Bibliographic record
Abstract
This thesis presents three chapters in empirical macroeconomics. The first chapter studies how the mortgage expansions in the early 2000s affect U.S. regional economies by estimating its impact on the local labor market from 2003 to 2017. Using a plausible exogenous measure of the credit supply shock, I find that counties with higher credit supply shocks have not seen significant changes in local unemployment but have shown slower wage growth. While the high-credit counties did not experience significantly different changes in local labor markets in the expansion period, they did experience larger increases in unemployment in the recession, but also recovered faster after the recession, summing to a zero net effect in the long run. Meanwhile, these counties experienced a slowdown in wage growth since the recession, resulting in a depressed wage level until 2017. Additionally, the wage decline was accompanied by a decrease in the employment share of young firms. In Chapter 2, I propose a mechanism to explain how mortgage market fluctuations affected the labor market, slowed down wage growth, and led to labor reallocation. I introduce two financial constraints, one on the household side and the other on the production side, both tied to the collateral values of houses. I show that changes in household borrowing constraints affect housing prices and thereby affect firms’ financial condition. When working capital constraint binds, mortgage market fluctuations affect firms' labor demand, which led to labor reallocation from financially constrained to unconstrained firms and a decline in wage. In Chapter 3, we study how small and micro enterprises (SMPE) respond to the policy in reducing the corporate income tax rate in China. Using gradual increases in the qualifying threshold for SMPEs during 2010-2016 as a natural experiment, we find that the rate cut led to significant increases in sales growth, investment, and productivity of affected SMPE firms. We further show that the rate cut induced micro-sized firms to enter the market.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".