Combining the contributions of behavioral economics and other social sciences in understanding taxation and tax reform
Bibliographic record
Abstract
This paper extends previous work presented at the SABE/IAREP conference at St Mary’s University, Halifax (James, 2009). In the earlier paper it was shown that conventional economic theory is used to make the case for tax reform but does not always adequately incorporate all the relevant factors. However, an approach based on behavioral economics can make the difference between success and failure. In this paper the contributions of other social sciences are also included. Taxation is a particularly appropriate subject to explore the integration of the social sciences since they have all devoted considerable attention to it. It can be seen that different social sciences suggest a range of variables that might be taken into account in addition to those included in mainstream economics. Other social sciences also offer different methodological approaches and consider the possibility of different outcomes of the fiscal process. The paper concludes that it is not easy to integrate the social sciences in a single approach to the study of tax and tax policy. There may also be the risk of encouraging inappropriate integration - researchers operating outside their expertise can produce results that are not helpful. However, comparing the contribution of behavioral economics with those of the social sciences more generally, it can be seen that behavioral economics can offer a framework within which these areas can be examined. Indeed, it may be a useful channel to add the contributions of other social sciences to mainstream economic research.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".