Bibliographic record
Abstract
This dissertation studies the effect of trade on wage dispersion, crime rates and alcohol consumption at the local labor market level in the the U.S. The first chapter develops a new measure for skill to investigate the effects of offshoring on wages of three types of workers: high-skilled, medium-skilled, and low-skilled. I also look at the effect of offshoring on wages of offshorable occupations. Although the previous literature emphasizes the impact of offshoring on the skill premium, I find that job characteristics such as offshorability is critical in explaining the wage effect. Chapter 2 analyzes the effects of increasing import exposure from the top 6 trading partners of the US (China, Canada, Mexico, Germany, Japan and Korea) on property and violent crimes for the period 1992--2006 at the commuting zone level. My results indicate that a {dollar}1000 increase in Chinese exposure increases the property crimes by about 3 percent. On the other hand, the same amount of increase in import exposure from three other developed country trading partners, Germany, Japan and Korea, reduces property crimes between 2 to 4 percent. I find no evidence on the change of violent crimes from any of the countries. The last chapter examines the effects of increasing import competition from China on alcohol consumption at the county level for the years 2002--2006. Recent literature has shown that increasing import competition from China worsens the labor market outcomes. Lower cumulative earnings and the fear of job loss may increase financial stress for workers who may resort to alcohol as a coping mechanism. I find that increasing import exposure from China increases both the prevalence of drinking and binge drinking among workers. The effect is more pronounced for men than women. Further, for men, binge drinking has a larger effect than prevalence of drinking, whereas for women, prevalence of drinking has a larger effect than binge drinking.
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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 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".