The Capital Structure of Business Start-Up: Is There a Pecking Order Theory or a Reversed Pecking Order? —Evidence from the Panel Study of Entrepreneurial Dynamics
Bibliographic record
Abstract
Using the Panel Study of Entrepreneurial Dynamics, we study if the problems of asymmetry and opacity of information, asset specificity, agency problem and signaling theory predict the financial structure at inception. Thus, we conduct a study in two steps. First, by analyzing the descriptive statistics, we find that novice entrepreneurs turn first to internal sources of finance. Then, they apply to external debts and finally to equity finance. We prove then the applicability of the Pecking order theory in case of entrepreneurial firms. Second, by analyzing the role of financial theory in predicting the capital structure of entrepreneurial firms we find the following results. In fact, evidence from analyzing the role of information opacity, asset specificity and signaling theory, proves that the main source of finance is equity rather than debt. In the majority of the cases, depth interviews show from studying the financial theory an inverted pecking order. Two main reasons for this pattern can be established. First, entrepreneurs consider debt as a personal liability as it requires to be underwritten by personal guarantees. Entrepreneurs place a self-imposed limit on the extent to which they are prepared to mortgage their assets. Second, entrepreneurs deliberately seek out equity investment as a means of obtaining added value. This external equity which has been viewed as expensive is viewed as good value. A well chosen investor can add business skills and social capital in the form of commercial contacts and access to relevant networks.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".