2021 Report on nonemployer firms
Bibliographic record
Abstract
The COVID-19 pandemic affected small businesses across the United States, with few escaping financial and operational challenges as a result of declines in economic activity and actions taken to reduce the spread of the virus. Among the most impacted firms were the smallest businesses—nonemployer firms—which are businesses with no employees other than the owner(s). Given nonemployers make up 81% of all small businesses in the United States, understanding the impact of the pandemic on those businesses and their ability to access emergency funding is important in assessing the overall well-being of the small business sector. This publication focuses on the experiences of nonemployer firms in the months leading up to the pandemic and the first six months of the crisis. The report supplements the findings from the 2020 Small Business Credit Survey (SBCS) described in the Small Business Credit Survey 2021 Report on Employer Firms, which explored outcomes for businesses with 1–499 paid employees other than the owner(s). Nonemployer firms are distinct from employer firms in more than just the employment size of the business. Nonemployers are concentrated in different industries and are more likely to be owned by women and people of color. While some nonemployers are gig workers supplementing their income, a majority of the respondents in the SBCS sample reported that their firm was the primary source of income for their household, and a quarter of them planned to become employer firms within the next year. This publication examines findings for nonemployer firms and highlights the differences in experiences between nonemployer and employer businesses. On average, nonemployer firms reported larger declines in performance in the 12 months preceding the survey than employer firms, and nonemployers also more often struggled to access the funding necessary to keep their businesses afloat. This report also underscores the importance of revenue size: Nonemployer firms with $100,000 or less in annual revenues faced more challenges and worse outcomes than larger-revenue nonemployers, which often reported conditions similar to those of smaller-revenue employer businesses.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.014 |
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".