Innovation and ProductivityChiara Criscuolo

Centre for Economic PerformanceLondon School of Economics

Paris 14th November 2007

Motivation• Technological knowledge is a driver of productivity • Not the only one:

– Organisational knowledge/ Non technological innovation (Topic 4)• “Productivity isn’t everything, but in the long run it is

almost everything” (Paul Krugman)• Productivity growth drives growth of real wages• Productivity growth can be used to finance government

expenditure• Therefore understanding differences in innovation

drivers (see also on IPRs and innovation) and outcomes and differences in the relationship between innovation and productivity will help us understand differences in performance across countries.

Background• Use of comparable cross-country data on innovation

inputs and outputs• We need firm level data: it is firms that innovate not

countries or industries• Large evidence of substantial and persistent intra-

industry heterogeneity in performance and characteristics across firms. (within countries and within industries!)

• Hurdle: firm level datasets compiled by national statistical offices must comply with confidentiality protection. – NOT possible to POOL the data together.– NEED a TEAM/Network willing to conduct the same

analysis in each separate country on similar data with “same” variables.

The project would have impossible...• …without the invaluable and extensive efforts and VOLUNTARY contributions of

country researchers and their institutions1. Australia: David (ABS)2. Austria: Martin Berger3. Belgium: Jeoffrey Malek4. Brazil: Bruno Araújo and João De Negri5. Canada: Petr Hanel and Pierre Therrien6. Denmark: Carter Bloch and Ebbe Graversen7. Finland: Mariagrazia Squicciarini Olavi Lehtoranta Mervi Niemi8. France: Stephane Robin and Jacques Mairesse9. Germany: Bettina Peters10. Italy: Francesco Crespi Mario Denni Rinaldo Evangelista and Mario Pianta11. Japan: Tomohiro Ijichi (could not participate for data problems)12. Korea: Seok-Hyeon Kim13. Luxembourg: Anna-Leena Asikainen14. Netherlands: George van Leeuwen, Pierre Mohnen, Michael Polder, Wladimir Raymond15. New Zealand: Richard Fabling 16. Norway: Svein Olav Nås and Mark Knell 17. Sweden: Hans Loof18. Switzerland: Spyros Arvanitis19. United Kingdom: Chiara Criscuolo

A big THANK YOU to all!

And without Innovation Surveys• What do we like about them?• rich source of information on innovation activities of firms:

innovation inputs/outputs external knowledge sources, factors hampering/fostering innovation and methods of protection of innovation.

• unique tool to study the innovation process within and across countries.

• harmonized across EU countries and similar across other countries

• in most countries can be matched to other data sources: – check the quality of the data and the representativeness of the sample– produce richer set of variables with which to investigate more questions

• it covers both the manufacturing and the services sectors – particularly suited to study innovation in the services sector

What we don’t like about innovation surveys• Although harmonized some cross-country differences

arise along various dimensions:– Sampling frames– Sector covered– Mandatory/voluntary – Questions asked – Phrasing and ordering of questions– Presence of filter questions

• Some of these issues can be accounted for in the analysis; but some of them cannot

• Limit the breadth of the analysis: “minimum common denominator” variables

Issues when doing comparative analysis

• Knowing the differences across different countries

• Which variables/information are the “minimum common denominator”?

• Define variables in the same way across countries

• Estimate the same model/specification across countries

…“Selection” issue

• In most countries innovation surveys “concentrates” on innovators (“yes to initial questions: product, process, ongoing and abandoned)

• those who have responded “no” to these questions (non-innovators) and do not have to respond to most of the rest of the survey.

• contains little information on non-innovators• “Selection” issue that needs to be dealt with in the

econometric analysis.

…poor productivity measure• For almost all countries innovation surveys contain

information only on one year (cross-sectional) information on sales (turnover) and employment.

• Sales/turnover is different from gross output• Only cross-sectional information, we cannot say anything on

growth• Sales per employee is a very rough measure of productivity.

Ideally we would want value added per employee (Labour Productivity measure) or Multi Factor Productivity measures

To improve on productivity measurement innovation survey must be combined with other production panel datasets (follow-up of current project?)

But what did we manage to do?

• We estimate: – the same model across 18 countries– extensions and variants of the model for smaller

subsets of countries• Within countries we distinguished where

possible – Small vs large firms– Manufacturing vs services– Control also for human and physical capital

And HOW did we manage to do?• We agreed on an acceptable model which could be run

in all participating countries:– Minimum common denominator variables– Corrected for selection– Simple productivity measure

• We had several meetings during the past 12 months to agree on the model

• Wrote programs/routines that could be easily estimated across countries (STATA do and ado files: Innov4Prod.do and *.ado available) and readme files that helped countries implement the routines

A brief outline of the model followed…

…To estimate the effects of innovation on productivity controlling for selection and endogeneity

Following the Crepon-Duguet Mairesse “tradition” we estimate a recursive 3 stage/4 equation model:

• 1st innovation equation• 2nd innovation input equation• 3rd innovation output equation• 4th productivity equation

The model• 3 stage with 4 equations • 1st stage explains firms’ decision whether to engage in innovation

activities or not and the decision on the amount of innovation expenditureProb(innovation=1)=f(size; Group; Foreign Market, Obstacles to innovation due to knowledge; costs and market; industry dummies)Ln(innovation expenditure per employee)=f(Group; Foreign Market; Cooperation; Financial Support; industry dummies)

• In the 2nd stage we estimate the knowledge production function where innovative sales depends on investment in innovation. Ln(innovative sales per employee)=f(Innovation expenditure; Size; Group; process innovation; Cooperation with clients; suppliers; other private and public agents; industry dummies; inverse Mills ratio [to correct for selection])

• The 3rd stage we estimate the innovation output productivity link using an augmented Cobb-Douglas production function using IV.Ln(sales per employee)=f(Size; Group; Process Innovation; log innovative sales per employee; inverse Mills ratio; industry dummies [Human Capital and Physical Capital])

Obstacles to innovation8.2 During the three years 2002 to 2004, how important were the following

factors for hampering your innovation activities or projects or influencing a decision not to innovate?

Degree of importance

High Medium Low Factor not

experienced

Lack of funds within your enterprise or group Lack of finance from sources outside your enterprise

Cost factors

Innovation costs too high

Lack of qualified personnel Lack of information on technology Lack of information on markets

Knowledge factors

Difficulty in finding cooperation partners for innovation

Market dominated by established enterprises Market factors Uncertain demand for innovative goods or services

Cooperation partnersPlease indicate the type of co-operation partner and location (Tick all that apply)

CUSTOMERS [Your

country] Other

Europe* United States

All other countries

C. Clients or customers

SUPPLIERS [Your

country] Other

Europe* United States

All other countries

B. Suppliers of equipment, materials, components, or software

PUBLIC [Your

country] Other

Europe* United States

All other countries

F. Universities or other higher education institutions

G. Government or public research institutes

OTHER PRIVATE [Your

country] Other

Europe* United States

All other countries

D. Competitors or other enterprises in your sector

E. Consultants, commercial labs, or private R&D institutes

Extensions/variations of the model ...

• Some countries could add Human Capital (H); Physical Capital (K) and Materials (M) in the productivity equation:– Austria (H,K); Belgium (H,K); Brazil (H,K,M); Canada (H,K); Finland

(H,K,M); Germany (H,K,M); New Zealand (K,M); Switzerland (H,K,M); UK (H)

• Sales per employee is a very rough measure of productivity. Ideally we would want value added per employee (Labour Productivity measure) or Multi Factor Productivity measures

• To do this innovation surveys must be combined with other production datasets. (follow-up of current project?)

…Extensions/variations of the model ...

• Most countries estimate separately for small vs large firms and manufacturing vs services firms;

• Korea and Canada only manuf; • Luxembourg serv. • Standard size threshold 250 employees • Different estimation strategies to deal with

endogeneity of innovation expenditure• Alternative definition of innovative firm

…Extensions/variations of the model…• Germany/Netherlands suggested a modification of

the model to deal with endogeneity• Canada: could only estimate on manufacturing and

weighted regressions and no information on obstacles to innovation

• Austria: estimate it on CIS3 rather than CIS4• Australia: no information on foreign market; inputed

group information and 2005 rather than 2004 • New Zealand: again differences in variable definitions• Switzerland no group variable

RESULTS

• “structural” model:– Heckman– Innovative sales eq. – Productivity eq.

• Some extensions:– Manufacturing vs services– Small (vs large)– Controlling for Human capital; Physical capital and

Materials

Controlling for Selection: innovation equation (Heckman selection eq.)

Selection Eq. GP FOR_MKT LEMP HAKNOW HAMARKET HACOST rho* Observations P-value LR test

Austria 0.213* 0.454*** 0.253*** -0.0765 -0.182 -0.00122 0.223 1001 0.226Belgium 0.198*** 0.617*** 0.267*** 0.0427 -0.0500 0.455*** 0.41 2695 0.0012Brazil 0.424*** -0.264*** 0.123*** 0.152*** 0.131*** 0.0320 2.019*** 9384 0.000

Canada -0.105* 0.290*** 0.140*** 1.005*** 5355 0.000Denmark 0.186** 0.637*** 0.253*** 0.243** 0.0288 0.391*** 0.324** 1729 0.0202Finland 0.0649 0.532*** 0.254*** 0.190** 0.259*** -0.0266 0.477*** 2155 0.00178France 0.227*** 0.778*** 0.204*** 0.201*** 0.0678*** 0.227*** 0.643*** 18056 0.000

Germany 0.144*** 0.529*** 0.0884*** 0.0144 -0.107 0.173*** 0.256** 3242 0.0656Italy 0.203*** 0.478*** 0.185*** 0.110*** -0.0680** 0.0908*** 0.753*** 15915 0.000

Luxembourg 0.267* 0.314** 0.248*** 0.191 -0.101 0.359* 0.192 545 0.701Netherlands 0.164*** 0.546*** 0.213*** 0.175*** -0.111** 0.0123 0.727*** 6858 0.000New Zealand 0.113** 0.349*** 0.0785*** 0.0892* 0.0270 0.138*** 1.337*** 3426 0.000

Norway -0.0724 0.643*** 0.320*** 0.301*** 0.0478 0.301*** 0.739*** 1852 0.000United Kingdom 0.174*** 0.464*** 0.0468*** 0.287*** 0.0883** 0.0883** 0.189 11162 0.261

Heckman outcome equation: innovation expenditure eq.

Outcome eq. GP FOR_MKT COOP FINSUP Observations

Austria 0.161 0.737*** 0.408*** 0.746*** 1001Belgium 0.233* 0.524*** -0.0205 0.714*** 2695

Brazil 0.875*** -0.204* 0.384*** 0.332*** 9384Canada 0.145* 0.448*** 0.173** 0.183* 5355

Denmark 0.477*** 0.762*** 0.182 0.735*** 1729Finland 0.260** 0.361* 0.495*** 0.460*** 2155France 0.231*** 1.158*** 0.427*** 0.683*** 18056

Germany 0.0538 0.610*** 0.402*** 0.469*** 3242Italy 0.268*** 0.511*** 0.310*** 0.412*** 15915

Luxembourg 0.212 0.434 0.102 0.352 545Netherlands 0.247*** 0.675*** 0.389*** 0.569*** 6858New Zealand 0.664*** 0.740*** 0.225*** Confidential 3426

Norway -0.0436 0.706*** 0.354*** 0.657*** 1852United Kingdom 0.0508 0.513*** 0.377*** 0.537*** 11162

Careful: group and foreign market are not marginal effects

Innovation Sales eq.GP LEMP PROCESS MILLSstrict COOP_client COOP_supplier COOP_private COOP_public Obs

Austria 0.32 0.0996 0.32 0.629 -0.142 0.0573 0.0828 -0.533 359Belgium 0.244 0.0432 0.0374 -0.0760 0.230* -0.150 0.307** -0.273** 411Brazil 0.748*** 0.136*** 0.0580 0.747** -0.0878 0.164 0.0340 0.313*** 1954

Canada 0.0717 0.0214 0.263*** 0.244 -0.0676 -0.0250 0.281** -0.145 2273Denmark 0.631*** -0.101* 0.0522 -0.0849 0.184 -0.111 -0.0260 0.0547 584Finland 0.264* -0.172* 0.297** -0.632 -0.189 0.00830 0.250 -0.0291 698France 0.552*** -0.0117 0.155** 0.0785 0.116* 0.0437 0.147** -0.0437 2511

Germany 0.0723 0.0367 0.0593 -0.469** 0.00700 0.127 -0.0507 -0.0601 1390Italy 0.0851 -0.0378 0.528*** -0.443 0.041 0.109 0.197* 0.0659 747

Luxembourg 0.183 -0.284** 0.509*** -1.130* 0.144 -0.127 0.0246 0.224 207Netherlands 0.320*** -0.0301 -0.0125 -0.186 0.0481 -0.000654 -0.0957 0.0889 1374New Zealand 0.300*** -0.0885*** 0.000126 0.548* -0.278*** 0.190* -0.103 -0.0765 993

Norway 0.239** 0.169** 0.147 0.219 -0.336* 0.304 0.0609 -0.00662 672United Kingdom 0.225*** -0.0349* 0.125** -0.163 0.0138 0.0660 0.00828 -0.00909 2989

LRTOTPE LRTOTPE

Austria 0.0501 Germany 0.340***Belgium 0.274*** Italy 0.210***Brazil 0.354*** Luxembourg 0.211***

Canada 0.367*** Netherlands 0.260***Denmark 0.141*** New Zealand 0.516***Finland 0.218*** Norway 0.471***France 0.214*** United Kingdom 0.263***

Productivity equation

GP LEMP PROCESS MILLSstrict LISPE Observations

Austria 0.137 -0.0236 -0.0103 -1.077** 0.401 359Belgium 0.289*** -0.0235 -0.125* -0.240 0.467*** 411Brazil 0.183** 0.140*** -0.211*** -0.315 0.647*** 1954

Canada 0.250*** 0.0772** -0.122** -0.00113 0.436*** 2273Denmark 0.186** 0.0732*** -0.0405 0.146 0.345*** 584Finland 0.244*** 0.0859** -0.0677 0.135 0.314*** 698France 0.232*** 0.0536*** -0.129*** 0.089 0.474*** 2511

Germany 0.0838** 0.0625*** -0.116*** -0.259** 0.500*** 1390Italy 0.093 0.00391 -0.192** -0.312* 0.485*** 747

Luxembourg 0.434*** 0.0349 -0.142 0.170 0.226* 207Netherlands 0.0219 0.0902*** -0.0440 -0.0765 0.409*** 1374New Zealand 0.128** 0.0662*** -0.135*** -0.200 0.682*** 993

Norway 0.256*** 0.0407 -0.0716 -0.168 0.344*** 672United Kingdom 0.150*** 0.0580*** -0.121*** -0.272*** 0.550*** 2989

…Manuf vs Serv; SME; HCManuf Serv small Large without with HC

Australia LISPE 0.399*** 0.0155Process -0.0443 0.515

Austria LISPE -0.35 0.394** 0.308** 0.401 0.334*Process 0.229 0.322** 0.157 -0.0103 -0.036

Belgium LISPE 0.446*** 0.525*** 0.607*** 0.353*** 0.467*** -0.00867Process -0.170** -0.0913 -0.109 -0.125 -0.125* -0.121

Brazil LISPE 0.758*** 0.589*** 0.647*** 0.117***Process -0.316*** 0.0218 -0.211*** -0.0373

Canada LISPE 0.507*** 0.368*** 0.436*** 0.380***Process -0.219** -0.0703 -0.122** -0.126**

Denmark LISPE 0.439*** 0.229 0.308*** 0.345*** noProcess 0.0536 -0.168* -0.0884 -0.0405 no

Finland LISPE 0.376*** 0.213 0.289*** 0.314*** -0.0929Process -0.119* 0.105 -0.0941 -0.0677 -0.00189

France LISPE 0.495*** 0.443*** 0.361*** 0.605*** 0.474*** noProcess -0.154*** -0.107* -0.115*** -0.0999* -0.129*** no

Germany LISPE 0.405*** 0.613*** 0.421*** 0.500*** 0.329***Process -0.107*** -0.137 -0.0979** -0.116*** -0.0877***

Luxembourg LISPE 0.450*** 0.226*Process -0.317** -0.142

Netherlands LISPE 0.459*** 0.390*** 0.386*** 0.429*** 0.409***Process -0.0803* 0.00225 -0.0507 -0.0116 -0.0440

New Zealand LISPE 0.589*** 0.707*** 0.685*** 0.639*** 0.682*** 0.245***Process -0.0629 -0.186*** -0.133*** -0.192* -0.135*** -0.0334

Summary of results

• When significant, coefficients are surprisingly similar• Serving a foreign market; being large and being part

of a group are generally associated with higher probability of being innovative and financial support and cooperation activity with higher investment in innovation

• Using a selection model is appropriate for most countries (exc. Austria; Luxembourg and UK)

• In the innovation sales eq. the elasticity of innovative sales to innovation expenditure is mostly between 20%-35%

• In productivity eq.: elasticity of productivity to product innovation is 30%-60%

Some counterintuitive results

• Obstacles to innovation have mostly positive coefficients. More innovative firms try harder and therefore find more obstacles?

• Process innovation is mostly negative in productivity equation. Measurement issues? Adjustment costs? Firms in crisis more likely to do process innovation? (Possibly future work?)

Lessons learned and the work ahead

• Surprising similar results across different countries

• Innovation matters for productivity• High coordination costs!• More work on the modelling and

understanding the differences in results• Extend the analysis to better measures of

productivity: match CIS with production data

THANK YOU!

C.CRISCUOLO@LSE.AC.UK

THE END