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Record W2783025291 · doi:10.1111/imig.12536

Fiscal Pressure of Migration and Horizontal Fiscal Inequality: Evidence from Indian Experience

2018· article· en· W2783025291 on OpenAlexfundno aff
Pinaki Chakraborty, Shatakshi Garg

Bibliographic record

VenueInternational Migration · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsEconomicsExtant taxonProsperityInequalityFiscal policyState (computer science)Per capitaHorizontal and verticalDevelopment economicsGeographyMacroeconomicsPopulationEconomic growthDemographySociology

Abstract

fetched live from OpenAlex

Abstract This article examines the patterns and trends in inter‐State migration across the Indian States and observes that along with the demographic factors, migration is also affected by the fiscal profile of States. Controlling for the economic prosperity of States as reflected in their per capita Net State Domestic Product and the nature of fiscal policy stance, econometric estimation using the gravity model suggests that level of vertical federal transfers and its horizontal distribution is an important determinant of the pattern of inter State migration. To correct for the extant horizontal fiscal inequality across the Indian States, the article suggests a relatively more progressive transfer system and a developmental fiscal policy stance at the State level to reduce the pressure of out‐migration to prosperous regions of the country.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.302
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.029
GPT teacher head0.337
Teacher spread0.308 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2018
Admission routes1
Has abstractyes

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