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Record W3120256977 · doi:10.1007/s10797-020-09642-1

Political competitiveness and the private–public structure of public expenditure: a model and empirics for the Indian States

2021· article· en· W3120256977 on OpenAlexaff
Stanley L. Winer, J. Stephen Ferris, Bharatee Bhusana Dash, Pinaki Chakraborty

Bibliographic record

VenueInternational Tax and Public Finance · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policies and Political Economy
Canadian institutionsCarleton University
FundersShastri Indo-Canadian Institute
KeywordsPublic financePublic expenditurePoliticsEconomicsPublic economicsGovernment (linguistics)Public goodState (computer science)Competition (biology)Socioeconomic statusPublic sectorPolitical scienceEconomyMacroeconomicsPopulationSociologyLawMicroeconomics

Abstract

fetched live from OpenAlex

Abstract Studies of government size usually try to identify the factors that explain what parts of economic activity are brought within the public sector and what parts are left strictly in private hands. Modern governments are now so large that the question of what determines the private/public composition, or privateness, of public expenditure is of comparable importance for understanding the role of government in society. In this paper, we use a model of the composition of public budgets to uncover the importance of electoral competitiveness and other factors in the evolution of the privateness of public expenditure across the Indian states. These states vary widely in their socioeconomic characteristics while sharing a common political heritage based on parliamentary government. New measures of public expenditure on private targetable goods and of electoral competitiveness at the Indian state level accompany the paper along with a primer on Indian public finance accounting practices in an Online Appendix. The empirical analysis shows that the degree of privateness in India’s more developed states falls substantially with greater political competition and with rising incomes, while in the less developed states it responds more weakly to these key factors and in some cases even inversely.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.417
Threshold uncertainty score0.407

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 studies0.0000.001
Scholarly communication0.0000.000
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 head0.036
GPT teacher head0.251
Teacher spread0.215 · 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 designTheoretical or conceptual
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

Citations14
Published2021
Admission routes1
Has abstractyes

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