Capital Income Taxation under Full loss offset Provisions of a Prospect Theory Investor
Bibliographic record
Abstract
In this paper we examine capital income taxation of a reference dependent sufficiently loss averse investor in a two period portfolio choice model under full loss offset provisions. Capital income taxation with loss offset provisions has been found to stimulate risk taking in expected utility models under certain assumptions about attitudes towards risk but would such effect be found under prospect theory type of preferences? We observe that the impact of capital income taxation depends on investors’ reference levels relative to their endowment income and thus we explore capital income taxation for different types of loss averse investors in terms of their ambition. We consider the less ambitious investors to be the ones with relatively low reference levels (they avoid relative losses in both periods) and more ambitious investors to be those with relatively high reference levels. We analyze two types of more ambitious investors: investors with higher time preference (who experience relative losses only in the second period under the bad state of nature) and investors with lower time preference (who experience relative losses only in the first period). We observe that capital income taxation stimulates current consumption in most cases which encourages risk taking, although the final outcome would depend on the investors’ degree of risk aversion, the rate of time preference and the tax rate in relation to certain thresholds. Current consumption could be discouraged for some ambitious type of investors that have relatively high second period reference levels but not necessary first period reference levels. in summary, to determine the impact of capital income taxation on the decision variables the reference levels in relation to endowment income play the most significant role. Ignoring reference depended preferences can lead to different conclusions for investors reaction to capital income taxation. We also find certain type of investors whose happiness level increases with capital income taxation under full loss offset provisions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".