Life Cycle and Intergenerational Effects of Income and Wealth
Bibliographic record
Abstract
This dissertation includes three essays in economic history and applied microeconomics. In the first chapter, I investigate the intergenerational effects of a negative shock in wealth for African Americans by examining the failure of the Freedman's Bank. I find that children of depositors were more likely to be literate than children of non-depositors after the bank failure. The positive literacy effect is explained by an increase in schooling and literacy for the depositors' children prior to the bank failure. I find that the bank was able to promote education for the depositors' children through its connection with a Christian educational organization, the American Missionary Association. While children from iifamilies who lost a higher proportion of wealth were less likely to attend school after the bank failure, the human capital gains which occurred prior to the bank failure outweigh and outlast the adverse effect of wealth loss. In the second chapter, I test whether the failure of the Freedman’s Bank contributed to the mistrust and underutilization of financial institutions by African Americans today using present day survey data. I find that African Americans are less likely to be banked if they reside in a county with higher exposure to knowledge of the bank failure. In addition, for unbanked households, those who reside in a county with higher exposure to knowledge of the bank failure are more likely to report “mistrust” in bank as the primary reason to be unbanked. The results suggest that the collapse of the Freedman’s Bank can partly explain persistent gaps in the utilization of financial services by African Americans. Finally, in the third chapter, I explore whether low-income individuals with a guaranteed income had a higher likelihood of occupation turnover using from a randomized experiment that occurred in the 1970s, the Manitoba Basic Annual Income Experiment. I find that guaranteed income treatment increased the probability of an occupation switch, where most of the individuals who switched were above the age of 35. From survey results, I find that occupation turnovers resulted in non-pecuniary gains for all occupation switchers. Overall, these results suggest that when relieved of financial pressure, a subset of low-income individuals were more likely to switch occupations. Those in the treatment group accrued more non-pecuniary gain simply because they switched occupations more often.
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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 | 0.005 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".