Consumption Smoothing and Optimal Savings
Bibliographic record
Abstract
Learning Objectives In this chapter, we look at consumption/investment decisions. During your working years, you have to decide how much of your salary you want to spend immediately and how much to save for the future. We use an economic concept called consumption smoothing , which suggests that people want to optimize their standard of living over their lifetime. They do so by balancing the spending and savings during different stages of their lives. We will see that under this concept, people's standard of living will not wildly fluctuate. Rather, it either remains the same or changes in a smooth fashion over their lifetime. You will learn to derive your optimal consumption and savings at various ages. You will also see how your financial and economic net worth change over time. Consumption Smoothing Imagine the following situation. You are twenty-five years old, and your long-lost and rather eccentric uncle, whom you rarely met and barely know, has just passed away. When the lawyers vet his will, they discover that he left you $25 million as an inheritance. Unbeknownst to your family, he was quite wealthy and he took a liking to you personally. Unfortunately (and there always is abut), themoney has been placed in an iron-clad trust that you cannot access for the next twenty-five years. Even the best lawyers in town cannot break or dissolve the trust documents. Your uncle was concerned about your financial maturity, and he decided it would be best to wait until you are (much) older before giving you the title to this unprecedented sum of cash.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".