Expenditure Visibility and Voter Memory: A Compositional Approach to the Political Budget Cycle in Indian States, 1959 – 2012
Bibliographic record
Abstract
In this paper we argue that the search for opportunism in government budgets is weakened by the absence of a strong reason for why such expenditures should be restricted solely to the period leading into the next election. Here we argue that the need to fulfill a set of election platform promises in combination with the characteristic that some budget items better attract the attention of voters (with deteriorating memories) will lead to a predictable reallocation of budgetary spending across the life of a government. Our test for a predictable pattern rather than a specific period of election motivated spending uses capital expenditures as our example of more politically visible budgetary items and a data set of 14 Indian states over 54 years (1959/60 – 2012/13). The results of the hypotheses that capital expenditures as a ratio of both total government expenditure and government consumption alone should rise across the entire governing interval are found to be consistent with this hypothesis and provide a fit with the data that is marginally better than more traditional models that use either all pre-election periods or only the pre-election year of scheduled elections to test for opportunism. The absence of a similar interval effect on aggregate state expenditures and on the net budgetary position suggests that evidence of political interaction with the budget is more likely to be found in its composition rather than in its overall level or in the size of its surplus or deficit.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
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".