Bibliographic record
Abstract
In this thesis, I use both structural- and reduced-form econometric models to analyze economic issues relating to the mortgage and labour markets. In chapter 2, I use a dynamic discrete choice model to quantify the impact of the Home Affordable Refinance Program (HARP) in the United States during the Great Recession, on mortgage default rates, as well as its monetary benefit to the government. I find that HARP led to a 71.6\% reduction in the total number of defaults and has saved the government US\$ 800 million. Furthermore, I also use the structural model to evaluate HARP under a counterfactual scenario of prolonged depression of house prices instead of the V-shaped recovery of house values we observed after the crisis. In chapter 3, I use a differences-in-differences (DiD) approach to establish the causal link between securitization and screening standards in the U.S. mortgage market. The estimation strategy exploits the discrete set of policy changes in the conforming loan limits that set the maximum loan size (conforming loans) that government-sponsored enterprises (GSEs) can purchase and securitize. I find that GSEs' purchase eligibility has increased the probability of loan origination by $\sim3\%$. Chapter 4, co-authored with Jason Adams and David Ros\'{e}, investigates the efficacy of the Ontario Works' (OW) active labour market programs (ALMPs) in helping welfare recipients find and retain employment using an instrumental variables (IV) approach. We find that structured job search activities and other skills-enhancing ALMPs reduce spells by between 1.6 and 3.1 months, whereas programs such as placement services increase spell lengths considerably, but lead to lower return rates to OW one and two years after the end of their welfare spells.
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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 | 0.018 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 0.009 |
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".