Three Essays in Labour Economics and Applied Econometrics
Bibliographic record
Abstract
The dissertation is comprised of three chapters. Chapter 1 investigates the marital behaviour of veterans.I first examine the extent to which military service makes men more marriageable using the NLSY97. I present evidence that non-college blacks enjoy the greatest return to military service in both the labour and marriage market. Second, to explain the relatively high intermarriage rates among veterans, I apply a local log odds framework to Census data to examine the social exchange hypothesis. Consistent with this hypothesis, I find both white and black women demand compensation to intermarry. Third, I present evidence in favour of the contact hypothesis. Chapter 2 evaluates the effects of racial integration during the Korean War (1950-1953). First, I evaluate whether the Army achieved its goal of improving efficiency as measured by the survival rates of wounded soldiers. Using casualty data, I develop a novel wartime integration measure to quantify exogenous changes in racial integration over time and across regiments. Based on a two-way fixed effects model, I find that a one standard deviation increase in integration improved overall casualty survival rates by 3%. Second, I explore the effects of wartime racial integration on the prejudicial attitudes of veterans after the war. To do so, I link individual soldiers to post-war social security and cemetery data using the expectation maximization algorithm. With these linked samples, I show that a one standard deviation increase in wartime racial integration caused white veterans to live in more racially diverse neighborhoods and marry spouses with less distinctively white names. Chapter 3 addresses the misuse of out-of-sample predictive performance tests for model selection. I compare in-sample alternatives for non-nested hypothesis testing of two possibly misspecified models developed by Vuong (1989), and compare those to the out-of-sample tests developed by Clark and McCracken (2014). Using Monte Carlo simulations, I demonstrate that the out-of-sample procedure indeed exhibits markedly worse statistical size and power. I then reproduce an applied example on GDP growth where the conclusions are found to be sensitive to the choice of out-of-sample versus full sample procedure.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.008 |
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".