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Innovation and Productivity
Chiara Criscuolo
Centre for Economic Performance
London 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 intraindustry 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 institutions
1.
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4.
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19.
Australia: David (ABS)
Austria: Martin Berger
Belgium: Jeoffrey Malek
Brazil: Bruno Araújo and João De Negri
Canada: Petr Hanel and Pierre Therrien
Denmark: Carter Bloch and Ebbe Graversen
Finland: Mariagrazia Squicciarini Olavi Lehtoranta Mervi Niemi
France: Stephane Robin and Jacques Mairesse
Germany: Bettina Peters
Italy: Francesco Crespi Mario Denni Rinaldo Evangelista and Mario Pianta
Japan: Tomohiro Ijichi (could not participate for data problems)
Korea: Seok-Hyeon Kim
Luxembourg: Anna-Leena Asikainen
Netherlands: George van Leeuwen, Pierre Mohnen, Michael Polder, Wladimir Raymond
New Zealand: Richard Fabling
Norway: Svein Olav Nås and Mark Knell
Sweden: Hans Loof
Switzerland: Spyros Arvanitis
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
expenditure
Prob(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 innovation
8.2 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
Cost
factors
Knowledge
factors
Market
factors
Factor not
experienced
High
Medium
Low
Lack of funds within your enterprise or group
Lack of finance from sources outside your enterprise
Innovation costs too high
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Lack of qualified personnel
Lack of information on technology
Lack of information on markets
Difficulty in finding cooperation partners for innovation
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
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


Market dominated by established enterprises
Uncertain demand for innovative goods or services
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



Cooperation partners
Please indicate the type of co-operation partner and location
(Tick all that apply)
[Your
country]
Other
Europe*
United
States
All other
countries




[Your
country]
Other
Europe*
United
States
All other
countries




[Your
country]
Other
Europe*
United
States
All other
countries
F. Universities or other higher education institutions




G. Government or public research institutes




[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




CUSTOMERS
C. Clients or customers
SUPPLIERS
B. Suppliers of equipment, materials, components, or software
PUBLIC
OTHER PRIVATE
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.
Austria
Belgium
Brazil
Canada
Denmark
Finland
France
Germany
Italy
Luxembourg
Netherlands
New Zealand
Norway
United Kingdom
GP
0.213*
0.198***
0.424***
-0.105*
0.186**
0.0649
0.227***
0.144***
0.203***
0.267*
0.164***
0.113**
-0.0724
0.174***
FOR_MKT
0.454***
0.617***
-0.264***
0.290***
0.637***
0.532***
0.778***
0.529***
0.478***
0.314**
0.546***
0.349***
0.643***
0.464***
LEMP
0.253***
0.267***
0.123***
0.140***
0.253***
0.254***
0.204***
0.0884***
0.185***
0.248***
0.213***
0.0785***
0.320***
0.0468***
HAKNOW HAMARKET HACOST
rho* Observations P-value LR test
-0.0765
-0.182
-0.00122 0.223
1001
0.226
0.0427
-0.0500
0.455***
0.41
2695
0.0012
0.152***
0.131***
0.0320 2.019***
9384
0.000
1.005***
5355
0.000
0.243**
0.0288
0.391*** 0.324**
1729
0.0202
0.190**
0.259***
-0.0266 0.477***
2155
0.00178
0.201*** 0.0678*** 0.227*** 0.643***
18056
0.000
0.0144
-0.107
0.173*** 0.256**
3242
0.0656
0.110*** -0.0680** 0.0908*** 0.753***
15915
0.000
0.191
-0.101
0.359*
0.192
545
0.701
0.175***
-0.111**
0.0123 0.727***
6858
0.000
0.0892*
0.0270
0.138*** 1.337***
3426
0.000
0.301***
0.0478
0.301*** 0.739***
1852
0.000
0.287***
0.0883**
0.0883** 0.189
11162
0.261
Heckman outcome equation:
innovation expenditure eq.
Outcome eq.
Austria
Belgium
Brazil
Canada
Denmark
Finland
France
Germany
Italy
Luxembourg
Netherlands
New Zealand
Norway
United Kingdom
GP
FOR_MKT COOP
FINSUP Observations
0.161
0.737*** 0.408*** 0.746***
1001
0.233*
0.524*** -0.0205
0.714***
2695
0.875*** -0.204* 0.384*** 0.332***
9384
0.145*
0.448*** 0.173**
0.183*
5355
0.477*** 0.762***
0.182
0.735***
1729
0.260**
0.361*
0.495*** 0.460***
2155
0.231*** 1.158*** 0.427*** 0.683***
18056
0.0538 0.610*** 0.402*** 0.469***
3242
0.268*** 0.511*** 0.310*** 0.412***
15915
0.212
0.434
0.102
0.352
545
0.247*** 0.675*** 0.389*** 0.569***
6858
0.664*** 0.740*** 0.225*** Confidential
3426
-0.0436 0.706*** 0.354*** 0.657***
1852
0.0508 0.513*** 0.377*** 0.537***
11162
Careful: group and foreign market are not marginal effects
Innovation Sales eq.
GP
Austria
Belgium
Brazil
Canada
Denmark
Finland
France
Germany
Italy
Luxembourg
Netherlands
New Zealand
Norway
United Kingdom
LEMP
0.32
0.0996
0.244
0.0432
0.748*** 0.136***
0.0717
0.0214
0.631*** -0.101*
0.264*
-0.172*
0.552*** -0.0117
0.0723
0.0367
0.0851
-0.0378
0.183
-0.284**
0.320*** -0.0301
0.300*** -0.0885***
0.239** 0.169**
0.225*** -0.0349*
PROCESS MILLSstrict COOP_client COOP_supplier COOP_private COOP_public
Obs
0.32
0.629
0.0374
-0.0760
0.0580
0.747**
0.263***
0.244
0.0522
-0.0849
0.297**
-0.632
0.155**
0.0785
0.0593 -0.469**
0.528***
-0.443
0.509*** -1.130*
-0.0125
-0.186
0.000126 0.548*
0.147
0.219
0.125**
-0.163
359
411
1954
2273
584
698
2511
1390
747
207
1374
993
672
2989
-0.142
0.230*
-0.0878
-0.0676
0.184
-0.189
0.116*
0.00700
0.041
0.144
0.0481
-0.278***
-0.336*
0.0138
0.0573
-0.150
0.164
-0.0250
-0.111
0.00830
0.0437
0.127
0.109
-0.127
-0.000654
0.190*
0.304
0.0660
0.0828
0.307**
0.0340
0.281**
-0.0260
0.250
0.147**
-0.0507
0.197*
0.0246
-0.0957
-0.103
0.0609
0.00828
LRTOTPE
Austria
Belgium
Brazil
Canada
Denmark
Finland
France
0.0501
Germany
0.274***
Italy
0.354***
Luxembourg
0.367***
Netherlands
0.141***
New Zealand
0.218***
Norway
0.214*** United Kingdom
-0.533
-0.273**
0.313***
-0.145
0.0547
-0.0291
-0.0437
-0.0601
0.0659
0.224
0.0889
-0.0765
-0.00662
-0.00909
LRTOTPE
0.340***
0.210***
0.211***
0.260***
0.516***
0.471***
0.263***
Productivity equation
GP
Austria
0.137
Belgium
0.289***
Brazil
0.183**
Canada
0.250***
Denmark
0.186**
Finland
0.244***
France
0.232***
Germany
0.0838**
Italy
0.093
Luxembourg 0.434***
Netherlands
0.0219
New Zealand 0.128**
Norway
0.256***
United Kingdom 0.150***
LEMP
-0.0236
-0.0235
0.140***
0.0772**
0.0732***
0.0859**
0.0536***
0.0625***
0.00391
0.0349
0.0902***
0.0662***
0.0407
0.0580***
PROCESS MILLSstrict LISPE Observations
-0.0103
-1.077**
0.401
359
-0.125*
-0.240
0.467***
411
-0.211***
-0.315
0.647***
1954
-0.122**
-0.00113 0.436***
2273
-0.0405
0.146
0.345***
584
-0.0677
0.135
0.314***
698
-0.129***
0.089
0.474***
2511
-0.116***
-0.259** 0.500***
1390
-0.192**
-0.312* 0.485***
747
-0.142
0.170
0.226*
207
-0.0440
-0.0765 0.409***
1374
-0.135***
-0.200
0.682***
993
-0.0716
-0.168
0.344***
672
-0.121*** -0.272*** 0.550***
2989
…Manuf vs Serv; SME; HC
Australia
Austria
Belgium
Brazil
Canada
Denmark
Finland
France
Germany
Luxembourg
Netherlands
New Zealand
LISPE
Process
LISPE
Process
LISPE
Process
LISPE
Process
LISPE
Process
LISPE
Process
LISPE
Process
LISPE
Process
LISPE
Process
LISPE
Process
LISPE
Process
LISPE
Process
Manuf
0.399***
-0.0443
-0.35
0.229
0.446***
-0.170**
Serv
0.0155
0.515
0.394**
0.322**
0.525***
-0.0913
small
0.308**
0.157
0.607***
-0.109
0.758***
-0.316***
0.507***
-0.219**
0.439***
0.229
0.308***
0.0536
-0.168* -0.0884
0.376***
0.213
0.289***
-0.119*
0.105
-0.0941
0.495*** 0.443*** 0.361***
-0.154*** -0.107* -0.115***
0.405*** 0.613*** 0.421***
-0.107*** -0.137 -0.0979**
0.450***
-0.317**
0.459*** 0.390*** 0.386***
-0.0803* 0.00225 -0.0507
0.589*** 0.707*** 0.685***
-0.0629 -0.186*** -0.133***
Large
0.353***
-0.125
0.589***
0.0218
0.368***
-0.0703
0.605***
-0.0999*
0.429***
-0.0116
0.639***
-0.192*
without
with HC
0.401
-0.0103
0.467***
-0.125*
0.647***
-0.211***
0.436***
-0.122**
0.345***
-0.0405
0.314***
-0.0677
0.474***
-0.129***
0.500***
-0.116***
0.226*
-0.142
0.409***
-0.0440
0.682***
-0.135***
0.334*
-0.036
-0.00867
-0.121
0.117***
-0.0373
0.380***
-0.126**
no
no
-0.0929
-0.00189
no
no
0.329***
-0.0877***
0.245***
-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
THE END
THANK YOU!
[email protected]