Public support for innovation, intangible investment and productivity growth in the UK market sector

Economics Letters 119 (2013) 195–198

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Public support for innovation, intangible investment and productivity growth in the UK market sector Jonathan Haskel a,b,c , Gavin Wallis d,e,∗,1 a

Imperial College Business School, UK

b

CEPR, UK IZA, Germany

c d

University College London, UK

e

Bank of England, UK

highlights • • • •

Between 1999 and 2007 the UK government trebled its support for research councils. We find a robust correlation between research council funding and market sector TFP growth. The correlation is robust to three updates of the data and to additional controls. The implied rate of return has fallen recently as the funding has been trebled.

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Article history: Received 25 October 2012 Received in revised form 29 January 2013 Accepted 11 February 2013 Available online 26 February 2013

abstract Does publicly-financed R&D spill over to private sector productivity growth? We document a robust correlation between UK public-sector financed R&D disbursed via research councils and market sector total factor productivity growth. © 2013 Elsevier B.V. All rights reserved.

JEL classification: O47 E22 Keywords: Intangible assets Productivity R&D Spillovers

1. Introduction Between 1999 and 2007 the UK government nearly trebled its support for UK Research Councils, who disburse money to peerreviewed grant applicants mostly from UK universities. This paper tests for productivity spillovers to the market sector from this and also from civil and defence public sector R&D spending (collectively known as the Science Budget). We also test for productivity

∗ Correspondence to: Department of Economics, University College London, Gower Street, London WC1E 6BT, UK. Tel.: +44 0 20 7601 4109. E-mail address: [email protected] (G. Wallis). 1 This research was not carried out while working at the Bank of England. Views expressed in this paper represent those of the authors and do not necessarily reflect those of the Bank of England. 0165-1765/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.econlet.2013.02.011

spillovers from private sector intangible investments such as R&D and other knowledge investments. A priori, one might expect university spillover effects to be high: (a) most support is for science and the UK science base ranks very highly in a number of OECD indicators (e.g. world citations of scientific papers and patents); (b) research findings are (comparatively) freely available; (c) research is likely to be basic (rather than applied); and (d) grants are competitively determined. As Salter and Martin (2001) discuss, one spillover measurement method is to document direct measures of knowledge flows e.g. via surveying firms’ use or rating of public R&D (Mansfield, 1981). This understates knowledge transfers if, for example, undocumented companies also use the knowledge (e.g. via the internet). So we adopt a complementary broader approach: looking for correlations between total public research spending and market sector total

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factor productivity (TFP) growth (following Griliches, 1992). The breadth of this second approach likely finds higher spillover effects; but such breadth is its weakness, since correlations might be hard to find in noisy data, which biases effects down: ultimately the matter is empirical. Further, we are unable to isolate any details of the transmission of knowledge, so our approach is complementary to other methods. Based on an econometric approach, Salter and Martin (2001) reports nine estimates of returns to publicly funded R&D, all for agriculture, only one dated after 1981. Guellec and Van Pottelsberghe de la Potterie (2002) find a positive correlation between R&D in government labs and universities and market sector TFP growth in 16 OECD countries (controlling for other factors). We add to this literature by identifying specific types of publicly funded R&D in the UK and estimating returns. We also capitalise private knowledge investment (e.g. R&D) in computing TFP avoiding the potentially severe biases outlined in Schankerman (1981). We find a statistically significant correlation between market sector TFP growth and Research Council spending (although the rate of return to such spend has fallen in recent years). This correlation is robust to three data revisions since the first draft of this paper (Haskel and Wallis, 2010) and other checks. We find little evidence of market sector spillovers from civil or defence public sector R&D spending.

Fig. 1. Publicly funded R&D, the ‘‘Science Budget’’, by administrative unit. Note: HEFC (Higher Education Funding Council) spend is university funding labelled for research purposes, see text. Source: BIS, 2011, www.bis.gov.uk/policies/science/science-funding/set-stats.

2. Data Fig. 1 shows the total Science Budget spend broken into (i) Research Councils, (ii) defence, (iii) civil and (iv) Higher Education Funding Council (HEFC) (the categories are mutually exclusive). In 1986, spend was dominated by defence. Research Councils funding nearly trebled over the 2000s. Concerning measurement the ‘‘science budget’’ is publiclyfunded R&D. Headline R&D data e.g. from Gross Expenditure on R&D, is by performer not funder. In practice, Research Council funding is almost entirely performed at universities. The ‘‘HEFC spend’’ is university budget labelled as research support, i.e. not competitively determined via peer review. However, the data source reports this is obtained by accounting assumptions that change over the period and so is likely unreliable.2 As set out in Haskel and Wallis (2010), in 2005/6, 82% of ‘‘research council’’ spending goes to medical and engineering based research councils. Of ‘‘civil’’, 36%, 15% and 12% went to Departments of Health, Foreign Development, and Environment and Rural Affairs respectively. Since the output of health, other countries and the environment are not measured as part of market sector GDP, this biases against finding an effect from this spend. On ‘‘defence’’ spend, aerospace and the like are in the market sector but public ‘‘defence services’’ are not. 2.1. Market sector TFP and spending on intangible assets We construct market sector TFP for the period 1980–2009 and using intangible investment following the approach of Corrado et al. (2005). The intangible asset classes are computerised information (mainly software), innovative property (mainly scientific R&D and design) and firm-specific resources (company spending on advertising, training and organisational capital).3 Fig. 2 sets out the relation between Research Council spending (as a proportion of market sector GDP) and (smoothed by a three

2 See Haskel and Wallis (2010) and www.bis.gov.uk/policies/science/sciencefunding/set-stats. We have no data on other (small sources) of research funding for universities from, for example, charities and EU grants. 3 See Goodridge et al. (2012) for details.

Fig. 2. Market sector TFP growth and research council spending (as % of market sector GDP). Source: Author’s calculations and BIS.

year centred moving average) TFP growth. As the figure shows, Research Council spend rose in the late 1980s/early 1990s, fell in the mid-1990s and then rose strongly from the late 1990s onwards. TFP growth rose in the early 1990s, fell back in the mid-1990s, rose again until 2004 and then slowed. Note the timing of the relation: a rise in R&D spend in 1988, and a TFP growth rise in 1990: a fall in spend in 1993 and TFP growth fall in 1994, and a spend rise in 1999 and the TFP growth rise in the early 2000s. There is no such relation with HEFC, defence and civil spend (Haskel and Wallis, 2010). 3. Model and results We estimate

1 ln TFPt = ρt



RPUB Y



+ α2 Zt + vt

(1)

t −1

where ρ is the rate of return on public  R&D, Z is other factors (see ¯X 1 ln X where Yt below) and 1 ln TFPt ≡ 1 ln Yt − X =L,K ,N PRIV s is real market sector value added, including capitalised intangibles, Lt , Kt NtPRIV and labour, tangible and intangible capital respectively, and s¯ their Tornquist share (all for market sector). RPUB is spending

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197

Table 1 Spillovers from public R&D (estimates of Eq. (1)). (Dependent variable is smoothed market sector 1 ln TFP.) (1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Combined

Separate

R Council

Old data

Add 1 ln K

1988–2006

1998–2006, 1(1 ln TFP)

1998–2008

31.8 (3.32)

35.4 (4.00)

15.9 (2.06)

15.0 (1.16)

Variables

(Res + HEF + Civ + Def)/Y (t − 1)

−4.5 (2.94) 25.4 (3.20)

Res Coun R&D/Y (t − 1) HEF/Y (t − 1)

−2.17

CIV/Y (t − 1)

−16.7

31.3 (3.05)

(0.16)

(1.71) 0.67 (0.26)

Defence/Y (t − 1)

−0.18

DlnK (tan)

(0.62) 0.07 (0.73)

DlnK (intan)

Res Coun R&D/Y (t − 1), t ≤ 2004

31.8 (2.92)

Res Coun R&D/Y (t − 1), t = 2005

27.1 (2.97)

Res Coun R&D/Y (t − 1), t = 2006

23.0 (2.93)

Res Coun R&D/Y (t − 1), t = 2007

21.5 (2.68)

Res Coun R&D/Y (t − 1), t = 2008

19.5 (2.16)

Res Coun R&D/Y (t − 1), t = 2009

14.8 (1.75)

Observations R-squared

17 0.34

17 0.63

17 0.43

17 0.48

17 0.45

19 0.26

19 0.07

22 0.57

Notes: Robust t-values in parentheses. Variables are R&D spending by Research Councils, HEFC (the part of university support apportioned to university research), Civil and Defence. Years are 1988–2004, in columns 1–5, other columns where indicated. 1 ln TFP smoothed by three-year moving average. In column 7, dependent is 1(1 ln TFP).

on public R&D and (RPUB /Y ) is lagged, following Griliches (1992). Results are robust to more lags. This should help with reverse causation.4 Guellec and Van Pottelsberghe de la Potterie (2004) use log changes in the stock of public knowledge. Both approaches are equivalent if such public knowledge does not depreciate (if for example, discarding and obsolescence are very small for basic research). In Table 1 columns 1–3 use new data, but on the same sample period as Haskel and Wallis (2010), 1989–2004. We obtain the same results as before: a negative relation between TFP growth (1 ln TFP) and total science spending as a proportion of market sector GDP ((R/Y )t −1 ) (see column 1) but a positive correlation with only Research Council spending (columns 2 and 3). The results are very similar to the old data shown in column 4. Column 5 shows the results on the significant of Research Council spending are robust to adding growth in tangible capital (DlnK (tan)) and intangible capital (DlnK (intan)). Intangible capital here includes privately-funded R&D.

4 We tried the fraction of parliamentarians with a science background as an instrument, but it failed the first stage F test.

Our previous work found that adding recent years to the data steadily lowered the marginal return on Research Council spending, which might be expected as Research Council spending tripled (an unprecedented change) over that short period. Column 6 shows a fall in the coefficient to 15.9 on the new data for 1988–2006, (cf. 15.0 (t = 2.33) on the old data for those years). Column 7 shows a very similar coefficient, but less well determined, when we difference the data again, i.e. regress 1(1 ln TFP) on 1(R/Y )(t − 1). Given how noisy the data is, a rise in standard error is to be expected. Column 8 extends the data to 2009 (the most recent data available) and adds (Res Council/Y ) times a year dummy for each post-2004 year. The marginal effect is 31.8 for the period to 2004 and then steadily declining, with the final year being statistically insignificant, confirming declining marginal returns. We carried out a number of robustness checks but none of them significantly affected the statistical significance or the magnitude of the coefficient on the Research Council spending variable. First, we added other spillover type variables that might help explain TFP growth, such as relative US/UK productivity levels, foreign government R&D/GDP weighted by relative GDP; the fraction of consumers with an internet connection, which grew very sharply over the late 1990s. Second, we have similar results for Research Council spending lagged by 2 or 3 years. Third, adding growth

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in market sector R&D gave 0.0096 (t = 1.62), implying a social return of 12%, with the Res Council/Y (t − 1) unaffected. Fourth, we used unsmoothed and EUKLEMS market sector 1 ln TFP growth and fifth added intangible capital (DlnK (intan)) broken into software, innovative property and economic competencies, but the significance of Research Council spending was unaffected.5 Our estimated rate of return is very high, perhaps consistent with the high international ranking of UK university science. Guellec and Van Pottelsberghe de la Potterie (2004, p. 366) find the elasticity of 1 ln TFP to 1 ln K (public), ϵ, = 0.17. The implied rate of return can be calculated from ρ = ϵ/[(R/Y )(1 + g )/(g +δ)] where g = 1 ln K (public) (=1.83% in their Table 1) and δ is depreciation (=0.15). For R/Y = 0.0025 (based on our data) this implies ρ = 11.2 (0.17/[(0.0025)(1 + 0.0183)/(0.0183 + 0.15)]) close to our ρ . 4. Conclusion We document a robust correlation between public-sector financed R&D disbursed via research councils and later market sector TFP growth. The correlation remains robust to three updates of the underlying data and additional controls. The implied rate of return has fallen in recent years as the research budget has been trebled. Acknowledgements Financial support for this research comes from the COINVEST project funded by the European Commission Seventh Framework Programme, Theme 9, Socio-economic Science and Humanities,

5 The significance of Research Council funding was also unaffected by adding unions (coverage and density), non-linear effects of research council spend and public capital stock (buildings, transport). Individual research council spend terms were too collinear for well specified results.

grant 217512 and the UK-IRC, ESRC grant RES-598-28-0001; the work draws on data supported by the NESTA Innovation Index project. We are very grateful for assistance from David Barnett and Martin Kenchatt and for comments from Adrian Alsop, Albert Bravo-Biosca, Tony Clayton, Fernando Galindo-Rueda, Dominique Guellec, Rosa Fernandez, Bruno Van Pottelsberghe, Julie Tam, and Robert Woods. All opinions and errors in this paper are those of the authors alone. References Corrado, C., Hulten, C., Sichel, D., 2005. Measuring capital and technology: a expanded framework. In: Corrado, C., Haltiwanger, J., Sichel, D. (Eds.), Measuring Capital in the New Economy. In: National Bureau of Economic Research Studies in Income and Wealth, vol. 65. The University of Chicago Press, pp. 11–45. Goodridge, P., Haskel, J., Wallis, G., 2012. UK innovation index: productivity and growth in UK industries. CEPR Discussion Paper No. 9063. Griliches, Z., 1992. The search for R&D spillovers. Scandinavian Journal of Economics 94, 29–48. Guellec, D., Van Pottelsberghe de la Potterie, B., 2002. R&D and productivity growth: panel data analysis of 16 OCED countries. OECD Economic Studies 33, 103–126. Guellec, D., Van Pottelsberghe de la Potterie, B., 2004. From R&D to productivity growth: do the institutional settings and the source, of funds of R&D matter? Oxford Bulletin of Economics and Statistics 66, 353–378. Haskel, J., Wallis, G., 2010. Public support for innovation, intangible investment and productivity growth in the UK market sector. CEPR Discussion Paper No. 7725. Mansfield, E., 1981. Composition of R&D expenditures: relationship to size of firm, concentration and innovative output. Review of Economics and Statistics 63, 610–615. Salter, A., Martin, B., 2001. The economic benefits of publically funded basic research: a critical review. Research Policy 30, 509–532. Schankerman, M., 1981. The effects of double-counting and expensing on the measured returns to R&D. Review of Economics and Statistics 63, 454–459.