1. What we want to know about entrepreneurship and the data we need for that

2. Entrepreneurship? Creation of new businesses Development and growth

3. Why do we care? Policy makers - interest in performance –Innovation, employment –mostly aggregate performance Heterogeneity across businesses –impacts differ –responses to shocks differ Understand micro-determinants of performance From individual to aggregate performance

4. What we want to know Where do new businesses come from? Why do people go into entrepreneurship? What do new businesses do? What separates those that succeed from those that do not? What is success? What happens to those that do not succeed? What can be done to improve their chances of success? Is this desirable?

5. What we need Longitudinal data –entrepreneur –venture –Key agents in support activities (e.g. finance, R&D) Sources with data on inputs and outputs Control groups (Researchers access to micro-data)

6. Longitudinal data Where do new businesses come from? What separates those that succeed from those that do not? What happens to those that leave entrepreneurship?

7. Linking inputs and outputs What separates businesses that succeed from those that do not? Data sets must contain data on inputs and outputs –Same dataset with data on determinants and performance for the same businesses

8. Creation of new businesses Different stages of creation of new ventures –From the idea to the IPO Individual entrepreneurs –Extended PESD approach –Partnerships New businesses as result of disagreements? Intrapreneurship –How companies reward new ideas –Practices that lead to new business Incubators –Exiting businesses and spin offs

9. Performance and determinants Performance - What is success? –Survival, employment, growth –Profit, value added, innovation –Own satisfaction Ex-ante and ex-post Example – exit can be associated with success –Sale / closing of existing business with a profit. –Serial entrepreneurs –Wages after entrepreneurial experience

10. Determinants History –What the person/venture did –Avoid recall bias Preferences –define success ex-ante Actions (e.g. who do they hire) Institutions (e.g. environment) Public policies (e.g. public support?)

11. Motivations - why go into entrepreneurship Opportunity vs. necessity Goals –define success (ex-ante) Preferences –Risk profile, preference for skewness –Loss aversion Perceptions –Self confidence, how good do you think you are compared to others –Optimism what is the probability that your business succeeds

12. Institutions European wide data –Different countries, different institutions –Need comparable (?) data legal definitions liability, etc. Providers –Official Statistics –Commercial databases (e.g. Amadeus)

13. Sources Official statistics –micro-data –matched data Own surveys (?) Experiments –Correlation vs. causality –Controlled conditions –Exogenous variation –Randomization

14. Data issues Lab experiments –Convenient samples Field experiments –Realistic samples –Policy evaluation Odd (?) questions in surveys –Hypothetical (behaviour) –Exogenous data Past – youth, etc.