Political competitiveness and the private–public structure of public expenditure: a model and empirics for the Indian States
Bibliographic record
Abstract
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.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".