Presentation at RSA by Enrico Vanino to ERC Research Showcase June 2017.

1. Bettina Becker
b.becker@aston.ac.uk
Stephen Roper
stephen.roper@wbs.ac.uk
Enrico Vanino
e.vanino@aston.ac.uk
Assessing the impact of business engagement
with publicly funded science:
An Exploratory Study
ERC Research Showcase
London, 21st June 2017

2. INTRODUCTION
UK Research Councils invest around £1.7bn annually in supporting scientific research.
Assessments of the impact of public R&D investment have been partial and largely case-
based, relying on limited information from innovation surveys.
Several previous studies add to the substantial evidence from a range of countries on
the positive role of research grants, subsidies and tax credits in helping firms to
innovate successfully (Zuniga-Vicente et al. 2014).
Public subsidies for private R&D generally justified in terms either of market failures or
strategic objectives linked to a desire to build capacity in specific sectors, technologies
or localities. Two possible causal effects:
a. a weak link from public support to R&D and innovation;
b. a strong link from public support to business performance (Porter and Van de
Linde 1995).

3. Conceptual Framework
Four alternative mechanisms may link public R&D subsidies to firms’ increased innovation
activity and performance:
1. Science creation: central rationale for public subsidies to private R&D is their impact
on knowledge creation which provide basis for subsequent innovation and value
creation;
2. Public R&D subsidies will be increasing liquidity and financial slack in recipient
companies which may help in over-coming risk aversion (Zona 2012);
3. Public subsidies for private R&D reduce the required investment and de-risk private
investment support its role in de-risking R&D activity (Calantone, Harmancioglu,
and Droge 2010; Mazzucato 2016);
4. Subsidies can play an enabling or bridging role, helping firms to access otherwise
unavailable new or pre-existing knowledge and to incentivise knowledge providers
to work with new partners (OECD 2010).

4. Research Questions
Need for more extensive research on the performance effects of publicly funded scientific
research (Scandura 2016):
1. Analyse the comprehensive effect on business performance of public science
investments in the UK
2. Identify the role of different Research Councils in supporting firms’ engagement in
basic science projects
3. Estimate the potential continuous “intensity” effect of engagement, considering the
value of research grant received
4. Compare levels of impact across sectors, firm size bands and regions
5. Assess time lags between firms’ engagement with the science system and any impacts
on firms’ growth in the short, medium and long term

5. DATA
1. Gateway to Research (GtR) Database developed by Research Councils UK providing
information about all publicly funded research since 2004 regarding:
a. Value of grants
b. Projects’ participants and leader
c. Funding Organization
d. Characteristics of participants (country, organization type, postcode)
2. Business Structure Database 1997-2016 providing information on the population of
UK firms about industrial classification, postcode, employment and turnover.
3. Contributions from other datasets: UK Innovation Survey, data on Science Parks in
the UK, BvD ORBIS.

6. DATA
Organizations Information Organizations Type
No. Organizations 34,120 Firms 18,533
Undefined 3,440 Universities 2,114
CRN 18,816 Public Research Institutes 2,667
Postcodes 20,721 Private R&D Centres 165
SIC code 12,001 Schools 430
Country 30,535 Hospitals 1,418
Gov. Authorities 1,308
GB 20,734 Research Councils 83
EU 3,854 Charities 2,142
US 2,193 Cultural Org. 716
ROW 3,754 Others 1,099
number share Value (M £) share
Tot. Projects 70, 178 100.0% 31,811 100.0%
AHRC 5,585 8.0% 742 2.3%
BBSRC 11,208 16.0% 3,750 11.8%
EPSRC 15,528 22.1% 9,270 29.1%
ESRC 5,675 8.1% 1,930 6.1%
Innovate UK 13,870 19.8% 4,920 15.5%
MRC 7,250 10.3% 7,190 22.6%
NC3Rs 248 0.4% 49 0.2%
NERC 6,963 9.9% 2,430 7.6%
STFC 3,851 5.5% 1,530 4.8%
Table 1: Organizations information and type
Table 2: Number and value of grants awarded by funding source

7. DATA
Figure 1: Tot. Grants Value per Research Council and Year Figure 2: Tot. Grants Value for UK firms per Res. Council and Year

8. DATA
Figure 3: Distribution organizations and firms funded by country

9. DATA
Figure 4: Distribution organizations and firms funded across the UK

10. DATA
Figure 5: Grants Value Intensity and Growth Rate for UK Firms

11. DATA
Figure 6: Industrial Distribution of UK Firms Funded and Grants Awarded: Manufacturing and Services Industries

12. RESULTS
All Manufacturing Services After 2008
ST MT ST MT ST MT ST MT
Employment ATT 0.0584*** 0.225*** 0.0779*** 0.242*** 0.0654*** 0.235*** 0.0642*** 0.211***
b.s.e. (0.0088) (0.0184) (0.0147) (0.0341) (0.0106) (0.0211) (0.0089) (0.0209)
Turnover ATT 0.0647** 0.279*** 0.135*** 0.331*** 0.0548* 0.260*** 0.0712*** 0.242***
b.s.e. (0.0174) (0.0343) (0.0269) (0.0553) (0.0211) (0.0419) (0.0177) (0.0376)
Labour Productivity ATT 0.0206 0.0626** 0.0413* 0.0788** 0.0126 0.0557 0.013 0.0292
b.s.e. (0.0164) (0.0275) (0.0230) (0.0367) (0.0215) (0.0369) (0.0178) (0.0320)
Untreated 2,969,032 1,356,914 112,984 63,941 2,856,048 1,292,973 2,358,352 912,893
Treated 5,657 3,665 1,665 1,166 3,981 2,491 4,395 2,429
Manuf. HT Manuf. LT KIS Non-KIS
ST MT ST MT ST MT ST MT
Employment ATT 0.0922*** 0.275*** 0.0761*** 0.238*** 0.0690*** 0.243*** 0.0377 0.179***
b.s.e. (0.0225) (0.0483) (0.0154) (0.0352) (0.0123) (0.0250) (0.0201) (0.0417)
Turnover ATT 0.162*** 0.398*** 0.130*** 0.342*** 0.0599* 0.247*** 0.0884* 0.258***
b.s.e. (0.0436) (0.0819) (0.0292) (0.0571) (0.0261) (0.0518) (0.0337) (0.0718)
Labour Productivity ATT 0.0612* 0.0644 0.0494* 0.0824** 0.0067 0.0766 0.0288 -0.003
b.s.e. (0.0331) (0.0510) (0.0234) (0.0370) (0.0280) (0.0472) (0.0280) (0.0528)
Untreated 26,038 16,048 68,168 37,439 2,411,136 1,076,811 444,912 216,162
Treated 922 676 1,559 1,100 2,882 1,816 1,090 667
Table 3: Impact of publicly-funded research grants on UK firms’ performance – ATT effects with nearest-neighbour matching
technique.

13. RESULTS
Research Grant Value
Q1 Q2 Q3 Q4
Employment ST ATT 0.0441** 0.0620*** 0.0584*** 0.0841***
b.s.e. (0.0153) (0.0186) (0.0162) (0.0181)
MT ATT 0.212*** 0.212*** 0.252*** 0.259***
b.s.e. (0.0359) (0.0358) (0.0337) (0.0390)
Turnover ST ATT 0.0397 0.0396 0.0465 0.0706**
b.s.e. (0.0285) (0.0372) (0.0337) (0.0350)
MT ATT 0.293*** 0.194*** 0.308*** 0.271***
b.s.e. (0.0619) (0.0648) (0.0643) (0.0698)
Lab. Productvity ST ATT -0.0109 -0.0368 -0.0195 -0.0315
b.s.e. (0.0237) (0.0319) (0.0286) (0.0297)
MT ATT 0.0400** -0.0557 -0.00214 -0.00789
b.s.e. (0.0153) (0.0501) (0.0438) (0.0505)
Control 2,969,032 2,969,032 2,969,032 2,969,032
Treated 1,659 1,151 1,395 1,454
Table 4: Impact of different levels of publicly-funded research grants received on UK firms’ performance
ATT effects with nearest-neighbour matching technique

14. CONCLUSIONS
First comprehensive analysis of the impact of publicly-funded research grants on the
performance of almost 10,000 UK firms using Gateway to research Data:
1. Receiving a research grant has on average a positive impact for employment
and turnover growth.
2. Employment grows faster both in the short and in the medium term (+5.8% -
+22.5%), while turnover (+27%) and labour productivity (+6%) growth effects
are stronger in the medium term - time lag between grant award and the
ability of firms to commercially exploit the outcome of their R&D activity
3. Impact of publicly-funded research grants is stronger for manufacturing firms, in
particular for high-tech manufacturing companies compared to low-tech
manufacturing and other services firms.
4. Positive impact is also incremental as the overall value of the grant increases,
productivity grows faster for firms receiving smaller grants.

15. NEXT STEPS
After extensive data cleaning and this preliminary evaluation the next steps will look at:
1. Network aspects: do specific types of collaborations yield more significant firm
performance effects?
2. Funding sources: are performance benefits more related to research grants
funded by certain Research Councils than others?
3. Firm heterogeneity: which other firms characteristics mediate the impact of
publicly-funded research grants on firms performance?
4. Spatial analysis: are performance benefits more concentrated in certain regions
than others? What’s the spillover effect on other firms operating in the same
industry and region?

16. If you would like any more information about this research please contact us:
Stephen Roper: stephen.roper@wbs.ac.uk
Enrico Vanino: e.vanino@aston.ac.uk
Bettina Becker: b.becker@aston.ac.uk
This work contains statistical data from ONS which is Crown Copyright. The use of these data does
not imply the endorsement of the data owner or the UK Data Service at the UK Data Archive in
relation to the interpretation or analysis of the data. This work uses research datasets which may
not exactly reproduce National Statistics aggregates.
THANK YOU – QUESTIONS and COMMENTS?

17. ROBUSTNESS
P-Score Mean Bias Equality of Means Ratio of
Variable Estimation Sample Treated Control Std. Bias Red. Bias t p>|t|
var.
Residuals
log(Employment) 0.866*** unmatched 3.006 1.132 118.2 220.66 0.000 7.98
(0.0106) matched 3.352 3.338 0.9 99.3 0.35 0.724 0.99
log(Lab.Productivity) 0.00422 unmatched 4.361 4.19 14.9 15.63 0.000 1.81
(0.0155) matched 4.425 4.397 2.4 84.1 1.19 0.235 1.36
log(age) -0.317*** unmatched 2.4 1.893 57.4 52.49 0.000 1.13
(0.0249) matched 2.665 2.658 0.8 98.6 0.54 0.587 1.02
Pre-Employment Trend 0.133** unmatched 0.076 0.013 27.2 27.55 0.000 2.12
(0.0563) matched 0.078 0.079 -0.5 98.2 -0.22 0.823 0.81
Pre-Productivity Trend 0.0657* unmatched -0.019 -0.021 0.4 0.33 0.743 1.5
(0.0376) matched -0.016 -0.026 2.1 473.7 1.12 0.265 1.45
Group 0.469*** unmatched 0.399 0.088 77.7 97.06 0.000
(0.0386) matched 0.453 0.455 -0.7 99.1 -0.28 0.777
Foreign Owned -0.409*** unmatched 0.121 0.019 40.7 65.65 0.000
(0.0507) matched 0.138 0.129 3.7 91 1.44 0.150
Science Park Area 0.567*** unmatched 0.183 0.077 31.9 35.13 0.000
(0.0393) matched 0.18 0.192 -3.7 88.5 -1.67 0.096
Agglomeration Index 1.213 unmatched 0.007 0.017 -54.2 -42.58 0.000 0.57
(1.998) matched 0.005 0.005 0.4 99.2 0.33 0.738 0.98
Regional R&D Intensity 0.0370*** Unmatched 8.916 9.109 -8.9 -7.41 0.000 1.04
(0.0086) Matched 8.869 8.858 0.5 94.5 0.26 0.796 1.01
Competition Index 0.752** unmatched -0.041 0.066 -53.1 -101.84 0.000 7.18
(0.372) matched 0.056 0.056 0.3 99.4 0.57 0.569 1.07
Region-Industry Productivity 0.00677 unmatched 4.744 4.711 6.3 6.22 0.000 1.47
(0.0541) matched 4.747 4.756 -1.8 71.9 -0.85 0.394 0.97
Region-Industry Employment -0.336*** unmatched 9.915 11.258 -88 -88.68 0.000 1.59
(0.0273) matched 9.887 9.905 -1.2 98.7 -0.59 0.554 1.11
High-Tech -0.417 unmatched 0.054 0.004 30.6 80.76 0.000
(0.397) matched 0.071 0.068 1.8 94.1 0.63 0.528
KIS 0.441*** unmatched 0.474 0.833 -81.4 -91.57 0.000
(0.150) matched 0.509 0.509 0 100 0.02 0.985
Sample Statistics R^2 LR-Chi^2 p>chi^2 Mean Bias Med. Bias B R
unmatched 0.251 20774.9 0 42.9 37.3 165.2 5.04
matched 0.001 19.61 0.295 1.6 1.2 8.3 1.14