• Founder Personality and Start-up Subsidies (with Gary Chapman) Download

This study investigates the role of founders’ personality, as captured by the dimensions of the ‘Big 5’ traits and entrepreneurial orientation, in their start-up’s access to and use of public subsidies. We document a limited role for the Big 5 personality traits but find a more significant role for founders’ entrepreneurial orientation, even after controlling for the observable founder and firm characteristics and the selection decision to seek external financing in the first place. We also document that founder personality influences the type (grants or loans) of subsidies that start-ups seek and obtain with more innovative founders being more likely to obtain grants and more competitive ones seeking subsidized loans. When comparing public subsidies to private sources of early-stage finance (banks, venture capital, family and friends), we find a larger role for founders’ baseline personality in private sources of financing and the role of founders’ entrepreneurial orientation to vary depending on the source. Finally, when disentangling application and allocation, we find little role for personality traits in explaining rejection, thus, suggesting that personality traits are more important in explaining start-ups’ self-selection into subsidies, rather than policymaker award choices. We discuss implications for research evaluating the effectiveness of subsidy programs as well as for policymaking.

Keywords: Start-up subsidies, start-up financing, entrepreneurship policy, entrepreneurial orientation,
Big 5 personality traits, venture capital

JEL codes: G24, L26, O25, O31


  • Research at the Frontier of Knowledge: Comparing text similarity indicators to citations for measuring scientific excellence (with Roman Fudickar) Download

Evaluating scientific excellence is a fundamental challenge for public science administrations. It currently primarily relies on peer-review and publication data analysis. This study proposes to add text-based indicators to the evaluation procedure, in order to get a more comprehensive picture of a scientists potential to do excellent science. We compare text-based similarity between publications of individual scientists in different scientific fields (biology, chemistry, economics and engineering) and text-documents of validated knowledge frontiers to citation-based indicators. We propose two knowledge frontiers for science evaluations (academic prizes and ERC funding) and show that text similarity approaches can be a valuable complement to standard bibliometric indicators. Moreover, survey data is used to study their relationship with alternative individual-specific measures of research quality, such as academic rank, institutional rank, and research budget. We find that overall text-based, citation-based and survey-based indicators provide a coherent picture. However, for young researchers for whom citations windows are short, text-based indicators may provide additional insights when evaluating research excellence. Moreover, the correlation between similarity scores and citation measures decreases with scientists’ age indicating their use also for established researchers.

Keywords: Scientific Excellence, Research Evaluation, Natural Language Processing

JEL Codes: I20, O30, O38


  • Knowledge spillovers from subsidized R&D and the productivity of non-subsidized firms (with Cindy Lopes-Bento)

Innovation policy encourages firms’ participation in co-funding schemes for R&D projects. Yet, the distribution of awarded grants tends to be highly skewed with few firms receiveing the lion’s share of the public money. From a welfare perspective, supporting a small number of selected firms can be an efficient policy provided that the subsidies trigger knowledge spillover generating R&D in the subsidized firms. This study investigates whether the effect of subsidies go beyond the subsidized firms by affecting productivity of non-subsidized ones. We find that research subsidies affect productivity of non-subsidied positivly, while development subsidies do not. Dynamic model specifications further show that in the longer run, development subsidies lead to business stealing by promoting product and process development in the subsizied firms with little positive spillovers to the non-recipients. When distinguing R&D-active and non-R&D-active firms among the non-subsidized firms, we see that adverse competitive effects are stronger for the latter. Conducting own R&D may therefore increase firms’ ability to realize effective knowledge spillovers and to avoid being harmed by competitive pressure from other firms product or process innovations.

Key words: Innovation Policy, Research & Development, R&D subsidies, Knowledge Spillovers, Competition

JEL classification: O31, O38, G30


  • R&D Subsidies and Firms’ Cost of Debt (with Sarah Demeulemeester) Download

Financing research and development (R&D) through loans is usually a costly endeavor. Information asymmetry, outcome uncertainty and low collateral value tend to increase the cost of debt. Based on a large panel of firms, this study shows that recipients of public R&D grants, on average, face lower costs of debt. Immediate effects on cost of debt suggest that a process of certification in which the subsidy signals the quality of the firm’s R&D to external lenders explains this observation. In addition, longer-term effects from an R&D grant receipt may point to a resource effect that facilitates investment in R&D such as prototyping and therefore provide tangible outcomes that may inform lenders on the firms R&D quality. The comparison between young ventures and established firms, however, shows that for the former short-term effects prevail primarily for subsidies for basic research. At a stage where outcome uncertainty and information asymmetries are particularly high, basic research grants may signal quality of more radical R&D endeavors. Young ventures also experience a longer-term impact from subsidies for development projects, which could point to funding of prototyping.

Key words: Innovation Policy, Research & Development, R&D subsidies, Cost of debt, Financial Constraints

JEL classification: O31, O38, G30