The supply and demand of high technology skills in United Kingdom, Norway and Netherlands : a report from the European Science and Technology Observatory (ESTO).
Date: 2012-08-20
Institutions: Institute for Prospective Technological Studies
Subjects: Netherlands, Norway, United Kingdom, economic growth, labour market, skilled worker, technology, vocational training

The supply and demand

of high technology skills in

United Kingdom, Norway and Netherlands

a report from the European Science and  Technology Observatory (ESTO)

Anders Ekeland
STEP Group,
Studies in technology, innovation and economic policy
Storgaten 1,
N-0155 Oslo,
Norway

and

Mark Tomlinson,
CRIC, University of Manchester
University Precinct Centre
Oxford Road
Manchester M13 9QH
United Kingdom

Co-ordinator at IPTS

Odd Björn Ure, visiting scientist

Seville, October 2001.

PREFACE

Policy  measures  in  a  knowledge-intensive  society  should  rely  on  solid  information  on  the  skill
structures in the sectors of which the economy is  composed. This  was  the underlying idea when
IPTS launched the study. It attempts to find out how useful statistics from the Labour Force Survey
are in the delineation of labour demand and supply in seven high technology sectors.

It  turned  out  that  there  are  substantial  differences  in  skill  structures  and  with  regard  to  the
provision of training, also at national level, among the three countries covered. Furthermore, this
phenomenon is observed in sectors that often are grouped together and considered as quite similar,
like computer and telecommunication services. A “one size fits all" training strategy is therefore
questioned by the authors.

A comparison of how the seven high  tech sectors contribute to  the overall employment share  in
national  economies,  indicate  that  these  sectors  are  not  employment  generators  per  se,  with  the
exception  of  computer  services  characterised  by  rapid  growth  and  high  skill  intensity.    When
interpreting these findings one should bear in mind that the study had a limited scope. However,
the  results  are  interesting  for  contemporary  research  and  debate  on  the  relationship  between
technology, economy and employment.

High Tech Sectors In A Knowledge-Intensive Economy

There  is  no  common  agreed  standard  on  what  economic  sectors  should  be  labelled  high  tech.
However,  the  seven  high  tech  sectors  chosen  for  this  study  refer  to  the  traditional  list  from  the
OECD from the late 1980s:

NACE

244

300

320

353

642

720

730

Manufacture of pharmaceuticals, medicinal chemicals and botanical products

Manufacture of office machinery and computers

Manufacture of radio, television and communication equipment and apparatus

Manufacture of aircraft and spacecraft

Telecommunications

Computers and related activities

Research and development

2

At that stage, OECD divided manufacturing into 22 sectors. Those having a share of own, internal
R&D of more than 4% of sales were labelled high tech, between 1 - 4% medium tech and those
below 1% were called low tech. For the purpose of this study pharmaceuticals, office machinery
and  computers,  radio/TV/communication  equipment  and  aircraft  manufacturing  are  included  in
high  tech.  The  inclusion  of  telecommunications,  computer  services  and  R&D  services  was  an
attempt to redress the bias towards manufacturing.

The  choice  of  sectors  opens  up  a  systemic  approach  that  takes  on  board  interactions  between
knowledge based services and high tech manufacturing. This perspective corresponds to innovation
policies in which producer services and manufacturing are considered as intertwined and dependent
on each other in a systemic manner.

Partners of the European Science and Technology Observatory (ESTO) carried out the study. Data
from Labour Force Surveys carried out in the United Kingdom, Norway and the Netherlands were
analysed.  The Labour Force Survey is based on a sample of 0,5-1,5% of the population. It turned
out  that  the  quality  of  LFS  data  varies  from  country  to  country.  This  is  due  to  the  "cell  size
problem", linked to the fact that the number of observations in each technology sector become so
small that the results lose reliability. In a (long-term) policy perspective it is important to overhaul
and redesign statistics for knowledge-intensive economies.

In the UK the employment share of the seven sectors rose from 3,6% in 1992 to 4,5% in 1999. The
Norwegian  data  show  a  decreasing  tendency  because  the  flourishing  oil  sector1  is  one  of  the
country's most knowledge-intensive sectors. When this sector is included for the UK and Norway,
the  employment  shares  become  more  equal  (1999:  4,8%  in  the  UK  and  4,1%  in  Norway).  The
quality of the Dutch data imposes cautious interpretations, but the share of high tech sectors (even
when extraction of petroleum and gas is included) seems to be lower than for Norway and the UK.

Labour Market Flexibility

The analysis of labour  force  mobility shows  that there  is  a  relatively large  degree  of  movement
between many sectors in all 3 economies. Having a lot of mobility of people - especially outflows -
may actually be damaging to a sector’s competitiveness.  This does not preclude the argument that
mobility is a useful way of diffusing knowledge in an economic system. The point is that a high
turnover  also  has  negative  aspects  due  to  the  loss  of  "tacit"  knowledge  with  the  departure  of
experienced personnel.

In  the  UK  telecommunications  and  office&computer  equipment  especially  suffer  from  high
outflows. Computer services  and  pharmaceuticals  appear  not  to  suffer  from this  problem having
higher  inflows  than  outflows2.    The  Norwegian  mobility  data  reveal  higher  fluctuations  to  the
extent that there seem to be higher mobility rates in Norway. One should be cautious to interpret
this in the sense that the Norwegian labour market is more flexible than in the UK.

Flexible labour markets are often seen as a cure for badly competitive sectors, but we observe that
some segments of the labour market are far from inflexible. Hence, general pleas for more labour
market flexibility can send the wrong signals to key players.

1 NACE 11:  Extraction of crude petroleum and natural gas
2 It should be noted that the study primarily dealt with mobility between sectors and that the data on mobility
inside sectors only were a by-product.

3

                                                     
The  Dutch  mobility  data  are  somewhat  unreliable,  but  interesting  observations  occur  in  the
literature  review  for  the  Netherlands.  Statistics  on  the  dispersion  of  fields  of  studies  over
occupations reveal that graduates in humanities are increasingly employed in occupational sectors
outside their traditional labour markets (i.e. public administration, education, socio-cultural sectors
and arts). These statistics underscore the tendency towards broader recruitment patterns in the ICT
sector.  On  the  other  hand,  Dutch  graduates  in  medicine,  veterinary  science  and  dentistry
predominantly  get  jobs  in  their  traditional  occupational  sector,  which  is  the  health  sector.  This
phenomenon can be explained by the fact that they are regulated by certificates and licences.

Polarisation of skills ?

There is quite an uneven opportunity for training across the seven high tech sectors. UK computer
services which have a high level of stocks of qualified staff, provide less training compared with
telecommunications. There are several possible explanations for this. When e.g. computer services
score low in the UK, this might be linked to the fact that many of these employees arrive already
trained.

In  the  UK  and  Norway  knowledge  workers  and  already  qualified  staff  are  more  likely  to  get
training than the overall worker. This suggests that a polarisation of skills and knowledge might
take place in certain sectors. This result is clear in Norway and the UK.  In the Netherlands we find
the opposite on the level of the whole economy, the low educated get more training than the highly
educated. If we look at the high tech sectors we get a more nuanced picture.

The Dutch training statistics indicate that the medium skilled echelons receive most training, apart
from  those  in  telecom&post  and  aerospace&other  transport.  Among  the  seven  high  tech  sectors
chemicals&pharmaceuticals are less training intensive than telecom&post and computer services.
A similar trend can be observed in the UK.

All in all, those who are already ahead in the game are more likely to receive further training. Staff
with  medium  formal  skills  is  more  often  trained  than  people  with  high  or  low  formal  skills.
Therefore it turns out that there is no clear-cut polarisation between high and low skilled. However,
the provision of training is clearly biased and we might never enter a lifelong learning society if this
state of affairs persists.

Key Observations From The Literature Surveys

In  addition  to  analysing  the  data  from  the  Labour  Force  Survey  the  project  partners  carried  out
literature surveys, from which two striking points occur:

- 

- 

there is a continuous disequilibrium between labour demand and supply

there is a certain (institutional) inertia in adapting to skill needs revealed in national economies

The consequence of the first point is that a sound analysis has to use a number of labour demand
and  supply  indicators.  The  second  point  entails  that  policy  formulation  also  has  to  reflect  the
problems of rapidly shifting education and training resources between subjects and sectors.

A  number  of  other  lessons  can  be  drawn  from  the  literature  surveys  forming  part  of  the  three
country reports.

4

The general impression from the literature review of skill problems in the UK economy points to
the need for the government to focus on several key areas:

-  Fostering more management skills among skilled employees

-  Fostering a more business like orientation and entrepreneurial style among professionals

-  Promoting multidisciplinarity

-  Reducing brain drain especially to the US in key sectors

-  Promotion of clusters of high technology industries

-  Encourage  the  creation  of  professional  bodies  for  new  high  technology  industries  such  as

photonics

The first three points could be addressed at university level. If more graduates are trained or given
the  opportunity 
to  undertake  some  management  courses  and  focus  on  courses  with
multidisciplinary awareness, this could reduce problems experienced by many high technology and
knowledge  based  firms.  The  last  three  points  suggest  a  focus  on  creation  and  maintenance  of  a
suitable infrastructure for certain key sectors. This would involve the maintenance of e.g. science
parks, regional development and grants for specific industrial clusters  as  well  as  the fostering of
dynamic local labour markets that reduce the level of brain drain.

From the Dutch literature review it is interesting to notice that the share of low educated employees
rose from 17,9% in 1993 to 30,8% in 1999. Apart from a change in statistical codes during this
period, the observation might confirm that an economic boom enhances the employment chances
for  the  lower  educated,  without  necessarily  making  them  more  employable  in  a  longer  time
perspective.  In  the  high  tech  sectors  the  share  of  high  educated  was  fairly  stable,  oscillating
between  12,7%  and  14,2%  over  the  same  period.  The  telecommunication  sector  shows  one
interesting deviation from this rule. Here, it appears that low skill employees have lost their jobs
while the higher educated telecom staff remains on the payroll.

The  Norwegian  literature  review  features  the  history  of  public  and  private  plans  to  provide  the
country with sufficient ICT skilled people. In 1986 an ambitious plan was set up containing radical
forecasts of the future needs in the ICT industry as well as in application sectors. Some follow-up
measures were put in place, but these were to a large extent shelved during a dip in the business
cycle  in  the  mid-90s.    In  the  late  1990s  the  need  for  ICT  skills  became  once  more  en  vogue.
Apparently, the implementation of the ICT upskilling plans was not robust enough to be sheltered
from temporary fluctuations. However, the 1986 skill plans were not necessarily apt for the ICT
industries by the late 1990s.

Open Method Of Policy Co-Ordination

The  open  method  of  co-ordination  was  introduced  during  the  spring  2000  presidency  of  the
European  Union  hosted  by  Portugal.  The  method  is  now  a  widely  used  policy  instrument.  The
present  ESTO  study  could  nurture  some  further  thought  to  policy  implications  of  comparisons
revealing substantial differences between European countries.

Perhaps  the  benchmark  best-practice  approach  widely  used  today  could  be  supplemented  by
different scenarios for each country in which the governments could compare differences between

5

each other and learn about potentially 'better practices' from other states 3. In such an open method
of co-ordination it would be vital to have an open ended dialogue between Member states as to
whether common policies would be effective  or whether  what is a best practice  for one country
might be less relevant in another. Moreover, polices in one country often have consequences for
others  (e.g.  immigration  policy  at  the  moment).  If  we  learn  by  comparing  policies  and  have  a
meaningful  dialogue  then  the  Member  states  could  improve  the  timing  of  the  introduction  of
policies.

Future Research

Two main lessons can be drawn with regard to follow-up research:

Forecasting of skills is a useful exercise when we refrain from drawing drastic conclusions,
but the data at hand are fraught with problems: changing classifications, small samples etc.

The  study  of  the  dynamics  regulating  the  stocks  and  flows  of  skills  in  the  economy  has
shown  itself  to  be  a  potentially  rewarding  approach.  However,  sectorial  stock  and  flow
indicators should be complementary to indicators based on other quantitative data (counting
of  the  number  of  vacancies,  including  career  data  and  education  statistics)  as  well  as
qualitative surveys.  Skill demand and supply is a multidimensional phenomenon and policy
formulation should rely on multiform methods.

3 One approach to the open method of co-ordination is described in
ftp://ftp.cordis.lu/pub/improving/docs/g_ser_abcproject.pdf.

6

                                                     
TABLE OF CONTENTS

PREFACE

Synthesis report

BACKGROUND TO THE STUDY

EXECUTIVE SUMMARY

2

12

13

PURPOSE .................................................................................................................................................................................... 13
PILOT STUDY.............................................................................................................................................................................. 13
COUNTRY STUDIES IN TWO PARTS: LITERATURE REVIEW AND STOCK/FLOW INDICATORS..................................................... 13
THE MAIN FINDINGS OF THE LITERATURE REVIEWS ................................................................................................................. 13
STOCK AND FLOW INDICATORS................................................................................................................................................. 14
THE LIMITATIONS OF THE LABOUR FORCE SURVEY................................................................................................................ 14
Challenges for Public policy................................................................................................................................................ 15

SYNTHESIS OF COUNTRY STUDIES

15

PURPOSE OF THE STUDY............................................................................................................................................................ 15
WORKING METHOD AND APPROACH......................................................................................................................................... 16
SKILLS: THE QUESTION OF DEMAND, SUPPLY AND EQUILIBRIUM ........................................................................................... 16
THE LITERATURE ON SKILL MISMATCHES ................................................................................................................................ 17
Forecasting the need for skills – can it be done – should it be done? ............................................................................... 17
The forces of substitution ..................................................................................................................................................... 18
INDICATORS AND DATA SOURCES............................................................................................................................................. 20
The limitations of the LFS as a data source........................................................................................................................ 22
STOCKS AND FLOWS.................................................................................................................................................................. 23
Employment shares .............................................................................................................................................................. 23
Training ................................................................................................................................................................................ 26
FLOWS – MOBILITY RATES........................................................................................................................................................ 27
The age effect........................................................................................................................................................................ 28
The dual nature of mobility.................................................................................................................................................. 28
A knowledge economy example: Comparing computer services with telecommunications in the UK. .......................... 30
CONCLUSIONS ........................................................................................................................................................................... 31

Country Report UK

FOREWORD

PART 1 INTRODUCTION

34

35

Part 2: Brief literature review ............................................................................................................................................. 35
Part 3: Data sources ............................................................................................................................................................ 35
Part 4: Analysis of the UK Labour Force Survey............................................................................................................... 35

PART 2

37

A REVIEW OF RECENT POLICY LITERATURE ON SKILLS AND MOBILITY IN THE UK ................................................................ 37
A) PSI Skilltrends 2000 study - a general overview of skill trends in the UK ................................................................... 37
B) The Skills Task Force and its programme at the Department for Education and Employment (DfEE)..................... 37
C) The recent research on skill demand done by the Institute of Employment Research (IER)....................................... 42

PART 3

46

POTENTIAL DATA SOURCES FOR FUTURE RESEARCH ON SKILLS AND MOBILITY IN THE UK .................................................. 46

7

PART 4

52

RESULTS OF USING THE UK LABOUR FORCE SURVEY TO EXAMINE SUPPLY AND DEMAND FOR SKILLS................................52
The importance of studies of mobility and human resources and the methodology employed.........................................52
The demand for skilled labour .............................................................................................................................................53
SOME GENERAL CONCLUSIONS FROM STOCKS AND TRAINING DATA.......................................................................................55
The supply side via mobility indicators................................................................................................................................56
General remarks concerning the mobility rates..................................................................................................................59
Synthesis of the results by sector - combining the demand and supply figures .................................................................59
Discussion and conclusions..................................................................................................................................................63

REFERENCES

APPENDIX A:  DATA FOR THE CHEMICAL SECTOR (WHICH INCLUDES PHARMACEUTICALS)

Country Report Norway

INTRODUCTION

PART 1 THE NORWEGIAN LITERATURE ON SKILL MISMATCHES

65

67

70

71

Are we able to foresee the future need for skills?................................................................................................................72
Forecasting the dimensions of different fields of education ...............................................................................................72
THE PROBLEMS OF SKILL MISMATCHES – DIFFERENT APPROACHES ........................................................................................73
Direct indicators – using registers and surveys ..................................................................................................................74
EMPIRICAL FORECASTING MODELS ...........................................................................................................................................75
Estimates and policy – the case of primary school teachers ..............................................................................................75
Estimates and policy – the case of IT-skills.........................................................................................................................76
The ability to forecast the need for ICT skills......................................................................................................................76
A very precise forecast of technological development........................................................................................................77
The expansion in the late eighties ........................................................................................................................................77
The case of ICT skills – the late nineties..............................................................................................................................79
ECONOMETRIC STUDIES OF SKILL MISMATCH...........................................................................................................................80
DATA..........................................................................................................................................................................................82
Labour force survey..............................................................................................................................................................83
Regular surveys.....................................................................................................................................................................82
Register data .........................................................................................................................................................................83
Future development of data sources on skill-mismatch......................................................................................................84
REFERENCES ..............................................................................................................................................................................85

PART 2 ANALYSIS OF NATIONAL DATA

87

LABOUR FORCE SURVEY (LFS) ................................................................................................................................................87
LFS VS. REGISTER DATA ...........................................................................................................................................................87
REGISTER DATA...........................................................................................................................................................................87
Employment in the hi-tech sectors .......................................................................................................................................93
Stocks of highly educated and knowledge workers .............................................................................................................95
Training.................................................................................................................................................................................98
Mobility according to the LFS ...........................................................................................................................................101
Mobility – register data ......................................................................................................................................................105

SUMMARY

110

SUBSTANTIAL FINDINGS ..........................................................................................................................................................110
DATA SOURCES........................................................................................................................................................................110

REFERENCES

112

8

Country Report Netherlands

INTRODUCTION

LITERATURE REVIEW:

114

114

THE STRUCTURE OF EMPLOYMENT IN THE NETHERLANDS.................................................................................................... 114
MAJOR EXISTING NATIONAL DATA SETS AND SURVEYS........................................................................................................ 117
LIST OF REFERENCES............................................................................................................................................................... 118

EMPLOYMENT IN THE DUTCH ECONOMY

119

INDUSTRIAL CLASSIFICATION ................................................................................................................................................. 119
GENERAL TRENDS IN EMPLOYMENT, 1993 – 1997 ................................................................................................................ 120

TRAINING PROVIDED DURING THE LAST FOUR WEEKS

MOBILITY

126

129

INDIVIDUAL MOBILITY – THE PROBLEMS WITH SECTORAL BREAKDOWN ............................................................................. 134

SUMMARY

137

REFERENCES............................................................................................................................................................................ 139
APPENDIX ................................................................................................................................................................................ 140

9

Synthesis report from the ESTO project

Anders Ekeland
STEP Group,
Studies in technology, innovation and economic policy
Storgaten 1,
N-0155 Oslo,
Norway

and

Mark Tomlinson,
CRIC, University of Manchester
University Precinct Centre
Oxford Road
Manchester M13 9QH
United Kingdom

Background to the study1

There is consensus between economists that high technology sectors stimulate economic growth.
An essential precondition for their emergence and competitiveness is the existence of highly skilled
workers employed in  these  sectors.  There  is  in  fact  convincing  macroeconomic  evidence  on  the
positive effect of skilled labour on productivity growth.  For instance,  Kahn  and  Jong-Soo found
that  between  1958  and  1991  US  manufacturing  industries  with  high  shares  of  skilled  labour
experienced significant economic growth2.

A study on skills and mobility of labour in high technology sectors should provide input to IPTS on
skills mismatches in Europe. Several reports in the frame of the FUTURES project have touched
upon this issue which, however,  needs  to be further deepened. Furthermore, the outlined  project
should support the GATEWAY project carried out by some institutions belonging to the JRC as
well as other European Commission services such as DG Education and Culture. This project aims
at setting up a European interactive information system providing access to high quality education
and training opportunities and resources as well as to a variety of learning experiences, across the
full  range  of  formal  and  informal  learning  contexts.  The  feasibility  study  on  the  GATEWAY
project was completed at the end of 2000.

In order to strengthen the foresight dimension in the field of education and training it is important
to better grasp the skills that are likely to be needed across Europe over the next few years in high
technology industries. As a consequence, the results of this study should be used by private and
public decision-makers (including SMEs) to begin to comprehend the following areas:

a) to identify areas where one would expect to find a mismatch between the skills offered by
institutions of higher education or other organisations and the skills needed by high technology
sectors.

b) to upgrade the content of educational programs and services. It is crucial for European decision
makers to be sure not only that higher demand for certain skills will be met by the relative supply, but
also that enough public resources are spent to impart those skills that are likely to play a major role in
the future development of high technology sectors.

The essence of the project is therefore to study whether in some selected European countries the
higher  demand  for  high  skilled  labour,  triggered  by  the  expected  future  development  of  high
technology sectors, will be matched by the relative supply.

1 This section is largely taken from the original project proposal.
2 Kahn J. A. and Jong-Soo L. 1998, “Skilled Labour Augmenting Technical Progress in US
Manufacturing”, in Quarterly Journal of Economics, 113(4), pp. 1245-1280.

12

                                                     
Executive summary

Purpose

To  investigate  the  possibility  of making  indicators  of  demand  and  supply  of  high  skilled  labour
based  mainly  on  the  Labour  Force  Survey  since  this  is  a  data  source  available  in  all  European
countries.

Pilot study

The study is a pilot study of three countries: the UK, Netherlands and Norway. The study is also a
pilot study in terms of resources. The whole study was budgeted to 120 days, 30 for each country
study and the rest for synthesis and coordination.

Country studies in two parts: literature review and stock/flow indicators

The  three  country  studies  start  with  an  overview  of  the  existing  literature  on  skill  gaps  and
mismatch –  including public policy and  conceptual/measurement  problems.  The  other  part  is  an
effort  to  use  various  stock  and flow  indicators  for  demand  and  supply  forces  and  their  dynamic
interaction. An important aspect of this effort is to investigate the strengths and weaknesses of the
existing data sources. The only common data sources are the national Labour Force Surveys (LFS).
In Norway there are in addition to the LFS, databases based on public registers.

The main findings of the literature reviews

There is no doubt that employers feel skill gaps and skill mismatches as a real problem. A problem
that  inhibits  their  innovative  performance  and  has  various  other  negative  consequences  for  their
business. This is revealed most forcefully by the results from the surveys done as a part of the UK
“Skills Task Force”. These surveys are among the most extensive skill studies recently done in a
European country. The problem is to address the revealed problems in an adequate way since there
is a danger that the employers are too myopic. They are acutely aware of the present skill gaps, but
less  aware  of  future  skill  needs.  So  a  public  policy  only  based  on  “revealed”  needs  would  be
incomplete.

At the same time there emerges from the literature on skill mismatches a more mature view on the
value  of  various  types  of  forecasting  efforts  than  was  the  case  from  the  mid-sixties  to  the  mid-
eighties (the first period of expansion of higher education). There seems to be a broad consensus
that using forecasting is very difficult – if not impossible – to foresee future skill needs in detail.
The  answer  to  such  forecasting  problems  is  not  resignation  or  inaction,  but  to  have  realistic
assessments of what can be done and to try to improve the methods being used, to be more clear
about what level of detail is needed and what time horizon should be used. The need for ICT skills
has been known for a long time - to take one example - and there are systemic reasons why not
more has been done about it. The present ICT skill deficit on a world scale has been forecast on
several occasions, but there are several intermediary links between the forecasts of researchers and
public policy making. But one part of the explanation is that since the other forecasts were wrong
why should the need for ICT skills be correct.

13

Stock and flow indicators

Seven sectors were chosen that according to OECD definitions are regarded as ”high-tech”3. These
sectors are:

NACE

244

300

320

353

642

720

730

Manufacture of pharmaceuticals, medicinal chemicals and botanical products

Manufacture of office machinery and computers

Manufacture of radio, television and communication equipment and apparatus

Manufacture of aircraft and spacecraft

Telecommunications

Computers and related activities

Research and development

Measured by the share of highly skilled workers they are above the average of most other sectors.
Their share of total employment is rather small, around 3-5% in the UK, the Netherlands and in
Norway.

There  is  one  small,  knowledge  intensive  and  rapidly  growing  sector:  computer  services.
Telecommunications has a changing skill composition, highly educated flows in, lower educated
out. There is a tendency for this sector’s share of employment to be declining or stagnant. For the
other sectors there are not such marked growth aspects.

The limitations of the Labour Force Survey

From the outset it must be borne in mind that the LFS was not designed for doing detailed studies
of stocks and flows. But it is the only data source that is available in all European countries. One of
the aims of this study was to investigate to what extent the LFS could be used for more detailed
studies of sectors, educational and occupational subgroups. The conclusion is that there are serious
limitations. These limitations are first of all due to the size of the sample in the national LFS, but
they also depend on the size of the subgroups one wants to study. They might vary considerably
from country to country as a result of a different size/composition of the national economy. For
example, the fact remains that sometimes there are too few observations to make robust estimates
of the size of the research sector, an important 2-digit sector. It is clear that the sectoral, educational
and occupational breakdowns have to be at a rather aggregate level to be reliable. Flow indicators
are even more difficult, since the mobile part of any subgroup of employees is somewhere between
5-25% of the total. If one wants to make studies with a degree of detail as in this study one has to
look for other data sources.

3 In manufacturing the OECD distinguish between high, medium and low technology sectors.
Thus pharmaceuticals, office machinery and computers, radio/TV/communication equipment and
aircraft manufacture are included in high tech. The inclusion of telecommunications, computer
services and R&D services was an attempt to redress the bias in discussions of this kind towards
manufacturing. Many producer services now also play a crucial role in innovation systems.

14

                                                     
The  digitalisation  of  all  kinds  of  data  about  firms,  employees,  education,  vacancy  registers,
advertisements etc. makes it feasible to have databases with provide input from an array of different
sources as the primary source of data for the mapping of skills in the economy. Such register-based
information is in the long run the only way forward for such studies. In the short and medium term
minor modifications of the LFS and greater harmonisation is a feasible way forward.

Challenges for Public policy

The fact that the seven high tech sectors have a stable share of the employment does not mean that
there  are  no  skill  mismatches  (for  example,  that  the  supply  of  ICT  competence  is  matching
demand). These sectors compete with the rest of the economy for highly skilled human resources.
They might often be  forced  to  use  persons whose competence  is not  the desired  one,  leading to
increasing  costs  due  to  more  resources  being  devoted  to  training  newly  employed  people.  So
although there are at any point in time uncertainties and contradictory forecasts about future skill
needs,  there  clearly  is  a  role  for  an  active  public  policy.  What  one  should  try  to  learn  from  the
failures of the past are the limitations of forecasting. Use the forecast more as a heuristic focusing
device; use dynamic models and scenarios. Do not see the forecasts as traditional estimates with a
greater or lesser standard deviation. Since there are many substitution mechanisms. It is important
that policy is made with an appropriate degree of detail and time horizons.

When formulating public policy one should take into consideration such substitution effects. More
knowledge of such effects would make policymaking easier. One should also not forget the fact
that  many  higher  education  qualifications  are  rather  general,  so  newly  graduated  people  need
substantial on the job training to get really productive. Since a good general education and problem
solving ability is the basis for adaptability with a minimum of costs, it might be discussed whether
one can overinvest in education and skill upgrading.

Synthesis of country studies

This  chapter  summarises  the  three  country  studies,  while  trying  to  highlight  some  policy
recommendations that will be put forward in order to deal with potential skills mismatches.

Purpose of the study

The purpose of the project was to undertake a first attempt to investigate whether the demand for
high  skilled  labour  in  some  selected  European  countries  –  triggered  by  an  expected  future
development of high technology sectors – is or will be  matched by  an  adequate  supply. Closely
connected with this is the question to which degree measuring future skill needs in various sectors
will induce policy initiatives that really are able to meet those challenges.

15

Working method and approach

This  study4  covers  three  European  countries  (UK,  Netherlands,  Norway),  reflecting  a  variety  of
education, training systems and institutional set-ups. The UK study has been the “template” for the
Norwegian and Dutch studies. Many of the findings are related to the challenges faced when one
tried to make comparable indicators for skill mismatches in the high-tech sectors that are used in
the UK study. Each country-study tries to accomplish two main tasks:

Firstly, to  look  at  past  developments,  the  work  analyses  the  evolution  of  demand  and  supply  of
skilled labour in high technology sectors over the last decade. Technological change may be one of
the driving forces behind the increase in skill requirements in the labour market especially in high
technology industries.

Secondly, assuming that previous trends will continue and considering other important factors that
are  likely  to  influence  the  supply  and  the  demand  for  skilled  labour  over  the  next  decade  (e.g.
change in educational policy priorities), future patterns of demand and supply of skilled labour in
high technology sectors are identified.

The studies proceed taking the following three levels into consideration:

•  Cross-sectoral (we examined and compared at national level flows of people from and into the
following sectors: The sectors are Aircraft; Office and computing equipment; Chemistry/Drugs
and  medicines;  Radio,  TV, Communications  equipment.  We  also  explored  certain  knowledge
intensive  service  sectors,  namely:  telecommunication  services,  computer  services,  R&D
services).

•  Occupational – we compared different types of worker within sectors
•  We  also  assessed  the  differential  access  to  training  and  human  capital  formation  by  different

sectors and occupational groups.

Skills: The question of demand, supply and equilibrium

Different schools in economics will treat the question of skills demand and supply differently. Most
mainstream  economists  will  try  to  treat  this  within  a  standard,  static,  partial  equilibrium
supply/demand framework. Graphically this is done using the very static, but very familiar analysis
of supply and demand curves. As many have pointed out, this type of model presupposes that the
crossing of supply and demand is actually an equilibrium point. But as we know by now this has
very little to inform us about the dynamic processes that determine at which quantity/quality/price
supply and demand will eventually match. For example, time is one often neglected, but crucial,
element in skill supply. The decisions taken about education and career paths are taken on another
timescale  than  those  that  determine  the  demand  for  skills5.  Furthermore,  people,  firms  and  the

4 It should be noted that this is a very small pilot study, and it could serve as a starting point for a
much more extensive research project. Each of the national studies was calculated to be 30
working days each and it goes without saying that what one can achieve during one month –
both summing up the existing literature, giving an overview of data sources and generating some
empirical results is indeed limited.
5 In addition one also have to face the tricky issues in actually estimating the demand and supply
curve, since what one observes are quantities sold at certain price. But that only gives us a
scatter, a cloud of points, but no basis for simultaneously estimating both the demand and the

16

                                                     
authorities engage in complex individual decision making processes (similar to those analysed by
proponents of game theory). This implies that the outcomes of these individual decisions do not
always  appear  rational  in  a  collective  sense  and  are  therefore  often  very  hard  to  analyse.  The
“heterodox”  schools  of  economic  thought  would  argue  –  with  different  emphasis  on  different
factors  and  mechanisms    –  that  there  is  continuous  innovation  and  consequently  a  continuous
situation of disequilibrium. In a paper from the  most  recent  major  study of  skills, the  UK Skills
Task Force, “The Dynamics of Decision-making in the Sphere of Skills’ Formation” (Penn 1999)
one of the conclusions is that:

“Decision-making within the sphere of skills’ formation is complex.  In particular, different actors
have differing perceptions and different goals.  This may not produce an equilibrating situation.
Indeed,  it  is  clear  from  the  wide  array  of  research  findings  reviewed  in  this  paper  that  such  an
equilibrium has not pertained during the last 30 or 40 years.  Nor will an equilibrium be produced by
improving knowledge and information per se.”

The  situation  of  continuous  disequilibrium  does  not  mean  that  the  development  of  supply  and
demand is totally unpredictable. However, it means there can be important non-linearities, limiting
the use of an overly simple and fully deterministic model. But one might be on a stable “path” that
can  be  described  by  a  fairly  simple  dynamic  model  or  a  model  with  two  or  three  “attractors”
leading to a limited number of possible scenarios.

This non-equilibrium situation is basically the reason why one – from a policy point of view – has
to try to develop various indicators of supply and demand and their dynamic interaction. This study
intends to be a small contribution to this indicator development process.

The literature on skill mismatches

From  the  literature  on  skills  and  skill  mismatches  there  emerges  a  set  of  questions  that  are
frequently raised.

Forecasting the need for skills – can it be done – should it be done?

There is no single answer to the question whether skills forecasting could or should be done. In this
respect  it  will  be  useful  to  keep  the  different  levels  and  time  horizons  in  the  different  types  of
forecasts apart. In the literature, there is a consensus that it is possible on a rather general level with
a time horizon of five to ten years to forecast the need for certain skills – ICT skills being one prime
example.  But  to  forecast  the  need  for  more  specific  skills  is  difficult  due  to  the  open,  dynamic,
“chaotic”  properties  of  socio-technological  development.  It  is  in  many  ways  like  forecasting  the
weather, clearly useful, not impossible, but inherently difficult. One has to use different methods
for short, medium and long-term forecasts.

It  seems  that  multidisciplinary  case  studies,  interviewing  employers,  collecting  data  about
vacancies etc. are giving the best indicators of urgently felt needs for specific skills. Since more and
more information  is available in databases one can today collect types and  amounts  of data that

supply curve, one has to make further assumptions so that one uses the scatter to estimate the
best fit of either supply or demand.

17

                                                                                                                                                    
were foreseeable, but not feasible ten yeas ago. But such an approach runs the danger of being too
limited when it comes to forecasting the need for medium and long-term skills.

Even  if  such  indicators  were  reliable,  the  problem  is  that  the  education  system  for  obvious  and
rational reasons generally cannot adapt quickly to such needs if they are of a more transient nature.
Generally the costs of rapid changes in staff, of all the mundane tasks connected to establishing
new lines of study, not to speak of new institutions, are probably too high. That means that there is
a certain necessary inertia in adapting to the revealed need for skills. The case of primary school
teachers is an excellent illustration of this point. In many countries the public is surprised that there
are  sometimes  too  few,  sometimes  too  many  primary  school  teachers;  especially  since  the
demographic  variations  in  school  children  cohorts  are  easily  predictable.  We  know  how  many
school entrants there will be 5-6 years in advance yet still the supply of primary school teachers is
not straightforward. Basically this is because it takes four years to train the teachers. When there is
a  lack  of  teachers  one  increases  the  number  of  students,  but  at  the  same  time  people  with  less
and/or different qualifications are employed as teachers. These people cannot be sacked when the
new large cohorts of graduates come onto the labour market. Some of them turn out to be excellent
teachers, but we then  get  a situation of oversupply.  This  sends  a  signal to people  not  to  go  into
teaching, so as time go by there are fewer students. These cyclical waves should then be matched to
the variations in birth rates6. However, even this is probably not rational, and in any case very few
politicians would be willing to undertake drastic, short-term up/down-sizing of the institutions that
educate  primary  school  teachers7.  This  is  not  to  say  that  the  present  educational  systems  in  the
countries concerned are as responsive, are as aware of present and future needs for skills as they
could be.

ICT skills might also serve as example of this. Although there has been a continuous under-supply
of  such  skills  for  two  decades,  public  policy  in  this  field  has  not  expanded  the  educational
capacities as much as needed to satisfy the demand. Part of the explanation is probably that in the
last  two  decades  anti-inflationary  policies  have  generally  made  it  difficult  to  radically  expand
certain fields of study like ICT while reducing drastically those not in such great demand. But a
note of caution is needed here, because in the long term there is a risk of a feedback loop between
ICT skills and new technology: the more people with ITC skills, the more projects are initiated, and
the  more  rapid  the  technological  development,  the  more  people  are  needed  to  staff  ICT
departments,  to  diffuse  the  knowledge  and  to  use  these  new  technologies  in  a  productive  way.
Since supply by such mechanisms creates more demand, we should consequently expect to be in a
situation  of  continuous  “under-supply”8.  The  real  questions  are  therefore:  How  great  should  the
under-supply  be?  How  many  of  those  wanting  to  get  an  ICT-education  should  be  given  the
opportunity to get one? What consequences are there when people educated and/or trained in other
fields are filling the empty ICT positions?

The forces of substitution

In  general  the  process  of  substitution  and  re-training  seems  to  be  widespread.  The  well-know
biologist and historian of science J. D. Bernal said: “Give me two years – and I shall be an expert in
any  scientific  field”–  and  there  seems  to  be  much  truth  in  this.  Partly  because  people  are  rather

6 It is a well know mathematical result that even a simple dynamic system of three deterministic
equations can generate chaotic behaviour, i.e unpredictable oscillations within certain bounds.
7 See the Norwegian literature study for details.
8 These sectors are notoriously labour intensive and although even software production is getting
more automated, increasingly using ready made components there is nothing like the increases
one gets in industry by introducing new machinery.

18

                                                     
flexible and able to learn new things – and because even if they have the appropriate education,
there is a lot of sectoral, firm, and technology specific knowledge– both codified and tacit that has
to  be  learned  in  any  case.  Even  specialised  professions  like  medical  doctors,  lawyers  and
economists need a substantial amount of on the job training before they really become productive.
This indicates that the difference between training a person with the “right” education and training
a person with the same level of education in a different field might not be so big for a considerable
number of occupations since a considerable proportion of the skills required  are  acquired  on  the
job.

If forecasting the need for skills is hard to do for policy makers, it is probably even more so for the
individual. There has been a long debate as to what extent the state should try to influence choice –
and it is well known that very often people behave differently from the paternalistic advice given
by the authorities. Penn (1999)9 illustrates this point in the following way:

Let  us  imagine  that  somehow  it  was  possible  to  ascertain  that  in  ten  years’  time  only  10%  of
current  16-year-olds  would  need  a  degree  for  their  job.    Is  it  likely  that  90%  of  this  age  group
would decide spontaneously to abandon their hopes and plans for higher education based upon this
knowledge?   It is far more likely  that the present trend for an increasing demand and  desire  for
higher  education  would  persist  and  the  net  effect  would  be  that  the  job  market  for  those  with
degrees would become far more competitive over the next decade.  It is also probable that a process
of credentialism’ would ensue whereby jobs that traditionally had not required degrees would use
them  as  a  new  entry  requirement.  Only  a  draconian  reduction  of  higher  education  places  could
ensure an equilibrium.

But the fact that children do not follow the advice  given by  their  parents  is  not to  say that such
recommendations should not be given by parents. Such explicit or implicit advice forms part of the
information and knowledge base that people use when they make their individual choices. There is
a complex relationship between individual choices and the resulting collective behaviour since the
individual choices of other people enter into the “equation”.

As a part of the UK DfEE “Skills task force” project a very interesting overview has been written
by  Jonathan  Haskel  and  John  Holt,  “Anticipating  Future  Skill  Needs:  Can  it  be  Done?  Does  it
Need  to  be  Done?”10.  The  paper  gives  a  very  useful  overview  of  these  problems.  In  their
concluding chapter they write:

At first sight the obvious conclusions of this paper would seem to be that individuals and employers
are not very good at seeing the skill needs of the economy, and not very good at responding to those
needs when they do see them; and that forecasts from economists are fraught with difficulties and so
not much help either.

In particular, most measures of skill shortages underestimate the  severity of the  problem  of  under-
investment in skills because they fail to pick up the extent to which employers adapt to skill shortages
by opting for product strategies that do not require high skills, thereby eliminating the skill shortages
but at the cost of a poorer performance for the economy at large. Equally, forecasts of skills are likely
simply to extrapolate such low skill traps into the future - so that if individuals use the forecasts they
will merely replicate the existing problem.

9 Penn, R. and Holt, R. (1999) Skills Issues in Other Business Services - Professional Services,
National Skills Task Force Research Paper 16, DfEE.

19

                                                     
Like most researchers Haskel and Holt conclude that it  is more  sensible to improve the  existing
forecasting  methods  than  to  refuse  to  try  to  think  rigorously  about  the  future.  These  are  some
trajectories for improved forecasting methods:

•  Better  data  –  not  the  least  as  the  spread  of  the  information  society  gives  us  much  more  in
structured, electronic form. If one really takes skills seriously one has to improve considerably
the scope and quality of data.

•  More dynamic (scenarios) and less linear models. Very many researchers are not trained in using
dynamic models, partly because they demand numerical solutions that need advanced software
and computers. Policy makers are not used to dynamic models either, and have not been very
demanding customers in this respect.

•  A  better  understanding  of  the  mechanisms  of  substitution,  of  life-long  learning,  shifting  the
emphasis  of  the  problem  from  forecasting  needs  for  specific  skills  to  proactively  adapting  to
identified skill needs – provided that the learners have a solid general education.

Indicators and data sources

The only  common data source in the UK, Netherlands and Norway is the Labour Force  Survey
(LFS). But as is well known this is not a standardised data source. The national LFSs are marked
by  their  history  and  are  not  constructed  according  to  any  common  standard.  There  has  been
ongoing  work  of  harmonising  the  national  LFS  in  Eurostat,  and  the  UK  has  been  part  of  this
process  since  it  started  in  the  early  1990s.  Norway  made  some  significant  steps  in  harmonising
their LFS from 1996 onwards.

When it comes to Dutch data the situation is that the Dutch Central Bureau of Statistics does not
regard  their  data  on  mobility  reliable  enough  to  be  used  as  a  basis  for  mobility  research.  The
authors of this study respect this point of view. But since the authors regard it is a pilot study partly
aiming to explore the data quality, we  have used  the  Dutch LFS  data that  Eurostat  uses in  their
work on the Community Labour Force Survey.

The question of data quality must always be discussed in relation to the problem to be analysed.
One of the aims of this study was to do a pilot study to investigate if the Labour Force Survey could
be used for mapping stocks and flows with a rather detailed industrial breakdown, and the short
answer to this is that the quality of the data is limited. Certainly, there are national differences, but
as  always  in  comparative  studies  the  weakest  data  sets  the  limit  for  what  kind  of  comparative
indicators  can  be  constructed.  In  this  case,  UK  was  used  to  set  a  standard  that  Norway  and  the
Netherlands were not able to match. As is clear from the table below, not even a basic variable such
as the NACE codes have the same degree of detail in the three countries. Only the UK has a three
digit-NACE required to study the high-tech sectors that we wanted to focus on. But the reason why
many countries do not have such detailed NACE codes is that the number of observations in each
industrial sector becomes so small that the estimates become unreliable.

10 Haskel, J. and Holt, J. (1999) Anticipating future skill needs: Can it be done? Does it need to be
done? National Skills Task Force Research Paper 1, DfEE.

20

                                                                                                                                                    
Table 1: Labour forces survey (LFS) variables in UK, NL and NO, plus Norwegian register data.

VARIABLES AND DATA SOURCES

UK - LFS

NL – LFS

NO LFS

NO REG.DATA

3-DIGIT NACE

OCCUPATIONAL CLASSIFICATION

EDUCATIONAL CLASSIFICATION

Yes

Yes

yes

no

Yes

Yes

no
yes11
yes

Yes

No

Yes

Table 2: Industrial sectors - preferred break down – used in UK study

NACE

3-DIGIT

TITLE

X

X

X

244

300

320

353

642

720

730

Manufacture of pharmaceuticals, medicinal chemicals and botanical products

Manufacture of office machinery and computers

Manufacture of radio, television and communication equipment and apparatus

Manufacture of aircraft and spacecraft

Telecommunications

Computers and related activities

Research and development

In the Dutch and Norwegian LFS there are industry data available only at NACE two digit level.
This is of course less of a problem in the Norwegian case, since the Norwegian register data have
the most detailed – i.e. 5 digit –classification. The persons interviewed in the LFS are of course also
in the registers and it is in principle possible to take the detailed industrial classification from the
registers and use it in the LFS. This has not been done in any study as far as we know – and would
demand more resources than have been available in this project.

Table 3: Industrial sectors, 2-digit level –Dutch and Norwegian study LFS

Extraction of crude petroleum and natural gas; related services, excluding surveying

Chemicals & Pharmaceuticals

Manufacture of office machinery and computers

Manufacture of radio, television and communication equipment and apparatus

Aerospace & other transport

Telecom and post

Computers and related activities

Research and development

11

24

30

32

35

64

72

73

ALL OTHER

ALL

Other Manufactoring (the manufacturing not belonging to the hi-tech sectors)

Other sectors (all remaining sectors, i.e. the rest of the economy)

As one will see, the sector breakdown is not identical in the three studies. In the Norwegian and
Dutch country studies the oil sector is included and the rest of manufacturing and all other sectors
are lumped together. The reason for the inclusion of the oil sector was to show that a non-industrial
extraction sector that often is regarded as a bit low tech at least in the Norwegian economy is one of

11 Occupational classification, i.e. ISCO 88 has been introduced from 2000, and has been
extended back to 1996, building on a national occupational classification used earlier.

21

                                                     
the most engineering intensive sectors12. The oil sector in Norway is much bigger than each of the
high-tech sectors that represent the core sectors in this study. In the UK and the Netherlands the oil
sector is very small.

It is well-know that the oil sector is attracting many engineers and consequently it contributes to the
shortage of engineers in other branches and probably to skill mismatches, since people with fields
of study that might be more relevant in other sectors have been “sucked” into the oil sector, causing
– to lesser or greater degree - skill mismatches in other sectors.

The limitations of the LFS as a data source

The Labour Force Survey, like all surveys, is based upon a sample of the population, from 0.5% -
1.5% of the population. And as with any other representative sample the data set has a sampling
error, i.e. when the number of observations gets small, the data become unreliable. The number of
observations  of  a  special  combination  of  characteristics  like  “male,  researcher,  Ph.D.”  will  be
represented with only a few observations in the sample. This small number will in relative terms
vary a great deal. Five observations in one quarter and ten in the next is an increase of 100%.  That
means  that  even  though  the  sample  is  a  randomly  drawn  representative  sample  you  do  not  get
reliable estimates of the total number in the economy. This is often referred to as the “cell-size”
problem.

For  the  present  purposes  the  LFS  has  shown  itself  to  be  of  limited  value  due  to  the  cell  size
problem. The sample size is too small to get reliable estimates – not only of detailed educational
and  occupational  subgroups.  The  estimate  of  whole  sectors,  for  example  the  2-digit  sector
“Research  and  development”,  is  unreliable.  If  one  wanted to  study  only  the  science  part  (that  is
going down to a 3-digit level) the problem of course gets worse. This cell-size problem makes it
very difficult to give very strong policy advice for “small” sectors and subgroups of the economy
based on the LFS. That does not at all mean that the results are of no value, but that they ultimately
need to be complemented by other data sources.

In  this  respect  the  register  data  is  one  alternative  as  is  shown  by  the  Norwegian  country  study.
Using register data eliminates the problems of sample size, since registers are a census of the whole
population. But the registers were made for administrative purposes and consequently they do not
contain all the variables one would like to use. One relevant example for this study is the lack of
occupational data. But since the LFS uses – in Norway at least – the official person ID number –
one can get information about occupation from the LFS. It is our opinion that the LFS and register
data should be used – and modified – in order to get the maximum synergies from them.

The use of registers do not always solve the problem of few observations. The chosen sector still
often gets very small, the numbers get very jumpy and hard to interpret; like in the case of “Office
and computer equipment in Norway”, a comparable small industry in a small country. That is of
course not due to a defect in the register data, it only shows that statistical analysis builds on the
law of large numbers. On such a detailed level –we are talking about a handful of firms and 300-
500  employees  –  the  noise  in  the  data  is  getting  much  more  influential.  In  such  cases  a
supplementary case study together with register data is probably the most appropriate method.

12 In the early eighties many were worried about the “deskilling”, the loss of competence when
the traditional shipbuilding industry turned its attention to this new expanding marked of building
oil platforms.

22

                                                     
Stocks and flows

Employment shares

The country studies should provide results that are intended to increase the knowledge base for the
policy discussion about future skill needs in Europe. Let us start with such a simple thing: the size
of these sectors in terms of employment.

Table 4: Employment shares, 1987 – 1999, UK, Norway and the Netherlands

87

88

89

90

91

92

93

94

95

96

97

98

99

HI-TECH, 3 DIGIT

HI-TECH, 3 DIGIT

HI-TECH, 3 DIGIT
W/OIL

HI-TECH, 3 DIGIT
W/OIL

HI-TECH, 2 DIGIT
W/OIL

HI-TECH, 2 DIGIT
W/OIL

OTHER
MANUFACT.

OTHER
MANUFACT.

OTHER SECTORS

OTHER SECTORS

UK

NO

UK

NO

NO

NL

NO

NL

NO

NL

3,6

3,6

3,5

3,6

3,7

4,0

4,1

4,5

3,1

3,0

3,0

2,9

3,0

2,9

2,9

2,9

2,8

2,5

2,5

2,4

2,7

3,9

3,9

3,7

3,8

3,9

4,2

4,3

4,8

4,2

4,1

4,1

4,1

4,2

4,2

4,3

4,3

4,2

3,7

3,6

4,2

4,1

13,5 13,2

13,1 12,8 13,1 13,1 13,0 12,8 12,5 10,9 10,8 10,4 10,8
6,6
7,2

6,0

6,5

6,2

7,5

6,5

15,2 15,0 14,6 14,0 13,8 13,5 12,9 12,6 12,5 11,7 11,9 11,9 11,7
14,4 14,0 14,3 14,1 14,0 13,8 13,5

71,3 71,8 72,3 73,2 73,2 73,4 74,1 74,6 74,9 77,4 77,4 77,7 77,6
78,1 78,8 79,5 79,9 79,5 79,7 79,9

The employment shares for 3-digit sectors in the UK and Norway have a marked difference if we
exclude the oil sector, the UK being more hi-tech by this definition.

If  we  include  oil,  one  of  the  most  engineering/knowledge  intensive  sectors  in  Norway,  this
difference  is  somewhat  reduced.  The  underlying  tendencies  in  both  countries  are  the  same.  All
other sectors than computer services are fairly stable. Computer services are increasing by around
one percentage point in all three countries. In UK the decline of the other hi-tech sectors (especially
telecomms.) is less, and consequently the hi-tech share is rising.

The Netherlands have a lower hi-tech share measured on a 2-digit level than Norway and the UK.
The reason for this is that oil is small, and transport consumes less labour than Norway for obvious
geographical reasons. As an employment generator one cannot expect too much from these sectors.
Only computers services are growing fast. So in terms of demand for skills the employment growth
in itself do not create a new great demand for skills. The exception being computer services. But
since  IT-skills  are  generic,  i.e  they  are  in  great  demand  in  most  sectors,  the  growth  of  the
employment share of computer services is not a very good indicator of the demand for ICT skills.

23

Another important point here is that any definition of hi-tech sectors is to a certain extent arbitrary
and  consequently  so  is  the  apparent  “hi-techness”  of  the  country.  The  international  division  of
labour and international competition tend to make countries specialise. It is not rational or possible
to have a large computer equipment industry in all countries. The inclusion or exclusion of the oil
sector  illustrates  another  example  of  this.  This  is  very  important  in  Norway  from  a  skill  and
competence perspective, less so in the UK and Netherlands.

Table 5: Sectoral shares of highly educated, 1987 – 1999, register data NORWAY

87

88

89

90

91

92

93

94

95

96

97

98

99

OIL AND GAS

CHEMICALS&

21,9

21,4

20,6

21,8

22,4

23,3

23,0

24,3

24,5

25,1

24,3

23,8

23,8

PHARMACEUTICA 7,8

7,8

7,1

8,3

9,0

10,0

11,5

13,0

13,6

14,3

15,2

15,5

15,6

OFFICE/COMP
EQUIPT

RADIO/TV/COMMS

AEROSPACE&
OTHER

TELECOM AND
POST

COMPUTER
SERVICES

RESEARCH

OTHER
MANUFACT.

OTHER SECTORS

TOTAL

20,3

19,8

19,5

18,9

19,0

19,6

16,1

19,6

17,7

16,6

19,0

27,4

21,2

7,2

7,7

10,4

12,2

13,5

14,9

16,7

19,5

21,8

23,3

25,3

24,8

20,4

1,8

1,9

2,2

2,7

3,0

3,1

3,1

4,1

4,6

5,5

5,6

6,4

6,9

5,3

5,7

5,9

6,5

6,7

7,0

7,3

7,9

8,6

9,5

10,3

11,3

12,5

17,2

17,5

19,2

20,3

21,7

23,3

24,6

27,2

29,9

32,3

33,2

34,9

36,7

43,7

45,5

47,0

50,3

53,1

51,5

52,1

52,9

54,5

53,2

53,5

55,5

56,7

2,5

2,7

2,8

3,2

3,5

3,7

3,8

4,4

4,7

5,2

5,5

5,7

6,1

11,2

11,6

12,0

13,0

13,9

14,7

15,5

16,7

17,4

17,3

18,0

18,7

19,2

9,6

9,9

10,3

11,4

12,1

12,8

13,6

14,7

15,4

15,5

16,2

16,9

17,5

24

Table 6: Highly educated, 1993 – 1999, Netherlands, LFS13

SECTOR

1993

1994

1995

1996

1997

1998

1999

OIL AND GAS

CHEMICALS&
PHARMACEUTICAL

OFFICE/COMP

RADIO/TV/COMM

AEROSPACE&
OTHER

TELECOM AND

COMPUTER

RESEARCH

OTHER MANUFACT.

OTHER SECTORS

ALL EMPLOYEES

31,0 %

30,5 %

26,6 %

29,6 %

19,2 %

21,3 %

56,5 %

63,6 %

11,3 %

24,3 %

22,9 %

38,9 % 47,1 % 45,3 % 36,4 % 28,9 % 28,5 %

30,6 % 29,9 % 29,3 % 28,3 % 29,1 % 24,1 %

34,9 % 32,0 % 20,5 % 26,9 % 28,7 % 19,2 %

28,5 % 31,8 % 39,1 % 37,7 % 30,1 % 33,3 %

15,5 % 17,2 % 20,2 % 11,5 % 7,6 %

13,5 %

21,4 % 21,6 % 23,6 % 26,2 % 22,2 % 23,8 %

56,8 % 56,6 % 57,9 % 56,4 % 47,6 % 52,9 %

64,3 % 63,8 % 61,6 % 66,3 % 62,5 % 62,2 %

10,6 % 10,9 % 11,0 % 12,7 % 11,8 % 12,7 %

25,1 % 24,8 % 25,9 % 26,7 % 25,0 % 25,3 %

23,5 % 23,2 % 24,3 % 25,4 % 23,6 % 24,2 %

Table 7: Highly educated, 1992 – 1999, UK, LFS

YEAR

1992

1993

1994

1995

1996

1997

1998

1999

AEROSPACE

OFFICE/
COMPUTER

RADIO/TV/
COMMS

PHARMA

TELECOMM

COMPUTER
SERVICES

RESEARCH

ALL

32,8

40,4

32,5

39,8

24,6

38,0

29,9

39,4

28,6

40,6

29,8

37,5

34,8

39,2

32,8

38,8

48,7

38,9

29,3

29,2

27,3

29,5

27,0

29,0

36,9

27,8

56,1

65,3

22,9

41,9

20,6

52,3

61,0

22,2

34,5

24,6

48,0

62,9

22,2

36,6

23,7

49,7

58,5

19,3

42,9

29,5

49,8

63,8

19,8

42,6

28,2

55,1

70,7

20,3

44,1

32,7

54,9

66,5

24,8

42,5

35,6

54,3

63,5

22,1

Looking at the sectoral shares of highly educated in the three countries one has to keep in mind that
the numbers cannot be directly compared. The educational classification in the Community LFS
that the Dutch data are taken from changed from “old” to “new” classification in 1997.  The group
highly educated is constructed in order to make “old” and “new” educational data comparable. It is
impossible to replicate without detailed knowledge of the Eurostat procedure. The Norwegian and
UK data have a common, stricter definition of highly educated, namely the “old” ISCED groups 6
and 7. There is no general rising trend in the Dutch and UK data, even if we look at all employees.
That is a bit surprising since the young generations generally have much higher average education
– so that  the educational level should  rise.  Maybe this  tendency  gets  obscured by the change of
classification in the Dutch case, in the UK case there is no obvious explanation.

13 Shaded figures means that the estimates are not reliable due to small sample size.

25

                                                     
When it comes to the sectoral differences, trends are rather similar, the same sectors have a high or
low  share.  The  very  high  share  of  computer  services  is  noteworthy,  being  second  only  to  the
research sector. Telecommunications (not combined with Postal services) increases with respect to
its share of the highly educated. This sector will be analysed in more detail below. The oil sector is
one of the most education intensive sectors in both the Netherlands and in Norway.

Training

There  is  also  quite  uneven  opportunity  for  training  by  sector.  For  example,  computer  services,
which have a high level of stocks, have a relatively low level of training provision compared to,
say, telecommunications in the UK. Since we can only look at the combined Post and Telecomms
sector  in  Norway  and  Netherlands  we  cannot  compare  this  directly.  There  are  several  possible
explanations.  One  being  that  employees  in  computer  services  arrive  already  trained,  while
telecommunications workers are more likely to  be  continually  upgrading or  being  re-trained.  Or
that  upgrading  skills  is  more  a  part  of  the  job  in  computer  services  since  there  is  a  continuous
stream of new hardware and software, while there are more distinct vintages in telecomms.

Table 8: Training, 1996-2000, LFS, Highly and low educated14

NO
HIGH

NO
LOW

NL
HIGH

NL
LOW

1992

1993

1994

1995

1996

1997

1998

1999

2000

28

27

24

24

22

15

15

14

14

13

22,0

20,4

22,4

20,8

20,5

16,9

19,3

26,1

26,0

26,6

20,9

22,3

22,5

23,0

UK
HIGH

24,2

23,6

24,2

22,8

13,8

22,2

22,7

23,3

UK
ALL

13,6

13,6

14,5

13,4

13,8

14,3

14,6

14,9

The table 8 shows that in the UK and Norway knowledge workers and already qualified staff are
more likely to get training than the overall worker. This suggests that there is the possibility of a
polarisation  of  skills  and  knowledge  taking  place  in  certain  sectors.  This  result  is  very  clear  in
Norway and UK15. In the Netherlands we find the opposite on the level of the whole economy, the
low educated get more training than the highly educated. If we look at the hi-tech sectors we get a
more nuanced picture.

14 Highly educated in the case of UK and Norway is ISCED-76, group 6 and 7 are roughly
speaking those with fifteen or more years of education. The lower educated is ISCED-76, 1-5. In
the Dutch case the high group should be roughly comparable, as it based on ISCED-76 and
ISCED-97, the low group is split in medium and low. Consequently the lower educated have less
education than in UK and Norway.
15 For the UK we compare High with All employees, so the difference should be somewhat larger
between low and highly educated.

26

                                                     
Table 9: Training, sectoral average, in NL, 1993 – 1999 (shaded = uncertain figures)

SECTOR

OIL AND GAS
CHEMICALS&PHARMACEUTICALS

HIGH MEDIUM LOW
18 %
22 %
18 %
14 %
22 %
18 %

OFFICE/COMP EQUIPT

RADIO/TV/COMMS

AEROSPACE&OTHER TRANSPORT

TELECOM AND POST

COMPUTER SERVICES

RESEARCH

OTHER MANUFACT.

OTHER SECTORS

16 %

16 %

14 %

31 %

25 %

14 %

17 %

20 %

18 %

19 %

13 %

28 %

28 %

25 %

17 %

21 %

9 %

14 %

9 %

17 %

31 %

12 %

17 %

23 %

In most high-tech industries, staff with medium formal skills  are  more often  trained than people
with  high  or  low  formal  skills.  Highest  propensity  to  training  among  medium-skilled  persons  is
found in Telecommunications and Postal, Computer services and Research, with respectively 28,
28 and 25 percent of the staff getting trained.

Why the Netherlands are different from the UK and Norway is not obvious. This might be a result
of the way education is classified, in three groups and not two as is the case in UK and Norway.

The  data  on  training  are  a  very  valuable  part  of  the  LFS  –  and  where  the  register  data  in  the
foreseeable future is no alternative16. In the national LFS there is more detail about the duration and
kind of training received that has not been used in this study. These data ought to be of interest to
researchers and policy makers concerned with life-long learning, human resources in the economy
and related issues.

Flows – mobility rates

There  are  several  ways  in  which  mobility  indicators  can  be  constructed,  i.e.  which  groups  of
persons to include in the numerator and denominator:

•  Only those employed in two consecutive years or also include unemployed, new graduates and

other not employed.

•  To look at all changes of workplace (establishment) or only those that at the same time are a

change of industrial sector.

•  Look at inflow or outflow rates. Outflow rates use the number of employees in the year T, inflow

rates the number in T+1 in the denominator.

There  is  no  canonical  way  of  calculating  the  mobility  rate.  The  various  calculation  principles
capture different aspects of mobility as a knowledge  diffusion  process. The  data also sometimes
limits what kind of mobility rates can be calculated.

16 Since most training providers do have databases over their students they could technically
report electronically to the education statistics in the same way as the higher educational
institutions do, but this will be a major statistical project to implement.

27

                                                     
The intrasectoral mobility says something with respect to the knowledge diffusion among sectors,
about the relation of a certain industrial sector17 to other sectors in the economy. If looking at all
changes  of  workplace,  including  those  internal  to  the  chosen  sector,  that  mobility  rate  will  by
definition be equal or higher. In most cases the mobility to firms in the same sector is a major part
of  all  mobility  pointing  to  the  fact  that  peoples’  skills  are  more  useful  in  firms  that  are  rather
similar.

The age effect

When  comparing  the  various  types  of  mobility  one  must  always  be  aware  of  the  “age  effect”
caused by the fact that persons from 16 – 24 years old are very mobile. They are searching for the
“right job”, they have difficulties getting a permanent job, they are the first fired, they are in part-
time  jobs,  they  switch  more  often  from  being  employed  to  other  states  (education,  caring  for
children, unemployment etc.). In this 16 – 24 age group the mobility rate might be as high as 50%
in  some  sectors.  After  25  years  of  age  the  mobility  rate  falls  significantly.  Consequently,  when
comparing sectors that demand long education and sectors which do not, one has to take the age
composition into consideration.

The dual nature of mobility

When  comparing  mobility  rates  one  must  avoid  the  temptation  to  think  “the  higher  the  better”,
because mobility clearly has a dual nature. On the one hand it is necessary for supplying firms with
fresh people, new ideas, new network connections, otherwise the firms and the economy as a whole
would  stagnate.  On  the  other  hand  too  much  mobility/turn-over  of  employees  has  well-known
negative  effects.  Key  persons  leaving  projects  before  they  are  finished,  increased  costs  of
constantly training new  employees,  sellers  market  for  labour  power  =  increasing  wages  with  no
corresponding increase in productivity.

The comparative results – the intersectoral rates

The comparative results in this study are limited due to data constraints. First of all the cell size
problem occurs once more. In the Dutch case the guidelines of Eurostat make it difficult to make
comparisons  on  a  sectoral  level.  In  the  UK  and  Norwegian  case  there  are  no  such  guidelines
actually prohibiting the publication of estimates which have a very large margin of error, but the
problem does not of course disappear. But the Norwegian data are not – at the time of writing –
structured in a way that allows for the calculation of outflow rates or intrasectoral rates, since the
industrial  classification  of  the  previous  workplace  is  not  a  variable  in  the  LFS.  The  Norwegian
register data can be used to calculate both outflow and intrasectoral rates. The consequence is that
only the Norwegian register data and the UK LFS data – for the time being – are capable of being
used to calculate intersectoral mobility rates.

17 Defined as a set of industrial classification codes, in most cases more than one.

28

                                                     
Table 10: Intersectoral mobility, 7 sectors, 1987 – 1999, highly educated, UK LFS, and Norwegian
register data

HIGHLY

88

89

90

91

92

93

94

95

96

97

98

99

PHARMA

PHARMA

OFFICE&

OFFICE&

RADIO/TV/

RADIO/TV/

AIRCRAFT

AIRCRAFT

TELECOM.

TELECOM.

COMPUTER

COMPUTER

RESEARCH

RESEARCH

UK

NO

UK

NO

UK

NO

UK

NO

UK

NO

UK

NO

UK

NO

12,5

11,4

8,6

2,2

3

7,4

6,8

6,7

4,2

2,5

7,3

4,0

2,9

12,9

20,4

20,9

13,9

60,3

35,7

3,4

1,2

5,6

8,3

3,3

9,7

9,3

7,2

8,1

2

5,1

8

8,9

9,7

9,4

8,8

14

7,9

8,8

7,9

10,5

5,8

16,1

46,6

8,6

12,8

14

9,5

4,4

11,0

15,4

9,7

31,2

10,1

12,1

5,1

12,1

7,9

15,8

33,6

10,4

4,4

1,4

6,1

12,7

7,1

7,5

12,3

4,2

8,9

5,5

5,7

4,4

3,4

4,0

3,5

3,9

3,8

11

2,2

8,5

3,6

3,8

5,2

7,8

3,6

4,9

7,1

7,4

6,2

5,1

7,4

6,2

3,3

14,0

29,8

6,9

3,4

15,6

8,7

34,0

12,6

11,6

14,2

10,1

8,5

14,5

13,3

10,6

11,2

12,2

12,9

10,9

11,5

14,6

9,9

14,2

9,3

17,3

10,6

12,9

3,7

4,8

6,2

2,8

5,2

1,4

5,3

13,8

13,6

11,4

10,3

18,5

13,8

10,2

12,0

21,1

11,9

12,6

12,3

8,8

The % highly educated people moving into the sector as a % of the total number highly educated
employees in the sector.

29

Table 11: Differences in intersectoral mobility, 7 sectors, 1992 – 1999, highly educated, UK LFS and
Norwegian register data

92

4,4

93

-2,5

PHARMA

OFFICE&

COMPUTER 53,5

33,2

RADIO/TV/

AIRCRAFT

TELECOM.

8,9

7,9

0,6

COMPUTER 8

RESEARCH 10,1

6,5

2,8

-0,1

3,6

5,4

94

95

96

97

98

99

-3,3

0,5

-3,2

6,3

0,2

-4,3

5,8

6,4

-2,1

4

2,6

12

4,1

18,3

3,1

0,9

-4,9

2,2

1,3

0,8

6,7

-0,3

-6,1

-0,9

7,3

1,8

-1,1

26,6

2,8

11,2

36,1

24,1

7,8

5,7

-2,7

7

2,8

6

26,5

8,2

2,3

-5

There  are  several  interesting  aspects  of  a  comparison  like  this.  First  of  all  the  variations  in  the
mobility rate in the register data are very great. In some years the mobility rate is over 50%. This is
caused by entities being outsourced, bought etc. getting a new firm ID. In such cases the mobility
rate of course get very high, although most probably it is the same people working together both
years. From a knowledge diffusion perspective such high mobility rates are statistical artifacts, they
do not measure any real mobility. This phenomenon is most visible in sectors with few employees
and/or  few  firms  (telecommunications).  The  rates  in  the  UK  vary  but  this  is  due  to  both  real
variations and the effects of few observations.

As can be seen from the table of differences the overall picture is that mobility is higher in Norway
and this is also found in one other comparative study done by Eurostat.18 where job-to-job mobility
(not intersectoral) rates are calculated for the EU, the EFTA and the EU candidate countries.

18 Lafia and Stimpson: Using the Community Labour Force Survey
to develop mobility rates on
human resources in science and technology, Eurostat 2001-06-15

30

                                                     
A knowledge economy example: Comparing computer services with telecommunications
in the UK.

As  an  example  of  how  this  type  of  analysis  might  be  used  within  a  specific  systemic  context
consider that in the 'knowledge based' or 'new economy'  two crucial sectors for  progress are the
telecommunications  services  (TS)  and  computer  services  (CS).  If  the  state  were  to  develop  and
implement a policy to enhance the knowledge driven economy based upon these two sectors then a
“one  size  fits  all”  strategy  would  be  rather  inappropriate  given  the  differences  in  mobility  and
human resource patterns in the two sectors. CS has very high stocks of educated workers and high
training provision while TS has lower stocks and higher training than CS. The high stocks required
by CS are already manifesting themselves to such a degree that getting qualified persons from other
parts  of  the  world  is  put  on  the  agenda.  The  inflow  rates  are  much  higher  than  outflow  rates
showing that the sector is expanding.

In TS on the other hand inflow and outflow rates are more or less congruent, but on the increase
throughout the 1990s. Therefore relatively speaking TS have more labour fluidity and more of a
problem than CS in terms of retaining people. The TS training figures may reflect this fact plus the
fact that the stocks are also lower. So in terms of policy, for telecommunications it would be more
useful  to  introduce  training  programmes  of  a  different  type  than  for  CS  and  also  to  attempt  to
reduce 
to
telecommunication firms that help to retain staff.

the  outflows  from  TS,  perhaps  by  offering  programmes  and 

incentives 

Note that this analysis tells us nothing about mobility within the sector, which may be of crucial
importance  for  individual  enterprises.  Whereas  the  computer  services  sector  seems  to  be  doing
relatively well in terms of retaining highly skilled staff and having less fluidity this tells us nothing
about  the  problems  that  individual  firms  within  sectors  may  be  having.  Firms  may  still  have
difficulties with poaching or training employees who then leave to go to rival firms.

It  seems  on  balance  that  if  policy  makers  wanted  to  target  training  programmes  or  influence
competitiveness  in  the  knowledge  based  economy  they  would  be  better  off  devoting  more
resources  to  telecommunications  than  to  computer  services  in  terms  of  flexible  labour  market
policies and training. On the other hand the high stocks in CS indicate that there is huge demand for
qualified staff in CS. This suggests that provision of computer related HE courses must also be a
priority.  Recent  employee  based  evidence  from  the  UK  also  increasingly  supports  the  need  for
computer based skills in general in any case (Green et al. 1999).

This kind of analysis could be done also in Netherlands and Norway. Not in the same way since the
Dutch and Norwegian LFS only can treat Telecommunications together with Postal services. The
the  skill  composition  of
combination  of  Norwegian  register  data  clearly  shows 
Telecommunications 
the  case  for  Dutch
is 
is  changing  rapidly,  most  probably 
telecommunications too. Consequently the same kind of policy challenges have to be addressed.

that 
that 

Conclusions

The very, very brief summary of the results of these three country studies is:

•  Forecasting of skills is a useful exercise when done in an non-naïve way, but the data at hand are

fraught with problems: changing classifications, small samples etc.

•  The labour force survey is a limited data source for both stock and flow variables due to cell size
problems. Register data is the only real data source for such data. The digitalisation of all kind of

31

employer – employee data in public registers makes this type of data potentially available. There
is of course an ongoing debate on privacy.

•  The  weakness  of  the  LFS,  the  only  common  data  source,  points  to  a  need  for  considering  if
something  could  be  done  on  a  European  scale  for  getting  data  sources  that  would  be  more
adequate  in  relation  to  the  ever  increasing  analytical  and  policy  demands.  Not  only  skill
mismatches in high-tech sectors, but more generally the need for a more precise mapping of the
human resources and their mobility in the new, ‘knowledge-based’ economy.

•  Despite the weaknesses of the data the study of the dynamics regulating the stocks and flows of
skills in the economy has shown itself to be a potentially rewarding approach. The rapid growth
and  high  skill  intensity  of  computer  services  is  but  one  example  of  a  stylised  fact  that  other
methods  might  not  uncover.  But  sectoral  stock  and  flow  indicators  will  always  have  to  be
complementary  to  indicators  based  on  special,  qualitative  surveys,  counting  of  the  number  of
vacancies in the sectors etc.  Skill demand and supply is a multidimensional phenomenon and
policy formulation has to rely on a larger set of indicators.

32

The supply and demand of high technology
skills in the UK

Mark Tomlinson

CRIC, United Kingdom

Foreword

This report outlines a  preliminary  pilot  study  of  possibilities  in the  under-explored  area  of  labour
mobility research and its relation to innovation and competitiveness in the UK. The report outlines
possible  data  sources,  other  published  sources,  and  case  study  material  that  can  be  used  within  a
national systems framework for future work in the areas of mobility and human capital allocation
and formation. It is not meant to be a definitive piece of work, but rather the beginnings of a possible
future  research  programme  analysing  labour  mobility  and  skills  in  knowledge  based  and  high
technology sectors.

The report is in 4 parts:

•  Part 1: 

•  Part 2: 

•  Part 3: 

•  Part 4: 

Introduction

Literature reviews

Potential data sources

Data results

•  Appendix A: 

Data for the Chemical sector (which includes pharmaceuticals)

34

Part 1

Introduction

The report consists of the following further sections:

Part 2: Brief literature review

Some  recent  published  documents  of  useful  information/data  are  reviewed.  This  includes  some
useful case study material on skills shortages in high technology sectors as well as some published
survey evidence among other things.

Part 3: Data sources

This  comprises  a  review  of  the  data  sources  already  available  in  the  UK  from  which  studies  of
mobility and skills (including training) can and have been made. These include the UK Labour Force
Survey,  various  panel  surveys  and  other  employment  surveys  etc.  Many  of  these  surveys  are
available to academics from the Data Archive at the University of Essex. Higher education statistics
are also reviewed.

Part 4: Analysis of the UK Labour Force Survey

To measure the demand for skills we use the Labour Force Survey (LFS) and measure the number of
qualified staff in different high technology and knowledge intensive sectoral categories . Using data
going  back  to  1992  we  can  see  how  this  is  changing  over  time.  We  repeat  this  using  broad
occupational categories to examine the stocks of managers, professionals, and technicians.

We also examine some training variables in the LFS to assess changes in the formation of human
capital within different sectors and occupations. This will also help to indicate in which sectors and
occupations there is a growing demand for skills.

This  evidence  gives  us  an  idea  of  the  demand  for  skilled  labour  in  the  economy  and  how  it  is
changing over time.

Turning to the supply side, the UK LFS asks people what  jobs they  are  doing now  and  in which
sector, and what they were doing 1 year previously. From these data we can calculate mobility rates.
This gives us proxy measures for fluidity of labour supply at a sectoral level which we break down
for  high-skilled  occupations  and  highly  educated  workers.  We  will  look  at  how  these  measures
change over time to see whether there are any increasing or decreasing trends in mobility rates.

Taking the results of the above analysis we attempt to identify trends in the supply and demand of
skilled labour and attempt to identify mismatches. We show whether there is an increasing trend for
educated workers or occupational types in certain sectors such as high tech sectors and knowledge
based producer services and assess whether there will be adequate supply by examining the mobility
indicators. This will be used (tentatively) to forecast where there will be problems in the future. This
will be coupled with the data on training. If certain sectors are increasingly having to train their staff
then this also might indicate that there will be problems in the future.

35

Drawing on the results of the single-country studies, some general conclusions will be outlined in a
further  synthesis  report.  Any  similarities  or  differences  in  the  trends  between  countries  will  be
emphasised.  Policy  recommendations  will  be  proposed  with  this  in  mind.  This  will  take  into
consideration  the  similarities  and  differences  between  national  systems.  If  there  are  common
European  strategies  that  can  be  suggested  then  this  will  be  given  priority.  On  the  other  hand
nationally specific cases may require different strategies for each country with respect to the supply
and demand of skilled labour.

36

Part 2

A review of recent policy literature on skills and mobility in the UK

A) PSI Skilltrends 2000 study - a general overview of skill trends in the UK

The  Skilltrends  2000  study  (PSI,  2000)  is  one  of  the  most  recent  reports  on  skill  trends  and
occupational  mobility  in  the  UK.  According  to  the  report  the  highest  employment  growth  is  in
regions with high service sector concentration rather than traditional manufacturing industries. There
is also an increasing diversity of labour required by the UK economy: different types of contract,
working arrangements, skills etc.

The study found that skill requirements are increasing in the UK with new technologies, changing
working  practices  and  increased  competition  being  the  main  reasons.  Recruitment  problems  are
rather severe with 23% of firms have hard to fill vacancies compounded by the fact that skills gaps
are also reported by a significant number of employers (15%).

In terms of labour supply the labour force is expected to rise in the next decade, but employers are
also  reporting  increasing  skills  shortages.  However,  educational  participation  is  increasing  which
will help to offset this. There are also increased tendencies by employers and government to create a
‘lifelong  learning  culture’  for  example  the  New  Deal,  Investors  in  People  and  other  forms  of
employer best practice.

With  respect  to  the  demand  for  skills  the  main  growth  areas  are  in  the  service  sectors  and  most
prominently in the Banking and Finance sector. The report also states that the decline in employment
in manufacturing is likely to be offset by increases in service sector employment. The authors note
that employment flow research is currently a priority, but not yet completed or advanced in any way.

Around two thirds of employers thought skill needs were increasing. IT and computer technology
has had a huge impact within the workplace for skills especially in large firms (this applies to both
manufacturing and service firms). Demands for IT skills are also likely to increase in the future. The
overall picture in the UK, then, is one of a continuous shift from manufacturing to services with the
resultant  flow  of  workers  this  entails.  Skills  are  in  short  supply,  but  at  least  the  labour  force  is
expected to increase in the next few years. Skill gaps are not just associated with new technology,
but  also  with  changing  working  practices  which  require  more  interpersonal  and  social  skills  than
previously needed. Employers appear to be worried that skill shortages are increasing.

B) The Skills Task Force and its programme at the Department for Education and
Employment (DfEE)

A  recent  major  attempt  to  study  skills  and  training  with  respect  to  deficiencies  and  supply  and
demand factors in the UK has been undertaken by the skills taskforce

The  skills  task  force  was  set  up  in  1997  in  an  attempt  to  deal  with  skills  shortages  in  the  UK
economy.  It  was  a  venture  proposed  by  David  Blunkett,  Minister  for  education  in  the  labour
government and  financed by the DfEE. The  members  of  the  task  force  are  academics  and  policy
makers, industrialists and local government officials. Several papers have been published addressing

37

the current shortages, supply and demand of skills in the UK economy. The following papers  are
available on their website:

Skills Task Force Research Papers published to date

ANTICIPATING FUTURE SKILL NEEDS: CAN IT BE DONE? DOES IT NEED TO BE DONE?

THE DYNAMICS OF DECISION MAKING IN THE SPHERE OF SKILLS' FORMATION

MANAGEMENT SKILLS

INTERMEDIATE LEVEL SKILLS - HOW ARE THEY CHANGING?

JUNGLE TREKKING: VOCATIONAL COURSES AND QUALIFICATIONS FOR YOUNG PEOPLE

THE LEISURE SECTOR

ENGINEERING SKILLS FORMATION IN BRITAIN: CYCLICAL AND STRUCTURAL ISSUES

THE MARKET VALUE OF GENERIC SKILLS

EMPLOYMENT PROSPECTS AND SKILL NEEDS IN THE BANKING, FINANCE AND INSURANCE SECTOR

NEW TECHNOLOGY INDUSTRIES

FUNDING SYSTEMS AND THEIR IMPACT ON SKILLS

SKILLS REQUIREMENTS IN THE CREATIVE INDUSTRIES

SKILLS ISSUES IN SMALL AND MEDIUM SIZED ENTERPRISES

SPATIAL SKILL VARIATIONS: THEIR EXTENT AND IMPLICATIONS

EMPLOYERS' ATTITUDE TO TRAINING

SKILLS ISSUES IN OTHER BUSINESS SERVICES - PROFESSIONAL SERVICES

SCIENCE SKILLS ISSUES

EMPIRICAL EVIDENCE OF MANAGEMENT SKILLS IN THE UK

MONITORING AND MEASURING OCCUPATIONAL CHANGE: THE DEVELOPMENT OF SOC2000

A  summary  of  some  of  the  findings  of  this  research  relevant  to  high  technology  and  knowledge
intensive sectors is now presented.

The possibility of forecasting skill trends

Haskel and Holt (1999) assess the efficacy of trying to measure and forecast skills and occupations.
They find that the forecasts perform quite well at the occupational level and can be used to forecast
qualifications and job opportunities  at national and  regional levels.  They argue, however,  that  the
major  issue  is  whether  such  forecasts  can  pick  up  changing  skill  requirements.  Multitasking  and
changing occupational characteristics make it much more difficult to perceive changes in the skills
structure through looking at occupational trajectories.

In  other  words  forecasting  of  occupations,  say,  should  be  supplemented  with  other  studies  that
attempt to analyse the underlying changes going on within occupations etc. These studies combined
are  essential  to  understand  and  make  allowances  for  skills  shortages  in  the  future  as  the  market
cannot adjust rapidly enough to changing demands in this area. The labour market and the education
system need to be honed substantially for an economy to remain competitive.

38

Engineering skills formation

Within sectors there are several studies available that focus on skills and training deficiencies. For
example,  Geoff  Mason  (1999)  of  the  National  Institute  of  Economic  and  Social  Research  has
explored recruitment difficulties and skill gaps among engineers in the UK. Engineering skills are a
core  competence  required  by  high  technology  industries  and  require  lengthy  periods  of  education
and training to produce. According to a survey carried out by the Engineering and Marine Training
Authority (EMTA 1999) approximately half of the engineering establishments surveyed in 1998 had
difficulties recruiting staff (of those that had vacancies to fill). This confirmed similar research done
by the Confederation of British Industry using their industrial trends survey data.

The  EMTA  report  also  suggested  which  areas  of  skill  deficiency  were  most  prevalent.  In  high
technology sectors such as aerospace, for example, practical skills were the highest reported skill gap
(in around one half of all aerospace establishments). This was the case also for most other sectors in
the  survey.  Other  serious  deficiencies  included  in  the  aerospace  industry  were  computer  literacy
(38%),  problem  solving  skills  (38%),  skilled  crafts  (35%),  management  skills  (32%)  and
communication skills  (29%).  In  other  words  around  a  third  of  companies  in  this  high  tech  sector
report  serious  deficiencies  across  a  whole  range  of  skills  from  practical  to  communication  to
managerial  etc.  a  similar  story  emerged  in  other  sectors  such  as  electrical  engineering  and
electronics.

Mason also noted the cyclical nature of skills shortages in the engineering industries. Engineering
sectors tend to experience peaks and troughs with respect to the difficulties in recruitment which in
turn affects production. Difficulties in recruitment correspond with intensifying existing recruitment
practices  rather  than  a  willingness  to  take  on  less  qualified  staff  or  retrain  existing  staff  (EMTA
1998). In fact the majority of firms surveyed by EMTA had not offered any on the job training in the
previous 12 months to their employees.  These factors represent  serious  deficiencies in  the overall
performance of engineering based sectors in the UK.

New technology industries

Hendry (1999) on the other hand, looks at new technology industries. The growing importance of
knowledge  based  industries  is  his  starting  point.  The  definition  of  new  technology  industry
incorporates mainly new scientific and highly advanced technological fields (quite often emerging
from the fusion of other advanced areas – Cf. Kodama  1992)  and  which  involve  manipulation of
materials  at  atomic  or  molecular  levels.  These  include  nanotechnology,  advanced  materials,
biotechnology  etc.  They  also  include  the  development  of  information  and  communication
technologies (ICTs).

This  study  explores  five  themes  with  respect  to  new  technology  industries:  growth  of  these
industries,  the  key  types  of  skills  required  by  them,  how  future  skills  can  be  identified,  how  to
combine the talents of scientists and technicians with business and other types of skilled worker, and
how the skills required are to be supplied (for example, by the education system).

The results of his study for 3 industries (advanced materials, biotechnology and opto-electronics) in
the UK are shown in table 1.

39

Table 1: Skill problems by sector
ADVANCED MATERIALS

BIOTECHNOLOGY

GROWTH
PROSPECTS

KEY SKILLS

SKILL GAPS

SKILL
UTILISATION

SKILL SUPPLY

THE UK IS WELL
PLACED TO GROW IN
THIS AREA

FUNDAMENTAL
KNOWLEDGE OF
MATERIALS;
SUPPORT SKILLS SUCH
AS TESTING
TECHNIQUES,
MODELLING AND
SIMULATION;
PROJECT MANAGEMENT
SKILLS.

SHORTAGE OF
TRADITIONAL
MATERIALS GRADUATES
(ESP. POLYMERS, BASIC
METALS AND
CERAMICS);
A MISMATCH BETWEEN
GRADUATE AND PHD
COMPETENCES AND
THOSE DEMANDED;
DIFFICULTY IN
PREDICTING SKILL
REQUIREMENTS.

A LACK OF
SUPERVISORY SKILLS,
TEAM WORK AND
MANAGEMENT SKILLS.

PHD PROGRAMMES;
INDUSTRIAL
COLLABORATIONS;
SUPPLY CHAIN
PARTNERSHIPS

UK HAS THE 2ND LARGEST
BIOTECH INDUSTRY.
STRONG GROWTH
FORECAST.
MULTIDISCIPLINARY
SKILLS;
STRONG SCIENCE BASE;
ENTREPRENEURIAL AND
MANAGEMENT SKILLS;
MARKETING SKILLS;
ALLIANCE FORMATION IS
CRUCIAL.

SHORTAGE OF
BIOCHEMICAL ENGINEERS;
SEPARATION OF PHYSICAL
SCIENCE AND BIOLOGY IN
THE NATIONAL
CURRICULUM A PROBLEM;
LACK OF SUITABLE
COURSES;
LACK OF COMMERCIAL
ACUMEN AND
MANAGEMENT SKILLS (ESP.
BY SCIENTISTS); BRAIN
DRAIN (ESP. TO US) A
SERIOUS PROBLEM.
PROBLEMS TRANSLATING
UNIVERSITY RESEARCH
OUT OF THE LAB.

LOTS OF PROFESSIONAL
BODIES;CLUSTERING USED
CONCENTRATE KEY
PLAYERS;

OPTO-ELECTRONICS
(PHOTONICS)

RAPID AND
CONTINUOUS GROWTH.

DIVERSE SKILLS;
SPECIALISTS WITHIN
SUB-SECTORS;
CRAFT SKILLS OFTEN
IMPORTANT;
STRONG EMPHASIS ON
LEARNING FROM
CUSTOMERS;
COMBINATION OF
SCIENCE AND BUSINESS
SKILLS.
LACK OF TECHNICAL
SKILLS;
LACK OF
COMMERCIALLY
AWARE SCIENCE
GRADUATES;
LACK OF AN
ORGANISATION
REPRESENTING THE
INDUSTRY AND A LACK
OF FOCUS;
SUGGESTIONS OF
BRAIN DRAIN.

LACK OF BUSINESS
ACUMEN;
IN HOUSE
DEVELOPMENT
SOPHISTICATED.
LACK OF
PROFESSIONAL
BODIES;CLUSTERING
USEFUL.

40

Clearly  the  results  of  Hendry’s  studies  imply  that  in  the  high  technology  industries  there  are
common  themes  and  problems  that  need  to  be  addressed.  For  example,  a  lack  of  commercial,
managerial and business skills is a major handicap for all three of the sectors surveyed. There are
problems  getting  scientists  in  universities  to  cope  with  the  demands  of  commercialisation  and
marketing of new products and ideas. Geographical clustering seems to be a useful way of fostering
a dynamic skills base, but on the other hand, there is a significant brain drain of skilled personnel
especially to the United States where higher salaries are usually blamed for attracting UK employees
abroad.

Business and professional services

Penn  and  Holt  (1999)  looked  at  business  and  professional  services  in  a  knowledge  economy
framework. These  producer  services  are  becoming  increasingly  important  in  fostering  innovation,
growth  and  competitiveness  through  their  linkages  with  manufacturing  sectors  (see  Tomlinson
2001).  Penn  and  Holt  look  at  three  broad  sectors  within  this  area:  management  consultancy,
advertising and market research.

In the management consultancy sector there is a polarisation between the actual consultants who are
almost  exclusively  graduates  (many  with  MBAs  or  accountancy  qualifications)  and  an  army  of
clerical  workers  and  support  staff.  The  graduates  are  usually  recruited  into  professional  or
managerial occupations. Much of the knowledge these workers possess is acquired on the job or in
large firms supplemented by in house training. New skills are beginning to emerge within the sector.
No longer are these consultants brought in to just advise on cost reduction or downsizing strategies,
but increasingly to offer advice on organisational issues, human resource practices, IT within firms
etc. These multifaceted skills are currently in rather short supply as the PSI report noted above.

In  the  advertising  industry  the  UK  has  a  strong  position  in  global  the  marketplace  although  its
fortunes fluctuate widely along with the business cycle. Again there is a strong distinction between
professional and non-professional staff. The professionals usually have degrees and acquire skills on
the  job.  Senior  professionals  thus  have  the  job  of  passing  on  knowledge  to  juniors.  Because
advertising is highly competitive agencies invest heavily in their own staff in order to remain ahead
of the pack.

Finally, the market research sector has, like advertising, a strong tacit dimension. Most of the skills
required  are  learned  on  the  job  although  there  is  an  increasing  emphasis  being  placed  on  formal
qualifications. There is also a growing demand in this sector for statistical and research credentials to
cope with the massive increases and availability of market data. Training often now includes courses
to assist in the dissemination of results and communication skills as well as the actual processing of
data.  New  technology  and  ICTs  in  particular  are  having  profound  effects  on  the  skills  required.
Serious skill shortages are prevalent in these areas and consequently high salaries are paid to workers
with such skills. Clustering is also high with the vast majority of employment in this industry being
located in London and the South East.

41

C) The recent research on skill demand1 done by the Institute of Employment Research
(IER).

The  IER  has  recently  reviewed  the  types  of  indicators  that  are  postulated  in  the  organisational
performance literature and that might be used as indicators of skills demands (Bosworth (1999) and
Bosworth and Wilson (1999)). This work has two main themes. Firstly a review of the literature on
national and regional development and second the collection of statistical information relating to
relevant  national  and  regional  indicators  of  skills.  We  review  the  limited  information  available.
More detailed results  are  expected later this year. Interested parties should consult  the  skillsbase
website for more details. The following is only a brief summary.

Review of the Literature

This work reviews the relevant regional and national literatures about developments which throw
light on the future skills requirements. The authors note that this ‘is a potentially enormous task’.
The review is limited to key papers that relate to the types of variables deemed most useful for the
present analysis.

In addition, the review covers some publications based upon unofficial data. There are several other
surveys and other studies of R&D and innovation at the regional level that are relevant to the skills
issue,  such  as  the  work  of  CURDS  at  Newcastle  University.  The  aim  is  to  report  on  relevant
developments as perceived by each author, as well as to examine how the focus and concerns of the
different publications has changed and is changing.

Indicators

IER note that it is possible to compile several statistical indicators at the national and regional levels
that can be used for forecasting future skills developments. These indicators are not without their
problems, but nevertheless they can provide an interesting picture of developments over time when
used in combination. The range of indicators is somewhat more comprehensive at the national than
the  regional  levels,  but  all  of  the  national  indicators  can  be  reproduced  for  other  countries.  We
focus only on the national indicators in this report as this is the focus of the later empirical work.
The IER report also deals with regional indicators.

According to IER many studies point to the importance of indicators such as R&D expenditure or
personnel levels and various intellectual property counts, such as patents, trademarks, etc. There are
problems with using some of these indicators (especially for the service sector which does a great
deal of research, but does not usually count it as 'R&D'. Services also tend not to issue patents; a
traditional  indicator  of  innovation  or  innovative  potential  in  manufacturing  sectors).  R&D  is  a
measure of input or effort in the inventive process, patents are often taken as a measure of industrial
inventive  output  and  trade  (and  service)  marks  have  been  argued  to  be  a  measure  of  product
innovation activity. These are all available at the national level, and can also be broken down by
sector.

Personnel measures are available from a number of different sources, including Business Monitor,
MA 14, ONS and OECD, Science and Technology Indicators. In addition,  Science,  Engineering
and Technology Statistics, ONS contains a compilation of R&D and other indicators. OECD 1999
(Benchmarking indicators for the knowledge based economy) also has data on levels of different

1 available on the skillsbase website

42

                                                     
types  of  skilled  worker  (for  example,  scientists  and  engineers  etc.)  in  different  categories  and
industries.

Other  time  series  of  indicators  at  national  level  include  a  wide  range  of  science  and  technology
indicators  for  most  industrialised countries  (such  as  OECD's  Science  and  Technology  Indicators
and  Industrial  and  Technical  Scoreboard  of  Indicators).  The  author's  also  note  that  there  are  a
number of other measures that may be useful, but that do not measure skills directly. One is foreign
direct investment into the UK, which, at the national level, is available from a variety of sources
(for  example,  OECD,  Reviews  on  FDI:  the  United  Kingdom).  According  to  IER,  FDI  is  an
important form of job creation and led to the introduction of new technologies, training and new
forms of management practice. Other measures that may be useful include general indicators of the
numbers (or proportions) of individuals passing through the further and higher education systems.
We  review  the  data  available  from  the  Higher  Education  Statistics  Association  (HESA)  below
which covers this area.

The Extent, Causes, and Implications of Skill Deficiencies

Another IER report (Hogarth and Wilson, 2000) focuses on the implications of skills shortages in
the UK. IER conducted research using employer survey data collected in 1999. This was conducted
as  part  of  a  major  project  sponsored  by  the  Department  for  Education  and  Employment  and
intended  to  meet  the  recommendations  of  the  Skills  Task  Force  'about  the  need  for  a  major
reassessment of the Extent, Causes and Implications of Skill Deficiencies (ECISD)'. Further reports
are  expected  which  will  provide  more  analysis  and  discussion  of  other  elements  of  the  ECISD
project. This will include a series of in-depth case studies. None of these appear to be available at
the time of writing. The survey work done so far focuses on the extent and nature of imbalances
between skills supply and demand as reported by employers. This is precisely the area of interest of
this  report,  but  unfortunately  only  summary  information  of  this  project  is  available  at  present.
Again later this year IER expect to publish more detailed results.

Skill Deficiencies

Two different kinds of skill deficiency have been identified in the UK. These confirm many of the
results of the skills task force case studies discussed above:

•  A. Recruitment difficulties, 'focusing in particular on hard to fill vacancies and what are referred
to as skill-shortage vacancies, (external recruitment difficulties explicitly attributed to a lack of
job applicants with the required skills, qualifications or work experience)'

•  B. Internal skill gaps 'defined as occurring where a significant proportion of existing staff in a

particular occupation lack full proficiency at their current jobs'

The  authors  state  that  recruitment  difficulties  are  traditionally  measured  by  vacancies.  Based  on
their  survey  data,  they  estimated  that  the  total  number  of  vacancies  reported  by  all  the
establishments in England was over half a million towards the end of 1999. They estimated also
that  the  total  number  of  'hard  to  fill'  vacancies  was  around  a  quarter  of  a  million.  Official
government statistics suggest that there were only a quarter of a million total vacancies at the time
of the survey. Thus the authors suggest that there is a serious underestimation of vacancies in the
official statistics.

43

The survey evidence highlighted the following problems:

•  Recruitment problems were widespread
•  Over 30 per cent of all establishments reported vacancies
•  16  per  cent  reported  hard-to-fill  vacancies.  (Of  these,  almost  half  were  due  to  skill  related

problems.)

•  Skill-shortage vacancies affected about one in twelve establishments. This represented about 43

per cent of all hard-to-fill vacancies.

•  The main causes of other types of recruitment difficulty were a lack of interest in particular kinds

of jobs and low numbers of applicants with the required personal attributes

In terms of occupation the survey found that occupations associated with skill-shortage vacancies
were  mainly  associated  with  craft  and  skilled  occupations  followed  by  associate  professional
occupations. Sales and personal service occupations were also significant. In terms of industries,
most affected were the craft-intensive construction and manufacturing sectors which makes sense
in terms of the occupations discussed above; but also there were problems in the financial service
industry and business services. We have already noted above that knowledge  based  services  are
becoming  crucial  drivers  of  growth  in  modern  economies.  Thus  the  fact  that  skill  shortages  are
associated with these services as well as traditional manufacturing sectors is a cause for significant
concern.  The  effects  of  this  type  of  problem  can  be  wide  ranging.  That  is  from  product
development and product quality all the way through to customer service provision.

Skill Gaps
With respect to skill gaps the survey found the following:

•  skill gaps were recognised by employers in about one in five establishments.
•  skill shortcomings had an impact on the performance of the establishment
•  a significant proportion of establishments with skill shortcomings reported that this problem had

led to a loss of business to competitors

•  a significant proportion of establishments also stated that they anticipated skill gaps having an

impact on their business in the future

•  In contrast to skill shortage vacancies skill gaps tended to occur in less-skilled occupations
•  Almost half of establishments with skill gaps acknowledged that these were at least in part due to

their own failure to train and develop their employees

•  The main factors increasing demand for skills were:

1. the introduction of new working practices,

2. the development of new products and

3. the introduction of new technology.

It has already been noted that skill deficiencies were associated with poorer performance. The IER
research went into more detail about this aspect of skill problems. Establishments suggested that
their plans to improve their products and services required extensive skill  changes.  Much  of  the
skill  changes  required  were  perceived  to  be  generic  rather  than  specific.  Many  involved

44

interpersonal skills such as communication skills, team-working, and management, but also general
problem solving skills.

The authors conclude  that a  shortage of  skills amongst the existing workforce  is  a  key  factor  in
inhibiting firms from achieving their goals. Moreover, 'there is some evidence that this is only the
tip of the iceberg. Unperceived, latent skill gaps could be at least as important. The implication is
that there is considerable scope for further investment in skill acquisition if the ambition of securing
a  long-term  improvement  in  economic  performance  is  to  be  achieved'.  Thus  there  are  crucial
messages  for  policy  makers  from  this  research  effort  in  terms  of  human  capital  formation  and
improvement.

45

Part 3

Potential data sources for future research on skills and mobility in the UK

Many  of  these  databases  are  freely  available  to  academics  in  the  UK  upon  application  and  are
managed  at  the  Data  Archive  at  the  University  of  Essex.  Full  details  are  available  at
http://dawww.essex.ac.uk. This list is not meant to be exhaustive.

The Labour Force Survey (LFS)

The Labour Force Survey has been carried out in the UK since 1973. From 1973 until 1983 the
survey was carried out biennially, and from 1984 until 1991 it was conducted annually. In 1992 the
quarterly  LFS  was  introduced.    The  quarterly  version  had  substantial  revisions  over  the  annual
survey  including  a  moving  panel.  In  addition  to  the  quarterly  database,  ONS  now  produce  two
databases for Unitary Authorities and Counties for Great Britain per quarter. This provides data at a
more detailed local level than was previously available from the County indicator and for the first
time makes available data for the  current administrative areas.  The Office  for  National  Statistics
(ONS) plan that these databases will be available for all quarters (from Spring  1992) as soon as
possible.

Data include occupation, sector, training and education as well as some questions concerning job
mobility.  The  sample  includes  unemployed  and  inactive  respondents  as  well  as  employees.  The
sample size is quite large facilitating more detailed disaggregation than in most surveys  (around
60000  employees  and  140000  respondents  in  total).  The  data  are  also  comparable  with  several
European labour force surveys and a European database (the Community Labour Force Survey -
CLFS) is created and  maintained at Eurostat  using these databases. The LFS  data  facilitated the
bulk of the empirical work presented later in this report.

The British Household Panel Study (BHPS)

The British Household Panel Survey (BHPS) is an ongoing project carried out by the Institute for
Social  and  Economic  Research  (incorporating  the  ESRC  Research  Centre  on  Micro-Social
Change)  at  the  University  of  Essex.  The  main  objective  of  the  survey  is  to  further  our
understanding of social and economic change at the individual and household level in Britain, to
identify, model and forecast such changes, their causes and consequences in relation to a range of
socio-economic variables.

The BHPS is designed as a research resource for a wide range of social science disciplines and to
support interdisciplinary research in many areas. The BHPS was designed as an annual survey of
each  adult  (16+)  member  of  a  nationally  representative  sample  of  more  than  5,000  households,
making  a  total  of  approximately  10,000  individual  interviews.  The  same  individuals  are  re-
interviewed in successive waves and, if they split-off from original households, all adult members
of their new households are also interviewed. Children are interviewed once they reach the age of
16; there is also a special survey of 11-15 year old household members from Wave Four. Thus the
sample should remain broadly representative of the population of Britain as it changes through the
1990s.

46

The BHPS collects extensive information on respondents' labour market status, (i) at the time of
interview at each wave of the panel, (ii) through the period between 1st September a year before
and the interview date, and (iii) retrospectively from first leaving full-time education. Thus several
longitudinal studies of labour market mobility and the impact of training and education, say, could
be carried out and followed up as more waves are added to the survey.

There are also several supplementary files available that have drawn together longitudinal and work
history  data  for  easier  use.  See  Halpin,  B.  (1997)  Unified  BHPS  work-life  histories:  combining
multiple sources into a user-friendly format, Technical Papers  of  the  ESRC  Research  Centre  on
Micro-Social  Change,  Technical  Paper  13,  Colchester:  University  of  Essex.  The  Work-Life
Histories  data  were  first  made  available  in  November  1997,  with  a  second  version  in  February
2000, which included information for Waves 6 - 8.

The Workplace Employee Relations Survey (WERS) and the Workplace Industrial Relations
Survey (WIRS)

The  Workplace  Employee  Relations  Survey  (WERS)  series  began  in  1980.  It  was  previously
known as the Workplace Industrial Relations Survey, or WIRS - the name was changed in 1998 to
better reflect the present content of the survey. The primary aim of the survey series is to provide
statistically reliable, nationally representative data on the current state of workplace relations and
employment practices in Britain.

The WERS  98 Panel Survey dataset contains data from interviews  conducted  with  management
respondents  at  the  same  establishment  in  both  1990  and  1998.  The  1990  interview  data  was
collected as part of the 1990 WIRS. A random selection of establishments that took part in 1990
were re-interviewed in 1998, using a questionnaire designed to update the picture of employment
relations in each establishment and investigate changes over the period.

Further information on the 1998 Workplace Employee Relations Survey data is also available from
the ESRC-funded WERS98 Data Dissemination Service, which has been set up to provide expert
advice about the survey and its analysis to both current and prospective users. The WERS98 Data
Dissemination Service is located at the National Institute of Economic and Social Research; it is
staffed  by  Neil  Millward,  John  Forth  and  Simon  Kirby.  For  further  details,  consult  the  Data
Dissemination Service's web-site at: http://www.niesr.ac.uk/niesr/wers98.

The Community Innovation Survey CIS2

The  2nd  Community  Innovation  Survey  was  conducted  in  1997.  It  has  firm  level  data  on
employment change and introduction of HRM practices from a nationally representative sample of
around 2000 firms. This database is comparable  with  the other European  community innovation
surveys. The data is mainly concerned with innovation, but it would in principle be possible to look
at the information of organisational change and the number of qualified staff within the responding
firms  and  relate  this  to  firm  level  performance.  In  fact  some  preliminary  work  was  done  in  this
respect which linked radical innovative performance with the proportion of qualified staff within
the firm (Tomlinson and Miles 1999).

The National Child Development Study (NCDS)

The  National  Child  Development  Study  (NCDS)  is  a  continuing  longitudinal  study  which  is
seeking to follow the lives of all those living in Great Britain who were born between 3rd and 9th

47

March,  1958.  The  aim  of  the  study  is  to  improve  understanding  of  the  factors  affecting  human
development over the whole lifespan. To date there have been five attempts to trace all members of
this cohort in order to monitor their physical, educational and social development including there
occupational trajectories. The first four sweeps were carried out in 1965 (when they were aged 7),
in 1969 (when they were aged 11), in 1974 (when they were aged 16) and in 1981 (when they were
aged 23).

In addition, in 1978, contact was made with the schools attended by members of the birth cohort at
the time of the third follow-up in 1974 in order to obtain details of public examination entry and
performance. Similar details were also sought from sixth-form colleges and FE colleges, etc where
these were identified by schools. The third follow-up assessed the progress of the children at age 16
and included medical history; IQ; medical and co-ordination tests; educational background; home
environment;  living  conditions;  ownership  of  consumer  goods;  relationship  between  child  and
parents; behavioural difficulties; educational expectations and aspirations; career expectations and
aspirations; spare-time jobs held, spare time activities.

The fourth follow-up, carried out when the children were aged 23, covered the following topics:
employment,  unemployment  and  periods  out  of  the  labour  force;  participation  in  government
special  schemes;  apprenticeship  and  training;  post-school  education;  marriage,  cohabitation  and
children;  housing  and  household  details;  family  income,  savings  and  investment;  respondent-
reported health state and health related behaviour; voluntary activity and leisure. The fifth follow-
up  when  the  cohort  group  were  aged  33  covered  the  following  topics:  education  and  training;
employment history; housing history; partnership and family formation; income and wealth; health;
health  related  behaviour;  citizenship  and  participation;  parenting;  cognitive  and  behavioural
development.  Thus  several  variables  based  on  childhood  development  and  post  childhood
development  (notably  different  training  and  educational  experiences  and  career  aspirations,
unemployment  and  voluntary  activities  etc.)  could  be  linked  with  actual  careers  and  mobility
patterns.

Further information about the NCDS can be found on the CLS website: http://cls.ioe.ac.uk/

Social Change and Economic Life Initiative (SCELI)

The questionnaire for this survey of around 6000 employees collected in 1986 in 6 different regions
in  the  UK  consisted  of  three  sections:  an  interview  schedule  including  questions  to  both
respondents and their partners, a respondent's self-completion and partner's self-completion. These
questionnaires include life history and work history data useful for analysing career and mobility
patterns. The data also include several questions about the respondents' jobs and working lives.

Employment in Britain (EIB) 1992

In 1992 a survey of employed and unemployed people was undertaken in the UK partly based on
the  SCELI  data.  The  project  was  performed  under  the  guidance  of  Duncan  Gallie  of  Nuffield
College, Oxford, and Michael White of the Policy Studies Institute. Around 3800 employed and
1000  unemployed  respondents  were  interviewed  about  their  working  lives  and  jobs.  Complete
work histories were collected for all respondents which can be used for longitudinal  studies  and
mobility  analysis.  Results  of  this  work  were  published  in  1998  (Gallie  et  al.  1998).  (NB  this
database is not yet in the Data Archive.)

48

The skills survey 1997

The Skills Survey undertaken in the UK in 1997 had the following objectives: to develop further
the concept of and methodology for measuring different types of skills and to investigate the impact
of  various  factors  on  skills  and  human  capital,  including  personal  characteristics  and  forms  of
education and training. Many of the questions on human capital in this survey are compatible with
questions in SCELI and EIB noted above thus allowing some limited time series to be constructed
mapping changes in the UK over a reasonably long period. The survey also aimed to investigate
which particular skills were changing during the 1990s, and to what extent as well as investigating
how skills were distributed among the employed population.

National adult learning survey

In  autumn  1996,  The  Department  for  Education  and  Employment  (DfEE)  commissioned  Social
and Community Planning Research (SCPR) to carry out a survey of people's experiences of and
attitudes towards learning.

This 1997 National Adult Learning Survey (NALS) was intended to collect data which would meet
the information requirements of the Department regarding adult learning, and to provide a basis for
future monitoring. In particular, the survey aimed to identify the extent to which people were taking
part  in  different  types  of  learning  (both  vocational  and  non-vocational),  the  costs,  reasons  and
problems involved and the benefits of learning.

This dataset has extensive information about all aspects of education, training and learning based
on  interviews  with  more  than  5,600  adults  aged  between  16-69  (both  recent  learners  and  non-
learners) in England and Wales. Careers, education or training advice or guidance received in the
past three years was also considered as well as the respondent's life event history - e.g. marriage,
moving home, losing a job, birth of children. Thus some mobility data is available that can perhaps
be related to learning.

Individual Commitment to Lifetime Learning: Individuals' Attitudes, 1993

The Employment Department commissioned three studies of attitudes to individual commitment to
learning.  Taken  together,  the  three  studies  have  been  designed  to  produce  an  overall  picture  of
attitudes to adult learning. The first in the series examined  individuals'  attitudes  to  learning. The
aim of this study, the first in the series, was to increase understanding of vocational learning. There
were around 1400 people in the sample.

Employers attitudes to learning 1994

The second study was concerned with employers' attitudes to learning. The aim of this study was to
increase understanding of the employers' role in lifetime learning; how they contribute to learning,
their  attitudes  towards  learning  and  the  factors  which  influence  their  policies  and  practices
including  such  things  as  the  type  of  training  provided,  human  resource  practices,  training
assessment, who pays for the training, training information and careers guidance. Around 580 firms
were interviewed.

49

Providers attitudes to learning 1994

This study, the third in the series, aimed to explore the extent to which providers encouraged and
supported commitment to adult learning, as well as identifying the factors which influenced their
attitudes and helped shape their agenda. The study consisted of both quantitative and  qualitative
work with a sample of around 800 establishments.

Family and Working Lives Survey, 1994-1995

The aim of this study was to examine how different aspects of people's lives affect their working
patterns in order to inform policy on equal opportunities and labour market flexibility. The study
provides longitudinal information on the ways in which people participate in the labour market, and
training, over the course of their lives.  There are about 9000 respondents in the database.

HESA databases

The  Higher  Education  Statistics  Agency  is  the  official  body  responsible  for  the  collection  and
publication of statistical data about higher education. The Agency was set up in 1993 and began its
data collection in the academic year 1994/95. Previously, a number of different organisations were
involved in this work: publications previously issued by the Universities' Statistical Record are now
available  through  HESA  Services.  For  the  future,  all  publications  prepared  by  HESA  are  being
marketed by HESA Services Ltd.

HESA also has a 'Data Provision Service' which aims to provide a customised data enquiry service
to cover information which may not be available from existing HESA publications. This falls into 4
categories:

Data on students in Higher Education

Data on first destinations of graduates and leavers from Higher Education

Data on Staff in Higher Education

Data on financial aspects of Higher Education Institutions.

Published data include the following titles:

Higher Education Statistics for the UK

Data Report - Students in Higher Education Institutions

Statistics Focus

Reference Volume - Students in Higher Education Institutions

Reference Volume - Resources of Higher Education Institutions

Reference Volume - First Destinations of Students Leaving Higher Education Institutions

Higher Education Management Statistics - Sector Level

50

Higher Education Management Statistics - Institution Level

HE Finance Plus

HE Planning Plus

There are also a number of 'datapacks' available which provide tabulations of essential statistics for
research into higher education (these are not free).

Research Datapack 1 - Ethnicity

Research Datapack 2 - Entry Qualifications in HE

Research Datapack 3 - Course results

Research Datapack 4 - First Destinations

Research Datapack 5 - Disability

Research Datapack 6 - Overseas Students

Research Datapack 7 - Regional Issues

Research Datapack 8 - Ethnicity in Higher Educations

Research Datapack 9 - Academic Staff in Higher Education

Research Datapack 10 - Non-credit-bearing Courses in Higher Education

51

Part 4

Results of using the UK Labour Force Survey to examine supply and demand for skills

The importance of studies of mobility and human resources and the methodology
employed

There  are  at  present  few  studies  that  link  together  the  generation  and  movement  of  human
resources within an innovation systems approach. Despite the fact that the mobility of knowledge is
mainly facilitated by the movements of people, most studies of occupational mobility are done by
sociologists who are on the whole interested in other matters. Attention is now beginning to turn to
the mobility of people as it affects and is affected by science, technology, innovation and industrial
change. For some early attempts at this see Tomlinson 1999, Miles and Tomlinson 1999, Akerblom
1999, and Ekeland 1998.

One of the first explicit recognitions of the importance of diffusion of knowledge through labour
mobility was the OECD Canberra Manual (OECD 1995). This document  explicitly  focussed  on
human resources as they affect the workings of the science and technology system and contribute
to  national  competitiveness.  Stocks  and  flows  of  personnel  were  recognised  as  essential
components and crucial objects of study for the analysis of economic systems. More recently the
OECD has set up a focus group within the National Innovation Systems theme on human mobility
which is for the first time attempting to produce comparable mobility data across countries. It is
still too early to assess the success of this exercise.

In what follows we attempt to measure stocks and flows in specific sectors of the UK economy
throughout the 1990s. the data employed are the Spring waves of the 1992-1999 UK Labour Force
Survey (LFS). Each wave has around 60000 economically active people out of a total of around
140000 adults (the sampling unit is the household, hence there is information about non-employed
people in the survey). The databases have information on mobility, training, education, job search
patterns, occupation and sector etc. This allows us to break down data by sector or occupation and
explore mobility patterns and training provision. Thus we can explore aspects of human resources
within  a  stocks  and  flows  framework  and  link  it  up  to  issues  of  supply  and  demand  within  a
national innovation system.

As  pointed  out  in  the  introduction  we  are  interested  in  certain  high  technology  manufacturing
sectors (aerospace; the chemical industry as a whole; pharmaceuticals; the oil sector; radio/TV and
communications equipment manufacture; office and computer equipment manufacture) and several
knowledge  intensive  service  sectors  (telecommunications;  computer  services;  the  research  and
development service sector).

We explore three areas with respect to these sectors: demand for skilled workers, supply of skilled
workers, and any mismatches that may be developing are isolated and assessed. Relevant policy
conclusions will then be drawn.

The demand for skilled workers can be measured in two ways: by the number of highly qualified
employees  within  the  sector  and  also  by  the  number  of  knowledge  workers  in  the  sector
(knowledge workers are defined by occupations that are managerial or professional corresponding
to the top 3 groups of the standard occupational classification). We also look at how high training

52

provision  is  in  each  sector  to  assess  the  demand  for  increasing  the  knowledge  and  skills  of
employees.  The  survey  asks  whether  any  job  related  training  was  received  within  the  last  four
weeks.

We  estimate  supply  of  skilled  workers  by  looking  at  mobility  rates.  Inflow  mobility  (i.e.  the
number proportion of workers flowing into a sector) gives us some estimation of the availability
and  willingness  to  move  from  another  sector  into  the  sector  under  consideration.  A  broader
definition can also take into account inflows from non-employment into employment. We can also
balance  the  inflow  rates  with  outflow  rates  (i.e.  the  proportion  of  employees  in  the  sector  who
leave). The labour force survey has data on the positions of every employee in the sample and also
information about what they were doing one year previously. It is using this time frame of one year
that we estimate the mobility rates (this question is only asked in the Spring quarter hence all our
figures refer to Spring of the respective years).

Once we have an idea of the demand and supply in the chosen sectors, we can make some tentative
remarks about mismatches and potential problem areas by combining the analyses noted. This is
the most interesting aspect of this work. Then policy conclusions can be drawn which may help to
focus  the  development  of  human  resource  practices  for  these  sectors.  We  now  proceed  to  the
analysis of the LFS data.

The demand for skilled labour

We have data on the number of highly qualified employees in each sector (defined as those with
tertiary level education ISCED 6 and 7) and also the level of training that was available to different
groups of workers. This is used to estimate demand for qualified employees on the one hand and
the demand for human capital development on the other at sectoral level.

Table 2: Stocks of highly qualified workers by sector
PHARMA
AEROSPACE

YEAR

1992
1993
1994
1995
1996
1997
1998
1999

32.8
32.5
24.6
29.9
28.6
29.8
34.8
32.8

OFFICE/
COMPUTER
40.4
39.8
38.0
39.4
40.6
37.5
39.2
38.8

RADIO/TV/
COMMS
48.7
38.9
29.3
29.2
27.3
29.5
27.0
29.0

TELECOMM COMPUTER
SERVICES
56.1
52.3
48.0
49.7
49.8
55.1
54.9
54.3

27.8
20.6
24.6
23.7
29.5
28.2
32.7
35.6

RESEARCH

ALL

65.3
61.0
62.9
58.5
63.8
70.7
66.5
63.5

22.9
22.2
22.2
19.3
19.8
20.3
24.8
22.1

36.9
41.9
34.5
36.6
42.9
42.6
44.1
42.5

This is the proportion of employees with tertiary education

53

YEAR

1992

1993

1994

1995

1996

1997

1998

1999

YEAR/Q
TR
1992

1993

1994

1995

1996

1997

1998

1999

AEROSPACE

Table 3: Training provision by sector
RADIO/TV/
OFFICE/
COMMS
COMPUTER
15.7
16.3

16.4

20.2

19.5

18.3

17.5

18.5

17.8

15.8

15.8

17.3

18.8

14.4

15.1

14.2

16.5

15.9

15.0

15.2

16.8

13.6

14.2

20.1

PHARMA

TELECOMM

24.0

23.4

21.1

16.7

18.8

16.9

22.1

19.9

23.0

20.5

15.6

19.3

16.8

15.2

19.2

19.4

COMPUTER
SERVICES
16.0

15.1

17.1

12.4

16.4

13.9

16.3

15.2

RESEARCH

ALL

23.8

22.7

17.6

15.6

12.9

13.4

20.0

19.2

13.6

13.6

14.5

13.4

13.8

14.3

14.6

14.9

This is the proportion of employees who received training in the last 4 weeks

TRAINING PROVISION BY SECTOR

KNOWLEDGE WORKERS (OCCUPATIONAL  GROUPS 1,2,3)

AEROSPACE

17.9

25.0

19.4

21.7

20.7

22.7

22.9

19.0

OFFICE/
COMPUTER
16.6

RADIO/TV/
COMMS
19.6

18.9

18.5

23.9

16.7

17.2

17.6

20.1

16.7

22.0

18.8

21.3

20.3

21.7

28.8

PHARMA

TELECOMM

31.7

27.3

25.4

21.7

19.5

21.6

28.7

23.1

24.2

26.8

13.6

19.3

16.7

16.7

18.8

20.9

COMPUTER
SERVICES
17.4

14.1

18.6

12.6

15.7

14.0

16.4

15.4

RESEARCH

ALL

24.7

24.7

17.6

18.0

13.5

14.3

19.8

19.7

19.6

19.4

20.2

18.8

18.9

18.7

19.2

19.8

This is the proportion of knowledge workers who received training in the last 4 weeks

TRAINING PROVISION BY SECTOR

EDUCATED WORKERS (ISCED 6+7)

YEAR

AEROSPACE

1992

1993

1994

1995

1996

1997

1998

1999

24.7

28.8

21.9

17.3

17.5

19.8

24.8

23.3

OFFICE/
COMPUTER
20.2

RADIO/TV/
COMMS
20.8

18.0

21.6

23.0

14.4

19.7

15.1

20.5

12.7

19.5

16.0

16.8

19.6

25.5

36.2

PHARMA

TELECOMM

34.6

29.4

32.4

25.8

18.8

20.3

24.2

20.3

25.2

24.3

13.9

21.8

16.8

16.2

23.1

22.3

COMPUTER
SERVICES
18.8

13.2

18.0

13.4

16.4

13.4

18.4

16.0

RESEARCH

ALL

25.7

24.8

17.3

20.3

12.9

13.5

20.9

20.0

24.2

23.6

24.2

22.8

13.8

22.2

22.7

23.3

This is the proportion of educated employees who received training in the last 4 weeks

54

Some general conclusions from stocks and training data
Some general conclusions from stocks and training data
Some general conclusions from stocks and training data
Some general conclusions from stocks and training data

Stocks of qualified personnel are very high in these sectors. The overall average for all sectors is
about one fifth of employees having tertiary education, but the sectors under consideration in this
study  have  substantially  more  stocks  of  qualified  personnel.  For  instance,  office  and  computer
equipment manufacture and computer services have around 40-50% while the research sector has
around  two  thirds.  Stocks,  however,  are  very  unevenly  distributed  between  these  sectors  (e.g.,
research and computer services are much higher than aerospace and telecommunications).

Although the figures fluctuate due to low numbers of cases in some instances, we can make some
general statements about changes in the levels of stocks over the 1990s in these sectors. In some
sectors stocks are generally falling (e.g., in Radio/TV/Communications equipment there appears to
be a general decline from quite high levels to relatively low levels by 1999) while in most of the
sectors  stocks  remain  fairly  constant  or  fluctuate  in  no  particular  direction  (e.g.,  aerospace  has
around  30%  for  the  whole  period  1992-99;  office  and  computer  equipment  and  pharmaceuticals
around 40%). Telecommunications appears to show a rise from around the low 20s to the around
30% in the later period.

Not  only  are  stocks  of  qualified  personnel  high,  but  also  training  provision  is  generally  high  in
these sectors also. This suggests that sectors that already have a high level of human capital are also
keen on developing their human capital further. This suggests that demand for and maintenance of
skill  levels  in  high  technology  sectors  and  knowledge  intensive  services  is  of  paramount
importance.

There  is  also  quite  uneven  opportunity  for  training  by  sector.  For  example,  computer  services,
which have a high level of stocks, have a relatively low level of training provision compared to,
say, telecommunications. This suggests that employees in computer services arrive already trained,
while telecommunications workers are more likely to be continually upgrading or being re-trained.
Other  points  to  note  are  that  in  most  sectors  considered  here  knowledge  workers  and  already
qualified staff are more likely to get training than the overall worker. This suggests that there is the
possibility of a polarisation of skills and knowledge taking place in certain sectors (see figure 1).

Figure 1

Training provision by sector 

40
35
30
25
20
15
10
5
0

e
c
a
p
s
o
r
e
A

r
e
t
u
p
m
o
C
e
c

/

i
f
f

O

/

s
m
m
o
c
V
T
o
d
a
R

i

/

Overall

Knowledge
workers

ISCED 6+7

m
r
a
h
P

s
m
m
o
c
e

l

e
T

v
r
e
s
p
m
o
C

h
c
r
a
e
s
e
R

l
l

A

55

 
For example, based on 1999 figures, in computer services and research there appears to be a more
equitable distribution of training than in radio/TV and communications equipment manufacture or
aerospace. In the economy as a whole there also appears to be a sharp polarisation in human capital
formation (14.9% of the whole sample received training in the last 4 weeks, compared with 23.3%
of highly educated workers).

There is thus evidence of polarisation of human capital formation even in some high technology
and knowledge intensive sectors (Cf. The OECD Jobs Strategy). This is a cause for concern. In a
knowledge  based  economy  it  may  not  be  wise  to  exacerbate  differentials  in  this  respect.  It  is
generally accepted that an overall upskilling of the population is required to reap the benefits of IT
and other new technologies. Giving significant advantages to those who are already ahead in the
game  seems  to  be  counter  to  prevailing  wisdom.  This  evidence  is  only  sketchy,  however,  and
further research would be needed to investigate this properly.

The other cause for concern is that it appears that most of these sectors show some decline (e.g.,
research, pharmaceuticals) or little change in overall training provision (e.g., in computer services)
whereas for the economy as whole it has remained fairly constant. This must have consequences
for national competitive advantage especially when many employers state that there are growing
skill shortages. It must surely be significant that two key sectors of the UK economy are reducing
their training provision, while very few are increasing it.

The supply side via mobility indicators

There are several ways in which mobility indicators can be constructed. The main differences are
whether non-employed persons are allowed to enter the equation. For example, a broad definition
of inflow mobility (i.e. the flow of workers into a sector as a proportion of the total employment in
that  sector)  might  include  entrants  from  unemployment,  the  education  system  or  other  non-
employed positions. Similarly outflows can include outflows to non-employed positions. A narrow
definition  might  only  include  employees  at  both  points  in  time  (whether  measuring  inflow  or
outflow). There are advantages and disadvantages to both methods. For example a broad definition
will give us more detail about the overall dynamics of an economy including the linkages between
the education system or the take-up of unemployed people (for inflows) or the problems of labour
scarcity due to retirements (for outflows). A narrow definition which only includes employees at
both  points  in  time  gives  a  clearer  picture  of  the  dynamics  of  the  more  economically  active
population. This helps to understand the dynamics of employees knowledge flows rather than other
types of knowledge flow. In what follows we concentrate on the narrow definitions of inflow and
outflow mobility. (The UK LFS can also be used to calculate using broader definitions as it has
non-employed and inactive members of the UK in its sampling frame at household level.)

The inflow and outflow rates give us some idea about the supply available to different sectors and
an indication of the levels of labour turnover in specific sectors. In the next section we attempt to
link together the supply indicators with the previously reported demand and training figures to form
employment profiles of the industries concerned.

56

Table 4: Inflow and outflow mobility rates

INFLOW MOBILITY RATES FOR HITECH SECTORS - EMPLOYEES AT TIME T AND T-1(%)

YEAR

AEROSPACE

1992

1993

1994

1995

1996

1997

1998

1999

3.0

1.2

1.6

2.2

5.8

6.8

6.7

4.9

OFFICE/
COMPUTER
8.3

RADIO/TV/
COMMS
2.2

7.9

7.9

13.2

12.4

10.8

13.5

11.8

5.2

9.0

12.1

12.6

9.9

12.1

9.7

PHARMA

TELECOMMS

6.0

5.4

6.0

4.5

4.8

8.2

10.3

8.7

2.6

3.1

3.7

5.0

5.8

9.2

10.4

12.5

COMPUTER
SERVICES
8.4

9.9

11.1

11.4

12.3

13.3

14.5

10.9

RESEARCH

3.6

4.4

4.9

6.5

7.5

3.8

7.5

12.9

This is the proportion of employees in year t-1 moved into sector in year t (ie narrow definition)

OUTFLOW MOBILITY RATES FOR HITECH SECTORS - ALL EMPLOYED AT TIME T AND T-1 (%)

YEAR AEROSPACE

1992

1993

1994

1995

1996

1997

1998

1999

4.6

4.1

5.5

5.9

4.2

5.3

4.0

3.9

OFFICE/
COMPUTER
11.5

RADIO/TV/C
OMMS
8.7

6.9

7.9

8.3

10.0

9.6

11.9

9.8

3.7

7.5

6.4

9.3

8.8

7.9

12.1

PHARMA

TELECOMMS

5.3

4.3

2.2

8.3

8.7

2.8

5.8

5.9

1.5

5.8

2.6

6.6

5.2

5.7

7.6

9.4

COMPUTER
SERVICES
6.2

7.2

6.4

6.7

6.0

5.7

7.5

6.3

RESEARCH

6.7

5.8

4.9

3.8

7.1

6.0

6.7

8.8

This is the proportion of employees in year t-1 moved out of sector but remained employed in year t
(ie narrow definition)

INFLOW MOBILITY RATES FOR HITECH SECTORS - EMPLOYED AT T-1 AND KNOWLEDGE WORKERS
(SOC GROUPS 1-3) AT TIME T (%)

YEAR AEROSPACE

1992

1993

1994

1995

1996

1997

1998

1999

1.2

1.7

2.2

2.6

1.8

6.4

6.7

4.3

OFFICE/
COMPUTER
5.5

RADIO/TV/
COMMS
0.0

4.5

4.9

9.2

9.9

7.7

11.4

7.0

5.9

10.6

6.3

13.8

8.6

3.2

4.9

PHARMA

TELECOMMS

0.9

5.1

5.4

2.5

3.3

7.7

9.2

5.9

4.0

2.7

1.8

5.6

5.3

3.0

9.0

6.7

COMPUTER
SERVICES
4.1

6.9

11.0

8.6

9.1

13.0

12.5

10.1

RESEARCH

2.2

5.2

4.0

3.7

5.9

2.5

4.7

13.4

This is the proportion of employees in year t-1 moved into sector in year t (ie narrow definition)

57

OUTFLOW MOBILITY RATES FOR HITECH SECTORS – KNOWLEDGE WORKERS AT TIME T (%)

YEAR AEROSPACE

1992

1993

1994

1995

1996

1997

1998

1999

4.2

5.5

8.7

4.2

6.0

4.1

3.8

4.3

OFFICE/
COMPUTER
9.6

RADIO/TV/
COMMS
6.9

7.0

5.6

6.8

9.3

7.7

12.7

8.3

4.0

6.7

1.7

12.5

3.6

7.6

7.9

PHARMA

TELECOMMS

4.3

5.1

2.8

7.1

7.8

3.2

3.6

3.8

2.0

4.7

3.5

8.6

6.5

3.5

5.2

10.8

COMPUTER
SERVICES
4.9

3.8

4.7

4.0

5.1

4.2

6.5

4.5

RESEARCH

7.4

4.6

5.9

2.7

4.6

2.5

4.1

8.4

This is the proportion of employees in year t-1 moved out of sector and employed in year t (ie narrow
definition)

INFLOW MOBILITY RATES FOR HITECH SECTORS - HIGH QUALIFIED WORKERS (ISCED 6+7) AT TIME T (%)

HI TECH SECTORS

KNOWLEDGE INTENSIVE SERVICES

YEAR AEROSPACE

1992

1993

1994

1995

1996

1997

1998

1999

4.4

1.4

2.2

5.2

4.9

6.2

6.2

3.3

OFFICE/
COMPUTER
6.8

RADIO/TV/
COMMS
1.2

2.5

2.9

9.3

8.0

8.8

10.5

5.8

5.6

8.3

8.1

12.8

14.0

9.5

4.4

PHARMA

TELECOMMS

3.0

6.7

7.3

3.3

2.0

9.7

14.0

8.8

3.4

3.9

3.6

3.6

7.4

7.4

6.9

10.0

COMPUTER
SERVICES
3.5

11.0

14.2

10.1

8.5

14.5

13.3

10.6

RESEARCH

3.7

4.8

6.2

2.8

5.2

1.4

5.3

13.8

This is the proportion of employees in year t-1 moved into sector in year t (ie narrow definition)

OUTFLOW MOBILITY RATES FOR HITECH SECTORS - HIGH QUALIFIED AT TIME T (%)

HI TECH SECTORS

KNOWLEDGE INTENSIVE SERVICES

YEAR AEROSPACE

1992

1993

1994

1995

1996

1997

1998

1999

2.3

4.8

9.0

8.0

2.5

6.2

2.8

2.5

OFFICE/
COMPUTER
11.7

RADIO/TV/
COMMS
6.6

8.4

5.7

10.1

8.0

8.8

13.0

9.3

4.2

5.7

2.5

14.6

6.5

13.6

6.5

PHARMA

TELECOMMS

3.0

6.7

3.3

10.1

6.8

3.8

3.9

6.4

1.4

6.7

3.6

7.8

5.0

5.3

9.4

9.4

COMPUTER
SERVICES
3.5

5.8

4.5

6.2

6.2

6.0

7.0

4.9

RESEARCH

7.1

5.6

7.0

3.5

3.8

3.4

7.1

9.4

This is the proportion of employees in year t-1 moved out of sector but still employed in year t (ie
narrow definition)

58

General remarks concerning the mobility rates

Inflow  mobility  to  the  high  technology  sectors  considered  seems  to  be  generally  increasing
throughout  the  1990s,  but  this  is  tailing  off  for  Radio/TV/Communications  and  Office  and
computer machinery manufacture. The inflow figures are generally high for Office and computer
machinery  manufacture,  Radio/TV/Communications  equipment  and  Pharmaceuticals  relative  to
Aerospace.

Inflow  mobility  to  knowledge  based  services  is  also  generally  increasing.  Inflow  mobility  of
Computer  services  has  been  the  highest  throughout  the  1990s,  but  now  Research  and
Telecommunications has caught up with it.

Outflow mobility is far less clear, but it is generally lower in the service sectors considered here.
Outflow mobility is also generally increasing in these services (except for computer services).  It
also  appears  to  be  increasing  in  office/computer  equipment  manufacture  and  radio/TV  and
communications  equipment  manufacture,  while  in  aerospace  it  has  remained  fairly  constant  and
any trend is rather unclear in pharmaceuticals

Synthesis of the results by sector - combining the demand and supply figures

The  following  graphs  plot  the  results  for  the  various  sectors.  They  show  the  stocks  of  educated
personnel in each sector, the overall training provision for each sector and the inflow and outflow
mobility rates of knowledge workers in each sector. This combination of figures gives us a useful
profile of each industry from a human capital supply and demand perspective which can be used to
assess  potential  problems  and  highlight  differences  between  sectors  at  the  national  level.  The
results are summarised in table 5 and discussed in the next section.

Manufacturing sectors:

Figure 2

Aerospace

Stocks

Training

Inflow  Kn

Outflow  Kn

40
35
30
25
20
15
10
5
0

1992 1993 1994 1995 1996 1997 1998 1999

Year

59

Figure 3

Pharmaceuticals

Figure 4

Figure 5

50

40

30

20

10

0

50

40

30

20

10

0

60

50

40

30

20

10

0

Stocks

Training

Inflow Kn

Outflow Kn

Stocks

Training

Inflow Kn

Outflow Kn

Stocks

Training

Inflow Kn

Outflow Kn

1992 1993 1994 1995 1996 1997 1998 1999

Year

Office and computer equipment manufacture

1992 1993 1994 1995 1996 1997 1998 1999

Year

Radio/TV/Communications equipt. manufacture

1992

1993

1994

1995

1996

1997

1998

1999

Year

60

Knowledge intensive Services:

Figure 6

Telecommunications

40
35
30
25
20
15
10
5
0

60

50

40

30

20

10

0

Figure 7

Stocks

Training

Inflow Kn

Outflow Kn

Stocks

Training

Inflow Kn

Outflow Kn

1992 1993 1994 1995 1996 1997 1998 1999

Year

Computer services

1992 1993 1994 1995 1996 1997 1998 1999

Year

61

Figure 8

Research and development

80
70
60
50
40
30
20
10
0

Stocks

Training

Inflow Kn

Outflow Kn

1992 1993 1994 1995 1996 1997 1998 1999

Year

Table 5: Summary table of the supply versus demand by sector

AEROSPACE

OFFICE/COMPUTER/EQUIPT.

RADIO/TV/COMMS. EQUIPT.

PHARMACEUTICALS

TELECOMMUNICATION

COMPUTER SERVICES

RESEARCH

STOCKS
GENERALLY
STABLE AT
AROUND 30%

VERY HIGH
AROUND 40%,
STABLE

DECLINED FROM
A HIGH OF 50%
IN 1992, NOW
STABLE AT 30%
HIGH AT AROUND
40%

INCREASING
RAPIDLY TO
AROUND 33%

VERY HIGH AT
AROUND 55%

HIGHEST
AROUND 65%,
ALSO SEEM TO
HAVE AN
UPWARD TREND

TRAINING
FAIRLY STABLE
AROUND 20%,
POSSIBLY SLOW
DECLINE
FAIRLY STABLE AT
AROUND 17%

FAIRLY STABLE AT
15%

APPEARS TO BE
SLOWLY
DECLINING TO
AROUND 20%, BUT
WITH SOME SIGN
OF RECOVERY
1999
FLUCTUATES
AROUND 16%
FROM A HIGH OF
23% IN 1992
RELATIVELY LOW
AT AROUND 16%

HAS DECLINED,
BUT SIGNS OF
RECOVERY

INFLOW
GENERALLY VERY
LOW

OUTFLOW
USUALLY HIGHER
THAN INFLOW, BUT
CONVERGING

QUITE HIGH AT
AROUND 10%

APPEARS ERRATIC
(LOW NUMBER OF
CASES)

GRADUALLY
INCREASING, BUT
LOW

QUITE HIGH AT
AROUND 10%,
GENERALLY
FOLLOWS INFLOW
APPEARS ERRATIC
(LOW NUMBER OF
CASES)

ERRATIC.
APPEARS TO BE
LOWER THAN
INFLOW 1997-99.

INCREASING
TREND

INCREASING
TREND

INCREASING
TREND. GREATER
THAN OUTFLOW.
FAIRLY STABLE,
SIGNS OF INCREASE
TOWARDS 1999

LOW AND STABLE
AT AROUND 4%

FAIRLY STABLE,
SIGNS OF INCREASE
TOWARDS 1999

62

Discussion and conclusions

Table 5 which summarises the results of the synthesis shows that the sectors with the highest stocks
are  Office  and  computer  machinery  manufacturing,  computer  services,  pharmaceuticals  and
research  and  development.  This  suggests  that  demand for  graduates  and  highly  qualified  people
will especially be in these sector. This suggests considerable investment might be made in higher
education  and  shifts  in  provision  of  courses  oriented  towards  these  sectors  might  be  prioritised.
Furthermore  there  has  been  a  significant  growth  in  demand  for  skills  in  telecommunications
towards  the  end  of  the  1990s.  Again  provision  of  training  and  education  oriented  towards  this
sector should be considered as essential (especially as training provision within this sector appears
to have declined).

Deficiencies in training provision are apparent in computer services, although this must be offset by
the  fact  that  this  sector  has  extremely  high  stocks  of  qualified  personnel.  Pharmaceuticals  and
research  and  development  also  show  a  decline  in  provision  which  is  potentially  worrying.  The
other sectors appear to have fairly stable training provision. In any case it must be borne in mind
that provision of training is higher in these sectors than the average for the whole economy.

In terms of mobility the sectors that appears to have high fluidity in terms of inflow and outflow are
telecommunications and office and computer equipment. Again steps could be taken to reduce the
outflow rates from these sectors if this seemed appropriate. Computer services and pharmaceuticals
appear  not to  suffer  from this problem  having  higher  inflows  than  outflows.  This  appears  set  to
continue.

The general impression of skill problems in the UK economy from the literature review point to the
need for the government to focus on several key areas:

•  Fostering more management skills among skilled employees
•  Fostering a more business like orientation and entrepreneurial style among professionals
•  Promoting multidisciplinarity
•  Reducing brain drain especially to the US in key sectors
•  Promotion of clusters of high technology industries
•  Encourage  the  creation  of  professional  bodies  for  new  high  technology  industries  such  as

photonics

The first three points could be addressed at university level. If more graduates are trained or given
the  opportunity 
to  undertake  some  management  courses  and  focus  on  courses  with
multidisciplinary awareness this could reduce problems experienced by many high technology and
knowledge  based  firms.  The  last  three  points  suggest  a  focus  on  creation  and  maintenance  of  a
suitable infrastructure for certain key sectors. This would involve the maintenance of, say, science
parks, regional developments and grants for specific industrial clusters and the fostering of dynamic
local labour markets which reduce the level of brain drain. We end the report with an example of
how the type of data generated above might be used in a more systematic way and with a warning
as  to  how  different  sectors  require  quite  different  strategic  policies  in  order  to  maintain
competitiveness.

63

A knowledge economy example: Comparing computer services with telecommunications in the
UK

As  an  example  of  how  this  type  of  analysis  might  be  used  within  a  specific  systemic  context
consider that in the 'knowledge based' or 'new economy' two crucial sectors for progress  are  the
telecommunications  services  (TS)  and  computer  services  (CS).  Two  sectors  which  have  been
analysed above with respect to the supply and demand of human resources in the UK. If the state
were to implement a strategy to bolster the knowledge driven economy based upon a focus on these
two sectors then a common strategy between the sectors would be rather inappropriate given the
differences in mobility and human resource patterns in the 2 sectors.

For example, CS has very high stocks of educated workers and high training provision while TS
has low stocks and higher training than CS. The high stocks required by CS are already manifesting
themselves  in  the  UK.  Immigration  controls  are  being  relaxed  in  order  to  allow  IT  qualified
graduates  from  abroad  to  work  in  the  country.  The  figures  on  mobility  show  that  there  is  no
problem keeping such workers in the industry once they enter in (inflow rates are much higher than
outflow rates).

In TS on the other hand inflow and outflow rates are more or less congruent, but on the increase
throughout  the  1990s.  This  is  the  case  whether  we  look  at  overall  mobility  or  just  knowledge
workers  (i.e.  professionals  and  managers).  Therefore  relatively  speaking  TS  have  more  labour
fluidity and more of a problem than CS in terms of retaining people. The TS training figures may
reflect  this  fact  plus  the  fact  that  the  stocks  are  also  lower.  So  in  terms  of  policy,  for
telecommunications it would be more useful to introduce training programmes of a different type
than for CS and also to attempt to reduce the outflows from TS, perhaps by offering programmes
and incentives to telecommunication firms that help to retain staff.

Note that this analysis tells us nothing about mobility within the sector which may be  of crucial
importance  for  individual  enterprises.  Whereas  the  computer  services  sector  seems  to  be  doing
relatively well in terms of retaining highly skilled staff and having less fluidity this tells us nothing
about the problems that individual firms within sectors may be having. Firms may have extreme
difficulties with poaching or training employees who then leave to go to rival firms.

It  seems  on  balance  that  if  policy  makers  wanted  to  target  training  programmes  or  influence
competitiveness  in  the  knowledge  based  economy  they  would  be  better  off  devoting  more
resources  to  telecommunications  than  to  computer  services  in  terms  of  flexible  labour  market
policies and training. On the other hand the high stocks in CS indicate that there is huge demand for
qualified staff in CS. This suggests that provision of computer related HE courses must also be a
priority. Recent employee based evidence also increasingly supports the need for computer based
skills in general in any case (Green et al. 1999).

There was also evidence of polarisation of training provision in TS, but not CS perhaps indicating
an area to be addressed also. It may be better to encourage telecommunications firms to train their
less skilled staff as well as their knowledge workers. While in CS there may be a more democratic
system in place. This quantitative evidence would need further investigation to be conclusive and
perhaps some qualitative work would help to shed more light on these results.

64

References

Åkerblom, M. (1999) Mobility of highly qualified manpower, a feasibility study on the possibilities
to  construct  internationally  comparable  indicators.  Report  for  Camire  (Eurostat)  and  OECD.
Statistics Finland, 14th December.

Bosworth, D.L. (1986). Intellectual Property Statistics, Oxford: Pergamon Press.

Bosworth,  D.L.  (1999).  "National  and  Regional  Indicators  of  Skill  Demands",  Institute  for
Employment Research, University of Warwick: Coventry

Bosworth, D.L. and R.A. Wilson(1999). "Skills and Performance: a Review of the Literature". Part
of a Report by Cambridge Econometrics, prepared for the London TEC Council

Ekeland, A. (1998) ‘Indicators for human resources and mobility’, IDEA Paper No.9, STEP Group,
Oslo.

EMTA (1999) Labour Market Survey of the Engineering Industry in Britain, Watford: Engineering
and Marine Training Authority

Gallie,  D.,  M.  White,  Y.  Cheng  and  M.  Tomlinson  (1998),  Restructuring  the  Employment
Relationship, Oxford: Oxford University Press.

Green,  F.,  Felstead,  A.  and  Gallie,  D.  (1999)  Computers  are  even  more  important  than  you
thought:  an  analysis  of  the  changing  skill-intensity  of  jobs.  Paper  presented  to  EEEG  Annual
Conference, July 1999

Haskel, J. and Holt, J. (1999) Anticipating future skill needs: Can it be done? Does it need to be
done? National Skills Task Force Research Paper 1, DfEE.

Hendry,  C.  (1999)  New  Technology  Industries.  National  Skills  Task  Force  Research  Paper  10,
DfEE.

Kahn  J.  A.  and  Jong-Soo  L.  1998,  ‘Skilled  Labour  Augmenting  Technical  Progress  in  US
Manufacturing’, in Quarterly Journal of Economics, 113(4), pp. 1245-1280.

Mason, G. (1999) Engineering skills formation in Britain: cyclical and structural issues. National
Skills Task Force Research Paper 7, DfEE.

OECD (1999) Science and Technology Industry Scoreboard, Paris: OECD.

OECD (1995) Canberra Manual, Paris: OECD.

Penn,  R.  and  Holt,  R.  (1999)  Skills  Issues  in  Other  Business  Services  -  Professional  Services,
National Skills Task Force Research Paper 16, DfEE.

PSI (2000) Labour market and skill trends 2000, London: Policy Studies Institute (for the DfEE
Skills Task Force).

65

Tomlinson, M. (2001), ‘A new role for business services in economic growth’, in Archibugie, D.
and Lundvall, B-Å. (eds.),  The globalising  learning  economy: major  socio-economic  trends  and
European innovation policy. Oxford: Oxford University Press.

Tomlinson, M. (1999), ‘The learning economy and embodied knowledge flows in Great Britain’,
Journal of Evolutionary Economics, Vol.9, No.4, pp.431-451.

Tomlinson, M. and Miles, I. (1999), ‘The career trajectories of knowledge workers’, in Mobilising
human  resources  for  innovation:  Proceedings  from  the  OECD  workshop  on  science  and
technology labour markets, DSTI/STP/TIP(99)2/FINAL, OECD: Paris.

66

Appendix A:  Data for the Chemical sector (which includes
pharmaceuticals)

INFLOW
OUTFLOW
KNOWLEDGE WORKERS:
INFLOW
OUTFLOW
TERTIARY EDUCATION:
INFLOW
OUTFLOW
STOCKS
TRAINING

1992
5.3
7.3

2.0
5.2

4.0
4.0
30.8
18.6

1993
4.6
5.3

3.4
5.4

4.0
7.6
30.0
18.1

1994
5.2
4.2

4.3
4.0

5.5
4.1
28.5
19.6

1995
6.0
7.8

4.3
6.7

5.6
9.7
28.4
17.3

1996
4.5
7.8

2.0
6.4

2.1
8.4
31.6
17.3

1997
5.7
4.5

5.1
4.2

4.8
4.1
31.0
16.9

1998
8.0
7.4

6.4
5.2

9.2
3.4
33.9
20.4

1999
7.2
8.1

5.0
5.9

8.1
8.1
34.6
16.7

67

The supply and demand
of high technology skills
in Norway

Anders Ekeland

STEP group, Norway

Introduction

This report is one of three country studies in a pilot project looking at the supply and demand
of high technology skills. The other two country reports are from the UK and the Netherlands.
In our study of supply and demand for high technology skills, we are going to take a closer
look  at  skills  in  the  high-tech  sectors  of  the  Norwegian  economy.  High-tech  sectors  are
characterised  by  requiring  advanced  skills,  they  are  often  R&D  intensive  and  they  often
experience  rapid  changes  in  products  and  technology.  These  factors  make  these  industries
particularly interesting and important when trying to study the problems of skill mismatch. In
addition,  these  sectors  are  often  regarded  as  potential  growth  sectors.  For  policy  makers
concerned with economic development, knowledge flows and technical change these sectors
are often regarded as the most interesting.

The actual choice of sectors to be defined as high-tech is based on the UK country study that in
many ways has functioned as “template”. We will in this study also to a certain extent discuss
that choice. Too often certain sectors are regarded as high-tech without much critical reflection
on what that actually means, running the danger of restricting analysis of high-tech skills to
certain sectors that are in fashion. And as we all know – fashion is a volatile phenomenon.

The following analysis has two main aims: First, to study the development of high-tech sectors
in  terms  of  labour  mobility  and  supply  of  training.  Such  a  study  is  important  for  the  very
simple reason that we are talking about very skill-intensive industries. Secondly, Norway has
both  register  data1  and  Labour  Force  Survey  (LFS)  data  that  can  be  used  whereas  most
countries only have LFS data. We will use both data sources and discuss their strengths and
weaknesses.

The report consists of the following sections:

Part1:

Part2:

Literature review

Analysis of national data

1 Register data is based on various public administrative registers.

70

                                                     
Part 1

The Norwegian literature on skill mismatches

There exists a considerable  literature  in  Norway  on  skill  mismatches  and  the  forecasting  of
future skill needs. The literature may roughly be divided into three according to the methods
and data used:

•  Direct indicators (registers of vacancies, surveys of employers and employees)
•  Empirical demographic/economic forecasting models
•  Macro economic econometric models

It is of course impossible to summarise this vast literature, written over the last four decades in
just a few pages. For the issues focused on in this report we think that there are a few topics
that are of special interest to the present study:

•  are we able to forecast the future need for skills - and if so - to what degree of detail -

and by which methods?

•  if we are able to foresee the need for skills - are the political systems willing to try to

implement the necessary policies?

•  if the political system is willing - will the implemented policy deliver the result
•  and if one misses - what are the costs of substituting and training?

The  basic  need  for  forecasting  the  output  of  various  types  of  skills  cannot  be  denied  on  a
general level. It takes several years to educate a highly skilled person, so to avoid bottlenecks
in the near future one has to be proactive. And very often it takes more years to expand the
staff, move to or build new buildings. All kinds of mundane tasks that require time if one must
expand more than marginally an existing field. Even more difficult and time consuming is to
build an entirely new field - the prime example in the last three decades is of course ICT.

A basic problem is of course who ought to formulate the needs for the expansion of existing
skills,  and  especially  difficult  is  the  creation  of  entirely  new  fields  of  education?  General
answers like “the public” or “the market” only raise further questions, because there are many
questions to deal with before one is down to an operative level. The public sector is a complex
system of actors, parliament, government, different levels and institutions.

The parliament is not going to make detailed decisions - at least not without expert advice -
and  if  expert  advice  is  given  are  the  parliament  to  overrule  the  expertise?  The  problem  of
policy  versus  expertise  is  repeated  at  various  levels  in  the  governmental  apparatus.  It  is  no
easier to decide who is going to be the voice of the "market", especially concerning long-term
issues like supplying skills for markets that do not exist, but that will emerge due to the future
development of various technologies.

In addition comes of course that the last four decades have seen an unprecedented expansion
of higher education, first during the sixties, for some periods the growth has slowed down a
little, but the secular trend has been very clearly expansive. But let us try to go through these
questions one by one - although they are highly interrelated.

71

To  describe  how  national  educational  systems  as  a  whole  and  the  various  subsystems  are
regulated one can use a model with three principal types of authority2:

•  Public authorities – various levels, both politicians and various types of expertise
•  Markets  –  the  demand  for  education  from  new  generations  and  the  demand  for

generic and specific skills from employers.

•  Academic and professional bodies

Every  national  system  of  skill  production  –  and  its  various  subsystems  –  is  of  course  not
governed by only one of these types of authority. They are the result of the relations between
these three types of authority.

Are we able to foresee the future need for skills?

Before one starts to summarise the track record of the efforts in Norway, one might ask the
more general question - are we able to satisfy today’s need for skills? Because one should by
no means take for granted that we are in an optimal situation now, and that the only task at
hand is to take into consideration the unknown and/or uncertain challenges of the future. There
is always a discussion how well adapted different types of education are to the multiple needs
of contemporary society. An eternal discussion of what those needs are and how well they are
met.

Different  political,  professional  and  pedagogical  views  structure  this  debate.  Even  very
homogenous interest groups might have different strategic and tactical points of view of what
is actually needed. Should education be more general, focusing on "eternal" elements of skill
or should is be very closely adapted to the technologies actually used. To take an example: a
university  level  study  of  economics  -  how  well  is  that actually suited  to  the  needs  of  those
employers that employ economists? These are not simple questions, and they get worse by an
order of magnitude when confronted with different emerging technologies.

Forecasting the dimensions of different fields of education

The track record of forecasting the future need for specific skills – either existing sets of skills
or new skills is not the best one, but of course there is not a single cause for that. Because if the
model were that simple then the forecasters would have corrected that a long time ago. Most
forecasts  have  of  course  not  missed  the  general  need  for  more  highly  educated  people.  At
times since the Second World War there were more or less strong voices warning that there
would be an overproduction of highly educated people. But today that is not regarded as a very
important  issue.  Experience  shows  that  all  the  educational  capacity  that  has  been  built  is
mostly  used, no  matter what the specific content.  The education  institutions  that  experience
less demand for their services in most cases manage to adapt to new needs – so there has been
no chronic oversupply of specific types of skills.

2 The model is taken from Burton Clarke (1983). This section draws heavily on Aamodt
(1999) where Burton Clarke’s model is used in analysing the regulation of higher education
in Sweden, UK and USA.

72

                                                     
The last four decades has witnessed a “massification” of higher education.  This process will
probably not stop before having at least 15-16 years of education becomes the general norm,
as seven and nine years of education has been the norm earlier. To have at least twelve years is
rapidly becoming the norm in Norway today.

But in case one fears that such a massification of education would result in an overproduction -
who is going to decide when enough is enough? There has up to now – as mentioned above -
been an insatiable demand for higher education in general. This demand is in our opinion  to a
large  extent  not  motivated  by  short  term  economic  motives  or  accurate  calculation  of  “net
present  value  of  future  income  streams”,  but  by  more  strategic  concerns  of  a  social  and
political  kind  by  broad  layers  of  society3.  Knowledge,  education  implies  social  recognition,
feeling  less  inferior  to  various  elites,  mostly  a  decent  income,  an  escape  from  hard  and/or
routinised work etc.

There  are  in  the  literature  clearly  two  different  worldviews.  On  the  one  hand  the
“particularists” – those  who  believe that various technologies demand a very  specific  set  of
skills.  The  particularists  often  see  skill  mismatch  as  a  technical  problem  in  a  “production
function”  framework.  On  the  other  there  are  the  "generalists",  who  emphasise  the  general
“ability-building”  aspects  of  education.  The  generalists  tend  to  believe  that  an  important
element of any higher education is the ability of problem solving, the self-confidence needed
to take difficult decisions etc. The generalists argue that a lot of education for example in the
humanities and social sciences is not directly useful in a narrow technical sense and that also
technical education becomes more useful when it is aware of the “other” aspects of technology
–  understanding  the  needs  of  the  end-users  of  technology,  understanding  economics,
entrepreneurship,  team-work  etc.  The  “generalists”  are  in  addition  as  a  rule  more  open  to
consider the social, cultural, political/democratic dimensions of education.

The problems of skill mismatches – different approaches

There are as mentioned above three main approaches to skill mismatches:

•  using “direct” indicators like vacancies and queues
•  empirical, demographic forecasting models
•  econometric models

Of course there is considerable overlap between these approaches. The use of direct indicators
of  course  is  based  on  a  more  or  less  explicit  model  of  how  labour  markets  function.  The
empirical/demographic  models  likewise  have  to  model  how  supply  and  demand  for
skills/education  and  the  econometric  models  uses  as  data  indicators  of  vacancies  and
demographic data, but still we think this typology is useful.

3  Cf. Aamodt (1999) and Skoie and Aamodt (1991)

73

                                                     
Direct indicators – using registers and surveys

The Nordic comparative study – Tema Nord 1995

One way of dealing with the problem of measuring skill mismatches is to try to study in detail
the  matching  process  on  the  labour  markets.  One  seeks  to  construct  indicators  for  the
mismatch between the skills of persons seeking new jobs and the kind of skills in demand by
employers. This is done in a comparative Nordic study from 19954.

The study starts by ordering the different phases and events on the labour market

Figure 1: Friction and mismatch on the labour market

Initial offer

Contract signed
Offer withdrawn
Renewed  public
offer
1st attempt

Contract signed
Offer withdrawn
Renewed public
offer
2nd attempt

job seeking
period

Negotiating
period

extraordinary friction
skill mismatch

normal friction

In order to define an indicator of skill mismatch one must first of all one has to draw a line
between  normal  friction  in  the  labour  markets  from  various  degrees  of  extraordinary
friction/skill mismatch. There are of course a lot of factors influencing the degree of friction,
like  attitudes to  geographical  mobility,  wages,  reaching  all  potential  job-seekers  in  a  labour
market segment with the job offer. But even if we assume that these factors are constant, it is a
complex  task  to  use  the  available  data  about  vacant  jobs,  number  of  people  applying  for
specific  jobs  etc.  to  get  a  good,  pure  indicator  for  skill  mismatch.  One  gets  for  example
different estimates for the number of job seekers using the register for the total number of job
seekers than the Labour Force Survey –due to different definitions.

The  study  defines  four  different  measures  of  skill  mismatch  and  estimates  them  by  using
register  data.  The  estimates  are  for  different  geographical  units  like  municipalities  and
counties. There are also estimates on the need for geographical mobility, the lack of/need for
more work experience and further education to reduce the mismatch. The study also looks at
the  occupations  and/or  branches  of  industry  with  the  greatest  disproportion  between  the
number of jobs offered and the number of job seekers. In Norway the sectors in 1993 with the
greatest need for more people was clearly education due to the baby boom in the late eighties
and early nineties and health. The next group comprises various services, including IT.

In addition to the estimates based on register data the study reports on a series of surveys done
in Norway where a sample of employers announcing jobs of more than a weeks duration were
asked about what would be the best qualification for this specific job. It is not asked only what
the employer realistically thinks they can get on the labour market today, what the firm regards
as  sufficient.  The  employer  is  asked  to  disregard  the  different  wages  for  kind  of  skills  –  to

4  Larsen, Eriksen and Devold (1995)

74

                                                     
hypothetically regard the wage as fixed to try to single out the importance of education and
previous  work  experience.  The  employer  is  also  asked  to  give  the  person  who  finally  was
employed in the job a questionnaire in order  to  get information  about their  background and
map the job seeking efforts of the employee.

Such  surveys  with  two  questionnaires  have  been  done  in  1989,  1990,  1991  and  1993  in
Norway  and  in  1991  in  Sweden.  The  categories  of  jobs  are  rather  broad  so  the  branches
focused in this study are not identifiable, but the methodology used is interesting and could be
used also for more specific branches and skills.

There  are  of  course  problematic  aspects  of  this  method  connected  with  the  fact  that  the
employers might be rather myopic – even if they are able to disregard wage differentials etc.
and actually tell what would be the best set of skills for the job. It is an open question how
good such a method is for forecasting their needs some years ahead due to technological (and
other) changes in society.

Since doing this kind of measurement exercise is rather costly - those who believe that a great
deal of substitution and/or  retraining  can take place  would not  see  detailed  measurement  of
skill mismatches using this kind of survey as being worthwhile.

Empirical forecasting models

There is a large literature on forecasting the needs for various occupational groups, the need
for recruitment to the universities and research institutes5. Such studies are done continuously,
In some countries this is part of a tradition of public planning and policy intervention, in others
they are presented more as analyses of specific markets. As mentioned above, the track record
of such forecasts is not that impressive, so today they are used as one piece of information to
fit  into  a  broader  picture.  Often  it  is  useful  to  make  such  an  estimate,  because  one  forces
oneself  to  try  to  single  out  the  most  important  determinants  for  the  demand  for  skills  and
possible skill mismatches – and to learn from the mistaken estimate what factors/mechanisms
were poorly understood. But it is also  important to realise that even if the estimates  for  the
future demand are sufficiently correct, it is by no means given what policy conclusions should
be drawn.

Estimates and policy – the case of primary school teachers6

Often one can hear in public debate around problems on the labour market that it is strange
that one cannot estimate correctly the number of teachers since we know at least six years in
advance how many children will start school, how many teachers that go into retirement etc.
There are more volatile factors influencing the demand for teachers and many of the estimates

5 A large part of this literature comes from the Norwegian institute of research on research
and education, Norwegian abbreviation is NIFU, but also other Norwegian research
institutions have contributed considerably to this field. The institute was set up in the sixties
when the optimism regarding the possibilities for planning and controlling the long-term
supply of skills and academic training was greater than it is today.

6 The case is discussed in Aamodt (1999), a Pro Memoria written by Lars Brandell in the
Swedish National Agency for Higher Education is printed as an appendix. Brandell deals
with this issue in great detail and illustrates the great variations in cohorts and the resulting
consequences for the demand for teachers.

75

                                                     
made for the demand for teachers have not been very close. But there is an aspect of this that
is often overlooked: namely that the number of children vary considerably due to considerable
changes in fertility. These variations are so great – the number of graduated teachers needed
on the top of the boom might be the double of the number needed when the small cohorts are
at school age. The variations happen within a decade or less so that it is simply impossible for
the education system to build up/reduce the capacity for graduating teachers accordingly. That
means that in the boom years people who are not qualified teachers are employed and one
might not want – or for legal reasons be able to dismiss – them rapidly enough. In addition
comes the question of the individuals right to become a teacher personally taking the risk that
the labour market might be adverse in a period after examination.

Estimates and policy – the case of IT-skills

In most countries one is experiencing a lack of ICT skills in the late nineties. It is generally
believed that ICT skills are vital in the development of key sectors of the economy and will be
so  for  a  good  number  of  years,  due  to  the  generic,  pervasive  character  of  this  set  of
technologies. The ICT skills are interesting since they pose all the classical questions of how to
minimise skill mismatches.

•  were different institutions able to foresee the demand for ICT skills - and the kind of

skills needed?

•  And if so - did the authorities take the necessary steps to meet the correctly estimated

need for ICT skills?

•  And if they did not - why - and have they learned anything from these experiences?

The ability to forecast the need for ICT skills

In  Norway  one  can  roughly  divide  the  period  after  WWII  in  an  early  period  –  until  the
mid-seventies  when  one  underestimated  the  potential  of  –  and  consequently  the  use  of
computers. But with the advent of mini-computers from firms like DEC7 and Norsk Data in
the  late  seventies  and  even  more  with  the  introduction  of  personal  computers,  “everybody”
realised in the mid-eighties that a major new technology shaping our lives in various ways had
arrived.

At  that  time  the  employer  organisation  Confederation  of  Norwegian  Industry  (Norges
Industriforbund)  had  their  yearly  conference  “Industrial  forum”  (Industriforum),  a  meeting
place for top leaders of industry and the political establishment – from the ministers of trade,
industry  and  top  civil  servants  to  the  leaders  of  the  political  youth  organisations.  The
universities  and  research  institutes  were  also  well  represented.  This  was  an  important  and
representative forum. In the opening speech, the president of the Confederation said:

There has always been a gap between the needs of industry and the capacity of the educational
system, this problem is now becoming more serious than ever …Especially we lack persons with
IT-skills and there is also a lack of people with education in various other fields of technology.

This opening speech was followed up by the Vice President of the Confederation providing a
much  more  detailed  analysis  of  the  needs  of  industry  and  the  bottlenecks  it  was  facing.  It

7 Digital Equipment Corporation, well know for machines like PDP11 and VAX.

76

                                                     
launched  a ten year  plan  with  very  specific  targets,  being  very  outspoken  about  the  lack  of
response from the political establishment to the very clear signals of bottlenecks. Underlining
that there was no lack of students or forecasts demanding a radical increase in the number of
graduates in key fields of science.

In  the  aftermath  of  the  conference  a  special  study  was  published  by  the  Confederation
specifically on this topic. In this study a ten-year plan with detailed quantitative targets was set
out.  These  targets  were  controversial.  The  leading  experts  –  the  institute  for  research  on
research and education held that lower numbers of engineers and ICT persons were needed.

A very precise forecast of technological development

Several  other  attempts  were  done  at  quantifying  the  need  both  for  technological  skills  in
general  and  ICT  skills  in  particular.  NIFU  made  a  study  with  the  title  “The  need  for  IT-
education” (“Behovet for datautdanning”) in 19858. This is a rather remarkable study because
it focused a great deal on discussing in detail both the possible technological scenarios – and
the corresponding educational needs. The study described the typical computer in 1974, 1984 -
the year of the study – and ten years ahead to 1994 – and they were remarkably clairvoyant
about the typical computer in 1994 on parameters like:

•  type and speed of processor
•  amount of RAM
•  size of hard-disk, development of other storage media
•  other related technologies (fiber-optics, e-mail, Local Area Networks etc.)

This was not pure luck or a stroke of genius, but reflected that the development of computers,
i.e. PCs at that time was on a certain technological trajectory that made it possible to foresee
further developments with a  sufficient  degree  of  accuracy  for  the  purpose  of  estimating  the
need for ICT skills.

The expansion in the late eighties

The demands both from the private sector and researchers probably led to an expansion of IT-
education in Norway, mainly as a part of the “National Action Plan for IT”. This expansion
might have continued into the nineties if the business cycle had not changed rapidly from late
1986. At that time the locomotive of the Norwegian IT industry, Norsk Data began to have
serious problems and the oil price plunged from above $30 a barrel to just above $10. The
banks – a very big employer of IT-skills were in crisis and had to be rescued by the state. The
oil industry also had to be much more careful about costs, starting big new IT-projects is not
the thing you do in such a business climate. This meant that the very optimistic forecasts from
the  mid-eighties  seemed  to  be  refuted  by  actual  events.  An  indication  of  this  is  the
unemployment rates for various groups of highly skilled IT-engineers.

8 NIFU/Jahnsen (1985)

77

                                                     
TABLE 1: UNEMPLOYMENT RATES 6 MONTHS AFTER GRADUATION*, 1987 - 1997

EDUCATION

1987

1989

1991

1993

1995

1996

CAND. SCENT FROM
UNIVERSITY

CIVIL ENGINEERS,
TECH. UNIV.

ENGINEERING
SCHOOLS (2- 3 YEARS)

OTHER HIGH
SCHOOLS, 3 YEARS

ALL HIGHER
EDUCATED

..

..

0,0

3,2

0,0

10,0

7,5

8,1

3,3

7,8

6,6

3,3

..

..

13,6

..

27,9

11,8

..

12,2

..

2,4

1,6

..

..

5,7

6,8

9,2

12,4

10,3

* The rates are for those who graduated in the Spring, for the Technical University in Trondheim the majority
graduates in the autumn, but one claims that the numbers are indicating the trend correctly.
Source: NIFU (1997), the survey of graduates (Kandidatundersøkelsen)

The table clearly shows the rapid adverse change in labour market conditions for the newly
graduated. There is no reason to think that there was any significant long-term unemployment,
but probably many had to take jobs that they were overqualified for.

One  aspect  studied  when  looking  at  the  post-graduates  6  months  after  graduation  is  their
subjective feeling of the relevance of their education to the job they have got (Arnesen 1996).
There are clear differences between the rate of unemployment and the “rate of relevance”. The
data seems to support the hypotheses that accepting jobs that are seen as less relevant are more
frequent when unemployment is high.

There are market differences between social scientists and natural scientists. But one should
not  jump  to  quick  conclusions  from  this,  for  several  reasons  all  of  them  related  to  the
mechanisms using certain types of education in new contexts, branches. The humanities, social
scientists and partly law have always been more “generalist”, while other educational branches
have been more narrowly used. Since the mid-nineties was the first period where the highly
educated experienced some major problems on the labour market there are reasons to believe
that this “shock” made the various professions more open minded about what could be seen as
areas where their skills could be used. Another finding is that there is not a simple relationship
either between the number of graduates, or the growth in the number of candidates, and the
unemployment rate. This is not surprising since there are various processes that interact: the
business  cycle,  search/matching  and  learning  processes  by  both  employers  and  job  seekers.
One cannot take for granted that the level of job satisfaction has some natural level, it might
increase in a period of rapid technological change, not because the graduates take jobs they are
overqualified  for,  but  simply  because  the  content  of  the  curriculum  is  lagging  behind  and
consequently they feel that it is less relevant.

The data covers the years 1989, 1991, 1993 and 1995. It would have been interesting to have a
full cycle – that is from the boom year 1987 to the next boom in 1997 - to see if the feeling of
match between education and job content stabilises again. If one could get data for the same
persons over several years that would have been very interesting.

78

The case of ICT skills – the late nineties

It is symptomatic that it is hard to find any forecasts from either industry or research of ICT
skills in particular between the late eighties and the late nineties. First when the up-turn started
in  1995,  after  the  highest  rate  of  unemployment  since WWII,  the  question  of  sufficient  IT-
skills again came on  the agenda. There  were  published official documents  in 1998,  like  the
Government white paper on IT-skills in a regional perspective (Stortingsmelding 38/98) and a
green paper: “IT-plan for 1998 – 2001 for private sector”).

Recently the question of the lack of ICT competent personnel has been raised with full force
again since on top of the general spread of ICT in society came the very proactive attitude of
firms in the light of the coming development of Internet computing. Only the making of home
pages and the setting up of web services increased the demand for ICT skills even more. One
clear indicator of this is the fact that the public sector – not having the possibility to match the
wage offers from the private sector was losing key personnel. Not only that: different parts of
public administration started to overbid each other, with various fringe benefits, in order to get
the human resources needed. As a consequence of this difficult situation there were launched a
series  of  studies  with  the  aim  of  forecasting  the  need  for  ICT  skills  in  the  central  state
apparatus.  The  main  method  used  for  the  quantitative  part  was  looking  at  the  share  of  ICT
persons in the different departments and institutions, making some estimate of the employment
growth/decline – and the necessary number of ICT persons followed deterministically9.

Other methods like counting the number of advertisements for ICT skills were also used. Early
in  1997  there  were  100  advertisements  per  week  in  the  major  Norwegian  newspaper
Aftenposten.  Of  these  40%  asked  explicitly  for  high  university  level  education,  the  total
number of candidates in Norway at that time equalled 7 per week10.

Since 1997 the extraordinary high demand for ICT competence has drawn university teachers
to the private sector and students into industry before graduating – no wonder, being offered
very  high  wages  and  many  other  benefits.  This  of  course  makes  it  difficult  to  increase  the
supply of ICT graduates – there were at the time 30 vacant university jobs out of 13011. The
fact that students do not graduate, do not finish their theses means less research and there is
less research experience being accumulated since efforts are directed towards solving the short
term needs of private sector – and not their long term needs.

It is of course not only a question of ICT skills in general, what was really in demand in recent
years was the communication part, TCP/IP, HTML, XML, web-design etc. This shortage of
very special ICT skill points to the problem of proactively preventing skill mismatches on such
a detailed level. There were established special arrangements to catch up in this field in 1996-
9712, but that was several years after the first graphical Internet browser Mozilla had made the
potential of the Internet pretty clear. But this is a question of whether it is possible – in advance
–  to  reform  the  curriculum,  change  the  resource  allocation  from  one  sub-field  of  Computer
Science to another. Who are going to make such decisions?

9 Statskonsult (1999a, 1999b, 1999c), ECON (1999) and Ekeland (1999)
10 Fløisbonn and Liestøl (1997)
11 NIFU (1997) quoting a survey done by Prof. Conradi at the University of Trondheim
12 A short memorandum written by administrative director of the Faculty of Mathematics and
Natural Sciences at the University of Oslo, Rune Fløisbonn and Prof. in Informatics, Knut
Liestøl gives a valuable overview over the situation in 1996/1997. (Informasjons- og
kommunikasjonsteknologi (IKT) – behov og potensiale for opptrapping av
utdanningskapasiteten.

79

                                                     
Econometric studies of skill mismatch

This  literature  uses  mainly  unemployment  rates  for  specific  groups  and  wages  to  construct
indicators for skill mismatch. The line of argument is that if  wages were  flexible, then they
would react to skill mismatches with lowering of the wages of those who’s skills are facing a
negative demand change, and rising wages for those who are much in demand. Since wages in
Norway is generally considered to be rigid, due to unionisation, central bargaining etc. a skill
mismatch would mean increasing unemployment for those groups with the wrong skills13.

One recent  study of this kind is  “The Effects  of  Skill Mismatch on  Wages  in  a  small  open
Economy with Centralised Wage Setting: The Norwegian Case”, Bjørnstad (2000).  The study
uses high-technology imports/exports as a proxy for rapid technological change. It then looks
at the development of the unemployment rates for workers with:

•  primary education, i.e. less than 11 year
•  secondary education, i.e. 11-12 years
•  vocational education, also 11-12 years
•  lower university education, 13-16 years
•  higher university education, more than 17 years

The unemployment rates for these groups have the following pattern: the higher the education,
the lower the unemployment

Figure 2:

Unemployment rates, five educational categories,  1975 - 1998, Norway

10,1

9,1

8,1

7,1

6,1

5,1

4,1

3,1

2,1

1,1

0,1

1975

1980

1983

1986

1992

1996

1998

primary education, i.e. less than 11 year

secondary education, i.e. 11-12 years

vocational education, 11-12 years

lower university education, 13-16 years

higher university education, more than 17

Source : Bjørnstad (2000), Statistics Norway

The stylised picture is clear, the lower the education the higher the unemployment, and also
the more variation in the unemployment. The persons with university education are much less

13 See Krugman (1994) for a more detailed analysis of these phenomena.

80

                                                     
influenced by the business cycle than the lower educated. In addition there is often a tendency
that in those groups with high unemployment more workers are withdrawing from the labour
market, so that the unemployment rates are biased downwards.

If wages were sufficiently flexible the labour markets should always clear and unemployment
rates should be close to “normal friction unemployment”, that is not more than 1- 2 percentage
points.

This  way  of  looking  at  skill  mismatch  could  of  course  try  to  find  data  for  unemployment
among  more  specialised  educational  categories  since  the  register  of  job  seekers  and/or
receivers of unemployment benefit do have individual data on education. As mentioned above
there are regular surveys  of  newly graduated persons  done to  map  when  they  get  their  first
regular job – and the corresponding rate of unemployment.

But there are some points that need to be addressed before such an exercise is started. First of
all it is hard to tell whether the higher rates of unemployment for the lower educated is really
because they do not have the necessary skills. Many employers would – when they can choose
– take the ones with the highest education available, even though those with lower education
do have the skills needed for the time being.

Even  though  increasing  your  skills  by  getting  more  formal  education  will  increase  your
individual chances of getting a job, in a recession you might just crowd-out someone with less
– but sufficient education for the job.

Since the level of education has been rapidly rising – one has to look at the same age groups
with the same education. But this does not solve the problem, since in the younger generations
where at least 12 years of education have become the norm, having less than 11 is in a way
stigmatised. Those with less than 11 years of education are overwhelmingly more than fifty
years old, so they would be harder to reemploy if they became unemployed, and consequently
have higher unemployment.

It  is  also  difficult  to  separate  the  effects  of  the  business  cycle  from  the  effect  of  new
technology  generating  a  skill  mismatch.  When  the  economy  is  booming  –  everybody  is
employed  –  but  that  does  not  mean  that  there  is  no  skill  mismatch.  When  the  economy  is
sluggish, different groups are hit by unemployment – both unskilled workers and architects. In
the case of the architects there is no case of skill mismatch even though they are unemployed.
It is just a consequence of construction being a very cyclical branch of the economy.

81

Data

Labour force survey

The most established and authoritative data source is the Labour force survey, even in Norway
where  public  register  data  is  available.  The  total  number  of  employed  people  and  other
important  macro  aspects  of  the  Norwegian  labour  force  are  estimated  by  using  the  Labour
force survey.

There  has  been  an  ongoing  process  of  development  of  the  Norwegian  LFS,  so  in  order  to
generate long time-series it is a considerable effort to prepare the data for analysis. There was a
major change in the LFS in 1996 when the Norwegian LFS was made to conform much more
with the EU standard for LFS, the so-called Community Labour Force Survey.

The Norwegian LFS is since 1996 a quarterly sample, people being questioned 8 times, with
partial renewal of the  sample each quarter. The LFS has all  the  “usual”  variables  about  the
present employment, including training. There was a national occupational code that has been
replaced by the ISCO-88 standard from year 2000.

The LFS also contains the key questions for mobility studies “when did your present job start”,
and  “when  did  your  last  job  end”.  Of  course  there  is  a  problem  how  these  questions  are
interpreted. Do the respondents regard it as a new job when they rise in the hierarchy, when
they move between different establishments in the same enterprise etc.? Since the respondents
are in the sample for 8 quarters one can also calculate the mobility between industrial sectors
on a yearly basis by looking at the NACE code of the present job between quarters.

Since the persons interviewed are asked for their personal identification number it is possible
to link the LFS to the registers, this is partially done today in order to get a consistent sectoral
classification.  But  there  are  many  other  potential  benefits  of  using  the  LFS  in  combination
with  the  registers.  One  of  them  is  that  using  the  registers  one  can  analyse  the  respondent’s
career  before  being  interviewed  by  the  LFS  and  later.  Of  great  importance  is  to  try  to  get
information about those who “drop-out” of the LFS for some reason or another.

Regular surveys

Other data are based on surveys with coverage and cycles varying from full censuses, like the
population census each decade or so, to samples like the annual industrial and biannual R&D
statistics. The latter often have a census of large firms, a sample of small and medium sized
firms. Statistics Norway is conducting these surveys.

Statistics Norway is making forecasts of the need for different skill categories based on their
large, empirical, macro-economic models.

The  Labour  market  authorities  also  publish  regular  reports  on  the  labour  market  and  the
demand for particular educational groups.

82

Register data

Beside the LFS the register data, parallel to those found in the other Nordic countries, are the
most  important  source  of  knowledge  about  the  labour  market.  It  is  a  widespread
misunderstanding that there is something inherently centralist about the register data. Even the
Canberra  Manual14  writes  that  these  countries  “have  a  tradition  of  centrally  co-ordinated
registration of characteristics of individuals”. But this is not correct - there has in fact been no
centrally  co-ordinated  registration  of  characteristics  of  individuals:  what  there  has  been  is  a
unique  identification  number  for  each  person  since  the  late  sixties.  In  the  various  parts  of
public  administration,  and  in  the  banking  system,  and  many  other  firms  and  institutions  –
databases have been made using this unique identification number. This was of course not part
in some “Big brother is watching you” plan from the Norwegian authorities. The main reason
was that the personal identification number cut through the problem of identical names, name
changes, address changes etc.

Even if we only look at the public sector, there was no co-ordination of which characteristics
to collect about individuals, definitions used, coding rules etc. were not co-ordinated. But the
unique  number  makes  it  possible  to  compare/merge/supplement/verify  information  from
various registers even if this was not the intention when the particular register was built up. It
is a well-known fact among those who have worked with data based on these registers, that
there  is  contradictory  information  in  the  various  systems,  for  example  the  industrial
classification  is  not  fully  harmonised  –  although  it  could  (and  should  be).  It  happens  that
persons are registered as employed and seeking employment at the same time, they have two
full jobs at the  same time, that  firms have different  industrial  classifications  in  the  different
registers  etc. These  problems are not major ones, the overall quality of the  registers  is  very
good, but there is room for improvement. The task of harmonising the registers, i.e. to agree on
definitions and maintenance rules and how to eliminate existing contradictions is a task which
has yet to be undertaken.

Although there is no centrally co-ordinated registration, the unique person number makes the
use of all register data possible. The data set used for research purposes is actually a merging15
of  information  from  uncoordinated  registers  such  as  the  educational  register  and  the  social
security register. In actual fact the authorities have what in database jargon is called “a virtual
distributed  database”.  It  is  “distributed”  because  the  administration  of  content  and  physical
handling  of  the  database  is  done  in  different  locations.  It  is  “virtual”  -  or  more  precisely
“potential” - because the various registers are not yet interconnected.  But this is  technically
quite feasible using existing database- and network technology.

The conclusion is that if one has various indicators for skill mismatch in a database using the
personal identification number one can get  - in a very cost efficient way – these skill
mismatch data analysed with a rich set of other personal characteristics over time. Incomes,
career, family, further education and so on.

14 The OECD manual for classifying HRST (Human Resources in Science and Technology),
made in Canberra, Canada in 1995.
 15 In database language this is called “a join”

83

                                                     
Future development of data sources on skill-mismatch

The general trend in Norway and the other Nordic countries is to try to base more and more of
the official statistics on registers, by using, modifying and extending already existing private
and public digital data sources. The reason is of course that registers are very cost efficient,
and when using person and firm identificators can be matched with a lot of other data.

Probably  this  will  also  influence  the  regular  surveys  like  the  LFS.  A  lot  of  the  information
collected by the LFS  could be extracted from the registers, and the LFS could concentrate on
that kind of information that is not  in the registers – like peoples judgement as to what degree
their education and skills match their present job.

But  before  there  are  going  to  be  any  major  developments  when  it  comes  to  measure  skill
mismatches, one has to decide if such an exercise is worthwhile – and not the least on the level
of detail and how to use the results. As the parameter for regulating access and funds to higher
education – or as just one piece of information in a much larger picture.

84

References

Arnesen, Clara Åse and Brandt, Ellen and Hovland, Grethe. 1997. IT-utdanning:

kapasitetsbehov og utbyggingsplaner (IT-education: needed capacity and plans
for further expansion). NIFU skriftserie 25/97. Oslo: NIFU

Fløisbonn, Rune and Liestøl, Knut 1997. Informasjons og kommunikasjonsteknologi
(IKT) – Behov og potensiale for opptrapping av utdanningskapasiteten.
(Information and communication technology. The need and potential for
expanding the capacity). Internal Memo, University of Oslo

Aamodt, Per O. 1994. Valg og gjennomstrømning i utdanningsystemet (Choices and

Through-put in the Educational System). in Lauglo (1994)
Aamodt, Per O. 1999. Dimensjonering av høyere utdanning. Et komparativt

perspektiv med særlig vekt på Storbritania, Sverige og USA (The Scale of
Higher Education. A Comparative perspective with special emphasis on the
UK, Sweden and the USA), NIFU Report 3/99. Oslo: NIFU

Adolfsen, Kari Anne and Gunnar E. Christensen, 1994, IT-utdanninger i Norge.

Rapport fra et kartleggingsprosjekt for Den norske dataforening. (IT-education
in Norway, Report from a mapping project on for the Norwegian Computing
Association, Oslo, DND

Bjørnstad, Roger. 2000,  The Effect of Skill Mismatch on Wages in a small open

Economy with Centralized Wage Setting: The Norwegian Case. Discussion
paper No 270, Oslo:Statistics Norway

Cappelen, Ådne og Nils Martin Stølen. 1994. Forecasting Labour Marked Imbalances,

Economic Survey" 4/94. Oslo: Statistics Norway

ECON. 1999. Behovet for IT-kompetanse i staten (The need for IT-skills in the State

administration. Report 57/99. Oslo: ECON, Center for economic analysis

Eikeland, Ole Johan and Olav Tvede. 1998. Rekrutteringsbehov i naturvitskaplege og
teknologiske fag: Status i 1997. Prognosar mot 2015 (The need for recruitment
in Natural Science- and Technological Subjects: Status in 1997. Forcasts to
2015). NIFU Report 20/98. Oslo: NIFU

Ekeland, Anders and Braadland, Thor Egil. 1999. Staten og IT-kompetansen: Offer

eller aktivist? (The State and the IT-skilled: Victim or Activist?). Working
Paper 9-1999. Oslo: STEP group

Hatlevik, Ida K. R. 2000. Styring og regulering av sykepleier-, lærer- og

ingeniørutdanningen i fire land. En sammenlignende studie av Norge,
England, Finland og Nederland. (Management and regulation of Nurse-,
Teacher- and Egnineering Education in Four Countries. A Comparative Study
of Norway, England, Finland and the Netherlands). NIFU Report 4/2000.
Oslo:NIFU

Norges industriforbund. 1986. Økt kunnskap og kompetanse (Increased knowledge

and skills) Debatt og studie hefte nr. 68. Oslo: Norges industriforbund.
Norges industriforbund. 1986. Industriforum 1986. (Industrial Forum 1986) Debatt og

studie hefte nr. 66. Oslo: Norges industriforbund

Larsen, Knut Aril. 1982. Høyere utdanning og arbeidsmarkedet mot år 2000. (Higher
Education and the Labour Market towards Year 2000) in Forskning og høgre
utdanning, Årbok 1982. Oslo: Universitetsforlaget

Larsen, Knut Arild and Devold, Edvard and Eriksen, John. 1995. Mistilpassning på

arbeidsmarkedet (Mismatch on the Labour Market). TemaNord 1995/505.
København: Nordisk Ministerråd

85

Lauglo, Jon (ed.), 1994, Norsk forskning om utdanning. Perspektiver og veivalg

(Norwegain Research on Education. Perspectives and Options??), Oslo: ad
Notam Gyldendal

Lindquist, K-G. and Skjerpen, T. 2000, Explaining the Cahnge in Skill Structure of
Labour Demand in Norwegian Manufacturing Industries. Discussion Paper
(forthcomming), Oslo: Statistics Norway

Nærings- og Handelsdepartementet. 1998. Næringsrettet IT-plan for perioden 1998-

2001 (Ministry of Trade and Industry: IT-plan for 1998 – 2001 for private
sector). Oslo: Nærings- og Handelsdepartementet

Næss, Terje and Ramberg Inge. (eds.) 1996. Utdanning og arbeidsmarked. (Education

and Labour Marked 1996). Oslo: NIFU

NIFU/Jahnsen, Torleiv. 1985. Behovet for datautdanning. Noen aktuelle

problemstillinger (The need for IT-education. Some recent challenges). NIFU
Notat 1/85. Oslo: NAVF’s utredningsinstitutt

Skoie, Hans and Aamodt, Per O. (eds.) 1991. Sykelys på høyere utdanning i Norge.

En del utviklingstrekk med vekt på 1980-tallet. (Focus on Higher Education in
Norway. Some trends with emphais on the eighties) NAVF Report 3/91,
NAVFs utredningsinstitutt

Statskonsult. 1999a. Intervjuer om IT og IT-kompetanse (Interviews about IT and IT-

skills). Notat 1999:6. Oslo Statskonsult

Statskonsult. 1999b. IT-personell, behov og tilgang (IT-skilled persons, demand and

supply. Notat 1999:11. Oslo Statskonsult

Statskonsult. 1999c. Staten og nerdene (The State and the Nerds). Notat 1999:5. Oslo

Statskonsult

Stortingsmelding 38 (1998). IT-kompetanse i et regionalt perspektiv (IT-skills in a

regional perspective), Oslo: Norwegian Ministry of Trade and Industry
Terje Næss og Inge Ramberg, 1996, Utdanning og arbeidsmarked, NIFU, Oslo,

TANO

86

Part 2

Analysis of national data

Labour Force Survey (LFS)

The  Norwegian  LFS  started  in  1975,  but  the  number  and  type  of  questions  have  changed
several times as in most other countries. Norway did not start to implement the new features of
the  1992  Community  Labour  Force  Survey  before  1995-96.  This  means  that  we  cannot  get
long time series based on LFS in Norway16.

Education

The educational classification is basically a national standard, but there is an official mapping
of national codes to ISCED which for the ISCED-76 is straightforward.

Industrial classification

The  NACE  classification  was  implemented  from  1995,  and  so  far  there  has  been  no
conversion from ISIC to NACE  for earlier years.

Mobility

The so called retrospective questions were first introduced in 1997. At the time of writing only
the  1996-2000  Norwegian  LFS  contains  the  retrospective  question  needed  for  calculating
mobility rates.

Occupational classification

Occupational classification has been used, but according to a national standard. A recoding to
ISCO-88 has been done for the years 1996 – 2000 and that will be the period of study of LFS
data.

LFS vs. register data

Register data

The Norwegian register data that makes a matching of employers and employees is available
for the period from 1986 - 1999. The data used here are the so-called“ employment files”17. As
is the case with the LFS data, the educational classification is a national system with a direct
mapping to the international standard, ISCED-76. There is no occupational classification in the
register data.

16 Using the register data to supplement the LFS one could construct the required data, but
this is a demanding task and as far as we know there are no plans to do this in the near
future
17 For a more detailed description, see Ekeland (1998)

87

                                                     
Industrial classification

NACE was implemented from 1995 in the Norwegian statistical system. To make it possible
to have time series with the register data an ad hoc recoding from ISIC to NACE had been
done by the STEP group for the years 1986 - 1994. The principle being – besides the trivial
cases of a one-to-one correspondence – to take advantage of the data at firm level. We take as
our starting point the year of classification change, in this case, 1995. This is the year when
both standards are used.  We then “write back” as far as possible for all firms existing in 1995
using the firm identification number as many years as possible.  For those firms that “died”
before 1995 we have used the most frequent of the possible ISIC-NACE combinations. That
means that some of the potential variation in the NACE codes is lost and consequently that the
intersectoral mobility estimates are biased. But  it is hard to tell which way they are biased,
since this depends on the complex relation between ISIC and NACE. Since this study is done
mainly  on  a  two  digit  level  we  do  not  think  this  will  influence  the  estimates  in  a  way  that
would alter the main results. The only real solution to this problem would be a very detailed
study  of  the  firms.  In  practice  that  would  amount  to  a  new  (re)classification  from  ISIC  to
NACE of the “dead” firms on an individual basis.

Industrial classification - the relation between LFS and register data

The high-tech industries are defined in the “template” UK study as a mix of two and three digit
NACE codes. However, it is not possible to get data for employment and education from the
Norwegian LFS on such a detailed level. When it comes to register data we will use both the
“common denominator”, i.e. a two-digit level and various more detailed breakdowns.

Table 2: Industrial sectors - preferred break down

NACE

3-DIGIT

TITLE

X

X

X

110

244

300

320

353

642

720

730

EXTRACTION OF CRUDE PETROLEUM AND NATURAL GAS; RELATED
SERVICES, EXCLUDING SURVEYING
MANUFACTURE OF PHARMACEUTICALS, MEDICINAL CHEMICALS AND
BOTANICAL PRODUCTS
MANUFACTURE OF OFFICE MACHINERY AND COMPUTERS

MANUFACTURE OF RADIO, TELEVISION AND COMMUNICATION
EQUIPMENT AND APPARATUS
MANUFACTURE OF AIRCRAFT AND SPACECRAFT

TELECOMMUNICATIONS

COMPUTERS AND RELATED ACTIVITIES

RESEARCH AND DEVELOPMENT

Table 3: Industrial sectors the actual break down LFS

11

24

30

32

35

64

72

73

Extraction of crude petroleum and natural gas; related services, excluding surveying

Chemicals&pharmaceuticals

Manufacture of office machinery and computers

Manufacture of radio, television and communication equipment and apparatus

Aerospace & other transport

Telecom and post

Computers and related activities

Research and development

88

Using  the  2-digit  industry  level  is  slightly  problematic  for  our  purpose,  as  we  now  cover
broader  activities  than  what  are  generally  regarded  as  high-tech  activities.  For  example,  we
wanted to study pharmaceuticals, but end up looking at both chemicals and  pharmaceuticals.
We cannot single out Aerospace, but have to look at “Aerospace” and  “Other transport”. We
wanted  Telecommunications,  but  can  only  get  Telecommunications  and  Postal  services
together.  This might not look like a very dramatic change, but as we will see below the size of
the sector increases threefold as a result of these minor changes in the Norwegian case. We
will take a more detailed look at the consequences of using a the 2-digit level in all sectors
instead of the 3-digit preferred breakdown in some of the sectors.

Table 4: Relative employment growth, three 2-digit ,1987 – 1999, register data

87

88

89

90

91

92

93

94

95

96

97

98

99

PHARMA+
CHEM.

TANSP+AIR

TELECOM
+POST

100 % 95 %

90 % 90 % 91 % 90 % 94 % 91 % 92 % 94 % 97 % 94 % 91 %

100 % 90 %

88 % 80 % 83 % 87 % 90 % 89 % 88 % 93 % 96 % 97 % 102 %

100 % 103 % 105 % 98 % 104 % 104 % 103 % 107 % 105 % 98 % 94 % 77 % 84 %

We  start  by  looking  at  the  development  of  employment  of  the  three  2-digit  sectors.  The
number of employees in Transport + Aircraft goes down and then up. This is mainly caused by
the business cycle. The employment in the other two sectors declines in relation to 1987.

Table 5: The 3 digit breakdown of 2 digit sectors, 1987-1999, all employees, register data

87

88

89

90

91

92

93

94

95

96

97

98

99

CHEM, NON-
PHARMA.

15381 14573 13735 13539 13354 13042 13569 12849 12782 12754 12998 12663 12165

PHARMACEUTICALS 1149

1116

1212

1297

1762

1813

1891

2267

2372

2811

2970

2915

2927

TRANSPORT
(NON-AIR)

35979 32590 31069 28347 29095 30783 31964 31780 31427 33141 34392 34822 36161

AIRCRAFT

1952

1584

2140

2105

2206

2213

2053

2030

1932

1996

2051

2100

2516

POST,

NON-TELECOM 28672 30918 33863 31235 34005 34609 34153 35664 34935 33692 32839 29299 30368

TELECOMMS

19349 18669 16413 15930 15937 15476 15229 15890 15517 13558 12418 7442

9842

CHEM, NON-
PHARMA.

100 % 95 % 89 % 88 % 87 % 85 % 88 % 84 % 83 % 83 % 85 % 82 % 79 %

PHARMACEUTICALS 100 % 97 % 105 % 113 % 153 % 158 % 165 % 197 % 206 % 245 % 258 % 254 % 255 %

TRANSPORT(NON-
AIR)

100 % 91 % 86 % 79 % 81 % 86 % 89 % 88 % 87 % 92 % 96 % 97 % 101 %

AIRCRAFT

100 % 81 % 110 % 108 % 113 % 113 % 105 % 104 % 99 % 102 % 105 % 108 % 129 %

POST, NON-
TELECOM

100 % 108 % 118 % 109 % 119 % 121 % 119 % 124 % 122 % 118 % 115 % 102 % 106 %

TELECOMMS

100 % 96 % 85 % 82 % 82 % 80 % 79 % 82 % 80 % 70 % 64 % 38 % 51 %

If we look at the 3-digit sector we want to study and the residual of the 2-digit sector we get
another  story.  Non-pharmaceutical  Chemicals  declines  steadily,  and  pharmaceuticals  more
than  doubles  its  number  of  employees.  Since  the  Non-pharmaceutical  part  of  the  sector  is

89

much bigger, the 2-digit sector employment declines. Telecommunications also have quite a
different trajectory than Postal services. Post is stable, in Telecom. employment is halved from
1987  to  1999.  Non-Aircraft  and  Aircraft  are  not  that  different  in  this  period,  so  here  the
employment development of the 2-digit sector is roughly the same as the 3-digit hi-tech sector.
But this is accidental because the size of Aircraft is roughly 5% of the combined sector, so it is
the rest of the sector that is decisive for the development of employment in the 2-digit sector.

Table 6: The three sectors as percentage of 2-digit sector, all employees, register data

87

88

89

90

91

92

93

94

95

96

97

98

99

PHARMA.

AIRCRAFT

TELECOM.

7 % 7 % 8 % 9 % 12 % 12 % 12 % 15 % 16 % 18 % 19 % 19 % 19 %

5 % 5 % 6 % 7 % 7 % 7 % 6 % 6 % 6 % 6 % 6 % 6 % 7 %

40 % 38 % 33 % 34 % 32 % 31 % 31 % 31 % 31 % 29 % 27 % 20 % 24 %

Pharmaceuticals and Aircraft are clearly small in relation to the rest of the actual 2-digit sector.
Telecommunications  start  out  in  1987  at  40%,  but  its  share  declines  continuously.  The
conclusion is that in the three cases where we want to study the 3-digit sector, but where the
LFS only allows us to study the aggregated 2-digit sector, the development of employment in
this 2-digit sector is not even a rough indicator of what is happening in the 3-digit sector since
the  hi-tech  sector  is  not  in  any  of  the  cases  more  than  a  quarter  of  the  2-digit  sector.  The
development of employment is actually quite different in two of the sectors.

The highly educated – and the question of 2- and 3-digit sectors

With rapid technological change leading to demand for new types of skill there is reason to
believe  that  the  share  of  highly  educated  employees  should  develop  differently  from  the
overall trends in employment.

Table 7: The 3 digit breakdown of 2 digit sectors, 1987-1999, highly educated, register data

87

88

89

90

91

92

93

94

95

96

97

98

99

CHEM, NON-PHARMA.

983

927

759

PHARMACEUTICALS

277

267

282

TRANSPORT(NON-AIR)

638

585

642

AIRCRAFT

36

33

58

POST, NON-TELECOM 227

246

288

TELECOMMUNICATIONS 661

766

872

868

338

763

58

302

990

431

850

70

454

925

76

900

1001 1276 1354 1369 1439 1602 1597 1517

468

568

654

741

772

764

790

978

1275 1443 1800 1892 2182 2445

76

85

81

87

412

462

538

658

711

618

108

639

125

581

159

718

1107 1196 1231 1478 1686 1923 2154 1782 2390

CHEM, NON-PHARMA.

100% 94% 77% 88% 92% 102% 130% 138% 139% 146% 163% 162% 154%

PHARMACEUTICALS

100% 96% 102% 122% 156% 164% 169% 205% 236% 268% 279% 276% 285%

TRANSPORT(NON-AIR) 100% 92% 101% 120% 133% 145% 153% 200% 226% 282% 297% 342% 383%

AIRCRAFT

100% 92% 161% 161% 194% 211% 211% 236% 225% 242% 300% 347% 442%

POST, NON-TELECOM 100% 108% 127% 133% 181% 204% 237% 290% 313% 272% 281% 256% 316%

TELECOMMUNICATIONS 100% 116% 132% 150% 167% 181% 186% 224% 255% 291% 326% 270% 362%

90

If we compare the employment of the highly educated with the overall employment it is very
clear that there is a change in the educational composition of the workforce in these sectors.
Some of it is of course caused by the general rise in educational level. The table shows that all
sectors have a rapid growth in their share of the highly educated, but the difference between
the  3-digit  hi-tech  sector  and  the  residual  is  most  marked  between  Chemicals  and
Pharmaceuticals, but the trend is the same in all three cases.

Table 8: Three digit sectors, percentage of 2-digit sector, highly educated , register data

87

88

89

90

91

92

93

94

95

96

97

98

99

PHARMACEUTICALS 22 % 22 % 27 % 28 % 32 % 31 % 27 % 30 % 32 % 34 % 33 % 32 % 34 %

AIRCRAFT

5 % 5 % 8 % 7 % 8 % 8 % 7 % 6 % 5 % 5 % 5 % 5 % 6 %

TELECOMS.

74 % 76 % 75 % 77 % 73 % 72 % 70 % 69 % 70 % 76 % 77 % 75 % 77 %

The share of highly educated in the 3-digit sectors of the highly educated in the 2-digit sector
is about the same in Aircraft as in the 2-digit sector. The two other sectors differ radically. If
we  compare  with  Table  6,  Pharmaceuticals  have  more  of  the  highly  educated,  and  for
Telecommunications 
in
Telecommunications, not in Postal services!

is  radically  altered.  The  highly  educated  are 

the  picture 

Conclusion – LFS not suitable for studying the chosen hi-tech sectors

The clear – and not surprising conclusion – is that one cannot use the 2-digit breakdown found
in the European Union Labour Force Survey to study a 3-digit sector like telecommunications.
One  is  only  able  to  study  this  sector  together  with  postal  services,  and  these  behave  very
differently  in  most  cases.  This  is  of  course  very  regrettable  because  making  a  comparative
study of   telecommunications  might be of great  policy  interest.  It  is  a  sector  that  is  heavily
influenced by public (de)regulation and of vital importance for the diffusion of various ICTs.

General trends in employment according to LFS and registers

In the period of study employment is influenced by the business cycle and a secular trend of
increasing  female  participation.  From  1987  to  1997  Norway  went  through  a  full  business
cycle. Starting with the last boom year of the previous cycle1987 with no unemployment and
surplus demand for labour, especially highly educated in the big cities. Then going down to the
bottom in the early nineties, 1992 and 1993. The upturn started slowly, but took off from 1996
onwards, levelling out in late 1999.

91

Table 9: Active population and employment, 1987 – 1999, comparing LFS data  and register
data

YEAR ACTIVE

IN %
OF 87

EMPLOYED
REG. DATA

IN % OF 87
REG. DATA

EMPL.
SHARE
NACE

EMPL. SHARE
FIRM ID

EMPL. SHARE
LFS

EMPLOYED
 LFS

3047419 100 % 1701051

100 %

3072498 101 % 1723773

101 %

3091150 101 % 1678527

3097819 102 % 1668969

99 %

98 %

3106601 102 % 16F62767

98 %

3115802 102 % 1675168

3125572 103 % 1687756

98 %

99 %

3138100 103 % 1710916

101 %

3135972 103 % 1945951

114 %

3142404 103 % 1985543

117 %

3151702 103 % 2032761

120 %

3165952 104 % 2080662

122 %

3183534 104 % 2093796

123 %

87

88

89

90

91

92

93

94

95*

96

97

98

99

55,8 %

56,1 %

54,3 %

53,9 %

53,5 %

53,8 %

54,0 %

54,5 %

62,1 %

63,2 %

64,5 %

65,7 %

65,8 %

56,9 %

71,2 %

57,2 %

71,0 %

54,7 %

69,6 %

54,1 %

69,1 %

53,6 %
52,6 %

52,9 %

53,1 %

53,6 %

56,1 %

57,5 %

58,7 %

60,9 %

68,4 %

68,4 %

68,2 %

68,5 %

69,7 %

71,3 %

72,6 %

73,4 %

73,3 %

2 171 000

2 183 000

2 151 000

2 142 000

2 126 000

2 130 000

2 131 000

2 151 000

2 186 000

2 240 000

2 288 000

2 323 000

2 333 000

The active population, all those from 16 years to 70 years old that are able to work is growing
from 1987 – 1999. Since there is an secular trend of higher participation rates of women, the
share of employed persons of the active population is growing.

The  downturn  –  and  for  Norway  a  high  rate  of  unemployment  -  in  the  early  nineties  only
meant  that  the  share  of  employed  persons  stagnated  between  1990  and  1995,  rising  as  the
economy recovered -  if we include the self-employed.  If we measure this by those that work
in a firm with a firm identification number –  including one-person firms –  the employment
share goes down from around 57% in 1987/88  to around 53% in 1992-1993, rising to 60% in
1999.

If  we  compare  the  share  of  employed  in  the  LFS  with the  register  data  it is  about  13  –  15
percentage points lower in the registers than in the LFS.

There are two main reasons why the register data has fewer employees than the LFS. First of
all the register  data has in practice a more restrictive definition of being  employed  than  the
LFS. The firms do not manage to get the temporary, accidental workers into their records –
and some of the ad hoc jobs are for firms that the statistical office do not get data from due to
the temporary, seasonal existence of some of these firms. This bias in the registers is reflected
in  that  the  employment  share  based  on  register  data  goes  down  while  the  LFS  rate  only
stagnates.

Secondly  there  was  less  coverage  in  the  sectors  with  a  lot  of  self-employment  (farming,
fishing, lawyers etc.). In these sectors the registers have been improving, especially from the
change  in  the  system  of  firm  identification  numbers  in  1995,  and  consequently  more
employment is registered.

92

There is a break in the series in1995 with the number of employees increasing by over 235.000
from 1994. The break in the time series is mainly caused by the fact that a lot of one-man firms
were given a NACE classification as a part of the transition from ISIC to NACE. Also some
persons that were not traditionally regarded as one man firms like farmers were given a NACE
code.  So  measured  by  those  with  NACE  codes  the  registers  increased  their  coverage  from
1995 onwards. This increased NACE coverage makes the definition of being employed more
equal in the registers and in the LFS. But the number of people that were employed in firms
(including one man firms) that got an establishment ID did not rise very dramatically so the
number of employed measured by firm ID is still considerably lower

Employment in the hi-tech sectors

We now turn to the sectors of interest for this analysis, the high-tech sectors. But as explained
above, we can either look at the preferred break-down, or the LFS “combined” sectors. First
the pure high-tech sectors.

Table 10: Employment shares, 1987 – 1999, register data

HI-TECH, 3 DIGIT

87

2,7

88

2,6

89

2,5

90

2,5

91

2,6

92

2,6

93

2,6

94

2,7

95

2,8

96

2,4

97

2,4

98

2,4

99

2,7

HI-TECH, 2 DIGIT 13,5

13,2

13,1

12,8

13,1

13,1

13,0

12,8

12,5

10,9

10,8

10,4

10,8

OTHER

15,2

15,0

14,6

14,0

13,8

13,5

12,9

12,6

12,5

11,7

11,9

11,9

11,7

OTHER SECTORS

71,3

71,8

72,3

73,2

73,2

73,4

74,1

74,6

74,9

77,4

77,4

77,7

77,6

Table 11: Employment shares, 1996– 2000, LFS

1996

1997

1998

1999

2000

HI-TECH 2-DIGIT

OTHER
MANUFACT.

OTHER SECTORS

10,8 % 10,9 % 10,7 % 11,0 % 10,5 %

12,1 % 12,0 % 11,8 % 11,2 % 10,8 %

77,1 % 77,2 % 77,6 % 77,8 % 78,7 %

The first table shows that the hi-tech sectors defined on a 3 digit level  have  a small,  stable,
2,7%  share  of  employment.  When  we  are  speaking  of  employment  growth  these  hi-tech
sectors do not seem to have much potential – at least not in Norway. As source of innovative
knowledge, diffused to other sectors as products/services and by human mobility, they are of
course much more important. But as we know – how much more is difficult to measure.

When  we  use  a  2-digit  level,  i.e.  the  breakdown  that  is  the  common  denominator  on  a
European level we are talking about quite another animal, four  times the  size  of  the hi-tech
sectors we wanted to study. And this 2-digit hi-tech sector  has  about  the  same  share  of  the
employment in Norway as in the Netherlands. Actually it is three or four percentage points
higher. The size of the 2-digit hi-tech sectors  are practically the same in the LFS and in the
register data.

The  overall  trends  are  well-known:  traditional  manufacturing  sectors  declining  and  services
growing. We shall not here enter the debate about how much of this that is “real” and how
much that is caused by our concepts of what “manufacturing” is and how we measure it, other

93

than  noting  that  this  is  a  very  interesting  debate  both  conceptually  and  when  it  comes  to
measurement methods. It is important for an empirical based debate about the  “information
society”,  “knowledge-based”  economy  etc.  that  one  avoids  taking  the  standard  industrial
classification as a given.

Table 12: Relative employment shares, nine sectors, 1987 – 1999, register data

OIL AND GAS

CHEMICALS&
PHARMACEUTICALS

OFFICE/COMP EQUIPT

RADIO/TV/COMMS

AEROSPACE&
OTHER TRANSPORT

TELECOM AND POST

COMPUTER SERVICES

RESEARCH

87

1,1

88

1,1

89

1,1

90

1,1

91

1,2

92

1,2

93

1,4

94

1,4

95

1,4

96

1,2

97

1,2

98

1,2

99

1,3

1,0

0,9

0,9

0,9

0,9

0,9

0,9

0,9

0,9

0,8

0,8

0,8

0,7

0,2

0,4

2,3

7,3

0,6

0,6

0,2

0,4

2,0

7,4

0,6

0,6

0,2

0,4

1,9

7,4

0,6

0,6

0,2

0,3

1,8

7,2

0,6

0,6

0,1

0,3

1,9

7,3

0,6

0,7

0,1

0,2

2,0

7,2

0,7

0,7

0,1

0,2

2,0

6,9

0,7

0,7

0,0

0,2

2,0

6,8

0,7

0,7

0,0

0,2

1,9

6,7

0,7

0,7

0,0

0,3

1,9

5,8

0,7

0,6

0,0

0,3

1,9

5,6

0,8

0,5

0,1

0,3

1,9

5,0

0,9

0,6

0,0

0,3

1,9

5,1

1,1

0,5

OTHER MANUFACT.

15,2

15,0

14,6

14,0

13,8

13,5

12,9

12,6

12,5

12,0

12,1

12,1

11,9

OTHER SECTORS

71,3

71,8

72,3

73,2

73,2

73,4

74,1

74,6

74,9

76,8

76,8

77,2

77,1

TOTAL 

100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0

Also on this detailed  level the overall picture is one  of  remarkably  stability.  The  only  clear
trends  among  the  hi-tech  sectors  are  oil  and  computer  services  (CS)  increasing;  and
Post&Telecom decreasing.

Table 13: Relative employment shares, highly educated, nine sectors, 1987 – 1999, register
data

87

88

89

90

91

92

93

94

95

96

97

98

99

OIL AND GAS

2,3

2,2

2,1

2,1

2,1

2,2

2,3

2,3

2,2

1,8

1,7

1,7

1,7

CHEMICALS&
PHARMACEUTICALS

OFFICE/COMP EQUIPT

RADIO/TV/COMMS

AEROSPACE&OTHER
TRANSPORT

TELECOM AND POST

COMPUTER SERVICES

RESEARCH

OTHER MANUFACT.

0,8

0,7

0,6

0,6

0,7

0,7

0,8

0,8

0,8

0,8

0,8

0,7

0,7

0,4

0,3

0,4

4,1

1,0

2,8

4,0

0,3

0,3

0,4

4,3

1,1

2,8

4,0

0,3

0,4

0,4

4,3

1,2

2,8

4,0

0,3

0,4

0,4

4,2

1,1

2,8

3,9

0,2

0,4

0,5

4,1

1,1

3,2

3,9

0,2

0,3

0,5

4,0

1,3

2,8

3,9

0,1

0,3

0,5

3,8

1,3

2,7

3,7

0,1

0,3

0,6

3,7

1,3

2,6

3,8

0,1

0,3

0,6

3,8

1,3

2,4

3,8

0,1

0,4

0,7

3,6

1,4

1,9

4,0

0,0

0,4

0,7

3,6

1,6

1,7

4,1

0,1

0,4

0,7

3,3

1,9

1,8

4,1

0,0

0,3

0,7

3,7

2,3

1,7

4,1

OTHER SECTORS

83,9

83,9

83,9

84,1

83,8

84,2

84,6

84,7

84,7

85,4

85,4

85,3

84,6

TOTAL

100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0

94

Again, the overall picture is one of remarkably stability. The only clear trends among the hi-
tech sectors are Oil whose relative share is declining. The oil sector had from the start a high
share  of  highly  educated,  and  has  become  a  mature  sector,  not  expanding  so  rapidly  any
longer, and not recruiting as many highly educated as before. But this treats all highly educated
as equal, if we had looked at fields of study the oil sector is one of the 2-digit sectors with the
highest share of engineers.

Stocks of highly educated and knowledge workers

Figure 3: Share of highly educated, 1987-1999, register data and LFS

25,0 %

20,0 %

15,0 %

10,0 %

5,0 %

0,0 %

87

88

89

90

91

92

93

94

95

96

97

98

99

20

H-Ed. Register data

H-Ed, LFS

The figure shows the rapid increase in the share of highly educated in the workforce. It is the
continuation of a trend that took off in the mid-sixties. In a historical perspective this will turn
the former elite in society having the highest education into a substantial group. We compare
the register data and the LFS for the last years. Again they are in general agreement. The small
difference in level is probably mainly the result that in the register data there is a time lag in the
education information used in relation to the LFS.

95

Table 14: The educational composition, 1987 –1999, register data

ISCED 1-5

87=100

ISCED 6+7

87=100

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

1467679

100 %

155179

1497039

102 %

164601

1506711

103 %

173044

1460243

1441421

1424597

1421876

1417633

1423916

99 %

98 %

97 %

97 %

97 %

97 %

187054

198598

209523

223400

243791

258413

1617209

110 %

293042

1633898

111 %

312611

1653783

113 %

334397

1675924

114 %

353284

100 %

106 %

112 %

121 %

128 %

135 %

144 %

157 %

167 %

189 %

201 %

215 %

228 %

The table shows that the absolute number of ISCED 1-5 (lower educated) is fairly stable, the
variations  in  the  share  of  number  of  employed  is  caused  by  the  business  cycle.  The  jump
between 1995-1996 is just a reflection of the increased coverage of the registers resulting in
more than a 10% increase. On the contrary all the entrants to the labour market –  that is in
practice  young  people  with  higher  education,  many  of  them  have  at  least  15-16  years  of
education, and many of them will be classified in ISCED 6 or higher. To a certain extent this is
the  question  of  “inflation”.  Some  major  educational  fields  like  health  and  engineering  have
changed from taking 2-years to 3-years and consequently moving from ISCED 5 to ISCED 6
so there is a certain “inflation” in the numbers.

Table 15: Sectoral shares of highly educated, 1997 – 1999, register data

87

88

89

90

91

92

93

94

95

96

97

98

99

OIL AND GAS

21,9

21,4

20,6

21,8 22,4 23,3 23,0 24,3 24,5 25,1 24,3 23,8 23,8

CHEMICALS&

PHARMACEUTICALS 7,8

7,8

7,1

8,3

9,0

10,0 11,5 13,0 13,6 14,3 15,2 15,5 15,6

OFFICE/COMP
EQUIPT

20,3

19,8

RADIO/TV/COMMS

7,2

7,7

AEROSPACE&

OTHER TRANSPORT 1,8

TELECOM AND POST 5,3

1,9

5,7

COMPUTER
SERVICES

17,2

17,5

RESEARCH

43,7

45,5

OTHER MANUFACT. 2,5

2,7

OTHER SECTORS

11,2

11,6

TOTAL

9,6

9,9

19,5

10,4

2,2

5,9

19,2

47,0

2,8

12,0

10,3

18,9 19,0 19,6 16,1 19,6 17,7 16,6 19,0 27,4 21,2

12,2 13,5 14,9 16,7 19,5 21,8 23,3 25,3 24,8 20,4

2,7

6,5

3,0

6,7

3,1

7,0

3,1

7,3

4,1

7,9

4,6

8,6

5,5

9,5

5,6

6,4

6,9

10,3 11,3 12,5

20,3 21,7 23,3 24,6 27,2 29,9 32,3 33,2 34,9 36,7

50,3 53,1 51,5 52,1 52,9 54,5 53,2 53,5 55,5 56,7

3,2

3,5

3,7

3,8

4,4

4,7

5,2

5,5

5,7

6,1

13,0 13,9 14,7 15,5 16,7 17,4 17,3 18,0 18,7 19,2

11,4 12,1 12,8 13,6 14,7 15,4 15,5 16,2 16,9 17,5

96

This table gives an overview of the share of highly educated. There are no great surprises, but
it is worth noting that Oil and Gas – an extraction sector – has the third highest share of the
highly educated. Research is first, followed by Computer  services.  But  again  one should be
aware of the consequences of the division of labour in society. If research was done – not in a
specialised  public/semi-public  institute  but  in  firms  “closer”  to  production  of  marketable
products/services, then one would not have a share of higher educated of 50%, but more like
Computer services and Oil.

Figure 4: Unemployment rates, five educational categories, 1975 - 1998

primary education, i.e.
l
11
th
vocational
d
11 12
higher university education,

ti

th

17

secondary
d
lower university
13 16
d

ti

ti

i

11 12

The  figure  shows  the  unemployment  rates  of  five  different  educational  groups.  In  1975
Norway was practically speaking in a full employment situation. As unemployment came with
the stagnating growth  of the  economy  the  lower  educated  were  hit  more  by  unemployment
than the highly educated. There are many possible explanations for this. The level of wages for
the  lower  educated  might  mean  that  they  are  not  competitive  at  the  going  wage.  Some
economists would argue that if  the  less educated just lowered  their  reservation wage  –  they
would be employed. Others would argue that if you cannot handle computers and other new
technologies, then you will be unemployed no matter how low the wages you are willing to
work  for18.  One  gets  the  combined  effect  that  there  are  just  fewer  jobs  that  do  not  demand
education – and that even in those jobs employers prefer the highest possible education.

18 One might also argue – inspired by Keynes – that the consequences for aggregate
demand might just start a downward spiral that does not stop before you fix wages, increase
government spending etc.

97

                                                     
Training

The information on training is only available from the Norwegian LFS. But before we start
analysing  the  provision  of  training  it  is  necessary  to  get  a  an  overview  with  respect  to  the
distribution of the three “top” occupational groups Managers, Professionals and Technicians.

Table 16: Stocks of knowledge workers (ISCO 1,2,3  Managers, Prof. and Technicians), 1996-
2000, LFS

SECTOR

1996

1997

1998

1999

2000

OIL AND GAS

CHEMICALS&
PHARMACEUTICALS

OFFICE/COMP EQUIPT

RADIO/TV/COMMS

AEROSPACE&OTHER
TRANSPORT

TELECOM AND POST

COMPUTER SERVICES

RESEARCH

OTHER MANUFACT.

OTHER SECTORS

58 % 51 % 50 % 61 % 60 %

35 % 37 % 33 % 35 % 37 %

63 % 62 % 100 % 76 % 34 %

57 % 55 % 53 % 45 % 38 %

18 % 21 % 19 % 24 % 30 %

50 % 52 % 53 % 56 % 62 %

81 % 87 % 91 % 90 % 88 %

73 % 76 % 78 % 78 % 92 %

23 % 22 % 22 % 23 % 25 %

37 % 37 % 38 % 39 % 40 %

ALL

36 % 37 % 37 % 39 % 41 %

The  very  high  share  of  knowledge  workers  in  all  the  hi-tech  sectors  and  the  rest  of  the
economy only shows that the classification of occupations – as the ISCO-88 standard does it –
in our opinion is not well adapted to a society with a general high level of formal education.
The use of both educational criteria and job content criteria make some of the ISCO groups
very inclusive. It makes it hard to use ISCO to tell what people actually are doing and what
their position in the workplace hierarchy is. It is not possible to decide if engineers are doing
more engineering or administrative work, and how that is changing.

98

Table 17: The amount of training last four weeks, 1996-2000, LFS, all employees

SECTOR

OIL AND GAS

CHEMICALS&
PHARMACEUTICALS

OFFICE/COMP EQUIPT

RADIO/TV/COMMS

AEROSPACE&OTHER
TRANSPORT

TELECOM AND POST

COMPUTER SERVICES

RESEARCH

OTHER MANUFACT.

OTHER SECTORS

ALL

1996

22 %

15 %

0 %

8 %

12 %

22 %

23 %

21 %

11 %

17 %

17 %

1997

24 %

22 %

0 %

24 %

12 %

22 %

16 %

14 %

12 %

17 %

17 %

1998

25 %

16 %

27 %

17 %

13 %

20 %

18 %

14 %

10 %

16 %

16 %

1999

22 %

20 %

0 %

11 %

15 %

21 %

22 %

15 %

10 %

16 %

15 %

2000

15 %

13 %

0 %

2 %

15 %

17 %

14 %

19 %

9 %

15 %

15 %

In the sectors  with  few  employees  the  numbers  are  very  unreliable.  The  extreme  case  is  of
course  “Office  and  computer  equipment” that  has  zero  observations  of  people  that  have
received training in four of five years, but also “Radio/tv/comms” are too small to get reliable
estimates.  In  the  sectors  with  more  reliable  estimates  there  are  some  differences.  “Other
Manufacturing”  has  a  low  share.  On  the  opposite  side,  “Telecom  and  Post”  are  among  the
highest. This is probably due to the long and strong traditions of “internal” training to cover
the need for specialised, practical skills not traditionally supplied by the educational system.

Table 18: The amount of training last four weeks, 1996-2000, LFS, Knowledge workers versus
other occupations

SECTOR

OIL AND GAS

CHEM. AND PHARMA.

OFFICE/COMP EQUIPT

RADIO/TV/COMMS

AEROSPACE&
OTHER TRANSPORT

TELECOM AND POST

COMPUTER SERVICES

RESEARCH

OTHER MANUFACT.

OTHER SECTORS

ALL

1996

K-
27 %

19 %

0 %

10 %

23 %

30 %

27 %

25 %

17 %

24 %

24 %

1996

1997

1997

1998

1998

1999

1999

2000

2000

OTHER
15 %

K-

OTHER K-

OTHER

K-

30 % 19 % 21 % 28 % 20 %

OTHER
OTHER
K-
24 % 16 % 13 %

12 %

33 % 16 % 25 % 12 % 36 %

12 % 14 % 13 %

0 %

5 %

9 %

15 %

5 %

12 %

10 %

13 %

13 %

0 %

39 %

0 % 27 %

0%

5 % 29 % 3 %

0 %

15 %

0 %

8 %

0 %

5 %

0 %

0 %

20 % 10 % 19 % 11 % 16 %

15 % 14 % 15 %

28 % 16 % 24 % 16 % 24 %

16 % 20 % 11 %

18 %

8 % 19 % 4 %

12 % 22 % 16 % 4 %

21 %

13 %

31 % 14 % 11 %

22 % 19 % 14 %

18 % 10 % 13 % 10 % 16 %

9 %

12 %

8 %

25 % 13 % 23 % 12 % 21 %

12 % 21 % 12 %

24 % 13 % 22 % 12 % 21 %

12 % 20 % 11 %

Although there are cases where the other occupations have received more training, the clear
tendency, both in the hi-tech sectors and for the economy as a whole, is that the knowledge
workers get significantly more training. The difference is almost 10 percentage points for the

99

economy as a whole. One might think that the other occupations in the hi-tech branches did get
relatively more training, being after all part of sectors marked by rapid technological change.
The numbers do not give much support for this hypothesis. There are of course problems with
small numbers of observations.  One  example  being  Computer  Services,  where  80-90  %  are
“knowledge  workers”,  the  non-knowledge  workers  become  a  very  marginal  group  and  the
estimates for “Other” (occupations) become very unreliable.

Table 19: The amount of training last four weeks, 1996-2000, LFS, Highly educated and lower
educated

1996

1997

1998

1999

2000

2000

HIGH

LOW HIGH LOW HIGH LOW HIGH LOW HIGH LOW

OIL AND GAS

21 % 23 % 24 % 25 % 16 % 28 % 18 % 23 % 10 % 16 %

CHEM. AND PHARMA.

21 % 13 % 36 % 20 % 10 % 18 % 28 % 19 % 12 % 14 %

OFFICE/COMP EQUIPT

0 %

0 % 0 % 0 % 28 % 26 % 0 % 0 % 0 % 0 %

RADIO/TV/COMMS

19 %

5 % 41 % 20 % 10 % 18 % 6 % 12 % 10 % 0 %

AEROSPACE&OTHER
TRANSPORT

31 % 11 % 35 % 10 % 20 % 12 % 16 % 14 % 7 % 16 %

TELECOM AND POST

36 % 20 % 27 % 22 % 23 % 20 % 27 % 19 % 25 % 15 %

COMPUTER SERVICES

42 % 13 % 13 % 18 % 24 % 13 % 23 % 22 % 16 % 13 %

RESEARCH

16 % 24 % 11 % 18 % 16 % 10 % 11 % 19 % 17 % 22 %

OTHER MANUFACT.

15 % 11 % 19 % 11 % 22 % 9 % 23 % 10 % 15 % 8 %

OTHER SECTORS

28 % 15 % 28 % 15 % 25 % 14 % 24 % 14 % 23 % 13 %

ALL

28 % 15 % 27 % 15 % 24 % 14 % 24 % 14 % 22 % 13 %

The overall picture is the same for the highly educated versus the lower educated. There is a
declining rate of training, the highly educated get more training than the lower educated, but in
one sector – “oil and gas” the relationship is reversed. Also in the research sector the lower
gets more training, except for one year. But the problem of small sample sizes makes it hard be
100% sure whether this is really so, or just a consequence of the “cell-size” problem. But since
the lower educated in Oil and Research is a substantial part of the sectors, 75 – 50%, this is
probably the case. One possible explanation might be that researchers and engineers in the Oil
sector in a certain sense are continuously trained, working with new and challenging projects,
conferences and seminars being a “natural part” of  the projects.  This  is  in  contrast to  lower
educated that are more explicitly taken away from routine work to get training.

100

Mobility according to the LFS

These are “job to job” mobility rates. That is the rate of those being employed in year T and
T+1 and changing “job”, meaning workplace. It is of course impossible to know exactly how
the respondents understand the questions: “When did your last job start” and “Have you been
in  this  job  more  than  a  year”,  which  are  the  two  questions  concerning  mobility  in  the
Norwegian  LFS.  But  the  way  questions  are  formulated  (in  Norwegian)  the  most  common
interpretation would be a change of establishment/workplace. Since these are the "job to job"
(j2j)  mobility  rates  they  are  not  comparable  with  the  UK  rates.  The  UK  rates  are  the
intersectoral rates, i.e. only those changing from one industrial sector to another are considered
mobile.

The  intersectoral  rates  can  in  principle  be  calculated  since  people  are  interviewed  eight
quarters in a row. But regrettably the data was not yet at the time of writing prepared as panel
data that would make possible the computation of the intersectoral rates from the LFS.

Table 20: Mobility, 1996-2000, LFS, all employees, job to job, inflow mobility rate

SECTOR

1996

1997

1998

1999

2000

OIL AND GAS

8,6 %

11,9 % 16,6 % 8,7 % 8,0 %

CHEMICALS&
PHARMACEUTICALS

OFFICE/COMP EQUIPT

RADIO/TV/COMMS

AEROSPACE&OTHER
TRANSPORT

TELECOM AND POST

8,4 %

12,0 % 12,1 % 8,4 % 7,7 %

0,0 %

23,8 % 0,0 % 0,0 % 50,9 %

29,6 % 12,2 % 6,6 % 19,8 % 8,0 %

12,7 % 12,7 % 12,1 % 12,7 % 10,6 %

12,0 % 13,1 % 16,5 % 14,1 % 15,1 %

COMPUTER SERVICES

18,8 % 22,8 % 31,5 % 31,0 % 23,6 %

RESEARCH

17,1 % 15,9 % 13,5 % 12,9 % 11,9 %

OTHER MANUFACT.

OTHER SECTORS

15,5 % 16,7 % 16,3 % 14,0 % 13,6 %

18,5 % 18,3 % 19,3 % 18,0 % 18,1 %

ALL

17,5 % 17,7 % 18,6 % 17,3 % 17,2 %

This  table  shows  that  the  hi-tech  sectors  do  not  have  a  higher  mobility  than  the  rest  of  the
economy. This is to a large extent an age effect. Since sectors with a higher portion of highly
educated will – on average – have persons who are older before they start working – and since
mobility declines rapidly and monotonically with rising age. On the other hand, people with
higher education are slightly more mobile than persons of the same age with less education.  In
“Computer services” where the employees are young and well educated the mobility rate is
consequently the highest.

101

Table 21: Mobility, j2j, 1996-2000, LFS, all employees, three age groups

16 - 24

25 -54 OVER 55

OIL AND GAS

46,8 % 9,9 %

2,5 %

CHEMICALS&
PHARMACEUTICALS

54,2 % 8,1 %

2,4 %

OFFICE/COMP EQUIPT

72,7 % 7,0 %

0,0 %

RADIO/TV/COMMS

90,2 % 13,7 % 5,6 %

AEROSPACE&OTHER
TRANSPORT

TELECOM AND POST

42,7 % 9,8 %

4,6 %

52,7 % 12,4 % 3,1 %

COMPUTER SERVICES

70,7 % 24,3 % 10,2 %

RESEARCH

58,6 % 15,4 % 4,1 %

OTHER MANUFACT.

48,7 % 11,9 % 3,6 %

OTHER SECTORS

51,9 % 15,7 % 4,8 %

ALL EMPLOYEES

51,6 % 15,0 % 4,6 %

The table shows the radical difference in mobility between young and older generations, but
still the sector specific mobility is clearly visible. Computer services is most mobile in all age
groups. This high mobility clearly shows the high rate of technical development in this sector.
It reflects the hectic “buy-up needed competence” strategy that is common in this sector. In
addition the high rate also reflects too strong competition for competent persons that in lead
the  “dot-com”  years  employers  to  overbid  each  other.  The  employees,  of  course,  took
advantage  of  this  “sellers  market”.  Such  high  mobility  is  a  way  of  diffusing  competence
rapidly, but it also has well-known negative consequences: Projects suffering from key persons
(the  most  competent)  leaving  before  they  are  finished,  increased  wage  costs  with  no
corresponding increase in productivity.

Table 22: Age composition, LFS, all employees, three age groups

16 - 24

25 -54 OVER 55

OIL AND GAS

3,8 % 88,6 % 7,5 %

CHEMICALS&
PHARMACEUTICALS

5,8 % 78,5 % 15,7 %

OFFICE/COMP EQUIPT

12,8 % 80,3 % 6,8 %

RADIO/TV/COMMS

3,9 % 84,2 % 11,9 %

AEROSPACE&OTHER
TRANSPORT

9,0 % 80,8 % 10,2 %

TELECOM AND POST

7,8 % 78,2 % 14,0 %

COMPUTER SERVICES

6,0 % 88,8 % 5,2 %

RESEARCH

3,9 % 79,8 % 16,2 %

OTHER MANUFACT.

12,6 % 73,4 % 14,0 %

OTHER SECTORS

12,5 % 73,2 % 14,4 %

ALL EMPLOYEES

11,9 % 74,0 % 14,1 %

102

The  table  shows  as  expected  that  the  hi-tech  sectors  have  less  young  employees  since  they
require more people with long education. The “young” branches like “Oil” and CS have less
over 55s, whereas an established sector like “Research” has more.

Table 23: Mobility, j2j, 1996-2000, LFS, Educational groups

HIGHLY EDUCATED

LOWER EDUCATED

YEAR

1996

1997

1998

1999

2000

1996

1997

1998

1999 2000

OIL AND GAS

9 % 18 % 11 % 9 % 17 % 9 % 9 % 19 % 8 % 5 %

CHEMICALS&
PHARMACEUTICALS

7 % 19 % 26 % 10 % 17 % 9 % 11 % 9 % 8 % 6 %

OFFICE/COMP EQUIPT

0 % 0 % 0 % 0 % 0 % 0 % 27 % 0 % 0 % 60 %

RADIO/TV/COMMS

24 % 35 % 0 % 22 % 0 % 31 % 8 % 8 % 19 % 10 %

AEROSPACE&
OTHER TRANSPORT

25 % 18 % 17 % 22 % 12 % 12 % 12 % 12 % 11 % 10 %

TELECOM AND POST

31 % 23 % 23 % 20 % 22 % 10 % 12 % 15 % 13 % 14 %

COMPUTER SERVICES

16 % 29 % 36 % 29 % 20 % 20 % 20 % 29 % 32 % 26 %

RESEARCH

18 % 10 % 19 % 13 % 7 % 19 % 22 % 6 % 12 % 29 %

OTHER MANUFACT.

17 % 19 % 31 % 24 % 20 % 15 % 16 % 15 % 13 % 13 %

OTHER SECTORS

17 % 19 % 20 % 18 % 16 % 19 % 18 % 19 % 18 % 19 %

ALL

17 % 19 % 21 % 18 % 17 % 17 % 17 % 18 % 17 % 17 %

If we look at the mobility rate for all highly educated employees it rises from 17% to 21% and
falls back to 17%. One hypothesis is that as everybody realises that the good times are back
again  in  1996-1998,  there  are  accumulated  needs  for  job  changes.  Firms  expand,  fuse  etc.
when  we  are  in  these  “dot-com”  years.  After  the  first  rush,  things  slow  down  a  little.  The
tendency is that the highly educated are more mobile.

Table 24: Mobility, j2j, 1996-2000, LFS, Occupational groups

MANAG., PROF.&TECH

OTHER OCCUPATIONS

YEAR

1996

1997

1998

1999

2000

1996

1997

1998

1999 2000

OIL AND GAS

7 % 12 % 17 % 9 % 9 % 11 % 12 % 16 % 8 % 7 %

CHEMICALS&PHARMACEUTI
CALS

2 % 10 % 8 % 9 % 5 % 12 % 13 % 14 % 8 % 10 %

OFFICE/COMP EQUIPT

0 % 0 % 0 % 0 % 0 % 0 % 63 % 0% 0 % 78 %

RADIO/TV/COMMS

20 % 14 % 0 % 27 % 0 % 43 % 10 % 14 % 14 % 13 %

AEROSPACE&OTHER
TRANSPORT

6 % 9 % 9 % 7 % 7 % 14 % 14 % 13 % 14 % 12 %

TELECOM AND POST

12 % 11 % 13 % 11 % 14 % 12 % 16 % 20 % 18 % 18 %

COMPUTER SERVICES

19 % 21 % 32 % 30 % 25 % 16 % 35 % 34 % 43 % 20 %

RESEARCH

22 % 13 % 16 % 9 % 15 % 9 % 25 % 4 % 25 % 23 %

OTHER MANUFACT.

10 % 13 % 12 % 12 % 13 % 17 % 18 % 18 % 15 % 14 %

OTHER SECTORS

15 % 15 % 16 % 14 % 13 % 21 % 21 % 22 % 21 % 22 %

14 % 14 % 16 % 14 % 13 % 20 % 20 % 21 % 19 % 20 %

103

Despite the fact that the definition of occupation builds on education the pattern of mobility is
not just a question of small variations. The mobility of “Other occupations” is markedly higher
than for than Managers, Professionals & Technicians. Why this is so is not obvious and it is
beyond the scope of this project to try to find out. But one hypothesis is that since there are
many  small  firms  in  Norway  the  managers  of  these  small  firms  will  be  classified  as  top
managers despite the fact that they have just a few persons below them. The directors of these
firms  are  classified  as  managers,  and  they  are  relatively  stable  since  they  are  often  the
backbone of the firm. In each of the hi-tech sectors the age composition is important. It is clear
that if a large part of the employees are educated, they are older – and the young ones, less
than 30 years, are very mobile.

104

Mobility – register data

Since the main data source for the country studies of UK and Netherlands is the LFS we are
just going to look at some of the mobility rates that one can calculate  with the register data.
The aim is to discuss the LFS-mobility rates data in order to compare levels and patterns.

In this part we will take a look at the difference between the total job-to-job and intersectoral
mobility. The total j2j mobility is every shift of job – also between firms in the same sector.
Intersectoral mobility disregards the mobility internal to the sector. The reason for looking at
intersectoral mobility is to focus on the spread of knowledge/human resources between sectors.
By  definition  the  intersectoral  mobility  rates  will  be  lower  than  the  overall  mobility  rates19.
This reduction of course will be stronger if the skills a person possesses are more or mostly
useful in the sector. The way the sector is defined also plays a great role in this. It is beyond the
scope of this study to discuss how the current industrial classification was constructed. But the
ISIC and NACE classifications are historical products, in many respect compromises between
conflicting needs. Consequently the various sectors are very heterogeneous when it comes to
size  both  generally  and  in  each  country.  The  technologies  used  and  the  skills  needed  in  a
NACE sector might be few or it might be very many. If the sector has a lot of job opportunities
– then internal mobility in the sector will be high, and the intersectoral mobility consequently
lower.

Table 25: Intersectoral mobility, 9 sectors,  1987 – 1999, all employees, register data

CHEM&
PHARMA

OIL
AND
GAS

OFFICE/
COMP
EQUIPT

RADIO/
TV/
COMMS

AEROSPACE&
OTHER
TRANSPORT

TELE
COM &
POST

COMP.
SERV

RESEARCH

OTHER
MANUF.

OTHER
SECTORS

10,0 % 12,1 %

9,5 %

11,7 %

4,7 % 10,9 %

10,6 %

12,9 %

5,4 %

3,9 %

14,8 %

12,3 %

3,3 %

3,6 %

16,8 %

9,2 %

3,2 %

5,5 %

13,5 %

28,4 %

4,9 %

5,1 %

54,5 %

10,7 %

4,5 %

4,4 %

41,5 %

4,3 %

3,4 %

10,5 % 4,9 %

9,0 %

5,6 %

6,4 %

3,6 %

14,9 %

8,1 %

3,9 %

8,5 %

9,6 %

8,4 %

8,9 %

7,9 %

12,0 %

4,8 %

6,4 %

35,9 %

22,1 %

10,7 % 9,0 %

12,0 %

26,7 %

87

88

89

90

91

92

93

94

95

96

97

98

99

17,6 %

14,6 %

10,3 %

8,7 %

6,7 %

7,0 %

7,6 %

7,1 %

10,1 %

10,1 %

12,4 %

11,3 %

18,6 %

6,4 % 16,6 % 10,7 %

7,6 % 13,0 % 11,6 %

7,5 % 11,3 % 10,4 %

5,4 % 12,3 %

8,5 %

5,0 % 10,4 % 13,3 %

5,1 % 10,2 % 11,1 %

5,6 % 12,7 %

7,7 %

4,6 % 9,8 %

10,3 %

10,1 % 13,7 % 15,6 %

7,9 % 10,0 % 17,3 %

15,6 % 19,3 % 12,1 %

10,6 % 12,1 % 11,6 %

9,3 % 14,0 %

9,2 %

8,7 %

9,1 %

6,7 %

5,8 %

5,5 %

5,8 %

4,7 %

5,2 %

8,0 %

7,0 %

9,6 %

9,4 %

9,2 %

2,8 %

2,6 %

2,0 %

2,1 %

1,8 %

1,6 %

1,5 %

1,4 %

2,4 %

2,3 %

2,6 %

2,7 %

2,5 %

19 If we presuppose that there is no change of NACE classification of the firms from one
year to the next. But this is in practice not the case. Firms change and their NACE
classification changes too. But in large sectors this phenomenon is dwarfed by the mobility
internal to the sector.

105

                                                     
The  table  shows  first  of  all  the  consequence  of  a  definition  of  sectors.  The  residual  group
“Other  sectors”  has  a  very  low  mobility  –  not  because  people  change  workplace  less,  but
because most of the mobility is internal to this very residual “sector”. In the extreme case when
the whole economy is defined as one sector there will be no mobility at all. What this low rate
shows  is  the  mobility  between  “the  rest  of  the  economy”  and  some  very  specific  hi-tech
sectors. In contrast the other, but much smaller residual group “Other Manufacturing” has a
level of mobility that is of the same order of magnitude as the high-tech sectors. That shows
that people changing jobs in the “Other manufacturing” sector change as much to the rest of
the economy as the much more narrowly defined high-tech sectors. A little surprising is the
low  mobility  from  “Post  and  Telecom”.  This  might  be  because  Telecommunications  are
dominated  by  the  deregulated  former  public  telecom  company,  the  Postal  service  is  still  a
public company. Both are scattered all over Norway and they are marked by formal rules and
traditions ensure that people who must or want change place, are employed somewhere else in
the  company,  if  possible.  Computer  services  is  a  thoroughly  dynamic  sector  with  a  high
internal and intersectoral mobility.

Table 26: Intersectoral mobility, 9 sectors,  1987 – 1999, highly educated, register data

OIL
AND
GAS

CHEM
&
PHARMA

OFFICE/
COMP
EQUIPT

RADIO/
TV/
COMMS

AEROSPACE&
OTHER
TRANSPORT

10,8 % 18,8 % 12,2 %

18,3 %

24,5 %

COMP.
SERV

TELE
COM
AND
POST
8,7 % 19,9 %

12,4 %

RESEARCH OTHER
MANUF.

OTHER
SECTORS

4,9 % 24,5 % 13,1 %

11,0 %

18,7 %

10,7 % 11,9 %

13,7 %

6,9 %

4,4 %

3,7 %

8,0 %

6,5 %

6,7 %

20,4 %

15,4 %

20,9 %

10,0 %

17,3 %

34,3 %

4,6 % 10,2 % 62,3 %

11,9 %

5,0 %

4,1 %

7,8 %

5,6 %

38,1 %

12,1 %

10,3 %

5,1 %

7,9 % 10,3 %

7,4 %

5,3 %

7,2 %

8,9 %

12,1 %

10,3 %

15,8 %

12,5 %

13,4 %

17,9 %

10,9 %

11,3 %

8,6 % 12,2 %

11,5 %

7,8 % 13,0 %

10,3 %

8,0 % 11,4 %

18,6 %

9,4 %

8,2 % 12,0 %

13,8 %

13,8 %

8,9 % 14,8 %

10,4 %

6,9 % 10,0 %

12,0 %

14,1 %

13,0 % 14,3 %

21,2 %

16,6 %

9,8 % 10,5 %

19,5 %

11,0 % 12,7 % 16,1 %

16,1 %

18,5 %

16,4 % 18,4 %

14,6 %

6,3 %

9,0 %

52,8 %

34,5 %

17,4 %

11,0 % 11,2 %

12,7 %

8,2 % 11,1 % 14,8 %

16,2 %

19,6 %

10,2 % 13,0 %

10,0 %

11,6 %

13,5 %

10,5 %

10,6 %

8,3 %

8,5 %

13,1 %

11,3 %

16,5 %

13,7 %

13,7 %

2,0 %

1,8 %

1,5 %

1,8 %

1,5 %

1,3 %

1,2 %

1,2 %

2,1 %

1,6 %

2,0 %

2,2 %

2,0 %

87

88

89

90

91

92

93

94

95

96

97

98

99

The table shows the highly educated (ISCED 6+7). If we compare with all employees we get
the well know result that the highly educated are more intersectorally mobile – as can be seen
from the table below.

106

Table 27: The difference in intersectoral mobility between all employees and the highly
educated, register data

OIL AND GAS

CHEMICALS&
PHARMACEUTICALS

OFFICE/COMP EQUIPT

RADIO/TV/COMMS

AEROSPACE&
OTHER TRANSPORT

TELECOM AND POST

COMPUTER SERVICES

RESEARCH

OTHER MANUFACT.

OTHER SECTORS

87

88

89

90

91

92

93

94

95

96

97

98

99

-0,80 -0,20

-1,40 -1,10 -0,50 0,30

-0,60 0,20

2,60 -0,90 -2,60 -1,50 2,50

-6,70 -13,60 -4,10 -3,00 -1,30 -5,10 -3,40 -2,20 -5,40 -1,70 -3,80 -2,60 -2,10

-2,70 -2,50

-5,50 -4,10 -3,80 -7,80

3,40 -1,30 -1,60 6,00 -8,10 -16,90 -2,80

-6,70

2,00

-3,10 -0,90 -5,90 -1,20 -4,00 -1,20 -3,60 -0,60 -4,20 -12,40 10,50

-6,90 -4,10

-5,60 -3,90 -6,70 -10,90 -3,30 -4,20 -4,00 -6,50 -6,10 -6,10 -1,00

-2,30 -3,10

-1,00 -2,50 -3,00 -3,10 -3,30 -2,30 -2,90 -1,80 -0,80 -0,40 -0,90

-3,30

1,10

-0,90 -0,70 -0,90 -1,80 -2,00 -0,20 -0,70 -0,50 0,90

0,90 0,90

-1,60 -2,00

-1,00 -1,80 -5,30 -2,70 -2,70 -1,70 -5,60 -2,10 -2,50 -1,00 -0,90

-2,90 -4,40

-3,80 -4,80 -3,90 -7,90 -3,60 -3,30 -5,10 -4,30 -6,90 -4,40 -4,50

0,80

0,80

0,50

0,40

0,30

0,30

0,30

0,30

0,40

0,70

0,60

0,50 0,40

The greyed cells are those where mobility rate for the highly educated is higher than for all
employees. This is mostly the case. There is only one exception to this rule – that is “other
sectors”.  The  reason  is  strange  is  that  it  includes  large  groups  of  highly  educated  in  health,
education and public administration in general. The highly educated are more internal to this
part of the economy than people with less education, who might take “manual” jobs just as
well in the manufacturing and hi-tech branches.

TABLE 28:  MOBILITY RATES, J2J, 1987-1999, REGISTER DATA

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

OIL AND GAS

23,2 10,9 19,8 22,7 8,2 12,6 11,7 7,9 30,6 11,7 13,3 14,3 12,5

CHEMICALS&PHARMACEUTICA 21,9 10,4 5,6

9,5

9,1

7,6 10,9 10,0 8,7

5,3 12,3 9,7 10,2

OFFICE/COMP EQUIPT

8,9 10,5 15,6 27,2 14,1 73,0 78,3 5,5

6,8 67,4 23,7 37,9 10,7

RADIO/TV/COMMS

14,5 39,0 21,5 20,4 28,1 10,1 8,5

5,8 10,7 8,4 22,4 34,2 21,0

AEROSPACE&OTHER

25,4 22,4 14,8 13,7 16,9 12,9 15,1 11,8 20,6 18,7 16,8 16,7 21,5

TELECOM AND POST

12,5 16,5 12,5 22,4 15,0 11,2 18,9 9,5 40,2 11,2 27,6 22,9 21,6

COMPUTER SERVICES

18,1 15,8 14,8 22,8 24,0 16,7 17,8 14,1 19,7 18,2 29,4 21,5 22,6

RESEARCH

15,3 13,8 13,9 14,2 20,0 16,3 24,0 21,0 18,7 11,8 13,5 14,9 9,9

OTHER MANUFACT.

17,1 16,6 14,0 12,5 10,9 11,7 10,4 11,2 14,7 13,2 16,8 16,6 16,5

OTHER SECTORS

18,5 19,6 17,8 18,0 19,5 17,0 15,7 14,5 22,6 20,8 30,4 27,9 25,7

107

This table shows all mobility between workplaces – not only that between sectors. The general
trends in the table have been commented on earlier and will not be repeated. But if one looks
closely  at  Telecom  and  Post  one  will  see  that  in  several  branches  the  rates  fluctuate  10-20
percentage points. So that even when we do not have a  problem of cell size, when a sector
becomes  small,  the  numbers  will  be  jumpy:  both  because  there  are  stochastic  elements,
localised business cycles, but also due to the importance of “firm demography”. In “Oil” the
rate  changes  several  times  more  than  10  percentage  points.  Some  of  this  is  the  result  of
hiring/firing  – huge contracts  lost  or  won,  but  also mergers    and  split-ups  will  “artificially”
influence the mobility rates. Take for example telecoms & post, that has great variations from
1994 onwards. Typically (like in oil&gas) there are a few big firms in these sectors and when
there are organisational changes, this will give high mobility rates without actually implying
any increased transfer of knowledge from one group of workers to another. The high rates are
in a certain sense statistical artefacts.

 Figure 5: Mobility rates, j2j, 1987-1999, register data

45,0 %

40,0 %

35,0 %

30,0 %

25,0 %

20,0 %

15,0 %

10,0 %

5,0 %

0,0 %

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

Oil and Gas
Computer services

Chemicals&Pharmaceuticals
Research

Aerospace&Other Transport
Other Manufact.

Telecom and Post
Other sectors

The very volatile behaviour from 1994 onwards also reflects, as mentioned before, that there
was a change in the firm ID system that, together with the Social Security Authority having
less resources for quality checking of the data, has made the numbers less reliable. Hopefully
these deficiencies will be amended in the future.

108

Table 29: Mobility from Computer Services to other sectors, highly educated

87

88

89

90

91

92

93

94

95

96

97

98

99 TOTAL

OIL AND GAS

2,5

2,0

1,2

1,7

3,0

4,5

1,4

1,9

0,0

1,6

0,8

1,9

0,4

1,4

CHEMICALS
&PHARM.

OFFICE/COMP EQUIPT

RADIO/TV/COMMS

AEROSPACE&
TRANSPORT

TELECOM AND
POST

0,0

1,2

0,6

0,0

0,6

0,8

0,2

0,2

0,9

0,4

0,1

0,4

0,6

0,5

1,4

0,4

11,9

0,2

0,4

0,0

0,2

1,2

0,3

0,4

6,5

0,1

0,2

1,6

1,4

0,4

1,5

0,4

1,1

0,8

1,6

6,8

2,2

1,5

0,9

1,2

1,8

1,6

0,0

0,0

0,0

0,2

0,4

0,6

0,2

0,2

0,7

0,3

0,4

0,6

0,5

0,4

10,1

8,4

7,3

8,4

5,3

3,9

4,7

6,3

7,7

8,5

6,8

6,2

3,8

6,2

COMPUTER SERVICES

22,5 25,1 29,4 44,3 51,5 39,8 31,0 32,1 35,6 47,5 37,0 54,2 41,4

40,8

RESEARCH

OTHER MANUFACT.

2,9

7,6

1,6

6,8

4,3

2,8

3,2

7,7

1,3

3,4

2,7

4,7

1,7

4,1

1,9

6,1

1,6

6,2

1,5

4,5

2,2

5,2

1,0

5,0

1,9

22,8

1,9

8,5

OTHER SECTORS

51,4 54,2 41,0 33,8 33,0 42,1 55,0 43,3 44,8 33,6 40,2 29,2 26,6

37,0

TOTAL 

100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0

The  table  shows  that  persons  from  Computer  Services  move  to  the  other  knowledge  and
intensive  branches  in  greater  proportion    than  the  size  of  these  sectors.  There  is  a  slight
declining  trend  for  Oil  and  Research  which  might  be  explained  by  these  sectors  having
established systems and consequently have moved from an intensive development phase into a
more mature operating phase. It might also be that they are buying more competence, instead
of doing in-house development.

99 TOTAL TOTAL

Table 30: Mobility from Computer Services to other sectors, less educated

OIL AND GAS

CHEMICALS&PHARM.

OFFICE/COMP EQUIPT

RADIO/TV/COMMS

AEROSPACE&TRANSP

TELECOM AND POST

89

1,7

0,4

7,5

0,4

0,8

7,2

90

0,6

0,4

0,3

0,0

1,1

11,6

91

0,8

0,1

0,7

0,2

0,2

6,0

92

1,6

0,6

0,0

1,1

0,2

6,9

93

0,7

0,4

0,0

0,4

0,2

9,8

94

0,5

0,2

0,6

0,8

0,3

5,1

95

0,1

0,1

1,8

0,1

0,3

6,7

96

0,8

0,1

0,3

0,5

0,4

5,8

97

0,3

0,1

1,0

0,2

0,5

4,8

98

0,9

0,2

0,0

9,3

0,5

5,3

0,1

0,2

0,0

0,7

0,3

4,1

0,7

0,2

0,8

1,3

0,5

6,6

COMPUTER SERVICES

22,0

47,5

60,0

42,1

27,8

32,9

28,7

53,0

38,0

41,9

38,3

38,4

RESEARCH

OTHER MANUFACT.

0,0

6,4

0,2

8,8

0,3

2,8

0,8

4,4

0,4

3,6

0,2

8,3

0,4

5,4

0,3

3,3

0,4

3,2

0,2

4,8

1,4

23,3

0,5

8,0

OTHER SECTORS

53,7

29,4

29,1

42,2

56,7

51,3

56,5

35,5

51,6

37,0

31,7

43,0

TOTAL 

100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0 100,0

109

1,4

0,5

1,6

1,6

0,4

6,2

40,8

1,9

8,5

37,0

100,0

The  mobility  patterns  for  those  with  less  education  are  similar,  as  expected  there  are  fewer
going  to Research  and  Oil, and  more  going  to  “Other  sectors”. For  both  groups  there  is  an
outlier in 1999 when “Other manufacturing” suddenly gets more than 20%. This is probably a
result  of  the  change  of  NACE  code  in  one  of  the  major  Computer  service  firms,  being
reclassified into “Manufacturing”.

Summary

The main findings in this part are on two levels:

1)  The  substantial  results  concerning  the  demand  and  supply  for  skills  in  high-tech

sectors.

2)  The usefulness of the two data sources, the Norwegian LFS and the register data.

Substantial findings

The high-tech sectors have a small but stable share of overall employment, about 2,75%  of
total employment.

In the high-tech sectors  the  share  of  highly  educated is  rising, more  than  for  the  rest  of  the
economy, but the variations are great and substantial.

Computer services is the only sector  that shows a rapid growth  in employment. The sector
has also very high mobility. Both indicators reveal a great demand for such skills and a lack of
them.

Telecommunication  goes  through  a  change  in  its  skill  structure  firing  the  lower  skilled  and
rapidly  increasing  the  share  of  highly  skilled.  This  reflects  the  technological  changes  in  all
parts  of  telecommunications,  from  an  analogue/mechanical  technological  base  to  a
digital/computerised base.

Data sources

The  Labour  Force  survey  has  notorious  problems  when  analysing  particular  sectors  and
subdividing  according  to  education  and  occupation.  In  this  case  the  Norwegian  LFS  –
following the EU-standard – does not have the level of detail as the UK LFS. Three sectors,
Aerospace,  Pharmaceuticals  and  Telecommunications  cannot  be  analysed  using  the  LFS.
These 3-digit sectors cannot be analysed on a two digit level, that is together with the other 3-
digit sectors in the same 2-digit group. The 2-digit sector is too heterogeneous. This is shown
by using the register data to analyse all the 3-digit sectors.

The LFS does have a lot of information on training but besides the eternal problem of cell size
(few  observations  of  sub-groups)  it  is  not  as  well  suited  to  analyse  skill  mismatches  as  it
should be.

The LFS also has occupational classification, but since the current standard, the ISCO-88, uses
education as one of its criteria, it does not give as much information about job content – and

110

consequently  on  skills  needed,  as  it  could.  One  work-around  would  be  to  use  the  detailed
occupational classification, but then we meet the problem of cell size again.

The register data are a very valuable source of information, but they lack occupational data.
When  it  comes  to  mobility  rates,  they  are  very  sensitive  to  the  way  the  firm  identification
number system handles such phenomena as, splits, mergers and change of NACE group.

111

References

Ekeland, A. (1998) ‘Indicators for human resources and mobility’, IDEA Paper No.9, STEP
Group, Oslo.

Braadland,  Thor  Egil,  Ekeland,  Anders,  (2001),  ”The  mapping  of  IT-competencies”,  STEP
working paper

OECD (1995) Canberra Manual,  Paris: OECD.

Nås,  Svein  Olav,  Anders  Ekeland,  Eric  J.  Iversen,  Mikael  Åkerblom,  Markku  Virtaharju,
Christian Svanfeldt, Johnny Ullström, 1998, Formal competencies in the innovation systems of
the Nordic countries: An analysis based on register data, STEP report 6-98.

Rosenberg,  Mikael,  1998,  “An  Inventory  of  National  Priorities  and  availability  of  data  in
OECD Countries to  Quantify  Science  and  Technology  Personnel  Mobility  Patterns”,  Room
Document No.2, joint NESTI/TIP/GSS workshop, 17. June 1998, OECD.

Stimpson, Alex. (2001) (forthcoming) Preliminary Results from the HRST Mobility analysis,
Procedings from OECDs Focus Group on Human Mobility, Camire Luxemburg.

Åkerblom,  M.  (1999)  Mobility  of  highly  qualified  manpower,  a  feasibility  study  on  the
possibilities to construct internationally comparable indicators. Report  for Camire (Eurostat)
and OECD. Statistics Finland, 14th December.

112

The supply and demand
of high technology skills
in the Netherlands

Anders Ekeland*, Sami Mahroum** and Thor Egil Braadland***

*Anders Ekeland

STEP group, Norway

**Sami Mahroum

TNO, Netherlands

***Thor Egil Braadland

STEP group, Norway

Introduction

In our study of supply and demand for high technology skills, we are going to take a closer look at
skills in the high-tech sectors of the Dutch economy. High-tech sectors are recognised by requiring
advanced skills, they are often R&D intensive and they often experience rapid changes in products
and  technology.  These  factors  make  these  industries  particularly  interesting  for  a  more  detailed
skills study. In addition, these sectors are often regarded as potential growth sectors. This make this
study important for policy makers concerned with economic development, knowledge flows and
technical change.

The following analysis has two main aims: First, to study the development of high-tech sectors in
terms  of  labour  mobility  and  supply  of  training.  Such  a  study  is  important  for  the  very  simple
reason that we are talking about very skill-intensive industries. Secondly, since this is one of the
first studies of its kind based on Dutch mobility data, the quality of the data is a major concern. A
central question is if the existing data give reliable and/or reasonable results.

The study is divided in four. After a literature review, we look at trends in the economy as a whole
in  order  to  delineate  more  general  trends  in  the  Dutch  economy  from  the  more  specific
development of the chosen high-tech sectors. Thirdly, we look at the distribution of training in the
Dutch  labour  market.  Fourthly,  we  look  at  mobility  of  highly  skilled  persons  in  the  Dutch
economy.

Literature review:

The structure of employment in the Netherlands

The  Netherlands  have  an  active  working  population  of  6.6  million  people,  a  number  which
continues to rise. Although, the number of jobs grew in virtually every sector since 1997, it was the
services sector, which was largely responsible for the 30% rise in the demand for labour. In 1999,
unemployment fell below the 5%. Much of the increased demand for labour came from the private
sector with the number of vacancies rising in that sector from 55000 in 1995 to 158000 in 1999.
Vacancies  in  the  public  sector,  however,  have  risen  from  8000  in  1995  to  14000  in  1999.  The
greatest  portion  of  these  vacancies  was  in  the  education  sector  and  in  local  government.  The
structure of employment in the Dutch labour market has changed radically in the last two decades.
The share of industry in the total employment figure reduced from 33% in 1960 to 18% in 1994,
while the share taken  by  the services sector (excluding  transport  and  communication)  rose  from
40% to 64%.

There has been a shift in the occupational structure of the labour force in the Netherlands: the share
of occupations in the traditional skilled trades, industrial and transport sectors shrank from 43% in
1960  to  23%  in  1994,  while  the  share  of  scientific  and  other  specialist  occupations  and  that  of
policy-making and senior management positions increased from 12% to 25% (SCP, 1996). Across
the  service  sector,  5.3  per  cent  of  vacancies  are  unfilled.  That  reflects  rapid  job  creation  in
industries  such  as  telecommunications  where  liberalisation  in  the  past  few  years  has  more  than
doubled  the  number  of  people  employed  the  sector.  Since  1960,  the  jobs  on  offer  in  the  Dutch

114

labour market have consistently become more highly skilled. Over the past 25 years the average
educational level of the Dutch labour force has also risen sharply. The number of people who have
attended secondary and tertiary education increased from 32% in 1970 to 65% in 1995. This figure
is forecast to exceed 70% in the years ahead.

Table 1: Level of Education of Labour Force between 1995-99

Education/Year

Senior vocational
education
Vocational colleges

1995

2,539

1,120

University education

513

Source: CBS 2000.

1997

2,696

1,172

582

1998

2,648

1,262

625

1999

2,692

1,309

652

The increase in the number of university-educated persons in the labour force between 1995 and
1999 is remarkable. There has been a total increase of 139000 persons in a period of 4 years. The
participation rate of these groups also went up by around 2%; in the case of university-educated
persons, it went up from 78% to 90%.  In a 1998 study analysing the future prospects of the Dutch
Labour market, De Grip et al. (from ROA: Research Centre for Education and the Labour market)
have expected future demand for university graduates to be higher than the demand for graduates
of the HBO (Higher Vocational Education) system. A large part of that is due to the upgrading of
skill  levels  (educational  level)  to  meet  demand  by  employers.  This  means  that  for  various
occupations employers will upgrade demands for education from lower to higher levels (De Grip et
al., 1998). This is particularly true for educational levels in the technical, economics, management,
and social studies fields. Table 2 below provides an insight into the employment situation of Dutch
university graduates one and half year after graduation.

Table 2: Situation of Dutch university graduates one and half year after graduation by field of
education (in %).
Agri.

Health

Techn

Social

Econ.

Law

Arts

Sci.

Total

PhD

Job

Study

Unemployed

Other

22

59

1

9

8

11

82

1

3

2

2

93

0

2

3

68

21

3

5

4

6

76

1

8

9

8

66

3

9

14

1

87

3

5

4

34

52

3

7

4

13

74

2

5

6

Source: Berkhout &Webbink (1997).

Table 2 shows that a large majority of university graduates (87%) were in paid employment one
and  half  years  after  graduation.  A  great  majority  of  medical  graduates  go  on  to  PhD  based
employment (scholarship), while 93% of economics graduates, 87% of law students, and 82% of
technology students land in regular paid jobs within one and half years after graduation. The link
between  the field  of study and area  of  employment  is  becoming less  direct  than  often  assumed.
This is particularly true for graduates in humanities, social sciences, agricultural and environmental
studies, law, and pharmacy. Indeed a survey in 1997 showed that 63% of all people employed in
the ICT sector in the Netherlands did not have a formal education in IT (Intermediair, 1998).

115

This highlights the fact that competition for skills does not take place within sectors only, but also
between  them.  Thus,  what  happens  in  one  particular  sector  of  the  economy  can  have  strong
influence and spill over effect on the labour market of other sectors in the economy. Indeed, in a
more recent survey of graduate employment in the Netherlands (see Allen et al., 2000) it was found
that for the majority of university graduates the content of their field of knowledge is generally not
a  problem,  but  rather  ‘well  developed  personal  characteristics  and  skills’  seem  to  be  the  most
critical  for  their  employment.  These  are  of  various  natures:  oral  and  written  communication,
motivation and interpersonal aspects, the ability to work in teams (social skills), and being sensitive
to commercial issues. Table 3 below provides an overview over the dispersion of graduates over
broad occupational domains. 0 indicates no dispersion while 1 indicates high dispersion. In other
words, 0 indicates that graduates work in the same fields they had graduated in, whereas 1 means
that they work in different fields.

Table 3: Dispersion of fields of studies over occupations of graduates in the Netherlands 1985, 1990,
1993 (Gini-Hirschamn Index)

University Grads.

1985

1990

1993

Humanities

Sciences/Engineering

Medicine

Social Sciences

0.41

0.84

0.36

0.73

Source: Webbink & Paape (1997)

0.68

0.82

0.38

0.88

0.77

0.80

0.48

0.89

As we can see from table 3 graduates in the field of humanities have been increasingly employed in
occupational  sectors  outside  their  traditional  domains  (outside  socio-cultural  sectors  and  arts).
Medicine graduates however continue to be concentrated in the medical and health sector. Sciences
&  engineering  maintain  a  quite  high  level  of  dispersion  across  occupational  sectors  and  are  not
limited to traditional technical occupations.

In contrast, university graduates in medical sciences, veterinary, and dentistry work predominantly
in  the  health  sector  (i.e.  one  specific  occupational  domain),  hence  their  traditional  occupational
sector.  This is perhaps expected due to the specificity of the medical profession, which restricts the
possibility for substitution or replacement with other types of graduates or occupational sectors. In
other  words,  you  cannot  work  in  the  field  of  medicine  health  or  veterinary  science  if  you  have
graduated in social sciences. However, the authors of the survey found that a growing number of
university graduates will find themselves employed in occupational domains (sectors) that are not
specific  to  their  educational  background  and  which  generally  recruit  from  broad  and  diverse
educational sources. The reason behind this trend according to the ROA’s survey is the shortages of
graduate  supply  in  certain  occupational  domains,  leading  to  recruitment  from  non-traditional
educational backgrounds. In some occupational domains, such as Civil Engineering, there will be a
shortage of demand for university graduates. Yet in order for those to adjust, some of them will
seek employment in other professional fields than their own.

The increase in the demand for university graduates in the fields of electrical engineering and IT is
expected to stem largely from occupational domains outside the IT sector itself (what the authors
call ‘alternative occupational domains’). This is perhaps a reflection of the continuing diffusion of
Information  Technology  in  greater  segments  of  the  economy  and  society  in  general.    In  other
words,  a  greater  number  of  IT  &  Electrical  Engineering  graduates  will  be  employed  in  non-
technical  professions.  Commercial  and  administrative  types  of  occupations  are  the  main
competitors with technical occupations for this type of graduate. A majority of the graduates are

116

coming from the HBO (polytechnics level) and MBO levels (two years post high school technical
studies – See Smits et al, 1998).

Figure 1: Total number of IT workers in the Netherlands and the change of their numbers over time.

Number of workers in the IT domain

175000

170000

165000

160000

155000

150000

145000

140000

135000

130000

1992

1993

1994

1995

1996

Smits et al, 1998.

Despite the sharp increase in the number of workers employed in the IT domain in the NL between
1995 and 1996, currently the Dutch labour market for IT graduates is  characterised  by tightness
with an officially recorded vacancy rate of 8.7 per cent. Industry surveys carried out by industry
consultants,  such  as  Berenschot,  put  the  IT  vacancy  rate  in  the  year  2000  higher  than  official
records,  namely  at  14.5  per  cent.  That  is  a  10  years  record  of  vacancy  rates  in  the  sector.  The
greatest type of demand is for systems analysts and programmers. In particular junior programmers
and Information Systems architects are the two positions most difficult to fill (Berenschot, 2000).
Table 4 provides various estimates of the labour shortage in this hi-tech sector for the year 1997 in
the Netherlands.

Table 4: Various estimates of the labour shortage in the ICT sector in the Netherlands in 1997
Survey

ICT shortages

ROA

FENIT

PWC

PWC: 1998

10,000

17800

21900

These numbers, however, do not take into consideration the substitute labour flows coming from
other  educational  and  occupational  backgrounds.  Since,  as  we  have  seen,  around  63%  of  those
employed in the ICT sector are ‘converts’ from other sectors or educational backgrounds.

Major existing national data Sets and surveys

The  main  existing  data  sets  on  the  characteristics  of  the  Dutch  employees  with  regard  to
educational and occupational background can be found in the OSA labour supply panel. The OSA
labour  force  survey,  first  fielded  in  1985,  is  a  face-to-face  biannual  panel  survey  among  a
representative  sample  of  some  2,000  households.  Its  logical  counterpart  is  the  biannual  OSA
labour demand panel study, first conducted in 1989, among some 2,500 firms and  institutions,
which is fielded in rotation to the labour supply panel. The OSA conducts a survey every two years

117

to collect data about the (potential) labour force in the Netherlands: the OSA Labour Supply Panel.
The panel targets members of households between 16 and 65 years of age, who are not following
daytime education. The survey is aimed at  finding  out about  respondents' employment  situation,
and about their behaviour in the labour market. Also, information is  collected about aspects  that
may be expected to influence their decision whether to participate in the labour market. The first
wave of the OSA Labour Supply Panel was carried out in the spring of 1985. Subsequent surveys
have taken place every two years, in the fall (from 1986 to 1998).

In  addition  there  is  the  Enquête  Beroepsbevolking  (EBB),  which  is  a  sample  survey  among
almost all Dutch households. Only people  living in institutions  and establishments are excluded.
Statistics Netherlands started the survey in 1987. Since then more than 100,000 persons have been
interviewed every year. The results of the EBB should be considered as an estimate. However, this
survey  does  not  provide  complete  or  wide  ranging  information  on  the  previous  occupational
background which makes it less useful for the purpose of our study in trying to track the mobility
rates between sectors. However, an extended version of the EBB, which is made available only to
Eurostat, does provide to some knowledge of the retrospective employment status of persons in the
technological  sectors  that  are  of  interest  to  us.  This  database  has  been  used  in  this  study.  The
advantage of the Eurostat database is that it is the best available data on the Dutch labour force that
is comparable to other EU and EEA countries.

List of references

Allen, J, Boezerooy, P., de Weert, E., & van der Velden,  R., (2000), Higher Education & Graduate
Employment in the Netherlands ROA paper # 157

Berenschot, 2000- www.berenschot.nl

Berkhout,  P.H.G  &  H.D.  Webbink  (1997)  Goede  Studies,  De  Beste  Banen.  Stichting  voor
Economisch Onderzoek der Universiteit van Amsterdam.

Centraal Bureau voor Statistics : www.cbs.nl

Enquête Beroepsbevolking:  www.cbs.nl

A. De Grip, Borghans, L., Smits W., 1998, Future Developments in the Job Level and Domain of
Highly-skilled workers. ROA paper # 126.

Intermediair, 1998: http://www.intermediar.nl/

Organisatie voor Strategisch Arbeidsonderzoek :  http://osa.kub.nl/

PriceWaterhouseCoopers 1998: An unpublished study for the Ministry of Economic Affairs.

ROA: Researchcentrum voor Onderwijs en Arbeidsmarkt (ROA)

Smits, W., Delmee, J.Grip, A.De. (1998) De arbeidsmarkt voor informatici. ROA-R-1998/2

SOC 1996: Social and Cultural Surveys 1996 [Sociale en culturele verkenningen 1996

Webbink , D., & Paape, A. (1997) De dynamisch relatie tussen hoger onderwijs en arbeidsmarkt
(Den Haag, Ministry of Education, Science, & Culture).

118

Employment in the Dutch Economy

Industrial classification

The study period is 1993 to 19991. We use three broad classes of education; Low, Medium and
High. The  “High”  group  includes  all  education to  University level,  from  short  one-  or  two-year
courses to a Ph.D. 2

The  high-tech  industries  are  defined  on  a  quite  detailed  level  in  the  traditional  industry
classification3. However, as it is not possible to get data for employment and education on such a
detailed  level,  we  had  to  settle  for  data  for  a  two-digit  level.  This  means  we  are  using  slightly
broader categories4. The industries studied in this paper are listed in Table 5.

Table 5: Industrial sectors, actual break down – general analysis
Extraction of crude petroleum and natural gas; related services, excluding
surveying
Chemicals & pharmaceuticals

11

24

30 Manufacture of office machinery and computers

32 Manufacture of radio, television and communication equipment and apparatus

35

64

72

73

Aerospace & other transport

Telecom and post

Computers and related activities

Research and development

Using the 2-digit industry level is slightly problematic for our purpose, as we now cover broader
areas  than  what  are generally regarded as high-tech activities. For example,  we  wanted  to  study
pharmaceuticals,  but  end  up  looking  at  both  chemicals  and  pharmaceuticals.  In  the  same  way,
Aerospace now includes Other transport, and Telecommunications includes Postal services.  This
compromises our study to some degree. Branches like manufacturing of chemicals, transport and
postal  services  are  generally  known  to  have  less  highly  educated  staff  and  being  less  high-tech.
However, only a detailed discussion building on information from other sources could really tell

1 Initially, we wanted to use the period 1992 – 1999, but in the Dutch case, the NACE standard
was not implemented before 1993, and although there exists some rough methods for making
the former ISIC classification comparable with NACE we felt that it was not appropriate to use
them in this context. Consequently the time series had to start with 1993.
2 During this period, there has been a change in educational classification, the years 1993 –
1997 using the “old” international standard, the ISCED-76 and from 1998 onwards the “new”
ISCED-97. Regrettably there is no mapping between the old and new standard that makes it
possible to have a reasonably detailed breakdown of educational levels. This is a general
problem and is not something specific to the Dutch data. In our opinion it would have been more
fruitful to make a group of the university level qualifications that are more than three years, but
that is not possible given the change in the educational classification – and as we will see below
– there are major problems of sample size in the Labour Force Surveys. Problems that would
only increase if one used a more detailed educational breakdown. See Appendix for details.
3 See Appendix, Table, for an overview of high-.tech industries and NACE codes
4 That may be due to the fact that the LFS data used here is the data from Eurostat. These data
are aggregated and harmonised versions of the Dutch LFS.  It is often the case that the data in
the national LFS have more detailed breakdowns of many variables.

119

                                                     
how much this would affect the results and consequently the conclusions. We do not have data to
judge the effects at this point.

General trends in employment, 1993 – 1997

The Dutch business cycle has followed the general European trend in the post 1993 period, with
growth in employment stagnating and later on a falling unemployment rate. The Dutch Economy
has had a longer period of high unemployment than Norway. From a low level in the mid seventies
it settled to a new level – between 6-11%. Only in the last two years did the unemployment rate go
lower.

120

Table 6: Registered unemployment rate, Netherlands 1970-1999

Males

Females

1970

1971

1972

1973

1974

1975

1976

1977

1978

1979

1980

1981

1982

1983

1984

1985

1986

1987

1988

1989

1990

1991

1992

1993

1994

1995

1996

1997

1998

1999

36

51

91

88

107

153

160

145

134

123

141

217

333

446

425

358

310

278

281

244

209

190

195

240

283

260

240

199

155

115

8

11

17

21

28

42

51

58

67

71

76

100

135

166

166

153

164

178

173

163

149

145

141

175

203

204

201

176

132

106

All

44

62

108

110

135

195

211

204

201

194

217

317

468

612

591

511

474

456

453

407

358

334

336

415

486

464

440

375

287

221

Males

Females

All

1,4

2,6

2,5

3,0

4,3

4,4

4,0

3,7

3,4

3,9

6,0

9,2

0,9

1,4

1,7

2,2

3,2

3,9

4,4

4,9

5,1

5,2

6,5

8,6

1,3

2,2

2,3

2,8

4,0

4,3

4,1

4,1

3,9

4,3

6,1

9,0

12,3

11,8

10,3

10,1

11,7

11,3

9,9

8,4

7,4

7,4

6,4

5,4

4,9

4,9

6,0

7,0

6,4

5,9

4,8

3,7

2,7

9,2

9,0

9,0

8,3

7,8

6,8

6,3

6,1

7,3

8,3

8,1

7,8

6,5

4,8

3,7

9,7

8,6

7,9

7,7

6,9

5,9

5,4

5,3

6,5

7,5

7,0

6,6

5,5

4,1

3,1

 Table  7  illustrates  this  by  presenting  the  basic  figures  on  active  population,  number  of  persons
employed and the share of persons employed of the total active population. The table shows that
the  upturn  started  early  in  1996  (the  numbers  are  from  the  2nd  quarter).  The  share  of  persons
employed passed 55 percent in 1996, and more than 7 million people now had a job.

121

Year

Table 7: Total employment growth in absolute and relative numbers, 1993-1997
Persons
employed

Active population

Percentage

1993

1994

1995

1996

1997

1998

1999

12.173.713

12.258.081

12.328.280

12.396.538

12.469.121

12.543.117

12.622.098

6.631.101

54,5 %

6.697.551

54,6 %

6.772.188

54,9 %

6.922.351

55,8 %

7.175.401

57,5 %

7.393.997

58,9 %

7.597.371

60,2 %

At  the  same  time,  unemployment  rates  went  down.  In  1995,  the  unemployment  rate  was  seven
percent, four years later the share had more than halved to 3.2 percent (Table 7).

Against the background of increased employment in the Dutch economy, what were the trends in
the high-tech sectors? Looking at all sectors (as defined in Table 6) we see in Table 8 that the high-
tech sector actually shows a slight decline measured as a share of total employment. In 1993, the
industries represented about 7,5 percent of total employment. In 1999, the share was reduced to 6,6
percent.

Table 8: Relative employment, three sectors, 1993 - 1999

1993

1994

1995

1996

1997

1998

1999

Hi-Tech

7,50 %

7,20 %

6,20 %

6,00 %

6,50 %

6,50 %

6,60 %

Other Manufact.

14,40 % 14,00 % 14,30 % 14,10 %

14,00 %

13,80 %

13,50 %

Other Sectors

78,10 % 78,80 % 79,50 % 79,90 %

79,50 %

79,70 %

79,90 %

Looking more closely at the individual industries, we find that the largest reductions (measured as
telecommunications.  Both  manufacturing  of
share  of  total  employment)  are  found 
radios/TVs/communication  equipment  (down  50  percent)  and  telecomm/post  (down  15  percent)
show marked reductions (Table 9). But also Aerospace/Other transport are reduced, as is chemicals
and pharmaceuticals.

in 

Growing high-tech industries are first and foremost computer activities. Office equipment grows
from 0,1 percent to 0,2 percent, and Computer services show a doubling in employment, from 0,8
percent to 1,5 percent. However, we are still talking about relatively small numbers. The total high-
tech employment in the Netherlands, even broadly defined as it here is, employs about 6,6 percent
of the total employed workforce in 1999.

122

Table 9:  Relative employment shares, nine sectors, 1993 - 1999

Sector

1993

1994

1995

1996

1997

1998

1999

Oil and Gas

0,2 % 0,2 % 0,2 %

0,1 %

0,2 % 0,2 % 0,2 %

Chemicals&
Pharmaceuticals
Office/comp equipt.

1,6 % 1,6 % 1,5 %

1,5 %

1,6 % 1,4 % 1,4 %

0,1 % 0,1 % 0,1 %

0,1 %

0,1 % 0,2 % 0,2 %

Radio/tv/comm

1,6 % 1,6 % 0,8 %

0,9 %

0,9 % 0,8 % 0,8 %

Aerospace& Other
Transport
Telecom and Post

0,6 % 0,5 % 0,5 %

0,4 %

0,5 % 0,4 % 0,4 %

1,9 % 1,7 % 1,8 %

1,6 %

1,6 % 1,6 % 1,6 %

Computer services

0,8 % 0,9 % 0,9 %

0,9 %

1,1 % 1,4 % 1,5 %

Research

0,5 % 0,6 % 0,4 %

0,3 %

0,5 % 0,5 % 0,6 %

Other Manufact.

14,4 % 14,0 % 14,3 % 14,1 % 14,0 % 13,8 % 13,5 %

Other sectors

78,1 % 78,8 % 79,5 % 79,9 % 79,5 % 79,7 % 79,9 %

The composition of the Dutch workforce has changed slightly during the 90s. Figure 2 provides an
overview of how different educational categories are represented as the share of all employees in
the economy in the period 1993 to 1999.

There are two trends. The first one is that share of higher educated persons is slightly increasing. In
1993 the share was 22,8 percent. In 1999 this share had increased to 23,9 percent. The second trend
is stronger, and that is that the share of persons with low formal education has increased: from 17,9
percent in 1993 to 30,8 percent in 1999. This could mean that the boost in the Dutch economy in
this period has to a large extent enabled people with low formal skills to enter the labour market.

123

Figure 2:

E d u c a t i o n a l g r o u p s , s h a r e s  o f a lle  e m p lo y e e s , 1 9 9 3 -1 9 9 7

22,8 %

23,4 %

23,1 %

23,8 %

25,0 %

23,3 %

23,9 %

59,3 %

59,7 %

59,6 %

44,8 %

44,6 %

46,1 %

45,4 %

17,9 %

16,9 %

17,3 %

31,4 %

30,5 %

30,6 %

30,8 %

19 93

1 994

199 5

19 96

1 997

199 8

19 99

L

M

H

100 %

90 %

80 %

70 %

60 %

50 %

40 %

30 %

20 %

10 %

0 %

The figure also shows  that there is  a  break in the series between  1995  and  1996.  This  could  be
caused by moves in the border between Low and Medium education. As we can see, the shares of
employees with respectively Low+Medium and High are fairly stable over time. One might have
expected an increase in the High educational group, since the general level of education is rising.
However, it seems to have been outweighed by previously unemployed people – with generally
less education – getting a job again.

If we look at higher educated staff within the high-tech sector, other manufacturing sector and other
industries sector, we find that on an aggregate level, the share is fairly stable. Table 11 provides an
overview of how all persons with higher education are distributed within three sectors of the Dutch
economy  in  the  period  1993  to  1999.  We  see  that  there  has  been  a  slight  increase  in  high-tech
sectors, from 13,2 percent to 14,2 percent. At the same time, the other two sectors show marginal
reductions.

Sector

Hi-Tech

Table 10: Relative employment shares, Highly educated, three - sectors, 1993-1999

1993

1994

1995

1996

1997

1998

1999

13,20 % 12,80 % 12,30 % 12,70 % 13,40 % 13,20 % 14,20 %

Other Manufact.

6,80 %

6,00 %

6,50 %

6,10 %

6,60 %

6,60 %

6,70 %

Other sectors

80,00 % 81,10 % 81,20 % 81,20 % 80,10 % 80,20 % 79,20 %

Table 10 shows the figures from the table above on a more detailed level. For most industries, the
share is quite stable. The most marked changes are in Radio/TV/Communication, going down, and
Computer services going up.  These findings are compatible with the findings in Table 9.

124

Sector

Table 11: Relative employment shares, Highly educated, nine sectors, 1993 - 1999
1998

1994

1997

1996

1995

1993

1999

Oil and Gas

Chemicals&
Pharmaceuticals
Office/comp equipt.

Radio/tv/comm

Aerospace&
Other Transport
Telecom and Post

Computer services

Research

Other Manufact.

0,2 %

1,8 %

0,1 %

1,8 %

0,5 %

5,6 %

1,9 %

1,3 %

6,8 %

0,2 %

1,8 %

0,2 %

1,7 %

0,3 %

5,3 %

1,9 %

1,4 %

6,0 %

0,3 %

1,8 %

0,2 %

1,0 %

0,3 %

5,6 %

2,1 %

1,0 %

6,5 %

0,2 %

0,2 %

0,2 %

0,2 %

1,7 %

1,6 %

1,7 %

1,3 %

0,1 %

0,1 %

0,2 %

0,1 %

1,4 %

1,2 %

0,9 %

1,0 %

0,3 %

0,2 %

0,1 %

0,2 %

5,9 %

6,3 %

6,0 %

6,6 %

2,2 %

2,6 %

2,9 %

3,5 %

0,9 %

1,2 %

1,2 %

1,3 %

6,1 %

6,6 %

6,6 %

6,7 %

Other sectors

80,0 %

81,1 %

81,2 %

81,2 % 80,1 %

80,2 % 79,2 %

The  question  that  now  arises  is  how  changes  in  highly  educated  staff  vary  or  deviate  from  the
general  trend  in  employment  in  the  different  high-tech  industries.  Table  12  shows  the  share  of
higher educated staff in each high-tech industry in the period 1993 to 19995. We find the highest
shares  in  Research  (62  percent)  and  Computer  services  (53  percent).  The  lowest  shares  are  in
Office equipment and Telecomm/Post.

The  share  of  employees  with  higher  education  has  decreased  in  most  high-tech  industries,  like
Chemicals & Pharmaceuticals, Computer services and Research. Telecomm/Post has increased its
share of high-skilled staff in this period – the same as radio/TV/communication has done.

Sector

Table 12: Share of highly educated per sector, nine sectors, 1993 - 1999
1997
1994

1995

1993

1996

1998

1999

Oil and Gas

31,0 % 38,9 % 47,1 % 45,3 % 36,4 % 28,9 % 28,5 %

Chemicals&
Pharmaceuticals
Office/comp equipt.

30,5 % 30,6 % 29,9 % 29,3 % 28,3 % 29,1 % 24,1 %

26,6 % 34,9 % 32,0 % 20,5 % 26,9 % 28,7 % 19,2 %

Radio/tv/comm

29,6 % 28,5 % 31,8 % 39,1 % 37,7 % 30,1 % 33,3 %

Aerospace&Other Transport

19,2 % 15,5 % 17,2 % 20,2 % 11,5 % 7,6 %

13,5 %

Telecom and Post

21,3 % 21,4 % 21,6 % 23,6 % 26,2 % 22,2 % 23,8 %

Computer services

56,5 % 56,8 % 56,6 % 57,9 % 56,4 % 47,6 % 52,9 %

Research

63,6 % 64,3 % 63,8 % 61,6 % 66,3 % 62,5 % 62,2 %

Other Manufact.

11,3 % 10,6 % 10,9 % 11,0 % 12,7 % 11,8 % 12,7 %

Other sectors

All employees

24,3 % 25,1 % 24,8 % 25,9 % 26,7 % 25,0 % 25,3 %

22,9 % 23,5 % 23,2 % 24,3 % 25,4 % 23,6 % 24,2 %

Set against the background that fewer persons are working in these two telecomm-industries but at
the same time the share of persons with higher education is increasing, we seem to have found an
important change in the composition of the Dutch telecommunication industry: Put bluntly, during

5 The shaded sectors are those where numbers get too small and unreliable, due to a wide and
stochastic variation.

125

                                                     
the  90s,  low-educated  persons  has  been  shed  while  the  high-skilled  staff  have  been  kept.  This
points  towards  a  more  skill-intensive  and  science-based  orientation  in  Dutch  telecomm-
manufacturing.  To  the  extent  that  higher  skilled  employees  indicate  more  advanced  innovation
processes, the trend we have discovered points toward a more science-based innovation process in
this industry.

Training provided during the last four weeks

The so-called knowledge economy is based on the increased role of knowledge as an input in the
production  process.  Although  empirical  studies  vary  in  how  they  judge  the  role  of  knowledge
compared to, for example, investments in physical equipment, knowledge is nevertheless to some
degree  an  important  input  to  innovation  and  economic  change.  Innovation  is  about  doing
something new, and doing new things involves learning. One might therefore claim that knowledge
is inseparable from innovation and economic development.

We have hitherto looked at the role of employees  with  high  levels  of  formal  skills in  the  Dutch
economy. Another important approach to knowledge use and development is the role of upskilling
employees. Given the often emphasised rapid change in technology, together with reduced product
life cycles and increased R&D efforts, a need for upskilling should be more necessary now than
before.

Looking at trends in the general provision for training, we find that such activities are surprisingly
stable. Table 13 provides an overview of the share of persons with either no training or training
between 1993 and 1999. The share of persons with some training has actually decreased from 77,7
percent to 77,0 percent. Given the change in the business cycle (Table 7 and Table 6) one might
have  expected  greater  variation  in  the  provision  of  training  as  a  consequence  of  the  change  in
labour market conditions over the period.6

Training

Table 13: Provision of training, all employees
1994

1995

1993

1996

1997

1998

1999

No training

77,0 % 78,1 % 79,2 % 79,6 % 79,0 % 78,5 % 77,7 %

Some training

23,0 % 21,9 % 20,8 % 20,4 % 21,0 % 21,5 % 22,3 %

What happens when we break down these figures by educational level (High, Medium, Low)? Do
the  persons  with  high  formal  skills  get  training  more  often  than  those  with  low  formal  skills  (a
specialisation  pattern)?  Or  will  we  find  that  those  with  the  lowest  formal  skills  more  often
participate in training (an equalisation pattern)?

Table 14 provides an overview of how provision of training is distributed by educational groups.
The table shows two interesting things. Firstly, that highly educated staff are more rarely than other
people provided with training. The share for highly educated people is 19,3 percent in 1999. For
medium  and  low  educated  persons,  the  share  is  approximately  23  percent  for  both  groups.
Secondly,  we  see  that  the  share  of  people  provided  with  training  has  increased  for  the  medium
educated group, while for the two other the share has decreased. The decrease has been about three
percent points for both groups.

6 No answer has been coded as no training

126

                                                     
Table 14: Provision of training, all employees by educational groups

1993

1994

1995

1996

1997

1998

1999

High

22,0 % 20,4 % 22,4 %

20,8 % 20,5 %

16,9 %

19,3 %

Medium

22,4 % 21,4 % 18,6 %

20,0 % 20,5 %

23,3 %

23,5 %

Low

26,1 % 26,0 % 26,6 %

20,9 % 22,3 %

22,5 %

23,0 %

The highly educated seem to have experienced a decline from 1996 onwards. That might reflect the
fact that there is less time for training when the business cycle is getting better.

We have also checked whether the occupational classification showed different trends in provision
of training (Table 15). Apart from large variations, predominantly between different occupations
and to some degree from year to year within the same occupations, there are no dominant trends to
be drawn from the material.

Table 15: Provision of training, all employees, four occupational groups

Armed Forces Managers

Professionals

Technicians

1993

1994

1995

1996

1997

1998

1999

22,3 %

29,8 %

25,2 %

25,6 %

27,7 %

28,0 %

33,5 %

14,3 %

13,0 %

12,0 %

11,8 %

12,8 %

13,9 %

15,8 %

23,4 %

21,9 %

22,5 %

19,5 %

18,7 %

20,0 %

22,5 %

28,7 %

27,9 %

23,0 %

21,4 %

21,6 %

21,5 %

23,2 %

Looking  at  the  occupational  main  categories7  one  can  see  that  there  are  certainly  differences  in
level.  Managers  clearly  get  less  training  than  the  three  other  groups.  There  is  a  possible  rising
frequency of training in the Armed Forces. On the other hand, we see that technicians seem to get
less training as the business cycle improves. This might relate to the fact that if mobility increases,
there are fewer incentives for firms to train their staff, as the risk of losing skilled people is getting
higher.

Table 6  shows  the provision of training by main  occupational  groups  (1993-1999  average).  The
two bottom groups are Managers and Plant and Machine operators in contrast to the top groups:
Service workers, Elementary occupations and Armed Forces.

7 1 digit level

127

                                                     
Table 16: Provision of training, main occupational groups, average 1993-1999
Some training
Occupational group

Armed Forces

Managers

Professionals

Technicians

Clerks

Service workers

Skilled agricultural and fishery workers

Craft and related trades workers

Plant and machine operators and assemblers

Elementary occupations

27,61 %

13,40 %

21,15 %

23,85 %

22,47 %

28,45 %

19,75 %

16,04 %

12,80 %

25,87 %

There are also some varieties in different industries with respect to average provision of training
(27).  Again  we  divide  the  economy  in  three  broad  sectors  (high  tech,  other  manuf.  and  other
industries), and divide the high-tech sector into the separate industries. There are no clear overall
sectoral  trends,  but  we  can  see  marked  differences  in  levels.  It  comes  as  no  surprise  that
Telecommunications and Post and Computer services are the top sectors in regard to provision of
training.

Table 17: Provision of training by industry, average 1993-1999

Some training

Sector

Oil and Gas

Chemicals&Pharmaceuticals

Office/comp equipt

Radio/tv/comms

Aerospace&Other Transport

Telecom and Post

Computer services

Research

Other Manufact.

Other sectors

20 %

19 %

15 %

17 %

12 %

27 %

27 %

17 %

17 %

21 %

It is possible to break down the results from table 17 by education level. We recall from what has
been said above that people with higher formal education had a lower propensity to get training.
How does provision of training vary by educational level if we categorise by industry? The general
picture is presented in Table 18.

128

Table 18: Training, sectoral average, 1993 – 1999 (shaded = uncertain figures)

Sector

Oil and Gas

Chemicals&Pharmaceuticals

Office/comp equipt

Radio/tv/comms

Aerospace&Other Transport

Telecom and Post

Computer services

Research

Other Manufact.

Other sectors

High

18 %

18 %

16 %

16 %

14 %

31 %

25 %

14 %

17 %

20 %

Medium

22 %

22 %

18 %

19 %

13 %

28 %

28 %

25 %

17 %

21 %

Low

18 %

14 %

9 %

14 %

9 %

17 %

31 %

12 %

17 %

23 %

In most high-tech industries, staff with medium formal skills  are  trained  more  often  than people
with high or low formal skills. The highest propensity to receive training among medium-skilled
persons  is  found  in  Telecommunications  and  Post,  Computer  services  and  Research,  with
respectively 28, 28 and 25 percent of the staff getting trained.

There are two industries where training is more frequent for high-skilled than medium-skilled, and
that is in Telecomm and Post, and Aerospace and Other Transport. However, in Computer services,
we  see  that  the  group  with  the  highest  propensity  to  receive  training  is  the  low-skilled,  with  as
much as 31 percent. This is also the single largest share for any group of persons, only equaled by
high-skilled Telecomm-workers.

Tentative conclusions with respect to training

•  stable differences in levels between groups
•  no strong tendencies – but the period is short – we ought to look at more years, if the

treatment of training is similar, from early eighties would be better

•  Again  hitting  cell  size  problems  –  shows  the  severe  limitations  of  the  LFS  for

analytical purposes of this nature.

Mobility

From earlier studies we know it is a  general  result  that when  unemployment  is  reduced  there  is
increased mobility. Normally, the cause is a micro chain reaction: New jobs are created – either by
established or new firms – and filled both by unemployed and employed people. One also often
finds increased mobility due to accumulated need for change. Both firms and persons become less
risk-averse as the economic prospects are getting brighter.

Labour mobility is one of the major adaptation mechanisms in a modern market economy. People
are  moving  between  occupations  inside  the  same  corporation,  crossing  state  borders,  people  get
new jobs in the same enterprise, but in different establishments (workplace). Finally people move
in  the  job  hierarchy  of  the  same  workplace.  From  a  skill  and  competence  perspective  all  these
processes are important, but when focusing on the demand for skill needed in a certain group of

129

industrial branches the change of workplace is of primary interest. There are many different, but
relatively  independent  and  relatively  interconnected  processes  that  lead  people  to  change
workplace.

Table 19: Mobility in the Dutch economy (shaded = uncertain figures)
Family
Self
worker
employed

Employee

Status in year T

Status in T-1

Self-
employed
w/empl.

Self-employed w/empl.

289 106

700

2 359

Not
employed

Total (start)

6 332

298 497

89 692

535 674

Self-employed

Employee

Family worker

Not employed

297

2 271

371

5 237

434 406

11 279

20 825

6 158 572

1 877

311 774

6 495 319

295

56 334

1 961

58 961

58 348

551 875

3 219

4 614 017

5 232 696

Total (destination)

297 282

514 279

6 724 380

61 430

5 023 776

12 621 147

The easiest way to read such tables is to start with a category, look at Total (start) and show how
these  people  move  to  various  destinations8.  For  example,  of  the  58.000  family  workers,  56.000
were  still  family  workers  the  year  after.  The  rest  of  the  group  became  either  self-employed,
employees or not employed9.

What  actually  dominates  the  picture  is  the  great  stability.  If  you  were  an  employee  one  year,
chances  are  high  you  will  be  so  next  year  too.  However,  under  the  surface,  there  is  large
movement. For example, of the more than five million “not employed” in year T, 550.000 became
employed the next year. But since 311.000 of the employees in T became “not employed” in T+1,
there was a net gain of 200.000 employees.

In  the  labour  mobility  literature  two  categories  of  mobility  have  emerged:  “wide  mobility”  and
“narrow  mobility”.  Wide  mobility  includes  both  those  coming  in  from  the  category  “not
employed”, i.e. recruitment; and those leaving employment (retirement etc). Narrow mobility is the
mobility of those who were working last year and are still working this year. This is typical for the
highly educated, very competent worker. They are not on the margins of the labour market. They
are not greatly affected by short or long spells of unemployment. That is why most of the tables in
the following part will discuss the “narrow” mobility rate for the chosen sectors and educational
and occupational groups. Narrow mobility is basically the change of work place of the 6,1 million
persons  that  were  employees  both  years.  There  are  also  nearly  300.000  self-employed  with
employees, and 430.000 self-employed, but these two groups stick to their one (wo)man firm in the
overwhelming majority of cases.

8 The grey shades indicate that the number is unreliable. Dark grey means totally unreliable, light
grey means that in a long time series – or together with other reliable information, it might give
some indication of what is happening.
9 Not employed here is not to be confused with unemployed. Here it means all kind of destinies,
besides being employed (illness, disability, military service, studies, unemployment, retirement ,
child birth etc.)

130

                                                     
Table 20: Broad mobility, inflow,10 by educational groups, 1993 - 1999

All

High

Medium

Low

1993

1994

1995

1996

1997

1998

1999

17,7 %

15,5 %

16,4 %

15,3 %

17,2 %

16,3 %

14,3 %

14,3 %

10,8 %

12,7 %

19,5 %

18,8 %

20,3 %

18,7 %

17,1 %

15,8 %

15,9 %

13,2 %

10,3 %

17,7 %

18,7 %

22,5 %

20,0 %

22,9 %

15,9 %

9,9 %

22,9 %

23,8 %

There is a sharp decline in the rate between 1997 and 1998 that is hard to explain by labour market
and/or business cycle factors, and it is probably to a large extent due to a change in data collection
and/or statistical methods used. A  clear  indication  of  this is  that the  number  of  non-responses is
much greater in 1995 and 1996 increasing almost ten times to 199711 (Table 21).

Table 21: Missing observations by year, 1993-1999
Missing observations

1 146

1 424

25 201

89 304

791 376

6 002

9 161

Year

1993

1994

1995

1996

1997

1998

1999

If  we  look  at  the  missing  observations  for  the  three  educational  groups,  the  pattern  is  generally
stable, i.e. the share of missing observations of the groups total observations are about the same for
all groups each year12.

Table 22: Missing observations by educational group, 1993-1999
Medium

High

Low

1993

1994

1995

1996

1997

1998

1999

0,10

0,17

0,77

7,40

63,76

0,86

1,62

0,15

0,12

2,97

8,50

0,35

0,59

10,52

23,89

100,56

218,18

0,57

1,35

1,15

0,70

10 The inflow rate is the number of people who changed job (workplace) from t-1, to t, divided by
the number employed in t.
11 We have asked the Dutch CBS for a possible explanation for this, but have not received any
answer at the time of writing.
12 The indicator is just (missing/total)*1000

131

                                                     
The table shows that very roughly the share of missing in High and Medium is about the same,
High being lower in most years. The share of missing for “Low” is at least twice as high as for the
Medium group. But the striking fact is the enormous increase in missing values in 1997.

Figure 3: Mobility rates, three educational groups, 1993-1999

Mobility rates, three educational groups, 1993 - 1999

0,25

0,20

0,15

0,10

0,05

0,00

1993

1994

1995

1996

1997

All

High

Medium

1998

Low

1999

The Medium group is the largest and dominant one, being almost as big as the other two groups
together. Since the High and Medium group has nearly the same level of mobility they dominate
the overall rate. The low rate is significantly higher. This is explained to a large extent by the fact
that it is the youth that is less educated, and they are very mobile. Regrettably, the dataset we have
for this particular project does not have an age variable, so we cannot isolate the effect of age.

As  mentioned  above  the  trends  in  the  mobility  rates  are  hard  to  interpret  due  to  the  enormous
increase in missing values in 1996 and especially 1997. The arrow indicates a slight upward trend
in mobility. Since mobility in most countries is increasing, we think that this is the most probable
development of the mobility rates from 1995 to 1998.

132

Figure 4: Mobility rates, two occupational groups, 1993-1999

25,0 %

20,0 %

15,0 %

10,0 %

5,0 %

0,0 %

1993

1994

1995

1996

1997

1998

1999

ISCO-High

ISCO-Other

All

In this graph we have the same “youth” phenomenon, i.e. that people with “high” occupations are
older and change job less frequently in relation to those who are younger, and in the other ISCO
classes.  The  mobility  rates  are  not  very  different  from  the  ones  we  get  using  an  educational
breakdown. That is not surprising since the “high” ISCO classes are to a large extent defined by the
educational level. Generally the ISCO classification is a problematic one since it does not classify
people according to homogenous dimensions. So even though the “High-ISCO” includes most top
leaders, it does not include army generals, because the 1-digit ISCO is partly your position in the
power  hierarchy,  partly  your  education  (implicitly  supposing  that  education  gives  power)  and
partly the sector of society you work in, so the Armed forces – generals and privates alike are in
ISCO 9 on the one digit level we are working with13.

13 In addition the pure problems of classification of occupation is qualitatively more difficult than
classification of education, which of course is by no means straightforward.

133

                                                     
Figure 5:

Broad, inflow mobility,  seven occupational groups, 1993 - 1999

Sen ior  offic. &
Ma n a ger s

P r ofession a ls

Techn icia ns

Cler ks

Ser vice & sa les

P la nt &m a ch in e
oper a t or s

Elem en t a r y
occu pa t ion s

35,0 %

30,0 %

25,0 %

20,0 %

15,0 %

10,0 %

5,0 %

0,0 %

1993

1994

1995

1996

1997

1998

1999

If we look at a more detailed breakdown of occupational groups, we see that the patterns are more
or less similar, but that there are marked differences in levels.

The “Legislators, senior officials and managers” group has significantly lower mobility than, for
example “Professionals”, “Clerks” or “Technicians”. The highest mobility is found in “Elementary
occupations”, second comes “Service workers and shop and market sales workers”. But here we
probably have the “youth” effect. But the “youth” effect can probably not explain all the difference
between  the  mobility  of  top  managers  and  other  highly  skilled  groups.  Part  of  the  difference  is
probably  due  to  the  fact  that  top  managers,  both  in  public  institutions  and  private  firms  have
reached  their  career  destination,  while  a  larger  share  of  the  other  groups  are  using  mobility  to
increase wages, get more challenging jobs, are more hit by down-sizing etc.

Individual mobility – the problems with sectoral breakdown

When  it  comes  to  mobility  in/out  of  economic  sectors,  we  directly  stumble  into  the  problem  of
sample size. If we want to perform a breakdown in the chosen economic sectors and educational
levels at the same time, we do not have enough observations to get robust results14. The number of
observations in each category is too small. Only in about one third of the categories do we have
enough numbers to gives us reliable results.

One good example is the research sector. As we can see from the table below, only in a few years is
the number or mobile persons above the minimum 4500 limit set by Eurostat, i.e. in 1995, 1996 and
1999.  In  all  the  years  the  number  of  mobile  persons  is  far  from  the  limit  of  10.000  persons  for
results to be published without warning.

14 More about this in the Appendix

134

                                                     
Table 23: The research sector – the problem of sample size

Year

1993

1994

1995

1996

1997

1998

1999

Stable

Mobile

26315

29309

18758

18521

26574

29686

32257

3591

4328

4876

4719

3500

2444

4612

Rate

12,0 %

12,9 %

20,6 %

20,3 %

11,6 %

7,6 %

12,5 %

The table shows a “dip” in the number of employees in 1995 and 1996 that is not straightforward to
explain. The mobility rate varies considerably, but in such a small sector (only 30.000 employees)
there will be considerable variation, even if we could follow every single employee – as can also be
seen from the Norwegian case.

Since the number of mobile persons is not that far from the Eurostat  minimum, one  might with
great caution try to interpret the numbers using other sources of supplementary information. The
real problem is that the focus of this study is skills, as measured by educational and occupational
codes. But breaking these numbers further down will of course only make things worse.

135

Table 24: The research sector – by educational groups  - the problem of sample size

Data

1993

Same job

Mobile

Rate

1994

Same job

Mobile

Rate

1995

Same job

Mobile

Rate

1996

Same job

Mobile

Rate

1997

Same job

Mobile

Rate

1998

Same job

Mobile

Rate

1999

Same job

Mobile

Rate

High

16430

2597

Medium

8346

994

13,6 %

10,6 %

19349

2280

7881

2048

Low

1539

0

0,0 %

2079

0

All

26315

3591

12,0 %

29309

4328

10,5 %

20,6 %

0,0 %

12,9 %

11612

3466

6522

1148

624

262

18758

4876

23,0 %

15,0 %

29,6 %

20,6 %

11714

2595

4748

1067

2059

1057

18521

4719

18,1 %

18,3 %

33,9 %

20,3 %

17339

2887

5555

547

14,3 %

9,0 %

18914

1158

5,8 %

19634

3177

13,9 %

9502

539

5,4 %

11295

1163

9,3 %

3680

66

1,8 %

1270

747

37,0 %

1328

272

26574

3500

11,6 %

29686

2444

7,6 %

32257

4612

17,0 %

12,5 %

As  expected  the  mobility  rate  is  even  more  erratic,  but  we  also  see  numbers  that  clearly  are
unrealistic, because there is always mobility from a firm even if it has only 15 employees. The table
above shows categories that are supposed to have several thousand employees, and no, or totally
marginal mobility (for example the “Low” educated). We know that this is not the case. There is
never any mobility when you have one thousand employees. To make things even worse, the rate
makes jumps from 0 to 37% in some cases.

136

Table 25: Overview of the “cell size “ problem by sector

ISCED -high

ISCED - low

ISCO-low

ISCO-high

Sector

Oil and Gas

Chemicals&
Pharmaceuticals
Office/comp equipt

Radio/tv/comms

Aerospace&Other
Transport
Telecom and Post

Computer services

Research

Other Manufact.

Other sectors

Warning

Not at all

A few

Not at all

Not at all

Not at all

Yes

Some

No warning

Warning

No warning

Not at all

Not at all

Not at all

Not at all

Not at all

Some

A few

Some

Some

Not at all

Not at all

Not at all

Yes

Most

Not at all

Not at all

Not at all

Not at all

Not at all

Yes

Some

Not at all

Not at all

Not at all

Not at all

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

The table shows the sectors, and the possibility to publish results from these sectors by education
and occupation. The conclusion is clear, only one of the high-tech industries “Telecom and Post”
can be studied in any detail due to the cell size problem. But that does not help very much since we
initially wanted to study only the “Telecom” part of this big sector. As pointed out several times
already, the LFS was not made for detailed studies like this. Not only was the LFS made at a time
when computers where much less powerful that today, but the costs of surveying a large enough
sample of the population makes it improbable that we in the foreseeable future will be able to get
around this “cell size” problem.

Summary

The  Dutch  literary  review  reports  on  a  sharp  increase  in  the  demand  for  jobs  from  1995  and
onwards.  The  number  of  vacancies  in  the  private  sector  almost  tripled  from  1995  to  1999,
corresponding to a reduction of the unemployment rate from 7% in 1995 to 3,2% four years later.
In the public sector the greatest proportion of vacancies was found in the education sector and in
local government.

A prospective analysis of the Dutch labour market foresees the demand for university graduates to
be higher  than  for  graduates  from  the  system  of  higher  vocational  education.  The  reason  is  that
employers seem to be on a track of upgrading the skill level for their employees, especially in the
service sectors.

The  tight  labour  market  puts  special  constraints  on  recruitment  to  the  soaring  ICT  sector  that
recruits candidates with a panoply of skill profiles. Hence, the fact that in 1997 63% of all Dutch
ICT  employees  did  not  have  formal  ICT  education  reflects  the  strength  of  the  skill  substitution
effect.  In  addition,  this  figure  might  indicate  new  recruitment  patterns  in  the  ICT  sector,  now
reflecting the need for a broader range of skills. Moreover, statistics on the dispersion of fields of
studies  over  occupations  reveal  that  graduates  in  humanities  are  increasingly  employed  in
occupational sectors outside their traditional labour markets (i.e. public administration, education,

137

socio-cultural  sectors  and  arts).  These  statistics  underscore  the  point  about  broader  recruitment
patterns in the ICT sector and perhaps in other labour market segments.

By contrast, graduates in medicine, veterinary science and dentistry predominantly get jobs in their
traditional  occupational  sector,  which  is  the  health  sector.  This  phenomenon  can  partly  be
explained by the specificity of these professions. They require very specialised skills, to the extent
that the substitution effects in this labour market segment are of little importance. Important here is
also the question of certificates for professions. The fact that you for example are not allowed to
practise as a medical doctor without a licence is a key regulatory factor in the labour market for
health care.

Moving on to the analysis of data from the Dutch Labour Force Survey it appears that the increased
employment from the mid-90s have not been translated into a growth in the seven high tech sectors
defined for this study. To the contrary, the relative high tech employment fell from 7,5% in 1993 to
6,6% in 1999. The only exception is computer services and office and computer equipment. We
should  bear  in  mind  that  similar  statistics  from  Norway  and  UK  confirm  the  role  of  high  tech
sectors as modest contributors to national employment.

It is interesting  to  notice that the  share  of  low  educated  employees  rose  from  17,9%  in  1993  to
30,8% in 1999. For the most part this is probably due to the change in the educational code from
“old ISCED-76, to “new” ISCED-97, but this also confirms the well-known fact that an economic
boom enhances the employment chances for the lower educated, without necessarily making them
more employable in a longer time perspective. In the high tech sectors the share of high educated
was  fairly  stable,  oscillating  between  12,7%  and  14,2%  over  the  same  period.  The
telecommunication sector shows one interesting deviation from this rule. Here, it appears that low
skill employees have lost their jobs while the higher educated telecom staff remains on the payroll.

The statistics on training provided during the last four weeks show a quite equal distribution among
the three categories high, medium and low education level. The only significant change applies for
highly educated persons, for whom the provision of training seems to decline when the business
cycle improves from 1995 and onwards.

When the same statistics are split down on occupational groups it looks like technicians receive less
training.  But  given  the  fact  that  the training  for  professionals  remains  quite  stable,  there  is  little
room for drawing any clear conclusion about decreasing training for high skill groups  when  the
economy is booming.

When splitting training down on sectors it turns out that the medium skilled echelons receive most
training, apart from those in telecom&post and aerospace&other transport. Among the seven high
tech sectors chemicals&pharmaceuticals is less training intensive than telecom&post and computer
services.

Finally, no general conclusion emerges from the Dutch statistics on mobility due to unreliable data.

138

References

Ekeland,  A.  (1998)  ‘Indicators  for  human  resources  and  mobility’,  IDEA  Paper  No.9,  STEP
Group, Oslo.

OECD (1995) Canberra Manual,  Paris: OECD.

Nås, Svein Olav, Ander Ekeland, Eric J. Iversen, Mikael Åkerblom, Markku Virtaharju, Christian
Svanfeldt, Johnny Ullström, 1998, Formal competencies in the innovation systems of the Nordic
countries: An analysis based on register data, STEP report 6-98.

Rosenberg, Mikael, 1998, “An Inventory of National Priorities and availability of data in OECD
Countries  to  Quantify  Science  and  Technology  Personnel  Mobility  Patterns”,  Room  Document
No.2, joint NESTI/TIP/GSS workshop, 17. June 1998, OECD.

Stimpson,  Alex.  (2001)  (forthcoming)  Preliminary  Results  from  the  HRST  Mobility  analysis,
Procedings from OECDs Focus Group on Human Mobility, Camire Luxemburg.

Åkerblom, M. (1999) Mobility of highly qualified manpower, a feasibility study on the possibilities
to  construct  internationally  comparable  indicators.  Report  for  Camire  (Eurostat)  and  OECD.
Statistics Finland, 14th December.

139

Appendix

Figure i: The relation between ISCED-76 and ISCED-97

ISCED '76

5
6
7

ISCED

High

ISCED '97

5b
5a
6

Employment and industry

In principle everybody classified as working should have a NACE code, but in reality this is not so
easy. A significant share of those being either

1

2

3

Self-employed with employees

Self-employed without employees

Employee

4 

Family worker

did not have a NACE code as can be seen from the following table:

Table i: Percentage of missing industrial classification (NACE)

No NACE
in 1000

All employees in
1000

Percentage
of all empl.

Percentage of
Highly educ.

152

143

206

398

420

349

394

6631

6698

6772

6922

7175

7394

7597

2,3 %

2,1 %

3,0 %

5,7 %

5,8 %

4,7 %

5,2 %

1,8 %

1,6 %

2,0 %

3,5 %

3,9 %

3,0 %

3,6 %

Year

1993

1994

1995

1996

1997

1998

1999

As expected there are less highly educated persons without industrial classification, but still 3,6% is
too high a share15. We get very similar results if we look at the occupational groups. The “Manager,
Professional, Technician” = ISCO groups 1, 2 and 3 have the same  level  of  persons  without  an
industrial classification.

The high-tech sector: Initial industries for the study

15 In comparison with the Norwegian register data, of all the persons classified as working less
than 0,003% did not have an industrial code.

140

                                                     
In this study we wanted to focus the following sectors:

Table ii: Industrial sectors - preferred break down

NACE

3-digit

Title

x

x

x

110

244

300

320

353

642

720

730

extraction of crude petroleum and natural gas; related services,
excluding surveying
manufacture of pharmaceuticals, medicinal chemicals and botanical
products
manufacture of office machinery and computers

manufacture of radio, television and communication equipment and
apparatus
manufacture of aircraft and spacecraft

Telecommunications

computers and related activities

research and development

Problems with few observations – “cell size” problems

The Community Labour Force Survey, like all surveys, is based upon a sample from 0,5% - 1,5%
of the population. It goes without saying that the number of observations of a special combination
of characteristics like “male, researcher, Ph.D.” will be represented with very few observations in
the sample. That means that even though the sample is a randomly drawn representative sample
there will be very few persons in a particular group and you do not get very accurate estimates of
the total number. This is often referred to as the “cell size” problem.

Based upon the sample size and design in the various Member States, Eurostat implements basic
guidelines  intended  to  avoid  publication  of  figures,  which  are  statistically  unreliable.    These
guidelines  consists  of  two  limits,  A  and  B  for  publishing  numbers  regarding  subgroups.  For
example the number  of highly educated persons in “Oil and Gas”  must  be  greater  than  A  to  be
published at all, and published only with a warning if greater than A, but less than B. In the case of
the Netherlands the limits are A = 4.500, and B = 10.000.

In the Dutch data, there are three sectors that are in the  “danger zone” of having numbers in danger
of being to small. That is “Oil and Gas”, and “Office machinery and computers” and “Aerospace
and  other  transport”.  The  first  two  are  just  around  10.000  before  dividing  into  subgroups.
“Aer&Transp” have three times that, but have very  few  highly  educated,  so  looking  at  this  sub-
group of the employees in this group is not possible, due to too few observations.

There are several possible solutions to this problem; the most obvious would be to combine sectors
with few observations with another sector. One possibility would be to combine “Manufacture of
office machinery” with “Computer Services”, arguing that the difference between them is not that
important for our purposes. One argument might be that office machinery has changed from being
a machine into being a dedicated computer like for example a desktop calculator. For a study of
skill and skill mismatches, the important question is whether the two sectors overlap each other to a
reasonable degree when it comes to skills.  One indicator would be the share of highly educated,
and that number is clearly different.

141

There is no obvious way this sector cannot be combined with one of the other high-tech sectors
focused in this analysis so the only solution would be to put it into “Other manufacturing”. That is a
pity, because we then lose the possibility to have a comparison with Norway – where the oil sector
is very “engineer intensive”, more so than many of the other high-tech sectors. From the meagre
information we have it emerges that this sector seems to be a fairly stable one in the Netherlands,
both in size and educational and occupational composition.

This merging of sectors is not a very attractive solution. One would get reliable estimates, but of
something other than we wanted to study. In the case of the 3-digit sectors we had no choice, but in
this case we have chosen to keep the problematic sectors – and shading the numbers to indicate that
these  numbers  are  not  estimates,  but  have  a  more  illustrative  character,  and  that  no  policy
conclusions whatsoever should be based upon them.

142