Investigating inflation and the dynamics of the revenue system of municipalities (case study: Isfahan Municipality)
Bibliographic record
Abstract
Municipal taxes are one of the main sources of revenue of municipalities, which includes a large part of the revenue. Each municipality needs an efficient system of revenue collection to perform its local tasks and meet the growing urban needs, which can be adjusted for fluctuating factors and outward shocks. One of the factors affecting the real revenues of municipalities is inflation, which, depending on the case, causes a decrease or increase in revenues. On the other hand, delaying the payment of taxes by the payers is one of the factors that cause income fluctuations; therefore, in the present study, in the form of a Tanzi model of the dynamics of the Municipal Income System, the relationship between inflation and real income is examined through the calculation of the length of lags in the collection of taxes and income elasticities for the municipality of Isfahan. The data used in seasonal time series ranges from the third quarter of 1385 to the second quarter of 1400, and the method used in this study is the ARDL model. The results indicate that the duration of the lag of the collection of complications was about 4 months and the income elasticity was 41/0, and the revenues were adjusted by a change of one percent in inflation to only 41/0. So, inflation has reduced the real revenues of the municipality, and the performance of the municipality in adjusting the revenues to inflation has been ineffective. JEL Classification: R51, E31, H20.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".