The Distribution of R&D Subsidies and its Effect on The Final Outcome of Innovation Policy

Liliana Herrera
Department of Business Administration, University of Leon, S

Joost Heijs
Economic Faculty of the Complutense University, Spain

     Full text: PDF
     Last modified: June 11, 2004

Abstract
There is no doubt, that the concept of additionality is one of the main criteria to evaluate innovation policy. Additionality can be defined as the extra R&D expenditure generated by State intervention. The results of the empirical evidence that analyses additionality (summarised in Capron 1992; David et. al., 2000 and Heijs, J. 2003) has not been conclusive, depending on the case, public funding can displace, complement or add to private funding. This divergence could be explained by the aid distribution process, and/or the non-estimation of a counter-factual state (what would have happened without the public support). The distribution of the support is a non-random process and could have an effect on the program’s effectiveness. For instance, if the aid is granted to firms that could have undertaken innovation activities without State support, then firms could end up substituting their private efforts by public support and would thus not increase the number of innovation activities. We argue, that it is important, to consider this process as a necessary pre-condition for the successful evaluation of policy effects. On the other hand, one of the most important flaws in evaluation studies is the non-estimation of the counter-factual state (Papaconstantinou, G. and Polt, W. 1997). The estimation of additionality needs to compare the dependent variable’s level before and after obtaining aid.

In this study, we try to overcome the before mentioned methodological problems by using the non-parametric approach of “Propensity Score Matching” (PSM). This method, formerly used in labour studies, has been used in the last few years by some authors to measure additionality (Czarnitzki y Fier, 2002; Almus y Czarnitzki, 2003 and Duguet E., 2003). PSM allows to control the aid distribution process and estimates the counter-factual state. A control group was formed with non-subsidised firms who had an equal propensity to obtain support. Unlike other studies applying the same methodology, this study widens the analysis by repeating the estimates with different classifications of firm sizes. This latter process was carried out with the intention of analysing whether the distribution and effect of innovation policy change according to the size of the firm.

The objective of this study is to evaluate the Spanish policy of innovation subsidies, controlling, while also explaining the aid distribution process and its effect on the R&D intensity of the firms. We have used a Data Base of about 645 Spanish manufacturing enterprises, obtained from the annual survey of business strategies (FUNEP), spanning a period of 3 years (1998-2000). The Data Base contains individual and very detailed annual data, including among others, the exact amount of money received by public support programmes.

Our analysis strongly supports three set of variables, including aspects not previously examined in the literature, namely: 1) the characteristics of the firms, 2) their market pressure, and 3) their technological level. In the first group we have included: size, sector, age, region, type of ownership, investment capacity, if the firm has had problems with financing innovation. In relation to the market pressure: main market evolution (in recession, expansion or stability), market quota (increasing, diminishing or in stability), export and import ratios. With respect to the technological level, we have considered: organization level of the innovation activity, if the firms import or export technology, and, if the firms have cooperation agreements. In addition, we have analysed all the innovation subsidy schemes available in Spain (regional, national and supranational support schemes). This analysis can be seen as an important methodological improvement with respect to earlier studies.

In a first section, we introduce the concept of additionality and make a review of the empirical evidence. In a second section, we discuss the methodological problems of previous studies regarding policy evaluation and introduce the PSM method. In the third section, we analyze the problem of aid distribution and its effect on the productive system and the industrial dynamic. In the next section, we present the results of the causal effect of the innovation policy, and in the last section, the conclusions.

A first part of the analysis, was directed towards studying and controlling the aid distribution process, this allowed us to conclude, that firms with a substantial capacity to guarantee the successful outcome of projects have a higher probability of obtaining support. The results were conclusive: large firms with a high investment capacity, with a public capital share, currently in markets in expansion and with a high level of innovation activity, have a significantly higher probability of obtaining aid. On the other hand, firms with problems to finance innovation, significantly have a lower probability of obtaining support. The aid distribution process is clearly oriented towards a “picking the winners” approach. In consequence, a weak additionality effect was found. The subsidised firms were 1.6% more innovative with respect to the non-subsidised firms (p<0.001). Controlling the estimation by size of firms we showed that small and medium sized firms have a higher level of additionality than large firms. These results reveal that the innovation policy has a greater effect on firms with fewer possibilities of obtaining a subsidy, as indicated by the general model.

In the Spanish case, the subsidy policy allows to broaden and deepen the technological activities of the innovation firms, nevertheless, the subsidy policy does not achieve to increment the number of innovative firms of the productive system.


References
Almus, M. y Czarnitzki, D. (2003). “The effects of public R&D subsidies on firms’ innovation activities: the case of Eastern Germany”. Journal of Business & Economic Statistics, Vol. 21, No.2, pp. 226-236.

Czarnitzki, D. y Fier, A. (2002), “Do innovation subsidies crowd out private investment? Evidence from the German service sector”. Applied Economics Quarterly, Vol. 48, No. 1, pp- 1-25.

David, P.A., Hall, B.H. and Toole, A.A. (2000). “Is public R&D a complement or substitute for Private R&D?. A review of the econometric evidence”. Research Policy, Vol. 29, pp. 497-529.

Duguet, E. (2003). “Are R&D subsidies a substitute or a complement to privately funded R&D? Evidence from France using propensity score methods for non-experimental data”. CNRS UMR 8594 – Université de Paris I, Maison des Sciences Économiques, Working Paper ftp://mse.univ-paris1.fr/pub/mse/cahiers2003/V03075.pdf

Capron, H. (1992) Economic and Quantitative Methods for the Evaluation of the Impact of R&D Programs, a State of Art. Union Europe (Cie. Monitor\Spear, Nov.)

Heijs, J. (2003): Freerider behaviour and the public finance of R&D activities in enterprises: the case of the Spanish low interest credits for R&D. Research Policy, Volume 32, Number 3, 2003 (Pp. 445-461).

Lerner, J. (1999), “The government as venture capitalist: The long-run impact of the SBIR program”. Journal of Business, Vol. 72, No.3, pp. 285-318.

Papaconstantinou, G.; Polt, W. (1997); Policy evaluation in innovation and technology: An overview. OECD Conference Policy evaluation in innovation and technology: towards best practices.

Rosenbaum, P.R. y Rubin, D.B. (1983), “The central role of the propensity score in observational studies for causal effects”. Biometrika, Vol. 70, No. 1, pp. 41-55.

Wallsten, S. (2000), “The effects of government-industry R&D programs on private R&D: the case of the Small Business Innovation Research program”, RAND Journal of Economics, Vol. 13, No. 1. pp. 82-100.