Lifecycle Consumption-Investment Policies and Pension Plans: A Dynamic Analysis
Bibliographic record
Abstract
This paper explores the optimal design of personal pensions based on the economic theory of the life cycle. It assumes that individuals derive utility from consumption of goods and leisure and that at some date they retire and stop earning income from labor. The existence of this retirement phase of the life cycle has a profound impact on optimal consumption and portfolio policy. We describe the properties of the optimal pension contract and derive the dynamic trading strategy that hedges the contract. In view of the popularity of age-based strategies — like target date funds — as default options in 401k and other defined contribution retirement plans, some of our results are particularly noteworthy. All target date funds start with a high proportion in equities at a young age and reduce it as a person ages. We identify conditions where the fraction of wealth optimally invested in equities increases or decreases over time as an individual ages. We also analyze the dynamics of pension plans, wealth and optimal policies. Distributional properties of endogenous variables are examined and the robustness of patterns to variations in parameters such as risk aversion and mortality risk is examined.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".