Abstract Research Summary Evaluating early‐stage ventures is difficult because observable signals of future quality are weak and noisy. How closely are evaluators' assessments related to venture quality, and do their subjective judgments add to the information already available about a venture? Using more than 1600 screening decisions from a venture incubator, we examine how judges use application information to evaluate ventures by rating them on a set of 14 criteria and providing an overall recommendation. Judges' beliefs as recorded in the grades of the criteria explain their recommendations well, but they are only weakly associated with venture quality. We document some systematic miscalibration in how criteria are weighted, though the miscalibrations largely even out when examining the correlation between recommendations and quality. Within the models we estimate, beliefs and recommendations predict venture quality no better than ventures' observable attributes. Experienced judges are more selective but identify high‐quality ventures only slightly better than novice ones. Managerial Summary Accelerators and incubators rely on expert judges to screen early‐stage ventures, but how much does this expertise add? Using more than 1600 screening decisions at a venture incubator, we assess how well judges identify high‐quality ventures and where their judgment falls short. We find that judges' recommendations are only modestly accurate. Judges form a recommendation from venture information by first rating a set of 14 criteria, but the beliefs formed are themselves only weakly tied to venture quality. Judges also misweight the evidence: several characteristics that matter most for quality carry little weight in their recommendation, while others sway decisions despite mattering little. Experience helps at the margin; seasoned judges are more selective and better at filtering out weak ventures, but they don't spot winners better than novices. A simple model using only the objective data that ventures report in their applications predicts quality as well as the judges do.

How do entrepreneurs act on their beliefs when probabilities of outcomes are unknown but subjectively perceived? We theorize that two distinct dimensions of ambiguity attitudes influence entrepreneurial action: ambiguity aversion - the unwillingness to bear ambiguity - and ambiguity sensitivity - how individuals discriminate between different levels of perceived chances of success. The second dimension determines how much entrepreneurs adjust their actions based on new information - a distinct aspect that cannot be captured by ambiguity aversion alone. Our theory suggests that entrepreneurs with different growth orientations have different ambiguity attitudes as compared to employees. Using incentivized measures from a large-scale survey, we find that incorporated entrepreneurs exhibit lower ambiguity aversion than employees, indicating that they are more willing to act under ambiguity. Distinctively, unincorporated self-employed individuals show higher ambiguity sensitivity, indicating that their actions are more responsive to changes in their beliefs. These patterns persist after controlling for risk attitudes, optimism, cognitive ability, and demographics. Our results highlight the distinct impacts of ambiguity aversion and ambiguity sensitivity on entrepreneurial actions.

Youth often decide what to study with limited exposure to many high-paying careers. In-person visits from role models can change behaviour but are difficult to scale and typically expose youth to only one person in one career. We test the impact of exposing youth to multiple role models, in both science, technology, engineering and mathematics (STEM) and entrepreneurship careers, using online video interviews to intervene at large scale in a randomized trial with 29,243 students in 813 Ecuadorian high schools. Girls treated with multiple role models reduce their likelihood of choosing a STEM major, increasing enrolment in business majors instead. Boys also shift their major choice away from STEM and move towards other majors such as agriculture. The contrast between fields appears to shift youth away from what they see as the more challenging career and reinforces girls' stereotypical college major choice.

The article first documents the general rise of female-led and ethnic minority led startups, while Venture Capital (VC) funding to these groups is still at very low levels. It then summarizes recent research on gender differences in founding choices, gender and ethnic bias in VC funding, the role of founder team diversity on team performance as well as on fund performance.

This report to the "Investigation on development of the innovation and entrepreneurship climate in Sweden" SOU 2016:72 regards governmental policies for university technology commercialization. It contains a literature review that spans several areas of research addressing the potential effect of changing the allocation of Intellectual Property rights between universities and their employees. There is also some original research; several secondary datasets are re‐analyzed and some primary interview and case data from a few universities are also added.

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We build a model of equity crowdfunding that incorporates the two major funding models: all-or-nothing (AoN) and keep-it-all (KIA). Both informed and uninformed investors arrive sequentially and rationally choose whether and how much to invest. The KIA solution turns out to be a reduced version of AoN without signalling. We test predictions using data from a leading European equity crowdfunding platform and find support. Results are consistent with rational information aggregation. However, negative information cascades may still appear. The AoN crowdfunding mechanism might therefore fail to finance a nonnegligible percentage of positive NPV projects.

We use a novel method to define the R&D team and use the unexpected death of an R&D worker to study its effect on the organization of R&D teams in profit-making firms. Average treatment effects on exit probability, entry probability, wages of stayers, and characteristics of leavers are null. Hires are slightly younger in treatment than control. Treatment effects do not vary by the size of the R&D team but vary in interesting ways by the characteristics of the deceased and with job market liquidity. Results suggest weak complementarity of personnel and almost perfect substitution of talent in business R&D. Management appears able to quickly adjust the team to the shock.

Many school systems across the globe turned to online education during the COVID-19 pandemic. This context differs significantly from the prepandemic situation in which massive open online courses attracted large numbers of voluntary learners who struggled with completion. Students who are provided online courses by their high schools also have their behavior determined by actions of their teachers and school system. We conducted experiments to improve participation in online learning before, during, and right after the COVID-19 outbreak, with 1,151 schools covering more than 45,000 students in their final years of high school in Ecuador. These experiments tested light-touch interventions at scale, motivated by behavioral science, and were carried out at three levels: that of the system, teacher, and student. We find the largest impacts come from intervening at the system level. A cheap, online learning management system for centralized monitoring increased participation by 0.21 SD and subject knowledge by 0.13 SD relative to decentralized management. Centralized management is particularly effective for underperforming schools. Teacher-level nudges in the form of benchmarking emails, encouragement messages, and administrative reminders did not improve student participation. There was no significant impact of encouragement messages to students, or in having them plan and team-up with peers. Small financial incentives in the form of lottery prizes for finishing lessons did increase study time, but was less cost-effective, and had no significant impact on knowledge. The results show the difficulty in incentivizing online learning at scale, and a key role for central monitoring.

research, and the emerging field of entrepreneurship (see Roy Thurik's contribution in the present article for more on this point).Those who knew David personally had been aware for some time that he was fighting a terminal illness.

Research Summary: We document that since 1997, the rate of startup formation has precipitously declined for firms operated by US PhD recipients in science and engineering. We explore how increasing knowledge complexity can be associated with fewer science-based startups. The decline in startup formation is accompanied by an earnings decline, increasing work complexity in R&D, and more administrative work for science-based founders. Founding a startup appears to have become increasingly harder over the past 20 years, while established firms are becoming more attractive workplaces for PhDs.

In An Inside Peek at AI Use in Private Equity, from the Summer 2021 issue of The Journal of Financial Data Science, Thomas Åstebro of HEC Paris explores the rapid adoption of artificial intelligence (AI) technology among private equity (PE) and venture capital (VC) firms. In particular, he examines AI use among four PE firms: Jolt Capital, Hone Capital, EQT Ventures, and SignalFire. While the use of AI technology ranges from comparatively modest applications, such as screening models, all the way to full-scale employment in end-to-end decision support systems (DSSs), the author argues that even limited use of the technology increases profitability and operational efficiency. He also explores how AI can potentially overhaul how VC firms conduct their day-to-day business and outlines new potential revenue streams and investment strategies enabled by AI. Åstebro predicts that adoption of AI technology among established firms will steadily increase while new, AI-based PE firms will continue to enter the market. Looking ahead, he argues that the influx of investors using AI will lead to competition and eventually an industry shakeout.

Recent research has shown that an embarrassingly small proportion of entrepreneurs who obtain venture capital (VC) funding in the United States are females or members of ethnic minorities. Although access to venture funding by gender and ethnic origin has been well documented in the United States, much less information is available about the situation in Europe. The authors address the financing of gender and ethnically diverse startup teams in Europe and examine the relationship between founder team diversity, VC funding, and startup performance at venture-financed businesses. They use data on all recorded European venture deals over the period 2010 to 2020 for startups that have raised more than USD 1 million including all rounds and financiers, augmented by identifying founders by gender and ethnicity. For these startups, gender diversity on the founding team is not associated with raising VC funding in their first round of financing, while ethnic diversity plays an important role in both the amount of VC funding raised and venture performance in certain sectors and countries. A carefully designed investment strategy that builds on a preference for diverse founding teams in some sectors may therefore provide favorable returns.

Research Summary Designing effective entrepreneurship training programs is still a challenge despite the investments in training made by governments and private institutions, and its importance for economic growth. We report a case of impact measurement of a social entrepreneurship program based on repeated randomized controlled trials (RCTs), discuss challenges of conducting repeated RCTs, and implications for policy evaluation. Impact measures from the first edition of the program showed no detectable treatment effects. The second edition was adjusted by reducing leadership training and increasing traditional entrepreneurial skills training, and had strong treatment effects on entrepreneurial activities, the creation of a new venture during the program, and subsequent start-up activity. Employing sequential field experiments can improve entrepreneurship training programs despite the challenges of executing RCTs in the field. Managerial Summary Governments, institutions, and businesses increasingly invest in innovative entrepreneurship training programs that tackle societal problems. However, we know very little about the effectiveness of such trainings. To measure the causal impact of a social entrepreneurship training program we use an experiment where we assign one group of applicants randomly to training. A comparable group is selected randomly to not obtain the training. There were no detectable differences between the two groups the first time the program was run. After substantial changes were made, where analytical skills training was boosted at the expense of a reduction in social leadership skills training and social entrepreneurial identity development, and individualized coaching intensified, we find a large impact on entrepreneurial activities, both during the program and 3 years later.

The number of private equity (PE) firms that have started to use artificial intelligence (AI) in investment decisions has risen rapidly over the past 10 years. This article provides a detailed account that can serve as a template for others in the industry who wish to make better investment decisions using AI. The news is both good and bad. The increased use of AI in PE and venture capital will greatly increase operational efficiency and transform the ways in which partners perform their work. It will allow for the entry of new firms but will also lead to a technological arms race and is predicted to cause an eventual industry shakeout. TOPICS: Private equity, big data/machine learning, performance measurement Key Findings ▪ The number of private equity firms that have started to use AI is rising rapidly. ▪ Use of AI will greatly transform the deal-making process. ▪ By increasing efficiency, AI will likely cause an industry shakeout.

We document that since 1997, the rate of startup formation has precipitously declined for firms operated by U.S. PhD recipients in science and engineering.These are supposedly the source of some of our best new technological and business opportunities.We link this to an increasing burden of knowledge by documenting a long-term earnings decline by founders, especially less experienced founders, greater work complexity in R&D, and more administrative work.The results suggest that established firms are better positioned to cope with the increasing burden of knowledge, in particular through the design of knowledge hierarchies, explaining why new firm entry has declined for high-tech, high-opportunity startups.

We study the behavioral drivers of market entry. An experiment allows us to disentangle the impact on entry across different types of markets of two key behavioral mechanisms: overconfidence and attitude toward ambiguity. We theorize and show that the causal effect of overconfidence on entry is limited to skill-based markets and does not appear in those that are chance based. Moreover, we also find that, independent of confidence levels, individuals exhibit ambiguity-seeking behavior when the result of the competition depends on their skills, which in turn leads to higher levels of entry. This preference for ambiguity can thus explain results that have previously been attributed to overconfidence. Our results challenge existing literature that has inferred overconfidence from differential entry levels across types of markets.

Is the Bayh-Dole intellectual property regime associated with more and better academic entrepreneurship than the Professor’s 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 for 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 supporting entrepreneurial decisions by younger, tenure-track academics may be more effective than are general incentives to increase academic entrepreneurship.

Recent evidence comparing earnings from entrepreneurship versus wage earning shows that, after allowing for obvious observable differences, most entrepreneurs in most developed countries earn less than similar wage-earning employees. Does this mean that the decision to become an entrepreneur should be discouraged? The answer depends in part on whether we believe that entrepreneurs report their income truthfully or not. Adjusting for what is considered to be underreporting by entrepreneurs lifts entrepreneurial earnings by between 10 and 40 %, reversing the fortunes of the entrepreneur such that they appear to be earning much more than their counterparts in a wage-earning job. If this adjustment should prove to be appropriate, then there is no obvious reason to increase the incentive for individuals to become entrepreneurs (such as with tax breaks or direct start-up subsidies) in developed countries, and there is reason, instead, to discuss decreasing these subsidies.