Government policy failure in public support for research and development

Abstract

Promoting Research and Development (R&D) and innovative activity is a key element of the EU Lisbon Agenda and is seen as playing a central part in stimulating economic development. In this paper we argue that, even allowing for benevolent policy-makers, informational asymmetries can lead to a misallocation of public support for R&D, hence government policy failure, with the potential to exacerbate preexisting market failures. Initially, we explore alternative allocation mechanisms for public support, which can help to minimize the scale of these government policy failures. Of these mechanisms (grants, tax credits, or allocation rules based on past performance), our results suggest that none is universally most efficient. Rather, the effectiveness of each allocation rule depends on the severity of financial constraints and on the level of innovative capabilities of the firms themselves.

Keywords:

• policy design
• policy-makers
• government policy failure
• R&D
• market failure
• subsidy allocation

Acknowledgements

We acknowledge helpful discussions with Stephen Roper on an earlier version of this paper.

Notes on contributors

Mika Haapanen is a Senior Researcher at the Jyväskylä University School of Business and Economics. His main research interests are in regional and labor economics, business subsidies, policy evaluation, and micro-level econometric analysis.

Helena Lenihan is an Associate Professor of Economics at the Kemmy Business School, University of Limerick. She has published extensively on industrial, enterprise and innovation policy evaluation, industrial and enterprise policy development, innovation policy, and the role of SMEs/entrepreneurship in economic development and firm growth.

Marco Mariani is Research fellow at IRPET (Tuscany's Regional Institute for Economic Planning). His main research interests lie in the area of applied industrial and business economics and include: R&D and innovation; small- and medium-sized enterprises; firm demography. In parallel, he is strongly concerned with enterprise and innovation policies and interested in quantitative programme evaluation methodologies.

Notes

1. Roper, Hewitt-Dundas, and Love (2004) provide an overview of the different mechanisms through which positive externalities of R&D can occur, while Borrus and Stowsky (1998) provide estimates of private and social returns from R&D activity. Specifically, Borrus and Stowsky (1998) estimate the private rates of return on investments in new technology to be between 20% and 30% in a variety of industries with social returns varying from 20% to 100% with an average of 50% (see also Mansfield et al. 1977).

2. The Arrowian argument has been criticized in light of a number of subsequent theoretical developments. A major point has been raised by industrial organization scholars, who have shown that competition between firms may result in a duplication of R&D efforts and, therefore, in a total investment level which is above the socially optimal level (Fundenberg and Tirole 1987; Dasgupta 1988; D’Aspremont and Jaquemin 1988). This non-optimality happens in a patent race, where two firms struggle to be the first to obtain a monopoly: only the winner will see its R&D investment rewarded by the patent, while the investment carried out by the loser cannot be recovered and represents a cost for society.

3. This assumption is consistent with pecking-order theory in corporate finance (Myers and Majluf 1984).

4. In the literature, these schedules are also called the marginal rate of return (MRR) and the marginal rate of cost (MRC) when no distinction is made between private and public returns (and costs).

5. By financial constraints we mean that in order to reach the private optimum, a firm will have to resort to external sources of funding.

6. We also assume for simplicity that subsidization of firm A does not affect the optimal investment of firm B (i.e., no displacement effect).

7. Note that the grant is independent of the firm's R&D expenditures. Later we will consider cases where the subsidy is proportional to the firm's R&D expenditures.

8. If we define by ∆I_i the increase in the R&D investment for firm i directly covered by the grant subsidy (i.e., I_i – ), then it is possible to identify a situation of additionality (or neutrality) if ∆I_i > subsidy (= ∆I_i) or a situation of crowding out if ∆I_i < subsidy. In Figure 1 the use of the grant subsidy on firm A results in partial crowding out.

9. In practice such a government subsidy can be awarded as a voucher (providing a subsidy of equal value to each firm). This approach minimises transaction costs on the part of both government and firms, and may have the political advantage of ‘fairness.’ In the case of the Dutch innovation voucher scheme which adopts this approach, both of these advantages have been important to the scheme's take-up and success (Cornet, Vroomen, and Van der Steeg 2006; Cornet, Vroomen, and Van der Steeg 2007). This voucher-type government subsidy is now available in most European regions (DG ENTR-Unit D2 2009; DASTI 2014; Technopolis 2010).

10. See Appendix for an example that compares grant subsidy allocation to tax credit allocation when a firm is financially constrained.

11. Note that in reality the grant or tax credit is often directly proportional to the pre-grant level of R&D, which suggests possible inefficiencies in the allocation of subsidies.