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
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it