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.
Highlights
One of the most fundamental questions of enterprise research: “How business start-ups are financed?” theoretical principles underlying the capital structure and financing choices can be generally described in terms of static trade-off or in term of Pecking-order framework
The following hypotheses regarding the problem of information opacity are proposed: H1: information opacity is positively correlated with the probability of using internal funds in financing new venture creation; H2: Information opacity is negatively associated with the probability of using bank debts in financing new venture; H3: Information opacity is negatively associated with the probability of financing the new venture by external equity; information problem may create some agency problems
Based on our review of the role of asset specificity in new venture financing, we propose to test the following hypothesis: H5: Asset specificity must be negatively correlated with the probability of using external debts; H6: Asset specificity must be positively associated with the probability of using internal fund for new venture financing; H7: asset specificity must be positively associated with the probability of using external equity for new venture financing
Summary
One of the most fundamental questions of enterprise research: “How business start-ups are financed?” theoretical principles underlying the capital structure and financing choices can be generally described in terms of static trade-off or in term of Pecking-order framework. Researchers demonstrated that the Pecking-order theory is more suitable for justifying the financial choice of new firm. This result is attributable to the importance of internal/external debts vs internal/external equity for the entrepreneur, [5,6]. [8] used a sample of new technology based firms; the study of [5] investigates a sample of business start-up The latter studied the problem of information opacity, asset specificity and human capital as determinants of capital structure of business start-up. We aim to study in one hand the possible application of the Pecking order Theory and in another hand the role of financial theory in predicting the capital structure of entrepreneurial firms. The organization of this paper is as follows: Section 2 is the study of the applicability of the pecking order theory; Section 3 is a summary of the role of financial theory in explaining the capital structure, Section 4 introduces the model construction, including selection of variables, data resources, model formulation as well as the estimation method and results and Section 5 draws conclusions and results
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