Essays on Human Capital, Inequality and Growth
Bibliographic record
Abstract
The first chapter studies heterogeneity in rates of college noncompletion in the United States. College noncompletion rates exhibit considerable heterogeneity along the lines of student ability and family resources. I study the origins of this heterogeneity in college noncompletion risk and its implications for productivity and the distribution of human capital. In general, we should expect noncompletion rates to rise as education subsidies become more generous. This occurs because 1) as subsidies become more generous, the average ability of enrollees falls, and 2) resulting lower skill premia drive noncompletion decisions by marginal students. Policy experiments show that although subsidy expansion increases human capital attainment, it is dominated by a merit-based subsidy. My findings suggest that the ``crisis'' of college noncompletion could be addressed with policies that affect students' work-study decisions. The second chapter studies the contribution of housing to the evolution of wealth and inequality in Canada during the recent housing boom. Combining data on household assets and quality-adjusted house prices with a portfolio share model, we estimate housing demand, mortgage demand and net worth in the absence of a housing boom. Increased wealth from the housing boom for low-earning and young households has been driven by an increase in the quantity of housing they own, whereas appreciation has been more important for older, affluent households. These effects vary considerably across cities. We also find that the housing boom in Canada has mitigated both wealth inequality and inequality of owner-occupied housing between high and low income households, as well as young, middle-aged, and older households. The third chapter studies the comparative development of settlements in Newfoundland, Canada after the incorporation of local governments, beginning in the 1950s. Earlier settlements, from the period of Admiralty Rule, were established with essentially no state, whereas later settlements, from the Dominion of Newfoundland, were established with only a centralized government. My results suggest that earlier settlements declined less slowly after incorporating local governments compared to more recent ones. I attribute this result to the lack of a state in early settlements crowding in the social capital that supports long-run institutional development.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".