Essays on time preference anomalies, intertemporal choice, insurance, and status
Bibliographic record
Abstract
The goal of my dissertation is to analyze individuals' behavior when they make choices over time and within a group. The first chapter is devoted to explaining some key time preference anomalies which are inconsistent with the standard discounted utility model. In the second chapter, I focus on how inter-personal comparisons would affect people's intertemporal choices. Finally, the last chapter studies how the concern for status affects the optimal risk sharing across individuals. The first chapter studies some key time preference anomalies. These include the time preference reversal characteristic of hyperbolic discounting, the magnitude effect and the extreme sign effect. I propose a simple explanation of discounting that accounts for these three anomalies simultaneously, within the context of the expected utility model with uncertainty, risk aversion and preference for precautionary saving. The second chapter develops an intertemporal model in which individuals care about consumption not only for its own sake but also for the status it implies. By putting an additive status term into the utility function, I show that the level of inequality in the initial wealth distribution affects individuals' saving and consumption behavior. The direction of the distortion in intertemporal choice relative to the standard model without status depends on the elasticity of intertemporal substitution in the utility from absolute consumption. I also analyze how changes in the initial wealth distribution affect saving. In the third chapter we develop a series of optimal social insurance models in which people care about both consumption per se and the status it implies. We show that the concern for status does impact the optimal contract under various information structures. Particularly, under complete information without commitment problem, the optimal contract may assign all the society resources to the minority group if the status term is convex enough. Under the limited enforcement regime, compared to the optimal allocation in the pure consumption model, it is optimal to transfer more resources to high income people when the status term is convex. Under moral hazard, the relatively lower status resulting from the higher effort level may make implementation of high effort level more difficult.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".