In economic theory, entrepreneurship is perceived as a driving force behind the process of creative destruction by introducing innovation to the market, crowding out inefficient businesses, and paving the way for economic growth. One central channel through which entrepreneurial activity can be realized is the founding a new firm (Carree & Thurik, 2003). Numerous empirical studies have investigated the causes of firm entry, the subsequent fate of these young businesses, and their impact on the economy (Geroski, 1995; Wagner, 2006). When assessing the importance of start-ups for economic development, their role in the creation and destruction of jobs is a key component. This thesis argues that start-ups’ role as employers should be evaluated not only by the quantity of jobs they create, but also by the quality of these jobs. If new firms differ from incumbents in terms of hiring patterns or employment conditions, this could have serious and long-lasting implications for the careers and personal lives of their employees. Against this background, the four essays of this thesis present new evidence on the hiring behavior of newly founded establishments, as well as on remuneration and employment prospects of workers joining a start-up.

Using representative linked employer-employee data for Germany, this paper analyzes short- and long-run differences in labor market performance of workers joining start-ups instead of incumbent firms. Applying entropy balancing and following individuals over ten years, we find huge and long-lasting drawbacks from entering a start-up in terms of wages, yearly income, and (un)employment. These disadvantages hold for all groups of workers and types of start-ups analyzed. Although our analysis of different subsequent career paths highlights important heterogeneities, it does not reveal any strategy through which workers joining start-ups can catch up with the income of similar workers entering incumbent firms.

We investigate how VC participation affects the failure of startups. Using a unique dataset of the survival of peer-to-peer (P2P) platforms in China, we identify two types of failures, bankruptcy, and run off with investors' money. The Competing Risk Model results show that while VC participation reduces bankruptcy hazard, it has little impact on the runoff failures. The findings are robust to the use of matched subsamples that disentangle the influence of pre-investment screening by VC. Further analysis of exit routes reveals that conditional on failure, VC participation is associated with a higher chance of running for the exit.

Measuring innovation is challenging both for researchers and for national statisticians, and it is increasingly important in light of the ongoing digital revolution. National accounts and many other economic statistics were designed before the emergence of the digital economy and the growing importance of intangible capital. They do not yet fully capture the wide range of innovative activity that is observed in modern economies.
This volume examines how to measure innovation, track its effects on economic activity and prices, and understand how it has changed the structure of production processes, labor markets, and organizational form and operation in business. The contributors explore new approaches to, and data sources for, measurement—such as collecting data for a particular innovation as opposed to a firm, and the use of trademarks for tracking innovation. They also consider the connections between university-based R&D and business startups, and the potential impacts of innovation on income distribution.
The research suggests potential strategies for expanding current measurement frameworks to better capture innovative activity, such as more detailed tracking of global value chains to identify innovation across time and space, and expanding the measurement of the GDP impacts of innovation in fields such as consumer content delivery and cloud computing.

Measuring innovation is challenging both for researchers and for national statisticians, and it is increasingly important in light of the ongoing digital revolution. National accounts and many other economic statistics were designed before the emergence of the digital economy and the growing importance of intangible capital. They do not yet fully capture the wide range of innovative activity that is observed in modern economies. This volume examines how to measure innovation, track its effects on economic activity and prices, and understand how it has changed the structure of production processes, labor markets, and organizational form and operation in business. The contributors explore new approaches to, and data sources for, measurement—such as collecting data for a particular innovation as opposed to a firm, and the use of trademarks for tracking innovation. They also consider the connections between university-based R&D and business startups, and the potential impacts of innovation on income distribution. The research suggests potential strategies for expanding current measurement frameworks to better capture innovative activity, such as more detailed tracking of global value chains to identify innovation across time and space, and expanding the measurement of the GDP impacts of innovation in fields such as consumer content delivery and cloud computing.

Young entrepreneurial firms are of critical importance for innovation. But to bring their new ideas to the market, these startups depend on investors who understand and are willing to accept the risk associated with a new firm. Perhaps the key reason as to why the US has succeeded in producing nearly all the most successful new firms of the 21st century is the economy’s ability to supply vast sums of capital to promising startups. The volume of venture capital (VC) invested in the US is more than 60 times that of Germany. In this policy note, we argue that differences in the regulatory and structural context of institutional investors, in particular life insurance companies, is a central driver of the relative lack of VC - and thereby successful startups - in Germany.

Many observers, and many investors, believe that young people are especially likely to produce the most successful new firms. Integrating administrative data on firms, workers, and owners, we study start-ups systematically in the United States and find that successful entrepreneurs are middle-aged, not young. The mean age at founding for the 1-in-1,000 fastest growing new ventures is 45.0. The findings are similar when considering high-technology sectors, entrepreneurial hubs, and successful firm exits. Prior experience in the specific industry predicts much greater rates of entrepreneurial success. These findings strongly reject common hypotheses that emphasize youth as a key trait of successful entrepreneurs.