THE IMPACT OF HIGHER EDUCATION INSTITUTION-FIRM KNOWLEDGE LINKS ON FIRM-LEVEL PRODUCTIVITY IN BRITAIN. RICHARD HARRIS, QIAN CHER LI AND JOHN MOFFAT.

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1 STRATHCLYDE DISCUSSION PAPERS IN ECONOMICS 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 DEPARTMENT OF ECONOMICS UNIVERSITY OF STRATHCLYDE GLASGOW

2 The Impact of Hgher Educaton Insttuton-Frm Knowledge Lnks on Frm-level Productvty n Brtan Rchard Harrs a, Qan Cher L b & John Moffat c a Ivy Lodge, 63 Gbson Street, Unversty of Glasgow, Glasgow, G12 8LR, Unted Kngdom. b Busness School, Imperal College London, South Kensngton Campus, London, SW7 2AZ, Unted Kngdom. c Correspondng Author: Department of Economcs, Sr Wllam Duncan Buldng, Unversty of Strathclyde, 130 Rottenrow, Glasgow, G4 0GE, Unted Kngdom. E- mal: john.moffat@strath.ac.uk; Tel.: Abstract Ths paper estmates whether knowledge lnks wth unverstes mpacts on establshment-level TFP. Usng propensty score matchng, the results show a postve and statstcally sgnfcant mpact although there are across producton and nonproducton ndustres and domestcally- and foregn-owned frms. JEL codes: D24, I23 Keywords: Unverstes Frm-level productvty 1. Introducton Ths paper estmates whether both sourcng knowledge from and/or cooperatng on nnovaton wth HEIs (Hgher Educaton Insttutons) 1 mpacts on establshment-level total factor productvty (TFP) usng a dataset created by mergng the UK government s Communty Innovaton Survey (CIS) wth the Annual Respondents Database (ARD). It also consders whether hgher graduate employment (as a measure of human captal) also mpacts postvely on TFP at the establshment-level. Many studes have nvestgated the relatonshp between unversty-frm knowledge lnks and nnovaton (see, for example, Mansfeld, 1991; Becker, 2003; Thorn et al, 2007). Most of these studes fnd a postve mpact. Fewer studes have nvestgated the mpact of unversty-frm knowledge lnks on productvty. Belderbos et al. (2004), usng the Dutch CIS, fnd that cooperaton wth unverstes has no statstcally sgnfcant mpact on the growth of labour productvty. Medda et al. (2005) fnd no statstcally sgnfcant effect of collaboratve research undertaken by Italan manufacturng frms and unverstes on the growth of TFP. Arvants et al. (2008), usng Swss data, show that unversty-frm knowledge and technology transfer has both a drect mpact on labour productvty and an ndrect mpact through ts postve mpact on nnovaton. In sum, there s as yet no clear consensus as to the mpact of unversty-frm knowledge lnks on productvty. 1 The actual questons used to defne HEI collaboraton are Q.16 and Q.18 (see for detals). 2

3 2. Data The dataset has been created by mergng the results from the 2007 CIS (coverng the perod 2004 to 2006) wth the ARD for The former gves nformaton on the nnovatve actvtes of some 14,872 UK establshments; whle the latter conssts of returned fnancal data on a stratfed sample of reportng unts from the Annual Busness Inqury (ABI) whch can be used to calculate Gross Value Added (GVA), factors nputs and thus TFP (see Robjohns, 2006). Mergng took place establshment level nformaton, wth all the relevant CIS establshments successfully lnked to the ARD. 2 Weghts are constructed (based on employment covered by the merged CIS-ARD dataset relatve to total employment n the ARD) 3 so that the results are representatve of the populaton of establshments. Based on such weghted CIS data, n 2006 on average 22.8% of UK establshments collaborated wth HEIs, rangng from over 70% n the Coke & Petroleum sector to 3.3% of Ar Transport companes. Larger enterprses were much more lkely to lnk wth HEIs (e.g., 41.4% of producton sector establshments employng 200+ workers, compared to around 4% of establshments employng 0-9 workers). The CIS data also shows that some 17.9% of the UK workforce held a degree, wth hgher proportons n establshments that exported, were foregn-owned, or were nvolved n producng nnovaton outputs. 3. Econometrc Model The basc model estmated s the followng producton functon: y = α + β e + β k + β x + β HEI + ε, (1) E K where y s the log of GVA for establshment ; e s the log of employment; k s the log of the captal stock; x s a vector of control varables; and HEI s a dummy varable that equals 1 f the establshment collaborates wth HEIs. The x varables consst of the followng: the percentage of the workforce that are graduates, the log of establshment age, the log of ndustral dversfcaton, the log of the Herfndahl ndex, a foregn-ownershp dummy, a sngle plant enterprse dummy, an exportng dummy, ndustry dummes, regon dummes and seven knowledge sourcng strategy dummes. 4 Because employment and captal are ncluded n the GVA equaton, the mpact of other varables s channelled through TFP (Harrs, 2005). A pror we assume that the mpact of collaboratng wth HEIs can dffer across domestcally owned and foregn-owned frms. To test for ths, n a second specfcaton, an nteracton varable between collaboratng wth HEIs and foregn ownershp s added to equaton (1). x ATT 2 Some ndustral sectors are omtted from the ARD such as agrculture and much of fnancal servces. 3 We calculated the weghts at the 2-dgt ndustry level, splt nto 5 employment sze-bands. 4 More detal on these varables s avalable n an unpublshed appendx, avalable on request. 3

4 4. Estmaton Strategy Because establshments that collaborate wth HEIs are a self-selected group of the populaton of establshments, they wll tend to have dfferent characterstcs from establshments that do not collaborate wth HEIs. Ths makes causal nference dffcult as these dfferences n characterstcs wll lead to dfferences n productvty performance that are unrelated to whether HEIs have any mpact on TFP (see, for example, Blundell and Costa Das, 2009, for a more detaled exposton of selfselected bas). One soluton to ths problem s to create a matched sample n whch treated and untreated establshments are observed for all values of the covarates. Ths was done usng propensty score matchng (see Deheja and Wahba, 2002), whch nvolved estmatng probt models of treatment status ncludng all varables that determne both productvty and whether an establshment collaborates wth HEIs, and then matchng on the estmated predcted values. 5 The advantage of propensty score matchng over other forms of matchng s that t overcomes the dffcultes of matchng on a large number of varables (Zhao, 2004). Table 1 OLS Estmates of Dfferent Versons of Equaton (1) usng a Matched Sample Model 1 Model 2 All Industres 0.113*** 0.119*** HEI Lnk (0.030) (0.035) HEI Lnk * Foregn Ownershp (0.067) Graduates Producton sector HEI Lnk HEI Lnk * Foregn Ownershp Graduates Non-Producton sector HEI Lnk HEI Lnk * Foregn Ownershp 0.404*** 0.405*** (0.051) (0.051) 0.092** 0.151*** (0.045) (0.055) ** (0.090) 0.429*** 0.445*** (0.085) (0.086) 0.140*** 0.104** (0.040) (0.045) 0.207** (0.095) Graduates */**/*** denotes sgnfcance at the 10%/5%/1% levels 0.445*** 0.443*** (0.063) (0.063) 5 The results from the (weghted) probt models are avalable n the unpublshed appendx. Dfferent models were estmated dependng on sector. 4

5 5. Results Table 1 gves the key coeffcents from estmaton of the two specfcatons for all ndustres, producton ndustres and non-producton ndustres. Table A1 n the appendx gves the coeffcents on the other varables for the frst specfcaton (.e. equaton 1). The sgn and sze of the latter are consstent wth expectatons and vary lttle across the dfferent specfcatons. For all ndustres, results from estmatng equaton 1 (the baselne model) show that collaboratng wth HEIs has a postve and statstcally sgnfcant mpact on TFP (the latter was around 12% hgher). Introducng the nteracton between HEI lnk and foregn ownershp has lttle mpact. For producton ndustres (the second part of Table 1), the coeffcent on the HEI lnk s postve and sgnfcant n the baselne specfcaton. Introducng the nteracton between HEI lnk and foregn ownershp leads to a large ncrease n the sze of the coeffcent on the HEI lnk dummy. Ths s because the coeffcent on the nteracton varable s negatve and statstcally sgnfcant. Ths mples that, for producton ndustres, there s a large dfference n the mpact of collaboratng wth HEIs on TFP between domestcally owned and foregn-owned frms wth the latter experencng no sgnfcant TFP gans from ther HEI lnkages. Ths suggests that foregn-owned frms operatng n the UK producton sector whch also collaborated wth HEIs were on average technology-seekng enterprses rather than explotng any ex ante technologcal superorty (see Fosfur and Motta, 1999; Cantwell et al., 2004; Love, 2003; Drffeld and Love, 2007). For non-producton ndustres, there s agan a postve and statstcally sgnfcant coeffcent on the HEI lnk varable n the basc specfcaton. Unlke for producton ndustres, the ntroducton of the nteracton between HEI lnk and foregn ownershp leads to a dmnuton n the coeffcent on the HEI lnk varable because the coeffcent on the nteracton varable s postve and statstcally sgnfcant. Ths suggests that n the non-producton sector, foregn-owned frms are explotng ther pror technologcal advantages wth the assstance of knowledge ganed from HEIs (overall foregn-owned frms wth an HEI lnk were some 36.5% more productve). The coeffcent on the graduates varable s postve and statstcally sgnfcant for all specfcatons. As ths varable s not logged, the coeffcents presented are not elastctes. Evaluated at the mean, a 10% ncrease n the percentage of graduates leads to an ncrease of between 0.6% and 1.4% n TFP, dependng on the sector and model chosen. 6. Concluson Ths paper has sought to estmate the mpact of collaboratng wth HEIs on TFP usng a dataset created by mergng CIS wth the ARD. Usng a sample created usng propensty score matchng, the results show that collaboratng wth HEIs had a postve and statstcally sgnfcant mpact on TFP although there are dfferences n the strength of ths effect across producton and non-producton ndustres and domestcally owned and foregn-owned frms. 5

6 Acknowledgements Support from the Economc and Socal Research Councl (Grant No. RES ), the Scottsh Fundng Councl (SFC), the Department for Educaton and Learnng (DEL) n Northern Ireland, the Hgher Educaton Fundng Councl for England (HEFCE) and the Hgher Educaton Fundng Councl for Wales (HEFCW) s gratefully acknowledged. Ths work contans statstcal data from ONS whch s Crown copyrght and reproduced wth the permsson of the controller of HMSO and Queen's Prnter for Scotland. The use of the ONS statstcal data n ths work does not mply the endorsement of the ONS n relaton to the nterpretaton or analyss of the statstcal data. Ths work uses research datasets whch may not exactly reproduce Natonal Statstcs aggregates. References Arvants, S., N. Sydow, M. Woerter, Is There Any Impact of Unversty- Industry Knowledge Transfer on Innovaton and Productvty? An Emprcal Analyss Based on Swss Frm Data. Revew of Industral Organzaton 32, Becker, W., Evaluaton of the Role of Unverstes n the Innovaton Process. (Unverstaet Augsburg, Insttute for Economcs). Belderbos, R., M. Carree, B. Lokshn, Cooperatve R&D and frm performance. Research Polcy 33, Blundell, R., M.C. Das, Alternatve Approaches to Evaluaton n Emprcal Mcroeconomcs. J. Human Resources 44, Cantwell, J.A., J.H. Dunnng, O.E.M. Janne, Towards a technology-seekng explanaton of U.S. drect nvestment n the Unted Kngdom. Journal of Internatonal Management 10, Deheja, R.H., S. Wahba, Propensty Score-Matchng Methods for Nonexpermental Causal Studes. Revew of Economcs and Statstcs 84, Drffeld, N., J.H. Love, Lnkng FDI Motvaton and Host Economy Productvty Effects: Conceptual and Emprcal Analyss. Journal of Internatonal Busness Studes 38, Fosfur, A., M. Motta, Multnatonals wthout Advantages. Scandnavan Journal of Economcs 101, Harrs, R., Economcs of the Workplace: Specal Issue Edtoral. Scottsh Journal of Poltcal Economy 52, Love, J.H., Technology Sourcng versus Technology Explotaton: An Analyss of US Foregn Drect Investment Flows. Appled Economcs 35, Mansfeld, E., Academc research and ndustral nnovaton. Research Polcy 20, Medda, G., C. Pga, D.S. Segel, Unversty R&D and Frm Productvty: Evdence from Italy. Journal of Technology Transfer 30, Robjohns, J., ARD2: The new Annual Respondents Database. Economc Trends 630, Thorn, K., A. Blom, M. Mark, D. Marotta, Human captal and unverstyndustry lnkages' role n fosterng frm nnovaton : an emprcal study of Chle and Colomba. (The World Bank, Polcy Research Workng Paper Seres: 4443). 6

7 Zhao, Z., Usng Matchng to Estmate Treatment Effects: Data Requrements, Matchng Metrcs, and Monte Carlo Evdence. Revew of Economcs and Statstcs 86,

8 Appendx Table A1: Weghted OLS Estmates of Equaton (1) usng a Matched Sample All Industres Producton Non-Producton HEI Lnk 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(Captal) 0.146*** 0.075*** 0.162*** (0.013) (0.021) (0.015) Foregn Ownershp 0.371*** 0.289*** 0.457*** (0.036) (0.047) (0.050) Exportng 0.203*** 0.172*** 0.203*** (0.035) (0.055) (0.045) Ln(Age) *** *** *** (0.025) (0.048) (0.030) Sze of graduates workforce 0.404*** 0.429*** 0.445*** (0.051) (0.085) (0.063) Ln(Herfndahl) *** *** (0.010) (0.015) (0.013) Ln(Dversfcaton) 0.083*** *** (0.017) (0.023) (0.024) Sngle-plant Enterprse *** *** *** (0.038) (0.051) (0.054) R-squared Observatons A full set of knowledge sourcng strategy, ndustry and regon dummes are ncluded but not reported for all specfcatons. */**/*** denotes sgnfcance at the 10%/5%/1% levels 8