission routes1
Has abstractyes

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