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Analysis of the Industrial PhD Programme

Analysis of the Industrial PhD

Programme

Published by :

The Danish Agency for Science, Technology and Innovation

Bredgade 40

1260 København K

Tlf: 35446200

Fax: 35446201

The report is produced by Iris Group for the Danish Agency for

Science, Technology and Innovation

Text: CEBR – Centre for Economic and Business

Research - Johan M. Kuhn, Ph.D.

ISBN: 978-87-92372-65-9 (web)

Design: Formidabel Aps

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CoNTENTS

ExECuTIvE SuMMARy

SAMMENFATNING (DANISh SuMMARy)

INTRoDuCTIoN

DESCRIPTIoN oF ThE INDuSTRIAl PhD PRoGRAMME

INDIvIDuAl lEvEl ANAlySIS

3.1 Results of the individual level analysis

CoMPANy lEvEl ANAlySIS

4.1 Data and methodology of the company level analysis

4.2 Results of the company level analysis

SuMMARy AND CoNCluSIoNS

APPENDIx 1: SElECTIoN oF CoNTRolS

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ExECuTIvE SuMMARy

This report has been prepared by the Centre for Economic and Business

Research (CEBR). It presents an analysis of the economic impact of the Danish

Industrial PhD Programme on participating companies and on wage and career

characteristics of Industrial PhD graduates.

The Industrial PhD Programme is funded by the Danish Council for Technology

and Innovation and is administered by the Danish Agency for Science,

Technology and Innovation (DASTI). The programme subsidises PhD studies

where the student is employed in a private sector company and simultaneously

enrolled as a PhD student at a university.

The analysis follows approx. 430 individuals and approx. 270 companies that

have participated in the programme and for whom relevant data is available in

the selected registers.

On the individual level, we compare wage income and occupation of Industrial

PhDs with regular PhDs and other university level graduates.

On the company level, we analyse company level developments within four

success parameters:

•

the number of patents applications,

•

gross profit growth,

•

total factor productivity, and

•

employment growth.

For a sample of companies which have hosted a maximum of three Industrial

PhD projects, we identify a control group of highly similar companies which

have not hosted any Industrial PhD projects. We then compare developments in

the success parameters in these two groups. Under identifying assumptions, the

difference between the sample group and the control group isolate the causal

impact of the programme on companies hosting Industrial PhD projects.

The results of the analysis can be summarised as follows: Industrial PhDs earn

approx. 7-10 percent higher wages than both regular PhDs and comparable

university graduates. This comparison is illustrated in FIGURE 1.

FIGuRE 1: hourly wage (DKK) in 2006, by Individual age

Industrial PhD Graduates

Regular PhD Graduates

31 32 33 34 35 36 37 38 39 40 41 42 43 44

Age (in years)

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They are also more likely to be found at the top levels of their organisations’

hierarchies compared to normal PhDs and more likely to be found in positions

requiring high-level specialist knowledge than regular university graduates.

Companies that host Industrial PhDs see on average increasing patenting

activity, illustrated by FIGURE 2. They are characterised by high growth in

gross profit, and more positive developments in gross profit and employment

growth than companies in the control group. We are not able to identify

robust relationships between hosting Industrial PhD projects and total factor

productivity developments.

FIGuRE 2: Number of patent applications, high-quality matches.

Average number of patent applications per company, change relative to year before

first initiating an Industrial PhD project

0,20

0,12

0,10

0,05

0,00

-0,05

-0,10

Age (in years)

-5 - 4 -3 -2 -1 0

1 2 3

5 6 7 8 9 10

Companies with Industrial PhD projects

Companies without Industrial PhD projects

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SAMMENFATNING (DANISh SuMMARy)

Denne rapport er skrevet af Centre for Economic and Business Research

(CEBR). Den beskriver en analyse af ErhvervsPhD-ordningens potentielle

effekter på udviklingen i de deltagende virksomheder og løn- og

karrieremønstre for personer, som har erhvervet deres ph.d.-grad gennem

ordningen.

Et ErhvervsPhD-projekt er et treårigt erhvervsrettet ph.d.-projekt, hvor den

studerende ansættes i en privat virksomhed og samtidig indskrives på et

universitet.

Ved hjælp af registerdata følger analysen ca. 430 individer og 270

virksomheder, som har deltaget i ordningen. På individniveau studeres

væksten i ErhvervsPhD’ernes lønindkomst i forhold til almindelige ph.d.’ere og

sammenlignelige kandidater.

For virksomheder studeres udviklingen i patentering, bruttofortjeneste,

totalfaktorproduktivitet og beskæftigelse. Hertil identificerer vi en gruppe af

kontrolvirksomheder, som ikke ansætter en ErhvervsPhD, men som ellers ligner

de ansættende virksomheder i størrelse, branche, alder og region.

Dermed kan vi besvare spørgsmålet om, hvorvidt de virksomheder, som ansatte

en ErhvervsPhD, har haft en mere positiv udvikling i succesparametrene, end

man ville have forventet på basis af udviklingen for kontrolvirksomhederne.

Analysens resultater kan sammenfattes som følger:

Efter uddannelsens afslutning har ErhvervsPhD’er i gennemsnit mellem 7 og 10

procent højere lønindkomst end normale ph.d.’er og personer med en afsluttet

universitetsuddannelse. Dette er illustreret i FIGUR 1.

FIGuRE 1: Timeløn (i kr.) in 2006, efter alder

ErhvervsPhD

Normal PhD

31 32 33 34 35 36 37 38 39 40 41 42 43 44

Alder

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ErhvervsPhD’ere har endvidere en væsentligt højere sandsynlighed for at blive

ansat i lederstillinger end almindelige ph.d.’ere og er stærkere repræsenteret

i gruppen af medarbejdere med jobfunktioner, som kræver specialviden på

højeste niveau. Virksomheder, som ansætter ErhvervsPhD’ere, har i gennemsnit

højere patenteringsaktivitet efter ansættelsen end før. Dette er illustreret i

FIGUR 2.

FIGuRE 2: Antal patentansøgniner, højkvalitetssammenligning

Gennemsnitlig antal patentansøgninger pr. virksomhed

(i afvigelser ift. året før første ErhvervsPhD-projekt)

0,20

0,12

0,10

0,05

0,00

-0,05

-0,10

År før/efter første

Erhvervs PhD

-5 - 4 -3 -2 -1 0

1 2 3

5 6 7 8 9 10

virksomheder med ErhvervsPhD-projekter

virksomheder uden ErhvervsPhD-projekter

De er også kendetegnet ved højere vækst i bruttofortjenesten/værdiskabelsen

og har en mere positiv udvikling i væksten i bruttofortjenesten og

medarbejderantallet end virksomhederne i kontrolgruppen.

Det er på nuværende tidspunkt ikke muligt at påvise, at ErhvervsPhD-ordningen

bidrager til højere vækst i virksomhedernes totalfaktorproduktivitet.

Analysis of Danish innovation policy - The Industrial PhD Programme and the Innovation Consortium Scheme

Performanceregnskab 2010

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INTRoDuCTIoN

This report has been prepared by the Centre for Economic and Business

Research (CEBR). It presents an analysis of the economic impact of companies

participating in the Danish Industrial PhD Programme in terms of growth

and value creation, and on wage income and career patterns of Industrial PhD

graduates.

Even though this analysis is an evaluation of a specific Industrial PhD subsidy

programme, its results might be of general interest, as programmes similar to the

Danish Industrial PhD Programme have been implemented or are considered for

implementation in a number of countries. However, general knowledge of their

effects which can be integrated into cost-benefit analyses of these programmes is

still rare.

The Industrial PhD Programme aims at increasing knowledge sharing between

universities and private sector companies, promoting research with commercial

perspectives, and taking advantage of competences and research facilities in

private business to increase the number of PhDs.

For this purpose, the Industrial PhD students typically spend 50 percent of their

time in a company and 50 percent of their time at a university while taking the

degree. The Danish Agency for Science, Technology and Innovation (DASTI)

subsidises the Industrial PhD’s salary with a fixed monthly amount, roughly

corresponding to 30-50 percent of the Industrial PhD’s total salary.

The Industrial PhD programme was initiated in 1971 under the name “The

Industrial Researcher Programme”. In 1988 it was made possible to qualify for

a PhD degree when graduating. The programme was subsequently reformed

to comply with Danish PhD regulations, making every graduate a formal

PhD graduate. Until 2009, approx. 1,200 projects have been started. As part

of its evaluation policy, DASTI has asked CEBR to analyse the company and

individual level effects of the Industrial PhD Programme. The main questions

of the evaluation are whether and how participating in the Industrial PhD

Programme is associated with company performance and, with regard to

individuals, to what extent an Industrial PhD degree is associated with future

career developments, measured by wage income and occupation.

To answer the questions outlined above, this analysis considers 430 individuals

and approx. 270 companies that have participated in the programme using a

matched employer-employer register dataset.

On the individual level, we compare wage income developments of Industrial

PhD graduates with regular PhD graduates and individuals with a university

level degree (and who have graduated at approximately the same time as the

Industrial PhD graduates).

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On the company level, we analyse company level developments within four

success parameters:

•

number of patent applications,

•

gross profit growth,

•

total factor productivity (TFP), and

•

employment growth

Gross profit is defined as annual net sales subtracted annual costs of variable

inputs (raw materials, energy, intermediate goods purchases, etc.), except labour

costs. Thus, gross profit is a measure of the company’s value creation.

Total factor productivity is gross profit corrected for the company’s use of capital

and the number of employees. It is measured as the percentage-wise deviation of

a company’s gross profit from the gross profit that would have been expected on

basis of the company’s number of employees and its capital stock.

To identify innovation, growth and productivity effects of hosting an Industrial

PhD, we analyse increases in the number of patent applications, gross profit

growth, total factor productivity and employment growth for a sample of

companies which have participated in the Industrial PhD Programme. By using

a control group of highly similar companies which have not participated in the

programme, we can compare the developments of the success parameters of the

two groups of companies to each other.

1 Gross profit is the most precise measure of the company’s value creation, but one should, of course,

keep in mind that a part of the company’s total value creation may be passed on to consumers, may be

retained in the company and increase its value (for which there is no data available for this analysis), or

may take the form of positive externalities, such as knowledge and/or innovations which benefit other

companies or society in general.

2 For this analysis, we measure TFP as the residuals of a Cobb-Douglas-production function estimation

with total assets and the number of employees as right hand side variables.

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DESCRIPTIoN oF ThE INDuSTRIAl PhD PRoGRAMME

An Industrial PhD project is a three-year industrially focused PhD project where

the student is hired by a company and enrolled in a university at the same time.

The company receives a monthly wage subsidy of (currently) DKK 14,500

(approx. €2,000) while the university has its expenses for supervising etc.

covered. The PhD student works full time on the project and divides his or her

time equally between the company and the university. There are additional

subsidies available for project-relevant stays abroad.

Currently, there are allocated annually approx. DKK 100-150 million (€15-20

million) for new projects. Approval rates for applications are currently above 60

percent.

Different aspects of the Danish Industrial PhD programme were addressed

in earlier evaluations. DASTI (2007a)

concludes that Industrial PhDs are

characterised by earning higher wages and are more likely to be a part of their

organisation’s management compared to regular PhDs. Companies hosting

Industrial PhD projects expect increased patenting activity and growth.

DASTI (2007b)

lists several positive benefits for the participating companies.

Among other things, companies may gain new knowledge, patents and licenses,

growth and new market opportunities, and an increased network inside the

academic world.

A similar conclusion is reached in a report by Right, Kjaer and Kjerulf from

2003. Based on interviews with participating candidates and companies in 2002,

they find that a majority of companies expect the Industrial PhD to contribute to

patents, while close to half of all companies expect increased earnings.

International evaluations include a report from the European University

Association,

which concludes that participating candidates enjoy better

employment opportunities due to improved skills. Two studies for the Swedish

agency KK-stiftelsen

have also been carried out. These conclude (a) that certain

conditions need to be met for projects to be successful, and (b) that the different

stakeholders of Industrial PhD projects report that the programme is achieving

its goals.

3 This section draws extensively on the information published by DASTI.

4DASTI, 2007a: ”ErhvervsPhD - Et effektivt redskab for innovation og vidensspredning”.

5DASTI, 2007b: ”ErhvervsPhD - Ny viden til erhvervslivet og universiteterne”.

6European university Association, 2009: “Collaborative Doctoral Education - university-Industry partners-

hips for enhancing knowledge exchange”.

7(a) KK-stiftelsen, 2003: ”KK-stiftelsens företagsforskarskolor - utvärdering av ett koncept för ökat sam-

arbete mellan akademi och näringsliv”.

(b) KK-stiftelsen, 2006: ”Småföretags- och institutsdoktorander för kunskaps- och kompetensutveckling”.

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INDIvIDuAl lEvEl ANAlySIS

For the individual level analysis, information gathered by DASTI on participating

individuals was merged with public register information typically referred to as

the “Integrated Database of Labour Market Research (IDA)”. These data cover the

period from 1980 onward and contain information on a multitude of individual

demographic background characteristics, like education, gender and age.

The IDA data have – for the period 1997 to 2006 – been merged with information

from the ”Wage Statistics Database”, which includes detailed information on

wages and occupation, including hierarchical levels.

Also, information from education-related registers has been added to the data, to

make it possible to control for inherent human capital endowments – pproximat-

ed by the grades of secondary education certificates – in the regressions.

The following analysis compares wages and careers of Industrial PhD graduates with:

(a) individuals with a university degree, but no PhD degree, and

(b) regular PhD graduates.

The validity of these comparisons depends on how similar the two groups are

with the Industrial PhD graduates and the potential to control for observable

factors presumably related to educational choices and, later, income and career

developments. Both objectives raise some issues regarding the optimal sampling

strategy, which is presented in the following:

When selecting the sample for the analysis, we obviously include all individuals

who have completed an Industrial PhD education. Individuals who have completed

a regular PhD education form the first control group.

With regard to university graduates, who form the second control group of

individuals for comparison, there is an issue which needs to be resolved: There is

a large number of secondary educations where it is not entirely clear whether they

should be defined as university level educations or not.

We choose to address this issue by identifying the highest educational degrees of

the Industrial PhD graduates before obtaining their Industrial PhD degrees. As a

first step in the sampling procedure, we only select individuals with the same set

of educations for the control group.

But without further conditions on sampling, the educational fields of the Industrial

PhDs and the university graduates would be very different. For example, there

would be a large share of individuals with university degrees in arts and Humanities

in the control group, while these degrees are relatively uncommon in the group of

Industrial PhDs. This would bias any comparison between the two groups.

For this reason, we also align the composition of the educational fields of

Industrial PhDs prior to obtaining the Industrial PhD degree and the educational

fields of the control group by selecting a fixed number of individuals into the

control group for each Industrial PhD graduate.

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Specifically, we select ten individuals into the control group for each Industrial

PhD. The number ten is a compromise between being able to find individuals

with the same educational degrees and a sample size large enough to isolate

relationships in the data.

These individuals, referred to as the group of ‘university graduates’ are

randomly selected, but must correspond to the educational field of the given

Industrial PhD graduate (before he/she obtains her PhD degree). In the selection

process, we also prefer persons of the same gender and origin (Danish vs. non-

Danish), and persons who are of similar age. This way, we base the comparisons

on groups of individuals similar not only in terms of their educational field, but

also age, gender and origin.

The individual level analysis is based primarily on information for the year

2006, which is the last year where the data provides detailed information on

wages and occupation.

3.1

Results of the individual level analysis

At present there are approx. 1,200 individuals who have participated or are

participating in the Industrial PhD Programme. In year 2006, which is the

last year for which all relevant data is available, 999 Industrial PhDs can be

identified in the register data.

Of these, the register data shows 442 completed their projects, i.e. obtained their

Industrial PhD degree, before 2006.

Additionally, there is wage information for 430 of these 442 individuals.

The wage concept used in the following analysis is Statistics Denmark’s ‘nw’-

variable of the Wage Statistics Database. This variable is a description of the

person’s hourly wage income excluding pension contributions and cleaned for

peculiarities such as overtime, dirty work premiums, etc.

Career developments are measured by Statistics Denmark’s ‘disco’-variable,

also from the Wage Statistics Database. This variable categorises occupation by

different hierarchical levels and work functions. The question to be considered

is whether Industrial PhDs are over- or underrepresented in leadership positions

(disco code 1000-1999) or positions which require high-skilled specialist

knowledge – these will be denoted as specialist positions in the following (disco

code 2000-2999).

Descriptive statistics

In this subsection, we describe the gross sample of all individuals associated

with the Industrial PhD Programme – with or without completed Industrial PhD

degrees - and of all individuals with a PhD degree in the last year in which they

are observed, and all individuals selected for the group of university graduates.

This ensures the most comprehensive description of these groups. However,

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when we turn to the comparison of wages and career patterns, we will concentrate

on those individuals with completed educations (either university or PhD) in 2006.

When taking a first look at the data (TABLE 3.2.1), we find that in 2006, the last

year for which data is available for this analysis, Industrial PhDs earn approx.

10 percent lower wages than regular PhDs, but are, in the current sample, almost

eight years younger on average.

Industrial PhDs have slightly lower grades than normal PhDs in their secondary

education examinations, but this difference is negligible relative to this variable’s

variation.

About four percent of the Industrial PhDs are represented at the top of their

organisation’s hierarchy, which is very similar to the two control groups.

Approx. 60 percent of Industrial PhDs work in specialist positions, a share which

locates them between regular PhDs and university graduates, where this is the

case for 74 and 44 percent, respectively. Obviously, we can expect differences

in both wages and positions to increase when focusing only on individuals with

completed educations in the next subsection.

TABlE 3.2.1: Descriptive statistics of the individual level data (2006), mean values

Industrial PhD

students and

graduates

hourly wage (DKK)

Female

Age (year)

Grade of university-

entrance diploma

(standard deviation: 8.9)

Non-Danish origin

leadership position

Specialist position

Number of

observations

228,61

0,35

34,55

91,83

0,06

0,04

0,58

999

Regular PhD

students and

graduates

223,44

0,34

42,66

92,63

0,08

0,04

0,74

12369

university

graduates

223,44

0,35

34,72

87,33

0,05

0,03

0,44

9625

All

243,06

0,34

38,98

90,07

0,07

0,04

0,61

22993

Cf. TABLE 3.2.2, we find that approx. 38 percent of all Industrial PhDs had a degree in engineering, and

another approx. 23 percent had degrees in chemistry or electronics engineering before receiving their

Industrial PhD degree.

obviously, the group of university graduates is supposed to only consist of individuals who actually have

graduated. Thus, when we formally compare the different groups of individuals in the results subsection,

please note that we will not consider individuals registered as having a university-entrance diploma as

their highest educational degree in 2006.

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TABlE 3.2.2: highest educational degree in 2006 (for Industrial PhD and regular

PhD graduates: highest degree before receiving the PhD degree), in percent

Industrial PhDs

Regular PhDs

university

Graduates

32,95

0,08

4,01

11,16

11,42

All

Master's in engineering

unknown

Master's in medical science

Master's in biology

Master's in chemical

engineering

Master's in electronics

engineering

university-entrance diploma

Master's in pharmaceutics

Master's in biochemistry

37,99

3,89

2,52

9,84

10,53

12,38

30,24

25,42

14,26

5,3

20,99

18,08

16,61

12,94

7,76

12,81

3,46

12,02

6,99

9,38

6,41

6,64

0,6

4,04

4,3

15,79

6,73

5,84

6,54

5,13

4,96

Before turning to wage and career comparisons, we take a brief look at the

kind of PhD degrees of Industrial and regular PhDs - see TABLE 3.2.3 for the

most popular subjects. We find these two groups to be quite different in their

compositions of the specific degrees. Consequentially, later comparisons of

wages and careers between Industrial and regular PhDs will have to take these

differences into account.

Interestingly, there are a number of Industrial PhDs in medical sciences, yet

only relatively few of these individuals had medical science university degrees

before taking their PhD.

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TABlE 3.2.3: Type of PhD degrees, in percent

Industrial PhDs

Technical sciences

Natural sciences

other disciplines

Medical sciences

veterinarian/agricultural

Pharmaceutical sciences

Social sciences

59,5

10,6

3,4

14,3

6,3

3,2

2,7

Regular PhDs

24,5

22,5

20,8

19,3

8,9

2,0

1,9

All

25,9

22,0

20,1

19,1

8,8

2,1

2,0

Results

As a first step, we compare average hourly wages of the different groups of

individuals under consideration in 2006 and graph the averages as a function of

age in FIGURE 3.2.1.

FIGuRE 3.2.1: hourly wage (DKK) in 2006, by Individual age

Industrial PhD Graduates

Regular PhD Graduates

31 32 33 34 35 36 37 38 39 40 41 42 43 44

Age (in years)

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We find that wages of Industrial PhD graduates are higher than those of both

regular PhD graduates and university graduates. Differences are largest in the

early and mid-forties. Regular PhDs have lower wages relative to both Industrial

PhDs and university graduates.

An obvious explanation of these differences may be sought in different

employment patterns of the different groups of employees, with regular PhDs

being overrepresented in public sector research institutions, which are generally

characterised by lower wages than private sector employers.

Comparisons between Industrial PhDs and regular PhDs

In this section, we compare wages between Industrial and regular PhD

graduates by using a linear regression, holding constant a set of (pre-

determined) background characteristics (age, gender, etc.).

These comparisons are based on the 430 Industrial and approx. 5,850 regular

PhD graduates. We choose a logarithmic specification of the wage variable,

implying that regression coefficients are the expected (approximately)

percentage-wise changes in the wage when the condition of the associated

explanatory variable is fulfilled.

TABlE 3.2.4: hourly wage of Industrial and regular PhD graduates, linear regression results,

dependent variable: log (hourly wage), sample: Industrial and regular PhD graduates (2006)

variables

The person is an Industrial PhD graduate

The person is female

Coefficient

0.090

-0.060

***

Standard

error

0.014

0.007

Coefficient

0.063

-0.065

***

Standard

error

0.014

0.007

The person is an immigrant (or descendant)

Grade of secondary education diploma (normalised)

Age (in years)

0.028

0.026

0.016

***

0.027

0.004

0.001

0.005

0.019

0.021

0.027

0.003

0.001

***

Additional controls

Secondary education: elective

courses (7 categories)

Secondary education: elective

courses (7 categories); specific

PhD degree (10 categories); age

when receiving the PhD degree

6.283

Number of observations

6.283

Notes: ***: significant at the 1% level. Heteroscedasticity-consistent standard errors.

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The regression results confirm the findings of FIGURE 3.2.1: Industrial PhDs

earn approx. (exp(0.090)=1.094) 9 percent higher hourly wages compared to their

counterparts who have taken a regular PhD degree. When including additional

variables in the regressions (which control for the different compositions of the

subjects of the PhD projects and for age differences when obtaining the PhD degree),

the difference drops to approx. 6 percent, but remains statistically highly significant.

Here, it might be noted that the result of positive wage income differences is

robust when considering gross hourly wages (i.e. total wage income including

pensions divided by the number of working hours) or annual income instead of the

current wage concept. In the first case, the relevant coefficient dropped to approx.

5 percent (instead of approx. 9 percent). In the second case, when considering

annual income without correcting for working hours, the coefficient increased

to between 10 (in the specification with additional controls) and 15 percent (in

the more simple specification). This indicates that Industrial PhDs register more

working hours than regular PhDs.

Comparing the career developments between the two types of PhDs, we first note

that 6.3 percent of Industrial PhD graduates are employed in leadership positions,

as opposed to 3.9 percent of regular PhD graduates. The formal comparison is by

estimating a so-called binary choice model (assuming a logistic distribution). The

coefficients of this model are displayed in TABLE 3.2.5.

TABlE 3.2.5: occupation of Industrial and regular PhD graduates, binary choice (logit) model,

sample: Industrial and regular PhD graduates (2006)

Dependent variable:

The person has a

leadership position

variables

The person is an Indu-

strial PhD graduate

The person is female

The person is an immi-

grant (or descendant)

Grade of secondary

education diploma

(normalised)

Age (in years)

Additional controls

Number of observations

Coefficient

1.087

-0.577

0.985

***

Standard

error

0.217

0.178

0.403

Dependent variable:

The person has a

specialist position

Coefficient

-0.738

0.268

-0.344

***

Standard

error

0.112

0.064

0.227

0.108

0.103

***

0.078

0.020

0.077

0.010

0.033

0.009

Secondary education: elective courses (7

categories)

7,214

Secondary education: elective courses

(7 categories)

7,214

Notes: ***: significant at the 1% level, ** significant at the 5% level.

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The exponents of the model’s coefficients equal the increases in the probability

that the individual is a leader or a specialist when the logical conditions of the

corresponding variables are true. We find that Industrial PhDs are almost three

times more likely (exp(1.087)=2.95) to hold a leadership position than regular

PhDs when holding constant the set of background characteristics included in

the regression.

Industrial PhDs have an approx. (exp(-0.738)=0.48) 50 percent lower probability

of being employed in a specialist position than regular PhDs. We conclude

that, while regular PhDs are almost entirely employed in specialist positions,

Industrial PhDs are more evenly distributed across the different occupational

levels.

Comparisons between Industrial PhDs and university graduates

In the following, we compare wages between Industrial PhD graduates and

university graduates.

TABLE 3.2.6 summarises the results of the comparison of hourly wages.

They suggest that Industrial PhDs earn a wage premium of approx. 7 percent

relative to university graduates in similar fields of study while controlling for

demographic factors, secondary education grades and course specialisation.

TABlE 3.2.6: hourly wage of Industrial PhD and university graduates, linear regression results, de-

pendent variable: log (hourly wage), sample: Industrial PhD graduates and university graduates (2006)

variables

The person is an Industrial PhD graduate

The person is female

Coefficient

0.066

-0.129

***

Standard error

0.014

0.009

The person is an immigrant (or descendant)

Grade of secondary education diploma (normalised)

Age (in years)

-0.053

0.043

0.028

***

0.034

0.005

0.001

Additional controls

Secondary education: elective courses (7 categories)

Number of observations

5,246

Notes: ***: significant at the 1% level. Heteroscedasticity-consistent standard errors.

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Again, it may be noted that the result of positive wage income differences is

unaffected by considering gross hourly wages (i.e. total wage income including

pensions divided by the number of working hours) or annual income instead of

the current wage concept. In the first case, the relevant coefficient dropped to

0.056 (instead of 0.066). In the second case, when considering annual income

without correcting for working hours, the coefficient increased to 0.11 -

again indicating that Industrial PhDs register more working hours than other

graduates.

The results of the career development comparisons are found in TABLE 3.2.7.

In comparison with university graduates, Industrial PhDs are overrepresented in

leadership positions and specialist positions. However, the difference regarding

leadership positions is not statistically significant and must be regarded as

tentative.

For specialist positions, the coefficient 0.397 corresponds to an approx. 50

percent higher probability that Industrial PhDs are employed in specialist

positions than university graduates.

TABlE 3.2.7: occupation of Industrial PhD graduates and university graduates, binary choice (logit)

model, sample: Industrial PhD graduates and university graduates (2006)

Dependent variable: The person

has a leadership position

variables

The person is an Industrial PhD graduate

The person is female

Coefficient

0.182

-0.741

***

Standard

error

0.217

0.156

Dependent variable: The

person has a specialist position

Coefficient

0.397

0.105

***

Standard

error

0.113

0.051

The person is immigrant (or descendant)

Grade of secondary education diploma (normalised)

Age (in years)

-0.572

0.139

0.095

***

0.710

0.064

0.015

-0.120

0.151

0.072

***

0.217

0.025

0.006

Additional controls

Secondary education: elective

courses (7 categories)

7,465

Secondary education: elective

courses (7 categories)

7,465

Number of observations

Notes: ***: significant at the 1% level, ** significant at the 5% level.

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4.1

Data

CoMPANy lEvEl ANAlySIS

Data and methodology of the company level analysis

The data used for the company level analysis is from three sources:

•

First, data from DASTI on the participation of companies and individuals

in the Industrial PhD Programme.

•

Second, information on financial reports that companies above certain

size thresholds must file to a public authority.

•

Third, information on patenting activity from the European patent office.

The data from DASTI on the participation of companies and individuals in the

Industrial PhD Programme contain information on the year an individual was

employed as an Industrial PhD student, and in many cases also the employing

company’s registration number (‘cvr-number’), which is filed at the public

authorities and which is also available in the other datasets used in this study.

Data on financial reports is from the private information provider company

Købmandsstandens Oplysningsbureau, now Experian A/S. This dataset, henceforth

denoted as the KOB data, contains information from the financial reports that com-

panies with a certain size and ownership structure must file to the public authorities.

Data on patenting is from the CEBR patent database, which has information on

all patent applications at the European Patent Office by at least one applicant

residing in Denmark.

The sample

In the original data from DASTI, there are 1,224 Industrial PhD projects in 536

different companies; 47 projects are registered as abandoned. Excluding these

projects from the sample (including one project which lacks information on when

the project was started) leaves us with 1,177 projects and 514 different companies.

However, it should be noted that in the original sample, the 514 different

companies are defined by their names. This number is partly due to registering

the same company under slightly different names in the DASTI data.

For the following performance analysis, we have to merge the sample of 1,177

projects in 514 companies with the information from the KOB database.

To accomplish this, we first had to find company registration numbers (‘cvr’-

numbers) of companies with missing or erroneous registration numbers in the

original DASTI data. We managed to find these registration numbers for 509

different companies as defined by their names (hosting 1,161 projects). These

509 different company names in the DASTI data correspond to 445 different

companies as defined by their company registration numbers. This is the

definition of companies we will use henceforth.

The first Industrial PhD projects were initiated in 1988. Up to 2003, the number of

projects initiated each year was relatively stable at approx. 30 to 50. However, in

recent years the number of projects initiated per year has increased steadily and is

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now in the range of 80 to 120 projects. It should be noted that approx. 30 percent

of the companies in the sample have hosted more than one project, and that some

companies have hosted a considerable number of projects (e.g. more than 20).

1,053 out of the 1,161 projects and 387 out of the 445 companies can be identified

in the KOB database. A large share of the attrition is related to companies which

have either been established too recently to be covered by the KOB database or

closed down before the KOB database assumed full coverage.

For 383 companies, there is financial report information in the KOB data. These

companies are observed on average for 15.6 years, which implies that there are a

total of 5,018 annual financial reports for companies that have hosted at least one

Industrial PhD project. However, it should be noted that any potential bottom-

line effects of Industrial PhD projects may take a couple of years to materialise,

and that a considerable share of Industrial PhD projects was initiated at the end

of the observation period.

Of the 383 companies in the KOB data, 72 companies are not observed after

first initiating an Industrial PhD project. These obviously cannot be used for the

following analysis, leaving us with 311 different companies have hosted a total of

851 Industrial PhD projects. Out of these, 195 companies have hosted only one

Industrial PhD project, 48 percent have hosted two projects, 27 companies three

projects, 9 companies four projects, and 32 (approx. 10 percent) companies more

than five projects. There are also a few companies which have hosted more than 20

projects.

The companies with many projects are typically large companies for which it

is difficult, if not impossible, to find similar companies for the comparisons in

the statistical analyses to follow. Also, the statistical model which is preferred

by the precision of its estimates requires fixing a year before a company first

participates in the Industrial PhD Programme. For companies with many

projects, this year is not well-defined, and the year before hosting the first

Industrial PhD is often before the KOB database assumes full coverage.

Accordingly, we will only consider companies that have hosted a maximum of

three projects for the following analysis. These represent approx. 85 percent of

all companies participating in the Industrial PhD Programme, which leaves us

with 270 companies for the company level analysis.

Of the 270 companies, approx. 120 are observed five years before first initiating

an Industrial PhD project, approx. 160 are observed five years after, and 86 are

observed ten years after first initiating a project.

The characteristics of the companies in the sample used for analysis are

however, it should be noted that missing information for a number of observations means that the

number of records which can be used for the analysis is reduced. For example, total factor productivity

figures are available for 91 companies five years after first initiating an Industrial PhD project, and for 46

companies ten years after first initiating a project.

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TABlE 4.2.1: Descriptive statistics of the matched treatment-control samples

All companies with a maximum

of three projects

high-quality matches

Companies that have

hosted at least one

Industrial

table,

PhD project

129

0.090

Control

companies

All

companies

described in greater detail in the

TABLE 4.2.1. In this

we also

of companies

the characteristics of two control groups of companies,

Number

summarise

270

539

809

which are identified by a matching procedure briefly presented in the next

Total factor

-0.056

-0.023

section and explained in greater detail in Appendix 1.

-0.006

productivity

Gross profit per

employee (DKK1,000)

1529.4

689.5

971.9

Companies that have

hosted at least one

Industrial PhD project

leftmost column of

Control

companies

All

companies

283

0.016

412

0.039

445.5

466.6

460.0

Patent applications

Number of employees

Gross profit (DKK1,000)

Total assets (DKK1,000)

Establishment year

Industries

Business services

Research and

development

Medical equipment,

instruments

manufacturing

Finance

Wholesale trade

Chemicals,

pharmaceuticals

Food production

Manufacturing

other

Zip-codes

1000-1999

2000-2999

3000-3999

4000-4999

5000-5999

6000-6999

7000-7999

8000-8999

9000-9999

2.4

520.0

651460.8

18800000

1978.8

0.7

212.0

154181.9

951008

1977.8

1.3

314.8

320482.4

6894683

1978.2

0.8

28.8

14828.6

22515.98

1988.6

0.3

31.8

15841.2

22661.74

1988.2

0.5

30.9

15522.5

22616.1

1988.4

18.52

9.26

8.89

18.55

9.09

8.91

18.54

9.15

8.90

26.87

14.93

11.94

24.09

12.54

11.22

24.94

13.27

11.44

8.52

8.53

7.46

7.92

7.78

8.52

7.78

8.53

7.79

8.53

7.79

5.97

6.60

4.95

6.41

5.26

4.81

4.82

3.73

3.96

3.89

4.44

3.33

25.93

4.45

3.34

25.97

4.45

3.34

25.96

0.00

2.99

0.00

2.97

2.31

0.00

2.06

2.52

0.00

11.85

41.85

10.00

4.07

7.41

3.33

4.81

9.63

7.04

11.13

39.89

9.83

4.27

7.98

5.38

3.53

11.13

6.86

11.37

40.54

9.89

4.20

7.79

4.70

3.96

10.63

6.92

12.69

44.03

11.19

3.73

8.21

1.49

0.75

8.96

5.22

11.55

38.61

10.89

4.62

6.93

4.29

1.98

10.23

4.29

11.90

40.27

10.98

4.35

7.32

3.43

1.60

9.84

4.58

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Unsurprisingly, we find that Industrial PhDs are typically hosted by companies

in knowledge-intensive industries. Also, hosting companies are geographically

concentrated in the Copenhagen area (zip-codes below 3000).

Companies hosting Industrial PhD projects are, on average, relatively large

companies with sometimes very high capital intensities (which is mostly due to

the presence of large financial sector companies).

Methodology of the company level analysis

Our statistical model compares two groups of companies:

(a) companies that have hosted at least one Industrial PhD project, and

(b) companies that have not hosted any Industrial PhD projects.

In accordance with the academic project evaluation literature, the group of

companies which have hosted Industrial PhD projects will henceforth be called

the ‘treatment group’, while the comparison group of companies which have not

hosted any Industrial PhD projects will be denoted as the ‘control group’.

When interpreting the results of the statistical comparisons, one must take into

account the fact that it is not possible to include all relevant factors in the models

because they are unobservable in the data. Examples include different kinds of

company competences and other immeasurable company characteristics.

This implies that interpreting any systematic treatment-control differences in

company performance developments as genuine causal effects of hosting an

Industrial PhD project will have to rest on an ‘all-else-equal’ assumption, i.e.

the assumption that factors omitted from the model are either irrelevant or, on

average, equal for treatments and controls.

To maximise the validity of this ‘all-else-equal’ assumption, we identify the

control group using a matching procedure which ensures that we compare the

treatment group companies with a control group of highly similar companies.

The identification procedure is described in greater detail in Appendix 1. Here,

it may be sufficient to note that in the analysis to follow, we will compare

developments in the success parameters over time of two groups of companies

highly similar in a number of observable characteristics.

Of interest in the following analysis is whether treatment group companies

experience more positive developments in the success parameters in association

with hosting Industrial PhD projects compared to control group companies.

The modelling setup was chosen to generate the most precise estimates possible.

However, it should be noted that the associated before/after comparisons imply

that this procedure is only applicable to analysing companies that have hosted

one or very few projects, as otherwise the timing issue cannot be resolved.

As a compromise between the precision of the before/after time period definition

and having a sufficient number of observations for the analysis, we consider

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companies with a maximum of three Industrial PhD projects. As noted earlier,

these companies represent approx. 85 percent of the participating companies.

The year that separates a company’s pre-participation period from its post-

participation period will be denoted as “year 0” or the “base year”. For

companies hosting an Industrial PhD project, year 0 is defined as the year

before initiating the first Industrial PhD project. For a company in the control

group, year 0 is the year in which it most resembled one of the project hosting

companies in its base year.

Using this method, we can measure participating companies’ developments

in the success parameters before and after their base year - the year before

initiating the first Industrial PhD project - and compare these developments to

the developments of the control group companies.

4.2

Results of the company level analysis

In the following sections, the results of the company level analysis will be

described. Here, two introductory remarks should be made:

Firstly, it must be assumed that it is practically impossible to isolate any

performance effects of hosting Industrial PhD projects on large companies,

as any contribution of an Industrial PhD project on aggregate company

performance would be small relative to the companies’ considerable

heterogeneity in the success measures. For this reason, we will also present

results for an alternative sample where companies with more than 300

employees or total assets of at least DKK 100 million in year 0 are not

considered. This sample will be denoted the ‘sample of small companies’.

Secondly, it proved to be difficult to find highly similar control companies for

a number of treatment companies. For this reason, we also consider a separate

sample of companies with less than 300 employees and total assets of less than

DKK 100 million where these low-quality matches are excluded. This results in

a sample of highly similar treatment and control group companies, denoted as

the sample of ‘high-quality matches’.

Before turning to the comparisons of the company performance parameters,

we will address the question of how successful the matching procedure is in

finding highly similar groups of treatment and control companies. Turning back

to TABLE 4.2.1, which is a snapshot of the companies in year 0, we can compare

the observable characteristics of the treatment and control group companies –

both for the sample of all companies with a maximum of three Industrial PhD

projects and their corresponding control companies, and for the sample of high

quality matches.

Note that the sampling procedure implies that the base years of the two groups of companies is distri-

buted highly similarly over time.

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While industry and geographical distributions are almost identical for treatment

and control group companies in the two samples (implied by the matching

procedure), some of the very large Industrial PhD companies in the sample

of all companies lack counterparts in the control group. In this sample of all

companies, treatment group companies have a lower total factor productivity

and a higher gross profit (which is consistent with a higher capital intensity) than

the control group companies. However, the large heterogeneity in these variables

implies that these differences are not statistically significant.

Companies in the high quality match sample are on average considerably

smaller, younger and, of course, generally more similar in their observable

characteristics.

We conclude that it was possible to find highly similar matches in terms of

geographic location and company age. For the sample of high-quality matches,

controls are also highly similar in company size.

Patenting activity

Patenting activity is measured by the company’s number of patent applications

per year.

To isolate any Industrial PhD programme participation effects, we calculate for

every company and year the difference between the number of patent applications

in the given year and the number of patent applications filed in year 0.

FIGURES 4.2.1-3 display developments of these differences, i.e. current

patenting activity relative to activity in year 0 for treatment and control group

companies, respectively.

We find large movements over time for companies that host Industrial PhD

projects relative to companies in the control group. This is likely to be a result of

generally higher absolute patenting activity in treatment companies.

FIGuRE 4.2.1: Number of patent applications, all companies

1,00

0,08

0,06

0,04

0,02

-0,02

-0,04

-0,06

-5

-3

-1

years before/after

year o

Companies with

Industrial PhD projects

Companies without

Industrial PhD projects

An alternative measure would have been to consider granted patents. however, the long patent ap-

proval process renders it difficult to associate this variable to current innovation output.

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FIGuRE 4.2.2: Number of patent applications, small companies

Average number of patent applications per company relative to year 0

0,15

0,1

0,05

-0,05

-0,1

years before/after

year o

-5

-4

-3

-2

-1

Companies with

Industrial PhD projects

Companies without

Industrial PhD projects

FIGuRE 2: Number of patent applications, high-quality matches

Average number of patent applications per company, change relative to year before

first initiating an Industrial PhD project

0,20

0,15

0,10

0,05

-0,05

-0,10

years before/after

year o

-5

-4

-3

-2

-1

Companies with

Industrial PhD projects

Companies without

Industrial PhD projects

All graphs indicate that after year 0, the developments over time for treatment

companies are equal to or larger than developments for control companies,

indicating greater increases in patenting activity for the group of treatment

companies compared to the group of control companies.

One could note that there are also differences between pre-base year trends in

patenting activity depending on the sample under consideration, indicating the

difficulties of finding control companies with patenting activities similar to the

companies hiring Industrial PhDs.

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Whether or not one is willing to interpret the graphs as evidence of positive

effects of hosting Industrial PhD projects depends on one’s underlying

assumptions. E.g. in FIGURE 4.2.1, there is a positive trend of increasing

patenting activity before hosting the first Industrial PhD project, but not in the

years after. But over a longer time horizon, activity is higher after year 0 than

before. So the interpretation of the results depends on whether one assumes that:

(a) trends would continue in the absence of programme participation, or,

(b) activity would stay at the same level in the long run in the absence of the

programme.

The estimates of the statistical model presented below will be based on a pre-

participation period specified as the five years up to year zero, and the post-

participation period as the ten years after year zero. Obviously, the lengths of

these time periods are computed are arbitrary, and the robustness of later results

when choosing different before/after time intervals needs to be checked in the

numerical analysis.

A look at the raw data reveals that participant firms in the sample of high-

quality matches apply for on average 0.07 patents per year before year zero,

and 0.18 after year zero (i.e., an increase of 0.11). Control firms have almost the

same patenting activity both before and after year zero. Under the assumption

that both groups of firms would have experienced the same developments

in their patenting activity in the absence of the programme, the programme

increases patenting activity with 0.11 patent applications per year.

To address the robustness of the graphs’ suggestions and to quantify the strength

of these associations in the data, we apply a model that estimates the expected

percentage-point changes in the number of patent applications in a given year

depending on whether the company is a treatment or a control company, and on

whether the year under consideration is before or after the base year.

The results of this model are presented in TABLE 4.2.2. Of particular interest

are the coefficients for the variable “The observation is after the base year and

belongs to an Industrial PhD company”. Under the assumption that patenting

of treatments and controls would develop in similar ways in the absence of

the programme, this variable identifies the genuine causal effect of hosting an

Industrial PhD project on patenting activity.

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TABlE 4.2.2: Count data regression results, dependent variable: number of patent applications in

a given year. The table presents exponentiated coefficients, i.e. multiples of the number of patent

applications when the logical conditions of the associated variable are fulfilled.

Sample: all compa-nies with a

maximum of three Industrial PhD

projects

Sample: small

companies

Sample: high-

quality matches

variable

The observation is

after year 0

The observation

belongs to an

Industrial PhD

company

4,13

***

4,17

***

4,36

***

0,88

0,76

0,84

The observation is after

year 0 and belongs to an

Industrial PhD company

1,70

2,19

1,94

Constant term

0,06

***

0,04

***

0,04

***

Notes: ***: significant at the 1% level; **: significant at the 5% level; *: significant at the 10% level. All regressions based on STATA

Corp.’s ’xtpoisson’ routine.

Findings of the statistical analysis of patenting activity can be summarised as

follows: We find positive potential effects of hosting an Industrial PhD project

for the sample of all companies with a maximum of three Industrial PhD

projects. According to the estimates, hosting an Industrial PhD almost doubles

(1.70) the number of patents per year in the years after year 0.

For the other samples, associations between hosting Industrial PhD projects

and changes in patenting activity are also positive, and stay significant the

ten-percent significance level also for the considerable reduced sample of high-

quality matches.

In sum, one can conclude that there is evidence of positive associations between

hosting Industrial PhD projects and changes in patenting activity in the data.

These relationships were robust when changing the lengths of the before- and after-base year periods

considered for the estimations. Also, computing average numbers of patents of both participants and

controls both before and after year zero, and estimating a linear model of the pre-post base-year dif-

ferences revealed very similar (and also statistically significant) results.

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Gross profit growth

The analyses of gross profit and TFP in the next subsection follow the same

blueprint as the previous look at patenting activity. Recall that gross profit is

the surplus of annual revenues over costs (excluding wages), and accordingly

measures the value creation of a company in a given year.

First, for every year we calculate the difference between the year’s gross profit

and the gross profit in the base year (the year before the company first initiated

an Industrial PhD project). Next, we calculate the average of these differences

for both the group of treatment companies (which have hosted Industrial PhD

projects) and the group of control companies (which have not hosted any

Industrial PhD project).

FIGURES 4.2.4-6 show these averages for the treatment and control companies

for the three different samples. They suggest that companies which host

Industrial PhD projects are characterised by high growth in gross profit. While

FIGURE 4.2.4, which compares all sampled companies both with and without

Industrial PhD projects, show a decrease in the growth trend in association

with hosting the first Industrial PhD project, FIGURE 4.2.5 and FIGURE 4.2.6,

respectively comparing small companies and high quality matches, show a

consistent gross profit growth which has no equivalent in the corresponding

control group’s gross profit growth pattern.

FIGuRE 4.2.4: Gross profit developments (in DKK1,000), all companies

Average values relative to year 0

150.000

100.000

50.000

-50.000

-100.000

-150.000

years before/after

year o

-5

-4

-3

-2

-1

Companies with

Industrial PhD projects

Companies without

Industrial PhD projects

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FIGuRE 4.2.5: Gross profit developments (in DKK1,000), small companies

Average values relative to year 0

200.000

150.000

100.000

50.000

-50.000

-100.000

-150.000

-5

-4

-3

-2

-1

years before/after

year o

Companies with

Industrial PhD projects

Companies without

Industrial PhD projects

FIGuRE 4.2.6: Gross profit developments (in DKK1,000), high-quality matches

Average values relative to year 0

200.000

150.000

100.000

50.000

-50.000

-100.000

-150.000

-5

-4

-3

-2

-1

years before/after

year o

Companies with

Industrial PhD projects

Companies without

Industrial PhD projects

Thus, assuming that treatment group companies in the absence of the

programme would experience similar gross profit growth (or in this case: a

similar decline in growth) as control group companies, the vertical distance

between the graphs shows a considerable genuine (causal) effect on the gross

profit growth of companies hosting Industrial PhD projects.

We turn now to formally estimating before/after year 0 differences in gross profit

growth for treatment and control groups respectively.

Accordingly, we divide

We consider before/after differences in growth rather than levels, since gross profit levels show clear

time trends which need to be taken into consideration in the estimations to avoid generating biased

estimates.

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each company’s observation period into two periods: one period before year 0, and

one period after year 0. For every company and for both two periods, gross profit

growth is measured by the average of the annual (absolute) increases in gross profit.

We can now compare these averages both over time and between treatment and

control group companies. In the statistical model, we use the same variables as in

the count data regression used of the patenting analysis as right-hand-side variables.

Hence, the difference between the developments in gross profit growth before/

after year 0 for treatment group companies and the gross profit growth

developments before/after year 0 for control group companies is measured by the

coefficient associated with the variable: “The

observation is after the base year

and belongs to an Industrial PhD company”

14.

The results of this comparison, which is again carried out by using a simple

linear regression model, are summarised in TABLE 4.2.3. The table shows the

results for high-quality matches, i.e. the treatment and control group companies

most similar to each other with regard to their observable characteristics, and for

which the comparison accordingly has the highest validity.

TABlE 4.2.3: linear regression results, dependent variable: annual increase in gross profit (in

DKK1,000, in prices of 2007), sample: high-quality matches.

observation period: three years

before to five years after year 0

observation period: three years

before to ten years after year 0

Coefficient

-1422,68

Standard

error

797,83

variable

The observation

is after year 0

The observation

belongs to an

Industrial PhD

company

The observation is after year 0 and

belongs to an Industrial PhD company

Constant term

Number of

observations

Coefficient

-1792,35

Standard

error

764,36

-458,33

905,19

-458,33

906,09

2267,23

1259,42

1458,82

1457,88

1488,27

381

607,25

1488,27

321

607,86

Notes: ***: significant at the 1% level; **: significant at the 5% level; *: significant at the 10% level. Estimated with heteroscedasticity-

consistent standard errors.

E.g., if the increase in annual gross profit of treatment companies is on average DKK 5m before the

base year and DKK 7m after year 0, and if gross profit for control firms increases on average DKK 3m

before and DKK 4m after year 0, the coefficient associated with “The observation is after the base year

and belongs to an Industrial PhD company”, measured in DKK, is equal to (7m-5m)-(4m-3m)= 1m.

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In the first model, which compares growth trends in the three-year period before

and the five-year period after year 0, the coefficient of “The

observation is after

year 0” (-1,792.35) suggests that gross profit growth has slowed down by almost

DKK 2m per year.

But the estimate of the coefficient for the variable “The

observation is after the

base year and belongs to an Industrial PhD company”

of 2,267.23 implies that

gross profit growth of Industrial PhD companies maintains its positive trend.

So, for Industrial PhD companies, growth after first initiating an Industrial PhD

project is approx. DKK 2m higher per year than would otherwise be expected

if they had experienced a similar decline in gross profit growth as the control

group companies.

So the approx. DKK 2m growth difference per year, implying an additional

gross profit of (2+4+6+8+10) DKK 30m in the first five years of programme

participation, is the genuine causal effect of programme participation, assuming

that Industrial PhD companies’ growth in gross profit would otherwise have

followed the exact same pattern of the control companies if they had not

participated.

It becomes clear that the programme might be considered successful even if only

a part of this difference is because of a genuine causal effect of the Industrial

PhD Programme.

When we compare the growth patterns of the two groups of companies between

both the three-year time period before and the ten-year time period after year

0, the difference still suggests higher growth for participating companies, but

becomes statistically insignificant (i.e. it becomes more likely that the finding is

coincidental).

Total factor productivity

For this analysis, total factor productivity (TFP) was calculated on an annual

basis for all companies in the entire KOB database in the given year.

Total factor productivity is gross profit ‘corrected for’ the number of employees

and total assets. It is calculated as the residuals of a Cobb-Douglas-production

function regression. In other words, TFP is the share of the company’s value

creation which cannot be explained by its number of employees or its capital

stock.

Thus defined, TFP approximates the percentage-wise deviation in gross

profit from the gross profit that we would have expected to observe, given the

company’s number of employees and its stock of assets.

For the analysis, we first take a look at the developments using a graphical

depiction of the data. FIGURES 4.2.7-9 summarise.

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FIGuRE 4.2.7: Total factor productivity developments, all companies.

Average values relative to year 0

0,15

0,10

0,05

-0,05

-0,10

-0,15

-0,20

-5

-4

-3

-2

-1

years before/after

year o

Companies with

Industrial PhD projects

Companies without

Industrial PhD projects

FIGuRE 4.2.8: Total factor productivity developments, small companies

Average values relative to year 0

0,15

0,10

0,05

-0,05

-0,10

-0,15

-5

-4

-3

-2

-1

years before/after

year o

Companies with

Industrial PhD projects

Companies without

Industrial PhD projects

FIGuRE 4.2.9: Total factor productivity developments, high-quality matches

Average values relative to year 0

0,10

0,05

0.00

-0,05

-0,10

-0,15

0,20

-5

-4

-3

-2

-1

years before/after

year o

Companies with

Industrial PhD projects

Companies without

Industrial PhD projects

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The figures illustrate that developments are very different depending on whether or

not large companies are excluded from the sample observed.

While there is a negative trend in TFP for the sample of all companies, there are

no such trends for the subsamples. The erratic movements in the graphs (in spite of

smoothing) suggest large heterogeneity in TFP over time and between companies.

For the subsamples, which are unaffected by the presence of very large companies,

TFP is between 5 to 10 percentage points higher approx. two to six years after year

0 in the subsamples.

Again, we qualify the suggestions of the graphs by use of linear regression, the

results of which are depicted in TABLE 4.2.4.

TABlE 4.2.4: linear regression results, dependent variable:

(TFP in a given year) - (TFP in year 0)

Sample: all companies

with a maximum of three

Industrial PhD projects

variable

The

observation

is after

year 0

The

observation

belongs to

an Industrial

PhD

company

The

observation

is after

year 0 and

belongs to

an Industrial

PhD

company

Constant

term

-0,002

0,040

0,042

0,064

0,068

0,067

0,057

0,028

0,008

0,043

-0,027

0,043

-0,084

***

0,023

-0,050

0,036

-0,066

0,038

Coefficient

Standard

error

Sample: small

companies

Coefficient

Standard

error

Sample: high-quality

matches

Coefficient

Standard

error

0,003

0,014

0,013

0,019

0,020

Notes: ***: significant at the 1% level; **: significant at the 5% level; *: significant at the 10% level.

Estimated with heteroscedasticity-consistent standard errors

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We see the negative TFP trends of companies hosting Industrial PhD projects and

their counterparts in the high-quality match control group corroborated by the

negative coefficients associated with the variable “the

observation is after year 0”.

Also, TFP has increased more (or decreased less) in the treatment group

companies compared to the control group the samples of small companies and

that of the high-quality matches.

This is indicated by the positive coefficients of the variable “the

observation is

after year 0 and belongs to an Industrial PhD company”,

which, for high-quality

matches, show that companies which have hosted Industrial PhD projects have on

average approx. 7 percentage points higher TFP than would otherwise be expected

if they had experienced a TFP development similar to the control group companies.

Under the assumption that treatment group companies would experience TFP

developments similar to those for control group companies in the absence of

initiating Industrial PhD projects, this 7 percentage point difference is the most

qualified assumption of the Industrial PhD Programme’s causal total factor

productivity effect. However, although positive, the TFP differences between

treatment and control groups are too small compared to the large variations

in TFP to interpret them as statistically significant, and must accordingly be

interpreted tentatively. In conclusion, one cannot claim any strong association

between hosting Industrial PhD projects and TFP development.

Employment growth

We conclude the company level analysis by taking a look at employment growth.

The finding of high growth in gross profit but not in total factor productivity

might be an indication that companies hosting Industrial Phd projects are

high-growth companies. This is strongly supported by a closer look at the data,

illustrated by FIGURE 4.2.10, with companies hosting Industrial PhD projects

being characterised by high growth in their number of employees both before

and after first initiating a project.

To establish the statistical significance of this result, we formally test the growth

difference by means of linear regression, the results of which (for high-quality

matches) are presented in TABLE 4.2.5. The results of these regressions suggest

that companies participating in the programme sustain an annual employment

growth of approximately (-3.48-1.33+3.44+2.95=) 1.58 employees per year in the

first five years after first initiating an Industrial PhD project, while companies

in the control group decrease their number of employees by approximately

-(2.95-3.48=) 0.5 employees per year. Qualitatively, this finding is independent

of whether one follows the firms for five or ten years after the base year, and is

statistically highly significant.

This finding was robust to changes of the lengths of the time periods before and after the base year

which were considered in the regressions. The findings was also robust to changing the regression

model, e.g. using each firm’s average total factor productivity in the time periods before and after the

base year as the dependent variable, or using different specifications of the production function which

was employed for the calculation of TFP.

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FIGuRE 4.2.10: Number of employees developments, high-quality matches

Average values relative to year 0

0,15

0,10

a0,05

0.00

-5

-0,05

-4

-3

-2

-1

-0,10

years before/after

year o

Companies with

Industrial PhD projects

Companies without

Industrial PhD projects

TABlE 4.2.5: linear regression results, dependent variable: annual increase in number of employees.

Sample: high-quality matches

observation period: three years before to five

years after year 0

variable

Coefficient

Standard error

observation period: three years

before to ten years after year 0

Coefficient

Standard error

The observation is after year 0

The observation belongs to an Industrial

PhD company

The observation is after year 0 and belongs

to an Industrial PhD company

Constant term

Number of observations

Notes: ***: significant at the 1% level; **:

significant at the 5% level; *: significant at the

10% level. Estimated with heteroscedasticity-

consistent standard errors.

-3.48

***

0.77

-3.13

***

0.73

-1.33

1.00

-1.33

1.00

3.44

2.95

349

***

1.18

0.65

2.73

2.95

267

***

1.19

0.65

Notes: ***: significant at the 1% level; **: significant at the 5% level; *: significant at the 10% level.

Estimated with heteroscedasticity-consistent standard errors

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SuMMARy AND CoNCluSIoNS

This analysis considers approx. 430 individuals and 270 companies which have

participated in the Industrial PhD Programme and can be found in register

data. On the individual level, we compare wage income and the occupations of

Industrial PhD graduates with regular PhDs and individuals who have a university

degree (and who are similar in terms of their fields of study, gender, etc.).

In the analysis, we take into account a set of demographic background

characteristics, like age and gender, but also the average grade of the school-

leaving examination, which to some extent controls for individual abilities.

On the company level, we analyse developments across four success parameters:

the number of patents, gross profit and employment growth and total factor

productivity. For a sample of companies which have hosted a maximum of

three Industrial PhD projects before 2009, we identify a control group of highly

similar companies which have not hosted any Industrial PhD projects, and

compare developments in the success parameters between these two groups.

Under identifying assumptions, these models isolate the causal impact of the

programme on companies hosting Industrial PhD projects.

The results of the analysis can be summarised as follows: Industrial PhD earn

approx. 7-10 percent higher wages than both regular PhDs and university

graduates. They are more likely to be found at the top levels of their

organisations’ hierarchies compared to regular PhDs and more likely to be found

in positions requiring high-level specialist knowledge than regular university

graduates. Companies which host Industrial PhD projects see on average

increasing patenting activity in association with hosting the projects. They are

characterised by high growth in gross profit (value creation) and employment.

The comparison with a control group of highly similar control companies

suggests that companies hosting Industrial PhD projects would have

considerably less positive gross profit and employment developments if they did

not participate in the programme.

We cannot find robust differences in total factor productivity developments

between companies which have hosted Industrial PhD projects and companies

which have not. This finding might be due to firm growth being negatively

associated with productivity developments.

The relative high wages of

Industrial PhD graduates, on the other hand, indicate that they have high

individual productivity.

Summing up, earlier studies which found that Industrial PhDs are characterised

by positive labour market outcomes have been corroborated. Findings on

the company level indicate that the Danish Industrial PhD Programme also

has positive effects for participating companies in terms of firm growth and

patenting activity.

This would be the case if there are decreasing returns to labour, which is one of economic theory’s

most standard arguments. Empirical support for this argument can be found in: Bingley, P., Westerga-

ard-Nielsen N., 2004, “Personnel policy and profit.” Journal of Business Research 2004; 57: 557-563.

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APPENDIx 1: SElECTIoN oF CoNTRolS

The KOB dataset is a panel dataset which has repeated observations for most

of the companies - one for each annual account filed to the authorities. So for

every company, there are typically multiple company-year observations (where

a company-year observation refers to a record, i.e. a data-point of a given

company in a given year). In the following, we will use the expression ‘control

observation’ to describe a single company-year observation (record) of a control

company.

Control companies are selected in the year in which they most closely resemble

one of the companies participating in the programme, based on the participating

company’s characteristics in the year before hosting its first Industrial PhD

project.

Note that the similarity between participating companies and potential control

companies is determined by (a) the companies’ region, size, age and industry,

and (b) the expected probability of participation, which is derived as follows:

We run an auxiliary regression on the universe of approx. 370,000 company-year

observations in KOB in the period from 1994 to 2008 which roughly resemble

the group of participants (for example, we do not consider industries in which

there is no single participating company).

The auxiliary regression is formulated as a simple probit model where the

dependent variable is initiating an Industrial PhD project the following year, and

company size, industry, region, productivity, total assets and time period as the

model’s right-hand-side variables. The regression’s pseudo R squared, which is a

measure of the model’s goodness-of-fit, is 0.29, which we consider to be high.

The probit regression predicts how likely programme participation is for a given

company. This allows us to find pairs or groups of companies for which this

predicted probability is very similar. For two companies, A and B, with similar

participation probability, the fact of company A participating and company B not

participating can accordingly be interpreted as coincidental.

Under this interpretation, the identification setup resembles an experiment where

programme participation is random, which would allow systematic differences

in outcome variables between participants and controls to be interpreted as the

programme’s causal effect on participating companies.

Yet, even companies with similar predicted participation probabilities can

be quite different, and to avoid systematic differences in industry affiliation,

size, etc. between participants and controls, we also require that a number of

observable characteristics are equal for a given participant and its matched

control company(s).

To do this, we divide the total number of company-year observations into groups

with the same industry affiliation, same geographic location, of similar size and

observed in the same year.

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For each participating company, we select the company-year observation of

a non-participating company within the same group and with a participation

probability closest to the participating company’s participation probability.

This selected company-year observation defines the participating company’s

control company, and the control company’s ‘base year’ (or ‘year 0’) – which

is the year in which it is most similar to one of the participating companies in

its base year, and in which it is selected as a control company. For each of the

control companies found by this procedure, the base year forms the basis for

comparisons of given success parameters over time.

By repeating the matching procedure, we can find an arbitrary number of control

observations for each participant. Here, a greater number of control observations

increases the robustness of later results. However, increasing this number also

makes it increasingly difficult to find highly similar control observations for

some of the participants.

As a compromise between these two considerations, we choose to find two

control observations (company-year observations of non-participants) for

each participating company. The selection of the two control observations per

participating company is made in two rounds. In each of the rounds we select

one control observation for each participating company.

In the first round, we find 270 control observations of non-participating

companies. In the second, we find another 269 control observations of non-

participating companies (the reason for only 269 instead of 270 is that in a single

case, one company-year observation is chosen as a control observation for two

participants).

In each of the two rounds, we first require that many factors are highly similar

when selecting control observations. This leaves a number of participating

companies for which no control observations could be found. In subsequent

steps, we reduce the number of factors and start choosing control observations

which are increasingly less similar, until each round has identified one control

observation for every participating company.

When control observations are equal in terms of industry (when distinguishing

between at least 36 different categories), number of employees (at least 11

different categories), gross profit (at least 7 categories), time period (at least 7

different categories) and company age (at least 3 different categories), they are

regarded as ‘high-quality matches’ in the analysis.

Note that in each of the rounds, we select only one control observation per

participating company. This does not rule out selecting different control

observations (belonging to different years) of the same control company. This

implies that there are a number of control observations that occur more than

once in the data forming the basis

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