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We propose a task for eliciting attitudes toward risk that is close to real-world risky decisions which typically involve gains and losses. The task consists of accepting or rejecting gambles that provide a gain with probability $$p$$ and a loss with probability $$1-p$$ . We employ finite mixture models to uncover heterogeneity in risk preferences and find that (i) behavior is heterogeneous, with one half of the subjects behaving as expected utility maximizers, (ii) for the others, reference-dependent models perform better than those where subjects derive utility from final outcomes, (iii) models with sign-dependent decision weights perform better than those without, and (iv) there is no evidence for loss aversion. The procedure is sufficiently simple so that it can be easily used in field or lab experiments where risk elicitation is not the main experiment.

This paper contains the on on-line appendix to the paper with the same title forthcoming in ILRR.; Is the Bayh-Dole intellectual property regime associated with more and better academic entrepreneurship than the Professor Privilege regime? The authors examine data on US PhDs in the natural sciences, engineering, and medical fields who became entrepreneurs in 1993–2006 and compare this to similar data from Sweden. They find that, in both countries, those with an academic background have lower rates of entry into entrepreneurship than do those with a non-academic background. The relative rate of academics starting entrepreneurial firms is slightly lower in the United States than in Sweden. Moreover, the mean economic gains from becoming an entrepreneur are negative, both for PhDs originating in academia and non-academic settings in both countries. Analysis indicates that selection into entrepreneurship occurs from the lower part of the ability distribution among academics. The results suggest that policies aimed at supporting entrepreneurial decisions by younger, tenure-track academics may be more effective than general incentives at increasing academic entrepreneurship in general.

International audience; A review of recent evidence on relative earnings from entrepreneurship versus wage work presents a puzzle: why do individuals become entrepreneurs when entrepreneurs on average apparently earn less than employees? After considering several potential explanations, we empirically analyze one: income underreporting by entrepreneurs. Using a nationwide panel survey representing U.S. households over 15 years, we estimate that entrepreneurs on average earn 4% less per year than employees. However, after correcting for income underreporting, the mean financial gain to entrepreneurship is positive and large, greater than 42%. However, we show that this estimate is built on some unpalatable model assumptions.

We debate the motivation for and effectiveness of public policies to encourage individuals to become entrepreneurs. Reviewing established evidence we find that most western world policies do not greatly reduce or solve any market failures but instead waste taxpayers’ money, encourage those already intent on becoming entrepreneurs, and mostly generate one-employee businesses with low growth intentions and a lack of interest in innovating. Most policy initiatives that would have the effect of promoting valuable entrepreneurship would not be recognizable as such, because they would primarily address other market failures: a central-payer healthcare would remove health-care related distortions affecting employment choices; greater STEM education would produce more engineers of which some start valuable new firms; and labor market reform to encourage hiring immigrants in jobs they have been educated for would reduce inefficient allocation of talent to entrepreneurship.

International audience; There is a growing body of evidence that many entrepreneurs seem to enter and persist in entrepreneurship despite earning low risk-adjusted returns. This has lead to attempts to provide explanations--using both standard economic theory and behavioral economics--for why certain individuals may be attracted to such an apparently unprofitable activity. Drawing on research in behavioral economics, in the sections that follow, we review three sets of possible interpretations for understanding the empirical facts related to the entry into, and persistence in, entrepreneurship. Differences in risk aversion provide a plausible and intuitive interpretation of entrepreneurial activity. In addition, a growing literature has begun to highlight the potential importance of overconfidence in driving entrepreneurial outcomes. Such a mechanism may appear at face value to work like a lower level of risk aversion, but there are clear conceptual differences--in particular, overconfidence likely arises from behavioral biases and misperceptions of probability distributions. Finally, nonpecuniary taste-based factors may be important in motivating both the decisions to enter into and to persist in entrepreneurship.

We propose a task for eliciting attitudes towards risk that is close to real world risky decisions which typically involve gains and losses. The task consists of accepting or rejecting gambles that provide a gain with probability p and a loss with probability 1 − p. We employ finite mixture models to uncover heterogeneity in risk preferences and find that (i) behavior is heterogeneous, with slightly less than one half of the subjects behaving as expected utility maximizers, (ii) for the others, reference-dependent models perform better than those where subjects derive utility from final outcomes, (iii) models with sign dependent decision weights perform better than those without, and (iv) there is no evidence for loss aversion. The procedure is sufficiently simple so that it can be easily used in field or lab experiments where risk elicitation is not the main experiment.

The topic of Entrepreneurial Finance involves many issues, including but not limited to the risks and returns to being an entrepreneur, financial contracting, business planning, capital gaps and the availability of capital, market booms and busts, public policy and international differences in entrepreneurial finance stemming from differences in laws, institutions and culture. As these issues are so extremely broad and complex, the academic and practitioner literature on topic usually focuses on at most one or two of these issues at one time. The Oxford Handbook of Entrepreneurial Finance provides a comprehensive picture of issues dealing with different sources of entrepreneurial finance and different issues with financing entrepreneurs. The Handbook comprises contributions from 48 authors based in 12 different countries. It is organized into seven parts, the first of which introduces the issues, explains the organization of the Handbook, and briefly summarizes the contributions made by the authors in each of the chapters. Part II covers the topics pertaining to financing new industries and the returns and risk to being an entrepreneur. Part III deals with entrepreneurial capital structure. Part IV discusses business planning, funding and funding gaps in entrepreneurial finance with a focus on credit markets. Part V provides analyses of the main alternative sources of entrepreneurial finance. Part VI considers issues in public policy towards entrepreneurial finance. Part VII considers international differences in entrepreneurial finance, including analyses of entrepreneurial finance in weak institutional environments as well as microfinance.

International audience; Lazear (2005) suggests that entrepreneurs should be generalists, while those who work for others should be specialists. Many prospective entrepreneurs will develop varied skills by engaging in a variety of employment activities prior to becoming an entrepreneur, and incomes are higher for those that do so. An alternative view predicts that those with greater taste for variety are more likely to become entrepreneurs. Varied employment prior to becoming an entrepreneur is simply an expression of this taste, and is associated with lower earnings. Data from a survey of 830 independent inventors and 300 individuals from the general population are used to discriminate between these two theories. The results show that inventor-entrepreneurs typically have a more varied labor market experience, and that varied work experience is associated with lower household income.

Direct individual-specific evidence on the profit motives of entrepreneurs has been lacking. Indeed, recent work suggests that entrepreneurship may be a non-profit-seeking activity, and that entrepreneurs evaluate risk oddly. In contrast, using direct prior measures of economic potential for inventions, we find that inventors respond sensibly to pecuniary incentives. We construct an empirically-relevant economic theory, with heterogeneous inventions and inventors, outside opportunities, sunk and non-sunk costs, and risk, to guide our investigation of the data. We collected assessment data from a center paid by inventors to assess their inventions' economic potential, and followed up with surveys to determine concrete economic outcomes. We find that inventors decide whether to commercialize their inventions (and later whether to exit) in a manner consistent with profit-seeking motives and risk aversion.

This article examines the impact of key success factors on the survival of innovations that have reached the market and were developed by inventors outside of established organizations. It is of interest to learn which characteristics predict, at an early stage, the duration of the innovation’s length of sales, because this duration is important to the financial success of new products. A focus on survival also can contribute conceptual clarity to the study of new product development. This study uses the Inventor’s Assistance Program (IAP) at the Canadian Innovation Centre (CIC) in Waterloo, Canada, as the source of data. The CIC is a not-for-profit agency that provides various services to foster business development involving innovative products and services. Analysts in the IAP evaluate a specific product idea or invention on 37 dimensions before it has reached the market. The data for the present study involved these 37 variables evaluated each with a three-point linguistic scale. As the evaluations of the criteria are subjective, they might be argued to contain inaccuracies compared to objective data. On the other hand, the analysts use multiple related measures of concepts that have been shown to increase predictive accuracy. The use of experts who are unrelated to the projects avoids decisionmaking biases potentially associated with project managers’ assessment of their own projects, such as unrealistic optimism. The recording of the expert evaluations of the ideas before they reached the market and independent of the measure of success, rather than using post-project completion evaluations, eliminates measurement biases such as hindsight bias and common method variance bias. Identifying information was used in these records to conduct a telephone survey of the inventors. An exploratory method of data analysis is identified and used that distinguishes research-appropriate constructs and their indicators in these data. Cluster analysis was performed, and survival regression correlated cluster scores with survival. Three variables were found to significantly affect survival: anticipated stable demand, price required for profitability, and technical product maturity. In addition, the degree of competition had a marginally significant effect. Because these variables can be assessed at an early stage of an inventions’ development, the expected survival time for a specific invention may be computed by entering these assessment values into the described survival model. Then this and other information may be used to compute the expected return of an invention.

International audience; I propose that sunk costs of learning and the output over which these costs are spread determine the probability and depth of technology adoption. Depth of adoption describes the extent to which firms exploit the advantages of the technology. I find that plant size but not firm size predicts CAD and CNC adoption. Learning costs are lumpy, are closely connected to technology adoption and determine both the probability and depth of adoption. Depth of adoption is considerably more plant idiosyncratic than the decision to adopt.

The impact of 36 innovation, technology, and market characteristics on the probability that early stage R&D projects will reach the market is examined. Analysis is based on data from 561 R&D projects developed by technological entrepreneurs. Four characteristics stand out as most predictive: expected profitability, technological opportunity, development risk, and appropriability conditions. These predict future commercial success well with an out-of-sample accuracy of 80.9 %. This model performs better than R&D managers' and venture capital's (VC's) forecasts of success and has the potential to be used as a screening tool for early stage R&D investment reviews by, for example, VCs. Implications for both research and practice are discussed.

International audience; We investigated the relation between the survival of new small businesses and bank loans. This was done using a model that included other loan sources, human capital variables, and company and industry descriptors. We found there is a negative correlation between having a bank loan and business survival, and a positive correlation between having a non-bank loan and survival. However, having a bank loan was a ceteris paribus positive predictor of the survival of start-up companies. Our findings enabled some inferences about the process of loan source selection by start-up business owners, and about the banks' loan granting process.

International audience; The authors analyze the efficiency of six missing data techniques for categorical item nonresponse under the assumption that data are missing at random or missing completely at random. By efficiency, the authors mean a procedure that produces an unbiased estimate of true sample properties that is also easy to implement. The investigated techniques include listwise deletion, mode substitution, random imputation, two regression imputations, and a Bayesian model-based procedure. The authors analyze efficiency under six experimental conditions for a survey-based data set. They find that listwise deletion is efficient for the data analyzed. If data loss due to listwise deletion is an issue, the analysis points to the Bayesian method. Regression imputation is also efficient, but the result is conditioned on the specific data structure and may not hold in general. Additional problems arise when using regression imputation, making it less appropriate.

International audience; The question of how venture capitalists assess the unique characteristics of potential ventures lies at the basis of all venture investing. Yet the data on human versus purely statistical prediction models is troubling, often indicating that humans are poor decision makers in the face of uncertainty. This article looks at over 500 potential new ventures, each reviewed by skilled engineers with some business background. From a list of 37 criteria, four were found to be statistically significant: expected profitability; development risk; functional performance; and IP protection. The rate of accuracy of the skilled predictors was surprisingly high.

International audience; We derive the mean and variance of the random discounted sum [[[sigma].sup.N].sub.n=1] [[theta].sup.n][X.sub.n], when N is uncertain, as are the [X.sub.n]'s. This quantity arises in applications involving random cash-flows over an uncertain number of years. One such application is R&D projects, where both the magnitude and duration of cash-flows are uncertain at the time of investment decision. Previous models have assumed cash-flow duration to be certain. We relax this assumption. We then specialize these results to geometric, mixed-geometric and Poisson distributions of the cash-flow duration.