James Foreman-Peck and Peter Morgan Cardiff Business · PDF fileInnovation in 13 European Countries James Foreman-Peck and Peter Morgan Cardiff Business School

Innovation in 13 European Countries

James Foreman-Peck and Peter Morgan

Cardiff Business School

The Questions

• Are firms supported by government more likely to innovate and if so by how much on average?

• Do innovating companies increase their turnover and if so by how much on average?

DataThe source of the data is the Community Innovation Survey for period 2006-2008 (CIS 2008)

The data set we are using contains data on 16 countries within the European Union.

For the purposes of this study three of the countries were omitted from the data set due to

anomalies in their variables.

Weights were not present for all the countries and hence were not used.

BG CY CZ DE EE ES HU LT LV PT RO SI SK

Frequency of Companies by Country

Country Code

Fre

quen

cy

050

0015

000

2500

035

000

Code Frequency

BG Belgium 15859

CY Cyprus 1024

CZ Czech Rep. 6804

DE Germany 6026

EE Estonia 3986

ES Spain 37400

HU Hungary 5390

LT Lithuania 2111

LV Latvia 1077

PT Portugal 6512

RO Romania 9631

SI Slovenia 2593

SK Slovakia 2296

Original Sample 100 534

Reduced to 89 584

Due to Missing Values,

zero turnover, etc.

Tableplot for raw data sorted by Log(Turnover Ratio), TRVisual representation of Data Table using colour/horizontal bars for categorical/numerical variables93 316 cases grouped into 150 bins – each coloured bar is broken into segments with lengths proportional to number of cases of that category

And the length of the bar gives the average value for that bin.

Gds Srv Proc Fund R&D HdOff NACE Cntry Loc Eur Nat Other Size LgTurn TR BsPr WkPr Extnl Dsgn Prom Plac Price

High

Growth

More of

almost

everything

goes on in

the middle!

Anomaly!

Zooming in on an Anomalous Region to Individual Firm Level

Exactly the same

turnover in 2006

and2008

Gds Srv Proc Fund R&D HdOff NACE Cntry Loc Eur Nat Other Size LgTurn TR BsPr WkPr Extnl Dsgn Prom Plac Price

Abbreviated Flow Chart1 Sector

1.1 Group, Head Office of

Group gp, c_ho

1.2Geographic markets, Largest market marloc, marnat, mareur, maroth,

larmar

2.1New goods/services inpdgd, inpdsv

2.2 – 2.3 details of product innovation

Both

answered

as NO

3.1 – New processes – method,logistic,support Inpspd, inpslg, inpssu

3.2 – 3.3 details of process innovation

4.1 Abandoned/Ongoing Inaba, inong

5.1 Innov. activities – Process and Product

In-R&D, Ext-R&D, Equipping, Training, Market Introduction, Other

rrdin, rdeng, rrdex, rmac, roek, rtr, rmar, rpre

5.2 Innovation expenditures

In-R&D, Purchase Ext-R&D, Equipping, External Knowledge, Total

rrdinx, rrdexx , rmacx, roekx, rtot_msk

5.3 Public Support, Local-Regional, Central Govt., EU, EU Framework

funloc, fungmt, funeu, funrtd

6.1 – 7.1 Information about Information Sources, Cooperation and

Objectives

8.1 Organizational Innovation

Business Practices, Work responsib., Extrnl relations orgbup, orgwkp, orgexr

All answered

as NOAll of 2.1, 3.1 and

4.1 answered as

NO

8.2 Organizational

Objectives

9.1 Marketing Innovation

Dsign, Promtn, Placemnt, Pricing mktdgp, mktpdp, mktpdl, mktpri

9.1 Marketing Innovation objectives

10.1 – 10.3 Environmental Variables

11.1 Turnover turn06, turn08

11.2 Employees size06, size08

Other branching not affecting

variables used herein has been

suppressed

Composition of the sampleSize (2008) and Head Office Location

Breakdown of Firms by Country and Size in 2008

Num

ber o

f Em

ploy

ees


10 –

49

50 –

249

>=25

0

0.0

0.2

0.4

0.6

0.8

1.0

In an Enterprise Group?

Not in a

group %

In a group %

75.4 24.6

Home

% total

sample

EU+ Non-EU+

14.8 8.2 1.6

Location of Group Head Office

Composition of Sample - Distribution across Industry Sector

Belgium

Cyprus

Czech R.

Germany

Estonia

Spain

Hungary

Lithuania

Latvia

Portugal

Romania

Slovenia

Slovakia

Variables Used in the StudyInnovation Outcomes

Innovation Variable Firms Innovating %

New goods? inpdgd 18.6

New services? inpdsv 11.5

New processes? inpspd 19.4


Proportions of Firms engaged in Goods(red), Services(blue) and Process(orange) Innovation

Country

Propo

rtion

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Belgium Cyprus Czech R. Germany Estonia Spain Hungary Lithuania Latvia Portugal Romania Slovenia Slovakia

Variables used in the studyMarket and Organizational Practices


Proportions of Firms engaged in Market Innovation Design(grey), Promotion(green),Placement(purple) and Price(yellow)

Country

Pro

porti

on

0.0

00.

050.

100

.15

0.2

00.

25


Proportions of Firms engaged in Business Practice(green), Work Responsibility(cyan) and External Relations(blue) Innovation

Country

Pro

porti

on

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Market Innovation % Yes

Design/Pack mktdgp 11.3

Promotion mktpdp 12.5

Placement mktpdl 9.4

Pricing mktpri 9.8

Organizational Practices % Yes

Business Practices orgbup 21.7

Working Practices orgwkp 23.3

External Relations orgexr 11.7

Missings

Variables used in the studyMarkets Addressed

Market

Location

% Yes

Local

marloc

75.6

National

marnat

70.7

EU+

mareu

40.6

Rest of

World

maroth

21.2


Proportions of Firms engaged in Local, National,European and Other Markets (from dark to light grey)

Country

Propo

rtion

0.0

0.2

0.4

0.6

0.8


Proportions of Turnover engaged in Local, National,European and Other Markets (from dark to light grey)

Country

Propo

rtion

0.0

0.2

0.4

0.6

0.8


Proportions of Firms Carrying Out Internal R&D

Country

Proportion

0.0

0.1

0.2

0.3

0.4

Variables in the StudyProportions of Companies doing Internal R&D and Country Sample Size

The patterns of

R&D use and

sampling across

countries are quite

ill-matched.

Germany has

highest proportion

of firms doing In-

house R&D but

carries a small

portion of the

total sample.

Hence measures

are required to

mitigate this

disparity.


Proportions of Companies by Country

Country Code

Fre

quen

cy

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

Spineplot of Raw Data for Acquisition of State Funding

Central Government Support or not (1=blue,0=red, missing=white)


01

Missing values for Spain and

Lithuania already set to zero

Variables in the StudyCentral Government Funding for Innovation


Proportions of Firms obtaining Central Government Funding

Country

Pro

porti

on

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

6% of companies in

the sample get

Central Government

Funding for

Innovation

Propensity Score Matching

• If enterprises were entirely randomly assigned to the two groups, state aided establishments and others, the difference in mean innovation outcome could be attributed to the state aid.

• Each firm getting state aid is therefore matched where possible with a business with an identical probability (propensity score) that did not receive aid.

• All firms that can be matched are ‘on support’.

• The difference in the mean innovation chances of these two groups is then attributed to the aid.

Raw Treated

Propensity Score

Den

sity

0.0 0.2 0.4 0.6 0.8

01

23

4

Matched Treated

Propensity Score

Den

sity

0.0 0.2 0.4 0.6 0.8

01

23

4

Raw Control

Propensity Score

Den

sity

0.0 0.2 0.4 0.6 0.8

02

46

810

14

Matched Control

Propensity Score

Den

sity

0.0 0.2 0.4 0.6 0.8

01

23

4

Distributions of Propensity Scores for

State Support before and after matching

Unmatched data sorted by State Funding variable

Cases not randomly

allocated between

State Funded and

Unfunded situations!

Gds Srv Proc Fund HdOff NACE Cntry Loc Eur Nat Other Size LgTurn BsPr WkPr Extnl Dsgn Prom Plac Price

How Propensity Score Matching Works for State Funding as TreatmentAnyIn Fund HdOff NACE Cntry Loc Eur Nat Other Size LgTurn TR BsPr WkPr Extnl Dsgn Prom Plac Price

Clear increase

in innovation

for State

Funded firms

at lower end of

the turnover

rangeVariables which

predict State

Funding have no

seeming difference

between the treated

and untreated

groups

Heckman Selection to Mitigate Unmeasured Variable Bias

• We may have a non-random assignment to funding in which case matching would not be enough.

• We do not observe innovation and funding of those not sampled

• The inference about the effects of funding on innovation may not extend to the unobserved group.

• This sample selection is equivalent to omitted or unmeasured variable bias.

• We can improve our estimates of the determination of innovation using the residuals (in our case) from the (whether or not) R&D equation (Heckman selection)

Type

of

Innovation

Unmatched

Coeff. on Funding

Variable

(Odds Ratio)

Means from

Unmatched

Data

No or Yes to

Funding

Means from

Matched

Data

No or Yes to

Funding

Matched

Single variable

Regression

Coeff. on Funding

Variable

(Odds Ratio)

Matched

Full regression

Coeff. on Funding

Variable

(Odds Ratio)

Using Probit

Heckman

Selection on

Matched Data

Marginal Effects

New

Goods

1.31

(3.70)

No: 0.160

Yes: 0.595

No: 0.352

Yes: 0.588

0.97

(2.63)

1.28

(3.58) 0.050

New

Services

0.89

(2.44)

No: 0.099

Yes: 0.359

No: 0.215

Yes: 0.351

0.68

(1.97)

0.86

(2.37) 0.025

New

Processes

1.08

(2.95)

No: 0.170

Yes: 0.567

No: 0.358

Yes: 0.561

0.83

(2.29)

1.05

(2.85) 0.030

Results for Effect of State Funding on Three Types of Innovation

Medium size €10 million turnover Belgian metal manufacturer addressing local markets, no market or organizational innovation

Predicted Prob(Innovation) goes from 0.18 to 0.37 upon gaining State Funding

Same company in all markets, with all organizational and market innovations – predicted Prob(Innovation) goes from 0.96 to 0.98

Selection on In-House R&D

What is the justification for selecting on doing R&D?

• Firms that do R&D may be more likely to innovate

Why not include (whether or not) R&D as an explanation for innovation instead?

• If there were no unmeasurables that predict selection into the sample then we could include the selection factors in the innovation equation or (if selection into the sample was completely random).

Aggregate Growth Results from Propensity Score Matching

At least one of the

primary

innovations

Possible ancillary

innovations

New Goods only

vs. No innovation

Possible ancillary

innovation

New Services only

vs. No Innovation

Possible ancillary

innovation

At least one

ancillary

innovation activity

Either, both or no

primary

innovation

All ancillary

Innovations

carried out vs. Any

of them

Either, both or no

primary

innovation

All ancillary

Innovations

carried out vs.

None of them

Either, both or no

primary

innovation

Yes: 1.18 Yes: 1.09 Yes: 1.23 Yes: 1.18 Yes: 1.16 Yes: 1.12

No: 1.19 No: 1.19 No: 1.24 No: 1.16 No: 1.18 No: 1.19

Primary Innovation: New Goods, New Services

Ancillary Innovation: New Processes, New Logistics, New Support activities

Ratio of total

turnover in 2008

to total turnover

in 2006 for Yes

(treated) and No

(untreated)

There seems to be little difference save for the New Goods only group where there is a negative apparent effect on growth!

Yes: 1.22 Yes: 1.20 Yes: 1.30 Yes: 1.19 Yes: 1.30 Yes: 1.31

No: 1.19 No: 1.18 No: 1.26 No: 1.17 No: 1.26 No: 1.26

Ratio of median

turnover in 2008

to median

turnover in 2006

for Yes (treated)

and No

(untreated)Heavy tails in turnover distributions must be distorting the aggregate measure

Growth Regressions – Matched by Innovation

Variable Unmatched Matched

Robust

Matched

(Intercept) 3.87*** 3.56*** 1.10***

anyinnov 0.06*** 0.07*** 0.03***

c_ho2Home 0.16*** 0.15*** 0.03***

c_ho2EU 0.28*** 0.29*** 0.07***

c_ho2RestOfWorld 0.27*** 0.27*** 0.06***

lgturn06 -0.28*** -0.26*** -0.07***

marloc -0.03*** -0.04*** -0.02**

mareur 0.07*** 0.06*** 0.02***

marnat 0.09*** 0.07*** 0.03***

maroth 0.04*** 0.03*** -0.00

size081 0.43*** 0.39*** 0.11***

size082 0.87*** 0.79*** 0.22***

rrdin2 0.04*** 0.07*** 0.02***

fungmt2 0.06*** 0.05** 0.03***

orgbup 0.03*** 0.03** 0.02***

orgwkp 0.02** 0.03** 0.02***

orgexr 0.04*** 0.02* 0.01*

mktdgp 0.00 0.00 -0.01

mktpdp 0.02** 0.04** 0.02**

mktpdl 0.01 0.00 -0.01

mktpri -0.02* -0.02* -0.02**

Adjusted R-squared 0.24 0.23 0.15

Variable Unmatched Matched

Robust

Matched

ManFood -0.01 0.02 0.06***

ManCloth -0.45*** -0.41*** -0.19***

ManWoodPaperRec -0.14*** -0.13*** -0.06**

ManChem -0.05* -0.04 -0.04*

ManMetal -0.03 -0.01 0.03

ManElecMechTrans -0.09*** -0.09** -0.02

ManOther -0.17*** -0.15*** -0.05**

Energy 0.34*** 0.35*** 0.12***

WaterWaste 0.03 0.05 0.06***

Vehicles 0.18*** 0.14*** 0.04*

Transport 0.02 0.04 0.03

Logistics 0.01 -0.02 0.04*

Media -0.11*** -0.11** -0.010

ITC -0.02 0.032 0.10***

FinServ 0.19*** 0.19*** 0.14***

Technical -0.08*** -0.06. 0.05*

Construct 0.02 0.02 0.07***

Hospitality -0.15*** -0.18*** -0.02

RealEst -0.21*** -0.19* -0.11*

ManageLegalAcc -0.14*** -0.12** 0.03

OtherTech -0.14** -0.08 0.05

AdminSupport -0.21*** -0.19*** 0.03

Agriculture -0.18*** -0.15** -0.01

Unmatched Matched

Robust

Matched

CY 0.19 *** 0.21*** 0.01

CZ 0.05 *** 0.08*** -0.02*

DE 0.16 *** 0.15*** -0.11***

EE -0.06 *** -0.04* -0.10***

ES 0.12 *** 0.13*** -0.14***

HU -0.10 *** -0.05* -0.10***

LT -0.02 -0.01 -0.05**

LV 0.01 0.00 -0.05*

PT -0.04 *** -0.02 -0.13***

RO 0.05 *** 0.07*** -0.01

SK 0.11 *** 0.18*** 0.05**

Sectors Countries

Control Treated

All 57060 32524

Matched 16587 16587

Unmatched 40473 15937

Aggregate

Growth

Ratio Median

Turnover

2008:2006

Ratio Total

Turnover

2008:2006

Non-

Innovators1.15 1.18

Innovators 1.18 1.21

Relative Proportions of Innovators (Matched on State Funding)National Innovation Differences by State Funding

Funding BG CY* CZ DE EE ES HU LT LV* PT RO SKNo 0.46 0.52 0.83 0.86 0.89 0.76 0.68 0.85 0.74 0.89 0.77 0.80Yes 0.77 1.00 0.95 0.87 0.95 0.84 0.92 0.97 1.00 0.98 0.99 0.95

Germany has the narrowest gap between proportions of funded and non funded innovators – both groups

being highly likely to innovate. Diminishing innovation returns to state funding when innovation is high.

Portugal is the country with next smallest innovation gap between funded and non-funded and also a high

level of innovation

BG CY CZ DE EE ES HU LT LV PT RO SK

Service Innovation

Proportion

0.0

0.4

0.8


Goods Innovation

Grey=Unfunded, Blue=Funded

Proportion

0.0

0.4

0.8

CY* and LV*

Very small

sample

Proportion of firms, with or without state funding, doing some kind of innovation

Relative Proportions of Innovators (Matched on State Funding)National Innovation Differences by State Funding


Process Innovation

0.0

0.2

0.4

0.6

0.8

1.0


Logistics Innovation

0.0

0.2

0.4

0.6

0.8

1.0


Support Innovation

0.0

0.2

0.4

0.6

0.8

1.0

What do these results imply is the maximum worth spending from government funds to stimulate innovation?

• (Pr. innovation|funding)*(turnover growth|innovation) = extra turnover growth induced by funding ≈ 0.03*0.03 =0.0009=0.09%

• But turnover growth consumes resources – it is not all gain

• Suppose profit growth is exactly proportional to turnover growth, this could be gain

• Which given our specification is larger the bigger is the firm.

• If a firm had, for example, a turnover of around 10 million euros. Suppose 10% was profit (1,000,000 Euros). 0.09% is a mere 900 Euros ( the maximum worth spending)

• Though this supposes there is no turnover rise from the innovation in subsequent three year periods

Acknowledgments

The authors wish to thank

EUROSTAT for the use of the Community Innovation Survey data

Project: European Small and Medium Enterprise Innovation Policies

(CIS/2012/07)

The Conference Organizers for the Opportunity to Present this Work

Documents

James Foreman-Peck and Peter Morgan Cardiff Business · PDF fileInnovation in 13 European Countries James Foreman-Peck and Peter Morgan Cardiff Business School