Abstract
This paper assesses UK innovation policy impact on a large, population weighted, sample of both service and manufacturing SMEs. By focussing on self-reported innovation the study achieves a wider coverage of the effects of SME innovation policy than possible with more traditional indicators. Propensity score matching indicates that SMEs receiving UK state support for innovation were more likely to innovate than unsupported comparable enterprises. Innovating enterprises are shown to have grown significantly faster over the years 2002–2004 when other growth influences are appropriately controlled. Combining these two results and comparing the outlays on SME innovation policy with the estimated effects suggests that policy was efficient as well as effective. There is evidence that SME tax credits were expensive compared with earlier support instruments. But the overall high returns estimated suggest that, even in times of public spending cuts, persisting with SME innovation policy would be prudent.
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Notes
SMEs are defined throughout the present paper as enterprises employing fewer than 250 persons.
Kleinkncht’s study showed that according to the official R&D survey, 91 % of private R&D in Dutch manufacturing firms was undertaken by large firms (with 500 and more employees). According to Kleinknecht’s estimate, this percentage would fall to 82.4 % (when considering only firms with 50 and more employees), and declines even more to 77.3 % when adding the R&D of firms with 10–49 employees not covered in the official survey.
If this point is accepted then the Crepon et al. (1998) model using R&D as an intermediate variable, standard for larger firms, is not appropriate for SMEs.
The UK CIS5 cannot be used for this purpose because the public policy questions were dropped.
Following H M Treasury (2003, p. 52) for the definition. “The success of government intervention in terms of increasing output or employment in a given target area is usually assessed in terms of its ‘additionality’. This is its net, rather than its gross, impact after making allowances for what would have happened in the absence of the intervention.”
Communication from Competition DG, European Commission, 20 May 2009.
‘Innovation is defined as major changes aimed at enhancing your competitive position, your performance, your know-how or your capabilities for future enhancements. These can be new or significantly improved goods, services or processes for making or providing them.’ Innovation here is measured as either or both of process or product innovation, either new to the firm or to the market.
Rosenbaum and Rubin (1983) showed that matching enterprises on just the propensity score, rather than matching firms on the vector of characteristics of Eq. (2), was appropriate. This is because firms receiving state aid but with the same value of the propensity score as those not receiving it have the same distribution as the entire vector of regressors.
Matching estimators do not rely on linear extrapolation (outside the common support region) or functional form assumptions. Nor do they require an exclusion variable or impose joint normality assumptions.
Combining the propensity score matching with a difference-in-differences estimator (Blundell and Costa Dias 2000) might improve the efficiency of estimates. Unfortunately, Community Innovation Survey 4, the data source for the present exercise, does not include the observations on SME innovation in two distinct periods that would permit this approach.
These are generalized or Gourieroux et al. (1987) residuals because Eq. (1) is a probit equation. Where y is the dependent variable, the residuals are:
u = [pdf(xB)/cdf(xB)] * [(1 − cdf(xB)) * (y − cdf(xB)]
when y = 1, u = pdf(xB)/cdf(xB),
when y = 0, u = −pdf(xB)/(1 − cdf(xB)),
where pdf and cdf are the p.d.f. and c.d.f. of N(0,1).
This is a version of the Durbin–Wu–Hausman (DWH) test (Davidson and MacKinnon 1993).
The CIS survey questionnaire refers to enterprise, but defines this as a reporting unit. ‘An enterprise is defined as the smallest combination of legal units that is an organisational unit producing goods or services, which benefits from a certain autonomy in decision making, especially for the allocation of its current resources. An enterprise carries out one or more activities at one or more location.’ So the reporting unit may be a subsidiary of a larger firm, or it may be a single plant or even several plants in the same or different regions. Some enterprises operating in one region may be owned by enterprises located in another region and so classified to this other region. The smaller the unit size the more likely it is to be a single plant firm operating at a single location. The theoretically ideal unit is one with substantial operational autonomy at the location where it is recorded. In practice the unit could affect places where it controls other units without substantial autonomy. For empirical purposes much depends upon how ‘a certain autonomy’ is interpreted in the data.
The weight assigned to each enterprise was the number in the population divided by the number of responses in that stratum. On average each respondent represents 11 enterprises in the population. In our smaller sample there is a maximum of 13,367 enterprises, the median weight is 7.4 and the mean 10.4. The largest percentile has a weight of 45.7 and the smallest 1.43.
The proportions of science and engineering graduates employed in the labour force and the proportion of other graduates did not prove significant explanatory variables.
In Appendix C in the Electronic Supplementary Material the value of ‘foreign sales’ of aid recipients after matching is 0.527 and for the control group it is 0.573. This difference is significant at the 3.9 % level. However dropping the foreign sales variable from the state innovation support equation reduces the equation fit, leads to a significant difference at the 5 % (but not the 1 %) level after matching between treated and control SMEs for one region and one SIC, and raises the estimate of the effect on innovation of support. For these reasons, and because the difference was not significant at the 1 % level, the specification of Table 4 (Eq. iii) was preferred.
This estimate, that receipt of UK state support for innovation raises the chances that a service or manufacturing SME will innovate by 27 percentage points, is not readily compared with other results, which generally refer to different groupings of firms, model specifications and/or policy instruments. For example, Griffith et al. (2006) find that UK national funding increases the chances of whether R&D is undertaken (by all firms) by 19 percentage points. Restricting themselves to SMEs (as in the present study but focussing on a different policy instrument) in England and Wales, Wren and Storey (2002) estimate a 30 to 1 ratio between the increase in turnover and the outlays on the state funded marketing initiative. For a sample of 3,000 English SMEs, Mole et al. (2009) find significant employment expansion from intensive business support but not from other types.
In accordance with the strategy of downwardly biasing the estimate of returns, Eq. (i) Table 6 includes foreign sales because the variable slightly lowers the policy coefficient compared with the specification excluding exports.
The higher value of the IV estimate of the innovation coefficient is consistent with (unobserved) innovative SME managements being less prone to expand their firms than others. The first stage F statistic of 80 is not consistent with weak instruments, nor is the significance of Shea’s partial R 2. The Hanson J coefficient does not reject the joint null hypothesis that the instruments are valid (uncorrelated with the disturbance term) and that the excluded instruments are correctly excluded from the estimated equation.
Griffith et al. (2006) estimate 5.5 % higher labour productivity for firms with product innovation, less than 0.073 but the present estimate is for the effect on turnover not on labour productivity (for reasons explained at length in Appendix A in Electronic Supplementary Material). Moreover a sensitivity analysis is conducted in Sect. 5 below. Here the coefficient is reduced by one standard error, from 0.073 to 0.058 (similar to the Griffith et al. figure) and the large returns are not substantially dented. Using CIS3 Griffith et al. consider only firms with more than 20 employees and cover the full size range above this level, in contrast to the present exercise.
Government weight 0.225 removed from £1075 billion GDP for 2002 from Meader and Tily (2008).
Calculated from BIS (Table 1) 2002 and 2007 Enterprise and Small Businesses. http://webarchive.nationalarchives.gov.uk/+/http://www.berr.gov.uk/whatwedo/enterprise/enterprisesmes/index.html.
Abramovsky et al. (2004)’s named support programme totals (using the earlier years, where there are two) amount to £156 million per annum. Multiply by 0.8 (because in January 2004, there were 899 Knowledge Transfer Partnerships, roughly 80 % of which involved SMEs; about 2,400 firms, mostly SMEs, and about 200 research base institutions were involved in 75 LINK programmes since the launch of LINK in 1986) to get an approximation to the SME allocation. Add in SME R&D tax credits of about £0.2 billion p.a. to reach approximately £ 320 million p.a. as the cost of SME innovation policy 2002–2004.
The profits and consumers’ surplus performance measure are ratios with turnovers as denominators so the same turnover to value added ratio is needed to calculate the impact in these terms.
Unless tax credits triggered innovations that were disproportionately productive.
References
Abramovsky, L., Harrison, R., & Simpson, H. (2004). Increasing innovative activity in the UK? Where now for government support for innovation and technology transfer? IFS Briefing Note no. 53.
Acs, Z., & Audretsch, D. (1988). Innovation in large and small firms: An empirical analysis. American Economic Review, 78, 678–690.
Baghana, R., & Mohnen, P. (2009). Effectiveness of R&D tax incentives in small and large enterprises in Quebec. Small Business Economics, 33, 91–107.
Belderbos, R., Carree, M., & Lokshin, B. (2004). Cooperative R&D and firm performance. Research Policy, 33(10), 1477–1492.
BERR. (2008). SME statistics for the UK and regions 2007, http://webarchive.nationalarchives.gov.uk/20110920151722/http://stats.bis.gov.uk/ed/sme/index.htm.
BIS. (2010). Internationalisation of innovative and high growth SMEs. BIS Economics Paper No. 5 March, Department for Business Innovation and Skills.
Blundell, R., & Costa Dias, M. (2000). Evaluation methods for non-experimental data. Fiscal Studies, 21, 427–468.
Cassiman, B., & Veugelers, R. (2002). R&D cooperation and spillovers: Some empirical evidence from Belgium. American Economic Review, 92(4), 1169–1184.
Cassiman, B., & Veugelers, R. (2005). R&D cooperation between firms and universities, some empirical evidence from Belgian manufacturing. International Journal of Industrial Organization, 23, 355–379.
Commission of the European Communities (2006). Framework for state aid for research and development and innovation (2006/C 323/01)
Crepon, B., Duguet, E., & Mairesse, J. (1998). Research, innovation, and productivity: An econometric analysis at the firm level. Economics of Innovation and New Technology, 7(2), 115–158.
Czarnitzki, D., Hanel, P., & Rosa, J. (2011). Evaluating the impact of R&D tax credit on innovation: A microeconometric study on Canadian firms. Research Policy, 40(2), 217–229.
Davidson, R., & Mackinnon, J.G. (1993). Estimation and Inference in Econometrics. New York: Oxford University Press.
Department for Innovation Universities and Skills. (2008). Persistence and change in UK innovation 2002–2006. http://www.dius.gov.uk.
DTI. (2006). Innovation in the UK: Indicators and insights. DTI occasional paper no. 6 July.
DTI Innovation Report. (2003). Competing in the global economy: the innovation challenge. December, http://www.berr.gov.uk/files/file12093.pdf. Accessed 11 March 2012.
Freel, M. S. (2000). Do small innovating firms outperform non-innovators? Small Business Economics, 14, 195–210.
Gourieroux, C., Monfort, A., Renault, E., & Trognon, Al. (1987). Generalised residuals. Journal of Econometrics, 34(1/2), 5–32.
Griffith, R., Huergo, E., Mairesse, J., & Peters, B. (2006). Innovation and productivity across four European countries. Oxford Review of Economic Policy, 22(4), 483–498.
Griffith, R., Redding, S., & Van Reenen, J. (2001). Measuring the cost-effectiveness of an R&D tax credit for the UK. Fiscal Studies, 22(3), 375–399.
Griliches, Z. (1990). Patent statistics as economic indicators: A survey. Journal of Economic Literature, 28, 1661–1707.
H M Treasury. (2003). The Green Book: Appraisal and evaluation in Central Government. London TSO. http://www.hm-treasury.gov.uk/d/green_book_complete.pdf. Accessed 11 March 2012.
H M Treasury, DTI, & DfES. (2004). Science and innovation investment framework 2004–2014. July. http://www.bis.gov.uk/files/file40398.doc
Hall, B. (2009). The financing of innovative firms. EIB Papers, 14(2), 8–29.
Hall, B., Lotti, F., & Mairesse, J. (2009). Innovation and productivity in SMEs: Empirical evidence for Italy. Small Business Economics, 33(1), 13–33.
Hall, B., & Van Reenen, J. (1999). How effective are fiscal incentives for R&D? A review of the evidence. Research Policy, 29, 449–469.
Harris R. (2008). An empirical study of the respective contributions of exporting and foreign direct investment to UK R&D, UK Trade and Industry, November. http://www.tradeinvest.gov.uk/download/file/115272.html
Harris, R., Cher Li, Q., & Trainor, M. (2009). Is a higher rate of R&D tax credit a panacea for low levels of R&D in disadvantaged regions? Research Policy, 38, 192–200.
Heckman, J. J., Ichimura, H., & Todd, P. (1997). Matching as an econometric evaluation estimator: Evidence from evaluating a job training program. Review of Economic Studies, 64(4), 605–654.
Heckman, J. J., & Navarro, S. (2004). Using matching, instrumental variables, and control functions to estimate economic choice models. Review of Economics and Statistics, 86(1), 30–57.
Ientile, D., & Mairesse, J. (2009). A policy to boost R&D: Does the R&D tax credit work? EIB Papers, 14(1), 145–168.
Jaumotte, F., & Pain, N. (2005a). Innovation in the business sector, OECD Economics Department Working Papers no. 459.
Jaumotte, F., & Pain, N. (2005b). From innovation development to implementation: Evidence from the community innovation survey, OECD Economics Department Working Papers no. 458.
Jones, C., & Williams, J. (1998). Measuring the social return to R&D. Quarterly Journal of Economics, 113, 1119–1136.
Kleinknecht, A. (1987). Measuring R&D in small firms: How much are we missing? Journal of Industrial Economics, 36, 253–256.
Leuven, E., & Sianesi, B. (2003). PSMATCH2: Stata module to perform full Mahalanobis and propensity score matching, common support graphing, and covariate imbalance testing. http://ideas.repec.org/c/boc/bocode/s432001.html. Accessed 11 March 2012.
Lev, B. (2001). Intangibles: Management, measurement, and reporting. Washington, DC: Brookings Institution Press.
Meader, R., & Tily, G. (2008). Overview of UK national accounts and balance of payments: Blue Book and Pink Book 2008. Economic and Labour Market Review, 2(10), 33–43.
Mole, K., Hart, M., & Roper, S. (2009). Assessing the effectiveness of business support services in England. International Small Business Journal, 27, 557–583.
North, D., & Smallbone, D. (2000). The innovativeness and growth of rural SMEs during the 1990s. Regional Studies, 34, 145–157.
OECD. (2003). Tax incentives for research and development: Trends and issues. Paris: OECD.
Oh, I., Lee, J.-D., Heshmati, A., & Choi, G.-G. (2009). Evaluation of credit guarantee policy using propensity score matching. Small Business Economics, 33, 335–351.
Pakes, A., & Ericson, R. (1998). Empirical implications of alternative models of firm dynamics. Journal of Economic Theory, 79, 1–45.
Peters, B. (2004). Employment effects of different innovation activities: Microeconometric evidence. ZEW—Centre for European Economic Research Discussion Paper No. 04-073.
Rammer, C., Czarnitzki, D., & Spielkamp, A. (2009). Innovation success of non-R&D-performers: Substituting technology by management in SMEs. Small Business Economics, 33, 35–58.
Roper, S., Du, J., & Love, J. H. (2008). Modelling the innovation value chain. Research Policy, 37, 961–977.
Rosenbaum, P., & Rubin, D. (1983). The central role of the propensity score in observational studies for causal effects. Biometrica, 70, 41–50.
Skuras, D., Tsegenidi, K., & Tsekouras, K. (2008). Product innovation and the decision to invest in fixed capital assets: Evidence from an SME survey in six European Union member states. Research Policy, 37, 1778–1789.
Smith, K. (2005). Measuring Innovation. In J. Fagerberg, D. C. Mowery, & R. R. Nelson (Eds.), Oxford handbook of innovation. Oxford: Oxford University Press.
Staiger, D., & Stock, J. (1997). Instrumental variables regression with weak instruments. Econometrica, 65(3), 557–586.
Sutton, J. (1997). Gibrat’s legacy. Journal of Economic Literature, 35, 40–59.
Tellis, G. J. (1988). The price elasticity of selective demand: A meta-analysis of econometric models of sales. Journal of Marketing Research, 25, 331–341.
Tether, B. S., Smith, I. J., & Thwaites, A. T. (1997). Smaller enterprises and innovation in the UK: The SPRU innovations database revisited. Research Policy, 2, 19–32.
van Leeuwen, G., & Klomp, L. (2006). On the contribution of innovation to multi-factor productivity growth. Economics of Innovation and New Technology, 15(4–5), 367–390.
Wieser, R. (2005). Research and development productivity and spillovers: Empirical evidence at the firm level. Journal of Economic Surveys, 19(4), 587–621.
Wren, C., & Storey, D. J. (2002). Evaluating the effect of soft business support upon small firm performance. Oxford Economic Papers, 54, 334–355.
Acknowledgments
Thanks to Peng Zhou and Tom Nicholls for excellent research assistance and to anonymous referees for their comments on an earlier draft.
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Foreman-Peck, J. Effectiveness and efficiency of SME innovation policy. Small Bus Econ 41, 55–70 (2013). https://doi.org/10.1007/s11187-012-9426-z
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DOI: https://doi.org/10.1007/s11187-012-9426-z