Entrepreneurial Income and Wealth Dynamics
Bibliographic record
Abstract
This thesis studies wealth inequality, entrepreneurship, and financial frictions. The first chapter focuses on how the uninsurable nature of entrepreneurial risk reduces entrepreneurial activity and affects aggregate output, productivity, and the distribution of wealth. I build a model where individuals choose to become workers or entrepreneurs, and where entrepreneurs choose how risky a business to start. My model features two distinct financial frictions. First, a missing market for entrepreneurial risk prevents entrepreneurs from insuring themselves against their income risk and the risk of business failure. Second, borrowing constraints limit the size of an entrepreneur's business. I contribute to a literature on financial frictions and entrepreneurship by studying the missing market for entrepreneurial risk. The model is calibrated using micro data on new U.S. firms. I find that completing the missing market for entrepreneurial risk improves aggregate productivity by 9% and reducing the share of wealth held by the wealthiest 1% by two thirds. In a policy experiment, a partial insurance scheme for unsuccessful entrepreneurs can increase aggregate productivity and output by encouraging entrepreneurs to start riskier businesses. In my jointly-authored second chapter, we study how the composition and distribution of household wealth affects the distribution of MPC's. We document facts in the Survey of Consumer Finances about the composition of household portfolios between housing, mortgage debt, and more liquid financial assets over the distribution of wealth. We then build a rich quantitative model with heterogeneous returns that matches both the composition and concentration of wealth. We use the model to decompose the importance of return heterogeneity, long-term fixed rate mortgages and refinancing, and owner-occupied housing for the average MPC and find that each factor significantly contributes to generating a higher average MPC, with a cumulative effect of 0.25 percentage points. In my third chapter, I study how different government policies can promote entrepreneurship by mitigating financial frictions. In the presence of both a missing market for entrepreneurial risk and borrowing constraints, I compare how entrepreneurial insurance, a universal basic income, government-backed business loans, entrepreneurial income tax deductions, and tax progressivity will impact the decisions of potential entrepreneurs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".