Fiscal decentralization and local tax effort
Bibliographic record
Abstract
In India an important policy initiative has been the devolution of financial responsibilities to village level local governments called the Panchayats. The Preamble to this initiative is two fold. First such devolution would not only lead to increased public expenditure but also such expenditures being targeted in a manner consistent with the preferences and needs of the local population. Second, the local tax base would widen, thereby reducing the magnitude of the equalization transfers. However, the incentive structures behind the granting of such additional financial powers have been inadequately articulated. The results have been in the form of reduction in taxes collected, as well as a perceived shrinking of the tax base. These outcomes are posited by us to be due to ignoring the impact of cost of collecting taxes, as well as perverse impacts of devolution of expenditure decisions on local wages and profits. The extant literature has been so far unable to adequately explain the perverse outcomes of devolution especially where reactions to local tax efforts to transfers from the higher level governments are concerned. This paper has attempted to fill this gap. It models and measures the cost of taxation and uses this and the ratio of transfers that augment the local wage rate to those that do not, after controlling for a number of other village level characteristics, to explain tax collected at the local level within a framework that allows for mutual endogeneity of tax collected and transfers. We find that both the cost of tax collection and the ratio of transfers that augment the local wage rate to those that do not have a significant negative effect on tax collection, thus validating the conclusions of the theoretical model developed in this paper. Several policy conclusions are derived. Keywords: Devolution, Incentive Effects, Equalizing transfers, Panchayats and Local Government
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".