Essays in Economics of Education, Aging and Trade Unions
Bibliographic record
Abstract
How does one's success in high-stakes exams at the end of high school affect future life outcomes? In Essay I, my co-author and I estimate the effect of receiving a higher grade on a high school exit exam on labor market and education outcomes in Finland. Identification comes from comparing students on different sides of grade cutoffs. Being above a cutoff in an exam leads to (i) an increase in quality of education but no change in years of schooling, (ii) an increase in yearly earnings that peaks between 1 and 5% at age 48, but no change in employment. Better education opportunities explain at most 60% of the increase in earnings. \n \nThe age composition of developed economies has been in flux because of the baby boom after World War II and the following reduction of birth rates to sub-replacement levels. In Essay II, I study how the age composition of a country's population affects the distributions of wages, hours worked and wealth. Macroeconomic models usually abstract away the age composition, but the model parameters are calibrated to real-world data, which is affected by the age composition. I use a reweighting method to quantify the composition effects of age on the distribution of wages, hours worked and wealth in Finland, Germany, Italy, the UK and the US. I find that the age composition affects the distribution of wealth the most. Mean wealth in 2010 is 15% lower for Germany, 14% for Italy and 7% for the US on a uniform age composition. The age composition affects wealth inequality and the overall distribution of wages and working hours to a much lesser extent. \n \nIn Essay III, I study credible wage promises by a labor union when firms invest in capital. A labor union promises a low wage. Firms respond by investing in capital. If the firms invest too much, the union cannot resist the temptation to renege on its promise and set a high wage. When the promise of a low wage is not credible, the level of investment is inefficiently low. When the interaction is repeated, the union can be punished in the future. Previous literature has assumed some specific punishment strategy (typically one-shot Nash equilibrium). I build a monopoly union model of an industry and use recursive methods to find the worst subgame perfect equilibrium to act as punishment. When the punishment is the worst subgame perfect equilibrium instead of the one-shot Nash equilibrium, unionization reduces consumer surplus from 0 to 50% less depending on the elasticity of demand for the output good, capital intensity of production and the discount factor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 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 teacher head, 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".