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Publications Secretary Satu Holm
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| Author: | Tanayama, Tanja |  | |
| Title: | Allocation and effects of R&D subsidies : selection, screening and strategic behavior | ||
| Series: | Acta oeconomicae Helsingiensis. A, ISSN 1237-556X; 309 | ||
| Series no: | A-309 | ||
| Year: | 2007 Thesis defence date: 2007-08-20 | ||
| Discipline: | Economics | ||
| Electronic dissertation: | » download dissertation in pdf-format  [3090 KB] | ||
| Index terms: | innovaatiot; tutkimus ja kehitys; tytäryhtiöt; innovation; research and development; subsidiary companies | ||
| Language: | eng | ||
| Bibid: | 101851 | Availability info (HELCAT) | ||
| ISBN: | 978-952-488-157-9 | ||
| Abstract: |
Allocation and effects of R&D subsidies: selection,
screening, and strategic behavior Tanja Tanayama Helsinki School of Economics Abstract This dissertation consists of four essays on the functioning of public research and development (R&D) subsidy programs. The topic of the first three essays is the selection process, also referred to as the participation process. The participation process consists of two decision problems: firms have to decide on whether to apply for a subsidy or not and the government agency allocating subsides has to decide on the subsidy. In the first essay we develop a fully structural model describing the two decision problems in the context of firms that engage in R&D to maximize profits and a public agency that decides on R&D subsidies to maximize its benefits. In the second and the third essay I examine more in detail the application and the allocation decisions respectively. In the fourth essay I take a different angle and develop a theoretical model to examine whether R&D subsidies can alleviate financial constraints and through which channels this effect comes from. In the first essay we develop a new structural method to estimate the expected returns to R&D, their distribution, and their determinants, including the effect of possible subsidies. First a model of continuous optimal treatment with outcome heterogeneity is developed, where the treatment outcome depends on the applicant’s investment. The model takes into account application costs, and isolates the effect of the treatment on the public agency running the treatment program. Under the assumption of a welfare-maximizing agency, the model generates expected general equilibrium treatment effects and social returns to R&D. Then the model is taken to project level data from the Finnish Funding Agency for Technology and Innovation (Tekes) granting process of R&D subsidies. The findings indicate that expected returns on R&D are high, their distribution skew, and treatment effect heterogeneity substantial. Agency’s utility not appropriated by the applicant is linear in R&D. The median increase in this expected agency specific utility from subsidies is 44 000 euros. Ignoring application costs severely biases the estimated treatment effects and returns upwards. In the second essay I analyze the application for R&D subsidies. Finnish firmlevel data on applicants and potential applicants is used to characterize the application behavior of firms. In addition to analyzing the characteristics underlying application for R&D subsidies, also the use of count data models in modeling the application for R&D subsidies is examined. The findings suggest that firms that are the most likely to have eligible projects, are also aware of the 1 R&D subsidy program. The results also suggest that the opportunity cost of applying is lower for firms quite at the beginning of their life cycle. In addition the results provide evidence that external knowledge is important in lowering the application cost. Industry level heterogeneity in application behavior seems to be related to the application activity of potential applicants rather than the awareness of the program. The model selection exercise indicates that in using a count data framework to model the application behavior it is important to take into account both unobserved heterogeneity and excess zeros. The third essay examines the allocation rule of the public agency. R&D subsidies to business sector constitute a selective policy tool to encourage private R&D activities. The efficiency and functioning of this tool depends on how the public agency allocates subsidies. The program under scrutiny is that of Tekes. The results indicate that in general Tekes adheres to the stated funding policy and criteria. The technological content of a project proposal and risks related to the implementation of the project are important in determining both whether an application is accepted and the subsidy-level. In addition being a small and medium size company increases the acceptance probability. Also the extent of collaboration matters. All these findings are in line with the stated funding policy and criteria. However, Tekes seems to be averse to risks related to the commercial potential of the project proposal. It can be questioned whether this observation is in line with the stated objectives. The fourth essay analyzes the role of R&D subsidies in reducing possible adverse selection based financing constraints related to innovation financing. Asymmetric information about the quality of an innovation project between the entrepreneur and the financier leads to a higher cost of external than internal capital, creating a funding gap. This funding gap may prevent especially small and new technology-based firms from undertaking economically viable innovation projects. Results indicate that under certain conditions, public R&D subsidies can reduce these financing constraints. Two different channels generate this effect. First, the subsidy itself reduces the capital costs related to the innovation projects by reducing the amount of external capital required. Second and more important, the observation that an entrepreneur has received a subsidy for an innovation project provides an informative signal to the market-based financier. Keywords: R&D subsidy, participation process, structural econometrics, treatment effects, count data models, adverse selection, financial constraints. 2 | ||
| Thesis defence announcement: |
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| Opponents: | Van Reenen, John professor London School of Economics, Great Britain
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| Chairperson: | Ilmakunnas, Pekka professor |
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