An Analysis of Individual Tax Morale for Russia: Before and After Flat Tax Reform
Bibliographic record
Abstract
This paper examines individual tax morale in Russia before and after the introduction of flat tax reform in 2001. The World Values (WVS) and European Values Survey (EVS) are used to compare individual tax morale in 1999, 2006 and 2011. An ordered probit regression model is applied to study the effects of socio-demographic and institutional variables on individual tax morale. A new variable for employment sector that appeared in 2006 and 2011 values surveys is included in our model. The probit regression results revealed significant coefficients for income scale and the employment sector variables with negative marginal effects on tax morale. Socio-demographic variables have varying effects on tax morale while institutional variables are positively related to individual tax morale for the three years. To detect linear trend associations, Mantel-Haenszel hypothesis test results indicate that individual tax morale for Russia has not changed in the years before and after flat tax reform.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".