Bibliographic record
Abstract
For first-time, lower-income and credit-constrained entrepreneurs (“entry-level entrepreneurs”), the employment tax savings proffered by a longstanding tax shelter known as the “Sub-S Shelter” can be particularly salient. Such hypersalience is problematic from a policy perspective. It not only increases the costs and complexity of the entry-level entrepreneur’s deliberation process concerning the appropriate entity for her business, but it distorts her incentives to choose the entity that best supports her business’s future growth. I argue that because the hypersalience of the Sub-Shelter is likely to be more pronounced for entry-level entrepreneurs than for entrepreneurs with more experience or better access to capital, the burdens of the shelter are distributionally regressive. As an alternative to full-scale reforms that would eliminate the demand for the Sub-S Shelter but may be politically infeasible, I suggest that the shelter’s regressive hypersalience can be addressed by government measures to provide choice-of-entity information tailored to the needs and concerns of entry-level entrepreneurs. Such targeted information can mitigate the hypersalience of the Sub-S Shelter by underscoring the risks of relying on it, while highlighting the real option value of choosing a more flexible business entity such as an LLC. By nudging entry-level entrepreneurs towards neutrality in regard to their choice-of-entity decisions, this approach has the potential to improve both the efficiency and the equity of a key step in formalizing a new business.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".