Bibliographic record
Abstract
In this thesis, I add to the literature which uses microdata to assess how economic outcomes are influenced by the financial condition of firms and households. In my first chapter, I analyze whether leverage impacts the growth rates of capital expenditures and employment for US and Canadian publicly traded firms in a non-linear manner. I find that the negative marginal impact of leverage on future growth tends to be higher for firms in higher leverage quartiles for firms with similar growth prospects. I also show that for firms with similar leverage, the impact of leverage on future growth is larger for firms with low growth opportunities compared to those with better growth opportunities. These findings provide new evidence supporting the use of debt to reduce the agency conflicts between shareholders and managers for firms with low growth opportunities. The second chapter of my thesis challenges the findings of a lead AER article which had analyzed how a firm's investment responds to changes in its collateral values. I demonstrate that their results are highly sensitive to arbitrary Winsorization thresholds. I also show that a firm's investment responds to shocks to national real estate prices rather than shocks to the value of collateral it actually owns. The extent to which real estate shocks affect corporate investment---the collateral channel---therefore, remains an open question. The final chapter of my thesis analyzes the impact of macroprudential housing finance rules changes in Canada between 2005 and 2010. I start by combining loan-level administrative data with household-level survey data to determine the financial condition of potential first time homebuyers in Canada. I then use a microsimulation model of mortgage demand to determine the impact of rule changes on potential first time homebuyers. Policies targeting the loan-to-value ratio are found to have a larger impact than policies targeting the debt-service ratio, such as amortization. In addition, I show that loan-to-value policies have a larger role to play in reducing default than income-based policies. Disclaimer, the views in this thesis are mine and do not necessarily reflect those of the Bank of Canada.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".