Entrepreneurs’ Legal Status Choices and the C Corporation Survival Penalty
Bibliographic record
Abstract
Foundational to the American Dream is the ability to easily and rapidly start a new business. Over the past quarter‐century, the limited liability company (LLC) dramatically shifted the choice of legal status calculus for entrepreneurs, and in its wake a consensus against the use of traditional C corporations by closely‐held firms emerged. The C corporation, scholars argued, had fatal drawbacks despite its simplicity: tax disadvantages as well as governance inflexibility. Due to historically limited sources of data, there has been little empirical research on choice of entity generally and none that explores the anti‐C corporation thesis in particular. Have C corporations underperformed as compared to similarly situated businesses with alternative legal statuses? This article exploits a large panel dataset that contains legal status, owner, business, financing, and other firm‐specific information collected from an eight‐year survey of nearly 5,000 enterprises that were formed in 2004. It presents four main results. First, C corporation status is associated with firm failure rates that are 38 percent higher (significant at 0.1 percent) than those of non‐C corporations with similar characteristics. Second, this C corporation survival penalty persists at nearly the same magnitude and significance even after a subset of “anticipated cash‐exit” C corporations with (a) venture capital investors or (b) employee stock option plans are separated out. Third, nonwhite and foreign‐born entrepreneurs have a significantly higher likelihood of choosing C corporation status. Fourth, within the subset of firms that appear eligible to elect out of the default (subchapter C) corporate tax classification and into the tax‐advantaged subchapter S classification, nonwhite and older entrepreneurs are significantly more likely to remain in C corporation status. These findings suggest that increasing legal status complexity is unlikely to be neutral from a distributive perspective, and may be disproportionately burdensome for marginalized entrepreneurs.
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 | 0.003 | 0.024 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".