The Precarity of Self-Employment among Low- and Moderate-Income Households
Bibliographic record
Abstract
Abstract Many people in the United States have achieved economic stability through self-employment and are often seen as embracing the entrepreneurial spirit and seizing opportunity. Yet, research also suggests that self-employment may be precarious for many people in the lower socioeconomic strata. Drawing on a unique dataset that combines longitudinal survey data with administrative tax data for a sample of low- and moderate-income (LMI) workers, we bring new evidence to bear on this debate by examining the link between self-employment and economic insecurity. Overall, our results show that self-employment is associated with greater economic insecurity among LMI workers compared with wage-and-salary employment. For instance, compared with their wage-and-salary counterparts, the self-employed have 78, 168, and 287 percent greater odds of having an income below basic expenses, and experiencing an unexpected income decline and high levels of income volatility, respectively. We also find that differences in financial endowment and access to health insurance are key drivers in explaining the relationship between employment type and economic insecurity, as being able to access $2,000 in an emergency greatly lowers the odds of budgetary constraint, whereas lack of health insurance increases those odds. These findings suggest that formal work arrangements with wages and benefits offered by an employer promotes greater economic stability among LMI workers compared with informal work arrangements via self-employment. We discuss implications of these results for future research and policy initiatives seeking to promote economic wellbeing through entrepreneurship.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".