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Record W7028358480

Fiscal Federalism and Decentralization in Indiaâ

2007· article· en· W7028358480 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship (California Digital Library) · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicLocal Government Finance and Decentralization
Canadian institutionsnot available
FundersCanadian Institute for Advanced Research
KeywordsDecentralizationAccountabilityFiscal federalismLegislatureRevenueFederalismContext (archaeology)Public service
DOInot available

Abstract

fetched live from OpenAlex

This paper surveys the state of fiscal federalism in India, in the broader context of decentralization. We begin with an overview of the basic features and recent developments in intergovernmental fiscal relations, including the role of political institutions, the specifics of legislative and budgetary autonomy, assignments of expenditure responsibility and revenue authority, revenue collection mechanisms, the system of intergovernmental transfers, and institutions and mechanisms for borrowing by subnational units. We then provide a diagnosis of accountability mechanisms, examining their quality of functioning and distortions and constraints. In particular, we analyze to what extent, and through what mechanisms, lower-level governments are held accountable to higher-level governments, the extent to which policies of subnational governments are affected by the competition for mobile firms, taxpayers, and investment capital, the functioning of credit markets, including the responsiveness of subnational governments to credit ratings and bond yields, and the role of democratic electoral channels at national and subnational levels in providing accountability in the provision of subnational public goods. Next, we review the implications of the intergovernmental system and accountability mechanisms on the cooperation of subnational governments and the quality of service delivery. We examine the resources and capacity of the subnational entities that are responsible for key services such as water, sanitation, education and health care, and the impacts of decentralization on service quality and the distribution of benefits among elected officials, citizens and interest groups. Finally, we offer a concluding assessment with suggestions for reform priorities.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.605
Threshold uncertainty score0.961

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.004
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 head0.012
GPT teacher head0.241
Teacher spread0.229 · 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

Citations0
Published2007
Admission routes1
Has abstractyes

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