This paper estimates whether knowledge links with universities impacts on establishment-level TFP. Using propensity score matching, the results show a positive and statistically significant impact although there are across production and non-production industries and domestically- and foreign-owned firms.Universities, Firm-level productivity

John Moffat

Qian Cher Li

Richard Harris

English

Research Papers in Economics

 
STRATHCLYDE 
 
DISCUSSION PAPERS IN ECONOMICS 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
DEPARTMENT OF ECONOMICS 
UNIVERSITY OF STRATHCLYDE 
GLASGOW 
 
 
THE IMPACT OF HIGHER EDUCATION INSTITUTION-FIRM 
KNOWLEDGE LINKS ON FIRM-LEVEL PRODUCTIVITY IN 
BRITAIN. 
 
BY 
 
 
RICHARD HARRIS, QIAN CHER LI AND JOHN MOFFAT. 
 
 
NO. 10-17 
 
   2 
 
The Impact of Higher Education Institution-Firm Knowledge Links on 
Firm-level Productivity in Britain 
Richard Harris
a, Qian Cher Li
b & John Moffat
c 
 
a Ivy Lodge, 63 Gibson Street, University of Glasgow, Glasgow, G12 8LR, United 
Kingdom. 
b Business School, Imperial College London, South Kensington Campus, London, 
SW7 2AZ, United Kingdom.  
c Corresponding Author: Department of Economics, Sir William Duncan Building, 
University of Strathclyde, 130 Rottenrow, Glasgow, G4 0GE, United Kingdom. E-
mail: john.moffat@strath.ac.uk; Tel.: +44-141-330-8523 
 
 
Abstract 
This paper estimates whether knowledge links  with  universities  impacts on 
establishment-level TFP. Using propensity score matching, the results show a positive 
and statistically significant impact although there are across production and non-
production industries and domestically- and foreign-owned firms. 
 
JEL codes:  D24, I23 
 
Keywords:    Universities  Firm-level productivity 
 
 
 
1. Introduction 
This paper estimates whether both sourcing knowledge from and/or cooperating on 
innovation with HEIs (Higher Education Institutions)
1
 
 impacts on establishment-level 
total  factor  productivity (TFP)  using a dataset created by merging the UK 
government’s  Community Innovation Survey (CIS) with the Annual Respondents 
Database (ARD). It also considers whether higher graduate employment (as a measure 
of human capital) also impacts positively on TFP at the establishment-level. 
Many studies have investigated the relationship between university-firm knowledge 
links and innovation (see, for example, Mansfield, 1991; Becker, 2003; Thorn et al,  
2007). Most of these studies find a positive impact. Fewer studies have investigated 
the impact of university-firm knowledge links on productivity.  Belderbos et al. 
(2004), using the Dutch CIS, find that cooperation with universities has no 
statistically significant impact on the growth of labour productivity. Medda et al. 
(2005) find no statistically significant effect of collaborative research undertaken by 
Italian manufacturing firms and universities on the growth of TFP. Arvanitis et al. 
(2008),  using Swiss data, show that university-firm knowledge and technology 
transfer has both a direct impact on labour productivity and an indirect impact through 
its positive impact on innovation. In sum, there is as yet no clear consensus as to the 
impact of university-firm knowledge links on productivity.  
 
                                                 
1  The actual questions used to define HEI collaboration are Q.16 and Q.18 (see 
http://www.bis.gov.uk/policies/science/science-innovation-analysis/cis/cis4_questionnaire for details).   3 
 
2. Data 
The dataset has been created by merging the results from the 2007 CIS (covering the 
period 2004 to 2006) with the ARD for 2006. The former gives information on the 
innovative activities of some 14,872 UK establishments; while the latter consists of 
returned  financial  data on a stratified sample of reporting units from the Annual 
Business Inquiry (ABI) which can be used to calculate Gross Value Added (GVA), 
factors inputs and thus TFP (see Robjohns, 2006). Merging took place establishment 
level information, with all the relevant CIS establishments successfully linked to the 
ARD.
2
 
 
Weights are constructed (based on employment covered by the merged CIS-ARD 
dataset relative to total employment in the ARD)
3
 
 so that the results are representative 
of the population of establishments. Based on such weighted CIS data, in 2006 on 
average 22.8% of UK establishments collaborated with HEIs, ranging from over 70% 
in the Coke & Petroleum sector to 3.3% of Air Transport companies. Larger 
enterprises were much more likely to link with HEIs (e.g., 41.4% of production sector 
establishments employing 200+ workers, compared to around 4% of establishments 
employing 0-9 workers). The CIS data also shows that some 17.9% of the UK 
workforce  held a degree, with higher proportions in establishments  that exported, 
were foreign-owned, or were involved in producing innovation outputs. 
 
3. Econometric Model 
The basic model estimated is the following production function: 
  , i i ATT i x i K i E i HEI x k e y ε β β β β α + + + + + =   (1) 
where yi is the log of GVA for establishment i; ei is the log of employment; ki is the 
log of the capital stock; xi is a vector of control variables; and HEIi is a dummy 
variable that equals 1 if the establishment collaborates with HEIs. The xi variables 
consist of the following: the percentage of the workforce that are graduates, the log of 
establishment age, the log of industrial diversification, the log of the Herfindahl index, 
a foreign-ownership dummy, a single plant enterprise dummy, an exporting dummy, 
industry dummies, region dummies  and seven knowledge sourcing strategy 
dummies.
4
 
 Because employment and capital are included in the GVA equation, the 
impact of other variables is channelled through TFP (Harris, 2005). 
A priori we assume  that the impact of collaborating with  HEIs  can  differ  across 
domestically owned and foreign-owned  firms. To test for this, in  a  second 
specification,  an interaction variable between collaborating with HEIs and foreign 
ownership is added to equation (1). 
 
 
                                                 
2  Some industrial sectors are omitted from the ARD –  such as agriculture and much of financial 
services. 
3 We calculated the weights at the 2-digit industry level, split into 5 employment size-bands. 
4 More detail on these variables is available in an unpublished appendix, available on request.    4 
 
4. Estimation Strategy 
Because establishments that collaborate with HEIs are a self-selected group of the 
population of establishments, they will tend to have different characteristics from 
establishments  that do not collaborate with  HEIs.  This makes causal inference 
difficult as these differences in characteristics will lead to differences in productivity 
performance that are unrelated to whether HEIs have any impact on TFP (see, for 
example,  Blundell and Costa Dias,  2009,  for a more detailed exposition of self-
selected bias). 
 
One solution to this problem is to create a matched sample in which treated and 
untreated establishments are observed for all values of the covariates. This was done 
using propensity score matching (see Dehejia and Wahba,  2002),  which  involved 
estimating probit models of treatment status including all variables that determine 
both productivity and whether an establishment collaborates with  HEIs,  and then 
matching on the estimated predicted values.
5
 
  The advantage of propensity score 
matching  over other forms of matching is that it overcomes the difficulties of 
matching on a large number of variables (Zhao, 2004). 
Table 1 – OLS Estimates of Different Versions of Equation (1) using a Matched 
Sample 
  Model 1  Model 2 
All Industries 
HEI Link  0.113***  0.119*** 
(0.030)  (0.035) 
HEI Link * Foreign Ownership 
−  -0.031 
  (0.067) 
   
Graduates  0.404***  0.405*** 
(0.051)  (0.051) 
Production sector 
HEI Link  0.092**  0.151*** 
(0.045)  (0.055) 
HEI Link * Foreign Ownership 
−  -0.208** 
  (0.090) 
   
Graduates  0.429***  0.445*** 
(0.085)  (0.086) 
Non-Production sector 
HEI Link  0.140***  0.104** 
(0.040)  (0.045) 
HEI Link * Foreign Ownership 
−  0.207** 
  (0.095) 
   
Graduates  0.445***  0.443*** 
(0.063)  (0.063) 
*/**/*** denotes significance at the 10%/5%/1% levels 
                                                 
5 The results from the (weighted) probit models are available in the unpublished appendix. Different 
models were estimated depending on sector.   5 
 
 
5. Results 
Table 1 gives the key coefficients from estimation of the two specifications for all 
industries, production industries and non-production industries. Table A1 in the 
appendix gives the coefficients on the other variables for the first specification (i.e. 
equation 1). The sign and size of the latter are consistent with expectations and vary 
little across the different specifications. 
 
For all industries, results from estimating equation 1 (the baseline model) show that 
collaborating with HEIs has a positive and statistically significant impact on TFP (the 
latter was around 12% higher). Introducing the interaction between HEI link and 
foreign ownership has little impact.  
 
For production industries (the second part of Table 1), the coefficient on the HEI link 
is positive and significant in the baseline specification. Introducing the interaction 
between HEI link and foreign ownership leads to a large increase in the size of the 
coefficient on the HEI link dummy. This is because the coefficient on the interaction 
variable is negative and statistically significant.  This  implies that, for production 
industries, there is a large difference in the impact of collaborating with HEIs on TFP 
between domestically owned and foreign-owned firms with the latter experiencing no 
significant TFP  gains from their HEI linkages.   This suggests that foreign-owned 
firms operating in the UK production sector which also collaborated with HEIs were 
on average technology-seeking enterprises rather than  exploiting any ex ante 
technological superiority (see Fosfuri and Motta, 1999; Cantwell et al., 2004; Love, 
2003; Driffield and Love, 2007). 
 
For non-production industries, there is again a positive and statistically significant 
coefficient on the HEI link variable in the basic specification. Unlike for production 
industries, the introduction of the interaction between HEI link and foreign ownership 
leads to a diminution in the coefficient on the HEI link variable because the 
coefficient on the interaction variable is positive and statistically significant. This 
suggests that in the non-production sector, foreign-owned firms are exploiting their 
prior technological advantages with the assistance of knowledge gained from HEIs 
(overall foreign-owned firms with an HEI link were some 36.5% more productive). 
 
The coefficient on the graduates’ variable is positive and statistically significant for 
all specifications. As this variable is not logged, the coefficients presented are not 
elasticities. Evaluated at the mean, a 10% increase in the percentage of graduates 
leads to an increase of between 0.6% and 1.4% in TFP, depending on the sector and 
model chosen. 
 
 
6. Conclusion 
This paper has sought to estimate the impact of collaborating with HEIs on TFP using 
a dataset created by merging CIS with the ARD. Using a sample created using 
propensity score matching, the results show that collaborating with  HEIs had  a 
positive and statistically significant impact on TFP although there are differences in 
the strength of this effect across production and non-production industries and 
domestically owned and foreign-owned firms.   6 
 
 
Acknowledgements 
Support from the Economic and Social Research Council (Grant No. RES-171-25-
0032), the Scottish Funding Council (SFC), the Department for Education and 
Learning (DEL) in Northern Ireland, the Higher Education Funding Council for 
England (HEFCE) and the Higher Education Funding Council for Wales (HEFCW) is 
gratefully acknowledged. This work contains statistical data from ONS which is 
Crown copyright and reproduced with the permission of the controller of HMSO and 
Queen's Printer for Scotland. The use of the ONS statistical data in this work does not 
imply the endorsement of the ONS in relation to the interpretation or analysis of the 
statistical data. This work uses research datasets which may not exactly reproduce 
National Statistics aggregates. 
 
 
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Appendix 
Table A1
 
: Weighted OLS Estimates of Equation (1) using a Matched Sample 
All Industries  Production  Non-Production 
HEI Link  0.113***  0.092**  0.140*** 
(0.030)  (0.045)  (0.040) 
Ln(Employment)  0.872***  0.972***  0.850*** 
(0.015)  (0.024)  (0.019) 
Ln(Capital)  0.146***  0.075***  0.162*** 
(0.013)  (0.021)  (0.015) 
Foreign Ownership  0.371***  0.289***  0.457*** 
(0.036)  (0.047)  (0.050) 
Exporting  0.203***  0.172***  0.203*** 
(0.035)  (0.055)  (0.045) 
Ln(Age)  -0.184***  -0.226***  -0.192*** 
(0.025)  (0.048)  (0.030) 
Size of graduates workforce  0.404***  0.429***  0.445*** 
(0.051)  (0.085)  (0.063) 
Ln(Herfindahl)  -0.039***  0.017  -0.064*** 
(0.010)  (0.015)  (0.013) 
Ln(Diversification)  0.083***  0.034  0.130*** 
(0.017)  (0.023)  (0.024) 
Single-plant Enterprise  -0.413***  -0.324***  -0.422*** 
(0.038)  (0.051)  (0.054) 
R-squared  0.663  0.718  0.633 
Observations  6928  2699  4280 
A full set of knowledge sourcing strategy, industry and region dummies are included but not reported 
for all specifications. 
*/**/*** denotes significance at the 10%/5%/1% levels 

Academic research and industrial innovation.

Alternative Approaches to Evaluation in Empirical Microeconomics.

ARD2: The new Annual Respondents Database.

Cooperative R&D and firm performance.

Economics of the Workplace: Special Issue Editorial.

Evaluation of the Role of Universities in the Innovation Process. (Universitaet Augsburg,

Human capital and universityindustry linkages' role in fostering firm innovation : an empirical study of Chile and Colombia. (The World Bank, Policy Research Working Paper Series:

Is There Any Impact of UniversityIndustry Knowledge Transfer on Innovation and Productivity? An Empirical Analysis Based on Swiss Firm Data.

Linking FDI Motivation and Host Economy Productivity Effects: Conceptual and Empirical Analysis.

Multinationals without Advantages.

Propensity Score-Matching Methods for Nonexperimental Causal Studies.

Technology Sourcing versus Technology Exploitation: An Analysis of US Foreign Direct Investment Flows.

Towards a technology-seeking explanation of U.S. direct investment in the United Kingdom.

University R&D and Firm Productivity: Evidence from Italy.

The Impact of Higher Education Institution-Firm Knowledge Links on Firm-level Productivity in Britain

http://www.strath.ac.uk/media/departments/economics/researchdiscussionpapers/2010/10-17.pdf

The Impact of Higher Education Institution-Firm Knowledge Links on Firm-level Productivity in Britain

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