The socio-economic impact of projects financed by the Cohesion Fund : a modelling approach. Volume I, Introduction and summary : literature review : Vector auto-regression models.
Date: 1999-07-15
Institutions: European Commission
Subjects: Cohesion Fund, EU financing, EU regional policy, economic and social cohesion, economic consequence

A modelling approach 

Volume 1 

Introduction and summary 
Literature review 
Vector auto-regression models 

European  Commission 

The socio-economic  impact  of  projects 
financed  by the Cohesion  Fund 

A modelling approach 

Volume 1 

Introduction and summary 
Literature review 
Vector auto-regression models 

European  Commission 

A great deal of additional information on the European  Union is available on the  Internet, 
¡t can be accessed through the Europa server  (http://europa.eu.int). 

Cataloguing data can be found at the end of this publication. 

Luxembourg: Office for Official  Publications of the  European Communities,  1999 

ISBN  92-828-3064-0 

©  European Communities,  1999 
Reproduction  is authorised provided the source  is acknowledged. 

Printed in Italy 

Preface 

The  Commission  entrusted  the  present  study  to 
the  London  School  of  Economics  and  Political 
Science  in  July  1995  in  response  to  the  provi 
sions  of  the  Cohesion  Fund  Regulation.1 These 
specify  not  only  that  individual  projects  should 
be thoroughly  appraised  before  approval  for  as 
sistance  by the Cohesion  Fund, but also that the 
Commission should report annually on the  "eco 
nomic  and  social  impact  of  the  Fund  in  the 
Member  States and on economic  and social co 
hesion in the Union". The study thus aims to fulfil 
the  requirement  for  regular  assessments  to  be 
made  of  the  socio-economic  impact  of  assisted 
investments  on  the  beneficiary  Member  States 
and  on  the  wider  economy  of  the  European 
Union. 

In the  period  1993  to  1999  the  Cohesion  Fund 
will  have  contributed  over  Ecu16  billion  towards 
the financing  of transport  infrastructure  and envi 
ronment  projects  in Spain, Portugal, Greece  and 
Ireland.  A  rigorous  evaluation  of  this  important 
commitment  is  an  indispensable  part  of  the 
Commission's  responsibility  to  ensure that  Com 
munity  resources are allocated effectively, to pro 
vide  evidence  of  the  positive  effect  of  economic 
and  social  cohesion  policies  and  to  improve fu 
ture projects. 

At  the  same  time,  it  is  hoped  that  the  study  will 
encourage  the  development  of  evaluation  tech 
niques  in  the  fields  of  infrastructure  investment 
impact  and regional economic  modelling. The re-

Council Regulation (EC) N° 1164/94 of  16 May 1994. 

suits are expected to  be of  interest  and  practical 
value to  many working  in these fields, as well as 
those  responsible for financing transport  and en 
vironmental infrastructure projects in the EU. 

The  study  of  the  London  School  of  Economics 
was  based  on  an  innovative  approach  to  the 
subject,  with  teams  of  economists  competing  to 
produce  the  most  appropriate  models.  The  out 
come has been the development  of three distinct 
modelling approaches to tackle the problem. The 
study  also  involved  the  setting  up  of  a  detailed 
regional database for the four Cohesion countries 
and  their  EU  neighbours.  The  models  are  thus 
capable  of  tracing  the  impact  and  spillover  ef 
fects  of  investments  at the  provincial, regional or 
national economy levels. 

The study shows, among many important results, 
that: 

there  is  a  strong,  positive  relationship  be 
tween  public  infrastructure  investment  and 
private  investment  in  the  Cohesion  countries 
over the longer-term: 

infrastructure  projects  give  rise  to  significant 
employment  impacts  both  in  the  short-term 
and long-term; 

estimated  regional  income  effects  in  some 
cases exceed those resulting from more tradi 
tional approaches to project appraisal such as 
cost-benefit analvsis: 

Preface 

important  spillover  effects  are  evident  be 
tween the Cohesion countries and their neigh 
bours. 

of  such  applications  will  be  published  in  ad  hoc 
reports as well as in the annual reports of the Co 
hesion Fund. 

The  models  have  been  produced  and  tested 
within a relatively short  period and will require ad 
ditional development  and refinement. They wil  be 
further  tested  and  applied  to  a range  of  projects 
in  the  transport  and  environment  fields  financed 
by the Cohesion  Fund and the ERDF The results 

The  study  is  intended  to  complement  other 
methods of analysing the economic  impact of as 
sisted  investments.  It  is  presented  by  the  Com 
mission  as  a contribution  to  the  effort  to  identify 
and quantify the lasting benefits generated by the 
European Union's cohesion policies. 

Section I — Introduction and summary 

Acknowledgements 

This  study  was  commissioned  by  the  Cohesion 
Fund directorate of  DGXVI  — Regional  Policy and 
Cohesion  — of  the  European  Commission,  and 
was carried out by the London School of Econom 
ics and Political Science under the direction of: 

Data  collection  and  analysis  within  the  countries 
covered  by the  study  were  undertaken  by teams 
of researchers and statisticians as follows: 

Spain 

Dr  Robert  Leonardi,  Director,  the  Economic  and 
Social  Cohesion  Laboratory,  the  European  Insti 
tute, LSE. 

Instituto  Valenciano  de  Investigaciones  Económi 
cas, Valencia: 

The economic  modelling was undertaken by: 

Professor Matilde Mas 

Department  of  Economics,  London  School  of 
Economics & Political Science: 

Professor Francisco  Perez-Garcia 

Professor Charles Bean 

Professor Christopher Pissarides 

Professor Danny T. Quah 

Professor Anthony Venables 

Mr Henry Overman 

Mr Etienne Wasmer 

Mr  Michael  Gasiorek,  School  of  European  Stud 
ies, University of Sussex 

Data  co-ordination  and  database  construction 
were the  responsibility  of  a team within the Eco 
nomic and Social Cohesion Laboratory of the Eu 
ropean Institute, LSE, including: 

Mr Richard Walker 

Ms Ana Maresso 

Acknowledgements 

Professor  Ezequiel Uriel 

Mr Lorenzo Serrano 

Portugal 

Centro  de  Investigação  de  Desenvolvimento  e 
Economia Regional, Universidade do Algarve: 

Professor Adriano Pimpão 

Ms Antonia Correia 

Greece 

Regional  Development  Institute,  Pantheon  Uni 
versity, Athens: 

Professor Nikolas Konsolas 

Mr Costas Rontos 

Ireland 

Italy 

CEEPA, University College, Dublin: 

Professor  Raffaella  Nanetti,  CUPA,  University  of 
Illinois, Chicago 

Dr Richard Sinnott 

Ms Ricky Schoen 

UK 

France 

Professor  Nigel Spence,  Queen  Mary  and West- 
field College, University of London 

Dr  Robert  Leonardi, Economic  and Social Cone-
sion Laboratory, the European institute, LSE 

Section I — Introduction and summary 

ontents 

SECTION I: 

INTRODUCTION AND SUMMARY 

1.  Terms of Reference 
2.  introduction to the Modelling 
3.  VARs — Regional  Labour Market  Issues 
4.  VARs — Regional output and Employment in the Presence of  Interregional Spillovers 
5.  Computable General Equilibrium  Models 
S.  Models of Explicit  Distribution  Dynamics 
7.  General Comments 
8.  Conclusions 

SECTION II :  LITERATURE REVIEW 

1.  General Introduction 
2.  Regional Labour  Markets 
3.  Supply  Effects 
4.  Vars and Stochastic  Kernels 
5.  Computable General Equilibrium  Models 
6.  Current Commission  Modelling Work 
7.  Where Next? 
Glossary 
List of References 

SECTION III :  VECTOR AUTO-REGRESSION  MODELS 

A.  Regional labour market issues 

introduction 
1.  Modelling  results 
1.1.  Methodology 
1.2.  Spain 

Contents 

Page 

9 

11 
13 
15 
17 
21 
23 
27 
29 

31 

33 
37 
43 
47 
51 
57 
61 
63 
67 

75 

77 

77 
79 
79 
82 

1.3.  Portugal 
1.4.  Greece 
1.5.  Ireland 

2.  Simulation results 
2.1.  Introduction 
2.2.  Evaluation strategy 
2.3.  The impact of projects financed by the Cohesion Fund 
2.4.  Spain 
2.5.  Greece 
2.6.  Portugal 
2.7.  Ireland 
2.8.  Additional Remarks on Labour Market Variables 

Conclusions  — Summary of results 
Appendix 1 
Appendix 2 (Spain, Correction of Wage Series) 
Appendix 3 (ADF Tests for the Stationarity of the Series) 
Appendix 4 (GDP per Capita in the Regions) 
Appendix 5 (Some Economectrics) 
Bibliography 

B.  Regional output and employment  in the presence of interregional spillovers 

1.  Introduction and summary 
2.  Theory and comparisons with other work 
3.  Methodology 
4.  Data and basic results for Spain 
5.  Differential effects from different sorts of public capital 
6.  Interregional spillovers 
7.  Cohesion spending simulations 

7.1.  Madrid Ring Road 
7.2.  Rias-Bajas Motorway 
7.3.  Simulation of environmental projects 
7.4.  Simulations of a «  standard »  project 

8.  Concluding remarks 
Bibliography 
Tables 
Figures 

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Section I — Introduction and summary 

SECTION I 

INTRODUCTION AND SUMMARY 

1. Terms of Reference 

On 9 June  1995 the Economic and Social Cohe 
sion  Laboratory  of  the  London  School  of  Eco 
nomics  was  informed that  it  had won the tender 
(6/SG.H/1994-Study  of  the  socio-economic  im 
pact of Cohesion Fund projects). Initially a "cohe 
sion  financial  instrument"  was  introduced  on  30 
March 1993 by Council Regulation 792/93 to im 
plement  Article  130d  of  the  Maastricht  Treaty. 
Given  the  delays  in  the  ratification  of  the  new 
Treaty,  the  Cohesion  Fund  was  finally  set-up  on 
16  May  1994  by  Council  Regulation  1164/94  to 
provide  financial  support  for  environmental  and 
transport  infrastructure  projects.  The  Cohesion 
Fund  was  allocated  15  150  million  ECU  (1992 
prices)  to  cover  the  period  between  1993  and 
1999. One of the key elements  in the two Regu 
lations  concerning  the  Cohesion  Fund  was  the 
need to ensure that  projects financed by the new 
fund contributed to economic  development  in the 
countries where the investment  was taking  place 
but  also to  economic  and  social  cohesion  in the 
European Union. 

detailed description of the work programme fore 
seen  by  the  contract.  The  study  was  broken 
down  into  four  elements:  (1) literature  review, (2) 
recommended  methodology,  (3)  data  collection 
and  coding  and  (4)  construction  of  economic 
model or  models. All of these  elements  are  con 
tained  in  the  three  sections  of  the  Final  Report 
presented here. 

Section  II of  Volume  1 covers  the  literature  re 
view  and  reflects  very  closely  the  requirements 
stipulated  by  the  research  contract:  "A  thorough 
and  objective  review  and  evaluation  of  the  latest 
academic  and  other  work  in the  field,  including 
work  undertaken  to  quantify  the  impact  of  the 
Structural  Funds  in the  Cohesion  countries,  with 
conclusions  on  the  relative  strengths  and  weak 
nesses of the different approaches used". In com 
pleting this section, we reviewed all of the relevant 
academic literature in addition to looking at the re 
ports  produced  by  the  Evaluation  Unit  of  DGXVI 
and other branches of the European Commission. 

Work on the study  began on 2 July  1995. Article 
1 of the contract  stipulated that the objectives of 
the contract  were: "to undertake a study  into the 
socio-economic  impact  of  projects  and  aggre 
gate  interventions  financed  by  the  Cohesion 
Fund  as  they  affect  the  regional  and  national 
economies  of  Spain,  Portugal,  Greece  and  Ire 
land  and,  if  appropriate,  those  of  neighbouring 
countries;  where  such  a  study  is to  include  the 
recommendation,  development  and testing  of an 
economic  model or set  of  models for  quantifying 
such  effects  and  the  necessary  data  collection 
and  construction  of  a  regional  database  of  key 
economic indicators," and in Annex II there was a 

Section  III  of  Volume  1  as  well  as  Volume  2 
bring together  the  results  of the  work  undertak 
en  to  meet  the  requirements  spelled  out  in  the 
(a)  recommended  methodology  and  (b)  con 
struction  of  the  appropriate  economic  mod 
els. We have fully  met the objectives  outlined  in 
the  contract  of  :  "...the  econometric  model,  or 
models,  are  to  be  constructed  by  the  various 
teams  involved  in the  study  making  fully  explicit 
the  assumptions  underlying  the  models,  the 
model  variables  and  specifications,  strong  and 
weak  points  and the  probable  robustness  of re 
sults."  The  four  models  developed  (two  VAR 
models, one regional Computable General Equi-

Terms of Reference 

11 

librium  model  and one Stochastic  Kernel  model) 
fully  meet  these  stated  objectives  and,  in  a 
number  of  instances,  go  well  beyond  these  ob 
jectives.  All  four  models  have  been  fully  tested 
on a sample  of  Cohesion  Fund  projects,  and as 
the Final Report  illustrates the results are robust. 

Volume  3  brings  together  the  data  collected 
and coded for the study. The contract  stipulated 
that the data collection  had: "to include the iden 
tification of key economic  indicators at the appro 
priate geographical  level, the collection  and  cod 
ing of such data, and the identification of any se 
rious gaps and recommendations for dealing with 
these. Data collection should include national and 
regional data in the economies concerned as well 
as the  data  required  on  Cohesion  Fund  projects 
and  aggregate  interventions.  In  addition  to  the 
Cohesion  countries,  data  on  neighbouring  coun 
tries  (the  UK,  France  and  Italy)  will  also  be  col 
lected.  Full  use  of  existing  data  sources  will  be 
made  and  close  collaboration  with  Eurostat 
maintained." These  requirements  on  the  stipula 
tion  of  appropriate  units  of  analysis,  key  socio 
economic  variables  and  input  from  Cohesion 
Fund  projects  were  fully  implemented.  In  the 
process  we assembled  a detailed data set for all 
four  Cohesion  countries  plus the three  "spillover" 

countries  of the  UK,  France and  Italy. Using data 
from  these  three  additional  countries  we  were 
able  to  trace  the  spill  over  impact  of  Cohesion 
Fund  projects  in the  four  more  peripheral  coun 
tries  to  their  more  immediate  non-Cohesion 
neighbours. With data from the other eight coun 
tries not  included  in this study, the analysis could 
be extended to all of the European Union. 

The  innovations  produced  by  the  study  are nu 
merous. We have not only produced new models 
for measuring in detail the overall socio-economic 
impact  of  Cohesion  Fund  projects.  Rather,  these 
models  can  also  be  used  (with  slight  modifica 
tions)  to  analyse  the  socio-economic  impact  of 
investments  undertaken  by  other  Funds  and 
Agencies  of the  European  Commission. Second, 
the  models  are  operative  at  a  level  of  analysis 
— regional and provincial  — which  has not  been 
possible  through  the  use  of  previous  techniques 
or  models  in  a  rigorous  scientific  manner. Third, 
our data set is unique not only for the four Cohe 
sion  countries  but  also  for  the  other  three  non-
Cohesion  countries  included  in the  study.  Finally, 
combining  our  seven  country  data  set  and  our 
models  we  are  able  to  trace  the  ripple  effect  of 
Cohesion  Fund  projects  across  regional  and na 
tional borders into neighbouring areas. 

12 

Section I — Introduction and summary 

2, Introduction to the Modelling 

This  report  outlines the  results of  modelling work 
undertaken  by  the  Economic  and  Social  Cohe 
sion  Laboratory  at  the  London  School  of  Eco 
nomics  on  behalf  of  the  European  Commission 
DGXVI  (Cohesion  Fund).  In  addition  to  outlining 
the  models,  it  gives  results  from  the  analysis  of 
specific  Cohesion  Fund  investment  expenditure 
undertaken  using  these  models.  The  various 
modelling  approaches  aim to  provide  a coherent 
framework  for  analysing  the  impact  of  Cohesion 
Fund spending  in the four  Cohesion  countries — 
Greece, Ireland, Portugal and Spain. The different 
approaches  incorporate  exciting  new  theoretical 
and  empirical  concepts.  We  believe  that  these 
models  are  well  suited  to  addressing  the  issues 
that are relevant  here, and provide the best avail 
able way to analyse the impact of Cohesion Fund 
spending. 

In  developing  the  models  we  have  sought  an 
swers  to  three  fundamental  questions.  How  do 
regionally focused changes  — investments, infra 
structure  development  and  redistributions  — af 
fect  regional  and  aggregate  economies?  How 
might  these,  in  turn,  impact  neighbouring 
economies  and the  union  as a whole?  What im 
plications  do  such  local  disturbances  have  for 
convergence, the tendency  or ability of the poor 
er regions in the Union to catch  up with those al 
ready  richer?  A  number  of  different  modelling is 
sues must be tackled to answer these questions. 
How  have  local  disturbances  propagated  them 
selves  through  the  nation's  economic  system  in 
the past? What transmission mechanism  — eco 
nomic  structure  — underlies  these  dynamic  ad 
justments?  How  do different  market  competitive 
ness,  industrial  organizations  and  transport  cost 

structures  affect  these  predictions?  The  models 
outlined  here,  represent  our  attempts  to  resolve 
these  issues  and  provide  answers  to  these  fun 
damental questions. 

The  main  modelling  reports  are  in three  sections 
of this report. Section III, Volume 1 outlines vector 
autoregression  specifications  (VARs)  and  sug 
gests  how  they  could  be  used  to  consider  the 
dynamic  impact  of  Cohesion  Fund  spending. 
Section  I, Volume 2 describes the specification of 
regional Computable  General  Equilibrium  models 
(CGE models), and shows how they can be used 
to  simulate the short  and  long  run effects  of Co 
hesion fund  infrastructure  projects. The final sec 
tion  (Section  II, Volume  2)  reports  results  from 
models of explicit distribution dynamics (MEDDs). 
The  first  paper  of  this  section  characterizes  the 
historical evolution of the distribution of economic 
indicators  within  and  across  Cohesion  Fund 
countries. The  second  paper  outlines  a method 
ology  for  explaining  these  evolving  distributions 
and considering the impact  of whole distributions 
of Cohesion Fund expenditure. 

The papers are ordered according to the regional 
"specificity"  of  each  model's  outputs.  The VAR 
models  analyse the  impact  of  an investment  in a 
region  on  the  economic  performance  of  that  re 
gion.  They  provide  information  on  output,  busi 
ness  investment,  employment  and  other  labour 
market  indicators.  In addition they  provide  useful 
information  on  spillovers  between  regions,  which 
give  an  indication  of  the  aggregate  and distribu 
tive  impact  of  expenditure.  The  regional  CGE 
models  are  less  region  specific.  They  are  most 
useful  for  analysing  the  impact  of  investments 

Introduction to the Modelling 

13 

which  change  the  transport  costs  between  re 
gions. The model output  provides  information  on 
the impact  across all affected  regions. Finally, the 
MEDDs  provide  the  broadest  possible  frame 
work. They can be used to analyse the impact of 
investments  on the distribution  of  economic  vari 
ables  across  all  regions  of  a  country,  or  even 
across all regions of all Cohesion and non-Cohe 
sion countries in the European Union. 

Why  have we  adopted  three  different  approach 
es?  Firstly,  because  different  models  do  not 
need  to  rely  on  one  set  of  theoretical  assump 
tions.  This  is  important,  as  our  current  knowl 
edge  about  economic  growth  processes  is  in 
complete.  Secondly,  different  models  can  tackle 
different  questions.  Finally,  because  there  is  no 

all encompassing  model which  can  begin to an 
swer  the  important  questions.  We  have  chosen 
to  develop  new  approaches  because  traditional 
approaches  sometimes  use  methodologies  that 
we think  are invalid. More  importantly,  traditional 
approaches  do  not  account  for  things  that  we 
think  are  crucial;  namely  spillovers  and  dynamic 
effects. 

The rest of this introduction  provides a brief sum 
mary  of  each  paper,  considers  the  relationships 
between  models  and discusses their application. 
Readers interested in getting an initial overall pic 
ture  of  the  current  and  historical  development  of 
each  economy  may  like to  refer to  the  statistical 
and  graphical  analysis  provided  in the  historical 
outcomes paper of Section II, Volume 2 first. 

14 

Section I — Introduction and summary 

3, VARs — Regional Labour Market Issues 

This  paper  estimates  the  relationship  between 
public  investment,  business  investment  and 
labour  market  variables  through  econometric 
analysis  of  a vector  autoregressive  model. The 
VAR methodology  captures the dynamic relation 
ship between these variables. 

In the  model,  public  investment  spending  Influ 
ences aggregate economic activity as follows: 

1.  Public  spending  has  a  direct  positive  impact 

on private investment decisions. 

2.  Tnis  increase  in  business  investment  further 
enhances  the  positive  effect  that  public 
spending  has on the employment  variables in 
the region. 

3.  In turn, the movement  in labour demand influ 
ences  other  labour  market  variables,  such as 
employment, regional activity rates and migra 
tion between regions. 

Hence, with this  model, we can evaluate the im 
pact  of  exogenous  changes  in public  investment 
spending  on  the  regional  economies  of  the  four 
Cohesion countries. The analysis is carried out at 
the  regional  ¡evel, but  some  spillovers  at the na 
tional  level  are  also  highlighted.  We  investigate 
the  impact  of  public  spending  on  private  invest 
ment  decisions  using  a  bivariate  VAR. The  esti 
mated  parameters  are  elasticities,  reflecting  the 
percentage  increase  in  the  dependent  variable 
(private  investment,  in this  case)  due to  a  1%  in 
crease  in the  exogenous  variable  (public  spend 
ing). The same  methodology  is applied to the bi 
variate  system  involving  private  business  invest 

ment  and  employment.  We  also  estimate  a 
trivariate  system  in  employment,  population  and 
activity rate, to examine the effect of employment 
shocks on other labour market variables. 

The parameters of the relationships are estimated 
utilising  panel  data  for  each  of  the  Cohesion 
countries.  Coefficients  can,  therefore,  be  calcu 
lated by exploiting the information contained both 
in  the  cross-sectional  and  time-series  dimen 
sions.  In this way, we model the impact  of  public 
spending  on  the  economy  of  a  "representative" 
region  for  each  country.  For  some  countries  we 
carry out the analysis for groups of regions of dif 
ferent economic  well-being.  In the case of Spain, 
the  very  comprehensive  data  set  allows  for  fur 
ther  disaggregation  for  different  subsectors  of in 
vestment  spending,  and  for  distinctions  to  be 
made in the workforce by level of education. 

The  main  findings,  by  country,  can  be  sum 
marised as follows: 

The  empirical  results  for  Spain,  for  which  the 
most  extensive  data  set  is  available,  support  a 
number of conclusions. 

•  A  one  per  cent  increase  in  public  investment 
spending  eventually  generates  an  increase  in 
private  business  capital  in the  local  economy 
of  0.16%.  The whole  of  this  increase  is in re 
sponse  to  productive  investment  expenditure 
(total  investment  expenditure  less  education 
and  health  expenditures).  Spending  on  edu 
cation  and  health  does  not  stimulate  private 
investments (at the regional level). 

VARs — Regional Labour Market Issues 

15 

•  The  long-run  effect  of  productive  investment 
spending  is  approximately  the  same  in  differ 
ent types  of regions. There is, however,  some 
evidence that the positive spillovers for private 
investment  are  slightly  stronger  in  poorer  re 
gions  and,  to  a  lesser  degree,  richer  ones 
(and weaker in middle-income ones). 

•  Public  investment  increases  local employment 

but has a very small effect on wages. 

•  An increase  in local  skilled employment  is as 
sociated with a fall in local unemployment and 
immigration  (conversely  for  a  decrease).  In 
contrast,  a rise in unskilled employment  is as 
sociated almost exclusively with an increase in 
the local activity rate. 

•  The  effects  of  shocks  on  regional  employ 
ment,  unemployment  and  inactivity  persist in 
definitely. This contrasts with US regional evo 
lutions,  where  only  employment  shocks  are 
persistent. 

For Portugal, the data indicate that: 

•  Private  investment  has  a  positive  and  signifi 
cant correlation with total and productive pub 
lic spending. This correlation exists both in the 
short-run and in the long-run, but the latter ef 
fect is only slightly significant. 

• 

• 

In  contrast  with  the  results  for  Spain,  public 
investment generates more private investment 
in the  richest  regions, but  in the other  regions 
no significant estimates were obtained. 

In  response  to  labour  demand  shocks,  re 
gional population is unaffected, and increased 
activity rates absorb most of the new jobs. 

For Greece, the data indicate that: 

•  Private investment  is positively and significantly 
correlated  with  total  and  productive  public 
spending, both in the short-run and in the long-
run. In contrast, non-productive investment has 
no significant impact on private investment. 

•  Public  investment  generates  more  private  in 

vestment in the poorest regions. 

•  Employment  shocks  have  little  effect  on  re 
gional activity,  most  of the  shock  is  absorbed 
by population and unemployment changes. 

For  Ireland,  the  data  set  is  still  incomplete,  but 
initial results suggest that: 

•  Private  manufacturing  investment  is  positively 
and significantly  correlated with total and pro 
ductive  spending  both  in  the  short-run  and 
the long run. 

•  Employment shocks are much less persistent, 
and  activity  increases  with  a positive  employ 
ment shock. 

•  Population  fails  to  adjust  to  the  shock,  given 
the  lack  of  positive  correlation  with  employ 
ment. 

•  We have not  been able to find a positive and 
significant  impact  of  public  investment 
spending  on  employment.  The  employment 
data  are  insufficient  to  estimate  this  model. 
We  have  not  been  able  to  use  longer 
time-series  for  national  employment  data  to 
impute  regional  employment  over  a  longer 
period as we would not be able to identify the 
impact  of  regional  shocks  on  the  regional 
economies. These  problems  may  be  specific 
to  this  model  which  does  not  include 
spillovers  across  regions.  The  small  size  of 
Irish regions  suggest  that  these spillovers  will 
be  far  more  important  than  for  the  other 
countries. 

The paper  also gives examples of  how the  mod 
el can  be  used to  analyse the  impact  of particu 
lar Cohesion Fund projects. The projects that are 
analysed  are  sections  of  the  Madrid  Ring  Road 
and  Rias  Bajas  Motorway  (Spain),  Spata Airport 
and  the  Evinos  Dam  (Greece)  and  the  Tagus 
Crossing (Portugal). For the Spanish projects, re 
sults  are  presented  under  two  scenarios.  The 
first  uses  the  estimates  from  total  public  invest 
ment, the second  uses estimates from total pro 
ductive  public  investment.  For the  moment,  pro 
ject  evaluation  taking  this  form  is  only  possible 
for  Spain  for  which  homogenous  and  disaggre 
gated  data  are  available.  For  Portugal  and 
Greece  the  data  are  not  as  disaggregated.  For 
these  countries  we  report  results  using  data  on 
total  public  investment. The  robustness  of these 
labour  market  results  is  then  tested  by  pooling 
data  across  all  Cohesion  countries.  The  tests 
suggest that these labour  market  results are sat 
isfactory. 

16 

Section I — Introduction and summary 

4. VARs — Regional Output and Employment in the 

Presence of Interregional Spillovers 

The  paper  outlined  in  the  previous  section  uses 
VAR techniques to study the impact of local pub 
lic  investment  spending  on  regional  economic 
decisions  by  the  private  sector.  This  section  of 
the report develops that work by extending it in a 
number  of  directions.  Using a structural VAR ap 
proach  the  impact  on  public  investment  and 
labour  market  outcomes  are  integrated  into  the 
same  system.  Thus  the  model  allows  a  richer 
range of  interactions  between these variables. In 
addition regional output  is added into the picture, 
permitting the evaluation of the consequences  of 
increased  public  investment  for  local  output  and 
productivity. The effect  on output  is, of  course, a 
natural  welfare  measure  to  use  in  evaluating  the 
effectiveness  of  increased  Cohesion  spending. 
The price of greater complexity is greater data re 
quirements.  For  this  reason  we  concentrate  our 
analysis  on  Spain,  for  which  homogenous  data 
are available for a somewhat  longer period. How 
ever,  the  lessons  learned  from  this  exercise 
should  also  be applicable  to  the  other three Co 
hesion countries in due course. 

The  main  theoretical  ingredient  required  to  ex 
tend the  earlier  work  is an augmented  local pro 
duction  function  relating  regional  output  to  local 
private and public  inputs. The existing theoretical 
and  empirical  literature  on  local  production  func 
tions is considered and we take issue with sever 
al assumptions: 

• 

that technical progress is the same across dif 
ferent regions; 

• 

that  measured  factor  inputs  are  effectively 
employed in the production process; 

• 

that adjustment costs are not important. 

We argue that, in fact: 

• 

technical  progress  may  differ  across  regions 
because of differences in industrial structure; 

•  measured  factor  inputs  may  differ  from  those 
effectively  employed  in  the  production 
process  because  of,  for  example,  labour 
hoarding and capacity utilization; 

•  adjustment  cost  may  be  important  and  that 
we  should  allow for  a more  sophisticated  dy 
namic structure. 

We extend the existing work  by introducing  more 
complex  dynamic  behaviour  and  allowing  for the 
simultaneous  determination  of  employment  and 
output.  To  do  this,  we  specify  and  estimate  a 
Structural Vector Autoregression  (VAR) for private 
capital,  public  capital,  employment,  unemploy 
ment  and  output.  The  standard  VAR  formulation 
specifies  each  variable  as  a  function  of  lagged 
values  of  both  itself  and  all  the  other  economic 
variables  in  the  system.  Nearly  any  dynamic 
macroeconomic  model  can  be  recast  as  a VAR. 
Because  the  VAR  formulation  involves  only 
lagged  values  of  variables  it  is  easily  estimated 
using  standard  techniques,  without  falling foul  of 
the simultaneity  problems  that  bedevil  estimation 
of  standard  dynamic  macroeconomic  models. 

VARs  — Regional Output and Employment  in the Presence of Interregional Spillovers 

17 

Identification  is  the  process  of  recovering  the 
form  of  the  underlying  macroeconomic  model 
(that  is,  how  all the  economic  variables  interact) 
from the estimated VAR. Our Structural VAR uses 
economic  theory  to  identify  our  macroeconomic 
model. 

Our  economic  model  is  essentially  of  the  stan 
dard neo-classical  synthesis variety.  Its main fea 
tures are that: 

•  There is an equilibrium, or natural, level of out 
put  which  occurs  when  capital  is  fully  em 
ployed  and  the  unemployment  rate  is  at  its 
equilibrium levei. 

•  The  equilibrium  unemployment  rate  is  deter 
mined  within  a  "battle-of-the-mark-ups" 
framework.  One  relationship  describes  the 
way wages are set (by firms to motivate work 
ers or as a bargaining outcome). Another rela 
tionship  describes  firms  pricing  and  employ 
ment  decisions.  This  framework  is  like  stan 
dard  competitive  equilibrium  but  without  the 
usual  efficiency  results  — there  can  be  equi 
librium unemployment. 

•  This  period's  natural  unemployment  rate  may 
depend  on  previous  period's  natural  unem 
ployment  rates  (a  hysteresis  effect)  but  is in 
dependent of the size of the labour force. 

•  Deviations  around these  natural  levels  of  out 
put  and  unemployment  occur  when there are 
unexpected  disturbances  to  aggregate  de 
mand  or when  there  are shocks  to  private or 
public  investment. 

•  During  recessions  firms  may  be  off  their  pro 
duction  functions  because  of  hoarding  of 
labour and other factors of production. 

This  model allows  us to  specify  interrelations  be 
tween the fundamental  shocks of our  macroeco 
nomic  model  and  the  observed  disturbances  oí 
the Structural VAR. Using these interrelations, we 
show  how  the underlying  macroeconomic  model 
can  be  identified  and  outline  a  simple,  but  con 
sistent, estimation  procedure.  Results from these 
models  are presented  both  statistically  (reporting 
coefficients  and significance tests) and grapnical-
ly (using impulse response functions). 

from the definition of public capitai. We show that 
motorway  expenditure  has no explanatory  power 
for the five other variables in the system. Further 
more, the (impulse) response of other variables to 
motorway  investment  is  perverse  (output  is un 
changed,  but  unemployment  increases).  Plots of 
motorway  investment  show  that  large jumps  in 
investment do not affect  regional output and em 
ployment,  which  suggests  that  benefits  may  be 
accruing  outside  the  region. We  exclude  motor 
way  investment  from  subsequent  analysis.  Sec 
ond,  the  "base"  model  is  extended  by disaggre 
gating  public  capital  into  transport  (roads,  ports 
and airports)  and other  infrastructure  capital (wa 
ter  and  urban  structures).  The  impulse  response 
functions are almost identical for the two forms of 
capital.  Statistical  tests  suggest  that  the  disag 
gregated  model  is preferred,  but  only  because  it 
improves the forecast  of public  investment. Thus, 
we  continue  to  use  a  single  aggregate  public 
capital variable for subsequent analysis. 

Finally,  the  importance  of  economy-wide 
spillovers and local spillovers from one region into 
neighbouring  regions  is  explored.  These  interre 
gional  spillovers  could  be a result  of  positive ag 
glomeration  effects  and  negative  congestion  ef 
fects. The average  GDP of  physically  contiguous 
neighbours  is  used  to  capture  locai  spillovers. 
However,  large asymmetries  in regional wealth at 
the  NUTS  11  level  makes  it  sensible  to  carry  out 
this  analysis  at the  NUTS  HI level. A  lack  of  data 
on NUTS III capital stocks means that we have to 
assume  that  all  NUTS  HI stocks  move  equipro-
portionately  with  their  NUTS  II counterparts.  We 
add total current  and lagged Spanish GDP as an 
exogenous  variable  to  capture  economy-wide 
spillovers.  Unsurprisingly,  economy-wide 
spillovers are large and positive. More interesting 
ly,  local  spillovers  are  positive,  but  temporary, 
suggesting  that  such  spillovers  are  demand-side 
driven.  Re-estimation  of  the  "base"  model  sug 
gest  that  differences  in  impulse  responses  are 
from spillovers and not from disaggregation. 

The main findings are summarized as follows: 

•  Allowing  for  the  associated  implications  for 
employment  and private investment,  the elas 
ticity  of  regional  output  with  respect  to  net 
productive public investment  is around a third, 
that  is, a 3%  increase in the local public  capi 
tal stock raises output by around 1 %. 

We consider two refinements to this "base" mod 
el.  First,  the  "base"  model  excludes  motorways 

•  There  ¡s no  major  difference  in the  impact  of 

Section I — Introduction and summary 

transport capital and other infrastructure capi 
tal,  with  the  major  exception  of  motorways 
where much of the  benefit  appears to  accrue 
to  regions  away  from  where  the  motorways 
are located. 

There  is  evidence  of  important  interregional 
spillovers,  not  only  from  aggregate  economic 
activity, but also from activity  in geographically 
adjacent provinces. 

The estimated  marginal  return  from  public  in 
vestment varies widely across Spanish regions, 

being  apparently  five times  greater  in  Madrid 
(the highest) than in Extremadura (the lowest). 

The paper also gives examples of how the model 
can  be applied  to  analyse the  impact  of  specific 
Cohesion  Fund expenditures. We use the  model 
to  analyse  the  impact  of  sections  of  the  Madrid 
Ring  Road and the  Rias  Bajas  Motorway  (Spain) 
and a sample  of  environmental  projects. We use 
the  Madrid  Ring  Road  results to  discuss  the dif 
ferences  between  this VAR specification  and the 
previous model. We return to this issue in Section 
7 below. 

VARs — Regional Output and Employment  in the Presence of Interregional Spillovers 

19 

5. Computable General Equilibrium Models 

This  paper  outlines  the  structures  of  three com 
putable  general  equilibrium  (CGE)  models devel 
oped  to  analyse  the  impact  of  infrastructure  in 
vestment  by the Cohesion fund. The methodolo 
gy  employed  in  these  models  is  very  different 
from  that  used  in  the  rest  of  the  study.  Rather 
than  undertaking  a  rich  empirical  analysis  using 
time-series  data, and  then  using  simple  reduced 
forms,  this  approach  builds  a  model  with  a fully 
articulated  micro-economic  structure.  Incorporat 
ing  this  micro-economic  structure  allows  the 
models to discriminate between different  projects 
in  a way  that  empirical  macroeconomic  models 
cannot.  For example, the CGE models can distin 
guish  between  two  similarly  sized  projects  in the 
same  region which  affect  two  separate  transport 
corridors and can trace their overall effects. Thus, 
these  models  can  provide  distinctions  more diffi 
cult to incorporate into the macroeconomic  mod 
els. Since the  models  are  built  or  "calibrated" on 
existing information, however, the microeconomic 
structure  is  imposed  and  consequently  not  sub 
ject to econometric testing. 

The models are structured as follows: 

•  Each  model  incorporates  a  set  of  regions  in 
which economic activity takes place, linked by 
a transport  network through which goods and 
services  can  be traded. NUTS  II regions were 
used  for  Spain/Portugal  and  Greece,  while 
NUTS III regions were used for Ireland. 

•  Along with interregional trading partners within 
a  country  area, the  models  allow  for  two  ex 
ternal trading  partners: the  EU and the rest of 
the world. These two external trading partners 

introduce  import  supplies  and  export  de 
mands to the regions analysed. 

Each  region  has  an  endowment  of  three  fac 
tors: skilled labour, unskilled labour and capital. 

For each of the three country areas, we divide 
the industrial sectors in the economy  into per 
fectly  competitive  and  imperfectly  competitive 
industries; for example, Spain/Portugal has 16 
sectors. 

We  construct  price  indices  for  each  industry 
at each location. These price indices form the 
basis for  consumer  choices  and for  firms' in 
put decisions. 

All sectors  use primary factors  and intermedi 
ate goods as inputs. 

Similarly,  all sectors'  outputs  are used  as final 
products and as intermediate goods. 

Both  consumption  goods  and  intermediate 
goods  are  traded  between  regions,  and  the 
volume  of  this  interregional  trade  is  regulated 
by  the  interregional  transport  costs.  Thus,  a 
high  level of  transport  costs  between  two  re 
gions  will  drive  up  the  price  of  goods  traded 
between  those  regions.  This  will  inhibit  the 
level of trade  passing through the segment  of 
the  transport  network  linking  these  two  re 
gions. 

Regions  in  the  models  also  serve  as  ports 
through  which  exports  and  imports  with  the 
two external trading partners flow. 

Computable General Equilibrium Models 

21 

Using  this  structure,  we  can  capture  the  effects 
of  infrastructure  investment  on  all  regions  in  a 
country area in the following way: 

1.  The resulting reduction in transport  costs from 
an infrastructure  project  directly  affects  prices 
of products  in different  regions, which, in turn, 
changes firm sales and profits. 

2.  In response to  these changes  in profits, entry 
and exit of firms  may occur,  causing a further 
round of effects. 

3.  Entry  of  a firm  increases  competition,  which 
reduces profits, and bids up factor prices, fur 
ther reducing profits. 

4.  Entry of  a firm also  increases  demands for in 
termediates, which increases suppliers' profits. 

5.  This, in turn, may cause supplier firms to enter. 

Thus,  reduction  in  transport  costs  changes  the 
structure  of  industry  in different  regions,  creating 
forward  and  backward  linkages  and  agglomera 
tion  effects.  These  models  capture  these  phe 
nomena and translate them into resulting impacts 
on each region within a country area. 

After  explaining the structure  and  underlying the 
ory of the  models, we report  results for  six simu 
lation  experiments:  the  Madrid  Ring  Road  and 
Rias Bajas Motorways (Spain), the Pathe and Eg-
natia  motorways  (Greece),  The  North-South 
Road  corridor  (Ireland)  and  the  Tagus  Crossing 
(Portugal). We run the  model for  each simulation, 
incorporating  the  changed  transport  costs.  We 
then  compare  the  new  equilibrium  with  the  old. 
The simulations proceed in four stages: 

1.  Compute  direct  effects.  We  find  the  value  of 
the  transport  costs  savings  with  all  endoge 
nous  variables  held  constant.  To  do  this,  we 
compute  the  effective  distance  saved  by  the 

infrastructure  project  between  and  within  re 
gions.  We  then  compute  trade  costs  as  a 
function  of  distance,  translating  the  effective 
distance saved into transport costs savings. 

2.  Compute  the  effects  holding  location  of  firms 
and  workers  fixed,  but  allowing  output  and 
sales  to  change.  This  approximates  the  in 
duced effects of the project  in so far as it per 
mits  changes  in some  endogenous  variables, 
but ignores the possibility that the project may 
cause  industrial  relocation.  This  gives  short 
run effects. 

3.  Compute  the  effects  allowing  the  number  of 
firms  to  change  but  holding  the  number  of 
workers constant. The changes in the number 
of  firms  occur  in  response  to  short  run 
changes in the level of profits and, as they oc 
cur,  they  further  change  the  profits  through 
the  linkage  and  factor  market  mechanisms. 
This gives medium run effects. 

4.  Compute  the  effects  allowing  workers  to 
move  in  response  to  induced  changes  in 
wages  between regions. This stage of the ex 
periment  allows  'perfect'  factor  mobility,  in so 
far as both labour and capital move to ensure 
equalisation of  returns across  all regions. This 
gives long run effects. 

Using  these  four  stages,  we  can  compute  the 
welfare and labour income changes  by region re 
sulting from  specific  Cohesion  infrastructure  pro 
jects  in the short  run, medium  run, and  long run. 
The  CGE approach  can  offer  more  specific,  de 
tailed insights into the overall effects of infrastruc 
ture investment,  building on the basic  information 
of  standard  cost-benefit  analysis.  This  approach 
highlights  the  spillover  effects  of  changes  in 
transport  networks  which  often  ripple  through 
numerous regions in a country area, giving a rich 
er  understanding  of  the  results  of  Cohesion 
spending. 

22 

Section I — Introduction and summary 

6, Models of Explicit Distribution Dynamics 

The  first  section  of  this  paper  — historical  out 
comes  — documents  the  dynamics  of  growth 
and convergence  across the NUTS III economies 
in  the  four  Cohesion  countries.  The  techniques 
consider  changes  over  time  in the  distribution  of 
income  over  the whole  cross-section  of  regions. 
in  contrast,  more  standard  techniques,  such  as 
regression  analysis,  will  only  track  movements  in 
the  average.  The  second  section  of  this  paper 
extends these techniques. This extension  is flexi 
ble enough to allow economies to  become  'clos 
er'  over  time, for  example,  through  the  Improve 
ment  of  connecting  transport  infrastructure,  and 
enables  the  importance  of  spillover  effects  to  be 
assessed. 

Section  A  describes  the  dynamics  of  income 
growth and convergence  patterns at the NUTS  111 
level for Cohesion  Fund countries. We consider a 
number  of  graphical  and  statistical  representa 
tions of these dynamics: 

•  Plots  of  per  capita  GDP  on  a  "iongitude  and 
latitude"  base,  for  each  of  the  countries,  re 
vea!  information  on  the  dynamic  spatial  pat 
terns of regional growth in each economy. 

•  Density  estimates  of  relative  per  capita  GDP 

across  NUTS  111  regions  give  an  idea  of  the 
nature  oí  movements  within  each  economy's 
regional Income distribution. 

•  Tukey  Box  plots  indicate  whether  observa 
tions  at  the  extremes  of  the  distribution  are 
large  relative  to  the  median  and  dispersion of 
the distribution. 

•  Cross  profile  plots  highlight  the  degree  of 
churning  within  the  income  distribution  as  it 
moves over time (ie the degree of intra-distrib-
ution dynamics). 

Ali these methods allow us to develop a more in 
tuitive  feel for  the  dynamics  of  the  cross  section 
income distribution. 

Having considered these  point  in time  snap shots 
we develop a more forma! methodology to investi 
gate  intra-distribution  dynamics  and  long  run ten 
dencies.  Initially, we  calculate transition  probability 
matrices for each income distribution. To construct 
these  matrices,  we  first  discretize  the  income 
space by dividing the range of  incomes into differ 
ent  states  (for  example,  between  0  and  VA  of  the 
distributions weighted  average, between % and  Vá 
etc).  Each  cell  of  the  transition  probability  matrix 
then  gives the  probability  of  moving  from  one in 
come  state  to  another  (the  probability  of  moving 
from  rich to  poor,  the  probability  of  staying  poor, 
etc). The diagonal of each matrix describes the de 
gree of  persistence: the  probability  of  remaining in 
the  current  income  state.  We then  conduct  a hy 
pothetical  experiment.  Assuming  that  the  system 
described  by the transition  probability  matrix  con 
tinues on its evolution, with no change in structure, 
the system will eventually  reach a long-run steady 
state.  We  use the  transition  probability  matrix  to 
calculate the "ergodic" distribution which describes 
this  long-run  situation. These ergodic  distributions 
suggest what may happen to these income distrib 
utions without Cohesion Fund intervention. 

Unfortunately,  we  show  that  the  ergodic  distribu 
tion  results  are  not  always  robust  to  changes  in 

Models of Explicit Distribution  Dynamics 

23 

the (arbitrary) discritization of states. For example, 
assigning  regions  to  classes  between  0  to  Vi?  of 
the  population  weighted  average and  between Vé 
and  1, may  change  the  ergodic  distribution.  Our 
solution  is to  let  the  set  of  states  be  all  possible 
income  intervals,  including  those  infinitesimally 
small  (technically,  we  consider  the  natural  Borei 
sigma-algebra).  The  transition  probability  matrix 
with its finite number of rows and columns  is now 
replaced  by  a  stochastic  kernel  (a  "transition 
probability  matrix"  with  a continuum  of  rows  and 
columns). These  kernels  can  capture  persistence 
(the  poor  staying  poor,  the  rich  staying  rich)  and 
the long run tendencies of the income distribution. 
These  stochastic  kernels  form  the  basis  of  the 
conditioning work in the next section of the paper. 

The  main  empirical  results  from  this  first  section 
are that: 

•  The collection of all Cohesion  NUTS III regions 
from  Spain,  Portugal  and  Greece  has  grown 
more  unequal. That  increase  has occurred  at 
a rate faster than that seen across countries. 

•  While  Greece  has  grown  slowest  in  aggre 
gate,  its  NUTS  III  have  shown  the  greatest 
static and dynamic cohesion. 

•  Aggregate  growth  has  been  relatively  high  in 

both Portugal and Spain. 

Spain's  regional  inequalities  have  kept  pace 
with  its  aggregate  growth;  Portugal's  have In 
creased more than proportionately. 

Greek  average  per  capita  income,  initially 
higher  than  that  of  Portugal's,  fell  rapidly  be 
low the latter. 

If the  historical  pattern  of  development  were 
to  continue,  the  cross-section  NUTS  III in 
come distribution will progressively  be skewed 
towards the relatively poor. 

An emerging twin-peaks  property  — polariza 
tion  of  the  income  distribution  into  multi-tier 
groupings  — is  observed  across  the  world's 
cross-section  of  countries.  However,  i:  does 
not occur across the Cohesion economies. 

Europe-wide  regions  do  not  display this twin-
peaks  property  either.  However,  most  of  the 
interesting  changes are hidden at gross levels 
of disaggregation. When we consider the Co 

hesion  "neighbours"  (UK,  France  and  Italy) 
separately  a  more 
interesting  picture 
emerges. 

•  The UK shows the greatest increase in region 
al  inequality,  while  that  in  France  and  Italy 
grows only roughly in step with overall growth. 
UK  and  French  regional  inequality  has  grown 
because  of  a  few  leading  regions  pulling 
ahead,  in  contrast  Italy  shows  emerging  po 
larization between rich and poor. 

Because of  data problems,  results for  Ireland are 
not yet available. 

In section  8, we develop  the  basic  framework  to 
allow  us  to  evaluate  alternative  explanations  for 
the  phenomena  described  in  Section  A.  We be 
gin  by  constructing  conditional  income  distribu 
tions.  The  process  that  tells  us  how  to  manipu 
late the  unconditional  data to  get the  conditional 
data  is  called  a  conditioning  operation.  We  give 
some simple examples of conditioning operations 
based  on  spatial  location  and  trading-bloc  (or 
convergence club) membership  and suggest why 
these  factors  might  be  important.  To  generalize 
these  ideas,  we  develop  the  formal  concept  of 
conditioning  schemes.  Each  conditioning 
scheme  tells  us  how  to  construct  a  conditional 
distribution  by  providing  information  for  each 
economy on: 

ti)  the  economies  associated  with  that  specific 

economy in each time period; 

(2) the  importance  of  each  of  those  associated 

economies to the specific economy; 

(3) the  time  lag  with  which  those  associated 

economies affect the specific economy. 

Once  a  conditional  distribution  is  calculated  we 
estimate  stochastic  kernels,  which  capture  the 
relationship  between  the  unconditional  distribu 
tion  and  this  conditional  distribution. These  sto 
chastic  kernels allow us to evaluate how much of 
the  unconditional  distribution  is explained  by our 
conditioning. 

Conditioning  schemes  can take  into  account  the 
fact  that  the  'closeness'  of  regions  may  depend 
on  whether  they  are  in the  same  country  or ad 
ministrative  region, and that  Cohesion  Fund pro 
jects  affect  this  'closeness'  by  improving  the 
quality of transportation and communications be-

24 

Section I — Introduction and summary 

tween  regions.  The  technique  is flexible  enough 
to  allow  different  economies  to  have  different 
spillover  influences,  and  for  that  influence  to 
change over time. 

Spatial conditioning for the EU-12  NUTS II re 
gions  show  that  a  large  part  of  income  in 
equalities  across  the whole  of  Europe can  be 
explained by spatial spillovers. 

For  the  Cohesion  NUTS  III regions  we  construct 
a spatial conditioning scheme as follows: 

(1) each  economy  is associated  with  its  physical 

contiguous neighbours; 

(2) each  associated  economy  is  weighted  ac 

cording to its population size; 

(3) it is assumed that associated economies have 

immediate impact (the time lag is zero). 

When the  relevant  stochastic  kernel is calculated, 
most of the persistence  in the unconditional distri 
bution  is  explained.  For  the  NUTS  III regions  of 
Portugal  and  the  NUTS  II  regions  of  Spain  and 
Greece, we also construct  a number of kernels to 
consider conditioning by private and public invest 
ment. Finally, we investigate the nature of spillovers 
from  Cohesion  to  non-Cohesion  countries  using 
as  specific  examples  the  three  "Cohesion  neigh 
bours": France, Italy and the UK. We construct the 
distribution  dynamics  among these  non-Cohesion 
economies,  conditioning  on  hypothetical  changes 
in the  Cohesion  ones. This gives  us important in 
sights  into how Cohesion  Fund expenditure might 
help economies outside of the Cohesion Four. Fur 
ther  analysis  is  possible,  but  extension  to  other 
non-Cohesion  countries  would  benefit  from com 
plete and exhaustive data for all European regions. 

In Greece,  private  investment  in  individual  re 
gions  helps  explain  distribution  dynamics. 
Public  investment  and  investment  (public  and 
private)  in  neighbouring  regions  have  little  ef 
fect. 

• 

In  Spain,  private  investment  matters,  but  so 
too  does  public  productive  investment  in 
neighbouring  regions  — spillovers  matter  a 
great deal here. 

•  Portugal  is intermediate  between  Greece and 
Spain. Here the dynamic effects of investment 
alter at different  levels of development: the co 
hesion  impact  of  investment  is  strongest  at 
low levels of income; the effect is muted as in 
comes  rise,  and  disappears  after  a threshold 
income level is reached. 

• 

If the  Cohesion  economies  see their  incomes 
increase and experience higher tendencies to 
wards greater equality, then the  non-Cohesion 
economies of France, Italy and the UK will ex 
perience similar outcomes. 

• 

It  is the  relatively  poorer  non-Cohesion  coun 
tries  that  will  benefit  most  from  Improving 
conditions in the Cohesion economies. 

The main results are as follows: 

•  Spillovers from physically adjacent  regions are 
very  important  in  explaining  income  distribu 
tion dynamics in the Cohesion countries. 

These  results  provide  key  insights  into  distribu 
tion dynamics across  and within Cohesion coun 
tries  which  allow  for  analysis  of  whole  distribu 
tions of Cohesion Fund expenditure. 

Models of Explicit Distribution Dynamics 

25 

7, General Comments 

We  have  chosen  to  use  a  number  of  different 
modelling  approaches  to  assess  the  impact  of 
Cohesion  Fund  expenditure.  A  combined  model 
capable  of  assessing  all  the  dimensions  of  the 
problem  does  not  exist.  Nor  do  we  think  that 
such a model could be developed within a sensi 
ble time frame. Thus the  key advantage  of  using 
four  different  modelling  approaches  is that  each 
provides  immediate  insights  into the different  as 
pects  of  the  impact  of  Cohesion  Fund expendi 
ture.  There  are  however  some  disadvantages  to 
the different approaches which we address here. 

Model  Outputs 

The  VARs  are  macro  based  empirical  models 
whose  coefficients  have  been  estimated  using 
time series data. Thus the CGE model's output is 
in  terms  of  indicators  we  usually  associate  with 
micro-economic  evaluation  such  as Cost  Benefit 
Analysis.  Results  from  the  different  approaches 
are  not  directly  comparable.  For  example 
changes in "labour income" from the CGE are not 
capturing  the  same  effect  as the  VAR  estimates 
of  changes  in employment.  The  output  from  the 
models cannot  be easily "harmonised". While this 
is a  long term  objective,  at  present  comparisons 
across  approaches  should  be  made  with  care. 
Instead, the models provide different  but comple 
mentary pictures of the impact  of Cohesion Fund 
expenditure. 

The  different  models  do  not  always  have  the 
same  variables  as  model  output.  The  VAR  re 
gional  labour  markets  model  estimates  (differen 
tial)  employment  and  investment  impacts.  The 
second  VAR  model  also  considers  employment 
effects,  but  also  analyses  the  impact  on  GDP, 
capital  stocks  and  unemployment.  In  contrast, 
the  CGE  models  provide  information  on  welfare 
change  and  "shadow  prices".  Finally the  MEDDs 
consider  solely  GDP  impacts.  In  some  cases 
these  differences  are  intentional. The  Bean VAR 
model was developed to extend some aspects of 
the  Pissarides/Wasmer  VAR  and  thus  considers 
additional  economic  indicators.  Similarly  the 
MEDDs  model  concentrates  solely  on  GDP im 
pacts to  allow  in-depth  consideration  of  spillover 
and  distribution  effects.  Other  differences  are 
methodological.  The  CGE  model  is  a  micro 
based theoretical model which has been calibrat 
ed  to  actual  economic  data  from  a  given  year. 

Unresolved  differences 

and  Employment  VARS 

The  difficulty  of  using  more  than  one  modelling 
approach is that differences  between model con 
clusions  are  bound  to  arise.  Our  study  is  no ex 
ception to this general rule. In particular there are 
a  number  of  reported  differences  between  the 
Regional  Labour  Market  VARs  and  the  Regional 
Output 
(the 
Pissarides/Wasmer  VAR  and  the  Bean  VAR). 
When we  can, we  explain  why  these  differences 
arise. Thus  we  are  able  to  provide  technical  ex 
planations  as  to  why  the  Bean  VAR  predicts  a 
much larger immediate employment  effect of Co 
hesion  Fund  expenditure,  and  why  it  predicts  a 
different  impact  on  local  private  investment.  Sta 
tistically we are unable to test for the  significance 
of  these  differences.  More  importantly,  economi-

General Comments 

27 

cally the differences are small relative to the over 
all estimated  impact  of  the  projects. The  models 
also  differ  in the  results  for  the  impact  of  motor 
way  expenditures.  Again  we  believe  the  differ 
ences are technical  and arise from the  difference 
in initial treatment  of the raw economic  data. The 
technical  problems  are  explained  in  the  second 
paper  of  the  report.  In  one  important  case  we 
cannot  explain  completely  why  differences  arise, 
although we put forward some  possible explana 
tions.  Specifically  the  models  differ  in  their  as 
sessment  of  the  impact  of  environmental  prob 
lems.  We  return  to  the  issue  of  environmental 
projects  below.  At  the  present  time  these  differ 
ences  between  the  two  models  could  not  be 
completely  resolved. We hope that  as we contin 
ue  to  analyse  Cohesion  Fund  expenditure  using 
the  models  that  we will  be  able to  better  under 
stand and reconcile these differences. 

Environmental  Projects 

Environmental expenditure is a significant  propor 
tion  of  Cohesion  Fund  expenditure  and  we  have 
tried  to  address  the  analysis  of  the  impact  of 
such  projects.  Unfortunately  the  problems  in as 
sessing the  impact  of  environmental  projects  are 
numerous.  There  are  a  number  of  general  prob 
lems that  would  hamper  any  attempt  to system 
atically  analyse  the  impact  of  environmental  ex 
penditure.  Environmental  expenditure  is very het 
erogeneous,  but  data  on  different  categories  of 
expenditure  is  scarce.  We  would  expect  these 
different  categories  of  expenditure  to  have  very 
different  economic  effects.  For  example environ 
mental expenditure that has very long term health 
benefits  and  high  standard  of  living  value  will 
have  little  effect  on  current  economic  indicators. 
In  contrast,  a  targeted  environmental  project 
which  improves the  water  supply  to  key  regional 
industries  could  be  expected  to  have  significant 
economic  as well  as social  impact.  The  problem 
is compounded  by the lack of a generally accept 
able  theoretical  model  of  the  impact  of  environ 
mental expenditures on economic  indicators. Our 

specific  problems  in estimating the  impact  of en 
vironmental  expenditures  partly  stems  from  this 
general problem. 

What  can our  models tell us about  the Impact of 
environmental  expenditures?  The  CGE  models 
tell  us  little. These  models  assess  the  Impact  of 
transport  projects  by  considering  the  implied 
changes  ¡n  transportation  costs  for  industrial 
products.  Unless a specific  environmental  project 
had  very  significant  cost  implications  for  a large 
number  of  industrial  sectors  in  specific  regions 
this approach could not be used to isolate the af 
fects  of  the  project.  Even  then,  analysis  using  a 
model  specifically  developed  to  analyse  infra 
structure  investments  would  not  really  be appro 
priate.  The  MEDDs  approach  does  not  distin 
guish  between  environmental  and  infrastructure 
investment,  and  would  be  inappropriate  for  the 
analysis of individual projects even if it did so. We 
have attempted to analyse the impact  of environ 
mental  projects  within  the  two  VAR  models,  but 
as  indicated  above  this  is  an  area  where  some 
differences  remain  unresolved.  Whereas  Bean 
finds that  environmental  projects  have an  impact 
on  employment  that  ¡s  not  statistically  different 
from  infrastructure  projects,  Pissarides/Wasmer 
find  that  environmental  projects  have  an  impact 
that  is  no  different  from  infrastructure  projects  in 
the  short  run,  but  not  significantly  different  from 
zero  in the  long  run. Thus  our  models  should al 
low  us to  predict  the  short  run  employment  im 
pacts  of  environmental  expenditure.  Predicting 
long  run  impacts  will  require  additional  informa 
tion  if we are to discriminate  between the two al 
ternatives  suggested  by  our  VAR  formulations. 
Additional information about the characteristics of 
an individual project, for example from Cost  Ben 
efit  Analysis,  may  allow  us  to  assess  whether 
long  run  benefits  are  likely  to  be  large  or  small. 
Using  this  additional  knowledge  to  help  us  dis 
criminate  between  the  two  different  scenarios 
would then  be appropriate  (after all we are trying 
to  capture  the  macroeconomic  impacts).  The 
model  results  would  then  provide  an  upper  and 
lower  bound  on  the  possible  employment  im 
pacts of environmental projects. 

28 

Section I — Introduction and summary 

8. Conclusion 

The models we have developed  have been used 
to  simulate  the  impact  of  a number  of  individual 
projects.  In addition, the  models  of explicit distri 
bution dynamics  have been used to  provide poli 
cy  implications  for  whole  patterns  of  Cohesion 
Fund  expenditure.  We  should  be  explicit  that  all 
four  different  models  cannot  be used to  simulate 
the  effects  of  all  types  of  projects.  This  reflects 
our  choice  to  trade  complete  coverage  by every 
model, for in-depth and hopefully accurate analy 
sis of the dynamic  impact  of a narrower  range of 
projects.  In  addition  the  models  do  not  always 
agree. We have tried to  explain these  differences 
when  they  occur,  and  to  assess  the  importance 

of them. Unfortunately, economics is not an exact 
science and sometimes we have had to leave dif 
ferences unresolved. When this is the case we try 
to  draw  the  reader's  attention  to  any  remaining 
differences  and  allow  them  to  draw  their  own 
conclusions. 

Despite  these  difficulties  we  believe  that  the 
models outlined here can be used to  help formu 
late  Cohesion  Fund  policy.  Both  as  tools  for 
analysing  specific  project  impacts  and  as guides 
for policy priorities they provide information which 
has hitherto been unavailable when making these 
important  investment decisions. 

Conclusion 

29 

SECTION II 

LITERATURE REVIEW 

1. General introduction 

In looking at the current  modelling  literature, as it 
relates to the analysis of Cohesion Fund spending 
on  infrastructure  and  environmental  projects,  we 
need  to  identify  a  number  of  basic  questions: 
How  do  regionally  focused  changes  — invest 
ments, infrastructure development, and redistribu 
tions  affect  an  aggregate  economy?  How  might 
these, in turn, impact on neighbouring economies 
and the  Union as a whole?  What  implications  do 
such  local  disturbances  have  for  convergence, 
the tendency or ability of the poorer regions in the 
Union to catch-up with those already richer? 

There  are  a  number  of  economic  dimensions 
which  we  consider  central  to  answering  these 
questions: 

Outline of the  literature  review 

Because  of  the  nature  of  the  existing  literature, 
and the differences  between  our  proposed  mod 
elling approaches, the review that follows is divid 
ed into six sections: 

— Regional Labour Markets (Section 2) 

— Supply-side Effects (Section 3) 

— VARs and Stochastic  Kernels (Section 4) 

— Computable  General  Equilibrium  Models 

(Section 5). 

— Current Commission Modelling Work (Section 6) 

— regional  analysis,  economic  geography,  ag 

— Where Next? (Section 7) 

glomeration; 

— trade theory, economic exchange across regions; 

— labour market flexibility, mobility, location deci 

sions of firms; 

— aggregate  business  cycles,  macroeconomic 

fluctuations; 

— local public finance, welfare of people in differ 

ent regions; 

— economic growth, convergence. 

As  yet,  no  one  research  stream  analyses  all  the 
interactions  between  these  different  fields.  Our 
work should establish new linkages: linkages that 
will be useful both empirically and theoretically. 

In addition,  a  Glossary  of  technical  terms  and  a 
full list of references is provided. 

In Section 2 we consider the impact of  proposed 
regional  projects  on  regional  labour  markets. 
When a project  ¡s allocated  to  a region we  need 
to  be  able  to  predict  the  short  and  long  run  ef 
fects  on  labour  supply  and  demand  in  the  re 
gion.  By  doing  so,  we  hope  to  be  able  to  char 
acterize the dynamic  employment  effects  of spe 
cific  projects. The existing literature has generally 
adopted  a  partial  approach  to  regional  labour 
markets, and we  have to  allow for this  in our re 
view. Thus, we  consider,  in turn,  the  large litera 
tures on regional wage determination and supply 
responses (both migration and participation), be 
fore  considering  the  much  smaller  literature  on 
regional  employment  adjustment.  Only  by  con 
sidering  the  demand  and  supply  responses  that 

General introduction 

33 

determine  regional  employment  adjustment  can 
we  hope  to  accurately  predict  the  effect  of  re 
gional shocks. 

In Section 3, we outline some of the more recent 
literature  on  the  supply  side  effects  of  public  in 
vestments.  In the  medium  to  long term these  ef 
fects  will  be  a  key  factor  determining  the  re 
sponse to Cohesion Fund spending. We consider 
the impact of public  infrastructure  on the produc 
tivity  of  regional  economies  and  also  analyze 
some work  relating to  the  locational  decisions  of 
firms.  We  suggest  how  our  models  will  capture 
different  elements of this supply side  adjustment, 
an issue which we return to below. 

In  Section  4,  we  consider  new  econometric  ap 
proaches  to  analysing  the  dynamic  response  of 
economic  variables  to  regional  shocks.  These 
new approaches  use historical time series data to 
specify  dynamic  relationships  between  variables. 
We find  applications  of these techniques  in three 
different  strands  of  the  literature:  in  the  regional 
forecasting literature; in the growth empirics litera 
ture;  and  more  recently  in  studies  that  explicitly 
consider  regional  evolutions.  In  our  review  we 
consider  each  of  these  strands.  In  addition  we 
provide  a  brief  theoretical  justification,  and  com 
ment on the suitability of these approaches for the 
sort of problems we are considering. 

In Section  5, we outline the applied  (computable) 
general  equilibrium  approach  which  is  used  ex 
tensively in the international trade and taxation lit 
eratures. We consider  the basic  constant  returns 
to  scale, perfect  competition  models, and exten 
sions  to  these  models  that  allow  for  increasing 
returns  and  imperfect  competition.  We also  out 
line  a  small  number  of  models  which  explicitly 
model regional economies and some models that 
allow  for  transportation  costs.  These  models 
form the building  blocks  of the model that we in 
tend to construct. We believe that combining fea 
tures of  industrial  organization and regional  mod 
els will give us a model that  can  help  predict  the 
welfare impacts of a whole range of regional pro 
jects. 

Section  6  provides  an  overview  of  the  current 
modelling  work  undertaken  on  behalf  of  the 
Commission.  We  have  concentrated  on  the  de 
tailed  reports  of  ex-ante  modelling  which  outline 
work  on:  large scale  macro  models;  computable 
general  equilibrium  models;  endogenous  growth 
theory  models;  and  input-output  models.  We 

have tried to highlight how our modelling work re 
lates  to  these  current  models,  and  why  we  be 
lieve  that  our  modelling  strategies  represent  an 
improvement on these existing approaches. 

In the final section we provide a brief  overview of 
the  relationship  between  the  existing  literature 
and our new modelling techniques. 

Supply side  issues 

Our  models  will  have  to  capture  the  impact  of 
Cohesion  Fund spending  on the  production  side 
of  the  economy  if  they  are to  effectively  predict 
the  long  term  impacts.  A  lot  of  the  earlier  litera 
ture  has tended to  concentrate  on  demand-side 
effects  while  examination  of  supply  side  effects 
is a relatively  recent  concern1. The  nature of our 
projects  is  such  that  we  do  not  explicitly  esti 
mate  'behavioural  equations'  that  claim  to  ex 
plain  the  supply  side  of  the  economy.  Instead 
our  modelling  approaches  seek  to  capture  sup 
ply  side  issues  in  ways  that  make  use  of,  and 
contribute  to,  our  current  understanding  without 
making  our  impact  predictions  reliant  on  com 
plete  and  accurate  modelling  of  these  issues2. 
The  regional  labour  market  models  will  consider 
supply side effects  when estimating  employment 
adjustment  (firm  location  and  hiring  decisions 
determine  the  demand  side  of  the  regional 
labour  markets).  The  VARs  and  stochastic  ker 
nels will capture supply side effects  naturally, be 
cause  information  on  the  regional  supply  reac 
tions to  previous  changes  are encoded  by these 
techniques  and  used  when  making  predictions. 
Finally, the regional nature of the CGE model and 
the  incorporation  of  increasing  returns  to  scale, 
imperfect  competition  and  differing  degrees  of 
factor  mobility are a result of our desire to explic 
itly model the long run supply side effects of Co 
hesion Fund spending. 

Impact  of environmental  projects 

Unfortunately,  the  existing  literature  deals  exclu 
sively with the environmental  impact  of economic 

To a certain extent  we  have  had to  reflect  this  bias  in our  review of 
the existing literature. 
See  also  our  comments  on  the  Commission's  current  modelling 
work. 

34 

Section  II  —  Literature  Review 

projects  (ie  environmental  impact  assessment) 
and  we  are  not  aware  of  any  work  concerning 
the  macro-economic  impacts  of  environmental 
projects.  Obviously,  it  is  possible  within  a  cost 
benefit  framework  to  forecast  the  potential  im 
pact  of  environmental  projects  at  the  micro-level 
(eg improved tourism  numbers,  reduced costs to 
local  industry).  But  these  analyses  are  partial  in 
nature and would  be of little use in predicting the 
macro-economic  impact  of a wide variety of pro 
jects  implemented  over  a  given  time  period. 
There are two  possible  courses for capturing the 
effects  of  these  projects  within  our  modelling 
frameworks. The first would  be to  incorporate in 
formation  from  cost  benefit  analysis  of  individual 
projects as an input  into the models. The second 
would  involve  considering  the  macro-economic 
impact of whole groups of projects. After examin 
ing a number of environmental  project  proposals, 
we  would  tend  to  favour  the  second  approach. 
Such an approach would still allow us to consider 
the  impacts  of  larger  projects1  individually,  and 
would  be a necessary  pre-requisite for  predicting 
the  macro-impacts  of  large  numbers  of  small 
scale projects. Given the lack of relevant theoreti 
cal or empirical literature, we feel that it would not 
be prudent,  at this  point, to  commit  ourselves to 
a  specific  solution  to  this  modelling  problem. 
However,  we  are  aware  of  the  importance  the 
Commission  places  on  differentiating  between 
environmental  and  transport  projects  and  will 

1  For example, the water distribution projects for Dublin and Athens. 

seek  to  reflect  these  differences  within the  mod 
elling frameworks. 

Three  different 
modelling  approaches 

Although we intend to adopt three rather different 
modelling  approaches,  there  is  a  unifying  theme 
which we will emphasise throughout  the literature 
review:  we  believe  that  dynamic  interactions  be 
tween  a whole  host  of  regional  variables  will de 
termine  the  impact  of  regional  spending.  Our 
modelling  approaches  reflect  this  belief,  and  we 
think  that  they  will  prove  both  theoretically  and 
empirically useful in the analysis of regional issues. 

Only once the three different  modelling  processes 
are  complete  will  we  be  able  to  assess  to  what 
extent  the  different  approaches  can  be  integrat 
ed.  However  we  have  never  intend  to  provide  a 
single  "magic  bullet"  model  that  will  provide  all 
the answers. Given the current state of economic 
modelling  we  believe  that  attempts  to  construct 
such  a  model  would  involve  strategies  that  will 
not  give  accurate  answers  to  any  of  the  impor 
tant  questions.  By  concentrating  our  attentions 
on  modelling  the  areas  of  regional  economies 
that we best understand, we hope to provide ac 
curate  predictions  for  key  economic  variables 
The  diversity  of  our  different  approaches  is  pre 
cisely one of the strengths that we offer. 

General  introduction 

35 

2, Regional Labour Markets 

Introduction 

It would  be fair to say that the dynamics of wage 
and  employment  determination  ¡n  local  labour 
markets  are  little  understood.  While  there  is  a 
vast literature concentrating  on partial analysis of 
the  Individual  components  of  regional  labour 
market  adjustments,  the  interaction  of  these  ef 
fects  within  a  dynamic  context  have  only  been 
formally  considered  very  recently.  This  new  em 
phasis  on  dynamic  interactions  has  provided 
several  interesting  insights,  but  in  what  follows 
we are, for the main part,  Interested  in the larger 
body  of  literature  covering  partial  approaches  to 
our problem. 

When a region is allocated a project, the immedi 
ate effect  is to  increase the demand for  labour in 
that  region.  Higher  demand  in the  labour  market 
puts  upward  pressure  on  wages,  and  induces  a 
number  of  supply  responses  from  workers. 
Eberts and Stone (1992)  identify four  possible ef 
fects: workers  increase the number of hours they 
work;  the  unemployed  receive  more  job  offers 
and unemployment  falls; individuals who  are cur 
rently  outside  the  labour  force  look for  work  and 
labour  force  participation  rises; workers  who  live 
outside  the  region  enter  the  local  labour  force 
(i.e., workers  migrate into the region). In the short 
run,  we  would  expect  that  the  supply  response 
may  not  be  complete  and  that  both  wages  and 
employment  will rise. In the long run the equilibri 
um  is  reestablished  in  the  local  labour  market. 
What  does  this  new  regional  equilibrium  look 
like?  Has there  been a permanent  effect  on em 
ployment?  Has  unemployment  returned  to  its 

pre-project  levels?  Are  regional  wages  higher? 
How have demand spillovers  affected  neighbour 
ing regions and the rest of the economy? 

In line with the existing literature we will subdivide 
this adjustment  process  into several separate but 
interrelated problems. We will consider: 

I)  Regional  wage  determination  — what  factors 
determine  how  regional  wages  will  adjust  to 
the initial demand shock? 

il)  Supply  responses  — what  factors  determine 
labour force participation  changes  and migra 
tion between regions? 

iv)  Employment  adjustment  — how do these fac 
tors  combine  to  determine  regional  dynamic 
adjustment? 

Regional wage  determination 

When a project  is allocated to  a region, we  have 
suggested that the initial effect  is to decrease un 
employment  and  raise wages. To the  extent  that 
the  induced  supply  response  is  a  result  of 
changes  in these  variables,  we  are  interested  in 
the determination  of these variables and the rela 
tionship between them. 

There  is  a  well  established  theoretical  literature 
that  hypothesises  a  long  run  positive  correlation 
between  regional  unemployment  rates  and  re 
gional wages.  If we view  the  unemployment  rate 
as  a  proxy  for  the  probability  of  employment, 

Regional Labour  Markets 

37 

then an individual who  stays in a high unemploy 
ment  region  faces  a  higher  probability  of  being 
out  of  work.  Workers  must  be  compensated  for 
this  higher  probability  of zero  income  by a higher 
wage  when  in  employment.  While  the  classical 
tradition  predicts  that,  in  the  short  run,  lower 
wages  should  be  associated  with  higher  unem 
ployment,  in the  long  run  expected  incomes  will 
be equated across regions and we see a positive 
relationship  between  wages  and  unemployment. 
Detailed outlines  and extensions  of this  hypothe 
sis,  together  with  empirical  tests  using  long  run 
data,  can  be found  in  Harris  and  Todaro  (1970), 
Hall  (1970,  1972),  Reza  (1978),  Behman  (1978) 
and Adams (1985). The specifications and empir 
ical  procedures  in  these  articles  are  essentially 
static  (pooling  across  states  and  time  periods) 
and so we concentrate on papers which highlight 
issues important to our dynamic considerations. 

The  existing  studies  tend  to  consider  either  mi-
croeconometric  or  macroeconometric  wage 
equations. The former  group  highlight  the  impor 
tance  of  individual  characteristics  in wage  deter 
mination and attempt to estimate the effect  of re 
gional variables on individual wages after control 
ling for  these characteristics.  They tend  to  use a 
mixture  of  panel  and  regional  data.  On the  other 
hand macroeconometric  wage equations consid 
er  the  determinants  of  average  regional  wages. 
From the micro studies we know that the charac 
teristics  of  individuals  are  important.  However,  in 
order  to  concentrate  on  the  aggregate  relation 
ships, macro studies allow for regional  character 
istics  by using compositional variables  and/or re 
gional  dummies.  They  tend  to  use  aggregate 
time  series  and  cross  sectional  data.  More  re 
cently  there  have  been  attempts  to  combine  the 
two traditions. 

Blackaby and Manning (1987) is one of the earlier 
studies to include local unemployment  in different 
specifications of a microeconometric  wage equa 
tion. The study  uses data from the  1975 General 
Household  Survey  on  7288  white  male  employ 
ees  who  had  worked  during  the  previous  year. 
For  each  individual,  data  on  years  of  schooling, 
work  experience,  work  experience  squared, 
weeks  worked  in  previous  year,  marital  status 
and  industry  of  work,  are combined  with  a mea 
sure of  regional  unemployment.  From pooled es 
timation  of  Mincer-type  earnings  functions  the 
authors  conclude  that  "individual  attributes  and 
the regional  industry  mix are significant factors in 
[the] explanation of regional earnings differentials" 

(p. 161). The unemployment  rate seems to have a 
negative  affect  on  earnings  both  over  time  and 
between  regions.  In addition, the  paper  presents 
estimates  of  regional  Phillips  curves  which  sug 
gest that  both the level and rate of change of re 
gional  unemployment  can  affect  regional  wage 
inflation.  The  authors  suggest  that  this  evidence 
is  consistent  with  determination  of  regional 
wages  through  a  bargaining  process  where  the 
regional  unemployment  rate  in an  important  fac 
tor  in  the  negotiations.  Reported  results  from 
causality  tests,  which  consider  the  explanatory 
power  of  regional  wage  spillover  variables  on 
residuals  from  the  Phillips  curve  estimation,  lend 
some support  to the  hypothesis  of a "leading re 
gion"  in  the  wage  determination  process  (the 
South  East).  In  Blackaby  and  Manning  (1990), 
the  Mincer  type  equations  are  reestimated  using 
General  Household  Survey  data  from  1975  and 
1982 with similar results. 

Jackman  and  Savouri  (1991)  consider  the sensi 
tivity of wages to  regional, as opposed to nation 
al,  labour  market  conditions.  They  specify  real 
wages  as  a function  of  regional  unemployment, 
leading  sector  unemployment,  national  unem 
ployment,  lagged  regional  wages  and  a  time 
trend.  Real wages  are defined as average  hourly 
wages  deflated  by  the  GDP  deflator.  They  esti 
mate  fourteen  separate  equations  (classified  by 
sex  and  the  level  of  sectoral  aggregation)  using 
aggregate  data  from  the  New  Earnings  Survey. 
The  more  aggregated  equations  (eg  male earn 
ings)  allow  for  regional  composition,  less  aggre 
gated  (eg  male  manual  workers  earnings)  allow 
for sectoral  differences,  while the most disaggre 
gated  equations  (eg male  manual  manufacturing 
earnings)  do  not  account  for  compositional  ef 
fects.  The  authors  suggest  that  omitted  variable 
bias  explains  the  positive  coefficient  on  national 
unemployment  (it  picks  up  the  effect  of  supply 
shocks). The coefficient  on  leading sector unem 
ployment is always positive and insignificant while 
that  on the  regional  unemployment  rate  is nega 
tive and significant. 

These  absolute  wage  equations  suffer  from  a 
number  of  problems.  In  particular,  there  are  a 
large  number  of  coefficients  given  the  available 
data and considerable  scope for  misspecification 
given current  understanding of national wage de 
termination.  Empirical  evidence  points  to consis 
tency  of the absolute  wage  equation  coefficients 
across  regions,  which  allows the  specification  of 
a  relative  wage  equation  that  reduces  these 

38 

Section I 

Literature Review 

problems:  there  are  fewer  coefficients  and  all 
variables  enter  as differences  from  national  aver 
ages.  The  estimation  results  imply  that  regional 
labour  market  conditions  only  matter  for  the 
wage  determination  of  manual  male  workers. 
Possible  explanations  are that  the aggregate un 
employment  measure  better  reflects  changes  in 
the  manual  market,  or  that,  because  of  mobility 
differences, the labour  market for  non-manuals is 
national in character. 

Although these papers do suggest  possible theo 
retical  models  which  are consistent  with the em 
pirical findings (bargaining wages or efficiency hy 
potheses),  they  are  not  complete  models  of  re 
gional  wage  determination.  Similarly,  Pissarides 
and  McMaster  (1990)  are  interested  in  the  long 
run relationship  between  regional wages and un 
employment  and the nature of adjustment  to this 
long run relationship, rather than the exact under 
lying wage determination  process. They consider 
adjustment  equations  for  the  region's  relative 
wage in terms of the unemployment  differential of 
the  region.  Estimation  results from  an  Error  Cor 
rection Mechanism suggest that short  run relative 
wages  are  correlated  with  unemployment  move 
ments  — a rise in relative unemployment  induces 
a fall in pay  (a regional  Phillips curve).  In the long 
run,  however,  the  relationship  between  regional 
wages  and  unemployment  is  a  compensating 
one1,  but  adjustment  to  this  long  run  equilibrium 
is slow. 

Blackaby and Manning (1992) also estimate Error 
Correction  Mechanisms  to  consider  regional 
wage  and  unemployment  determination.  Inde 
pendent  variables  are  measured  relative  to  the 
national  average.  Dependent  variables  are  differ 
ences  between  the  actual  and  expected  values 
(the wage which regions should pay given nation 
al wage  levels and the  regions  industrial  compo 
sition).  The  results  suggest  a  complex  relation 
ship  between  unemployment  and  wages  where 
unemployment  (in particular  long term unemploy 
ment)  reduces  earnings  in the  short  term,  but  is 
associated with higher wages in the long run. 

Bartik (1991) is more concerned with the effect of 
regional employment  on regional wages. He esti 
mates wage equations  using  U.S.  Current Popu 
lation  Survey  data  (1980-1987).  The  explanatory 

1  That  is,  higher  regional  wages  compensate  workers  for  a  higher 
probability  of  unemployment  thus  equating  expected  lifetime earn 
ings across regions. 

variables  are  demographic  factors  and  a  time 
trend.  Unchanging  area  characteristics  are  con 
trolled for  by differencing. A number  of specifica 
tions  (including  several  that  use  shift  share  vari 
ables  for  potentially  endogenous  employment) 
find  a  positive  relationship  between  wages  and 
employment.  The  estimated  equations  are  actu 
ally the quasi reduced forms of a structural model 
of  regional  wage  determination  in  which  an  effi 
ciency  wage  hypothesis  holds and there  is Inter 
action  between  land  and  labour  markets.  Bartik 
suggests  that  this  long  run  relationship  supports 
a hypothesis of regional hysteresis. An increase in 
local job growth leads in the short  run (at least) to 
unemployed  residents  getting  jobs  that  they 
would  not  have  got  otherwise.  These  residents 
who obtain jobs in the short run acquire skills that 
increase  their  long  run  employability  and  wage 
even after migration has responded. The relation 
ship between this hysteresis effect and the struc 
tural  model of regional wage determination  is not 
made explicit. 

Eberts and  Stone (1992) are explicitly  concerned 
with  regional  dynamics.  They  identify  dynamic 
demand and supply curves through  postulating a 
recursive  dynamic  structure.  Employers  respond 
to  a shock  by  changing  employment  not  wages 
—  employment  is  a  function  of  exogenous 
shocks and lagged wages. Workers make partic 
ipation  decisions  within  a  reservation  wage 
framework  — wages are a function of exogenous 
shocks  and  lagged  employment.  Skill  adjusted 
wage  differentials  are  estimated  as  the  coeffi 
cients  on  dummy  variables  for  regions  in  wage 
equations which control for  personal characteris 
tics and are estimated using panel data. Regional 
equations  for  these  skill  adjusted  differentials 
suggest a cumulative positive response of region 
al  wages  to  regional  employment  shocks.  Im 
pulse  response  functions  calculated  from  these 
equations  show  that  in  response  to  demand  or 
supply  shocks,  wages  and  employment  over 
shoot  before settling down to a long run equilibri 
um 

Blanchard  and  Katz  (1992)  are  also  concerned 
with regional dynamics. They construct  two  mod 
els to explain regional evolutions (with and without 
full  employment);  however,  the  fundamental  em 
pirical  work  involves  VAR techniques  and  will  be 
considered  in more detail later.  In common with a 
number  of  papers above,  results  suggest  a short 
run negative relationship and long run positive re 
lationship between wages and unemployment. 

Regional  Labour  Markets 

39 

More  recently,  Blanchflower  and  Oswald  (1994) 
have estimated  wage  curves  for  a  large  number 
of  countries  using  individual  data  supplemented 
by  macroeconomic  data  on  regional  unemploy 
ment  levels. They consistently  find a negative re 
lationship  between  unemployment  and wages af 
ter compensating for individual and regional char 
acteristics.  The  authors  offer  several  possible 
theoretical  explanations.  "These  include  the  Idea 
that  it  [the  negative  relationship]  is  an  efficiency 
wage  no-shirking  condition,  a  contract  curve,  a 
bargaining power locus, or a scatter of disequilib 
rium points" (p. 13). 

Although  these  papers  differ  in  their  theoretical 
and  empirical  approaches  they  do  highlight  sev 
eral important issues: 

a)  the regional wage determination  process  may 
differ  in the short  and  long  run. Specifically, in 
the short  run unemployment  may have a neg 
ative  effect  on wages,  while  in the  long  run it 
may have a positive effect; 

b)  the  personal  characteristics  of  a  region's 
labour force and the  industrial  composnon  of 
the  region  can  explain  some  regional  wage 
differentials; 

c)  there  is  a  complex  relationship  between  re 
gional unemployment and regional wages. 

Supply  responses:  migration 

The  distribution  of  wages  across  regions  implies 
a  distribution  of  employment  across  regions  as 
firms  decide  where to  hire  as well  as  how  much 
to  hire. Given the underlying  distribution of popu 
lation  across  regions,  these  spatial  hiring  deci 
sions determine the distribution of  unemployment 
across  regions.  To  understand  how  the  regional 
labour  market  develops,  we  need  to  know  how 
labour  supply  responds  to  these  distributions  of 
unemployment  and wages. The most  extensively 
studied  labour  supply  response  is the  migration 
response to which we now turn. 

In  his  seminal  article,  Greenwood  (1975)  points 
out  that  the  vast  majority  of  economic  literature 
which  considers  migration  is concerned  with  the 
determinants,  rather than the effect,  of migration. 
Here,  we  need  to  consider  both  these  issues. 

Greenwood  identifies  several factors  that  can  af 
fect  gross  migration  rates.  Firstly,  distance  is a 
serious  deterrent,  usually  treated  as  a  proxy  for 
transport  costs,  psychic  costs  and  information 
availability.  Secondly,  possible  income  streams 
affect  the  migration  decision.  Migrants  select 
destinations  for  which  the  present  discounted 
value of their expected future stream of returns is 
greatest.  Thirdly,  greater  information  availability 
will increase migration: migrants are more likely to 
move to areas about which they know more. This 
effect  is  captured  in  the  "well-trodden  path"  or 
"chain-migration" theories  in which  migrant  flows 
into  a  region  are  self-perpetuating.  Finally,  per 
sonal characteristics  are key in explaining individ 
ual migration decisions.  Migration decreases with 
age and increases with education. 

A large number of papers analyze the determinants 
of  the  probability  of  migration  of  a  single house 
hold:  see, for  example,  Davanzo  (1978),  Hughes 
and McCormick  (1981,  1985,  1987), Topel (1986), 
Molho  (1987),  Pissarides  and  Wadsworth  (1989) 
and Jackman and Savouri (1992). 

In  a  recent  paper,  Hughes  and  McCormick 
(1994),  consider  an  integrated  approach  to  both 
the  decision  to  migrate  from  a  region  and  the 
choice  of  destination  region. They  use  a  hierar 
chical  decision  tree,  with  the  first  tier  containing 
the  migration  decision  and  the  second  tier  the 
destination  decision. There  is feedback  between 
the  second  and first tier  as the  possible destina 
tions  will  influence  the  initial  migration  decision. 
This  feedback  is  incorporated  by  the  use  of  an 
"inclusive  variable"  whose  coefficient  captures 
dissimilarity  of the regions, in addition to the vari 
ables  describing  the  attractiveness  of  the  origin 
region relative to the mean. They use a variant of 
McFadden's  conditional  logit  model  to  estimate 
the  two  equations.  From  the  migration  decision 
equation they find that the unemployed are signif 
icantly more likely to  migrate. They find that  high 
er  regional  unemployment  for  a  socioeconomic 
group  is  associated  with  a  lower  probability  of 
out-migration  in that  group.1 In addition, high lev 
els  of  real  wage  discourage  out-migration.  Re 
sults from the destination equation suggest a sig 
nificant  positive  effect  of  relative wages  on desti 
nation  choice.  Both  relative  turnover  rates  and 

These first  two findings appear  contradictory,  however  one  refers to 
the  individual's  unemployment  history,  the  other  to  the effect  of ag 
gregate regional unemployment. 

40 

Section I 

Literature  Review 

relative  unemployment  rates  enter  with  positive 
coefficients  that,  although  insignificant,  hint  at  a 
possible perverse effect on destination choice1. 

Pissarides  and Wadsworth  (1989)  use  data from 
the  Labour  Force  Survey  (1984)  to  estimate  the 
probability  of  migration  but  without  destination 
choice feedback.  Estimates from a migration de 
cision equation suggest that individual unemploy 
ment  experience  increases  the  likelihood  of  mi 
gration.  In addition, the effect of high regional un 
employment  has  an  indeterminate  effect  on  the 
probability  of  migration.  However,  given  the  sig 
nificant  positive  influence  of  regional  vacancy 
rates  and  relative  regional  wages,  the  authors 
conclude that there is evidence of statistically sig 
nificant  migration flows in the "right" direction (i.e. 
higher  gross  outflows  from  high  unemployment, 
low vacancy,  low wage  regions).  Finally, they find 
that at higher overall unemployment  rates the mi 
gration probabilities of all groups are reduced. 

The relationship between  migration flows and na 
tional  employment  conditions  is  considered  in 
more depth  by  Gordon  (1985).  From an  "at  risk" 
model,  migration  is found to  depend  on the  rate 
of creation of employment  opportunities  at desti 
nation, differences  in the expected value of those 
opportunities,  distance,  and  a  cyclical  mobility 
factor.  He suggests that  this cyclical  phenomena 
could  be  a  result  of  changes  in  liquidity  con 
straints,  employment  opportunities  and  the  level 
of  uncertainty  about  the future which  occur  over 
the  business  cycle. Results from  migration equa 
tions  provide  evidence  that  national  unemploy 
ment and regional differentials are important. 

Gordon does  not  include regional earnings  differ 
entials  in the migration equation specification be 
cause he is primarily  interested in cyclical interac 
tions.  In  addition,  results  from  Molho's  (1984) 
pooled cross section time series estimations sug 
gest  that  earnings  differences  are  not  significant 
influences  on  the  migration  decision  once  em 
ployment  growth  rates  are  included. The  impor 
tance  of  future  expectations  of  employment  lev-

els (and thus of employment growth rates) is con 
sidered further by Topel (1986). 

These  papers  consider  factors  that  are  funda 
mentally  important  in determining gross flows be 
tween regions. Plane and Rogerson (1990) stress 
the  importance  of  feedback,  linkage  structures 
and  interregional  Interdependence  in  migration 
flows. The flow  of  persons from  origin  region  i to 
destination  region j  depends  not  only  on  condi 
tions ¡n i and j but also on those in other regions. 
In addition, they  suggest  that  one cannot  under 
stand these  net  flows  purely from the analysis of 
individual  migration  behaviour  (we  often  see  net 
flow to low wage areas). 

Studies  that  model  aggregate  net  flows  use vari 
ables identified by these micro data papers. How 
ever, they tend to  concentrate  on  relative  regional 
unemployment  and  wage  rates  as  explanatory 
factors  capturing  compositional  factors  using  re 
gional  fixed  effects.  For  example,  Pissarides  and 
McMaster  (1990)  derive  net  migration  rates  by 
considering  the  determinants  of  the  probability  of 
migration of a single household. The gain from mi 
gration depends on relative wages and relative un 
employment  rates.  Regional  fixed  effects  capture 
the  differences  in  composition  of  households 
across  regions  (which  change  only  very  slowly 
over time). The authors  find that  while  unemploy 
ment  ratios  influence  migration  decisions  perma 
nently  only  changes  in  regional  wage  growth  are 
significant. That is, high unemployment  regions will 
loose  population  forever,  but  it appears that  peo 
ple have preferences across regions that are partly 
independent  of  relative  wage:  only  when  relative 
wages  change  are  they  induced  to  migrate. 
Eichengreen  (1993)  estimates  a similar  equation 
for Italy but finds that migration flows only respond 
to regional wage differentials entered in levels. 

For  similar  empirical  approaches  see  Barro  and 
Sala-i-Martin  (1991)  and  Blanchard  and  Katz 
(1992). Earlier studies  of the migration  phenome 
na  using  aggregate  data  can  be  found  in  Hart 
(1970),  Creedy  (1974),  and  Elias  and  Molho 
(1982).  For  a justification  of  this  approach  see 
Greenwood (1975). 

care  is  needed  here.  Firstly,  there  is  considerable  interaction  be 
tween  these  variables  and, one  would  suspect,  a considerable  de 
gree of  collinearity.  Secondly,  it  is difficult  to  separate  two  opposing 
effects.  Higher  unemployment  may  reflect  higher  long  term  unem 
ployment  and  thus  a  lower  expected  lifetime  income  stream  which 
would reduce in-migration. On the other hand, high turnover may in 
crease  the  probability  of  getting  a job  and  increase  in-migration. 
From the  reported  results  it  is not  possible  to  distinguish  these  two 
opposing effects and one should be careful when interpreting the re 
gression results. 

Supply  responses:  participation 

Explanations  of  interregional  differences  in labour 
force participation are less contentious. Gallaway, 

Regional  Labour  Markets 

41 

Vadder and  Lawson (1991), using  U.S.  data, find 
that  participation  is lower  in states with lower  rel 
ative wages,  lower transfer  payments  and  higher 
unemployment  rates.  Results  from  Eberts  and 
Stone  (1992)  and  Houseman  and  Abraham 
(1990) show that  changes  in labour force partici 
pation  account  for  a  significant  proportion  of 
overall changes in employment. 

Employment  adjustment 

We  have  outlined  some  of  the  vast  literature  on 
wage  determination  and  migration  patterns.  In 
contrast, there are very few papers which "close" 
the regional labour market system  by considering 
the interaction of these forces. Only by consider 
ing this interaction, can we map out the changes 
in employment  patterns as a result of a particular 
project. 

Pissarides  and  McMaster  (1990)  do  close  their 
regional labour market  model. Given estimates of 
migration and wage determination equations they 
postulate  a  relationship  between  unemployment 
differentials  and  the  level  of  migration.  Because 
the net migration data ¡s for all age groups, not all 
migration into a region will change the unemploy 
ment  rate. The actual change will  depend  on the 
level  of  labour  force  participation.  However,  mi 
gration will  create additional  demand  in a region, 
which  must  also  be  considered  when  specifying 
the relationship. Once the system is closed  it can 
be used to trace through the effects of shocks on 
regions.  Although  the  paper  postulates  an  in 

traregional  multiplier  effect,  it  does  not  consider 
interregional multiplier effects. 

The  recursive  dynamic  structure  used  by  Eberts 
and  Stone  (1992),  to  identify  the  demand  and 
supply  for  labour,  determines  employment  and 
wages  simultaneously.  As  with  all  regional  struc 
tural  models  the  equations  are  reduced  forms of 
the underlying model and subject to the usual cri 
tique.1 When the model is estimated for two sep 
arate  but  interacting  sectors  (manufacturing  and 
non-manufacturing)  tracing  out  the  responses  to 
a  shock  highlights  the  possible  importance  of 
multiplier  effects  within  regions.  Ignoring  regional 
interactions  between  model  sectors  predicts  a 
speed  of  adjustment  that  is far  too  high  and  in 
addition  fails  to  predict  significant  overshooting 
and spillover effects. We would expect that ignor 
ing  interactions  between  regions  could  have  an 
even greater effect. 

There  are  a  large  number  of  regional  structural 
models which claim to trace out some of these dy 
namics. We do not consider these models here. In 
addition  to  problems  of  estimation  and  identifica 
tion2, there are considerable  doubts  regarding the 
constancy  of  parameter  values  under  changed 
states  of  nature.  This  problem  is well  recognized 
and generally  known  as the  'Lucas  critique'3. We 
return  to  this  problem  in  our  evaluation  of  the 
Commission's current modelling work. 

See  Below. 
See the VARs and stochastic kernels section. 
See  Lucas  (1976).  The  debate  is  far  older  than  the  current  label 
would  suggest,  for  an  early  exposition  see,  for  example,  Robbins 
(1932) 

42 

Section I 

Literature  Review 

3. Supply effects 

In the  short  run,  the  impact  of  Cohesion  Fund 
spending  will  be  determined  through  Keynesian 
demand effects and the relevant short  run supply 
responses.  In the medium and long term, supply 
side effects will play a major role. A number of re 
cent  studies  analyze  the  supply  side  effects  of 
public  investment  at the regional level. This litera 
ture generally applies standard econometric tech 
niques to evaluate (a) the impact  of public  invest 
ment  on  the  productivity  of  regional  economies, 
and  (b) the  determinants  of  the  location  of  firms 
and economic  activity across  states and regions. 
In our  view,  standard  time  series,  cross-section, 
and  panel  data  regression  analyses  provide  an 
unsatisfactory  approach  to  the  investigation  of 
dynamic  comovements  of  regional  disaggre 
gates. We nevertheless review briefly some of the 
main findings of this literature, as they represent a 
benchmark  with  which  our  methodology  and 
findings will be confronted. 

Regional  productivity 
and public  investment 

In the first group of studies the empirical strategy is 
to  evaluate  the  role of  public  infrastructure  in the 
determination of output, productivity, and growth of 
regional economies. These works generally suggest 
a quantitatively important relationship between pub 
lic  sector  capital  accumulation  and  private  sector 
productivity,  thus  supporting  the  policy  conclusion 
that  publicly  provided infrastructure  is an important 
element  of  regional  economic  performance. Some 
authors,  however,  have  recently  questioned  the 
methodology by which these results are obtained. 

Most  of  this  literature  can  be traced  back  to  the 
work  by  Aschauer  (1989,  1990)  on  the  relation 
ship  between  aggregate  productivity  and  stock 
and flow government  spending variables. The key 
message  of Aschauer's  analysis  is that  the  stock 
of public infrastructure, as well as the stock of pri 
vate capital, may be a key to  explaining  changes 
in  output  and  productivity  in  the  private  sector. 
Munnell  (1990a)  builds  upon  Aschauer's  insight 
and  explores  whether  changes  in the  amount  of 
public  capital,  combined  with  the  growth  of  pri 
vate capital and labour, can explain the slowdown 
in  United  States  productivity.  Munnell  (1990b) al 
so considers the impact  of public  capital on eco 
nomic activity at the state and regional level, find 
ing evidence that  those states that  have invested 
in infrastructure tend to have greater output, more 
private  investment,  and  more  employment 
growth. Garcia-Mila and McGuire (1992) specify a 
regional  production  function  that,  in  addition  to 
labour  and  private  capital,  includes  two  publicly 
provided  inputs  (highways  and  education).  They 
employ  a  panel  data  set  of  annual  observations 
on  the  48  contiguous  U.S.  states  from  1969  to 
1983  to  estimate  input  elasticity  coefficients  un 
der a specification that allows for differences over 
time and  across  states.  Both  of the  publicly  pro 
vided  inputs  are shown to  have a significant  and 
positive effect on output. 

These studies generally find significant supply side 
effects of public  infrastructure. However, these re 
sults have recently been criticized on econometric 
grounds. Holtz-Eakin (1994) presents estimates of 
production  functions  that  use  standard  tech 
niques  to  control  for  unobserved,  state-specific 
characteristics: these reveal essentially  no role for 

Supply effects 

43 

public-sector  capital  in  affecting  private  sector 
productivity.  It is thus  claimed that  only  estimates 
of  state  production  functions  that  do  not  include 
such controls find substantial productivity  Impacts 
(with  the  exception  of  Garcia-Mila  and  McGuire). 
Region-level  estimates  are  essentially  identical  to 
those from state data, suggesting no quantitative 
ly important  spillover  effects.  Hulten  and  Schwab 
(1991)  had  earlier  provided  estimates  of  the  link 
between  public  infrastructure  and  economic  per 
formance, based on accounting techniques which 
effectively  control  for  state-specific  effects.  Their 
findings  also  suggested  that  this  link  is  weak. 
Sturm  and  de-Haan  (1995)  review  empirical  evi 
dence  for  the  U.S.  on  the  impact  of  the  oublie 
capital stock on productivity. They show that if the 
model of Aschauer  is estimated in first  differences 

— which  is  a  minimum  requirement  as  the  vari 
ables used are neither stationary  nor cointegrated 
—  ambiguous results are obtained. 

Firm locational  decisions 

The  second  group  of  empirical  studies  concen 
trates on the determinants  of the location of eco 
nomic  activity  across  states  and  regions. The fo 
cus  is  on  the  interregional  analysis  of  the  birth, 
death,  and  mobility  of  firms,  ¡n order  to  evaluate 
how  different  types  of  incentives  can  influence 
economic  activity  location  at  the  regional  level. 
Papke  (1991)  examines  the  impact  of  state  and 
local  tax  differentials  on  the  location  of  industry 
using  a  panel  data  set  of  manufacturing  firm 
start-ups.  The  number  of  firm  births  ¡s  modeled 
as  a  Poisson  count  process  and  the  estimation 
technique  explicitly  accounts  for  unobserved  lo 
cation  or  state  heterogeneity  in  the  estimation. 
After controlling for state and industry effects, the 
estimates  Indicate  that  a  high  state  margiral  ef 
fective tax  rate reduces the number  of firm births 
for  half  of  the  industries  examined.  Audretsch 
(1994)  analyses  the  linkages  between  increasing 
return to production within a spatial unit of obser 
vation  and the tendency  towards  increased  con 
centration  of  economic  activity,  measured  in 
terms  of the  birth  of  new  firms.  Based  on  75 re 
gions  in  West  Germany,  evidence  ¡s found  sug 
gesting that the location of new economic  activity 
tends  to  occur  in  regions  exhibiting  characteris 
tics reflecting convexities in production. 

Audretsch  and  Zoltan  (1988,  1989)  conduct  a 
cross-section  empirical  analysis  to  examine  the 

determinants  of  mobility  and  entry  behaviour  of 
small  firms.  Small-firm  entry  is  at  least  partially 
determined  by  entry  barriers,  industry-specific 
characteristics  facilitating  retaliatory  conduct  by 
Incumbent  firms,  and  the  reliance  upon  innova 
tive strategy  by small firms. Small-firm  mobility  is 
promoted  in  growing  markets  where  skilled 
labour  and  technological  change  play  an  impor 
tant  role, and  hindered  in high capital and adver 
tising  intensive  industries  and  ¡n  concentrated 
markets. In Audretsch  (1991) the survival rates of 
over  11,000  firms  established  in  1976  are com 
pared across  manufacturing  industries. The exis 
tence  of  substantial  scale economies  and  a high 
capital-labour  ratio  appears  to  lower  the  likeli 
hood of firm survival, although these  results tend 
to vary considerably with the time interval consid 
ered.  Holtz-Eakin  (1992)  examines  the  extent  to 
which  liquidity  constraints  increase the  likelihood 
of entrepreneurial failure. The results suggest that 
liquidity  constraints  exert  a  noticeable  influence 
on the viability of entrepreneurial enterprises. 

Recent  empirical  investigations  of  the  dynamics 
of  new  firm  formation  at  the  regional  level  ¡n a 
number of EU countries are provided by Hart and 
Gudgin  (1994),  Davidsson,  Lindmark,  and Olofs 
son (1994), van-der-Laan (1991), and Keeble and 
Walker  (1994).  Reynolds  (1994)  explores  the im 
pact  of  various  characteristics  of  382  United 
States  labour  market  areas  on  subsequent  new 
firm  birth  rates  for  1986-88  using  linear  regres 
sion  modelling  techniques.  Growth  in  local  de 
mand  has a major  impact  in all situations. Statis 
tically  significant  evidence  of an impact  of urban 
ization/agglomeration,  unemployment,  personal/ 
household  wealth,  and  small  firm  presence/eco 
nomic  specialization  is present,  but tends to vary 
for different  types  of  labour market  areas and dif 
ferent economic sectors. 

Capturing supply  side  effects 

We  have  identified  two  ways  in  which  Cohe 
sion  fund  spending  may  affect  the  supply  side 
of  the  regional  economy.  All  our  modelling 
strategies  will  seek  to  capture  the  productivity 
effects  of  Cohesion  fund  projects1.  Firstly,  the 

As  explained  in the  introduction,  the  nature  of  our  projects  is  such 
that  we will not  explicitly  estimate  'behavioural  equations'  that  claim 
to explain the supply side of the economy.  Rather, we will try to cap 
ture supply  side effects  using a number  of different  approaches  that 
are best suited to each specific modelling strategy. 

44 

Section  II  —  Literature  Review 

regional  labour  market  model will  consider  pro 
ductivity  effects  when  analysing  employment 
adjustment.  Secondly,  the  stochastic  kernel 
methodology  is  based  on  historical  data  in 
which  both  demand  and  supply  effects  are 
present.  Finally,  one  of  the  distinguishing  fea 
tures  of  our  Computable  General  Equilibrium 
modelling  strategy  is  the  explicit  consideration 
of  agglomeration  and  industrial  organizational 
Issues. 

In  addition,  we  have  suggested  that  Cohesion 
fund  spending  will affect  firm  locational  decisions 
through  two  channels:  Firstly,  they  directly  affect 
the  relative  cost  attractiveness  of  different  loca 
tions.  Secondly,  they  affect  the  pattern  of  local 
demand  as  the  regional  population  distribution 
adjusts.  Both  these  effects  are  captured  by  the 
Stochastic  kernels and the CGE modelling strate 
gies. The regional labour  market  model will better 
capture the effects of the second channel. 

Supply effects 

45 

4, VARs and stochastic kernels 

Introduction 

We  will  consider  econometric  methods  of  pre 
dicting future patterns of economic variables from 
the  past  history  of  those  variables,  and  attempt 
to  extend  this  to  predict  the  response  of  those 
variables  to  the  shocks  generated  by  regional 
fund  injections.  Specifically  we  will  consider  less 
structured  models,  in the  form  of  vector  autore 
gressions  (VARs)  and  stochastic  kernels.  Previ 
ous  applications  of  the  VAR  technique  are  out 
lined  below.  Existing  applications  of  stochastic 
kernels  involve applications  of  Markov  chain the 
ory and these too are considered. 

These  applications  fall  into  three  broad  cate 
gories.  Firstly,  VARs  that  have  been  specified  to 
improve  regional  forecasting  performance,  given 
the  poor  results  from  existing  structural  models. 
Secondly,  studies  that  consider  VARs  and  sto 
chastic  kernel  methods  because  of  dissatisfac 
tion  with  cross-sectional  approaches  to  the 
analysis  of  convergence.  Finally,  VAR  specifica 
tions  that  have  been  used  to  calculate  impulse 
response  functions,  and  trace  through  the  dy 
namics of regional specific shocks 

In Section  6 we outline the  'Lucas  critique'  of si 
multaneous  equation  models1.  We  have  also 
suggested  that  these  models  can  suffer  from 
identification  problems2.  These  problems  have 
meant  that  researchers  have  often  had to  resort 

to  large  numbers  of  exclusion  restrictions3  to 
achieve  identification  of  particular  models.  Sims 
(1980)  describes  many  of  these  restrictions  as 
'incredible'.  He  suggests  that,  even  restrictions 
which  are  useful  for  partial  equilibrium  analysis, 
may  not  work  well  as  a  system  of  restrictions. 
These  identification  problems  are  further  com 
pounded  in dynamic  models, as many  previously 
'exogenous'  variables  become  'endogenous' 
when  we  allow  for  effects  to  be  distributed  over 
time. As a result  of these, and other  problems of 
large  scale  models,  Sims  advocates  adopting 
modelling  strategies  "without  restrictions  based 
on  supposed  a  priori  knowledge"  (p. 15).  The 
dominant  modelling  strategy  to  emerge from this 
'atheoretical'4 approach  is the VAR  methodology. 
Using  historical  data  patterns  to  estimate  VAR 
specifications  may  allow  us  to  test  important 
economic  hypotheses,  without  the  burden  of  a 
whole host of maintained  hypotheses, that  would 
be  needed  to  specify  a  comparable  structural 
model.  Given the  current  state  of  knowledge  re 
garding  the  workings  of  the  regional  economy, 
reducing  the  importance  of  a-priori  knowledge 
may  pay  considerable  dividends  in  improved 
forecasting  performance. 

Forecasting and regional VARs 

Before  turning  to  existing  regional  VAR  applica 
tions,  we  will  briefly  outline  some  of  the  reasons 

1  See the comments on large scale macro-models in Section 6. 
2  That  is, the problem of whether there is one unique underlying para 
meterization  of  a  model  that  is  consistent  with  the  observed  data 
(considered in the regional labour markets section). 

An exclusion restriction specifies that a particular dependent variable 
does  not  appear  as an explanatory  variable  In the  equation  for an 
other dependent variable. 
Used  here  in a  loose  sense  — all  models  include  some  theoretical 
content: even if only in the choice of what variables to model. 

VARs  and  stochastic  kernels 

47 

for  considering VARs for  regional forecasting  and 
policy  analysis.  Kinal  and  Ratner  (1986)  suggest 
that:  i) VARs  have  lower  data  requirements  be 
cause  fewer  variables  are  needed  to  forecast 
than to  trace  out  structural  linkages;  ii) Accuracy 
of  forecasts  should  be  enhanced  because  we 
can  allow  for  national-regional  and  intra-regional 
interaction  and  we  do  not  require  adhoc  fore 
casts  of  exogoneous  variables;  iii)  VARs  have 
lower model construction and updating costs. 

There are a number of VAR applications  in the re 
gional  economic  literature.  Anderson  (1979), 
builds a VAR model of the  Ninth  Federal Reserve 
District  (Minnesota,  Montana,  North  and  South 
Dakota,  northwestern  Wisconsin,  Upper  Penin 
sula of  Michigan), using quarterly  data.  Knal  and 
Ratner  (1986), consider  similar  monthly  data, but 
for a single state (New York). Gruben and others, 
in  two  papers  (1984  and  1988),  compare  the 
forecasting  performance  of  a number  of  different 
time  series  models  of the Texan economy.  Sher 
wood-Call  (1988)  uses  variance  decompositions 
to analyze the differences between comovements 
of state and national variables. Lesage and Reed 
(1989a)  examine  the  hypothesis  of  downward 
transmission  of  wage  effects  through  tiers  of  an 
urban hierarchy,  by considering the (Granger) ca 
sual  relationships  among  variables.  Using  similar 
methodology,  Lesage  and  Reed  (1989b)  test 
some dynamic  hypotheses  suggested  by the Ex 
port  Base Model. 

These  applications  highlight  a  number  of  impor 
tant  specification  issues, but  in themselves  suffer 
from  serious  technical  errors  in  application  and 
estimation.  Firstly, the treatment  of stationarity is 
sues  is  generally  arbitrary.  Many  of  the  papers 
recognize  the  potential  for  nonstationarity  prob 
lems,  but  the  subsequent  use  of  differencing  or 
power  transformations  is  essentially  adhoc,  and 
there  is  little  formal  testing  for  stationarity  of  the 
transformed  data.  For  forecasting  purposes  this 
may not be too bad, but the time path of Impulse 
responses  will  be  significantly  distorted  as  a re 
sult  of  the  failure  to  tackle  the  stationarity  issue 
consistently. Secondly, the papers tend to specify 
arbitrary  model  orders  to  "fully  capture  the  dy 
namic  properties  of the series", or avoid degrees 
of  freedom  problems.  Some  of  the  forecasting 
papers  use  various  forecasting  performance 
measures  when  specifying  lag  length.  Remark 
ably few  papers  use any of the order  determina 
tion statistics that  are common  in the  macroeco 
nomic  literature. Finally, regional economists have 

concentrated  on  Bayesian  VAR  (BVAR)  ap 
proaches. A number of comparative studies (see, 
for example, Shoesmith  (1990)), suggest  that the 
BVAR  approach  provides  better  forecasts  for re 
gional  economies.  However  the  BVAR  approach 
usually  fits  models  which  are  closer  to  Indepen 
dent  univariate  processes  for  each  variable  than 
a  true  multivariate  specification.  Given  the  sta 
tionarity  problems  and  the  multicollinearlty  prob 
lems  (which  are  a  result  of  arbitrary  lag  length 
specifications),  it is perhaps  unsurprising that the 
"near"  univariate  processes  produce  more 
favourable  results. Cromwell  and  Hannan (1993), 
provides  a  useful  critical  summary  of  regional 
VAR  literature.  In  addition  it  reports  results  and 
impulse  response functions from a number  of al 
ternative bivariate VAR specifications for a region 
al  economy,  which  highlight  the  Importance  of 
stationarity and order determination. 

Endogenous growth  theory 
and time series  approaches 
to the empirics  of  growth 

There is a large and growing literature which con 
siders the cross sectional growth  performance of 
nation  states  and  regions  within  states.  This  re 
newed  interest  in  the  empirics  of  economic 
growth reflects, in part, the challenge to the clas 
sical  growth  model  from  the  new  endogenous 
growth  literature.  While  the  classical  model  pre 
dicts  convergence  of  output  across  economies 
(be  they  national  or  regional),  the  new  endoge 
nous growth models allow for the continuation, or 
even the widening, of existing output differentials. 

Initial studies of this issue (see for example, Barro 
and Sala-i-Martin (1991)), used cross-section da 
ta  to  test  for  the  convergence  of  various  per 
capita  income  measures  across  different  groups. 
Initial dissatisfaction with this empirical approach, 
has lead to  an emphasis  on time series  methods 
by  a  number  of  researchers  (see,  for  example, 
Bernard  and  Durlauf  (1993),  and  Quah  (1993a, 
1993b)).  These  methods  use  historical  time  se 
ries data to  predict the future patterns of regional 
/ country income distributions. 

The early applications  of stochastic  kernels in the 
growth  empirics  literature  sought  to  emphasise 
the  importance  of  considering  intra-  distribution 
movements,  as well as the movement  of  income 
distributions  through  time.  Markov  chains  are 

48 

Section II — Literature Review 

used to approximate and estimate laws of motion 
for  the  evolving  distributions.  The  information 
about  intradistribution  dynamics  is encoded  in a 
transition  probability  matrix.  Calculating  the  er 
godic  distribution associated with this matrix pre 
dicts  the  long  term  behaviour  of  the  underlying 
Income  distributions.  Using  this  approach  Quah 
(1993b),  predicts  a  movement  of  the  distribution 
of  relative world  Incomes towards  a bimodal dis 
tribution,  with  a  small  rich  group,  a  much  larger 
poor  group  and  little  movement  between  the 
poor  and  rich  groups.  For  applications  of  the 
same methodology to  European data, see Neven 
and Gouyette (1994) and Larch (1994). 

An  outgrowth  of  this  empirical  literature  is  the 
Quah (1994)  paper  which  explicitly  considers the 
relationship  between  regional  disaggregates  and 
national  aggregates.  The  methodology  is  similar 
to  the  growth  orientated  papers,  but  the  issue 
here  is  whether  regional  fluctuations  that  leave 
aggregate  variables  unchanged  can  affect  the 
dynamic  performance  of  an  economic  system. 
U.S.  data are  used to  construct  a time  series of 
quantiles of the distribution  of relative regional in 
comes. Alternative  realizations  of this  distribution 
reflect  different  patterns  of  regional  income, 
which  leave aggregate  activity  levels unchanged. 
After  conducting  (Granger)  causality  tests,  Quah 
concludes  that,  contrary  to  standard  macroeco 
nomic theory, the distribution of regional incomes 
can interact  with the time  path of  national aggre 
gates.  It is precisely this sort of  interaction that is 
important  in  determining  the  short  and  long  run 
effects of a proposed  project. 

These  papers  do  not  explicitly  address  the  im 
pact  of  different  policies,  concentrating  instead 
on future economic  performance  under  constant 
policy  regimes.  The  suitability  of  these  models 
for  the  analysis  of  policy  changes  is  an,  as  yet, 
unresolved  issue.  We  follow  Sims  (1982),  and 
model  regional  performance  under  different  pat 
terns  of  regional  spending,  as draws  from  some 
underlying  complex  'distribution'.  By  analyzing 
how this 'distribution'  has behaved when subject 
to shocks  in the past, we can map out  how  cur 
rent  shocks1  may  effect  that  'distribution'  in the 
future. The  regional  economies  that  we  consider 
are  continuously  subject  to  outside  shocks.  We 
can predict the future response to shocks gener 

'  The VAR literature deals in 'shocks' and 'impulse response functions' 
because  policy  changes  are  usually  modelled  as  effecting  the  error 
terms  in the system  of  equations,  i.e.  policy changes  are  'unexpect 
ed'  given the history of the system. 

ated  by  regional  projects  because  there  is  no 
reason  to  believe  that  these  shocks  will  affect 
the  underlying  distribution  differently  to  past 
shocks. 

Regional  dynamics 

We  will  briefly  outline  the  Blanchard  and  Katz 
(1992) contribution to the regional evolution litera 
ture.  There  are  a  number  of  key  characteristics 
that  separates  the  Blanchard  and  Katz  paper 
from other  regional VAR applications.  Firstly, their 
VARs  are  properly  specified  after  testing  for  sta 
tionarity  of  included  variables.  Secondly,  they 
specifically  consider  the  identification  of  labour 
market  shocks  and test the sensitivity  of their re 
sults to this  assumption.  Finally they are interest 
ed  in  studying  the  dynamic  propagation  of 
shocks through the system, not in forecasting.2 A 
number  of  papers  outlined  in the  regional  labour 
markets  section  estimate  VAR  specifications  de 
rived from some underlying theoretical model. We 
consider the Blanchard and Katz paper separate 
ly  because,  although  the  VAR  specifications 
could be viewed as reduced forms of two  simple 
models,  data  limitations  and  extensions  mean 
that  they  are  often  estimating  "atheoretical" 
VARs. 

Estimates of univariate processes for a number of 
regional  variables  highlights  the  importance  of 
stationarity  issues.  After  testing  the  authors  im 
pose  a  unit  root  ¡n  the  relative  employment 
process  — shocks  have  largely  permanent  ef 
fects  on  employment  levels.  In  contrast,  similar 
tests for  relative  unemployment  rates find  no unit 
root  — states  return to their mean relative unem 
ployment  rate fairly  quickly  after  a shock.  Allow 
ing for  these  test  results, the  authors  estimate  a 
number of different VAR specifications. 

A  trivariate  VAR  for  an  employment  variable,  a 
participation  variable and an  unemployment  vari 
able3 is estimated. The implied impulse  response 
functions  for  the  employment  rate,  participation 
rate and unemployment  rate can then be plotted. 

Although  some  of  the  other  regional  VARs  calculate  impulse  re 
sponses  we  do  not  consider  them  here  because  of  the  technical 
problems already outlined above. 
The employment  variable  is the first  difference  of  the log of  employ 
ment  in a state minus the first difference of the log of U.S. aggregate 
employment;  the  participation  variable  is  the  log  of  the  ratio  of  the 
labour  force to the working age population  minus the same variable 
for  the  entire  U.S.;  the  unemployment  ratio  is  the  ratio  of  employ 
ment to the labour force minus the same variable for the entire U.S. 

VARs  and  stochastic  kernels 

49 

The  difference  between  the  employment  change 
and  the  corresponding  changes  in  participation 
rates and unemployment  rates must  be account 
ed for  by  net-migration. The results suggest  that 
after  an adverse  shock,  wages  initially  decrease, 
and  unemployment  initially  increases.  These 
changes  induce  out-migration  of  labour  and in-
migration  of  firms.  The  resulting  changes  in 
labour  demand  and  supply  increase  wages  and 
decrease  unemployment,  and  there  is  no  long 
run  effect  on  either  variable.  Most  of  the  adjust 
ment  to  an  adverse  shock  to  labour  demand  is 
through  out-  migration  of  labour,  not  changes  in 
participation  rates,  or  in-migration  of  firms. Thus 
in  contrast  to  the  effect  on  unemployment  and 
wages,  shocks  will  have  a  long  term  impact  on 
the level of employment  in the state. 

A second  trivariate VAR for  the  employment  vari 
able, a wage variable and a house prices variable1 
is  estimated.  The  impulse  response  functions 
show that house prices exhibit a large negative re 
sponse  to  an  adverse  labour  demand  shock, 
which suggests that real wages don't change very 
much, and that it is unemployment which must ex 
plain the adjustment of labour force (ie migration) 

1  The employment variable is as above; the wage variable is the differ 
ence  between  the  log  of  hourly  earnings  in  manufacturing  in  the 
state  and  its  national  counterpart;  the  housing  price  variable  is the 
log of the median sales price of existing houses  minus the log of the 
national median price. 

A comparison  of these findings with those  of the 
regional  labour  market  literature,  based  on  more 
theoretical  models,  suggests  the  scope  of  the 
VAR methodology  in considering a number of im 
portant  regional  issues.  For  example,  the  over 
shooting  of  wages  and  employment  in  response 
to  demand  shocks  is  also  found  by  Eberts  and 
Stone  (1992),  and  the  importance  of  unemploy 
ment  differentials,  rather  than  wage  differentials, 
in explaining  migration  is a feature of the Hughes 
and McCormick (1994) model. 

Conclusion 

We have outlined the key features of current VAR 
and  stochastic  kernel  applications.  The  strength 
of  these  methods  is that  they  can  trace  out  the 
complex  dynamic  reactions  of  economic  vari 
ables  when  shocks  hit  the  system,  allowing  for 
interactions  between  those  variables.  By  apply 
ing,  and  extending,  existing  VAR  and  stochastic 
kernel techniques, we will be able to consider in 
teractions  between  regional  and  national  vari 
ables.  It is these  interactions  which  will  be a key 
factor  in  determining,  and  thus  predicting,  the 
economic effects of proposed regional projects. 

50 

Section I 

Literature  Review 

5. Computable general equilibrium models 

Introduction 

Static  Computable  /  Applied  General  Equilibrium 
models (AGE) have been developed over the past 
twenty years to analyze a whole range of policy is 
sues  in  both  the  developed  and  the  developing 
world. At a basic level, these models are comput 
er  representations  of  the  real  world.  The  equa 
tions that define the models are derived from con 
sideration  of  the  behaviour  of  economic  agents: 
consumers,  producers  and  governments.  Con 
sumers  purchase  products  from  producers  and 
supply  inputs  to  the  production  process.  If  the 
model  includes  a  government,  consumers  may 
pay  taxes,  and  in  return,  may  receive  income 
transfers  and  public  goods  and  services.  They 
make decisions to  maximize utility subject to their 
budget constraints. Producers meet the demands 
of these consumers by combining  primary factors 
and  intermediate  goods. Their  outputs  are  deter 
mined  by  specific  production  technologies,  and 
they  choose  output  and  input  levels to  maximize 
profits. The government  may also be modelled as 
an utility maximizing economic agent subject to Its 
own particular budget constraint. 

For our purposes, we distinguish "early" AGE mod 
els as those  which  are characterised  by  constant 
returns  to  scale  and  perfect  competition.  We will 
briefly  consider  some  representative  examples  of 
these  models  below. They  allow  us to  outline the 
approach  and  highlight  some  of  the  important 
methodological  issues. Next, we will consider vari 
ous extensions to these  models. The most  impor 
tant of these have emerged from the efforts of vari 
ous writers to  analyze the  importance  of  industrial 

organizational issues for trade models. These stud 
ies  attempt  to  incorporate  increasing  returns  to 
scale,  imperfect  competition,  and  differentiated 
products. Again we will outline some representative 
models.  Finally, we will consider  the  much smaller 
literature which  concerns  the  explicit  modelling  of 
regional and transport issues within AGE models. 

The early age  models 

We will  briefly  outline the  issues  in the  construc 
tion of  constant  return to  scale, perfect  competi 
tion  models,  using  examples  from  the  models 
found in Whalley (1985) and other models, where 
necessary. We will consider the issues of specifi 
cation and model predictions but do not consider 
solution  methods  — for  a  discussion  of  these 
see, for example, Shoven and Whalley (1992). 

Multicountry or single country models? 

Multicountry  models  disaggregate  the  world  into 
a  number  of  different  regions.  The  regions  in 
these  models  are  countries  or  blocks  of  coun 
tries, the exact specification depends on the poli 
cy  issues  under  consideration.  There  are  two 
models  discussed  in Whalley  (1985a),  a four  re 
gion  AGE  (model  1)  and  a  seven  region  AGE 
(model 2). Model  1 was developed to analyze the 
impact  of  the  1973  GATT  round  on  the  devel 
oped  countries  involved: the four  regions are the 
EEC, the U.S., Japan and the"Rest of the World". 
Model  2 developed  from  model  1, but was  used 
to  analyze  policy  issues  regarding  North-South 
trade:  the  seven  regions  are the  EEC, the  U.S., 

Computable general equilibrium models 

51 

Japan,  Other  Developed  Countries,  Organization 
of Petroleum Exporting Countries, Newly Industri 
alised Countries, and Less-Developed Countries. 

Single  country  models  split  the  world  into  the 
country  of  interest  and  the  rest  of  the  world. For 
example, the well known ORANI model outlined in 
Dixon  et  al (1982), was  developed  to  analyze the 
Australian  economy.  Single  country  models  allow 
more  detailed  modelling  of  the  target  country 
(more industrial sectors, more consumers etc), but 
are  less suitable for  analysing the  interactions  be 
tween  countries.  Our  regional  model  will  incorpo 
rate elements of both models. Considering regions 
within  a country  will  allow  a  rich  specification  of 
sectors and consumers (a feature of single country 
models), but will also allow Interaction between the 
regions (a feature of multicountry models). 

Dimensionality 

A  related  issue  is  the  degree  of  disaggregation 
within regions, both in the demand and production 
specifications.  Smaller  multicountry  models  will 
usually  have finer  disaggregation.  Model  1 allows 
for 33 commodities in each of the four regions and 
a total  of  41  consuming  groups1.  Model  2  allows 
for  six  commodities  in  each  of  the  seven  trade 
blocs,  and  only  seven  demand  side  agents  (one 
for  each  bloc).  Single  country  models  can  cope 
with  far  greater  production  disaggregation.  While 
ORANI  has  only  one  representative  household, it 
allows for 114 different commodities. 

The  multicountry  models  highlight  an  important 
feature  of  many  AGE  models:  goods  are  often 
not  modelled  as homogeneous  across countries. 
A Japanese  car  is modelled  as a qualitatively dif 
ferent  good  from  an American  car.  This Arming-
ton assumption2 is contrary to theoretical reason 
ing but  in applied  models  it is needed for  a num 
ber of reasons. Firstly, it helps explain cross-haul 
ing of  products  (i.e. the simultaneous  import  and 
export  of  essentially  the  same  good).  Secondly, 
models  without  the  Armington  assumption  pre 
dict unrealistically strong specialization effects. Fi 
nally, model calibration uses the available data on 
specific  Import-export  elasticities.  Models  which 
explicitly  consider  differentiated  products  do  not 
have to rely on this arbitrary assumption. 

1  A government  and a corporate  sector  for  each of  the US.  EEC and 
Japan;  10 household  sectors  for the US stratified  by income;  1 rep 
resentative  agent  for each  of  the nine  EEC countries,  16  household 
sectors for Japan stratified by income. 

2  Armington (1969). 

Functional forms 

The  choice  of  functional  forms  is  governed  by 
two  considerations: the model  needs to  be theo 
retically  consistent  and  analytically  tractable. To 
gether  with  the  assumptions  of  constant  returns 
to scale and perfect  competition, these consider 
ations  have lead to  a reliance  on the  Cobb  Dou 
glas,  Constant  Elasticity  of  Substitution  (CES) 
and  Linear  Expenditure  System  (LES)  functional 
forms.  The  use  of  CES functions  gets  round  the 
unitary-elasticity  problems  of  the  Cobb  Douglas 
function. In turn, the use of nested CES functions 
gets round the constant elasticities of substitution 
across  different  types  of  good, that  are a feature 
of the standard  CES. The level of  nesting will de 
pend  both  on  data  availability  and the  perceived 
use  of  the  model.  LES functions  allow  non-uni 
tary  income  elasticities.  They  will  often  be  incor 
porated within a nested utility function. 

Model  1 uses  a 4  level  CES/LES  functional  form 
for  the  utility  function  of  each  household  sector. 
At  the  fourth  level,  consumers  substitute  be 
tween  import  types  under  a CES specification  to 
give  import  composites  for  each  product.  At  the 
third  level,  there  is  (CES)  substitution  between 
this import  composite and the domestic  product, 
to  give a composite  good for each  product type. 
An LES functional form  ¡s used at this level which 
specifies  minimum  requirements  for  each  import 
composite,  and  thus  allows  the  income  elastici 
ties  of  demand  to  be  different  from  unity.  At  the 
second  level,  there  is  (CES)  substitution  within 
the  different  categories  of  composite  goods,  to 
give a composite  non-energy good. Finally, at the 
first  level  there  is substitution  between  this  non-
energy composite  and the energy good. Produc 
tion  occurs  under  a  CES  value  added  function. 
The  nested  hierarchy  for  intermediate  inputs  is 
similar to  that  for  consumption  goods. There are 
two primary inputs: capital and labour services. 

Parameter values 

The  models  are  calibrated  using  benchmark  data 
(actual data adjusted to reflect the specified equilib 
rium requirements). Because the system is not fully 
identified  by the  benchmark  equilibrium  (i.e. many 
parameter values are consistent with the actual da 
ta) a literature  review  is used to  provide  additional 
elasticities. No statistical tests of the model specifi 
cation are used, because  parameters  are calculat 
ed determlnistically  under the assumption  that the 
benchmark data represent an equilibrium. 

52 

Section I 

Literature  Review 

Counterfactual equilibria 

Once the model is specified and the parameter 
values calibrated, ¡t can be used to calculate 
counterfactual equilibria, i.e. new equilibria under 
proposed policy changes. New values of policy 
variables are specified, for example tariff values are 
lowered, and the model ¡s run again. The model 
gives a new equilibrium which is compared to the 
benchmark data or to the equilibria from other pol­
icy simulations. There is no theoretical argument 
that guarantees the uniqueness of these new 
equilibria. However, Shoven and Whalley (1984), 
report that adhoc methods, such as different start­
ing points for solution algorithms, have not re­
vealed non­uniqueness ¡n previous applications. 

Shoven and Whalley (1984) identify the major 
themes from a whole host of policy simulations us­
ing different CRTS, perfect competition models. 
Firstly, the welfare effects of changes ¡n trade policy 
are usually relatively small compared to the effects 
of other kinds of policy change (generally changes 
in tax policy). Secondly, the terms of trade effects 
associated with changes in trade policy can be sig­
nificant. Finally, there is a considerable difference 
between national and global interests. 

Because of a number of problems with AGE 
models — data limitations, sensitivity to specifica­
tion of functional forms — a number of propo­
nents of AGE modelling do not advocate the use 
of such models in a strict forecasting sense: see 
for example, Whalley (1985b). However, evalua­
tions of forecasting performance are reported by 
a number of modellers. For example, in a recent 
paper, Kehoe, Polo and Sancho (1995), report 
statistics for the forecasting performance of an 
AGE model for the Spanish economy. They also 
assess the sensitivity of the model to various dif­
ferent closure rules and labour market specifica­
tions. The policy change considered1, which has 
mainly distributional effects, ¡s well suited to their 
static model and the results suggest that it per­
forms well. The model is relatively sensitive to dif­
ferent labour market specifications. For other as­
sessments of model performance see, for exam­
ple, Polo and Sancho (1993) or Lolos et al. 
(1995). 

There are a number of books and survey articles 
which provide detail on specifications, calibration, 
solution algorithms and existing models: see, for 

' The change in the Spanish taxation system upon entry to the EEC. 

example, Shoven and Whalley (1984), Scarf and 
Shoven (1984), Whalley (1985a), and Shoven and 
Whalley (1992). 

Industrial organization 
and age models 

Towards the end of the 1970s, economists be­
gan to reconsider the Importance of increasing 
returns to scale and imperfect competition, as 
factors in explaining the observed patterns of In­
ternational trade: see for example, Krugman 
(1979, 1980), Corden (1980) and Either (1982). 
Harris (1984) was the first paper to incorporate 
these features into an AGE model. This AGE 
model, produced significantly different results 
from comparable "early" AGE models. These em­
pirical results, together with new approaches de­
veloped ¡n the theoretical literature, have encour­
aged a number of researchers to allow for differ­
ent forms of industrial organization in AGE model 
specifications. We will provide a brief summary of 
the key ΙΟ­AGE models, before considering the 
important specification issues using examples 
from the recent literature. 

The model outlined in Harris (1984) and Cox and 
Harris (1985), was developed to analyze the Im­
pact of the proposed free trade agreement be­
tween the United States and Canada, on the 
Canadian economy. Norman (1990), considers 
the response of a number of two region (EC and 
EFTA), three sector models built under different 
assumptions, to the removal of trade barriers. He 
considers a pure comparative advantage model 
with CRTS and perfect competition, a second 
similar model utilizing the Armington assumption, 
and a third which models one industry as an oli­
gopoly with and without a free entry/exit assump­
tion. Gasiorek, Smith and Venables (1991) build a 
six region model (France, Germany, Italy, UK, 
Rest Of EC, Rest of World (ROW)) with fourteen 
sectors, to analyze the Impact of completing the 
internal market. Burniaux and Waelbroeck (1992), 
extend this regional structure (ROW is divided in­
to Japan, North America, LDC and a residual 
ROW) but at the cost of less sectorial differentia­
tion. The model is used to analyze the same poli­
cy change, as is Mercenier (1995), which is a five 
region, nine sector model mainly concerned with 
the unemployment Impact of 1992. Kehoe and 
Kehoe (1994b) summarize four recent models 
which have been built to analyze the Impact of 

Computable general equilibrium models 

53 

NAFTA  on  the  US,  Canada  and  Mexico  at  the 
countrywide, regional and sectoral levels. 

the  estimated  model  prices  are  formed  using  a 
weighted  combination  of  the  two  prices  derived 
under the alternative behavioural assumptions. 

Not all sectors will be modelled as producing un 
der increasing returns. For example, Harris (1984) 
has  20  scale  economy  manufacturing  sectors 
and  9  competitive  sectors.  Burniaux  and Wael-
broeck  (1992)  have  four  scale  economy  sectors 
(household  appliances,  steel,  shipbuilding  and 
automobiles)  and  a  residual  perfect  competition, 
constant returns to scale sector. 

Increasing  returns  may  be  incorporated  through 
specification  of  fixed  inputs  in  production.  Vari 
able  input  requirements  are then  modelled  using 
a  standard  constant  returns  functional  form.  For 
example,  Harris  (1984)  includes  fixed  require 
ments  for  primary  factors,  and  models  variable 
input  requirements  using  a  constant  returns  to 
scale Cobb  Douglas specification. Alternatively, a 
scaling  function  can  be  introduced  explicitly  in 
the  cost  function.  Gasiorek,  Smith  and Venables 
(1991)  multiply  a  standard  cost  function  by  a 
scaling function, which  is decreasing  in total  firm 
output. 

Once  increasing  returns  have been incorporated, 
models  can  specify  imperfect  competition  and 
product  differentiation  in  several  ways. Theoreti 
cal  considerations  cannot  help  us  choose  be 
tween  different  imperfect  competition  specifica 
tions: there  is no dominant  account  of  how firms 
maximize  profits  in  imperfect  markets.  Similarly, 
there  is  little  literature  on  the  gain  from  allowing 
for  product  differentiation,  rather  than  assuming 
symmetric firms within the industry. 

The  Harris  (1984)  and  Cox  and  Harris  (1985) pa 
pers  consider  two  different  behavioural  specifica 
tions.  Both  specifications  have  homogeneous 
products  within  country  industries,  but  allow  for 
product  differentiation  between  countries  (the 
Armington  assumption).  The  first,  assumes 
Cournot  competition  with  symmetric  oligopolistic 
firms  competing  in  quantities  rather  than  prices. 
Prices are calculated  as a mark-up  over  marginal 
costs, using the standard (perceived) elasticity for 
mula. The second, uses the "Eastman-Stykolt as 
sumption"1:  domestic  firms  effectively  collude, by 
setting domestic  prices equal to the foreign  price 
plus  an  adjustment  to  cover  protective  tariffs.  In 

From the Eastman and Stykolt  (1966) findings that  Canadian manu 
facturing prices tended to track  U.S. prices for similar goods with an 
adjustment that reflects tariff levels. 

Allowing  products  to  be  differentiated  within  in 
dustries  has  two  major  effects:  it  removes  the 
need for the arbitrary Armington assumption, and 
it  allows  Bertrand  type  equilibria  i.e.  oligopolistic 
firms  compete  in  prices  instead  of  quantities. 
Bertrand  markets  are  more  competitive  than 
comparable  Cournot  markets.  Mercenier  (1995), 
considers  equilibrium  generated  under  assump 
tions  of  Bertrand  markets  to  analyze  the  impact 
of  "1992"  on  European  welfare.  Model  results 
suggest  that  the  welfare  effects  under  Bertrand 
competition  are  almost  non-existent  (a  result 
much closer to those of the CRTS, perfect com 
petition models). The preferred assumption in the 
current  literature  is  that  firms  play  a  Cournot 
game: see, for example, Brown and Stern (1989), 
Norman  (1990),  Gasiorek,  Smith  and  Venables 
(1991), Kehoe and Kehoe (1994a). 

Once  differentiated  products  are  specified  there 
are several ways to construct the demand side of 
the model. For any perfectly competitive  markets 
the  demand  side  specification  possibilities  are 
identical  to  the  "early"  AGE  model  options  (ie 
composite  goods  are  constructed  allowing  for  a 
number  of  nested  levels of substitution). The use 
of  differentiated  products  allows  us  to  construct 
composite  goods  and  price  indices  by  consider 
ing  substitution,  at  one  level,  between  all avail 
able  products  using, for  example,  a CES aggre 
gator.  Calibration  of  such  aggregators  is difficult: 
models  that  assume  constant  rates  of  substitu 
tion between all products from all countries, can 
not  account  for  the  higher  shares  of  domestic 
products  that  we  observe  in the  data. There are 
several  solutions  considered  in  the  literature. 
Gasiorek,  Smith  and  Venables  (1991)  calculate 
industry  wide  substitution  elasticities:  EC  wide 
averages  of  price-cost  margins  imply  a  measure 
of  product  differentiation  (under  the  assumption 
of a long  run, zero  profit  equilibrium), from which 
industry  specific  substitution  elasticities  are ob 
tained.  The  persistence  of  differences  in  market 
shares  between foreign  and  domestic  producers 
is  accounted  for  by  demand  parameters,  which 
describe the preferences for a consumer in coun 
try  a,  for  product  produced  in  country  b.  These 
parameters effectively  give a higher weight to do 
mestically  produced  products.  In contrast, Burni 
aux  and  Waelbroeck  (1992)  account  for  different 
market  shares  by  estimating  separate  substitu-

54 

Section  I 

Literature  Review 

tion  elasticities  within  countries  reflecting,  they 
suggest,  the  influence  of  nonborder  trade  barri 
ers. The  procedure  used  by  Mercenier  (1995) al 
so  produces  estimates  of  scale  elasticities  that 
differ  across  countries  and  are jointly  determined 
with the monopoly power of domestic producers. 

There  are  two  related  issues  on  the  production 
side: the determination  of perceived elasticities of 
demand,  and  the  calculation  of  the  elasticity  of 
substitution  between  differentiated  intermediate 
inputs.  All  models  assume  that  firms  calculate 
perceived elasticities correctly1.  Some models al 
low  no conjectural variation2 and no  intermediate 
inputs,  for  example,  Norman  (1990),  Mercenier 
(1995). Others allow for conjectural variations, for 
example,  Burniaux  and  Waelbroeck  (1992). 
Gasiorek, Smith and Venables (1991) allow for in 
termediate  inputs,  but  are forced to  assume val 
ues for  substitution  elasticities  due to  lack of da 
ta.  In addition  pricing behaviour  is complicated in 
the  market  for  intermediate  goods,  where  all de 
mands  are  derived  demands,  and  buyers  may 
have  some  monopsony  power.  Gasiorek  et  al. 
assume that  intermediate goods are priced at av 
erage cost. 

There are a number  of other  specification issues, 
some  as  yet  unresolved.  Firstly,  the  models  can 
allow for different  entry and exit  assumptions, for 
example,  reflecting  the  short  (no  entry/exit)  and 
the  long  (entry/exit)  run.  Gasiorek,  Smith  and 
Venables  report  results  under  both  assumptions. 
Secondly,  Ginsburgh  (1990)  shows that ambigui 
ties can arise from the choice of numeraire. It has 
been  suggested,  see for  example,  Burniaux  and 
Waelbroeck  (1992),  that  possible  problems  are 
reduced  by  a  choice  of  numeraire  from  a com 
petitive sector.  Thirdly,  non-uniqueness  problems 
Increase:  Mercenier  (1995)  reports  non-unique 
ness for a model specification that  assumes  rigid 
wages  with free  entry  and  exit  of  firms.  Finally, a 
number  of  papers  report  increased  sensitivity  of 
results  to  parameter  specifications  and  closure 
assumptions. 

That  is, they  calculate  the  percentage  change  in  demand  resulting 
from  a change  in their  own good  price taking  into account  that  this 
change in price will affect the composite industry  price index directly, 
and indirectly  via induced changes  in the equilibrium  prices of  other 
firms. 

'  Allowing  for  conjectural  variation  means  that  firms  will  take  into  ac 
count  the effect  of  changes  in their  price  levels on their  competitors 
price levels when  deciding  optimal  output  (and not just  when calcu 
lating elasticities to determine price given output). 

Regional  models 

There are two approaches to the modelling of re 
gional  phenomena.  Firstly,  top  down:  economic 
behaviour  is modelled  at the economy  wide level 
and  projections  are  disaggregated  to  a  regional 
basis,  without  feedback  from  the  regions.  Sec 
ondly, bottom  up: behavioural theory is applied at 
the regional  level, with economy wide  projections 
derived as aggregates of results. 

The  usual  AGE  approach  is  top-down.  The 
ORANI model outlined in Dixon et al. (1982) is ca 
pable  of  producing  regional  predictions.  These 
predictions  are  simply  decompositions  of  the 
models  aggregate  results  using  various  (fixed) 
measures  of  regional  sectorial  composition.  Hig-
gs,  Parmenter  and  Rimmer  (1988)  suggest  a hy 
brid  top  down,  bottom  up  modification  of  the 
ORANI model. This hybrid  model  partially region 
alises  ORANI,  by  defining  Tasmanian  sub-indus 
tries for  a number  of  nationally  trade  goods. The 
main changes come because some of these Tas 
manian  disaggregated  sectors  then  meet  the 
models  20%  export  rule,  and  thus  their  export 
demand  is  modelled  as  endogenous..  However 
as  Higgs  et  al.  point  out:  "The  hybrid  system  is 
suited primarily to the analysis of the regional Im 
pact of economywide  shocks  , not the economy-
wide  impact  of  regional  shocks  ...It  can  handle 
some  regional  shocks,  in particular,  ones that di 
rectly affect the subsectors of the regionalised in 
dustries  [but]  analysis  of  regionwide  shocks  re 
quires  the  more  demanding  bottom  up  ap 
proach" (p.326). 

There are a limited number  of existing  bottom-up 
regional  models.  Kimbell  and  Harrison  (1984), 
specify  a  "single  country"  model  to  analyze  the 
impact  of tax  changes  on the  Californian  econo 
my.  Liew  (1984),  specifies  a  theoretical  "multi 
country"  regional  Johansen  model.  Morgan 
(1989), builds a six region seven good  model us 
ing  US  data  to  analyze  the  effects  of  the  tax 
structure  on factor  movements  and  regional pro 
duction. Jones and Whalley (1989) outline a more 
sophisticated  model for  six  Canadian  regions al 
lowing  for  thirteen  industrial  sectors.  A  recent 
model  by  Buckley  (1992),  explicitly  incorporates 
transportation  costs  in the formulation  of  a three 
regional, five sector model of the US economy. 

Although  these  models  are formally  similar to  the 
"early" AGE models there are a couple of specifi-

Computable  general  equilibrium  models 

55 

cation  issues  which  are  not  as  important  in  the 
multicountry  models.  Firstly,  there  are  a  greater 
range of  possible  assumptions  regarding  mobility. 
Multicountry  models may allow for capital mobility, 
but other factors are generally  immobile. Regional 
models  need  to  allow  for  some  labour  mobility. 
The three primary factors  of the Jones and Whal 
ley (1989) model can have varying degrees of mo 
bility. Capital may be interregionally,  intersectorally 
or  internationally  mobile.  Labour  is  internationally 
immobile,  sectorially  mobile  and  interregionally 
partially  mobile.  Resource  inputs  are  immobile. 
The  model  allows  for  partial  labour  mobility  by 
specifying  a distribution  of  individuals  within  each 
region,  who  differ  in  their  intensity  of  locational 
preference,  and  who  migrate  when  they  will  get 
greater  utility  from  consuming  their  current  con 
sumption  bundle  elsewhere.  Different  factor  mo 
bility assumptions are capable of producing differ 
ent  simulation  results.  Secondly,  these  models 
need  to  allow  for  multiple  levels  of  regional  gov 
ernment,  and  possibly  transfers  of  income  be 
tween regions. Thus the Jones and Whalley mod 
el  includes  a federal  government  which  is  mod 
elled as an eighth trading  region, although  it only 
imports  goods  from  other  regions  (which  ignores 
public  goods  provision).  Regional  governments 
are modelled simpllstically with all revenues raised 

redistributed  in lump  sums.  Finally, regional  mod 
els are spatial models: there is a cost of transport 
ing  goods  between  regions  which  depends  on 
the  distance  between  those  regions.  Modelling 
the  demand  for  transporting  and  warehousing 
services  as  a  derived  demand,  allows  Buckley 
(1992) to  explicitly  account  for  these  costs  when 
considering trade flows between regions. 

Conclusion 

In  outlining  the  existing  literature  we  have  con 
centrated  on  the  issues  that  will  be  involved  in 
the specification  of a regional computable  gener 
al equilibrium  model. Many of the papers consid 
er some of the issues we will be concerned  with, 
none  of  them  consider  all  the  issues.  Thus  we 
see  CRTS,  perfect  competition  models  with  a 
specific  regional or transportation  component,  or 
multicountry  models  which  allow  for  increasing 
returns  to  scale  and  industrial  organizational 
components.  It  is the  combination  of  these  two 
modelling streams that will allow us to specify ap 
propriate models for the analysis of the impact of 
proposed projects. 

56 

Section I 

Literature Review 

6. Current Commission modelling work 

Introduction 

In our  evaluation  of  previous  modelling  work,  we 
intend to concentrate on a very limited number of 
reports  that  deal  explicitly  with  modelling  the 
macroeconomic  effects  of  investments  by  the 
Structural  Funds and  investments that  are a part 
of  the  Community  Support  Frameworks.  Before 
providing  a  more  in-depth  analysis  we  would 
make the following general points: 

— Much  of  the  current  work  deals  with  general 
'philosophical', allocational, efficiency and imple 
mentation issues: that work is not covered here. 

— There  are  a  number  of  thematic  evaluations 
which  deal with  spending  priorities  within  one 
particular area (for example telecommunications 
and  transportation).  We  see these  reports  as 
complementary  to  our  current  modelling  work. 
In the  future  such  work  could  provide  initial 
rankings of projects and provide some input in 
to the new modelling strategies. However, such 
thematic  evaluations  cannot  capture  the com 
plex  interactions  between  projects  and are not 
adequate for prioritising a long list of competing 
projects that relate to different areas.1 

— The current  work  that  looks  at the  macroeco 
nomic  effects  of  different  Commission  Pro 
grammes is aimed at predicting the overall im-

For example, "The Evaluation of Actions  in the Fields of Telecommu 
nications and Transportation  in Greece, Ireland and Portugal" (Booz-
Allen and Hamilton)  provides  separate rankings for different  road, rail 
and  port  projects.  While  useful,  the  rankings  do  not  allow  us  to 
choose  between  competing  projects  and, more  importantly,  cannot 
allow for the dynamic effects of these infrastructure changes. 

pact  of  entire  programmes.  The  Commission 
has identified the need to look at the impact of 
small  scale  programmes.  The  current  large 
scale modelling work has little chance of effec 
tively  assessing  such  impacts.  The  methods 
we  intend  to  use are the  best  (currently avail 
able or being developed) that have any chance 
of  evaluating  the  dynamic,  spillover  and  ag 
gregative  impacts  of  the  small-scale  projects 
targeted by the Cohesion Fund. 

Large scale  macro-models 

Large  scale  macro-models  or  structural  models 
consist  of  large  numbers  of  related  equations 
each of which explains the formulation of one key 
economic  variable  (the  dependent  variable). The 
dependent  variable  from  one  equation  may  be 
the  explanatory  variable  in  other  equations,  and 
thus structural models explicitly capture the com 
plex  interaction  between  economic  variables. 
Various  estimation techniques  are used to calcu 
late  specific  parameter  values  for  each  of  the 
equations.  Once  the  model  has  been estimated, 
it  can  be  used  to  predict  the  effect  of  a  policy 
change,  by  changing  the  values  of  policy  vari 
ables.  The  problem  is that  the  'structural'  equa 
tions  that  have  been  estimated  reflected  the be 
haviour  of  economic  agents  under  the old  policy 
regime. If economic  agents are rational, then they 
will, in general, change their behaviour when con 
fronted  with  a  different  policy.  Unless  the  equa 
tions  that  have  been  estimated  are true  behav 
ioural  equations  (and they  are generally  not),  the 
parameter  values  of  these  equations  will 

Current  Commission  modelling  work 

57 

change.1 Variables that  are determined  by equa 
tions  whose  parameters  have  changed  are  pre 
dicted  incorrectly.  Worse  still,  the  allowance  for 
the  interaction  amongst  variables  then  propa 
gates these errors through the rest of the system. 
The poor forecasting  performance  of these  mod 
els  is well  documented  and we  have little faith in 
their results. 

These criticisms are particularly appropriate given 
the  nature  of  the  projects  that  we  wish  to  ana 
lyze.  Cohesion  Fund  spending  is  intended  to 
have significant  effects  on the  supply  side  of the 
economy.  Advocates  of  large  scale  structural 
models  are  well  aware  of  the  weaknesses  of 
these  models  when  it  comes  to  capturing  the 
supply  side  effects  of  spending  proposals.  In 
deed  much  of  the  current  work  on  the  HER-
MES/HERMIN  models  has sought to  specify  and 
estimate  behavioural  equations  for  the  supply 
side2.  As  those  involved  in  this  sort  of  analysis 
would  admit,  our  understanding  of  the  supply 
side of  the  economy  is at  best  limited. The eco 
nomics profession continues to seek a better un 
derstanding  of  these  issue  and  we  realise  that 
the  Commission  may  want  to  contribute  to  this 
process.  However,  given  the  current  level  of 
knowledge  existing  large  scale  models  will  be 
particularly  vulnerable  to  the  Lucas  critique.  We 
believe  that  in the  medium  term3, the  only  hope 
of predicting the impact of Cohesion fund spend 
ing, comes from the  modelling strategies that  we 
propose:  strategies  which  will  Incorporate  and 
expand  our  understanding  of  supply  side issues, 
but  that  are  not  extremely  susceptible  to  failures 
to model these issues completely. 

Computable  general 
equilibrium  models 

A  number  of  Computable  General  Equilibrium 
(CGE) models have been used to analyze the im 

This is the so-called Lucas critique'. 
See, for example, "The role of the Structural Funds: Analysis of Con 
sequences  for  Ireland  in the  Context  of  1992"  Bradley  ef  al (ESRI), 
"The  Effects  of  the  CSF  1994-99  on  the  Irish  and  Portuguese 
Economies"  Bradley  ef  al  (ESRI/UCP),  "The  Effects  of  the  CSF 
1994-99  on the Spanish  Economy: An Analysis  based on the HER-
MIN Model" Herce/Sosvilla-Rivero (FEDEA) and "The HERMIN-S4  -
A  Four  Sector  Structural  Model  of  the  Spanish  Economy  for  the 
Analysis of CSF" Herce/Sosvilla-Rivero (FEDEA). 
This medium term could be very long: the idea that we will have ac 
curate  behavioural  equations  for all the  relevant  variables  (or even a 
few  of  them)  In the  near  future,  would  be  incongruous  to  the  vast 
majority of the economics profession. 

pact  of  the  CSF4.  These  models  are,  in  some 
senses, the precursor of the CGE models that we 
will  consider.  Our  model  differs  in  emphasis  and 
theoretical background as follows: 

— the  model  will  be  regionally  based,  with  a 

comparable sectoral decomposition. 

— transport  costs will be modelled explicitly. 

— the  new  model  will  incorporate  increasing  re 
turns  to  scale  and  imperfectly  competitive 
markets. 

— activities are potentially  mobile, with their equi 
librium  allocation  across  locations  determined 
by:  sectoral  and  locational  specific  factors, 
and by demand and cost linkages for firms ac 
tive in imperfectly competitive markets. 

It  is  this  new  emphasis  on  dynamics,  imperfect 
competition  and  increasing  returns,  previously 
absent  in static  CGEs, that  will  make  our  model 
relevant  to  the  development  questions  posed  by 
the Cohesion Fund projects. 

We  have  also  considered  the  dynamic  general 
equilibrium  analysis  of  the  CSF  and  Structural 
Funds  outlined  in  a  number  of  papers  by  A. 
Pereira5.  The  models  outlined  in  these  reports 
share  an  obvious  theoretical  heritage  with  the 
modelling  strategies  that  we  intend  to  pursue. 
They provide a useful analysis of the ways in which 
Community transfers  may affect  economic  perfor 
mance  within  an endogenous  growth  framework. 
In addition the models give estimates of the overall 
macroeconomic  effect  of  entire  spending  pro 
grammes. While useful as a first  step  in analysing 
the  growth  impact  of  transfers,  this  model  is un 
able to  capture the  dynamic  interactions  between 
different  firms /  sectors  /  regions  of the  economy 
which  is emphasised  in the  endogenous  growth 
literature.  When  it  comes  to  analysing  the  impact 
of  individual  projects  these  interactions  and  other 
spillover effects need to be considered. 

See, for  example,  "Evaluation  of  the  Economic  Impact  of  the Com 
munity  Support  Framework  on  the  Greek  Economy  using  an  Ex 
tended  Computable  General  Equilibrium  Model"  Lolos/Zonzilos 
(DMP  Ltd). 
"A  Dynamic  General  Equilibrium  Analysis  of  EC  Structural  Funds 
(with  an  application  to  Portugal)"  (1991),  "Structural  Policies  in the 
European  Community:  An  International  Comparison"  (1994)  and 
"Evaluation of the Community Support  Framework for  1994-1999". 

58 

Section  I 

Literature  Review 

Input-output  analysis 

The  quantitative  analysis  of  the  impact  of  public 
investment  programs  is  often  carried  out  within 
an  input-output  (i-o)  framework1  The  i-o  ap 
proach is generally advocated on the basis of lev 
el of sectoral  disaggregation  it allows, and of the 
relative simplicity of its implementation. 

Nevertheless,  a  number  of  factors  make  the  in 
put-output  framework  unsuitable  for the  purpos 
es of our  empirical  analysis.  Firstly, and  most im 
portantly,  i-o  models  are  intended  to  perform 
comparative  static  analysis.  They  can  thus  pro 
vide little information on the dynamic behaviour of 
the  variables  of  interest  over  different  time  hori 
zons.  In addition, these  models  are explicitly  de 
signed to quantify  demand effects, while they are 
not  meant  to  capture  any  supply-side  effects. 
Secondly,  the  i-o  framework  suffers  from  the  re-
strictiveness  of the assumptions  it is built  on: the 
demand  for  inputs  is  independent  of  the  level of 
output, i.e. the percentage of total inputs required 
from  each  industry  does  not  vary  with  the  scale 
of production; technology  is such that, for a given 
level  of  output,  no  substitutions  may  be  made 
between  inputs.  Finally,  significant  problems  of 
data  availability  are  generally  encountered  in the 
practical  implementation  of  i-o  models,  particu 
larly when the objective  is to  evaluate the  impact 
of projects at the regional level. 

The latter issue of data availability and practical im 
plementation of impact analysis requires some ad 
ditional comments, as it has been the focus of the 
efforts in much of the recent literature on i-o  mod 
els. The  regional  i-o  framework  requires  a survey 
of  an appropriately  stratified  sample  (by  size  and 
industrial  composition)  of  all the firms  within  a re 
gion. As a result  of the  high costs  of  constructing 
survey-based  regional  tables, the  development  of 

'  See, for  example,  "The  Economic  Impacts  of  the  Community  Sup 
port  Frameworks  for  the  Objective  1  Regions  1989-93"  and  "The 
Economic  Impacts  of  the  Community  Support  Frameworks  for  the 
Objective  1 Regions 1994-99" by J.Beutel. 

input-output  tables  at  the  sub-national  level  has 
generally  relied  on  two  alternative  options:  non-
survey and partial survey techniques. 

Non-survey  techniques  consist  essentially  of  ei 
ther  modifying tables  assembled from survey da 
ta  for  other  regions  or  (in  most  cases)  comple 
menting the existing national tables with very lim 
ited  information  at  the  regional  level  (Brucker, 
Hastings,  and  Latham  (1990)  provide  an empiri 
cal comparison of five such regionally customized 
models).  Partial  survey  techniques  (see  e.g.  De-
whurst  (1992))  require  some  data  collection  but 
not  on  the  scale  envisaged  for  the  complete  re 
gional tables, and are based  on a number  of dif 
ferent techniques to  make optimal use of the lim 
ited information available. 

A vast literature  has developed  recently  in the at 
tempt  to  improve on the techniques  of  construc 
tion  of  i-o  models  and  estimation  of  regional co 
efficients.  Martins  (1993)  presents  a new  method 
for  use  in the  construction  of  hybrid  regional  in 
put-output  tables,  based  primarily  on  individual 
returns from the Census of Manufactures. Jensen 
(1990)  provides  a  general  overview  of  the 
progress  of  regional  i-o  models,  and  argues  that 
hybrid  models  provide a preferable alternative for 
future  direction  in  regional  tables.  Jackson  and 
Comer (1993) present  a table disaggregation and 
adjustment  (TDA)  method  for  combining  the  in 
formation  from  the  detailed  benchmark  tables 
with the more current but aggregated data. 

Despite  the  efforts  of  this  empirical  literature, 
there  seems  to  remain  substantial  room  for  im 
provement  in the  existing  methods  of  construc 
tion  and  updating  of  i-o  tables.  Together  with 
the  more  substantial  objections  expressed 
above  towards  the  comparative  static  nature 
and the  restrictive assumptions  of the i-o frame 
work,  these  limitations  point  at  the  need  for al 
ternative  empirical  approaches.  These  should 
be  explicitly  intended  to  describe  the  dynamic 
response  of  economic  variables  to  regional 
shocks,  while  not  restricting  the  attention  to 
Keynesian demand effects. 

Current  Commission  modelling  work 

59 

7. Where next? 

Regional  labour  markets 

We have outlined the existing literature on: 

— regional wage determination 

the  impact  of  investment  spending.  Only  by  ex 
tracting  from  minutiae  and  incorporating  these 
key  factors  can  we  hope  to  adequately  predict 
the impact of Cohesion Fund spending. 

— migration 

—  participation 

— firm locational decisions 

— employment  adjustment 

We  have  suggested  that  when  projects  are allo 
cated  across  regions  all  of  these  factors  will  be 
important  in determining their impact. Different al 
locations of projects  lead to different  distributions 
of  labour  demands  across  regions.  Given  the 
population  distribution,  a  pattern  of  regional 
wages  ¡s determined  (regional  wage  determina 
tion).  Workers  make  locational  and  participation 
decisions  dependent  on the  resulting  distribution 
of unemployment  and wages (migration  and  par 
ticipation).  The  regional  labour  supplies  adjust 
over time. As projects  continue the flow  of funds 
and the  locational  decisions  of firms  determine a 
pattern  of  labour  demand  which  adjusts  over 
time. The interaction of labour  market supply and 
demand  determine  the  dynamic  pattern  for  em 
ployment (employment adjustment). 

Our  regional  labour  markets  modelling  strategy 
takes  into  account  all of  these  factors.  It  cannot 
deal with the gritty detail of each highway, railway, 
bridge and port facility.  However,  it does deal ex 
plicitly  with  the  regional  spillover  effects  of  pro 
posed Cohesion  Fund spending. We believe that 
it is these effects which will be key to determining 

VARs and stochastic  kernels 

We have outlined the existing literature on: 

— forecasting and regional VARs 

— endogenous  growth  theory  and  time  series 

approaches to the empirics of growth 

— regional dynamics 

Our  modelling  strategy  builds from these existing 
regional  applications,  using  techniques  that  rec 
ognize  the  nature  of  the  dynamically  evolving 
cross-sectional  distribution  of  disaggregates.  By 
working  directly  off  the  entire  distribution  we 
hope  to  analyze  characteristics  of  resource  mo 
bility and flow across  regions, increasing inequali 
ty  across  rich  and  poor  regions  and the  interac 
tion of all regions with aggregate fluctuations. Be 
cause these techniques  work  with the entire dis 
tribution  we can  use past  data on movements of 
the key variables to predict the future response of 
the distribution to Cohesion Fund spending 

Computable  general 
equilibrium  models 

We have outlined the existing literature on: 

—  the early AGE models 

Where next? 

61 

—  industrial organization and AGE models 

—  regional models 

Our  modelling  strategy  is  a  natural  progression 
from  this  existing  work.  The  model  we  develop 
will be a regional model with explicit treatment  of 

transport  costs  and  factor  mobility  with  imper 
fect  competition /  increasing returns to scale for-
m at  v\/e  believe  that  the  new  emphasis  on  dy 
namics, imperfect  competition and increasing re-
turns  will  be  vital  if  a  CGE  model  is  to  capture 
the  long  run  growth  impact  of  Cohesion  fund 
spending. 

62 

Section II — Literature Review 

Glossary 

Bayesian Vector Autoregression: Specifies prior distributions for the parameter values on certain vari­
ables which are then updated during the estimation procedure. If the modeller has a strong feeling that χ 
is not important in explaining y then they will specify a prior with a mean close to zero and a small vari­
ance. If the modeller has a strong feeling that χ is very important in explaining y then they may specify no 
prior distribution and let the estimation technique give the parameter value. 

Bimodal: The mode is the most frequently occurring observation and is represented by a 'hump' ¡n the 
probability distribution. A bimodal probability distribution has two 'humps'. 

Compositional Variable: A variable that reflects certain characteristics of a region; eg the sectorial com­
position or the sex and age composition. 

Differencing: The first difference of a variable is the change in that variable from one period to the next. 
The second difference of a variable is the change in the change in that variable from one period to the 
next. Economists often difference nonstationary variables in an attempt to get a stationary variable. 

Dummy Variables: Typically a binary variable (taking the value 0 or 1) that allows us to control for differ­
ences across units that are not explained by a model, but that are not a result of random errors; eg in a 
migration equation we may use a dummy variable for regions that have a better climate and are thus 
favoured for retirement — migration flow to such a region will be larger than their relative unemployment 
and wage rates would predict, and this effect is captured by the dummy variable for 'hot regions'. 

Ergodic Distribution: The 'long run' equilibrium distribution which is unchanged by the application of 
the relevant stochastic kernel (see below) 

Forecasting Performance Measures: A measure that is constructed by comparing the predicted val­
ues from a model with the actual values that are observed. 

Granger Causality: A variable χ 'Granger Causes' a variable y if knowledge of current and past values 
of χ can improve forecasts of future values of y, even when we know the entire history of the variable y, ie 
knowledge of the χ process gives us information about future y that we do not get from knowledge of 
the y process alone. 

Impulse Response Functions: When a model is specified to explain the determination of a variable (the 
dependent variable) there will be movements in that variable that cannot be totally explained by that 
model. Given values for the explanatory variables the model will predict a value for the dependent vari­
able. If the model is correctly specified (ie is an accurate representation of the real economy) then the 

Glossary 63 

model errors (the difference between the predicted and actual values) should be random. When a model 
is constructed these random errors will be included as an additional  'explanatory'  variable. The effect of 
an event that  changes the value of the dependent  variable  but  not the explanatory  variables  ¡s not pre 
dictable by the model. Such events are labelled shocks, and their effect  must  be incorporated  in the er 
ror term.  Many  dynamic  models  include  lagged  (past)  values  of the  dependent  variable  as  explanatory 
variables. Thus shocks  in one period can effect the value of the dependent  variable for many periods to 
come. The dynamic  effect  of  a shock  is given  by the  impulse  response function  which  shows  how  the 
dependent  variable  is changed  over time  by changing the value of the error term  in a certain period (or 
periods).  In multivariate  models  every  equation  may  have an error term  and the effect  of  each  of these 
error terms on each of the dependent variables can be represented by an impulse response function. 

Markov Chain: A process which generates a random variable is a Markov process if all the usable infor 
mation  about  future values  of the variable  is given  by  knowledge  of the current  value of the variable, ie 
the best possible prediction of future values can be made using only the current value; Information about 
past values would  not  improve this prediction. A Markov  Chain is a Markov  Process that takes only dis 
crete values 

Nonstationarity: A variable that  ¡s not stationary  (see below) ¡s nonstationary. The level of GDP and the 
level of  prices tend to  drift  up over time which  suggests that they are nonstationary.  Nonstationary  vari 
ables are often classified  by their order of integration. A variable is integrated of order d if it needs to be 
differenced d times before it is stationary. An equation that uses variables of different  orders will generally 
be misspecified unless special (cointegrating ) relationships exist between the variables of higher order. 

Panel  Data:  Data  collected  for  a cross  section  (of  individuals,  regions,  firms  etc)  at  different  points  in 
time. The combination of cross section and time series data allows us to control for individual effects (ef 
fects that  vary over  individuals  but  not  over time, eg sex) or time effects  (effects that  vary over time  but 
are constant over individuals, eg the national inflation rate). 

Phillips  Curve:  Strictly,  a  negative  relationship  between  unemployment  and  the  rate  of  wage  inflation 
first  suggested  by  Phillips  (1958).  Now  used  to  describe  a  broad  range  of  (negative)  relationships  be 
tween unemployment and prices or wages. 

Power  Transformations:  In a  similar  way  to  differencing,  economists  use  power  transformations  (eg 
taking  logs, or square  roots) to  obtain a variable that  is  'better  behave'  less variable and thus  easier to 
model. Some apparently nonstationary variables may become stationary under a power transformation. 

Quantiles: The values  of the variable for which there  is a certain  probability  of  making a draw from the 
distribution which is less than that value. For empirical distributions it is the value for which there is a cer 
tain  percentage  of the  population  with  outcome  lower than that  value.  For  example the  lowest  Income 
decile is the income value at which  10% of the population earn less than that value. 

Stationarity: A weakly  stationary variable  has mean, variance  and covariances  that  are independent  of 
time.  A  strongly  stationary  variable  has  a  distribution  that  is  independent  of  time.  Stationary  variables 
fluctuate  around  a constant  mean value without  ever  moving  permanently  away from that  mean value. 
Unemployment  and GDP growth  rates are often thought to be stationary values. Various statistical tests 
exist which help determine whether a variable is stationary. 

Stochastic  Kernels: A mathematical  operator  which encodes the  information about  movements  within 
a  distribution  and  movements  of  the  entire  distribution  over  time.  When  this  mathematical  operator  is 
applied to the 'description' of a given distribution, the outcome is a 'description' of a new distribution. 

Transition Probability  Matrix: If the  'world' of interest  is divided into different  discrete states and ¡s ob 
served over time, then the transition probabilities are the probabilities that an 'individual' moves from one 
state  in one  period to  another  state  in a different  period. For example  given an income distribution (the 
world), we  consider  people's  income  levels and  define them  as  poor  or  rich  (the states). The transition 

64 

Section II — Literature Review 

probabilities  then  give the  probability  of  becoming  rich,  given  that  one  is  poor;  staying  poor  given that 
one ¡s poor etc. A transition probability matrix records this information  in matrix form. In our example we 
would  have a two  by two  transition  probability  matrix.  Information  about  Markov  Chains  ¡s conveniently 
stored  ¡n transition  probability  matrices. A transition  probability  matrix  is the concrete  manifestation  of a 
general stochastic  kernel when the distributions are over only discrete states. 

Unit Root: A process with a unit root is a nonstationary variable with certain (well Investigated) properties. 

Univariate  process: A  model  that  attempts  to  explain  the  behaviour  of  a  variable  using  only  lagged 
(past) values of the variable (an autoregression) or only current and lagged values of a random variable (a 
moving average process) or only a mixture of both (an autoregressive moving average model). 

Variance  Decomposition:  Because  the  equations  of  models  usually  Include  an  'explanatory'  term 
which  is a random  error, the  dependent  variables  themselves  are  random  variables  (the observed  time 
series represent draws from the random variables joint distribution). The variances of the dependent vari 
ables will depend on the variances of the random errors. A variance decomposition gives the percentage 
of the variance of one of the variables that can be explained by the error term from one of the equations 
for different  points in time. 

Vector Autoregression: A model which explains a vector of (n) variables in terms of lagged (past) values 
of those (n) variables. It is the multivariate equivalent of a univariate autoregression. 

Glossary 

65 

List of references 

Official  Publications 

Barret,  G. (1995) Meta-evaluation  of the CSF and Operational Programmes for the Objective 1  Regions 
1994-1999 Ireland. 

Beutel, J. (1995)  The Economic Impacts of the Community Support Frameworks for the Objective 1  Re 
gions,  1994-99, Report  to  the  Directorate-  General for  Regional  Policies,  Commission  of the  European 
Communities. 

Booz-Allen  & Hamilton  (1993) Evaluation  of Actions in the Fields of  Telecommunication  and  Transporta 
tion in Greece,  Ireland and Portugal. 

Bradley,  J.,  Gerald,  J.,  and  Kearney  I.  (1990)  The Role of  the  Structural  Funds: Analysis of Conse 
quences for Ireland in the Context of  1992.  ESRI. 

Bradley, J., Coimbra, R.,  Modesto, L,  Neves, P., and O'Donnell, N. (1994) The Effects of CSF 1995-99 on 
the Irish and Portuguese Economies. An Analysis based on the HERMIN Model, ESRI and UCP, Lisbon. 

Brady Shlpman Martin (1994) Tourism as an Element of Local Economic Development. 

Deloltte & Touche (1992) Evaluation of the Community Support Framework 1989-1993: Ireland. 

Deloitte & Touche (1994) Prior Appraisal of the Operational Programme for Industrial Development  1994-
1999 Republic of Ireland Final Report. 

Evalion (1995) Evaluation ex post of the Greek CSF. 

Fitzgerald, J., (1992) The Macroeconomic Effects of the Operational Programme on Peripherality (Report 
to the Technical Assistance Programme Steering Committee), ESRI —  DKM. 

Fitzpatrick Associates (1994) Irish Operational Programme for Tourism  1994-99 Prior Appraisal. 

Herce, J. and Sosvilla-Rlvero, S., (1994)  The Effects of the Community Support Framework 1994-99 on 
the Spanish Economy; An Analysis based on the HERMIN Model, FEDEA. 

Herce,  J.  and  Sosvilla-Rlvero,  S.,  (1994)  HERMIN-S4,  A  Four Sector  Structural Model  of  the Spanish 
Economy for the Analysis of Community Support Framework,  FEDEA. 

List of references 

67 

Honohan, P. (1994) Ex Ante Appraisal of the Irish National Development Plan 1994-99. ESRI. 

Karantinos,  D.,  Konsolas  N.,  Mitropoulos,  C,  Prodromidis,  and  K,  Vakakis,  F,  (1994)  Greek  Regional 
Development Plan 1994-1999 Appraisal,  KANTOR. 

Kennedy, K. (1993) Evaluation of Industriai Policy Issues: Ireland,  ESRI. 

Lolos, S. and Zonzilos, N., (1992) Evaluation  of the Economic Impact of the Community Support Frame­
work on the Greek Economy using an Extended Computable General Equilibrium Model, DM Ρ  Ltd. 

O'Connell, P.J. (1993) Evaluation  of Structural Funds Expenditures in Favour of Human Resourses in  Ire­
land, ESRI. 

Pereira, A (1995) Evaluation of the Community Support Framework  1994-1999. 

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74 Section II — Literature Review 

SECTION 

VECTOR AUTO-REGRESSION  MODELS 

Α. REGIONAL LABOUR MARKET ISSUES1 

Chris Pissarides and Etienne Wasmer 

Department of Economics, London School of Economics 

Introduction 

A key question for analysing the impact of Cohe­
sion Fund expenditure is how this public invest­
ment spending influences private investment ac­
tivity and employment at the local level? In this 
paper we investigate this question for the four 
cohesion countries at the regional level (NUTS2 
or NUTS3 depending on data availability). Unfor­
tunately, the labour market data for Ireland does 
not allow a complete analysis for that country. 

We carry out the analysis for an average ("repre­
sentative") region and also for poorer and richer 
regions separately. Our approach is to ask first, 
how do private and public Investments interact at 
the regional level and what does that Imply for 
the overall effect of public investment spending 
on regional investment? Then we ask, how does 
public investment influence local employment 

and wages? The question of whether an increase 
¡n local employment is realised through an in­
crease in the region's population (in­migration), or 
through a fall ¡n unemployment or discourage­
ment of workers, is taken up next. This latter 
question is investigated separately for skilled and 
unskilled workers ¡n the case of Spain. We carry 
out the analysis for overall Investment spending 
and also for some of the different components of 
spending. 

This paper comprises three parts. In the first part 
we describe the modelling methodology and the 
econometric analysis of the data. In the second 
part, we report results from the simulation of ac­
tual Cohesion Fund expenditure on the regional 
economies in the four Cohesion countries. The 
third part summarizes our results and concludes. 

We want to thank Charlie Bean, Henry Overman and Danny Quah for numerous comments on the previous versions of this report. We also thank 
Monika Reti and especially Katharina Gassner, who made a very important contribution to the simulation report. 

Introduction 

77 

1, Modelling Results 

We begin with an outline of the methodology  and 
the  results  of  econometric  analysis.  In  the  first 
section  we  describe  the  methodology  that  we 
apply  to  the  analysis  of  the  impact  of  cohesion 
fund  spending  on the  functioning  of  local  labour 
markets.  In the  next  four  sections  we  describe 
the  results  of  the  econometric  analysis  for  each 
of  the  four  cohesion  countries,  Spain,  Portugal, 
Greece and Ireland. 

1.1.  Methodology 

Our  goal  is to  measure  the  impact  of  structural 
public  investment  spending  on  the  regional and, 
to  a  lesser  extent,  national  economies.  We  first 
evaluate  the  effect  of  spending  on  business  in 
vestment,  employment  and  labour  supply  by av 
eraging  across  regions and  computing  the  effect 
on  the  economy  of  the  "representative"  region. 
We  also  study  some  of  the  spillovers  at  the  na 
tional  level  within  the  same  framework.  Subse 
quently, we disaggregate and estimate the effects 
of public  investment spending in regions of differ 
ent economic well-being and for different  kinds of 
investment spending. 

In  order  to  estimate  the  impact  of  investment 
spending  on  the  economy  of  the  representative 
region,  we  express  all  local  variables  relative  to 
the  national average. We then take the  logarithm 
of each variable, so what  we  shall call for  brevity 
"regional  investment",  "regional  employment", 
etc., should be understood as the percentage re 
gional share or, ¡f the variable is an average, such 
as the employment  rate, wages  etc.,  as the  per 

centage  deviation  of  the  regional  variable  from 
the national average. 

Public  investments  have a variety  of  characteris 
tics  but  our  main interest  ¡s on the  impact  of the 
overall  spending.  So  we  conduct  most  of  our 
analysis  for  total  investment  spending  measured 
as a percentage  of  GDP. However,  where possi 
ble,  we  also  disaggregate  investment  spending 
into  "productive"  and  "non-productive".  "Non 
productive"  investments  include spending  on ed 
ucation  and  health.  These  "non-productive"  in 
vestments  must  surely  have  beneficial  economic 
effects.  However,  these  effects  are  likely  to  be 
long  term, and  not  confined  to  the  region  where 
the expenditure take place. 

Public  investment  spending  influences  aggregate 
economic  activity through direct job  creation and 
also  indirectly,  by  increasing  the  productivity  of 
private  capital. The  latter  channel  stimulates  pri 
vate  Investment  which  leads  to  more  job  cre 
ation. At the aggregate  level, such job  creation is 
likely to  lead to  a fall in unemployment,  since the 
labour  force  ¡s not  likely  to  respond  one for  one 
to the new job  creation. But  at the  regional level, 
the  supply  of  labour  is  more  responsive  to  eco 
nomic  incentives,  because  of  inter-regional  mi 
gration.  Despite  the  frequent  assertion  that  mi 
gration  rates  in Europe are low, inter-regional mi 
gration  is  still  a  much  larger  cause  of  regional 
populations than natural growth is. 

The response of both private investment and inter 
regional  migrations  to  public  investment  spending 
is likely to  be slow and spread over time. We pick 
up the response functions through vector auto-re-

Modelling Results 

79 

gressions (VAR) models. This amounts to estimat­
ing the effect of the lagged values of all variables 
on the present value of each. By making use of 
the estimates, we can then simulate the dynamic 
response of each variable to a unit change in a 
given exogenous variable. 

In terms of equations, we estimate: 

These restrictions could be criticised because the 
impulse response we derive is dependent on 
them in the short­run. Moreover, there is a prob­
lem of self­correlation of the residuals: it can be 
shown that cov{ef, ef.,) = 0 if and only if «5 = 0 or 
if a.' ­ ­β, which need not to be true. The non­
zero covariance of the residuals will induce a bias 
on the least square estimators of the coefficients. 

X;  A M X, 

+ ε; 

d) 

where Xit is a vector of variables at time t in re­
gion /, A,(L) is a polynomial matrix of the lag oper­
ator /_, defined by L­X, = X,_7, and (e ) is a vector 
of disturbances. The matrix A may or may not be 
region­specific, according to the restrictions im­
posed on the estimate. 

The restrictions imposed on the shocks e are im­
portant. A simple example with public investment 
and private investment with one lag is helpful. 
Suppose we are interested in the coefficients a, 
α', β and ß' in the equations: 

Plt = C + a ■ P/(_7 + β · Bl,_, + ef 

(2) 

and 

S/f = C' + a'­P/i_7+ß'­e//_7+ef 

(3) 

where PI is public investment, Bl is private (busi­
ness) investment, and the e are such that 
E(e) = 0, and V(e) = σ. Now, there might be an 
immediate positive impact of public Investment 
on private capital productivity and therefore on 
business investment. In the model above, the im­
mediate effect of PI on Bl can only arise from the 
shock ef. Therefore, we need to consider the 
correlation between ef and ef, where we expect 
Cov{ef, ef) > 0 if public investment has an im­
mediate impact on private investment. In con­
trast, as public investment is sometimes counter­
cyclical ¡n order to decrease the impact of busi­
ness cycles fluctuations, the above covariance 
may be negative. Given the complexity of the 
problem, we may impose identification restric­
tions on the shocks, i.e. define a matrix linking 
the structural variables (the pure exogenous 
shocks) to the innovations (estimated as the 
residuals of the regressions above). In all the cas­
es, we have to rewrite the model by decompos­
ing ef into the sum of two terms: <5­ef character­
ising the immediate effects, and a ζ, such that 
cov(sf, ζ,) = 0. 

A convenient way of dealing with these two prob­
lems, which relies only on one restriction on the 
data, is the following. If we assume that at time t 
an exogenous shock in business investment (im­
plied for instance by a regional business shock) 
has no contemporaneous effect on public invest­
ment, then a natural model to estimate is: 

Ρ /(=0 +  α·Ρ/,_7+β·Β/(_7+μί 

(4) 

and 

Bl, = C' + a' ■ P/(_7 + β' ■ Blt_, + γ ■ PI, + μΕ, 

(5) 

where γ represents the immediate effect of public 
investment on private investment. In this model, 
we assume that a shock in private investment is 
uncorrelated with public investment at time t. Also 
it can be shown that now, cov (ßf, ¿¿f_,) = 0. This 
way of modelling the dynamic Interactions be­
tween the regional series was used by Blanchard 
and Katz (1992) in their study if the US regional 
evolutions. They assumed that a shock at time t 
on activity, or population, or wage rates, had no 
impact on the level of employment in the same 
year, but only ¡n the next year. This assumptions is 
obviously restrictive but the payoff is that it re­
duces the arbitrariness of the assumptions on the 
identification matrix. The parameter γ giving the 
short­run effect of one variable on another ¡s esti­
mated (consistently) from the data, and not pos­
tulated. A discussion of another source of bias, a 
correlation between PI, and μ,, which would affect 
the estimate of the coefficient γ, is reported in the 
Appendix, where we show that this leads to sys­
tematic errors which will be small in our case. 

Once the parameters have been estimated, we 
can simulate the response of a one percent in­
crease in public investment on the other vari­
ables. Given the logarithmic transformations that 
we have already described, the estimates are 
elasticities, that is, they show the percentage in­
crease in the dependent variables due to a 1 % 
increase in one of the exogenous variables. If our 
main interest, however, is in the effect of one ad­

80 

Section I  ■ Vector Auto­regression Models 

ditional unit of public investment on spending in 
the business sector, employment, e t c ., we can 
easily convert from elastlcises to real numbers by 
multiplying the impulse response calculated 
above by an appropriately­ defined factor. For in­
stance in the short run, by definition: 

ABI _ API 
Bl ~T PI 

which implies that: 

ABI = γ ■ API  ÊL 
PI 

(6) 

(7) 

In others words, if the elasticity estimated for a 
group of regions is lower than for another group 
of regions, ¡t does not necessarily imply that the 
impact in terms of units of spending is lower on 
the first group. It will depend on the average ratio 
of the variables, BI/PI like above. We will con­
stantly refer to both the elasticities and the gross 
numbers in the sections detailing the econometric 
analysis. In the second part of our report, where 
we simulate the impact of Cohesion Fund expen­
diture, we will emphasize the gross numbers per 
unit of spending or per project, since this is obvi­
ously the primary interest of the Cohesion Fund. 

We can also estimate the effect of this shock on 
cumulated private investment, which can be in­
terpreted as the long­run effect on the stock of 
private capital, ¡f depreciation of capital is zero. 
An issue that was raised during discussions with 
the Commission was the relevance of the endo­
genelty or exogeneity of public Investment 
spending with respect to the private sector (that 
is, whether public investment responds to private 
sector conditions). In the VAR model defined by 
equations (3) and (4), the first equation describes 
the dynamic evolution of public investment, both 
as a function of its past values and of past busi­
ness investment. In this specification, public in­
vestment ¡s endogenous (it depends on past 
business investment). It may be thought that, in 
absence of any consensual theory of public in­
vestment, it is better to consider the model with 
restrictions on the coefficients of the first equa­
tion. If the model was estimated while imposing 
say β = 0, it would mean that public investment 
depends only on its past values (public invest­
ment is exogenous). The econometric innovation 
(the public Investment shock) would be followed 
by successive innovations with decreasing size. 
Alternative estimations could impose both α and 

β to be equal to 0. In the case of these partial (or 
restricted VAR), we have not found very strong 
significant differences in the results. Cohesion 
Fund spending could represent additive invest­
ments (case of exogenelty, restricted VARs) or a 
substitution for other investment (in which case 
the unrestricted VARs are more appropriate). 
Which scenario actually holds depends on the re­
sponse of member states to Cohesion Fund in­
tervention. Unless specified, we have chosen the 
first option. It should be emphasised that a 
change in this assumption leads to little addition­
al computational efforts, and that we are ready to 
consider both scenarios in future simulations. 

This methodology can be extended to many other 
blvariate systems: some of them Involving disag­
gregated series of public investment and private 
investment (for instance, productive public invest­
ment and non­productive investment, investment 
in roads, water infrastructures, etc.), and others 
looking for the effect of public Investment and em­
ployment on other relevant labour market vari­
ables. Of course, it would be interesting to esti­
mate a system with more than two variables. The 
problem here is that, in the case where two lags 
are imposed in the equations, the number of co­
efficients to estimate in each equation is equal to 
5 or 6 with two variables, 7 or 8 with three vari­
ables, and more generally 2xk+1 (+1) when k is 
the number of variables. Thus, there ¡s a trade­off 
between the improvement from taking into ac­
count more of the interactions between the vari­
ables and the cost of reducing the number of de­
grees of freedom of the estimates. 

There are two different estimators of the coeffi­
cients. Which of them ¡s optimal ¡s conditional on 
whether data are time­series or cross­section. 
The first estimator consists of estimating the co­
efficients state by state, and then averaging them 
by taking either a weighted or an unweighted av­
erage. This method is appropriate when the data 
are available for a long period of time, as for in­
stance in Spain. The second method consists of 
estimating the VAR as a panel, restricting the 
slope coefficients to be equal. The intercept can 
be either common, region­specific or a random 
effect. In our discussion below we always use the 
panel method with common slope coefficients. 
For Spain we estimated the parameters in both 
ways and a comparison of the results led to very 
similar conclusions. 

Modelling Results 

81 

The VAR methodology can also be applied to 
study what happens to labour market variables 
when regional employment is shocked by, say, 
one percentage point. We again estimate 

Having outlined our methodology, we now pre­
sent results from the application of this methodol­
ogy to each Cohesion country. 

X/ | f=A(/­)­X,,­,+ß­^+E;, 

(8) 

1.2. Spain 

where A(L) is a polynomial of matrices, L the lag 
operator, and S is a matrix of maximum rank 
(/c­1) where k has the dimension of X, and can be 
written as, 

β: 

0 
α 
ß 

0 
0 
0 

0 
0 
0 

(9) 

The term B­X represents the impact of labour de­
mand shocks on the other variables within the 
same period. This avoids defining the identifying 
restrictions by the assumption that the second, 
third etc. variables have no immediate effect on 
the first variable in the vector X. As in Blanchard 
and Katz, we will assume that the first variable is 
the level of employment, and the other variables 
will be chosen among the following set: unem­
ployment rate, activity rate (participation), 15­64 
population, total population and wages. Thus, 
our analysis will tell us whether a one percent 
employment shock is translated into more in­mi­
gration, a fall in unemployment, an increase in 
participation, an increase in wages, or a combi­
nation of these (refer to Appendix 1 for details). 

We test for the stationarity of the time series be­
fore obtaining the estimates. The level of disag­
gregation of the analysis is sometimes NUTS2 
and sometimes NUTS3, depending on data avail­
ability. The estimated elasticities are generally 
lower when the degree of disaggregation is high­
er (see Appendix 3). This is probably due to the 
fact that there are measurement errors which are 
inversely related to the size of the regional units, 
which biases downwards the estimates of the 
parameters. One solution is to weight the esti­
mates with the size of the region, or to use a two 
step OLS/GLS procedure to weight the estimates 
with the cross­section variance. There is a nice 
economic interpretation to this estimator. In the 
first step, we have information on which regions 
behave differently from the others (this is given by 
the variance of the residuals region by region). 
Then, in the second step, we estimate a model of 
a representative region with the biggest weight 
given to the core regions, where core means the 
regions behaving similarly. 

We begin our analysis with Spain. Good quality 
data are available at the NUTS2 level (17 Re­
gions) for public investment and the capital 
stock, disaggregated into eight categories from 
1964 to 1991. Private sector investment and 
capital stock are available for the same period for 
23 sectors of the economy. For the same period 
1964­91, data for population, population aged 
15­64, labour force, employment and unemploy­
ment are available at the NUTS3 level (50 
provinces) and disaggregated into five levels of 
skill (except for total population). Wages are avail­
able every two or three years between 1955 and 
1993 for NUTS3, and every year between 1980 
and 1991 from another source for NUTS2. For 
the six years where the data overlap, the differ­
ence between the two series is less than 3%, 
and we can predict 97% of the variance of one 
variable with a constant and the other variable. 
This close correspondence allows us to define a 
third regional wage series for NUTS2 with six 
more points (this is explained in Appendix 2). 

1.2.1. Dynamic relations between 

public investment, private investment 
and private capital stock 
at the regional level. 

Public investment spending, the sum of which 
represents on average 2.3% of GDP, is disaggre­
gated into two main categories: productive and 
"non­productive". Productive investment is mainly 
on infrastructures (roads, ports, airports, motor­
ways and urban structures). "Non­productive" in­
vestment is in the education and health sectors. 
Table 2.1 displays some summary statistics, and 
Figures 2.1 and 2.2 (at the end of this section) 
provide additional illustration. The second column 
of the table reports the annual growth rate for 
each form of investment. In comparison, real GDP 
growth in Spain over this period was 3.4% p.a. 

As described in Section 1, we estimate a bivari­
ate VAR between relative public investment and 
private sector investment. Augmented Dickey­
Fuller tests run region by region reveal that both 
series are stationary. Thus in the following, the 
variables are entered in their levels. 

82 

Section III — Vector Auto­regression Models 

TABLE 2.1.  Public 

and Private Sector  Investment. 

Summary 

Total Public  Investment 

Productive 
Roads 
Water Facilities 
Urban Structures 
Ports (Pub. Adm.) 
Ports (Non Pub.  ) 
Motorways 
Airports 
Non Productive 
Health 
Education 

Total Business Sector  Investment 

64-91  Av 

64-91 growth (%) 

100 (ref) 

77 
25.6 
25.1 
10.4 
0.6 
4.4 
8.0 
3.1 

23 
8 
15 
748 

3.9 
3.7 
4.3 
1.7 
10.9 
18.5 
-0.01 
-5.7 
2.1 
4.8 
4.3 
5.2 
2.0 

We estimate, with two lags, the equations: 

PI, =C  + a,  -P/f_7  +α2·Ρ/ί_2+β7  -ΒΙ,_,  + 

+β2-β/ί-2+μΓ 

and 

Bl,  = C + δ7  -PI, _7 + δ2  -PI,_2  + λ7  -Bl,_7  + 

+X2-BI,_2+yPI, 

+μ,β 

(10) 

(11) 

where  γ is the  important  coefficient  representing 
the  immediate  effect  of  public  investment  on 
business  Investment. The results are summarised 
in Table 2.2 and illustrated in Figure 2.3. 

The  numbers  reported  in  the  table  are  the  im­
pulse  response  to  a  unit  shock  in  public  invest­
ment,  in  the  short  and  the  long-run.  Private  in­
vestment  has  a  positive  and  significant  correla­
tion  with  total  and  productive  public  spending, 
both in the short-run and the long-run. (Appendix 
5 explains how the standard-error  of the estimat­
ed  effect  on  the  cumulated  private  investment 
was  calculated.)  Looking  at  the  different  compo­
nents  of  productive  investments,  the  main  effect 
on  the  business  sector  is  from  investments  in 
roads  and  motorways,  while  other  types  of  in­
vestments  are  too  small  to  produce  an  observ­
able  effect  on  the  regions.  In contrast,  non-pro­
ductive  investment  has  no  significant  impact  on 
private investment. 

The  insignificant  impact  of  education  investment 
on regional private investment  ¡s a result that de 
serves further  comments. We consider  all our re 

sults in terms of shares in the national aggregate. 
The  most  plausible  interpretation  of  this  result  is 
that  increasing  the  education  spending  in  a re 
gion  improves  the  outcomes  of  all  regions,  and 
that the relative regional Impact  is not significantly 
stronger  than  this  aggregate  Impact.  The  em 
ployment  results  suggest  one  possible  mecha 
nism: the  regional  mobility  of  unskilled workers is 
low,  and  the  mobility  of  the  skilled  workers  is 
much  higher.  Improving  the  education  level  in a 
region  may  lead  to  more  mobility,  which  is  cer 
tainly  in  aggregate  more  efficient,  but  does  not 
only  benefit  the  region  where  the  investment 
takes place. 

We can also predict the long-run impact of public 
investment on employment.  Because Augmented 
Dickey-Fuller  tests  tend  to  indicate  that  employ 
ment  is  non-stationary  (see  Appendix  3  for  the 
tests),  the  VAR  is  estimated  with  the  first  differ 
ence of employment. The results are summarised 
in Table 2.3  and for  aggregate  public  investment 
and  business  investment  only,  in  Figure 2.4. The 
results  are  similar:  we  have  a  positive  impact  of 
productive  investment  on  employment,  account 
ed for  mainly  by  investment  in roads  and  motor 
ways. 

We  finally  estimate  the  impact  of  public  invest 
ment  on  wages.  For  that,  we  need  to  calculate 
the wage rate for some years between  1964 and 
1979.  We  describe  our  calculations  in  Appendix 
2.  The results  are reported  in Table 2.4  and illus 
trated in Figure 2.5. 

Modelling Results 

83 

TABLE 2.2. Short­run and 
GLS 2 step estimators 

long­run estimated impact of public spending, 
in panel, weights=cross­sectional variance 

Shock on 

Total Public 
Investment 
(442 obs.) 

Productive + 
Non­Productive 

Productive Public 
Investment 
(442 obs.) 

Total 

(442 obs.) 

Water Facilities 

(442 obs.) 

Urban 
Infrastructures 

(442 obs.) 

Roads 

(191 obs.) 

Motorways 
(11 Reg.) 

(213 obs.) 

Ports (10 Reg.) 

(260 obs.) 

Ports Non Admin. 
(10 reg) 

(329 obs.) 

Airports (16 reg.) 

Non­Productive 
Public Investment 
(442 obs.) 

Total 

(442 obs.) 

Education 

(442 obs.) 

Health 

Effect on Private 
Investment 

Effect on Private 
Capital Stock 

Effect on Private 
Investment 

Effect on Private 
Capital Stock 

in the short­run 
(γ) 

in the long­run 

in the short­run 

in the long­run 

Elasticities' 

Elasticities 

Gross Number 

Gross Number 

0 . 0 3 7 *" 
[0.016] 

0 . 0 3 3 *" 
[0.013] 

­ 0 . 0 03 
[0.013] 

­ 0 . 0 0 04 
[0.01] 

0 . 0 3 5 "* 
[0.01] 

0.0033 
[0.004] 

­ 0 . 0 02 
[0.006] 

0.0015 
[0.012] 

0.0045 
[0.005] 

­ 0 . 0 01 
[0.0016] 

­ 0 . 09 
[0.14] 

­ 0 . 0 04 
[0.015] 

0 . 1 6 0" 
[0.05] 

0 . 1 3 7 *" 
[0.04] 

­ 0 . 0 35 
[0.04] 

0.0718* 
[0.05] 

0 . 1 8 8 *" 
[0.04] 

0.028** 
[0.013] 

­ 0 . 0 28 
[0.031] 

0.032 
[0.039] 

0.002 
[0.02] 

0.001 
[0.11] 

­ 0 . 0 27 
[0.08] 

0.049 
[0.08] 

0.28 

1.20 

0.32 

1.55 

NS 

NS 

1.02 

NS 

NS 

NS 

NS 

NS 

NS 

NS 

­1.04 

2.14 

5.49 

1.70 (+) 

NS 

4.40 (+) 

NS 

NS 

NS 

NS 

1 The columns headed "Elasticities" report the elasticity estimates from the logarithmic regression in the text. The columns headed 
"Gross Numbers" report the elasticity times the ratio of Business to Public Investment, which is the number of units of private in­
vestment or capital stock that follow an increase of one unit of public spending. 

(short­run: weighted standard errors ; long­run: standard errors computed from the covariance matrix on the estimators; (+) the 
average of Business over Public Investment was calculated in the sub­sample of regions and years where Public Investment was 
different from zero, ' , " , "" significantly different from 0 at the 10, 5 and 1 % level, NS means Not Significant) 

The results in the last three tables suggest that 
public investments have a positive impact on 
capital productivity, positive effects on employ­
ment, but not much effect on wages. 

Our estimates are for a representative region, es­
timated with a two­stage procedure to correct 
the cross­sectional heteroscedasticity. We can 
go a step further and examine the relevance of 
this representative region. First, we look at the 
size of the residuals for each region. This identi­

fies regions that behave differently from the repre­
sentative one, as ones with large residuals. The 
following Table 2.5 (column 2) gives the ratio of 
the standard deviation of the residuals for each 
region over the standard deviation for all the 
residuals. A value below one implies that the re­
gion is close to the representative region. A value 
above one implies that the region is different from 
the representative one. We can see that for three 
regions, there is a value significantly above one: 
Baleares, Extremadura and Aragon. 

84 

Section I  ■ Vector Auto­regression Models 

TABLE 2.3. Short­run and long­run estimated impact of public spending 
GLS 2 steps estimators in panel, weights=cross­sectional variance 

Shock on 

Total Public 
Investment 
(425 obs.) 

Productive + 
Non­Productive 

Productive Public 
Investment 
(425 obs.) 

Total 

(425 obs.) 

Water Facilities 

(425 obs.) 

Urban 
Infrastructures 

(425 obs.) 

Roads 

(191 obs.) 

Motorways 

(203 obs.) 

Ports (10 Reg.) 

(250 obs.) 

Ports 
Non Admin. 
(10 reg) 

(319 obs.) 

Airports 

Non­Productive 
Public 
Investment 
(425 obs.) 

Total 

(425 obs.) 

Education 

(425 obs.) 

Health 

Business 
Investment 
(425 obs.) 

Employment 

Employment 

Employment 

Employment 

in the short­run (γ) 

in the long­run 

in the short­run 

in the long­run 

Elasticities' 

Elasticities 

Gross Number 

Gross Number 

0.014— 
[0.004] 

0.012"* 
[0.003] 

0.004* 
[0.0026] 

0.0006 
[0.002] 

0.004* 
[0.0028] 

0.0015* 
[0.0011] 

­0.0010 
[0.0012] 

­0.006" 
[0.003] 

­0.001 
[0.0026] 

0.0040 

[0.0044] 

0.0030 
[0.0033] 

­0.002 
[0.004] 

0.03** 
[0.01] 

0.0084" 
[0.004] 

0.0076— 
[0.003] 

­0.0033 
[0.003] 

0.0016 
[0.004] 

0.0076— 
[0.003] 

0.0035*" 
[0.001] 

­0.0048" 
[0.002] 

­0.0024 
[0.003] 

­0.0004 
[0.002] 

­0.0024 

[0.007] 

­0.0040 
[0.006] 

­0.0003 
[0.005] 

0.032*** 
[0.006] 

73 000 

44 000 

81 300 

51 500 

81 520 

15 550 

­

­

­

­

­

­

­

­

­

­

­

­

­

­

­

­

­

­

21 000 

22 000 

' The columns headed "Elasticities" report the elasticity estimates from the logarithmic regression in the text. The columns headed 
"Gross Numbers" report the number of jobs created per 12 000 000 workers (Spanish employment) in response to an increase 
of public investment spending of 1 % of GDP. 

(short­run: weighted standard errors in parentheses; long­run: standard errors computed from the covariance matrix on the esti­
mators. *. '". '" significantly different from 0 at the 10, 5 and 1% level) 

Second, we can estimate the VAR for each re­
gion separately. Because we lose the cross­
sectional variance of the variables, the confi­
dence interval on the regressors is much larger. 
Moreover, we have calculated the standard de­
viation of the long run value of cumulated pri­
vate investment on the assumption that the co­

efficients are random normal. This assumption 
is asymptotically valid, so acceptable when the 
number of observations is large. Our panel has 
442 observations but our time series only 26, 
so the value of the standard deviation in the 
brackets ¡n the third column may not be accu­
rate. 

Modelling Results 

85 

TABLE 2.4. Short­run and long­run estimated impact of public spending, 
GLS 2 step estimators in panel, weights=cross­sectional variance 

Shock on 

Total Public 
Investment 

Productive Public 
Investment 

Non­Productive Public 
Investment 

Business Investment 

Wages 

in the short­run (γ) 

Elasticities 

0.0048 
[0.0038] 

0.0033 
[0.0030] 

­ 0 . 0 0 06 
[0.004] 

0.270* 
[0.20] 

(weighted standard errors in parentheses, *, ", "* significantly different from 0 at the 10, 5 and 1% level; long­run elasticities are 
not reported because they are all insignificantly different from zero) 

TABLE 2.5. 

Long­run impact, individual regional 
Total Public and Private Investment 

estimates, 

Com. Valenciana 
Andalucía 
Navarra 
La Rioja 
Pais Vasco 
Madrid 
Catalunya 
Asturias 
Murcia 
Galicia 
Castilla Y Leon 
Cantabria 
Canarias 
Castilla la Mancha 
Baleares 
Extremadura 
Aragon 

VAR Total Public Investment and Private 
Investment 

Long­Run Effect 
Estimated Individually 

0.39 
0.58 
0.61 
0.76 
0.81 
0.84 
0.85 
0.89 
0.98 
1.00 
1.04 
1.05 
1.08 
1.15 
1.40 
1.41 
1.47 

0.380 [0.29] 
0.540 [0.46] 
0.081 [0.04] 
0.122 [0.09] 
0.231 [0.97] 
0.23 [0.45] 
0.461 [0.17] 
0.223 [0.18] 
0.216 [0.06] 
0.140 [0.17] 
­0.570 [0.99] 
­0.368 [0.47] 
­1.03 [0.36] 
­1.87 [1.23] 
0.849 [0.64] 
­0.01 [0.74] 
­0.099 [0.15] 

(standard errors computed from the 

covariance matrix on the estimators) 

We can repeat the same exercise with the bivari­
ate system that has productive public investment 
and private investment, since the diffe'ence ob­
served across regions may be due to heterogene­

ity in the nature of investment. The dispersion of 
the long­run value estimated for each region (col­
umn 3) is smaller here, because the estimated se­
ries of investment are more homogenous. 

86 

Section III — Vector Auto­regression Models 

TABLE 2.6. 

Long­run impact, 

individual regional estimates 

Productive Public 

and Private Investment 

VAR Productive Public Investment and Pri 

/ate 

Investment 

Com. Valenciana 

Andalucía 

Navarra 

La Rioja 

Pais Vasco 

Madrid 

Catalunya 

Asturias 

Murcia 

Galicia 

Castilla Y Leon 

Cantabria 

Canarias 

Castilla la Mancha 

Baleares 

Extremadura 

Aragon 

oi / σ 

0.40 

0.57 

0.61 

0.76 

0.81 

0.83 

0.34 

0.89 

0.97 

1.00 

1.03 

1.06 

1.06 

1.15 

1.39 

1.41 

1.47 

Long­Run Effect 
Estimated Region by 
Region 

0.380 

[0.26] 

0.340 

[0.36] 

0.065 

[0.03] 

0.107 

[0.07] 

0.221 

[0.82] 

0.288 

[0.24] 

0.359 

[0.13] 

0.228 

[0.15] 

0.170 

[0.041 

0.111 

[0.12] 

­0.578 

[1.16] 

­0.268 

[0.35] 

­0.657 

[0.26] 

­1.336 

[0.97] 

0.789 

[0.54] 

0.002 

[0.71] 

­0.054 

[0.14] 

(standard errors computed from the  covariance 

matrix on the estimators) 

Given the problems of estimating region by re­
gion, we turn to groups of regions which can 
form natural smaller panels. We group regions 
according to their GDP per capita and ask: Do 
the poor regions benefit more from public invest­
ments than the rich ones? We divide the panel of 
17 Spanish regions into three groups: the six 
poorest, the five richest and the remaining six in a 
middle­income group (see Appendix 4 for de­
tails), and then run the previous regressions for 
each of the subgroups. Having less cross­sec­
tional variance, the confidence intervals are now 
larger. 

The results indicate that productive investments 
in the middle­income regions have stimulated 

less private investments than in the richest and 
poorest regions. Thus, substitutability between 
business and public investment appears to be 
stronger at the extremes of the distribution. This 
result, however, ¡s not likely to be robust, give 
the large standard errors of the estimate. The 
regressions with non­productive Investment do 
not give significant results. This is summarised 
in the Tables 2.7 and 2.8. The long­run elasticity 
of cumulated private investment with respect to 
public Investment is 35% higher in the bottom 
third of the distribution than ¡n the top third. The 
dynamic path of the response of private invest­
ment ¡s the same as ¡n the representative re­
gion. 

Modelling Results 

87 

TABLE 2.7.  Long-run estimated  impact  of  public  spending  on private  investment 
subgroups of regions, GLS 2 steps estimators in panel, weights=cross-sectional  variance 

Shock on different  groups of regions 

Elasticities in 

the long-run: cumulated private investment 

Poor, 156 obs. 

Intermediate,  156 obs. 

Rich,  130 obs. 

Total  Public 
Investment 

Productive  Public 
Investment 

Non-Productive  Public 
Investment 

Shock on different  regions 

Total  Public 
Investment 

Productive  Public 
Investment 

Non-Productive  Public 
Investment 

0.199 
[0.17] 

0.178* 
[0.12] 

- 0 . 31 
[0.27] 

0.104 
[0.10] 

0.093* 
[0.08] 

0.037 
[0.18] 

0 . 1 3 4" 
[0.06] 

0 . 1 1 3" 
[0.05] 

0.112 
[0.14] 

Elasticities in 

the long-run: cumulated private investment 

Catalunya, 26 obs. 

Andalusia, 26 obs. 

Galicia, 26 obs. 

0 . 4 6 1 "* 
[0.17] 

0 . 3 5 9 "* 
[0.13] 

0.632 
[0.92] 

0.540* 
[0.46] 

0.340 
[0.36] 

- 0 . 3 77 
[0.74] 

0.140 
[0.17] 

0.111 
[0.12] 

- 0 . 8 4" 
[0.39] 

(long-run: standard errors computed  from the covariance  matrix on the estimators,  *, ",  '**  significantly 
5 and 1 % level) 

different  from 0 at the 10. 

TABLE 2.8.  Long-run  estimated  impact 
subgroups of regions, GLS 2 steps estimators 

on employment  of  public  spending, 
in panel, weights=cross-sectional  variance 

Shock on different  groups of regions 

Elasticities in the long-run: employment 

Poor, 150 obs. 

Intermediate,  150 obs. 

Rich.  125 obs. 

Total  Public 
Investment 

Productive  Public 
Investment 

Non-Productive  Public 
Investment 

Shock on different  regions 

Total  Public 
Investment 

Productive  Public 
Investment 

Non-Productive  Public 
Investment 

0.0065 
[0.012] 

0.0076 
[0.009] 

- 0 . 0 0 36 
[0.017] 

0 . 0 1 3" 
[0.007] 

0 . 0 1 0" 
[0.006] 

0.0044 
[0.014] 

0 . 0 0 8" 
[0.004] 

0 . 0 0 6 9" 
[0.0038] 

0.0076 
[0.0095] 

Elasticities in the long-run: employment 

Catalunya, 25 obs. 

Andalusia. 25 obs. 

Galicia, 25 obs. 

0 . 0 4 0 *" 
[0.015] 

0 . 0 3 1 "* 
[0.012] 

0 . 1 2 6" 
[0.07] 

0.0067 
[0.04] 

0 . 0 5 0 *" 
[0.02] 

- 0 . 0 68 
[0.078] 

- 0 . 0 04 
[0.03] 

-0.0007 
[0.02] 

- 0 . 1 3 3— 
[0.055] 

(long-run: standard errors computed  from the covariance  matrix on 
5 and 1 % level) 

the estimators,  ',  '*,  '"  significantly  different  from 0 at the 10. 

Section III — Vector Auto-regression Models 

1.2.2. The Impact of labour demand shocks 

Public investment spending affects private capital 
accumulation  and  so  also  affects  labour  produc 
tivity,  wages  and  employment.  Here we  evaluate 
the impact  of the  public  spending  shocks  on the 
regional labour markets. We consider the dynam 
ic  effect  of  labour  demand  shocks  on  activity 
rates and regional population. 

All  the  relative  variables  "Regional  Employment", 
"Regional  Activity  Rate"  and  "Regional  15-64 
Population"  (in  logarithm)  are  non-stationary,  ac 
cording  to  appropriate  Augmented  Dickey-Fuller 
tests  reported  in  the  Appendix  3.  This  implies 
that the new equilibrium  reached after  a shock is 
different  from  the  previous  one  for  the  employ 
ment  level, the  activity  rate  and  the  population. 
As  discussed  in  Blanchard  and  Katz  (1992),  the 
migration of workers explains the  non-stationarity 
of  employment  and  population.  But  a new  result 
here  is that  the  activity  rate  and  unemployment 
are also  affected  by the  past  shocks  in the  long-
run.  During our sample  period, some  regions  did 
consistently  better  than  others  in  terms  of  em 
ployment, activity rates and in-migration. 

We  estimate  the  VAR  model  in  (8) for  the  three 
variables  Employment,  Activity  Rate  and  15-64 
Population. The VAR has two  lags and is estimat 
ed as a panel  with fixed  regional  effects. The re 
gressions  where  activity  and  population  are  the 
exogenous  variables  allow  for  an  immediate  ef 
fect  of  employment.  The  estimator  is  discussed 
in the technical Appendix 5. 

We  introduce  here  a  new  degree  of  disaggrega 
tion in the workforce: the skill level, which is avail 
able in the 50 provinces since  1964. We then es 
timate a VAR for the regional series, and for each 
skill  level  (00,  01, 02,  03,  04,  described  in  the 
Appendix  2).  It  appears  that  the  response  to  a 
1 %  employment  shock  was  similar  for  the  cate 
gories 00 and 01  and for  02, 03 and 04, but dif 
ferent  between  these  two  groups.  We  therefore 
aggregated  categories  00  to  01  (referred  to  as 
the unskilled), and categories 02, 03 and 04  (  re 
ferred to as the skilled). 

The  results  of  the  impulse  response  of  employ 
ment on the activity rate and population aged 15-
64  can  be  seen  in  Figures  2.6-2.8.  The elastici 
ties are summarized in Table 2.9. 

TABLE 2.9.  Impul se Response of Labour Market Variables 
Summary. 50 Provinces, GLS 2 steps panel estimator, weights 

to Employment Shocks, 
=cross-sectional variance 

VAR 

Spain 

Spain 

Spain 

Spain 

Spain 

Number of Observations 
Number of Regional Units 
Shock: 

1300 
50 
+1 % Total 
Employment 

1300 
50 
+1%  Skilled 
Employment 

1300 
50 
+1%  Skilled 
Employment 

1300 
50 
+1%  Unskilled 
Employment 

1300 
50 
+1%  Unskilled 
Employment 

Short-run elasticity of 
Total Activity Rates 
Total 15-64 Population 
Skilled Activity Rates 
Skilled 15-64 Pop. 
Unskilled Activity Rates 
Unskilled 15-64 Pop. 

Long-run Elasticity of 
Total Employment 
Total Activity Rate 
Total 15-64 Population 
Skilled Employment 
Skilled Activity Rate 
Skilled  15-64 Pop. 
Unskilled Employment 
Unskilled Activity Rate 
Unskilled  15-64 Pop. 

0.675 [0.01]*" 
0.026 [0.005]"* 
-
-
-
-

-
-
0.240 [0.01 ]*** 
0.282 [0.01]*" 
-
-

-
-
0.239 [0.01]*" 
0.266 [0.01]*" 
-
-0.05  [0.007]*" 

-
-
-
-
0.797  [0.01]*" 
0.072  [0.008]"" 

-
-
-
-0.04[0.01]"* 
0.807 [0.01]*** 
0.066[0.007]"* 

1.05 
0.62 
0.12 
-
-
-
-
-
-

-
-
-
1.28 
0.33 
0.44 
-
-
-

-
-
-
1.29 
0.32 
0.47 
-
-
0.01 

-
-
-
-
-
-
0.95 
0.68 
0.12 

-
-
-
-
-
0.09 
0.98 
0.68 
0.15 

(short-run: weighted t-stat in 
parentheses  : long-
'"  significantly different from 0 at the  10, 5 and + 

un: standard errors 
1 % level) 

computed from the covariance matrix on 

the estimators, ", *', 

Modelling Results 

89 

The important result here is that the skilled and 
the unskilled responses to the employment 
shock are very different from each other, both in 
the short­run and in the long­run. In response to 
a positive employment shock, the skilled move 
from unemployment to employment and from 
outside the region Into it. In contrast, the un­
skilled move from inactivity to employment and 
not at all from unemployment to employment. In­
migration for the unskilled is also much weaker 
than for the skilled. The Implication of this finding 
is that the main effect of negative shocks on em­
ployment for the unskilled is discouragement 
(workers leaving the labour force because of lack 
of employment opportunities). They do not leave 
the region, even if labour demand is high else­
where. In contrast, skilled workers migrate if they 
cannot find jobs in their region, and they respond 
quickly to the differential in demand for labour 
elsewhere. 

1.2.3. Conclusions for Spain. 

economy an increase in private business capi­
tal of 0.16%. The whole of this increase is ac­
counted for by public Investment spending ¡n 
Infrastructure. Spending on education and 
health do not stimulate private Investments. 

2. The long­run effect of productive Investment 
spending is approximately the same in differ­
ent types of regions. There is, however, some 
evidence that the positive spillovers for private 
Investment are slightly stronger in poorer re­
gions and, to a lesser degree, richer ones 
(and weaker in middle­income ones). 

3. Public investment Increases local employment 

but has very small effects on wages. 

4. An increase in local skilled employment ¡s as­

sociated with a fall in local unemployment and 
¡η­migration (conversely for a fall). In contrast, 
a rise in unskilled employment is associated 
almost exclusively with an Increase in the local 
activity rate. 

Our empirical results for Spain support the fol­
lowing five conclusions: 

1. A 1 per cent increase in public investment 

spending eventually generates in the local 

The effects of the shocks on regional employ­
ment, unemployment and inactivity persist in­
definitely. This contrasts with US regional evo­
lutions where only employment shocks are 
persistent. 

90 

Section III — Vector Auto­regression Models 

Figure 2.1: Components of Total Public Investment 
(Percentages, 1964-1991 Averages) 

Figure 2.2: Public Expenditure as a Percentage of GDP, 
1964-1991 

Spain 

0,04 

0,035 

0,03 

0,025 

0,02 

0,015 

0,01 

0,005 

0 

CD 

Water Facitilites 
25% 

Ports, Public 
Admin. 
1% 

Urban Structure 
11% 

Ports, Non-Public 
Admin. 
4% 

Motorways 
8% 

y 

■'■I** 

-; 

-= 

I 

, 

U——, 

■ Ports, Public Admin. 
Π Water Facitilites 
Θ Urban Structure 
Β Ports, Non-Public Admin. 
■ Roads 
O Motorways 
□ Airports 
Ξ Health 
Π Education 

o 
Q. 
= 
5' 

CQ 
33 
Φ 

CO 

CD 
ro 

en 
Φ o 

o 
o 
> 
c 
o 

à 

o α 

Φ 

Spain 

Figure 2.3: Cummulative Response of Private Business Investment to a 1% Shock in (a) Total Public Investment and (b) 

Productive Public Investment 

Years 

| f / Total Public Investment Shock 

Productive Public Investment Shock 

Spain 

Figure 2.4: Response of  Local  Employment  to  a 1% Shock to  (a)  Private 
Business  Investment  and (b)  Public  Investment 

/  Shock  to  Private  Investment 

/  Shock to Public  Investment 

Figure 2.5: Response  of  Wages to  a 1%  Shock to  (a)  Private  Business 
Investment  and (b)  Public  Investment 

Years 

/  Shock to  Private  Investment 
/ 

Shock to  Public  Investment 

- -/ 

Modelling Results 

93 

co 

ω 
Φ 
ο 
ο' 
3 

Ι 
Î 
> c 
δ* 

ο 
=3 
ο α 

Φ 

Spain 

Figure 2.6: Response of 
Employment to a 1% 
Shock in Employment 

Figure 2.7: Response of 
Activity Rate to a 1% 
Shock in Emploment 

1,6 τ 

Figure 2.8: Resonse of 
Population to a 1% Shock in 
Employment 

Figure 2.9: Population of 
Skilled and Unskilled 
Workers, 1964­1992 

35000 

30000 -

25000 

20000 

15000 

10000 

5000 

■ * j - r " - o c 5 c o o ) C \ j i r > c o i-
c o < o h - h » r ^ . r - ~ c o c o c o o> 
c n o i o i o i c n c n c n o i c ñ o) 

LEGEND 

Total Population 
Skilled Workers 
Unskilled workers 

Years 

Years 

Years 

1.3. Portugal 

We now turn to Portugal. Public investment by 
municipalities ¡s available at the district level (18 
regions, and for most years, the islands of 
Acores and Madères as well) between 1979 and 
1986 and from 1990 to 1993. Central Govern­
ment Investment Spending is also available, but 
only since 1990. Because the latter sample ¡s 
rather small, we focus only on local public spend­
ing by the municipal authorities. This causes two 
problems for a cross­country comparison: first, 
the nature of these investments is different from 
the investment by central government. Second, 
municipal investments in Portugal are strongly 
concentrated in the richest districts (Lisboa and 
Porto), so it is difficult to detect any strong effect 
of public spending in the regions where the 
shocks on municipal investments are small, since 
their Impact on the business sector may be mar­
ginal. 

Private investment is available at the NUTS3 level 
between 1980 and 1993, without disaggregation 
by type of spending. Wages are available be­
tween 1971 and 1993 (except for 1978, 1980 
and 1990) but before 1980 the series is very 
volatile and probably not reliable. We have em­
ployment, population and population aged 15­64 
since 1970 at the district level and unemploy­
ment since 1975. 

1.3.1. Dynamic relations between 

public investment, private investment 
and private capital stock 
at the regional level. 

The comparison of Portuguese investment data 
with Spanish data ¡s difficult: in Portugal, the ratio 
of business to local public investment ¡s 15.2, in­
stead of 7.4 in Spain. The interpretation of the re­
sults in Portugal must be done carefully. We 
again estimate a bivarlate VAR between relative 
public investment and relative private sector In­
vestment. The missing data between 1987 and 
1989 are a problem, and result in the loss of 5 
data points for each regional unit. 

We estimate with two lags the equations: 

PI, =C + a, -PI,_, + a2 -PI,_2 + ßj -Bl,_, + 

+β2·β/ί-2+μΓ 

Bl,  =C + δ, -PI,_, + δ2 -PI,_2 + λ,  -Bl,_, + 

+l2-BI,_2+yPI, 

+μ,β 

(11) 

(12) 

where γ represents the Immediate effect of public 
investment  on  business  investment.  The results 
are  summarized  in Table  3.1  and also  in Figure 
3.1.  The figure  shows  cumulative  investment de­
clining after the first 2-3 years. The reason is that 
some  of the estimated  coefficients  are negative, 
implying that  some of the initial positive  effect on 
private  Investment  is offset  by crowding  out at a 
later  stage.  Not  much  should  be  read  into  this 
fluctuation, however, given the data limitations. 

TABLE 3.1.  Short-run and long-run estimated impact of public spending, 
GLS 2 step estimators in panel, weights=cross-sectional  variance 

Shock on 

Private 
Investment 

Private Capital 
Stock 

Private 
Investment 

Private Capital 
Stock 

in the short-run 
(ï) 

in the long-run 

in the short-run 

in the long-run 

Elasticities' 

Elasticities 

Gross Number 

Gross Number 

Local  Public  Investment  (1 lag, 126  obs.) 

Local  Public  Investment  (2 lags, 90 obs.) 

0 . 1 7 0 *" 
[0.062] 

0 . 1 5 3" 
[0.078] 

0.046 
[0.05] 

0.050 
[0.06] 

2.58 

2.33 

0.70 

0.76 

The columns headed "Elasticities" report the elasticity estimates from the logarithmic  regression In the text. The columns headed 
"Gross Numbers" report the elasticity times the ratio of Business to Public  Investment, which is the number of units of spending 
of private investment or capital stock  following an increase of one unit of public spending. 

(short-run: weighted  standard errors in parentheses  : long-run: standard errors computed  from the covariance matrix  on the esti 
mators. *, ". '"  significantly different  from 0 at the 10, 5 and 1%  level) 

Modelling  Results 

95 

The numbers reported in the table are the im­
pulse responses to a one unit shock on public In­
vestment, in the short and the long­run. Private 
investment has a positive and significant correla­
tion with total and productive public spending, 
both in the short­run and in the long­run, the lat­
ter effect being slightly significant. See Appendix 
5 for a description of the calculation of the stan­

dard­error on the cumulated private investment. 
We can also predict the long run impact of public 
Investment on employment. Because Augment­
ed­Dickey Fuller tests tend to indicate that em­
ployment is non­stationary, the VAR is estimated 
with the first difference of employment. The re­
sults are summarised in Table 3.2 and in Figure 
3.2. 

TABLE 3.2. Short­run and long­run estimated  impact of public spending, 
GLS 2 step estimators in panel, weights=cross­sectional variance 

Shock on 

Local Public Investment (2 lags, fixed effects, 108 obs.) 

Local Public Investment (2 lags, random effects, 108 obs.) 

Business Investment (2 lags, fixed effects, 162 obs.) 

Business Investment 2 lags, random effects, 162 obs.) 

Employment 

in the short­run 
(γ) 

Employment 

in the long­run 

Elasticities' 

Elasticities 

0.001 
[0.05] 

0.054 
[0.068] 

0.029 
[0.038] 

0.010 
[0.037] 

0.015* 
[0.01] 

0.025 
[0.03] 

0.054* 
[0.035] 

0.007 
[0.006] 

' The columns headed "Elasticities" report the elasticity estimates from the logarithmic regression in the text. 

(short­run: weighted standard errors in parentheses; long­run: standard errors 
mators, ", *", *"* significantly different from 0 at the 10, 5 and 1 % level) 

computed from the covariance 

matrix on the esti­

In order to test for the relevance of the concept 
of the representative region, we first look at the 
size of the residuals for each region. This pro­
vides information on which regions behave like 
the representative one, and which are different. 
Table 3.3 (column 2) gives the ratio of the stan­
dard deviation of the residuals for the region over 
the standard deviation for all the residuals. A val­
ue below one implies that the region is close to 
the representative region. A value above one im­
plies that the region is different from the repre­
sentative one. There is a value significantly above 
one for two regions only: Bragança and Castelo 
Branco. In the Portuguese case we do not try to 
estimate the equations district by district, be­
cause the number of observations across time is 
too small. 

As in the Spanish case, we address the question 
of the distributive effect of public spending. Do 
poor regions benefit more from public investment 
than rich ones? We divide the panel of 18 Por­
tuguese districts into three groups, the 6 poorest, 
the 6 next and the 6 richest, and run the previous 
regressions for each of the subgroups. Having less 
cross­sectional variance, the confidence intervals 
will be larger. The results are different from the 
Spanish ones: for Portugal it is found that public 
investment generates more private investment in 
the richest regions, but in the other regions no sig­
nificant estimates were obtained. This last result is 
probably due to the fact that Portuguese regions 
differ a lot in terms of GDP per capita and in terms 
of the amount of local public spending. When the 
public investment shocks are small, it is difficult to 
detect any significant impact on the private sector 
and our estimator is not efficient. 

96 

Section I 

Vector Auto­regression Models 

TABLE 3.3. 

Relative standard deviation of 

residuals by region 

/AR Local Public Investment and Private 
Investment 

Long ­Run Effect 
Estimated Region by Region 

NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 
NA 

σι /σ 

0.41 
0.56 
0.19 
2.57 
2.35 
0.60 
0.30 
0.43 
0.94 
0.16 
0.10 
0.74 
0.37 
0.21 
0.75 
1,15 
0.32 
1.04 

AVEIRO 
BEJA 
BRAGA 
BRAGANÇA 
CASTELO BRANCO 
COIMBRA 
ÉVORA 
FARO 
GUARDA 
LEIRIA 
LISBOA 
PORTALEGRE 
PORTO 
SANTARÉM 
SETÚBAL 
VIANA DO CASTELO 
VILA REAL 
VISEU 

(standard errors computed from the 

covariance matrix on the estimators) 

TABLE 3.4. Short­run and Long­run estimated impact on private 
investment of public spending, subgroups of regions, 
GLS 2 steps estimators in panel, weights=cross­sectional variance 

Shock on different groups of regions 

Elasticities in the long run: cumulated private investment 

Poor, 42 obs. 

Intermediate, 42 obs. 

Rich, 42 obs. 

Productive Public Investment (1 lag) LR 

-0.065 
[0.20] 

0.040 
[0.11] 

0 . 1 7 0" 
[0.10] 

(short run: weighted standard errors ; long­run: standard errors computed from the covariance matrix on the estimators, 
significantly different from 0 at the 10, 5 and 1% level) 

1.3.2. Impact of labour demand shocks 

We consider the dynamic effect of labour de­
mand shocks on activity and population. Again, 
we estimate the VAR model described in (8), for 
the three variables Employment, Activity Rate 
and 15­64 Population, and then replacing the 

15­64 Population by the Total Population. The 
VAR has two lags, and is estimated in a panel 
without fixed regional effects, given the length of 
longitudinal (time) dimension. The regressions 
where activity and population are the exogenous 
variables allow for an immediate effect of employ­
ment. The elasticities are summarized in the fol­
lowing table: 

Modelling Results 

97 

TABLE 3.5.  Impulse Response of Labour Market Variables to Employment Shocks, 
Summary. 13 Regions, GLS 2 steps estimator in panel, 
weights=cross-sectional  variance 

VAR 

With  15-64 Population 

With Total Population 

Number  of  Observations 

234 

234 

Short-run  elasticity  of 

Activity 

Population 

Long-run  Elasticity  of 

Employment 

Activity 

Population 

0.974  [0.02]  *" 

- 0 . 0 41  [ 0 . 0 1 ] "* 

0.949  [ 0 . 0 1 ] *" 

0.015(0.015] 

0.852 

0.829 

- 0 . 0 27 

0.846 

0.782 

0.004 

| 

(short-run: weighted t-stat in parentheses : long-run: standard errors computed from the covariance matrix on the estimators. '.  ", 
'** significantly different from 0 at the 10, 5 and 1 % level) 

As it is also Illustrated by Figure 3.2, what  is strik 
ing here is the lack of mobility appearing from the 
figures:  regional  population  Is  hardly  influenced 
by  the  employment  change,  and  as  for  the  un 
skilled  in  Spain,  the  activity  rates  absorb  most 
employment shocks. 

1.3.3. Conclusions for Portugal 

For Portugal, the public  investment  data relate to 
local  investment,  not  government  investment. 
Therefore,  an  important  issue  is  the  degree  of 
comparability  between  the  results  for  Portugal 
and  the  other  countries.  Differences  could  result 
from  the  nature  of  investments  covered  by  the 
data.  Local  administrative  units  (Municipalities) 
have different  objectives  and spending from cen 
tral  governments.  The average  spending  of  local 
units  is a mixture of different  projects  (roads, wa 
ter  facilities...)  with  possibly  different  proportions 
from  the  proportions  of  central  government  in 
vestment  spending. The extent  of the  differences 
in  the  proportions  is  rather  difficult  to  evaluate, 
however.  The  major  difference  is  likely  to  be  the 
importance of the location of the returns to public 
investment.  In  the  case  of  local  administrative 
units, it is likely that the objective function is to in 
crease  private  investment/employment  locally, 

whereas the objective function of the central gov 
ernment  is  certainly  less  geographically  specific. 
Since our method does not account for spillovers 
across  regions, considering  only local  investment 
is likely to  lead to  an upwards  bias of the  impact 
of  public  investment  spending  in  the  region 
where  investment  is  spent.  The  degree  of  the 
bias is, however, impossible to evaluate. 

With this caution  in mind, our  results for  Portugal 
indicate that: 

1.  Private  investment  has  a  positive  and signifi 
cant correlation with total and productive pub 
lic spending. This correlation exists both in the 
short-run and in the long-run, but the latter ef 
fect is only slightly significant. 

In contrast  with  the  results  for  Spain,  public 
investment generates more private investment 
in the richest  regions, but  in the other  regions 
no significant estimates were obtained. 

In  response  to  labour  demand  shocks,  re 
gional population is unaffected, and increased 
activity rates absorb most of the new jobs. 

98 

Section III —  Vector Auto-regression Models 

Figure 3.1 : Response of  Cummulative  Private Business  Investment to a 1% Shock to Local Public  Investment 

Portugal 

Local  Public  Investment  Shock  ι 

o 
Q_ 
CD 

CQ 
DD 

CD 
CO 

o 
o 

ω 

CD 

CQ 

CD 

O 
Q. 
CD 

Portugal 

Figure 3.2: Response of (a) Employment (b) Activity Rate and (c) Population to a 1% Shock in Employment 

Years 

• Total Employment 

■ - Total Activity Rate 

■A —Total Population 

1.4.  Greece 

At  the  NUTS 2  regional  level  (13  regions),  public 
investment  and  private  investment  are  available 
from  1980  to  1993,  without  disaggregation  by 
type of spending. At the NUTS3 level (51 nomos), 
data  on  public  and  private  investment  are avail­
able for 4 years only (1980 to 1983). 

From  Eurostat,  we  have data  at  NUTS2  level for 
1988-94  on  Population,  15-64  Population, 
Labour  Force,  and  Employment.  Unemployment 
is available at the NUTS3 level. There is no disag­
gregation by skill level. These data can be partial­
ly  matched  with  the  Labour  Force  Survey  since 
1981, despite problems of changing definitions of 
the  regions.  Hourly  and  weekly  wages  of  salary 
and  wage  earners  are  available  between  1990 
and 1993 at NUTS2, with a disaggregation for 28 
sectors. We have also gross production  in manu­
facturing  at  current  prices  at  NUTS3  level,  be­
tween  1980 and  1993. Regional GDP (NUTS3) is 
available from 1970 onwards for 12 sectors. 

1.4.1. Dynamic relations between 

public investment, private investment 
and private capital stock at 
the regional level 

In Spain,  public  investment  is  7.4  times  smaller 
than private investment.  In Greece, public  invest­
ment  is  only  1.44  times  smaller  than  private  in­
vestment  during  our  sample,  so  the  definition  of 
public investment spending in Greece is broader. 

Again, we estimate a bivariate VAR between rela­
tive public  investment  and  private  sector  invest­
ment. We estimate, with two lags, the equations: 

PI, =C + a, -ΡΙ,_Ί  +α2-ΡΙ,_2  +β7 -S/(_, + 

+ β2- β /ί_2+ μί 

Ρ 

(13) 

and 

Bl,  =C  + b1 -PI,., +δ2-ΡΙ,_2+λ1 
o 
+K2-Blt_2+yPlt  +μ,β 

-Bl,., + 

(14) 

where γ represents  the  immediate  effect  of  public 
investment on business investment. The results are 
summarized in Table 4.1 and shown in Figure 4.1. 

TABLE 4.1.  Short-run and long-run estimated impact of public spending, 
GLS 2 steps estimators in panel, weights=cross-sectional  variance 

Shock on 

Private 
Investment 

Private Capital 
Stock 

Private 
Investment 

Private Capital 
Stock 

in the short-run 
M 

in the long-run 

in the short-run 

in the long-run 

Elasticities' 

Elasticities 

Gross Number 

Gross Number 

Total  Public  Investment 
(Sample  1,13  regions,  with  fixed 
effects,  1 9 8 0 - 9 3,  156  obs.) 

Total  Public  Investment 
(Sample  2,  51  regions, without  fixed 
effects,  1980-83,  102  obs.)2 

0.195*** 
[0.03] 

- 0 . 01 
[0.03] 

0 . 5 3 8 *" 
[0.07] 

0 . 6 0 3 *" 
[0.20] 

0.28 

0.77 

NS 

0.87 

'  The columns headed "Elasticities" report the elasticity estimates from the logarithmic regression in the text. The columns headed 
"Gross Numbers" report the elasticity times the ratio of Business to  Public  Investment, which is the number of units of spending 
of private investment  or capital stock following an increase of one unit of public spending. 

2  As an alternative  specification, we  have estimated for  the 50  regions  between  1980-83  a random  effect  model  with  two  lags, 
useful when the time series dimension  is small. The short  run elasticity  was found to  be 0.036  [0.07] and the long-run elasticity 
0.661  [0.271. so not strongly different  to the results of the table. 

(short-run: weighted standard errors in parentheses  ; long-run: standard errors computed  from the covariance  matrix on the esti­
mators, ", ",  '"  significantly different  from 0 at the 10, 5 and  1% level) 

Modelling  Results 

101 

The numbers reported in the table are the impulse 
response  to  a  one  unit  shock  on  public  invest 
ment,  in  the  short  and  the  long-run.  Private in 
vestment  has a positive and significant  correlation 
with total and productive  public spending, both in 
the short-run and in the long-run. On the contrary, 
non-productive  investment  has  no  impact  signifi 
cantly different from zero on private investment. 

We can also predict the long run impact of public 
investment  on  employment.  Because  Augment-
ed-Dickey  Fuller  tests  tend  to  indicate  that  em 
ployment  is non-stationary,  the VAR  is estimated 

with  the  first  difference  of  employment.  The re 
sults  are  summarized  in Table  4.2  and  in  Figure 
4.2.  We also  show  the  results  of the  estimate of 
the impact  of a shock  on  private capital  stock  to 
employment.  The  elasticity  of  the  impact  of  pri 
vate  investment  on  employment  is  smaller  than 
the corresponding elasticity for public  investment. 
To get a consistent  comparison, we have to  mul 
tiply  the  elasticities  by  the  average  size  of  each 
kind  of  investment.  In this  case,  it  appears  that 
public  investment  still  has  a  larger  impact,  since 
the ratio of private to  public  investment  is  1.44  in 
average over the period. 

TABLE 4.2.  Short-run and long-run estimated impact of pubi 
GLS 2 steps estimators in panel, weights=cross-sectional 

c spending, 
variance 

Shock on 

Total  Public  Investment 
(without  fixed  effects,  1988-93,  49  obs.) 

Business  Investment 
(without  fixed  effects,  1988-93,  49  obs.) 

Employment 

in the short-run 

Employment 

in the long-run 

Elasticities1 

Elasticities 

0 . 1 7 0" 
[0.09] 

- 0 . 32 
[0.52] 

0 . 1 2 6" 
[0.06] 

0.017 
[0.03] 

'  The columns headed "Elasticities" report the elasticity estimates from the logarithmic  regression in the text. 

(short-run: weighted standard errors  in parentheses  ; long-run: standard errors computed from the covariance  matrix  on the esti 
mators, *, **, "** significantly different from 0 at the  10, 5 and 1 % level) 

Do poor  regions  benefit  more from  public  invest 
ment  than  rich  ones?  As  with  Spain  and  Portu 
gal,  we  check  if  our  representative  region  con 
cept  is useful  by  looking  at the size of the resid 
uals  for  each  region. Table 4.3  (column  2)  gives 
the  ratio of the  standard  deviation  of  the residu 
als for the  region over the standard  deviation for 
all the  residuals. A value  below  one  implies  that 
the  region  is  close  to  the  representative  region. 
A value above  one  implies that  the  region  is dif 
ferent  from  the  representative  one.  Again,  for 
three  regions  only,  there  is  a  value  significantly 
above  one:  Anatoli  Makedonia  & Thrakl,  Sterea 
Ellada  and  Borio  Aigaio.  Second,  we  estimate 
region by region, as for Spain, the long-run elas 
ticity  of  public  and  cumulated  private  invest 

ment, with the  restriction that  the standard  error 
provided  in the cells is only valid asymptotically1. 
Again,  we  learn  less  from  the  region  by  region 
estimations,  than  from  the  aggregated  system. 
We can divide the  panel of  13 Greek  regions in 
to two  groups, the  7 poorest,  and the 6 richest, 
and  run the  previous  regressions  for  each these 
subgroups.  The  results  are  different  from  the 
Spanish  ones: for  Greece  it  is found  that  public 
investment  generates  slightly  more  private  in 
vestment  in the bottom  half of the regions. How 
ever, when we multiply the elasticities  by the av-

As explained  in Appendix  5 and in Section  2, when the sample size 
is small, we  cannot  make the assumption  of  normality  of  the coeffi 
cients and the formula for the variance of the long run limit is itself bi 
ased. 

102 

Section III —  Vector Auto-regression Models 

erage  ratio  of  business  over  public  investment 
(1.56  in the  richest  regions,  1.35  in the poorest), 
one third of the differences  in the elasticities dis­
appear.  Given  the  standard  error  (for  the  gross 

numbers,  0.18  for  the  richest,  and  0.11  for  the 
poorest),  the  impact  of  public  investment  is  still 
stronger  in the poorest  regions. See table 6.4 for 
a summary. 

TABLE 4.3. 

Relative standard deviation of residuals, 

by region 

VAR Total Public Investment  and Private 
Investment 

Anatoli  Makedonia  and  Thraki 

Kentriki  Makedonia 

Dytiki  Makedonia 

Thessalia 

Ipeiros 

Ionia  Islands 

Dytiki  Ellada 

Sterea  Ellada 

Attiki 

Peloponnisos 

Borio  Aigaio 

Notio  Aigaio 

Kriti 

(standard errors computed from the 

covariance matrix on the estimators) 

σ ί /σ 

1.95 

0.19 

0.98 

0.21 

0.69 

0.91 

0.72 

1.65 

0.20 

1.03 

1.27 

0.78 

0.53 

Long-Run Effect 
Estimated Individually 

1.09  [0.62] 

0.477  [0.40] 

- 1 . 18  [0.46] 

1.11  [0.39] 

- 0 . 1 91  [0.05] 

0.680(0.16] 

0.220  [0.03] 

- 0 . 3 07  [0.12] 

0.505  [0.10] 

0.183  [0.05] 

0.748(0.13] 

0.626  [0.08] 

0.678  [0.04] 

TABLE 4.4.  Long-run estimated impact on private investment of public spending, 
subgroups of regions, GLS 2 step estimators in panel, 
weights=cross-sectional  variance 

Shock on different groups of regions 

Elasticities on private investment (SR) 
and cumulated private investment (LR) 

Poor (84 obs.) 

Rich (72 obs.) 

Total  Public  Investment  (1lag,  1980-93,  with  fixed 
effects)  Short  Run 

Total  Public  Investment  (1  lag,  1980-93,  with 
fixed  effects)  Long  Run 

0 . 3 4 5 "* 
[0.05] 

0.765*** 
[0.08] 

0.039 
[0.033] 

0.329*** 
[0.12] 

(short  run: weighted standard errors  ; long-run: standard 
significantly different from 0 at the 10, 5 and 1 % level) 

error: 

>  computed from the covariance  matrix on the estimators,  *, **, ""* 

Modelling Results 

103 

1.4.2.  Impact of labour demand shocks 

We  consider  the  dynamic  effect  of  labour  de 
mand  shocks  on  activity  and  population. Again, 
we estimate the VAR model described  in  1.7,  for 
the  three  variables  Employment,  Activity  Rate 
and  15-64 Population. 

The  VAR  has  two  lags,  and  ¡s  estimated  as  a 
panel  without  fixed  regional  effects,  given  the 

length  of  longitudinal  (time)  dimension.  The  re 
gressions  when  activity  and  population  are  the 
exogenous  variables  allow  for  an  immediate  ef 
fect  of  employment.  From  these  impulse  re 
sponses,  it  is  possible  to  estimate  the  effects  of 
the  employment  shocks  on  the  gross  numbers 
(variation  in  active  population,  unemployment, 
15-64  population).  The  results  are  displayed  in 
Figure  4.3.  The  elasticities  are  summarised  in 
Table 4.5: 

TABLE 4.5.  Impulse Response of Labour Market Variables to 
Summary. 13 Regions, GLS 2 step estimator in 

Employment Shocks, 
panel, 

weights=cross- sectional variance 

VAR 

Number  of  Observations 

Short-run  elasticity  of 

Activity 

15-64  Population 

Long-run  Elasticity  of 

Employment 

Activity 

15-64  Population 

Total 

62 

- 0 . 1 93  [ 0 . 0 5 8 ] "* 

0.470  [0.073]  "* 

1.03 

-0.081 

0.504 

(short-run: weighted t-stat  in parentheses  ; long-run 
*"  significantly different from 0 at the  10, 5 and 1 % 

: standard 
evel) 

errors computed from the covariance matrix on the estimators. ',  ". 

We see here that activity slightly decreases with a 
positive  employment  shock,  which  is  consistent 
with  the  existence  of  an  added-worker  effect:  in 
downturns,  households  reduce  the  impact  of  a 
loss of income by supplying more hours, with ad 
ditional workers. This is a characteristic feature of 
poorer countries or poorer fractions of population 
(see  Mincer  (1962)  for  instance).  In the  long  run 
however, activity  rates do not exhibit the effect of 
the initial shock, contrary to Spain. 

Mobility  is on  average  more  important  in Greece 
than  in Spain  as a supply  of  labour  response. In 
conclusion,  population  changes  are  significantly 
correlated to  employment  changes, while  activity 
seems to play a minor role in Greece. 

1.4. Conclusions for Greece 

Our  empirical  results  for  Greece  support  the  fol 
lowing conclusions 

1.  Private investment is positively and significant 
ly  correlated  with  total  and  productive  public 
spending,  both  in  the  short-run  and  in  the 
long-run.  In  contrast,  non-productive  invest 
ment  has  no  significant  Impact  on  private in 
vestment. 

Public  Investment  generates  more  private  in 
vestment in the poorest regions. 

Employment  shocks  have  little  affect  on  re 
gional  activity,  most  of the shock  is absorbed 
by population and unemployment changes. 

104 

Section I 

Vector Auto-regression Models 

Figure 4.1:  Response of  Cummulative  Private  Business Investment to a 1% Shock to Total  Public  Investment 

Greece 

Years 

/  Total  Public  Investment  Shock 

;0,60% 

0,50% 

0,10% 

0,00% 

o 
D. 
ω 

CD 
DD 

o 
αϊ 

o 
CD 

CO 
CD 

I 

o 
o 
> 

Greece 

Figure 4.2:  Response of  (a) Employment  (b) Activity  Rate and (c) Population to  a 1%  Shock  in  Employment 

Years 

• 

Total  Employment 

-  β  -  Total Activity  Rate 

——Α —Total  Population 

1.5.  Ireland 

The  country  was  divided  into  8  units:  East, 
South-East,  South-West,  Mid-West,  West,  Mid­
lands,  and  Donegal&Northwest  and  North-East, 
the  last  two  being  sometimes  aggregated  into  a 
region called Border to fit with Eurostat data. 

Regional  Public  Investment  data are available for 
each  year  between  1976  and  1995.  Public  In­
vestment is disaggregated into 8 subdivisions: 

1.  Housing and Building 

2.  Roads, Transport and Safety 

3.  Water Supply and Sewerage 

4.  Development  Incentives and Control 

5.  Environmental Protection 

6.  Recreation and Amenity 

7.  Education, Health and Welfare 

8.  Miscellaneous Services 

Disaggregated  estimations  were  not  successful, 
so we aggregated  1., 2., 3.  and 4. to  get  a vari­
able that  we called  Productive  Public  Investment 
(68%  of  Total  Public  Investment).  This  variable 
should be consistent with the Spanish definitions. 

Regional  Private Capital  Stocks  data are available 
between  1979  and  1990,  for  the  Manufacturing 
Sector. We  have  used two  alternative  series  as a 
proxy for Business Investment, along the lines of the 
previous  estimations  for  other  countries.  First, we 
have used the Acquisitions of Capital Assets (BIT2), 
and  second  the  Net  Additions  to  Capital  Assets 
(BIT3)1. 

From  Eurostat,  we  have  Population  data  for  the 
period  1977-93,  and  data  from  Labour  Force 
Surveys for  Employment,  Unemployment  (84-95) 
and Wages (81-91). 

"Stocks"  are  equivalent  to  the  American  "Inventories".  Stock 
changes are calculated by subtracting the value of stocks at the be­
ginning  of  the year from the value at the end of  the year.  In general 
the value of  stocks  at the end of  one year does  not  equal the value 
at the beginning of the next. This is due to  reclassification  of estab­
lishments from one NACE category  to  another, changes  in reporting 
year,  commencements  or  cessations  of  business,  etc;  small  differ­
ences may also arise because of the method of estimation for small 
establishments and non-respondents. Capital assets (land, buildings, 
plant  and equipment)  are defined as goods with  an expected  useful 
life of  more than one year, intended for  use by the establishment  it­
self.  Acquisitions  include  purchases  from  other  establishments  and 
production  by the establishment  itself of capital for its own use. Ma­
jor alterations, improvements and repairs that extend the useful life of 
an asset  or  increase its  productivity  are included. The value of  work 
put  in  place  during  the year  is  included  whether  or  not  completed. 
Acquisitions  are  valued  at  total  cost  Including  installation  charges 
and fees or duties  but  excluding deductible VAT and financial costs. 
Sales are valued at the price actually received excluding VAT. 

1.5.1. Dynamic relations between 

public investment, private investment 
and private capital stock 
at the regional level. 

We  faced  a  few  difficulties  in  applying  the 
methodology that  we use for the other countries. 
First,  the  ratio  of  Manufacturing  Private  Invest­
ment  over  Total  Public  Investment  is  0.68.  In 
Spain,  this  ratio  was  7.4.  The  difference  arises 
from two factors.  First, Manufacturing  Investment 
is  smaller  than  Total  Private  Sector  Investment 
because Agriculture  and Services  are not includ­
ed.  Second,  the  definition  of  Public  Investment 
may  be  broader  in  Ireland than  in  Spain.  For in­
stance,  in  the  category  4,  Development  Incen­
tives  and  Controls,  we  find  Representational 
Functions,  Promotion  of the  Interest of the Com­
munity,  Twinning  of  Local  Authorities  Areas,  and 
Administration  & Miscellaneous,  that  account  for 
one  third  to  one  half  of  the  total.  With  the  de­
scriptions of the data available to us, we were not 
able  to  decide  which  category  to  use  as  Public 
Investment,  so  we  used the  aggregate  spending 
with the restriction that the definition covers more 
than in the case of Spain. 

Another  problem  encountered  was  the  high 
cyclicity  of  manufacturing  investment,  as  shown 
in the Figures 5.1  and 5.2, which  show the loga­
rithm of the share of the regions in manufacturing 
investment. This high cyclical component was re­
moved in a standard way, by filtering the series of 
business  investment  with a Hodricks-Prescott  fil­
ter2.  The  variables  HPBIT2  and  HPBIT3  are  the 
series  minus  their  cyclical  component,  taken  in 
logarithms  of  their  share  in  the  national  aggre­
gate. 

We then estimate the bivariate VAR between rela­
tive  public  investment  and  private  sector  invest­
ment. We estimate the equations: 

PI, = C + a-PI,_1 +β·Β/,_7  +mp 

and 

Bl,  =C  + 8-PI,_1+X-BI,_1+y-PI,  +μ,β 

where γ represents the immediate effect of public 
investment  on  business  investment.  The  results 
are summarized in Table 5.1. 

The  parameter  λ  was  chosen  equal  to  100,  which  is  the  standard 
yearly value to cut the business cycle frequencies. 

Modelling  Results 

107 

TABLE 5.1.  Short-run and long-run estimated impact of public spending, 
GLS 2 steps estimators in panel, weights = cross-sectional variance 

Shock on HPBIT2 (Gross Capital Changes) 

in the short-run 
Ol 

in the long-run 

in the short-run 

in the long-run 

Private 
Investment 

Private Capital 
Stock 

Private 
Investment 

Private Capital 
Stock 

Elasticities' 

Elasticities 

Gross Number 

Gross Number 

Total  Investment  (1  lag, 88  obs.) 

Productive  Public  Investment 
(1  lag, 88  obs.) 

0.026 
[0.021] 

0.011 
[0.007] 

1 . 2 1 *" 
[0.45] 

0 . 5 1 5 "" 
[0.20] 

0.017 

0.011 

0.76 

0.47 

1  The columns headed "Elasticities" report the elasticity estimates from the logarithmic  regression in the text. The columns headed 
"Gross Numbers" report  the elasticity  times the ratio of  Business  to  Public  Investment, which is thought  as the number  of  units 
of spending of private investment  or capital stock following an increase in one unit of public spending. 

(short-run: weighted standard errors  in parentheses  : long-run: standard errors computed  from the covariance matrix on the esti 
mators. ',  ",  '"  significantly different  from 0 at the 10, 5 and 1 % level) 

The  numbers  reported  in  the  table  are  the  im 
pulse  response  to  a one  unit  shock  on  public in 
vestment,  in  the  short  and  the  long-run.  Private 
investment  has a positive  and significant  correla 
tion  with  total  and  productive  public  spending, 
both  in  the  short-run  and  in  the  long-run.  The 
disaggregation  of  public  investment  into  produc 

tive  investment  Including  the  first  four  categories 
of  public  investment  leads  to  smaller  impact  on 
private investment. 

The  alternative  definition  of  business  investment 
HPBIT3  yields  similar  results  reported  ¡n Table 
5.2: 

TABLE 5.2.  Short-run and long-run estimated  impact of public spending, 
GLS 2 steps estimators in panel, weights=cross-sectional  variance 

Shock on HPBIT3 (Gross Capital Changes) 

in the short-run 
(H 

in the long-run 

in the short-run 

in the long-run 

Private 
Investment 

Private Capital 
Stock 

Private 
Investment 

Private Capital 
Stock 

Elasticities' 

Elasticities 

Gross Number 

Gross Number 

Total  Investment  (1  lag, 88  obs.) 

Productive  Public  Investment 
(1  lag, 88  obs.) 

0.038 
[0.021] 

0.011 
[0.008] 

1.44"* 
[0.45] 

0 . 5 7 2 *" 
[0.22] 

0.026 

0.010 

0.99 

0.57 

'  The columns headed "Elasticities" report the elasticity estimates from the logarithmic  regression in the text. The columns headed 
"Gross Numbers"  report the elasticity  times the ratio of  Business to  Public  Investment, which is thought  as the number of units 
of spending of private investment or capital steck following an increase in one unit of public spending. 

(short-run: weighted  standard  errors  in parentheses:  long-run: standard errors  computed  from  the covariance  matrix  on the esti 
mators, ',  '",  '"  significantly different from 0 at the  10, 5 and  1% level) 

108 

Section I 

Vector  Auto-regression  Models 

We have not been able to find a positive and sig 
nificant  impact  of  public  investment  spending  on 
employment  in the case of  Ireland. There are two 
main reasons. The first one, ¡s that the sample of 
employment  data was  Insufficient  to  allow accu 
rate  estimation  of  the  model. We  have  not  been 
able to use longer time-series of national employ 
ment  data to  impute  regional employment  over a 
longer  period. This  method  would  not  have al 
lowed us to identify the Impact of regional shocks 
on the regional economies. The second  reason is 
more  specific  to  the  nature  of  our  model  which 
does  not  include  spillovers  across  regions.  It ¡s 
likely that  part  of the employment  effect  generat 
ed  by  public  investment  spending  is  located  in 
other regions, which prevents  us from finding any 
significant  effect.  This  problem  was  unimportant 
in the case of  Spain and Greece, since the aver 
age size of the regions considered was sufficient 

ly  large. This  problem  is  certainly  very  significant 
for  Ireland since the country  is divided  into  much 
smaller  units.  Bean's  methodology  (inclusive  of 
spillovers) should lead to better estimates. 

1.5.2.  Impact of labour demand shocks 

Again,  we  estimate  the  VAR  model  described  in 
(8)  for  the  three  variables  Employment,  Activity 
Rate and  Population. The VAR has two  lags, and 
is a panel with fixed  regional effects. The regres 
sions  where  activity  and  population  are  the  ex 
ogenous  variables  allow  for  an  immediate  effect 
of employment.  From these Impulse responses, it 
is possible to estimate the effects  of the employ 
ment  shocks  on  the  gross  numbers  (variation  in 
active  population,  unemployment,  15-64  popula 
tion).  The  elasticities  and  gross  numbers  effects 
are summarized in Table 5.1 : 

TABLE 5.3.  Impulse Response of Labour Market Variables to Employment Shocks, 
Summary. 13 Regions, GLS 2 steps estimator in panel, 
weights=cross- sectional variance 

VAR 

Number  of  Observations 

Short-run  elasticity  of 

Activity 

Population 

Long-run  Elasticity  of 

Employment 

Activity 

Population 

Total 

52 

0.569  [0.047]*" 

-0.036  [0.029]* 

0.501 

0.269 

-0.050 

(short-run: weighted t-stat in parentheses ; long-run: standard errors computed from the covariance matrix on the estimators,  ', 
'"  significantly different from 0 at the 10, 5 and 1 % level) 

We  see  here  that  first  employment  shocks  are 
much  less  persistent,  second  that  activity  in 
creases  with  these  positive  employment  shock. 
Finally,  migration  seems  to  fail  to  adjust  to  the 
new  shock,  given  the  lack  of  a  positive  correla 
tion between employment  and population. 

With  respect  to  the  other  Cohesion  countries, 
this  denotes  a  lack  of  mobility,  maybe  due  to 
the  small  size  of  the  regions  in  Ireland:  workers 
in  regions  facing  decreasing  employment  are 
still  close  from  more  economically  active  re 
gions. 

Modelling  Results 

109 

Figure 5.1. 
Manufacturing: Acquisitions of Capital Asset by Regions 

^

—

—-

V 

N- 

^ 

"  \ 

/ 

/ 

\ 

/ 

ν 

\ 

^^ 

v.—-

x- 

^ 

I 

I 

Ι 

ι 

ι 

ι 

ι 

ι 

ι 

ι 

ι 

ι 

I 

I 

I 

I 

I 

I 

1.0-

1.5-

2.0-

2.5-

3.0-

3.5-

40 

76 

78 

80 

82 

84 

86 

88 

90 

92 

94 

BIT2E 
BIT2MID 
BIT2MW 

BIT2NE 
BIT2NW 
BIT2SE 

—  BIT2SW 
—  BIT2W 

110 

Section III —  Vector Auto-regression Models 

Figure 5.2. 
Manufacturing: Net Additions To Capital Assets by Regions 

■0.5 

■1.0-1 

■1.5 

-2.0 

-2.5 

-3.0 

-3.5 

-4.0 

τ 1 1 1 Γ 

76 78 80 82 84 86 88 90 92 94 

BIT3E 
BIT3MID 
BIT3MW 

BIT3NE 
BIT3NW 
BIT3SE 

BIT3SW 
BIT3W 

Modelling Results 

111 

2, Simulation Results 

2.1.  Introduction 

In this  second  part,  the  results  from  the  econo 
metric  analysis  outlined  in the  previous  sections 
are  used to  simulate  the  impact  of  selected  pro 
jects  financed  by  the  Cohesion  Fund  on  the  re 
gional economic environment. We briefly recall the 
results from the previous part.  Despite  differences 
¡n the  coverage  of  public  Investment  data  in the 
four  Cohesion  countries,  we  showed  a  robust, 
positive  and  significant  impact  of  public  invest 
ment  on  private  investment  and  employment, 
both  in  the  short  and  the  long  run.  For  Spain, 
where  the  most  comprehensive  data  set  was 
available,  our  results  indicated  that  infrastructure 
investment  (roads,  motorways  and  urban  struc 
tures)  had the strongest  impact, while other types 
of  Investment,  most  noticeably  non-productive 
public  Investment  (that  is,  health  and  education) 
had less significant impact in the long run. This re 
sult was also supported  in the other cases where 
disaggregation, according to  productive and non 
productive public investment was possible. 

We then investigated how the regional labour mar 
kets  were affected  by  employment  shocks  result 
ing from the Increase in public  Investment and the 
induced  increase  ¡n private  investment.  In most of 
the  cases,  employment  rose,  fuelled  by  higher 
labour force  participation  and immigration  Into the 
region.  Again, this  increase  occurred  in the  short 
and  the  long  run. Given  data  limitations,  weaker 
correlations  were  sometimes  found, as  ¡n Greece 
between  employment  and  activity  rates  and  ¡n 
Portugal between employment and population. 

This part consists of 7 additional sections. In Sec 
tion  2,  we  describe  our  approach  to  the  evalua 
tion  of  the  projects.  Section  3  details  the evalua 
tion  process  and summarizes the  main results. In 
Sections 4-7 we then report the results of the ac 
tual evaluations  for the  projects  in Spain, Greece, 
Portugal,  and  Ireland  respectively.  Section  8 
makes  some  further  comments  with  respect  to 
the simulation of labour market variables. 

2.2. Evaluation  Strategy 

We  have  estimated  the  coefficients  of  reduced 
form economic  relations  in the  previous sections, 
using  aggregated  data  at  the  NUTS2  or  NUTS3 
level  according  to  availability.  We  now  use  these 
results to  forecast  the  Impact  of  specific  projects 
financed  by the  Cohesion  Fund, which  represent 
an  increase  in  regional  public  spending. The im 
pact  on  regional  business  Investment  and  labour 
supply  is  evaluated. As  the  impact  of  the  whole 
project  on  the  region  has  to  be  evaluated,  we 
consider  total  spending  on  the  project,  rather 
than  the  85%  of  total  expenditure  which  is fi 
nanced by the Cohesion Fund. 

Guided  by  the  availability  of  the  data,  we  have 
proceeded  in the  following  way  for  the Spanish, 
Portuguese,  Greek  and  Irish  project  evaluations: 
For  all the  projects,  we  provide the  forecasts  on 
the  basis  of  results  obtained  from  the  national 
models  that  were  estimated  across  regions  of  a 
country, to  capture  specific  national structural  ef 
fects.  The  relationship  between  total  public  in 
vestment,  business  investment  and  employment 

Simulation Results 

113 

is estimated  for  each country,  with the  exception 
of  Ireland  where  the  relationship  between  public 
investment  and  employment  cannot  be  estimat 
ed  because  of  the  lack  of  sufficient  employment 
data.  Initially,  the  projects  in  each  country  are 
evaluated  using  the  national  coefficients  coming 
from these sets of estimations. 

given  the  differences  in the  definition  and  cover 
age of public investment across countries, such a 
panel  would  be  meaningless.  However,  pooling 
across  countries  has been employed to  estimate 
the  coefficients  for  the trivariate  system  Employ 
ment,  Activity  and  Population,  given  the  greater 
similarities across countries. 

Since the Spanish data set  is the most  complete 
of the four Cohesion countries, we are able to ex 
tend the model to include the following additional 
version for  more  detailed analysis of the  Spanish 
projects.  For the first scenario, a project  is evalu 
ated using the elasticities provided by estimations 
based  on  data  for  total  public  investment  in 
Spain.  Disaggregated  data then allow the follow 
ing variation: Scenario Two estimates the elastici 
ties  again,  now  using  data  sets for  total  produc 
tive public  investment.  For the two  scenarios, re 
lationships  between  these  public  investment  ag 
gregates,  business  investment  and  employment 
are  estimated.  The  resulting  elasticities  thus  re 
flect  the  nature  of  the  project  in  greater  magni 
tude.  However,  this  improvement  is also accom 
panied  by  a  loss  in  precision  of  the  coefficients, 
as the  basis  upon  which  they  are estimated  be 
comes  more  narrow.  This  fact  must  be  kept  in 
mind  when  interpreting  the  results.  For  the  mo 
ment,  project  evaluation  taking  this  form  is  only 
possible  for  Spain, for  which  homogeneous  and 
disaggregated data is available. 

The  approach  adopted  for  the  evaluation  of  the 
Greek  and  Portuguese  projects  is a  result  of  the 
more  limited  data  availability  (notably  no  disag 
gregation  of  public  spending  data)  and  the  re 
stricted time frame of this report. 

For  Greece  and  Portugal, we  use the  aggregate 
public  investment  model  (Scenario  1) to  evaluate 
the  impact  of  both  road  and  environmental  pro 
jects.  Results  obtained  are  only  approximate, 
since the  Spanish  results  based  on  disaggregat 
ed data show that there are strong  differences  in 
the intensity of impacts of projects once they can 
be differentiated  by investment type. An addition 
al scenario for these countries is created by pool 
ing the  data for  all four  countries  in order  to  de 
crease the variance on the estimated coefficients. 
Here,  a further  set  of  elasticities  is added  to  the 
ones estimated  on a solely  national  basis. These 
new coefficients then provide the input for an ad 
ditional model variation. This pooling  method has 
not  been  used  in  the  case  of  the  bivariate  sys 
tems  including  public  investment  spending since, 

The  Irish  project  simulations  are  still  largely  con 
strained  by  the  incomplete  data  set  available  for 
this country, notably by the lack of regional data for 
GDP. Only one scenario based on estimates of the 
relationship between public investment and private 
investment in manufacturing was simulated. 

2.3. The Impact  of the  Projects 

Financed  by the Cohesion  Fund 

2.3.1.  Methodology 

Having  specified  models  of  the  relationship  be 
tween  public  investment,  business  investment 
and  labour force variables  using coefficients  esti 
mated  as described  in the first  part, we  proceed 
in the following manner: 

Spending  on  each  project  is  considered  to  be a 
shock to  regional  public  investment  as compared 
to average regional public spending. In its first ap 
plication, the model evaluates the impact of these 
shocks on business investment in that region. The 
VAR  approach  allows  for  estimation  not  only  of 
the immediate effect that  occurs during the years 
of  actual  project  spending,  but  also  of  the  long 
term  effect  on  private capital stock.  In its  second 
stage of manipulation, the model is used to evalu 
ate the impact of the public investment shocks on 
regional  employment.  Movements  caused  in the 
regional  employment  variable  are  then  used  as 
the  initial shock  occurring  in the trivariate  system 
evolving  around  the  response  of  employment, 
population and activity rates to this shock. 

For  reasons  of  econometric  robustness,  models 
for  each  country  are  estimated  using  panel  data 
specific  to that  country. The elasticities thus refer 
to  a  "representative  region"  in  each  country. 
Hence,  impacts  of  total  project  spending  are 
evaluated  on  the  basis  of  these  representative 
elasticities combined with the region specific data 
for  GDP,  employment,  population  and  activity 
rates.  In this  way, the  project  investment  can  be 
evaluated in the context of the economic  charac-

114 

Section III — Vector Auto-regression Models 

teristics of the region in which  it is located. How 
ever,  we  stress  once  again  that  our  evaluations 
are restricted  by the data we have available. This 
is  particularly  relevant  for  Portugal  and  Ireland 
and to a lesser extent, Greece. 

2.3.2. Evaluation of the  Percentage 
Increase in Public  Investment 
Caused by the Projects 

The projects evaluated in these sections are 

-  Spain: Madrid Ring Road  (93/65/010+modif, 

94/65/011,  93/64/017) 

Rias Bajas Motorway (93/65/002, 94/65/002, 
95/65/004) 

-  Greece: Spata Airport 

Evinos Dam (93/09/61/011,  93/09/61/074 + 
modif,  94/09/61/011-1) 

-  Portugal: Tagus Crossing  (94/10/65/005) 

- 

Ireland:  North-South  Road Link 
(93/07/65/007-010,029,032,036; 
94/07/65/007) 

North-South  Rail Link (93/07/65/018,019-23-
24-25+modif.) 

Evaluation  on the  Odeleite-Beliche  Water  Supply 
project  in  the  Portuguese  Algrave  region  has 
been undertaken. However,  as some pivotal data 
are  still  missing  results  for  this  project  have  not 
been  included.  The  evaluation  of  the  two  Irish 
projects  has  to  be  interpreted  with  caution  be 
cause of limitations  in the  regional data available. 
Notably  the  lack  of  regional  GDP  data  prevents 
the  evaluation  of  the  Irish  projects  within  the 
same formal framework. The summarizing  results 
reported  below  refer  therefore  only  to  the Span 
ish, Portuguese and Greek projects. 

The projects have been evaluated with respect to 
their  total  eligible  costs,  85%  of  which  are fi 
nanced by the Cohesion Fund. Project size varies 
between  0.43%  and  1.88%  of  regional  GDP, the 
smallest  project  in  terms  of  this  measure  being 
the  Madrid  Ring  Road,  the  largest,  the  Tagus 
Crossing in Lisbon. 

10.8%  in  Greece  (See  Figures  3.1  to  3.3  for 
these  cross-country  comparisons  of  project 
spending).  Therefore,  the  size  of  the  projects 
should be compared on the basis of their relative 
magnitudes  with  respect  to  GDP  and  public 
spending. The magnitude of a project's  impact is 
seen as a shock to  regional  public  spending that 
also  depends  on  the  size of  the  country's  public 
sector.  For  example,  for  similar  project  sizes 
(Madrid  Ring  Road:  303.87  MECUs;  Spata  Air 
port:  292.12  MECUs),  total  spending  on  the 
Madrid  Ring  Road  represents  0.43%  of  Madrid's 
GDP, and  0.83%  of  Athen's  GDP. The  difference 
obviously reflects the higher GDP of Madrid. Nev 
ertheless, as a shock  to  average  annual  regional 
public  spending,  the  total  spending  on  the 
Madrid  Ring  Road  represents  a  18.7% increase, 
yet  the  shock  to  spending  due to  the  Spata Air 
port  is only 7.5%. The contrast  in relative magni 
tude  of these two  shocks  hence  reflects  the dif 
ference  in  size  between  the  Spanish  and  the 
Greek public sector (see also Figures 3.1 to 3.3). 

2.3.3. Evaluation of the Impact 
on Business Investment 

The impact  of the  public  spending  shocks  on the 
private capital stock (the long run effect) varied be 
tween 3% (Madrid Ring Road) and  10% (Rias Ba 
jas  Motorway).  This  last  project  was  also  among 
the  largest  in terms  of  percentage  expenditure  of 
GDP, with  total  spending  representing  1.58%  of 
the regional GDP of Galicia and Castilla y Leon. 

2.3.4. Evaluation of Project  Impact 

on Labour Markets 

The  number  of  jobs  created  varies  between 
3,000  and  20,000, the  extreme  values  occurring 
in  the  Greek  and  notably  the  Portuguese  case. 
However,  the  flawed  and  incomplete  data  for 
these  countries  recommend  caution  in the  inter 
pretation of these results. 

The  results  of  the  simulations  show  that  this job 
creation will  reduce unemployment,  lead to  inter 
regional immigration and cause an increase in the 
activity  rate.  This  result  has  been  obtained  and 
discussed in the first part of our contribution. 

The  magnitude  of  the  projects  with  respect  to 
public investment must be interpreted accounting 
for  the  different  sizes  of  the  public  sector  in the 
three  countries.  Public  spending  in  Spain  is,  on 
average,  2.3%  of  GDP,  3.1% in  Portugal,  and 

No  analysis  in  terms  of  skilled  and  unskilled 
workers  has  been  done  for  this  report.  Such  an 
investigation  could be undertaken for future eval 
uations  as far  as  Spain  is concerned; the possi 
bilities for other countries are less certain. 

Simulation Results 

115 

05 

o 

> 
c 
Õ 

o 
Q. 
CD 

Spain: 

Average PI/GDP 
Average  PIPT/GDP 
Average pipm/GDP 

Madrid Ring Road 

Project  Spending  (Millions  of  ECUs) 

1993 
1994 
1995 
1996 

Total  Spending 
Regional  GDP 
Total  Spending  as  % of  Regional  GDP 

Rias Baias Motorway 

Project Spending  (Millions of ECUs) 

1993 
1995 
1996 
1997 
1998 

Total  Spending 
Regional  GDP 
Total  Spending  as  % of  Regional  GDP 

0,023 
0,01771 
0,00184 

167,96 
71,88 
51,65 
12,38 

303,87 
70  512,20 
0,43% 

23,44 
156,75 
326,48 
217,87 
61,93 

786,47 
49  758,70 
1,58% 

Total Project Spending in Millions of ECUs 

Figure 3.1 

Rias  Bajas  Motorway 

Madrid  Ring  Road 

100 

200 

300 

400 

500 

600 

700 

~y 
800 

Total Project Spending as a Percentage of Regional 
GDP 

Rias  Bajas 
Motorway 

Madrid  Ring 
Road 

w 
3 
c_ 
S. 
õ' 
=3 

φ 

Greece: 

Average PI/GDP 

Figure 3.2 

0,108 

Total Project Spending in Millions of ECUs 

Evinos Dam 

Project Spending (Millions of ECUs) 

1993 
1994 
1995 

105,35 
107,58 
4,51 

Total Spending 
Regional GDP 
Total Spending as % of Regional GDP 

217,44 
35 796,46 
0,61% 

Spata Airport 

Project Spending (Millions of ECUs) 

1996 
1997 
1998 
1999 

16,41 
56,85 
101,39 
117,47 

Total Spending 
Regional GDP 
Total Spending as % of Regional GDP 

292,12 
35 796,46 
0,82% 

Λ 

Spata Airport 

Zl 

Evinos Dam 

100 200 300 400 500 600 700 800 

Total Project Spending as a Percentage of Regional 
GDP 

2,00% ' 

1,60% 

1,20% 

0,80% 

0,40% 

0,00% 

Evinos Dam 

Spata Airport 

Portugal: 

Average  PI/GDP 

0,03089006 

Total  Project Spending  in Millions  of  ECUs 

Figure 3.3 

Taqus  Crossinq 

Project  Spending  (Millions  of  ECUs) 

1995 
1996 
1997 

209,52 
122,15 
34,51 

Total  Spending  on Project 
Regional GDP 
Total  Spending  as % of GDP 

366,18 
19  426,63 
1,88% 

Tagus Crossine 

V 

100 

200 

300 

400 

500 

600 

700 

y 

800 

Total  Project Spending as a Percentage of  Regional 
GDP 

Tagus 
Crossing 

CO 

c? o 

> 
c 

o 
α 

CD 

y? 
3 
ç_ 
S 
õ' 

13 
1} 
CD 
ω 
c_ 
ω 

CD 

Figure 3.4 

Spending on Madrid Ring Road as a 
Percentage of Regional GDP 

Spending on Evinos Dam as 
Percentage of Regional G DP 

Spending on Tagus Crossing as a 
Percentage of Total Regional GDP 

0,7% ·' 

1,2% ' 

1993 

1994 

1995 

1996 

1993 

1995 

1996 

1995 

Spending on Rias Bajas Motorway 
as Percentage of Regional GDP 

Spending on Spata Airport as a 
Percentage of Regional GDP 

0,7% 

0,6% 

0,5% 

0,4% 

0,3% 

0,2% 

0,1% 

0,0% 

0,7% 

0,0% 

1993 

1995 

1996 

1997 

1998 

1996 

1997 

1998 

1999 

L 

2.4. Spain 

2.4.1. The Madrid  Ring Road 

The  project  is located  in the outskirts  of Madrid. 
Spending  on the project  takes  place  between 
1993  and  1996  and  represents a total  amount of 
303.87  MECU. This amounts to 0.43% of regional 
GDP in 1992. (Refer to Figure 3.4 for an illustration 
of the spending pattern over the four year period.) 

1 st Scenario:  Spending as a Percentage of Total 

Regional Public Investment 

Given that total average public  spending in Spain 
represents  approximately  2.3%  of  GDP,  total 
spending on the project amounts to 18.7% of av 
erage  annual  public  spending  in the reg on.  Fig 
ure  4.1  A1 illustrates  the spending  pattern  as a 
percentage  of annual  regional  public  spending 
over the four year period. 

The  first  scenario  considers  annual  project 
spending  compared  to the average  total  public 
spending  as the regional  shocks.  The shocks 
vary  between  0.76 and 10.4%  (Figure  4.1 A1) 
and  cause  an immediate  response  in  regional 
business investment of up to 0.5%. The cumulat 
ed long term effect on business investment  is 3% 
(Figure 4.1  B1). The additional  public  and  private 
investment  has according  to our simulation the 
following  positive  impact  on employment  ¡n  the 
region:  in the long  term,  approximately  2,900 
jobs  were created by the  project  (Figure 4.1  C1). 
This  increase  in employment  can partly  be  ac 
counted for by an increase in the activity rate and 
employment,  and partly  by immigration  into the 
region.  If requested,  a trivariate  model  involving 
the  variables  Employment,  Unemployment and 
Activity  could  be estimated  for future  reports. 
This  would  then  permit  a more  accurate  evalua 
tion of the impact of public  investment  shocks on 
unemployment of the region. 

2nd Scenario: Spending as a Percentage of 

Regional Productive Public 
Investment 

Knowing  that  total  public  productive  spending in 
Spain  represents  approximately  1.7% of GDP, 
the  project  amounts to 24.3% of average  annual 
public  productive  spending  in the region.  Figure 
4.1  A2 illustrates  the spending  pattern  over the 
four year period. 

This  second  scenario  considers  annual  project 
spending  compared to the average  spending on 
productive  public  investment  as the regional 
shocks.  The shocks  vary  between  0.99  and 
13.45% and cause an immediate  response in  re 
gional  business  investment  of up to 0.56%. The 
cumulated  long  term  effect  on business  invest 
ment  ¡s 3.3%  (Figure 4.1  B2). The impact  on em 
ployment  in  the region in  the long term is positive 
— approximately  3,400  jobs  are created  by  the 
project  (Figure 4.1  C2). We see that  for the  sec 
ond scenario, the impact on business  investment 
and  employment  is larger. This is due to the fact 
that the shocks on the system are naturally  larg 
er,  total  public  productive  Investment  being a 
subset of total public investment. 

2.4.2. Rias Bajas  Motorway 

The  project  is located  in the regions  of Galicia 
and  Castilla  y  Leon.  Spending  on the  project 
takes  place between  1993 and  19981 and repre 
sents  a total  amount  of  786.47  MECU.  This 
amounts  to  1.58% of bi-regional  GDP ¡n  1992. 
(Refer  back to Figure 3.4 for an  illustration of the 
spending pattern over the five year period.) 

1 st Scenario: Spending as a Percentage of Total 

Regional Public  Investment 

Given  that  total  public  spending  in Spain  repre 
sents  approximately  2.3%  of GDP, total  project 
spending  amounts  to 68.7%  of average  annual 
public  spending in the region. Figure 4.2 A1 illus 
trates  the spending  pattern  as a percentage of 
annual regional public spending over the five year 
period. 

The  first  scenario  considers  annual  project 
spending  compared  to the average  total  public 
spending  as the regional  shocks.  The shocks 
vary between  2.05% and 28.53% (Figure 4.2 A1) 
and  cause  an Immediate  response  in regional 
business  investment  of up to 1.17%. The cumu 
lated  long  term  effect  on business  investment is 
11.0%  (Figure 4.2 B1). The additional  public and 
private  investment  has a positive  Impact  on  em 
ployment  ¡n the region:  ¡n the long run , approxi 
mately 3,300 jobs are created by the project  (Fig 
ure 4.2 C1). 

The  initial  phase  of the project  started  in  1993.  a break  occurs in 
1994,  and the project  financing  begins  in  1995.  We assess the 
shocks were over a 5 year period, the break in 1994 being omitted. 

120 

Section III — Vector Auto-regression  Models 

2nd Scenario: Spending as a Percentage of 

Regional Productive Public 
Investment 

Knowing  that  total  public  productive  spending  in 
Spain represents approximately  1.7%  of GDP, to 
tal project  spending  amounts to  89.25% of aver 
age annual  public  productive  spending  in the re 
gion.  Figure  4.2  A2  illustrates  the  spending  pat 
tern over the five year period. 

This  second  scenario  considers  annual  project 
spending  compared  to the average spending  on 
productive  public  investment  as  the  regional 

shocks.  The  shocks  vary  between  7.03  and 
37.05%  (Figure 4.2 A2) and cause an  Immediate 
response  in  regional  business  investment  of  up 
to  2.0%.  The  cumulated  long  term  effect  on 
business  investment  is  12.28%  (Figure  4.2  B2). 
The  impact  on  employment  in  the  region  in  the 
long  term  is  positive  —  approximately  3,900 
jobs  are  created  by  the  project  (Figure  4.2  C2). 
We see that for the second  scenario, the  impact 
on business investment  and employment  is larg 
er. This  is due to  the fact  that  the  shocks  to  the 
system  are  naturally  larger,  total  public  produc 
tive investment  being a subset  of total  public In 
vestment. 

Simulation Results 

121 

Figure 4.1: 
Madrid  Ring  Road 

Response  of (a)  Business  Investment  and 

(b) Cumulated  Business  Investment to  Project 

Employment  Response  to  Project  Investment 
(Number  of Jobs  Created) 

Spending  on  Project  as a Percentage  of : 
Scenario  T. Total  Regional  Public  Investment 
Scenario  2: Regional  Productive  Public  Investment 
Scenario  3: Regional  Investments  in  Motorways 

A1 

Business  Investment 

LZ.J Cumulated  Business  Investment 

B1 

8,0% 

6,0% 

4,0% 

2,0% 

0,0% 

1993 

1994 

1995 

1996 

Years 

Scenario 1: 
Total  Public 
Investment 

10% 

5% 

0% 

Scenario 2: 
Productive 
Public 
Investment 

C1 

Years 

C2 

Years 

ro 
IV) 

ω 
Φ 
o 

c? 
o 

o 

O o. 
Φ 

Figure 4.2: 
Rias Bajas Motorway 

Spending on Project as a Percentage of : 
Scenario 1: Total Regional Public Investment 
Scenario 2: Regional Productive Public Investment 

Response of (a) Business Investment and 

(b) Cumulated Business Investment to Project 

Employment Response to Project Investment 
(Number of Jobs Created) 

A1 

J Business Investment 

[ZU Cumulated Business Investment 

B1 

Scenario 1: 
Total Public 
Investment 

40% 

30% 

20% 

10% 

0% 

Scenario 2: 
Productive 
Public 
Investment 

1993 1995 

1996 

1997 1998 

A2 

Years 

B2 

1993 1995 

1996 

1997 1998 

Years 

C1 

Years 

C2 

Years 

w 
3 
ç_ 
ω 
õ' 

IO 
œ 

ro 
ω 

2.5. Greece 

Consistency  with  the  general  results  as  outlined 
in the previous  report  can also be reported  in the 
Greek case. Both projects are situated  in the rich 
region  of  Athens,  where  we  have  reported  a 
stronger  impact  of public  investment  on ousiness 
Investment  than  in  the  previous  report.  The  im 
pact  of  Cohesion  Fund spending  on the  regional 
economy are found to  be positive and persistent. 
The  simulation  results  concerning  job  creation 
are  more  pronounced  than  in the  Spanish  case, 
but  because  the  data  sets  used  were  inferior, 
these results should be interpreted with caution. 

2.5.1.  Evinos Dam 

The  project  is  located  in  the  region  of  Athens. 
Spending  on  the  project  takes  place  between 
1993 and  1995  and  represents  a total  amount  of 
217.44 MECU. This amounts to 0.607% of GDP in 
1992.  (Refer back to Figure 3.4 for an illustration of 
the spending pattern over the three year period.) 

vestment  shocks  have  on  labour  market  vari 
ables.  Second  scenario  evaluations  done for  the 
Greek  projects  base  the  simulation  of  the  effect 
of  project  spending  on  the  coefficients  concern 
ing the trivariate  system  of  employment,  popula 
tion and activity rate obtained through pooled da 
ta  from  all  four  Cohesion  countries  (refer  to  the 
Initial sections of this report.) 

Figure 5.1  C2  Illustrates that this additional simu 
lation  supports  the  original  results  obtained  in 
Scenario  1, although job creation ¡s slightly higher 
in this simulation. 

2.5.2. Spata Airport 

The second Greek project to be evaluated is also 
located  in the  region of Athens. Spending on the 
project takes  place between  1996 and  1999 and 
represents  a total amount  of 292.12  MECU. This 
amounts to 0.816% of GDP in 1992. (Refer back 
to  Figure  3.4  for  an  illustration  of  the  spending 
pattern over the three year period.) 

1 st Scenario: Spending as a Percentage of Total 

Regional Public Investment 

1st Scenario: Spending as a Percentage of Total 

Regional Public Investmen: 

Given that total  public  spending  in Greece repre 
sents  approximately  10.8%  of  GDP, the  project 
amounts  to  5.6%  of  average  annual  public 
spending  in  the  region.  Figure  5.1  A1  illustrates 
the spending  pattern  as a percentage  of total re 
gional public spending over the three year period. 

The  first  scenario  considers  annual  project 
spending compared to the average annual public 
spending  as  the  regional  shocks.  The  shocks 
vary between 0.117% and 2.78% (Figure 5.1 A1) 
and  cause  an  immediate  response  in  regional 
business  investment  of  up to  0.67%. The cumu 
lated  long  term  effect  on  business  investment  is 
3% (Figure 5.1  B1). The additional public  and pri 
vate investment  has a positive impact on employ 
ment  in  the  region  — in  the  long  run, approxi 
mately 13,000 jobs are created (Figure 5.1 C1). 

Given that total  public  spending  in Greece repre 
sents  approximately  10.8%  of  GDP, the  project 
amounts  to  7.6%  of  average  annual  public 
spending  in  the  region.  Figure  5.2  A1  illustrates 
the spending  pattern as a percentage of total re 
gional public spending over the three year period. 

The  first  scenario  considers  annual  project 
spending  compared  to  the  average  annual  pub 
lic spending  as the  regional  shocks. The shocks 
vary  between  0.42  and  3.03%  (Figure  5.2  A1) 
and  cause  an  immediate  response  in  regional 
business  investment  of  up to  0.82%. The cumu 
lated  long term  effect  on  business  investment  is 
4.03%  (Figure 5.2  B1). The additional  public  and 
private  investment  has a positive  impact  on em 
ployment  in  the  region  —  in  the  long  run,  ap 
proximately  8,800  jobs  are  created  (Figure  5.2 
C1). 

2nd Scenario: Spending as a Percentage of Total 

2nd Scenario: Spending as a Percentage of Total 

Regional Public Investment — 
Estimation of the Coefficients with 
Pooled Data from all Cohesion 
Countries 

Regional Public Investment — 
Estimation of the Coefficients with 
Pooled Data from all Cohesion 
Countries 

The  difference  in  this  second  evaluation  of  the 
project  concerns  only  the  effects  that  public  in-

The  difference  in  this  second  evaluation  of  the 
project  concerns  only  the  effects  that  public  in-

124 

Section III — Vector Auto-regression Models 

vestment  shocks  have  on  labour  market  vari- 
ables.  Second  scenario  evaluations  done  for 
the  Greek  projects  base  the  simulation  of  the 
effect  of  project  spending  on  the  coefficients 
concerning  the  trivariate  system  of  employ- 
ment,  population  and  activity  rate  obtained 
through  pooled  data  from  all  four  Cohesion 

countries  (refer  also to the  initial sections  of this 
report.) 

Figure 5.2  C2  illustrates that this additional simu-
lation  supports  of  the  original  results  obtained  in 
Scenario  1, although job creation is slightly higher 
in this simulation. 

Simulation Results 

1 25 

Spending on Project as a Percentage of Total 
Regional Public Investment 

Response of (a) Business Investment and 

(b) Cumulated Business Investment to Project 

Employment  Response to  Project  Investment 
(Number of Jobs  Created) 

Figure 5.1: 
Evinos Dam 

A1 

Business Investment 

Γ~Ί  Cumulated  Business  Investment 

B1 

C1 

Æ*5  ¿3t 

16000 

12000 

8000 

¿7 

4000 

Years 

Years 

ro 
co 

o 
3 

cí 

> 
c 
Õ 

o 
Q. 
φ 

Scenario 1: 
Total Public 
Investment 

Scenario 2: 
Panel Data: 
Random 
Effects 

Q? 
3 

o 
3 
IO 
CD 

­vl 

Scenario 1: 
Total Public 
Investment 

Scenario 2: 
Panel Data: 
Random 
Effects 

Figure 5.2: 
Spata Airport 

Spending on Project as a Percentage of Total 
Regional Public Investment 

Response of (a) Business Investment and 

(b) Cumulated Business Investment to Project 

Employment Response to Project Investment 
(Number of Jobs Created) 

I Business Investment 

I Cumulated Business Investment 
Bl 

C1 

Years 

16000 

14000 

12000 

10000 

8000 

6000 

4000 

2000 

0 

16000 

14000 

12000 

10000 

8000 

6000 

4000 

2000 

0 

Years 

C2 

y*7  ¿m ¿3\ ¿3\ ¿ζ? 

Years 

2.6.  Portugal 

As  far  as  Portugal  is concerned,  data  shortages 
still  limit  the  evaluation  work.  The  positive  and 
significant  correlation  between  public  spending 
and  business  investment  is confirmed.  However, 
we could only  proceed with the evaluation  of the 
Tagus  Crossing  project  located  in  the  Lisbon 
area.  Regarding  the  water  sewage  project 
Odeleite-Beliche, there have been difficulties  with 
the  relevant  regional  data  that  could  not  be  re 
solved within the time frame of this report. 

2.6.1. Tagus Crossing 

The  project  is  located  in  the  region  of  Lisbon. 
Spending  on  the  project  takes  place  between 
1995  and  1997  and represents  a total amount of 
366.18  MECU. This amounts to  1.88%  of GDP in 
1992.  (Refer  back  to  Figure 3.4  for  an  illustration 
of the spending pattern over the two year period.) 

1 st Scenario: Spending as a Percentage of Total 

Regional Public Investment 

Given that total public spending in Portugal repre 
sents  approximately  3.09%  of  GDP, total  project 
spending  amounts  to  5.75%  of  total  public 
spending  in  the  region.  Figure  6.1  A1  Illustrates 
the spending  pattern  as a percentage  of average 
regional public spending over the two year period. 

The  first  scenario  considers  annual  project 
spending compared to the average annual public 
spending  as  the  regional  shocks.  The  shocks 
vary between 5.75% and 34.91%  (Figure 6.1 A1) 
and  cause  an  immediate  response  ¡n  regional 
business investment of up to 5.3%. The cumulat 
ed  long  term  effect  on  business  investment  is 
3.1%  (Figure  6.1  B1). The  additional  public  and 
private  investment  has  a positive  impact  on em 
ployment  in the region  — in the long run, approx 
imately  16,500 jobs are created (Figure 6.1 C1). 

2nd Scenario: Spending as a Percentage of Total 

Regional Public Investment — 
Estimation of the Coefficients with 
Pooled Data from all Cohesion 
Countries 

The  difference  in  this  second  evaluation  of  the 
project  concerns  only  the  effects  the  public  in 
vestment  shocks  have on the labour  market vari 
ables.  The  second  scenarios  done  for  the  Por 
tuguese  project  evaluations  base  the  simulation 
of  the  effect  of  the  project  on  the  coefficients 
concerning  the  trivariate  system  of  employment, 
population  and  activity  rate  obtained  through 
pooled  data from  all four  Cohesion  countries (re 
fer to the initial sections of this report.) 

Figure 6.1  C2 illustrates that this additional simu 
lation  supports  the  original  results  obtained  in 
Scenario 1. 

128 

Section I 

Vector Auto-regression Models 

ΰ? 
3' 
m 
õ' 
Z3 
DO 
Φ 

CO 

Scenario 1: 
Total Public 
Investment 

Scenario 2: 
Panel Data: 
Random 
Effects 

Spending on Project as a Percentage of Total 
Regional Public Investment 

Response of (a) Business Investment and 

(b) Cumulated Business Investment to Project 

Employment Response to Project Investment 
(Number of Jobs Created) 

Figure 6.1: 
Tagus Crossing 

B9 Business Investment 

LJCumulated Business Investment 

B1 

Years 

C1 

s~7 

¿5s! 

22000 

20000 

18000 

16000 

14000 
12000 
10000 
8000 
6000 
4000 
2000 
0 

A 

­

Years 

C2 

¿"3; 

¿H* 

μ 

Years 

Æ 

i 

1 
θ! 
Ί 
A-
A 
3 

22000 
20000 
18000 
16000 
14000 
12000 
10000 
8000 
6000 
4000 
2000 
0 

2.7.  Ireland 

2.7.2. North  -  South Rail Link 

Data  shortages  limit  the  evaluation  work  in  the 
Irish case: the relationship  between  public  invest­
ment  and  private  investment  in  manufacturing 
can  be  estimated,  not,  however,  the  relationship 
between  public  investment  and  employment  da­
ta. The lack of regional data concerning GDP and 
employment  make  it  difficult  to  evaluate the  Irish 
projects  and  the  results  should  be  interpreted 
with caution. 

2.7.1. North  -  South Road Link 

The North  — South Road Link comprises financ­
ing  of  a  road  extension  (Dunleer  — Dundalk)  as 
well  as  two  by-passes  (Drogheda  by-pass,  and 
Balbriggan By-pass). The project  is located at the 
North-Eastern  border  of  Ireland  in  the  two  re­
gions East and North-East. Total spending on the 
project  up  to  the  year  1999  will  be  198.36 
MECU.  This  represents  about  32%  of  public  in­
vestment  for the two  regions combined  (see Fig­
ure  7.1  A  for  the  repartition  of  the  investment 
across the years). 

Our  analysis  shows  that  work  on  the  project 
should lead to a direct response of private invest­
ment close to 2% in the first ten years of the pro­
ject (see Figure 7.1  Β for illustration). 

The North-South  Rail Link comprises financing of 
the rail links  between  Dublin and Cork  as well as 
Dublin  and  Belfast.  We  evaluated  the  project  in 
the  region  East  (Dublin).  Total  spending  on  the 
project  between  the  years  1993  and  1995  in 
70.53  MECU. This represents about  13% of pub­
lic Investment in manufacturing for the region (see 
Figure  3.2  A  for  the  repartition  for  spending 
across the years). 

Our  analysis  shows  that  work  on  the  project 
should lead to a direct  response of private invest­
ment  around  1 % in the first five years of the pro­
ject (see Figure 3.2  Β for illustration). 

2.7.3. Labour Market Variables 

The link between  investment  shocks and their ef­
fect  on  employment  could  not  be  estimated  be­
cause of data shortage. We have however simu­
lated the response of the labour market variables 
Employment, Activity  Rate, and  Immigration for a 
hypothetic  shock  of  1% on  labour  demand. The 
response in terms of job creation are illustrated in 
Figures 7.1 C and  7.2C  for the regions where the 
projects  are  located.  No  significant  impact  on 
population can be reported. 

130 

Section III — Vector Auto-regression  Models 

3 

o 

Ζ) 

Φ 

Ireland (MECU) 
Average PI/BI 0,6 

North­south Road Link 
Project Spending 

1993 
1994 
1995 
1996 
1997 
1997 
1997 

Total 

6,87 
32,46 
35,96 
37,14 
40,44 
28,25 
17,24 
198,36 

Regional PI (East & North­East) 
Total Spending as %age of Reg. PI 

614,36 
32,29% 

Figure 7.1: 
North­South Road Link 

Figure 7.1 Β 

Response of (a) Business Investment and (b) 
Cumulated Business Investment to Project 

18,0% 
16,0% 
14,0% 
12,0% 
10,0% 
8,0% 
6,0% 
4,0% 
2,0% 
0,0% 

Years 

Figure 7.1 A 

Figure 7.1 C 

Spending on Project as Percentage of Total 
Regional Public Investment 

Response of the Labour Market to a 1% Shock 
to Demand for Labour 

CO 

1993 1994 1995 1996 1997 1998 1999 

¿71 

t<7\ ¿7\ ff 

4^Λ ¿em áS& 

U L L Ä J iÄ 

Years 

IRELAND (MECU) 
Average PI/BI 

North ­ South Rail Link 
Project Spending 

0,6 

1993 
1994 
1995 

Total 

21 
24,25 
25,28 
70,53 

Regional PI (East) 
Total Spending as % age of Reg.PI 

536,26 
13,15% 

Figure 7.2: 
North­South Rail Link 

Figure 7.2 B 

Response of (a) Business Investment and (b) 
Cumulated Business Investment To Project 

9,00% 

8,00% 

7,00% 

¡ 
j 

6,00% 

5,00% 

4,00% 

3,00% 

-1 ét 

η 

n 

m 

Λ ι 

; 

ca 

m 

2,00% 

1,00% 

0,00% 

^mññññññmüeeúlaai 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 

Figure 7.2 A 

Figure 7.2 C 

Spending on Project as a Percentage of Total 
Regional Public Investment 

Job Creation as a response to a 1% shock to 
Demand for Labour 

Λ 

Æ Ζ 

M 

y 

5,oo% r 
4,00% 

3,00% 

2,00% 

1,00% 

0,00% 

y 

y 

1993 1994 1995 

% 

fåk f% 

Years 

ω 
ro 

o 
3 

> 
C 
o 

o 
Q. 
CD 

2.8. Additional  Remarks on  Labour 

Market  Variables 

0.17%  increase  is  estimated,  representing  a 
change in activity rate from 50.4% to 50.61 %. 

For  the  Spanish  projects,  the  simulation  results 
for  the  trivariate  system  including  employment, 
population and activity  rates support  the findings 
in  the  previous  report.  Namely,  an  increase  in 
employment  caused  by  project  spending  can be 
fulfilled  by two  effects:  (1) an  increase  in the  ac 
tive  population  (including  those  workers  who 
move  from  inactivity  to  the  labour  force  and 
those  who  immigrate  to  the  region)  and  (2) 
through  a decrease  in unemployment.  Insofar as 
the  impacts  of  the  two  projects  analysed  for 
Spain,  an  increase  in  employment  of  approxi 
mately  3,000  in the  case  of  both  projects  ¡s ac 
companied  by a population increase of 900. This 
can be interpreted as partly being the result of in 
terregional  migration  into  the  areas  in which  the 
projects  are  located. As  far  as  the  activity  rates 
are  concerned,  the  Madrid  Ring  Road  invest 
ment leads to a long-run increase of 0.05%. That 
is,  the  activity  rate  increases  from  49.02%  to 
49.07%.  In the  case  of  Rias  Bajas  Motorway,  a 

A  more  precise  interpretation  of  these  results 
would  separate the contributing  factors that  lead 
to increased employment.  Namely, by developing 
a  different  version  of  the  model, we  could  differ 
entiate between decreases in unemployment  and 
increases  in the  active  population.  The  trivariate 
system  would  now  also  include  unemployment, 
allowing for  more detailed  results on the  impacts 
of public investment expenditures. 

In the  Greek  and  the  Portuguese  case,  the  re 
sults  concerning  population  and  activity  rate  are 
subject  to  considerably  less  detailed  data.  We 
feel that  these estimates  have to  be  redefined in 
order  to  undergo  a  more  extensive  analysis  for 
these two countries. 

In the  Irish  case,  the  link  between  public  invest 
ment  and  employment  can  not  at  the  present 
stage be estimated  because the employment se 
ries  stated  only  in the  mid-80s.  For  this  reason, 
the impact of public  spending on the labour  mar 
ket cannot so far be simulated. 

Simulation Results 

133 

3. Conclusions 

Summary of Results 

S.1.  Impact of Public  Investment 

on the Business Sector and Implications 
of Inter-regional Transfers 
of Public Investment Spending 

This table is a summary of our empirical  findings 
on the responses  of private  business  investment 
spending: 

TABLE S.1.  Impact of Positive Aggregate Public Spending Shocks 

Short Run 

Long Run 

Rich 

Mid 

Poor 

Spain 

Portugal 

Greece 

Ireland 

++ 

++ 

++ 

0 

++ 
? 

TABLE S.2.  Long Run Impact of disaggregated  Public Spending Shocks in Spa n 

Infrastructure Spending 

Roads 

Ports Non Adm. 

Motorways 

Urban Infrastr. 

Non-Infrastructures 

++ 

++ 

+ 

+ 

0 

Conclusions 

135 

This  last  table  has  to  be  interpreted  cautiously. 
We  showed,  in the  case  of  Spain, that  non-pro 
ductive  investment  had  no  significant  relative ef 
fect.  It  does  not  mean  that  this  effect  does  not 
exist.  It could  be delayed, so that  our VAR  mod 
els with two  lags in the equations will not  capture 
them. Another  possibility  is that  there  may be an 
aggregate effect,  but  no relative effect:  improving 
education  in a region will  improve  human capital, 
but  may  not  have  a  relative  effect  locally.  This 
could receive some support from our table 2.9: if 
upgrading the skill level of a poor  region is a way 
to  increase the  mobility  of the  workers,  then this 
may also have adverse effects on this region. 

The next tables offer  a summary of the impact of 
public  investment  spending. The first  row  shows 
the  differences  across  groups  of  regions  of  the 
ratio  of  business  over  public  investment.  The 

second  and third  rows give the  shares  of the re 
gions  in these two  investments. The fourth  gives 
the long-run elasticities of cumulated business in 
vestment  ¡n  response  to  public  investment 
changes,  and the fifth  gives the same effect,  but 
converted  into  units  of  spending.  Finally, the  last 
row  is the  net effect  on business  investment  of a 
net  transfer  from  the  other  groups  of  regions  to 
the group  of regions of the column. For instance, 
in  Spain,  given  that  business  investment  reacts 
more  strongly  in the  richest  and  the  poorest  re 
gions,  increasing  their  share  in public  investment 
has  a  net  positive  effect,  while  increasing  the 
share of the middle-class regions has a net nega 
tive  effect.  It  should  be  recalled  that  the elastici 
ties  estimated  in Spain  are  not  statistically  differ 
ent.  However, this is not the case ¡n Portugal and 
Greece, where the differences appear to be more 
significant. 

TABLE S.3.  Summary of the impact of public spending, Spain 

Spain 

Poor 

Intermediate 

Rich 

Ratio  of  Business  Over  Public  Investment 

%  Share  of  National  Business  Investment 

%  Share  of  National  Public  Investment 

Effect  of  Public  Investment  on 
Business  Investment  :  Elasticity 

Effect  of  Public  Investment  on 
Business  Investment  : Units  of  Spending 

Effect  of  Public  Investment  on  Business 
Investment  : Increase  in National  Share  in  % 

Inter-regional  transfers  of  1 %  share 
of  Public  Investment  on  Business  Investment 

7.0 

34.6% 

43.2% 

0.199 

1.393 

10.0 

41.9% 

37.0% 

0.104 

1.036 

10.8 

23.4% 

19.8% 

0.130 

1.420 

+  0.159% 

+  0.128% 

+  0.154% 

+  0.022% 

-  0.029% 

+  0.009% 

TABLE S.4.  Summary of the impact of public 

spending, Portugal 

Portugal 

Poor 

Intermediate 

Rich 

Ratio  of  Business  Over  Public  Investment 

%  Share  of  National  Business  Investment 

%  Share  of  National  Public  Investment 

Effect  of  Public  Investment  on  Business 
Investment  :  Elasticity 

Effect  of  Public  Investment  on  Business 
Investment  : Units  of  Spending 

Effect  of  Public  Investment  on  Business 
Investment  : Increase  in  National  Share  in  % 

Inter-regional  transfers  of  1 %  share  of  Public 
Investment  on  Business  Investment 

4.37 

8 . 1% 

7.9% 

- 0 . 0 65 

-(0.28) 

4.39 

9 . 1% 

8.9% 

0.040 

(0.18) 

25.2 

82.7% 

80.5% 

0.170 

4.28 

(-0.22)% 

(0.12)% 

+0.099% 

- 0 . 0 8 9% 

- 0 . 0 8 9% 

+0.099% 

(the parentheses mean that the elasticity is not significant given 

the small size of local investment in these regions) 

136 

Section III —  Vector Auto-regression Models 

TABLE S.5.  Summary of the impact of public 

spending, Greece 

Greece 

Ratio of Business Over Public  Investment 

% Share of National Business Investment 

% Share of National Public  Investment 

Effect of Public Investment on 
Business Investment  : Elasticity 

Effect of Public Investment on Business Investment: 
Units of Spending 

Effect of Public Investment on Business Investment: 
Increase in National Share in % 

Inter-regional transfers of 1 % share 
of Public Investment on Business Investment 

Poor 

1.35 

38.3% 

44.1% 

0.765 

1.032 

0.66% 

+0.3% 

Rich 

1.56 

61.7% 

55.9% 

0.329 

0.513 

0.36% 

-0.3% 

S.2. Labour Mobility 

This  final  table  gives  the  sign  of  the  changes  at 
the  regional  level  of  employment  in the long-run, 
activity  and  changes  in  population  (proxying  mi 
gration  flows),  in  response  to  short-run  employ 
ment changes. 

TABLE S.6. 

Long Run Effects of Positive Employment Shocks 

Employment 

Activity Rate 

Mobility 

Total 
Skilled 
Unskilled 

Spain 

Portugal 
Greece 
Ireland 

+ 
++ 
++ 
+ 
+ 
0+ 

+ 
0 
+ 
++ 
-
+ 

+ 
++ 
+ 
0 
+ 
0 

Conclusions 

We have looked for a link between cohesion fund 
spending  and  local  economic  activity  and  found 
it. The  key  link  is provided  by  private  investment 
activity. Cohesion fund spending, like other types 
of  public  investment,  create incentives for  private 
investment.  Private  investment  then  in  turn  en 
courages  more investment  spending and this vir 
tuous cycle of activity  leads to  more employment 

at  the  local  level. There  are  also  spillovers  and 
national effects from this activity  but we were not 
concerned  with  those  effects  in  this  part  of  the 
study  — see  Bean's  chapter  for  the  spillover  ef 
fects. 

Data  for  Spain  were  richer  than  for  the  other 
countries and  let us go deeper  into the effects of 
cohesion fund spending. We found that the inter 
action  between  public  and  private  investment 
spending  was  strong  both  in the  short  term  and 
the  long  term.  It  was  especially  strong  in  richer 

Conclusions 

137 

regions,  though  also  in  some  of  the  poorer  re 
gions.  Mid-income  regions  did  not  fair  as  well. 
Results with  rich regions were similar in Portugal, 
though in Greece poorer  regions seemed to  ben 
efit  more from cohesion fund spending than rich 
er regions did. 

Of  the  different  types  of  spending,  spending  on 
roads and ports  had the greatest  impact  on eco 
nomic  activity.  Spending  on  'environmental'  pro 
jects  and  other  non-infrastructure  projects  had 
less  impact,  though  still  positive.  Unfortunately, 
data for the other countries were not rich enough 
to  enable  a  breakdown  of  spending  by  project 
type, but  results for Spain should be indicative of 
results elsewhere. 

The  investment  activity  that  is  generated  by  co 
hesion fund spending feeds mainly on to employ 

ment,  with  little,  ¡f any, wage  effects.  If anything, 
we  found  that  the  employment  effects  of  cohe 
sion fund spending were  'too  high', given our ex 
pectations. This  was  especially  true  ¡n Spain  but 
it  was  also  true  of  the  other  countries,  with  the 
possible  exception  of  Ireland, where  results were 
weaker. 

Higher  employment,  however,  did  not  always 
lead  to  a  reduction  in  local  unemployment.  In 
Spain,  increased  demand  for  skilled  labour  led 
mainly to  more in-migration, but  also to some in 
crease  in  the  region's  activity  rate.  Unemploy 
ment  benefited  only slightly, though  more for un 
skilled  labour.  We  found  similar  results  for  the 
other  countries.  In Portugal  and  Ireland the  main 
effect  of the  increase  in employment  was  on the 
region's activity  rate whereas  in Greece it was on 
labour mobility. 

138 

Section I 

Vector Auto-regression Models 

Appendix 1 

In the VAR system, if A, = AE/E,  l2 = AL/L -  AP/P 
and /3 = AP/P, then AL/L = AP/P + l2 = l3 +12- We 
also have AUE = (l2 +13) I {1  -u)  and AP/E = P/E. 
/3. Then, taking the average employment  and un- 
employment  rates  over  the  period  considered, 

we  can  have  a  approximation  of  the  number  of 

persons  who  will  enter  the  labour  force,  or  who 

will  leave the  region  in  response  to  a unit  labour 

for demand shock, 

Appendix 1 

139 

Appendix 2: Spain, Correction of the wage series 

Having  two  series  of  wages  for  the  wage  and 
salary  earners  available,  we  first  try  to  see  how 
consistent  they  are.  It can  be seen that  the av 
erage  deviation  of  the  first  wage  definition  to 
the  other  is  extemely  weak.  Region  by  region, 
the  same  picture  is  true.  The  table  A  provides 
some  regressions  in  panel  data.  In  the  fourth 

column,  we  predict  97%  of  the  variance  of 
W2TO. 

The  fourth  regression  is  used  to  define  W3TO, 
which is equal to W2TO if available, and the esti 
mator of W2TO for the other years, so for 6 years 
between 80 and 91. 

TABLE A-1.  Consistency of the two 

regional wage series 

W2TO 

W2TO 

W2TO 

W2TO 

Constant 

W1TO 

R square 

Total Panel Obs 

Time series length 

-0.019"* 
[0.007] 

1.205*** 
[0.08] 

0.696 

102 

6 

Region Specific 

-0.024"* 
[0.009] 

Region Specific 

-0.055 
[0.11] 

0.959 

102 

6 

Region Specific 

Region Specific 

0.908 

102 

6 

0.968 

102 

6 

(weighted standard errors in 

parentheses, ',  " 

*" significantly different from 0 at the 10, 5 and 1 % level) 

Appendix 2: Spain. Correction of the wage series 

141 

Appendix 3: ADF tests for the stationarity of the series 

The tables report the t­statistic of the coefficient 
b in the regressions: 

ΔΧ, = C + b ■ X, „j + a, · ΔΧ, _, + a2 ■ AX, _2 + ε, 

where Xt are the regional relative variables (log of 
the regional over the national value). 

Appendix 3: ADF tests for the stationarity of the series 143 

­PO­

ω 
co 
o 
õ' 
r3 

o 
q 
> 
c 

o 
Q. 
CD 

N obs 

Crit. val. ( 1 %, 5%, 10%) 

AND 

ARA 

AST 

BAL 

CAL 

CAN 

CAT 

CAY 

CNR 

COM 

EXT 

GAL 

MAD 

MUR 

NAV 

PAI 

LAR 

1 st order Self Correlation 

1/2­period persistence of shocks 

BITT 

25 

-3.72 

-2.98 

-2.63 

-1.71 

-2.30 

-1.95 

-2.54 

-2.19 

-0.80 

-1.45 

-1.71 

-1.65 

-1.11 

-2.10 

-1.70 

-1.59 

-0.09 

-2.17 

-1.50 

-2.02 

0.821 

3.5 

TABLE A­2. ADF tests, Spain 

EMTO 

UNMP 

ACTO 

PATO 

POPU 

W1T0 

25 

-3.72 

-2.98 

-2.63 

-2.61 

-3.44 

-3.00 

-3.05 

-2.06 

-1.57 

-1.92 

-2.61 

-2.29 

-2.85 

-1.79 

-2.65 

-2.37 

-0.83 

-2.77 

-2.79 

-2.50 

26 

-3.71 

-2.98 

-2.63 

-1.97 

-2.48 

0.38 

-1.44 

-2.77 

-0.43 

-1.89 

-1.85 

-1.30 

-1.37 

-2.16 

0.69 

-2.11 

-0.54 

-1.74 

-1.85 

-1.92 

26 

-3.71 

-2.98 

-2.63 

-1.94 

-3.09 

-1.80 

-2.41 

-0.93 

-2.00 

-1.52 

-0.72 

-2.05 

-0.96 

-1.84 

-1.40 

-2.51 

-1.41 

-2.70 

-2.46 

-0.65 

26 

-3.71 

-2.98 

-2.63 

-1.33 

-2.87 

26 

-3.71 

-2.98 

-2.63 

-2.23 

-0.98 

27 

-3.70 

-2.98 

-2.63 

-1.77 

-1.60 

10 

-4.32 

-3.22 

-2.76 

-1.38 

-2.35 

0.23 

0.41 

1.19 

0.29 

-3.16 

-1.61 

-0.82 

-1.56 

-2.20 

-1.71 

-0.32 

-2.45 

-0.57 

-2.91 

-1.18 

-2.12 

-2.05 

-1.88 

-1.72 

-2.99 

-0.29 

-2.77 

-2.48 

-0.77 

-2.59 

-2.71 

-1.82 

-3.70 

-1.31 

-2.52 

-3.43 

-1.68 

-0.64 

-2.80 

-1.90 

-2.80 

-2.33 

-1.30 

-2.64 

-2.58 

-1.69 

-3.31 

0.03 

-1.41 

-2.01 

-2.63 

-1.58 

-1.55 

-1.25 

-1.08 

-0.93 

-1.92 

-1.25 

-1.58 

-1.49 

-1.54 

-2.61 

-1.49 

-1.38 

-1.27 

0.695 

1.9 

0.672 

0.927 

0.871 

0.862 

0.924 

0.937 

1.7 

9.1 

5.0 

4.7 

8.8 

10.7 

> n 

"O 
ω 

> 
O 

Ν obs 

Crit.Val. (1%,  5%, 10%) 

AVEI 

BEJA 

BRGA 

BRGN 

CAST 

COIM 

EVOR 

FARO 

GUAR 

LEIR 

LISB 

PRTL 

PRTO 

SANT 

SETU 

VIAN 

VILA 

VISE 

1st order Self-Correlation 

1/2-period persistence of shocks 

4^ 
en 

TABLE A-3.  ADF tests,  Portugal 

PILT 

EMP1 

UNMP 

ACTO 

POPA 

POPU 

9 

-4.46 

-3.27 

-2.78 

-1.95 

-1.06 

-1.68 

-1.72 

-1.90 

-2.13 

-1.13 

-0.61 

-1.88 

-0.12 

-0.37 

-1.91 

-2.00 

0.12 

-1.04 

-1.90 

0.07 

-0.85 

0.176 

0.4 

6 

-5.25 

-3.55 

-2.93 

-0.58 

0.83 

0.32 

-0.53 

-1.95 

-0.64 

-1.40 

-2.64 

-0.81 

-14.2 

-0.74 

-0.61 

-1.47 

-1.17 

0.20 

-0.51 

-0.04 

0.24 

0.715 

2.1 

18 

-3.86 

-3.04 

-2.66 

-0.76 

-0.48 

-0.31 

-2.80 

-0.16 

-0.38 

-0.88 

-3.05 

-0.14 

-0.61 

-1.39 

-0.80 

-0.29 

-1.76 

-1.07 

-2.43 

-3.37 

-0.68 

0.916 

7.9 

18 

-3.86 

-3.04 

-2.66 

-0.72 

-1.80 

-2.54 

-2.48 

-1.50 

-0.39 

-2.03 

-1.88 

-1.79 

-2.25 

-1.79 

-1.17 

-0.71 

-2.00 

-1.94 

-1.99 

-1.26 

-1.45 

0.653 

1.6 

13 

-4.07 

-3.12 

-2.70 

-0.34 

-2.38 

-1.06 

-4.01 

-0.96 

-0.47 

-2.30 

-2.38 

-0.65 

-0.56 

0.16 

-2.31 

-1.32 

-0.87 

-1.00 

-1.59 

-3.59 

-0.49 

0.762 

2.6 

21 

-3.79 

-3.01 

-2.65 

0.081 

-1.30 

0.39 

0.89 

-2.21 

-1.41 

-1.13 

-1.86 

-1.62 

-1.98 

-1.83 

-1.26 

0.72 

-1.13 

-2.76 

-3.39 

-1.40 

-2.18 

0.774 

2.7 

21 

-3.79 

-3.01 

-2.65 

0.00 

-2.72 

0.21 

0.07 

-2.65 

-2.18 

-2.68 

-0.70 

-1.67 

-0.81 

-3.21 

-2.59 

0.18 

-2.33 

-2.63 

-2.72 

-1.43 

-2.22 

0.774 

2.7 

wn 

10 

-4.32 

-3.22 

-2.76 

-1.91 

-0.95 

-0.74 

-1.13 

-1.19 

-0.82 

-0.89 

-1.31 

-0.30 

-1.24 

-5.05 

-4.13 

-3.62 

-1.20 

-2.21 

-0.22 

-1.52 

0.32 

0.092 

0.3 

TABLE A­4. ADF tests, Greece 

UNMP 

ACTO 

POPA 

POPU 

Ν obs 

Crit. val. (1%, 5%, 10%) 

R1 

R2 

R3 

R4 

R5 

R6 

R7 

R8 

R9 

R10 

R11 

R12 

R13 

ω 
CD 

o 
> 

1 st order Self­Correlation 

1/2­period persistence of shocks 

': insufficient length of time series 

PITT 

11 

-4.22 

-3.18 

-2.73 

-1.63 

-5.40 

-2.80 

-3.62 

-3.56 

-4.17 

-6.35 

-3.10 

-2.03 

-1.92 

-2.40 

-2.56 

-3.08 

0.650 

1.6 

11 

-4.22 

-3.18 

-2.73 

-2.40 

-2.35 

-I2.0 

-1.80 

-6.95 

-2.03 

-0.45 

-2.27 

-1.36 

-9.60 

-1.23 

-1.31 

-4.47 

0.921 

8.4 

EMP2 

9 

-4.46 

-3.27 

-2.78 

■ 

* 

• 

9 

-4.46 

-3.27 

-2.78 

• 

« 

• 

9 

-4.46 

-3.27 

-2.78 

* 

• 

-1.66 

-1.22 

-2.96 

• 

• 

• 

• 

• 

• 

• 

• 

* 

* 

• 

• 

• 

• 

• 

• 

• 

• 

• 

♦ 

• 

• 

• 

-1.84 

0.552 

0.6 

-3.18 

0.278 

0.5 

-1.89 

0.140 

0.4 

20 

-3.80 

-3.01 

-2.65 

-2.49 

-2.52 

-2.58 

-2.54 

-2.38 

-2.52 

-2.63 

-2.61 

-1.34 

-2.51 

-2.59 

-2.54 

-2.56 

0.956 

15.4 

9 

-4.46 

-3.27 

-2.78 

­

« 

• 

0.24 

• 

• 

• 

• 

■ 

• 

-1.96 

0.760 

2.5 

> 

"O 
"O 
CD 

> 
D 

TABLE A-5.  ADF tests,  Ireland 

HPBIT2 

EMPI 

UNMP 

ACTO 

N obs 

12 

20 

POPU 

14 

wn 

Crit.Val. (1%,  5%, 10%) 

-4.32 

-3.86 

-4.46 

-4.46 

-4.46 

-4.01 

-4.64 

East 

SouthEast 

MidWest 

West 

Midlands 

SouthWest 

NorthEast 

Northwest 

-3.22 

-3.04 

-3.27 

-3.27 

-3.27 

-3.10 

-3.3 

-2.75 

-1.86 

-11.2 

-2.66 

-2.78 

-2.78 

-2.78 

-2.69 

-2.82 

-0.86 

1.19 

-3.19 

0.79 

-1.86 

-2.15 

-1.32 

-1.65 

0.62 

0.10 

0.24 

-1.22 

-3.01 

-2.67 

-1.11 

-2.60 

-1.17 

- 0 .Í 

-2.35 

-9.71 

-1.14 

-0.81 

-1.51 

-4.06 

-1.36 

-0.60 

-1.54 

-1.48 

-0.37 

-1.61 

-3.30 

1.16 

-1.36 

-4.24 

-3.06 

-2.63 

-1.87 

-1.86 

-0.29 

-0.06 

-3.54 

-2.06 

-4.21 

-0.89 

-0.83 

-1.70 

-1.36 

-1.71 

-1.36 

-2.92 

-0.94 

-1.16 

-1.10 

1 st order Self-Correlation 

0.961 

0.422 

0.258 

0.238 

0.274 

0.922 

0.480 

-Ë. 

1/2-period persistence of shocks 

17.4 

0.80 

0.5 

0.5 

0.5 

8.5 

0.9 

Appendix 4: GDP per capita in the regions. 

TABLE A-6.  Spain 

Panels 

Regions 

GDP per head 
(1 = Spain, average 80-92) 

Poor Regions 

Intermediate Regions 

Rich Regions 

EXTREMADURA 
ANDALUCÍA 
GALICIA 
CASTILLA-la-MANCHA 
MURCIA 
CASTILLA-y-LEON 
CANARIAS 
COMM. VALENCIANA 
ASTURIAS 
CANTABRIA 
ARAGON 
CATALUNA 
LA RIOJA 
MADRID 
PAIS VASCO 
NAVARRA 
BALEARES 

0.65 
0.77 
0.81 
0.82 
0.92 
0.93 
0.94 
1.01 
1.01 
1.03 
1.10 
1.19 
1.19 
1.21 
1.23 
1.25 
1.32 

Appendix 4: GDP per capita in the regions. 

149 

TABLE A-7.  Portugal 

Panels 

Regions 

GDP per head 
(1 = Portugal,  1992) 

Poor Districts 

Intermediate Districts 

Rich Districts 

VISEU 
GUARDA 
BEJA 
CASTELO BRANCO 
LEIRIA 
COIMBRA 
VILA REAL 
VIANA DO CASTELO 
FARO 
AVEIRO 
BRAGANÇA 
ÉVORA 
BRAGA 
PORTALEGRE 
SANTARÉM 
PORTO 
SETÚBAL 
LISBOA 

0.36 
0.40 
0.50 
0.52 
0.54 
0.54 
0.55 
0.57 
0.58 
0.58 
0.68 
0.84 
0.88 
0.95 
0.97 
1.04 
1.31 
1.72 

Panels 

Poor Ypa 

Rich Ypa 

TABLE A-8.  Greece 

Borio Aigaio 
Ipeiros 
Dytiki Ellada 
Ionia Islands 
Anatoli Makedonia and Thraki 
Thessalia 
Kriti 
Kentriki  Makedonia 
Dytiki Makedonia 
Peloponnisos 
Notio Aigaio 
Attiki 
Sterea Ellada 

GDP per head 
(1  = Greece, average79-92) 

0.72 
0.76 
0.87 
0.89 
0.91 
0.92 
0.92 
0.98 
0.99 
1.00 
1.03 
1.09 
1.24 

150 

Section III —  Vector Auto-regression Models 

Appendix 5: Some Econometrics 

Estimation of the variance of the limit 
of the cumulated private investment. 

public investment shock is given by a non­linear 
combination of the coefficients estimated in the 
VAR. This limit is equal to: 

C3 +C4 ­i C5 
1­C,­C2 

where C^ and C2 are the autoregressive coeffi­
cients of Business Investment and C3 to C5 are 
the lagged effect of Public Investment. A linear 
Taylor approximation of that function around the 
true value of the parameters gives (see Greene 
1993 for details): 

EstVarl ■­

d/(ß) , . dl (β) 

y ' -Var (β - b)· y ' 
dß 

dß 

where β is the vector of the estimated parame­
ters and b is their true value. The variance of the 
parameters is estimated as usually, s2­(X'X')~\ 
where s2 is the sum of squared residuals. We 
then refer to the normal distribution to provide an 
interval confidence around the estimated value. 

The choice of the estimators. 

We have developed the concept of a representa­
tive region, which is a useful benchmark to con­
sider. This consideration lead us to use the fol­
lowing econometric approach. 

In a first step, we estimate the coefficient from a 
standard OLS regression, assuming that the co­
efficient are the same, except the intercepts 
which are allowed to be region­specific. In a sec­
ond "step, we use the residuals estimated from 
the first step to build a estimated matrix of vari­
ance­covariance. Then, we assume that the true 
matrix of variance­covariance of the residuals is a 
block matrix of the matrices of variance­covari­
ance of the residuals is the identity matrix times a 
region­specific variance. The economic interpre­
tation ¡s straightforward: the closer the region is 
to the representative region, the smaller this re­
gional coefficient. Then, we estimate the coeffi­
cients a GLS estimator from the matrix of vari­
ance­covariance estimated above. In the text, we 
show the regional variance of the residuals from 
this second stage, and show that they are not 
strongly different. 

The choice of this GLS versus more sophisticat­
ed estimators like GMM to correct for a correla­
tion between the fixed effects and the regressors 
can be explained as follows. When we estimate a 
model like: 

ν;,(=ς.+βγ;ι(_;+ε,  I,t 

we  can  interpret  the C¡ as an intercept and an 
OLS estimator is consistent under the assump­
tion of no autoregressive errors. See Nickell 
(1981). If we interpret C, as a fixed effect such 
that ECj = 0 across regions, then we have a bias 
¡n the estimation of β coming from the correlation 
between the residuals and y,,_,. Then, the bias is 
proportional to the variance of the C, across re­
gions. Macroeconomists, considering small 

Appendix 5: Some Econometrics 

151 

cross­sections and long time­ series, usually do 
not take this into account, assuming that the bias 
is small. Moreover, the usual corrections imply a 
first order differentiation plus instrumentation with 
the lagged regressors, meaning a loss in the 
number of observations. Given our data limita­
tions, we have preferred to use the GSL estima­
tor, to offer an overview of what sort of estima­
tions of the impact of the Cohesion Fund we can 
provide. In a later stage, we think that in the case 
of Spain only we are able to provide other esti­
mates based on other estimators, as a check of 
the robustness of the results. 

Identification of the shocks. 

Let us come back on the following equation: 

Bl, = C' + a'­PI,_,+$'­BI,_,+y­Plt +μ,β 

The residuals μ, are shocks on regional business 
investment, linked for instance to regional busi­
ness cycles. A recurrent problem is the possible 
correlation between these residuals and the re­
gressors, here the public investment spending in 
the same year Plt. On the one hand, we expect a 
positive correlation because in downturns, bud­
get are smaller. On the other hand, investment 
spending could be used to absorb the shocks, 
and the correlation could be negative. 

We have assumed implicitly that, in absence of 
good theory to explain how governments spend 
their budget, the most reasonable assumption 
was to say the public investment spending is de­
cided one year before, which means that PI, is 
not correlated with μ,. Moreover, we consider our 
relative variables, and regional business cycles 
have much smaller sizes, from the data have 
considered. 

But as an alternative test of robustness of the re­
sults, we have explicitly considered these possi­
ble correlation. Assuming a range of [­a, a] for 
the correlation coefficient corrjPI,, μ^, we are able 
to estimate the size of the bias on the coeffi­
cients. 

Indeed, if we estimate: 

Y = b­X + z 

with a correlation coefficient c between one of 
the regressors X and e, then we can show that 
the OLS estimator β is biased, such that: 

E[ß-ö] = 

C 

se 

,1/2 χΙχ1/2 

where of course the first term of the right hand 
side goes to infinity when c goes to 1, and the 
second term can be rewritten as: 

\1/2 

Izßl 
R2 

where R2 is the fraction of the variance explained. 
In the estimation of the dynamic link between 
business investment and public investment, the 
Rsq are all close to 0.98 for the Cohesion coun­
tries, such that even for large correlations, 
c=+/­0.6, the bias is small, about 8% for Greece 
and Portugal, and 1 1% for Spain. Of course, 
when the size of residuals increases, the bias be­
comes larger. This could be an alternative expla­
nation for the negative and non significant effects 
of public investment spending that we found in 
Spain (especially Water Facilities and Non­Pro­
ductive Investment). 

152 

Section III — Vector Auto­regression Models 

Bibliography 

Blanchard O.J. and L. Katz,  1992, Regional Evolutions, Brooking Papers on Economic Activity (1),  pp1-
61 

Blanchard  O.J.  and  D.T  Quah,  1989,  The  Dynamic  Effect  of  Aggregate  Demand  and  Supply  Distur 
bances, American Economic Review 79(4), September,  pp655-73 

Greene W., 1993, Econometric Analysis,  second ed., Prentice-Hall International, Inc 

Mincer  J.,  1962,  "Labour  Force  Participation  of  Married Women: A  study  of  Labour  Supply",  in Gregg 
Lewis, ed, Aspects of Labour Economics, Princeton University Press 

Nlckell S., 1981, Biases in Dynamic  Models with Fixed Effects, Econometrica, 49(6), Nov, pp1417-26 

Bibliography 

153 

Β. REGIONAL OUTPUT AND EMPLOYMENT 
IN THE PRESENCE 
OF INTERREGIONAL SPILLOVERS 

Charles Bean 

1, Introduction and summary 

The previous chapter of this report used vector 
autoregressive (VAR) techniques to study the 
impact of local public investment spending on 
regional economic decisions by the private sec­
tor. First, it explored the impact of public 
spending on private investment decisions. Sec­
ond, it explored the effect of regional move­
ments in labour demand (which might, for in­
stance, be the consequence of movements in 
public investment) on other labour market vari­
ables such as local wages and migratory popu­
lation movements. This section of the report 
develops that work by extending it in a number 
of directions: 

(1) The impact on private investment and labour 
market outcomes are integrated into the 
same system, allowing a richer range of inter­
actions; 

(2) Regional output is also added into the picture, 
permitting also the evaluation of the conse­
quences for local output and productivity of 
increased public investment. The effect on 
output is, of course, the natural welfare mea­
sure to use in evaluating the effectiveness of 
increased Cohesion spending: 

(3) The importance of local spillovers in economic 
activity from one region Into neighbouring re­
gions is explored. An understanding of the 
strength and direction of these is important in 
moving from the impact on local economic 
variables to the aggregate Impact on the na­
tional economy. 

The price of this greater complexity is greater da­
ta requirements. For this reason we have 
presently concentrated on analysing Spain as ho­
mogenous data is available for a somewhat 
longer time period than for the other three Cohe­
sion countries. However, the lessons learned 
from the exercise should also be applicable to 
them and in due course the analysis should be 
capable of being extended to them. 

The main findings are as follows: 

(1) Allowing for the associated implications for 
employment and private investment, the elas­
ticity of regional output to net productive pub­
lic investment is around a third, that is a 3% 
increase in the local public capital stock raises 
output by around 1%; 

(2) There is evidence of important interregional 
spillovers, not only from aggregate economic 
activity, but also from activity ¡n geographically 
adjacent provinces. Consequently a localised 
public sector investment project has signifi­
cant economic effects on surrounding regions 
and, to a lesser extent, on remote regions. 
The aggregate output multiplier can be as 
large as seven, although for most regions this 
multiplier will be rather small; 

(3) The estimated marginal return from public In­

vestment varies widely across Spanish regions, 
being roughly five times greater in Madrid (the 
highest) than in Extremadura (the lowest). 

Introduction and summary 

155 

2. Theory and comparisons with other work 

The  main  theoretical  ingredient  required  to  ex 
tend the  earlier work  is simply  an augmented lo 
cal production function  relating regional output  to 
local private and public inputs: 

Yit = F(Ait, Kit, Git, Nit) 

(1) 

where Yit is output  in region I at date, A¡, is an in 
dex of the state of technical knowledge,  Kit is the 
private  capital  stock,  Git  is  public  capital  stock, 
and  Nit  is  employment.  Although  technical 
knowledge may be common across an economy, 
since  regions  differ  in their  industrial  mix  and the 
rate  of  technical  progress  differs  across  indus 
tries,  it  makes  sense  to  allow  the  Ait  to  differ 
across  regions,  as well  as  evolving  over  time  as 
the state of technical knowledge advances. 

A  number  of  authors  have  estimated  production 
functions, augmented to  include public  capital in 
puts. These include Aschauer  (1989), Holtz-Eakin 
(1992),  and  Hulten  and  Schwab  (1993)  for  the 
United  States,  and  Mas  ef  al.  (1995,  1996)  for 
Spain.  These  last  two  works  are  especially  rele 
vant  in this  case  as they  use similar  data to  that 
used  here.  In Mas  et  al. (1996)  a  Cobb-Douglas 
production function is assumed for F(.) 

ínY¡t = a¡ + bt + cínK¡, + dínG¡, + eínN¡„ 

(2) 

which  is then  estimated for  17 Spanish  (NUTS II) 
regions  over  the  period  1964-1991  (1964  and 
then  alternate  years from  1967).  Country-specific 
constants are allowed (the a¡), but the slope para 
meters are all assumed to be the same across re 
gions.  In particular  the  rate  of  technical  progress 
is assumed to be the same across all regions. 

It seemed natural to begin our investigation of the 
output  effects  of  public  investment  by  replicating 
their  results,  and  exploring  their  robustness. The 
results of re-estimating their basic equation is giv 
en  in  the  second  column  of  Table  1.  Regional 
output  is real gross value added, deflated  by the 
economy-wide GDP deflator since region-specific 
deflators are unavailable; the private capital mea 
sure  is  the  net  non-residential  private  capital 
stock;  and the public  capital  measure is the total 
productive net public capital stock (roads + water 
+  urban structures + ports + airports, but exclud 
ing  motorways;  the  reason  for  excluding  motor 
ways  is  discussed  further  below).  Similar  results 
(not  reported)  obtain  if  gross  capital  stock  mea 
sures are used. The results essentially  reproduce 
the findings of  Mas  et al}  The estimated elastici 
ties of output  with  respect  to the  inputs  are 0.11 
with  respect  to  public  capital,  0.48  with  respect 
to  private  capital,  and  0.6  with  respect  to  em 
ployment. 

While these results seem intuitively plausible, they 
are  not  without  their  problems.  First,  as  noted, 
Mas  et  al.  assume  that  the  rate  of  technical 
progress  is common  across  regions,  despite  the 
fact that is likely to differ because of differences in 
industrial  structure.  When  we  re-ran  their  model 
allowing for interregional  differences  in the rate of 
technical progress (i.e. b¡ rather than b) as well as 

The  minor  differences  that  remain appear  to  be  due to  small  differ 
ences  in the  data  set.  In  particular  Mas  er  al. use  observations  for 
1964,  1967 and biennially thereafter corresponding to the availability 
of the output  series. In order to ensure consistency of the VARs esti 
mated below, we prefer to work with regularly spaced data and have 
therefore  chosen  to  construct  an  estimated  output  series  for  1965 
by linear  interpolation  of the  1964 and  1967 observations, and then 
use data for every other year, beginning in 1965. 

Theory  and  comparisons  with  other  work 

157 

the  constants,  we  obtain  the  results  detailed  in 
the  third  column  of  Table  1. The  additional  re 
gion-specific  time  trends  are  statistically  highly 
significant  and the input  elasticities  become  0.16 
with  respect  to  public  capital,  0.49  with  respect 
to  private  capital,  and  0.41  with  respect  to  em 
ployment.  While  the  results  do  not  change  that 
much, the  low value of the employment  elasticity 
and  the  high  value  of  the  private  capital  share is 
somewhat  troubling  since  under  competitive  as 
sumptions the labour elasticity should be equal to 
labour's income share. 

Second, the results assume that measured factor 
inputs  are  the  same  as  those  effectively  em 
ployed  in  the  production  process.  In  practice 
firms  hoard  some  labour,  particularly  skilled 
labour that might be difficult to replace, during re 
cessions  rather  than  lay  it  off.  Similarly  capacity 
utilisation  varies  over  the  business  cycle.  The 
consequence  is that the estimated  parameters in 
Table  1 partly  reflect  short-run  business-cycle  ef 
fects and not just the underlying production para 
meters. This suggests the need to introduce sep 
arate variables to  control for  such business cycle 
effects. 

Finally,  and  partly  related  to  this,  the  model  ig 
nores  the  complications  introduced  by  adjust 
ment  costs.  For instance  newly  hired  labour  may 
be  less  productive  than  experienced  labour  be 
cause  it  needs  first  to  acquire  firm-specific  skills 
before  it  can  become  fully  productive  and  this 
takes time. Similarly the installation of new capital 
may temporarily  interfere with  current  production 
activities. This limitation  is revealed in the Durbin-
Watson tests  for  serial correlation  in the  estimat 

ed errors; these are highly significant and point to 
the  need  to  allow  for  a  more  sophisticated  dy 
namic structure. 

Mas  et al. (1995)  bears  many  similarities  to  Mas 
et  al.  (1996),  except  that  rather  than  estimating 
the  elasticities  of  output  with  respect  to  private 
capital  and  labour  directly,  as  in Table  1, it  relies 
on the fact that in competitive markets where fac 
tors  receive their  marginal  product  by way  of re 
muneration,  the  elasticity  of  output  with  respect 
to  the  labour  (capital)  input  will  be  equal  to 
labour's  (capital's)  income  share.  This  can  be 
used to construct  a "modified total factor produc 
tivity residual" which  in essence  is simply an esti 
mate  of  the  joint  contribution  of  technical 
progress  (Ait)  and  changes  in  public  capital  to 
output  growth. This has the advantage  of not re 
quiring  that  the  production  function  be  con 
strained  to  take  a  Cobb-Douglas  form,  but  the 
disadvantage  of  requiring  that  private  factors  re 
ceive their marginal products  by way of  remuner 
ation.  The  cumulated  estimated  productivity 
residual  is  then  regressed  on  the  public  capital 
stock  and  a  time  trend  (to  capture  technical 
progress)  in order to  estimate the former's contri 
bution  to  output.  Now  they  find  that  the  public 
capital  variable  does  not  seem  to  be  particularly 
important  in  explaining  movements  in  output, 
over  and  above  that  due  to  changes  ¡n  private 
factor inputs. The apparent inconsistency with the 
results of Mas et al. (1996) is left unexplained, but 
the analysis is open to all the same objections as 
apply to  Mas  et al. (1996), namely the apparently 
invalid  assumption  that  the  rate  of  technical 
progress  is the  same  in  all  regions,  and  the  ne 
glect of business cycle and dynamic factors. 

158 

Regional Output and Employment  in the Presence of Interregional Spillovers 

3. Methodology 

In this chapter of the report we shall extend this 
analysis by introducing more complex dynamic 
behaviour, as well as allowing for the simultane­
ous determination of employment and output. 
Vector autoregressive methods are ideally suited 
for situations where there may be complicated 
dynamic interactions of unknown form between 
variables. Suppose we have a set of n economic 
variables, Xt, whose evolution is described by the 
economic model 

BXt = d, + Α,ΧΜ + A2Xt_2 +..+ AX, ; + et 

(3) 

where the A¡ and Β are nxn matrices, d, is an 
nx1 vector describing the net effect of any deter­
ministic or exogenous variables that are deter­
mined outside the system, and e, is an nx1 vec­
tor of serially and mutually uncorrelated shocks 
with E[et] = 0 and Var[et] = D is a diagonal matrix. 
The presence of the Β matrix allows for arbitrary 
simultaneous interactions between the elements 
of Xt. Virtually any macroeconomic system one 
cares to write down can be written in essentially 
this form. While (3) may be the representation of 
interest, however, it cannot be estimated directly 
by Ordinary Least Squares in general if there are 
arbitrary non­zero elements off the diagonal of B, 
i.e. there are simultaneous interactions between 
the variables, because the right­hand side en­
dogenous regressors in any regression will be 
correlated with the equation error. We can, how­
ever, always estimate the reduced form of (3) 

X, = f, + Ο Λ ­ι + C2X,­2 +·■+ QXH + u, 

(4) 

where C¡ = Β"1Α, f, = B"1d„ u, = ß­1et. Equation (4) 
is the vector autoregression. The question is then 

under what circumstances one can recover the 
coefficients of interest, A¡ and B, from estimates 
of this reduced form? 

The classic way to identify models of this kind, 
following Sims (1981), has been to assume the 
economy has a recursive structure so that Β has 
a triangular form with ones along the diagonal 
and zeroes above the diagonal. This means that 
variables at the top of Xt are regarded as the 
"most" exogenous, whilst those at the bottom 
are the "least" exogenous, being heavily influ­
enced by contemporaneous developments in the 
other endogenous variables of the system. Mak­
ing this assumption leaves n(n­1)/2 coefficients to 
be identified in B, together with the η variances 
along the diagonal of D, I.e. a total of n(n+1)/2 
coefficients. This can be exactly accomplished by 
noting that ΒΩΒ' = D, where Ω = Var[uJ contains 
exactly n(n+1)/2 independent parameters. Since 
Ω can be estimated from the covariance matrix 
of the residuals in (4), it is straightforward to ob­
tain the implied estimate of Β from this residual 
covariance matrix. Such a triangular ordering is 
for instance invoked in the model of the previous 
chapter where it is assumed that investment in 
public capital is "more" exogenous than private 
capital. 

However, in many cases there is no compelling 
reason for assuming that Β is triangular, and in­
stead a number of authors have suggested in­
voking restrictions from economic theory con­
cerning either the short­run (e.g. Bernanke, 1986) 
or long­run (e.g. Blanchard and Quah, 1987) in­
teractions between variables; this is the so­called 
"structural VAR" approach. In order to address 

Methodology 

159 

the limitations of the results of Table 1, we shall 
be studying a sequence of VAR models describ­
ing the joint evolution of: the private (net) capital 
stock; the public (net) capital stock(s); employ­
ment; output; and unemployment, the last of 
these being included in the system so as to cap­
ture business cycle influences. While it may make 
sense to assume that, because of gestation lags, 
both private and public current investment are 
largely predetermined, this is not the case for 
output, employment and unemployment, which 
are likely to be simultaneously determined. We 
therefore choose the avenue of identifying the 
system by means of theory­based assumptions. 
In essence these assumptions will turn out to be 
similar to those employed in building traditional 
aggregate macroeconometric models. However, 
the VAR structure forces one to be quite clear ex­
actly what assumptions are being made, some­
thing that is not always the case with convention­
al macroeconometric models. 

The assumptions that we invoke here are of the 
following form. Let ux be the residual in the VAR 
corresponding to variable x, where χ is variously 
the logarithm of the private capital stock (k); the 
logarithm of the public capital stock (g), the loga­
rithm of employment (n); the logarithm of output 
(y), and the unemployment rate (u). The driving 
shocks, corresponding to the components of the 
vector e, in (3), are identified with shocks to: pri­
vate capital formation (e^, e.g. due to changes in 
interest rates or the animal spirits of investors; 
public capital formation (eg); the labour force (ef), 
e.g due to demographic developments; the nat­
ural rate of unemployment in the region (eu); and 
to aggregate demand (ed). Since investment in ei­
ther public or private capital is likely to have an 
effect on aggregate demand through the usual 
Keynesian multiplier channels, the last of these 
should be thought of as due to non­Investment 
related movements in aggregate demand, e.g. 
changes in savings behaviour. 

Our economic model is then essentially of the 
standard neo­classical synthesis variety. The 
equilibrium, or natural, level of output occurs 
when capital is fully employed and the unem­
ployment rate is at its equilibrium, or natural, lev­
el. This equilibrium unemployment rate is 
thought of as being determined within a "battle­
of­the­mark­ups" framework in the manner of 
Layard, Nickell and Jackman (1991) or Phelps 
(1994) and summarised in Bean (1994). In the 
models of those authors there is one relationship 

describing the way in which wages are set, ei­
ther by firms who need to motivate their workers 
or as the outcome of bargaining, which makes 
the real "supply" wage a decreasing function of 
the unemployment rate, and another pricing (or 
labour demand) relationship which describes 
firms' pricing and employment decisions and 
makes the real "demand" wage (usually) a de­
creasing function of the level of employment. 
Equilibrium employment, and unemployment, 
then corresponds to the level of activity at which 
the supply and demand wages are equalised. 
This is really just like a standard competitive 
equilibrium, except that it does not have the 
usual efficiency properties of a perfectly compet­
itive market, because of the presence of imper­
fections ¡n both goods and labour markets. 
Movements ¡n the natural rate can occur be­
cause of changes in e.g. unemployment bene­
fits, taxes, import prices, or the bargaining 
strength of workers. Today's natural rate may al­
so be a function of previous levels of the actual 
unemployment rate because of so­called "hys­
teresis" effects (see e.g. Blanchard and Sum­
mers, 1986; Layard and Bean, 1989). However, 
we will make the assumption that the natural 
rate of unemployment is independent of the size 
of the labour force. This is consistent with the 
observation that over long periods of time, 
stretching back to the last century, the labour 
force has steadily risen whilst the unemployment 
shows no persistent trend in either direction. 
Thus it must be the case that increases in the 
labour force ultimately show up entirely in em­
ployment. 

Deviations around these natural levels of output 
and unemployment will, however, occur when 
there are unexpected disturbances to aggregate 
demand if wages and/or prices are slow to ad­
just. Because changes in the capital stock will af­
fect the marginal product of labour, and therefore 
the demand for labour at any given real wage, it 
is also reasonable to believe that the effect of in­
vestment shocks on unemployment may be 
rather different from the effect of changes in de­
mand due to e.g. shifts in savings behaviour. This 
generates a conventional business cycle. In our 
model such business cycles can be driven either 
by shocks to private or public investment 
(through ek and eg respectively) or by other things 
(represented by ed). When a recession happens, 
firms may be "off" their production function be­
cause of the hoarding of labour and other factors 
of production. This reasoning leads us to the fol­

160 

Regional Output and Employment in the Presence of Interregional Spillovers 

lowing specification for the interrelations between 
the fundamental shocks, et, and the observed 
disturbances, ut: 

uy = auk + ßug + yun + ed (Production) 

uk = ek (Private investment) 

ug = eg 

(Public Investment) 

uu = 7iuk + pug + φβα + eu (Unemployment) 

un = §uu + e, (Employment) 

The following comments are in order. First, the 
parameters α and β on the two investment dis­
turbances in the production relationship reflect 
both their direct influence on supply (through the 
usual production function relation) and their Indi­
rect effect on the extent of labour hoarding via 
their impact on aggregate demand. Their sepa­
rate influences are not identifiable. 

Second, both private and public Investment are 
assumed to be exogenous within the current pe­
riod, and are only affected by other variables with 
a lag. This is justified by the presence of the con­
siderable planning and time­to­build lags involved 
in capital expenditures. 

Third, the unemployment rate is allowed to devi­
ate from its natural rate in the short run to the 
extent that there are cyclical disturbances pre­
sent (due to either investment, or other de­
mand, shocks) or because of changes in private 
or public capital which as noted above may 
change the marginal product of labour and 
hence shift labour demand independently of any 
effects they have through aggregate demand. 
The parameters π and ρ thus capture the influ­
ence on unemployment of shocks to private or 
public capital through both channels; again the 
size of the separate effects is not identifiable. 
The presence of "hysteresis" effects, etc., will of 
course be picked up by the lagged terms in un­
employment that appear as regressors in the 
VAR. 

Fourth, the term cpuu in the employment equation 
allows discouraged worker effects on labour 
supply. This occurs when unemployed workers 
stop looking for work because unemployment is 
high and the probability of finding work low. 
Consequently measured unemployment under­
states the true amount of slack in the labour 

market, and the measured labour force (identi­
cally equal to the sum of employment and un­
employment) understates the available labour 
supply. If such an effect were not present then 
one would have φ =1 and employment would In­
crease one­for­one with the labour force, and 
decrease one­for­one with increases ¡n unem­
ployment. However, with such an effect present 
an increase in unemployment will also generate 
a simultaneous decline in the measured labour 
force and therefore φ >1. 

Remembering that But = e,, this set of assump­
tions gives the following specification for the Β 
matrix, where X' = {k, g, u, n, y}, u' 
{uk, Ug, uu 
un, uy} and e' = (ek, eg, eu, e,, edj: 

1 0 0 0 0 
0 10 0 0 

B = 

α φ -π βφ-ρ 1 γφ -φ 

(5) 

0 0 -φ 1 0 
-α -β 0 -γ 1 

This has seven free parameters, whereas there is 
enough information to identify ten. Hence there 
are three overldentlfying restrictions which can be 
used to test the model. 

Since the parameters {α, β, γ, π, ρ, φ, φ} enter In­
to Β non­linearly, estimation of the model is at 
first sight not entirely straightforward. However, 
consideration of the structure suggests an easy 
way to proceed. First notice that our assumption 
that movements in the labour force are neutral 
with respect to the natural rate of unemployment 
means that uu is uncorrelated with e, and hence 
a consistent estimate of φ can be obtained by re­
gressing the residual from the employment equa­
tion in the VAR (an estimate of un) on the residual 
from the unemployment equation (an estimate of 
un), with the residual in this equation being an es­
timate of er. Next we can estimate α, β, and γ by 
regressing the residual from the output equation 
in the VAR (an estimate of uy) on the residuals 
from the private and public capital equations (es­
timates of uk, and ug> respectively) and the resid­
ual from the employment equation (an estimate 
of un). Estimation by Ordinary Least Squares will 
not give consistent estimates because un ¡s cor­
related with ed. However, we know that un is cor­
related with e, and that ed and e, are uncorrelat­
ed. Hence we can estimate α, β, and γ consis­
tently ¡f we estimate it by Instrumental Variables, 
using as an instrument for un the estimate of e( 

Methodology 

161 

just obtained. The residual in this regression is ing restrictions can be based on the quantity (see 
then an estimate of ed. Finally we can obtain con­ Bernanke, 1986): 
sistent estimates of π, ρ, and φ by regressing the 
residual from the unemployment equation (an es­ ­2/:n|B| + ÌT  ίηίΒΩΒ'ί  ~  y2ím) 
tímate of uu) on the residuals from the private and 
public  capital equations  (uk, and ug) and :he esti 
mate of ed just  obtained. Ordinary  Least Squares 
ts sufficient  here. Finally a test  of the overidentify- 

where  m  is the  number  of  overidentifying  restric 
tions. 

~^ 

® 

162 

Regional Output and Employment  in the Presence of Interregional Spillovers 

4, Data,and basic results for Spain 

The basic data used in the analysis is given in 
Figures 1­5. The data is biennial 1965­91 for 17 
Spanish NUTS II regions. Because of the large 
number of variables in the model it is not possible 
to estimate VARs separately for all the regions as 
there are only 14 time series observations for 
each region and after introducing lags we will 
have almost that many parameters to estimate. 
We therefore pool the data across regions, im­
plicitly assuming common dynamic responses 
across regions, although we do allow for region­
specific intercepts and time trends. However, be­
fore reporting results for our baseline model we 
first need to look at the time series properties of 
the various series to see whether they are sta­
tionary or not and, if the latter is true, whether 
there are any cointegrating relationships present1. 
To do this we again pool the data and assume 
that each region has its own intercept and re­
gion­specific time trend, but that otherwise the 
dynamic properties are the same across regions, 
and then carry out standard unit root tests. That 
is we estimate the model 

Axit = a¡ + b¡t + c­|Axit_­| + c2Ax¡,_2 + dxiM (7) 

for χ equal to k, g, u, n, and y in turn where Δχ( (= 
Xt ­ xt_i) ¡s the difference operatör. If d is signifi­
cantly below zero then the variables are best re­
garded as stationary around their region­specific 
trend. However, if d is close to, or above, zero 
then the variable in question is non­stationary 
and potentially needs to enter the VAR in first dif­
ferences. Table 2 gives the parameter estimates 

A cointegrating relationship is a linear relationship between a set of 
non­stationary variables, which is itself stationary. 

for d for each variable. Unfortunately the statisti­
cal properties of this test for a unit root in pooled 
data is presently unknown. However, in each 
case the estimate of d is so far below zero that it 
seems natural to assume that all the variables in 
the model can be treated as trend stationary. 

Table 3 reports the results of estimating our 
baseline VAR model allowing for 2 lags on each 
variable. For brevity the coefficient estimates for 
the intercepts and time trends are not reported. 
Table 4 reports F­tests for the joint significance of 
each variable in each equation. Finally Table 5 re­
ports the results of estimating the B matrix de­
scribing the simultaneous interactions between 
the variables, along the lines laid out in the previ­
ous section. In general the coefficients in this es­
timated B matrix are significant, have the right 
sign and are of plausible magnitude. Finally the 
test of overidentifying restrictions does not reject 
the model. 

The easiest way to evaluate the model, however, 
is not by inspection of the individual coefficients, 
but rather by examining how the model responds 
to exogenous shocks (the e, in equation (3)). 
These "impulse responses" are given in Figure 6. 
They are constructed by inputting a "typical" (one 
standard error) shock to ek, eg, eu, ef, and ed in 
turn and then tracing through their effects on the 
variables in the model. Reading down each col­
umn of the array gives the response of each vari­
able in turn to the particular shock noted at the 
head of the column, while reading across each 
row gives the response of the variable designated 
in the left­hand margin to each shock in turn. 
Thus the first column gives the response of each 

Data and basic results for Spain 

163 

of the variables to a private investment  shock (ej, 
the second column gives the response of each of 
the  variables  to  a  public  investment  shock  (eg), 
and so on. In reading the graphs it may be helpful 
to  remember  that  the  variables  are  all  in  loga 
rithms (other than the unemployment  rate which is 
already  in the  correct  units), so that  in each case 
a  .01  movement  corresponds  to  a  1%  point 
change.  In addition,  reading  across  any  row,  the 
scale on the vertical axis is the same for all of the 
shocks. Finally remember that the data ¡s biennial, 
so that the length of a "period" is here two years; 
the horizontal axis in each of the plots thus corre 
sponds to a 24-year horizon. 

Since  the  Impact  of  public  spending  is the  main 
focus  of  interest  in this  project  we  begin  by  dis 
cussing  the  impact  of  a  shock  to  public  invest 
ment  (the second  column). The size of the  shock 
is such that it adds 3% to the public capital stock, 
which  then  decays  away  over the  next  14 years. 
Since this might be thought of as the life of a "typ 
ical" public sector investment  project, this "shock" 
can be thought of as representing investment  in a 
project which is not subsequently  maintained as it 
depreciates1. We see that this has a complemen 
tary effect on private investment,  boosting the pri 
vate  capital  stock  by  a  maximum  of  0.75%  after 
about  10 years. The multiplier effects on demand, 
as well as any beneficial effects on the productivi 
ty  of  labour,  also  lead to  a  small  rise  in  employ 
ment  (peaking at 0.85%  after  6 years) and a cor 
responding  decline  in the  unemployment  rate (of 
0.44%  points  after  6  years).  The  increase  in all 
three  factor  inputs  implies  that  output  must  also 
rise, the maximum effect on output coming after 8 
years and registering at 0.96%. 

Is such an effect  on output  reasonable?  Suppose 
that  the  production  function  can  indeed  be  well 
represented by a Cobb-Douglas  production func 
tion  as  in  equation  (2),  and  that  the  supply  of 
labour  is fixed, as is the cost  of capital. Then  it is 
not  difficult  to  show  that  the elasticity  of employ 
ment  with  respect  to  public  capital,  allowing  for 
the induced effect on private capital formation will 
be d/(1 -c)2. If we plug in the estimates for c and d 
from  equation  1 of  Table  1 then  we  get  a value 

Analysing  a permanent  change  in the  level of  the  capital  stock  can 
be  achieved  by  inputting  an  appropriate  sequence  ot  eg  shocks, 
rather  than  a single  one.  However  It  is  usually  easier  to  assess  the 
reasonableness of a model by looking at one-off shocks. 
If  fr\Y  =  a  +  cfnK  +  ófnG  +  e/'nN  then  d(fnY)/d(fnG)  =  d  + 
c.d(fnK)/d(rnG)  if  N is fixed. The  profit-maximising  condition  for  pri 
vate capital is aY/K = r, where r is the user cost  of capital. Hence if r 
is fixed  d(fnY)/d(/nK)  =  1 and hence  d(rnY)/d(cnG)  = d(fnK)/d(mG). 
Hence the result in the text. 

for this elasticity of 0.31,  i.e. a 3% Increase in pri 
vate  capital  should  generate  a  1% increase  in 
both output and private capital. This is very much 
of  the  same  order  of  magnitude  obtained  from 
the Impulse responses. 

While  it  is the  effect  of  changes  in  the  stock  of 
public  capital that  ¡s of most  interest, it is also in 
structive to  look at the impulse responses to  oth 
er types  of  shock to  see if these too  are reason 
able. As far as exogenous  shifts  ¡n private invest 
ment  go  (column  one),  we  see  that  in the  short 
run this  boosts  employment  and output.  Howev 
er some years  down  the  road the effect  is to re 
duce employment  raise and raise unemployment, 
perhaps  because  the  extra  capital  is  predomi 
nantly directed to  substituting for labour.  Interest 
ingly  public  capital  also  declines  after  a  number 
of years. This may have something to do with the 
political  economy  of  how  public  investment  has 
been  directed  regionally  in  the  past,  e.g.  ¡t  has 
typically  been  directed  to  depressed  rather  than 
booming areas. 

A reduction  in the natural rate of  unemployment, 
e.g.  as a result  of the  improved  matching  of un 
employed  workers  to  vacant  jobs,  is  associated 
with  an  immediate  rise  in  employment.  There  is 
also  an  induced  expansionary  effect  on  private 
capital. Together  this  boosts  output.  In this  case 
the  numerical  magnitudes  are not  quite so  plau 
sible,  however. With a Cobb-Douglas  production 
function  as  in  equation  (2), the  elasticity  of  both 
output  and  private capital with  respect  to  an ex 
ogenous  increase  in  employment,  holding  the 
cost  of  capital  fixed, would  be e/(1-c);  using the 
estimates  in  Table  1  again  this  comes  out  at 
1.23.  However,  the  percentage  increase  in  both 
private  capital  and  output  is  less  than  a  half  of 
the percentage increase in employment.  One ex 
planation  for  this  may  be that  in  reality  the  extra 
workers  that  find jobs  as  a  result  of  a  decrease 
in the  natural  rate  are typically  of  lower  than av 
erage skill and quality. 

As far  as an  increase  in labour  supply  goes, this 
has  immediate  beneficial  effects  on  employment 
and output  (this is an automatic  consequence  of 
our  assumption  that  the  natural  rate  of  unem 
ployment  is  Independent  of  the  labour  force. 
There  is a small  induced  positive effect  on public 
capital  formation,  but  ultimately  a  slightly  de 
pressing effect on private capital formation. Again 
this is a slightly odd result. 

164 

Regional  Output  and  Employment  in the  Presence  of  Interregional  Spillovers 

Finally a demand  shock  has entirely  conventional 
effects, boosting employment  and, later, both pri­
vate and public  capital formation, with the conse­
quence  that  output  expands  while  unemploy­
ment falls. 

There  are  therefore  some  peculiarities  in  the 
model,  in  particular  in  relation  to  the  impact  of 
labour  market  disturbances, that  merit  some fur­
ther experimentation with alternative  identification 
schemes,  i.e.  alternative  assumptions  for  the  Β 

matrix.  However,  overall  the  results  seem  suffi­
ciently  sensible  —  particularly  in  respect  of  the 
Impact  of  public  investment  shocks  — to  make 
the  use of the  model to  evaluate the  regional im­
pact  of  public  spending  decisions  worthwhile.  In 
the rest of the chapter, we therefore explore a va­
riety  of  relevant  extensions  to  the  model,  whilst 
retaining  the  basic  identifying  and  overidentifying 
assumptions  as embodied  in B. We begin with a 
consideration  of the  implications  of  heterogeneity 
in public capital goods. 

Data and basic results for Spain 

165 

5. Differential effects from different sorts of public capital 

As noted earlier the definition of public capital 
(G) used in both the simple production function 
estimates of Table 1, and our basic VAR model 
excludes motorways. Is there any statistical or 
economic justification for this? A simple way to 
evaluate the impact of motorway expenditure is 
to note that in(1 +x) is approximately equal to χ 
¡f χ is small. If we let Gmot be the motorway capi­
tal stock, then in(G+Gmot) is approximately 
equal to (nG + Gmot/G. Consequently to evalu­
ate the Incremental impact of motorway expen­
diture all we need to do is add (Gmot/G), the ratio 
of motorway capital to other public capital, into 
the VAR and evaluate Its significance. When we 
do this, we find that the (Gmot/G) have no ex­
planatory power for the five other variables in 
our system (a formal test gives a test statistic of 
χ2(10)=12.81 which is barely significant at the 
75% level). Furthermore the impact of motor­
ways investment, such as it is, is very different 
from that obtained with the aggregate of the 
other sorts of public capital. This is illustrated in 
Figure 7 which gives the impulse responses 
from this augmented model1. Whereas an in­
crease in other public capital boosts output and 
employment by a significant, albeit modest, 
amount, increased motorway investment seems 
to have an essentially negligible effect on out­

put, and actually raises, rather than reduces, 
unemployment. 

The statistical reason why motorways seem to 
have such a nugatory effect is clear from a com­
parison of the plots for public capital (Figure 2) 
and output (Figure 5). If capital is measured in­
cluding motorway capital (dashed line) then there 
are very large upward movements in the mid­
1970s in a number of regions, particularly Navar­
ra and La Rioja, yet there is no corresponding in­
crease in output in these regions, or in any of the 
other variables. Consequently one would not ex­
pect to find a significant role for an aggregate 
capital measure which Included motorways2. 

All this begs the question as to why motorways 
should be excluded from the measure of local 
public capital, while other roads are included. 
While the motorways ¡n question are apparently 
actually operated by the private sector under 
franchise, it is not clear why this should affect 
their impact on local economic activity. One ex­
planation may be that the construction of a mo­
torway through a region has the effect of reduc­
ing transport costs and so encouraging the ag­
glomeration of economic activity at the ends of 
the motorway rather than being distributed more 
evenly as in the status quo ante. This sort of ef­

in both this, and the next, model the assumptions underlying the ma­
trix summarising the simultaneous interactions between the variables, 
Β (now 6x6). are generalised in the obvious manner and are therefore 
assumed to take the form, where the first three variables in the sys­
tem are private capital and then the two sorts of public capital: 

1 
0 
ao - π 
0 
-α 

0 
1 
β ο -ρ 
0 
-β 

0 
0 
δφ - σ 
0 
-σ 

0 
0 
1 

0 

0 
0 
γφ 
1 
-γ 

This does invite the question as to why Pissarides and Wasmer ap­
parently find an important role for motorway investment. We believe 
the answer lies in the fact that their approach, as emphasised in 
Section 7, models the behaviour of public and private investment 
relative to the economy­wide average. Now if motorway investment 
rises in all regions simultaneously, as is partly the case here, relative 
public investment will hardly change. Consequently they are not 
faced with having to deal with the troubling observation that the 
large increase in motorway expenditure did not apparently produce 
a large contemporaneous increase in output or employment. 

Differential effects from different sorts of public capital 

167 

feet comes out of the computable general equi­
librium simulations carried out in this project, for 
instance. In that case the economic effects on 
the region through which the motorway passes 
may be nugatory, or even perverse as here, al­
though it may well have beneficial effects on sur­
rounding regions. Identifying exactly which sur­
rounding regions will benefit probably requires 
being precise about exactly how the motorway 
system functions, however. By contrast general 
road networks whose impact on a region is more 
diffuse need not have the same effect of encour­
aging agglomeration. 

The next step is to see whether (non­motorway) 
transport capital (I.e. roads + ports + airports) 
have a different effect from other infrastructure 
capital (i.e. water + urban structures). Figure 8 
gives the impulse responses for a VAR model 
that includes the two sorts of capital separately 
(for brevity the full statistical results are not re­
ported). Looking down the second column (cor­
responding to an increase in water and urban 
structure investment) and the third column (corre­
sponding to an Increase in transport investment) 
we see that the impulse responses of output, 
employment, unemployment, and private capital 
are remarkably similar. The only minor asymmetry 
appears to be in the interaction between the two 
sorts of public capital, with increases in transport 
investment producing a small subsequent decline 
in water and urban capital, but the opposite be­
ing true, mutatis mutandis for an increase in wa­
ter and urban investment. However the differ­
ences appear to be slight, and one is naturally 
lead to ask whether they are statistically different. 
This is not an entirely straightforward question as 

the capital stock variables are in logarithms, so 
that the base model VAR ¡s not nested within the 
VAR with disaggregated public capital. However, 
we can construct a suitable non­nested test by 
taking the fitted values from the equations for k, 
u, n and y ¡n the VAR with disaggregated public 
capital together with the fitted values of a regres­
sion of the aggregate public capital stock, g, on 
the same set of regressors, and then including 
them in the respective equations of our base 
model VAR. If they are significant then it indicates 
that there is some explanatory power in the VAR 
with disaggregated public capital that is not pre­
sent in the base VAR model1. Somewhat surpris­
ingly given the similarity of the impulse responses 
in columns two and three of Figure 8, the model 
with disaggregated capital is preferred (the test 
statistic is χ2(5) = 21.70 which is significant at the 
99% level). However, this is entirely because it 
improves the predictability of aggregate public 
capital to model the two components separately! 
The improvement in the fit of the other equations 
of the system — which after all are the ones we 
are most interested in — is by contrast slight. 
Hence in what follows we have continued to use 
a single aggregate public capital variable2. 

This is just a multivariate generalisation of the Daviqson­McKinnon 
(1981) J­test. 
Again one may be tempted to compare this with some of the results 
that Pissarides and Wasmer obtain when they disaggregate across 
capital types. Two observations are worth recording. First, their dis­
aggregated estimates are often rather imprecise, so that while the 
point estimates for different categories may be different, those differ­
ences are often statistically not all that significant. Second, we have 
lumped together water and urban public capital on the one hand, 
and all transport capital on the other hand, a rather different disag­
gregation from them. Consequently one should exercise caution in 
comparing the results of the two studies. 

168 

Regional Output and Employment in the Presence of Interregional Spillovers 

6. Interregional spillovers 

The  analysis  so  far  has  treated  each  region  as 
though  it  were  a separate,  isolated,  economy.  In 
practice  this  is  not  a  particularly  plausible  as 
sumption as one would expect what is happening 
in one region to affect what is happening in anoth 
er.  Such  interregional  spillovers  can  have  the  ef 
fect  of  amplifying,  or  attenuating, the  effect  of in 
creased public investment and are thus potentially 
important  for  evaluating  the  economic  effects  of 
Cohesion  Fund spending. One obvious  source of 
Interregional  spillovers  would  be through  demand 
¡n the  goods  market.  Although  the  suppliers  of 
services,  such  as  haircuts,  may  be  affected  only 
by changes in the demand conditions in their local 
market,  manufacturers  are  obviously  affected  by 
demand  developments  throughout  the  economy, 
and indeed in other economies too. 

This  is  not  the  only  possible  source  of  interre 
gional spillovers, which  may occur  on the  supply 
side  as  a  result  of  externalities  from  producers' 
location  and  production  decisions.  These  exter 
nalities may be either positive or negative. First, it 
may  be sensible to  locate  near  suppliers  or cus 
tomers  in order to  reduce transport  and informa 
tion  costs.  Second, the concentration  of particu 
lar  sorts  of  production  in  a  given  locale  will en 
courage  specialised  labour  to  locate  there  too. 
Both of these considerations  will encourage  new 
producers'  to  site  their  activities  in the  same  lo 
cale  since  they  reap  extra  benefits  from  being 
close  to  existing  producers.  The  result  is  a  ten 
dency  to  the  agglomeration  of  economic  activity. 
The  chapter  by  Venables  in  this  report  exploits 
computable  general  equilibrium  techniques  to 
study these forces ¡n a variety of settings. 

Working  against  these factors  are so-called  con 
gestion  externalities  which  arise  when  the  entry 
of  another  producer  into  a  locale  puts  pressure 
on  existing  infrastructure  facilities  and  reduces 
the attractiveness  of the area to  other  firms. The 
obvious  example  is the effects  on traffic,  and the 
consequent  delays,  but  to  the  extent  that  the 
general  environment  deteriorates  such  conges 
tion  may  mean  that  workers  have  to  be  paid 
more to live and work there, etc. 

These  agglomeration  and  congestion  effects  are 
essentially  local effects. Accordingly,  in evaluating 
the  importance  of  interregional  spillovers,  it 
makes sense to consider  local, as well as econo 
my-wide,  spillovers.  While  the  latter  might  rea 
sonably be captured by the level of economic ac 
tivity  in  the  economy  as  a  whole,  the  choice  of 
variable  to  capture  local  effects  is  less  obvious. 
As a first stab at the question, however,  ¡t seems 
reasonable to  use the average  level of  economic 
activity  in the immediately  surrounding  regions as 
suitable  proxy variable.  However,  some of the  17 
NUTS  II  regions  we  have  been  working  with  so 
far  are  rather  large,  and  once  one  starts  aggre 
gating  activity  in surrounding  regions  it  begins  to 
look  quite  similar  to  aggregate  Spanish  GDP. In 
order  to  examine the  Influence  of  local  spillovers 
it would therefore be highly desirable to work at a 
finer,  NUTS  III, level  of  disaggregation.  Unfortu 
nately,  while  there  is  output  and  labour  market 
data available at NUTS III level for Spain, the cap 
ital stock data ¡s only available at the NUTS II lev 
el. We therefore are forced to compromise  by as 
suming  that  the  private  and  public  capital  stock 
in each  NUTS  III province  moves equiproportion-
ally with their regional NUTS II counterparts. 

Interregional spillovers 

169 

Before presenting results including regional 
spillover effects we first check that the results for 
our base model using the provincial data, is ap­
proximately the same as we obtained earlier us­
ing the regional data. Again for brevity the full sta­
tistical results are not reported, but Figure 9 gives 
the impulse responses from the model estimated 
on the new data set. Comparing these with Fig­
ure 6 we see that they are broadly similar, partic­
ularly in regard to the impact of public invest­
ment. This suggests that any subsequent 
changes to the results will be the consequence of 
introducing spillover effects rather than the move 
to a finer level of disaggregation. 

We then add into our VAR, as exogenous influ­
ences1, the current and two lagged values of (the 
logarithm of) Spanish GDP, and the current and 
two lagged values of the average level of (the log­
arithm of) output in those other regions that di­
rectly border any given province2. Tables 6 and 7 
give the associated statistical results for this case, 
while the impulse responses for provincial shocks 
are given in Figure 10 and for the spillover effects 
in Figure 11 The spillovers from both local output 
in the surrounding provinces and total output are 
statistically highly significant (the test statistics for 
their joint significance are χ2 (15) = 70.45 for local 
output and χ2 (15) = 219.9 for total output re­
spectively). This is still the case even if only 
lagged values of total and local output are includ­
ed (the test statistics in this case are χ2 (10) = 
22.54 for local output and χ2 (10) = 191.0 for total 

In treating them as exogenous influences, we are neglecting the fact 
that activity in the surrounding provinces is potentially affected by 
activity in the home province which will lead the current average level 
of activity in the surrounding provinces to be correlated with the er­
ror in the equation, which may lead to some bias in the coefficient 
estimates. In principle one could address this difficulty by using In­
strumental Variables, but unfortunately It is not obvious what could 
be used as an Instrument in this instance. 
For this purpose we assume that Las Palmas adjoins Tenerife, and 
vice versa, and that Baleares adjoins none. 

output). That total output should be significant is 
perhaps not too surprising, but the fact that out­
put in adjoining provinces also seems to have an 
independent influence is a quite striking result. 

Turning now to the impulse responses, there are 
some notable differences between the model 
with interregional spillovers (Figure 10) and the 
model without (Figure 9). In particular, and most 
relevant for this exercise, the effect on private in­
vestment is almost eliminated, while the effect on 
output and employment is almost halved ¡n mag­
nitude. Indeed the effect of most shocks is signif­
icantly attenuated (note that for ease of compari­
son the scales in the two figures are identical). 
However, it should be emphasised that this does 
not imply that the total output­enhancing effect of 
public investment is any lower, because one 
needs to take into account the spillover onto the 
rest of the economy, which are not included in 
the experiments of Figure 10. 

Some clue about the effects of these spillover ef­
fects is given by Figure 11. Here the experiment 
that is conducted is a once­off (i.e. temporary) in­
crease of 5% points in aggregate Spanish GDP 
(first column) and a once­off increase of 5% 
points in average output in the adjoining 
provinces (second column). Notable in each case 
is the strong positive spillover onto within­
province output, as well as the strong positive 
spillover of total output onto private capital for­
mation. The fact that the spillovers onto output 
are so short­lived suggests the primary channel 
at work here is through the demand side. It is 
possible that some of the supply­side effects dis­
cussed earlier might be better captured by in­
cluding average capital in the surrounding 
provinces, rather than average output, but unfor­
tunately in the absence of capital stock data at a 
finer level of disaggregation it is difficult to investi­
gate this hypothesis. 

170 

Regional Output and Employment in the Presence of Interregional Spillovers 

7. Cohesion spending simulations 

To conclude the analysis of the Spanish data we 
report  the  results  of  some  simulations  of the  ef 
fects  of increased  public  capital spending. There 
are at least two types of question that one might 
ask of the model(s). The first ¡s the positive ques 
tion  of  how  large  is the  impact  of  an actual Co 
hesion  Fund  project  on  the  Spanish  regions. 
Here we simulate the  impact  of the  Madrid  Ring 
Road, the Rias-Bajas  motorway, and a set of en 
vironmental  projects  covering  all  but  two  of  the 
NUTS  II  regions. The  second  type  of  simulation 
that  the  Commission  might  be  interested  in in 
volves  calculating  how  the  impact  of  a  given 
"standard  size" project  varies across  regions ac 
cording  to  its  location.  Knowing  the  answer  to 
this  should  help  the  Commission  address  the 
normative  question  as to  how  it should  direct Its 
spending  in the  most  cost-efficient  fashion.  Be 
fore  presenting  the  results,  however,  we  discuss 
a few general issues. 

Conceptually  there  are  at  least  two  ways  one 
might wish to  use the models for simulation  pur 
poses.  The  first  is  to  assume  that  Cohesion 
Fund spending  represents  a genuine  addition  to 
other  public  capital  spending,  and  leaves  the 
latter  unaffected.  In general  this  is the  objective 
of  the  Fund,  namely  that  funds  are  tied  to  par 
ticular  projects  and  are  not  supposed  to  be  an 
excuse  for  local  or  national  governments  to  si 
multaneously  reduce  their  spending  on  other 
projects.  In  order  to  do  this  we  therefore  need 
to  cut  off  the  endogenous  response  of  public 
capital  to  the  other  variables  of  the  model,  i.e. 
treat  public  capital  as  an  exogenous  process, 
and  simply  input  the  new  public  capital  se 
quence.  This  is  effectively  how  the  simulations 

in  the  chapter  by  Pissarides  and  Wasmer  are 
conducted. 

While  this  is  easily  accomplished  in  a  formal 
sense,  it  can  be  argued  that  ¡t  is  not  really  a 
valid  procedure,  since  any  change  in  the  time 
series  behaviour  of  a  policy  variable  like  public 
capital  spending  may  induce  a  corresponding 
change  in  private  sector  rules  of  behaviour.  In 
other  words  this  procedure  is  open  to  the  so-
called "Lucas Critique" which  requires that  mod 
els  for  policy  evaluation  be  specified  at  the 
"deep"  level  of  tastes  and  technology,  rather 
than  being  in  terms  of  estimated  parameters 
which  are  most  likely  complicated  convolutions 
of  these  "deep"  parameters  and  the  rules  gov 
erning  the  evolution  of  policy  and  exogenous 
variables. Whether  this  is a serious  practical  ob 
jection in the current  context  is, however,  debat 
able,  and  almost  certainly  the  procedure  of 
treating  the  public  sector  capital  stock  as  ex-
ogenously  determined  represents the way mem 
bers  of the  Commission  usually think  about  and 
evaluate the impact of projects. 

An alternative  approach  to  simulating  the  effects 
of  changes  in  public  capital  spending  is  to  input 
them as innovations  in spending, i.e. as an incre 
ment to  eg. Other  public  capital spending  is then 
allowed  to  respond. As  an  example,  the  conse 
quence of building the Madrid Ring Road may be 
to  lead  the  Spanish  government  to  do  less  (or 
more) spending  on other  roads  in the  Madrid re 
gion, or else on the local rail network, than it was 
originally  planning  to  do;  it  might  then  use  the 
funds  so  released  to  increase  public  consump 
tion or else to  lower taxes. While this may not be 

Cohesion spending simulations 

171 

what the Commission  intends to  happen as a re 
sult  of  Cohesion  Fund  spending,  it  is clearly  ap 
propriate  at  least  to  consider  the  possibility.  We 
have  therefore  explored  the  consequences  of 
simulating  the  model(s)  under  both  assumptions, 
namely  with  exogenous  public  capital  and  en 
dogenous  public  capital. The actual  outcome for 
any  particular  project  might  then  lie  somewhere 
between these two scenarios, depending how ef 
fective are the Commssion's  procedure for ensur 
ing  that  Cohesion  projects  represent  additional 
spending  and  do  not  end  up  merely  substituting 
for other public sector projects already scheduled 
to  be  undertaken.  As  noted  the  simulations  by 
Pissarides and Wasmer in the previous chapter of 
this  report  assume  that  public  sector  capital  is 
exogenous  and  therefore  do  not  allow  for  the 
"crowding-out",  or  "crowding-in",  of  non-Cohe 
sion  public  investment,  although  it turns  out  that 
it that  it makes relatively  little difference to the re 
sults if their model is simulated under the alterna 
tive  assumption  that  public  sector  capital  is en 
dogenous. 

A secondary  issue relates to the treatment  of de 
preciation. Since we have used  net  capital  stock 
measures  in the  model, the  value  of  the  original 
investment  will  be  declining  as  time  passes  un 
less  there  is  adequate  replacement  investment 
to  cover  depreciation.  The  capital  stocks  are  in 
fact  calculated  assuming  4%  depreciation. 
Therefore  in  conducting  the  simulations  with  an 
exogenous  public  capital  stock  we  have  as 
sumed  sufficient  continuing  replacement  invest 
ment  to  maintain  the  public  capital  stock  at  its 
new  higher  level  indefinitely.  Similarly  for  the en 
dogenous  public  capital  stock  assumption  we 
have input  as the sequence for eg not merely the 
immediate  costs  of  the  project,  but  also  a sub 
sequent  replacement  investment  allowance  of 
4%  per  annum  of  the  expended  initial  project 
costs. 

7.1. Simulations  of  selected 
road transport  projects: 
Madrid  Ring  Road 

mer.  The  ECU  value  of  spending  for  the  Madrid 
Ring Road as supplied by the Commission is giv 
en in Table 8, together  with the current  and con 
stant-price  peseta  equivalents.  Because  of  our 
difficulties  in  identifying  the  regional  Impact  of 
motorway  spending,  pending  resolution  of  that 
puzzle1  we  have  presently  treated  both  the 
Madrid  Ring  Road and the  Rias-Bajas  Motorway 
as though they were general spending on roads. 

We  begin  by  reporting  the  results  obtained  from 
simulating  the  base  model,  under  both  endoge 
nous and exogenous  public  capital assumptions. 
The  simulation  output  itself  ¡s  given  in  Table  9, 
while  Figures  12 (endogenous  public  capital) and 
13 (endogenous  public  capital)  give the same in 
formation  in  a  slightly  more  digestible  graphical 
form2. 

The most notable feature is the contrast  between 
the  results  under  the  two  different  assumptions. 
The  impact  effects  on  output,  employment,  etc., 
are  identical.  However,  in  the  medium  term  (by 
the  end  of  the  millenlum)  the  effects  under  the 
exogenous  public  capital  assumption  are  about 
twice  as  large  as the  effects  under  the  endoge 
nous  public  capital  assumption.  Thus  employ 
ment  is  56  thousand  higher  under  the  former, 
and  21  thousand  higher  under  the  latter.  By the 
end  of  the  simulation  period  (the year  2015)  the 
public  capital stock under the endogenous  public 
capital assumption  is just  20% of  its value under 
the  exogenous  capital  assumption.  The  effects 
on  the  other  variables  are  similarly  attenuated 
with  output  only  42  billion  1990  pesetas  higher 
rather than 325 billion higher as under the exoge 
nous capital assumption. Similarly there is virtual 
ly  no  effect  on  employment  and  unemployment 
by the  end  of  the  period  under  the  endogenous 
capital  assumption,  while  employment  is  some 
12-13 thousand higher and unemployment  some 
7  thousand  lower  with  exogenous  public capital. 
Overall  the  estimated  impact  on  employment  is 
much higher,  even under the endogenous  capital 
assumption, than  found  by  Pissarides  and Was 
mer who  obtain an effect  on employment  of 3 to 
4 thousand (there is little difference  between their 
medium and long-run estimates). 

In this section we explore  in depth  the  impact  of 
the Madrid Ring Road, using both the base mod 
el which  treats  regions  as  separate  entities,  and 
the full model with  interregional spillovers, as well 
as  attempting  to  reconcile  the  results  for  the 
same  project  obtained  by  Pissarides  and  Was-

1  It is perhaps worth recording that the particular features of the capi 
tal stock  data for  motorways  that  give rise to  these difficulties  is not 
due to any statistical oddity, such as a change  in definition, and evi 
dently does represent a genuine feature of the data. 

2  The  slight  discrepancy  between  the  numbers  in Table 9  and  those 
reported in preliminary  drafts of the report  is the result  of  uncovering 
a minor programming error in the earlier simulations. 

172 

Regional  Output  and  Employment  in the  Presence  of  Interregional  Spillovers 

We next turn to simulations with the full model  in 
corporating interregional spillovers. As noted in our 
earlier discussion of this model, the introduction of 
the interregional spillovers has the effect of attenu 
ating the impulse response functions and therefore 
might be expected to reduce the impact on output 
and employment  within the Madrid region, as  well 
as leading to a leakage into the rest of the econo 
my. While the  model is perfectly  well-behaved  un 
der the endogenous public capital assumption, we 
encountered  problems  in simulating  the model 
with  exogenous  public  capital.  Shutting  off the 
public  capital  equation  has the effect  of removing 
an important  stabilising  influence within the  model 
and leads it to become (slightly) unstable, with em 
ployment  and output  continuing to grow  beyond 
the  horizon  of the simulation  period,  despite the 
fact that public capital is held constant. This is less 
troubling than it seems, because as  already noted 
the validity of conducting simulations in this way ¡s 
open to question. We therefore only report  results 
with endogenous public capital. 

As the full  model  contains  250  separate endoge 
nous  variables  (five  variables  each  for the 50 
NUTS  III regions) we do not report  the full set  of 
estimates  in detail.  Instead  Table  10 reports nu 
merical  results for just the Madrid  region  and  the 
whole economy, as does Figure 14. However, Fig 
ures  15a to 15e do plot the simulated  effects on 
each of the five variables for all of the 50 NUTS III 
regions  and  give a clear  picture  of the  important 
role of the Interregional spillovers. (Note in reading 
these,  and similar  later,  figures  that  for any vari 
able the scales  are the same for all regions,  ex 
cept the region(s) in which the shock occurs.) 

Comparing these results with the relevant  portion 
of  Table  9, we see that  the effects  in Madrid is 
now  estimated  to be somewhat  smaller.  For  in 
stance  in 1997  regional  employment  ¡s 17 thou 
sand higher as against 25 thousand before, while 
the corresponding  numbers for 2001  are 7 thou 
sand as against  21  thousand. As a counterpoint 
to  this,  however,  we see considerable  spillover 
effects  into the  rest of the economy, with the ag 
gregate effects on output  and employment  some 
three  to four  times  greater  than  the effect on 
Madrid  alone.  Roughly  speaking  the  percentage 
increase  in each of these variables  in the rest of 
the  economy  is half  the percentage  increase in 
Madrid.  However, for private  capital the percent 
age  increase  in Madrid  and elsewhere  is similar 
(indeed  if anything  it is slightly  lower  ¡n Madrid), 
reflecting the absence of any marked direct effect 

of  local  public  investment  spending  onto  local 
private  investment,  other  than  via its effect  onto 
aggregate output. 

Are  these  results  more  consistent  with Pis-
sarides-Wasmer?  There  are two dimensions to 
consider:  the absolute  levels  of investment and 
employment;  and the shape  of the responses 
over time. As far as the first of these goes, at first 
glance  the results  of Table  10 still  seem  much 
larger  than  those  obtained  by  Pissarides and 
Wasmer,  ¡n at least the first  decade  or so of the 
simulation  period.  However  it  needs  to be  re 
membered  that  they  model the behaviour  of pri 
vate  investment  and employment  relative to  the 
average across all regions  (a sort of "representa 
tive"  region). Thus their  results pertain only to dif 
ferential  movements  between  a region  and  the 
rest of the economy. A shock that  raises employ 
ment  equiproportlonally  ¡n all regions  would  then 
register  as a nil response  in their  simulations. 
Consequently  their  estimates  for the number of 
jobs  created,  and for the increase  ¡n private in 
vestment,  in Madrid as a result of the  Ring Road 
are  strictly  only  valid  under  the assumption  that 
employment  and investment  elsewhere  in  the 
economy  does  not change.  But the simulations 
of the model  with  interregional  spillovers  shows 
that there is a significant  element of the response 
which  is common to all regions.  In that  case the 
Pissarides-Wasmer  results  should  be Interpreted 
as telling us about the excess  response of these 
variables  ¡n the Madrid  region, where by "excess 
response"  is meant  the additional  employment 
and  private  investment  over  and above  what 
would have happened if Madrid had behaved like 
the "representative" region. 

A  graphical  description  of the differences  in  the 
estimates  of these  differential  responses  is given 
in  Figure  15f. The variables  in the Pissarides-
Wasmer  model1 are (the logarithm  of) the  ratio of 
employment  in a region  to total  economy em 
ployment  and (the logarithm  of) the ratio  of pri 
vate  investment  in a region to total  economy  pri 
vate  investment.  The output  from  the simulation 
is thus  an estimate of the excess of the  percent 
age  change  ¡n employment  in Madrid  over the 
percentage  change  in employment  in the whole 
economy as a result of the  Ring  Road, and simi 
larly for private investment, mutatis mutandis. We 
can  easily  calculate  the same  statistics  for  the 

'' The simulations  of the Pissarides-Wasmer  model  here  treat  public 
investment  as endogenous, rather  than exogenous as in their  paper. 
However, the differences are minor. 

Cohesion spending  simulations 

173 

output from the model with spillovers1. Figure 15f 
graphs  the  output  from  the two  models, the up 
per  panel  being  for  the  differential  employment 
response  and the  lower  panel for the  Investment 
response. 

It is clear that the responses from the two models 
are  somewhat  different,  particularly  in  respect  of 
their  time  profile.  Thus  as  far  as  employment 
goes,  there  is  significant  front  loading  of  the  ex 
cess  response  in  the  model  with  spillovers  as 
compared  to  the  Pissarides-Wasmer  model 
where the  excess  response  is rather flat. And for 
investment, the share of the Madrid region in total 
private  investment  rises  initially  in  Pissarides-
Wasmer, but falls in the model with spillovers. We 
need  to  ask,  first,  how  the  differences  come 
about  and,  second,  whether  the  differences  are 
either  statistically  or  economically  significant.  As 
far as the first question goes this is relatively easy 
to  answer,  at  least  at  a  technical  level. The  key 
feature  that  determines  how  big  the  estimated 
excess responses are in the spillover model is the 
size  of  the  coefficients  on  the  spillover  terms  in 
aggregate  and  local  output  in the  five  equations 
of the  model. The bigger these are, then the  big 
ger  is the  réponse  of the  regions  outside  Madrid 
to  the  Ring  Road  project,  and  the  smaller  is the 
estimated  excess  response  of  Madrid.  In  a 
sense,  therefore,  one  can  say  that  the  spillover 
effects lead to "too little" response in employment 
outside  Madrid,  and  to  "too  much"  response  in 
private  investment  outside  Madrid, relative to the 
Pissarides-Wasmer  benchmark.  This  suggests 
that  further  work  to  narrow  the  differences  be 
tween  the two  models  might  in the first  instance 
concentrate  on refining the way the spillovers are 
modelled. 

As  far  as the  second  question  goes,  clearly  it is 
desirable that there be no difference  between the 
answers  given  by the two  models.  However,  the 
differences  could  be  either  statistically  (insignifi 
cant or economically  (in)significant or both. As far 
as  the  statistical  significance  of  the  differences 
go, this  is hard to give an answer  to.  In principle 
one  can  calculate  confidence  bands  for  the  im 
pulse  responses  for  both  models  by  stochastic 
simulation,  and  then  see  whether  they  overlap. 
However,  this  is  impractical  for  the  model  with 
spillovers,  as  it  would  require  re-estimating  the 

To calculate  Investment  we  use  our  knowledge  of  the  path  of  the 
capital stock together with the assumption of an annual depreciation 
rate of 4%. 

model on artificial data and simulating it at least a 
thousand  times.  The  best  we  can  do  is  simply 
note  that  models  of  this  kind  usually  do  have 
quite large confidence bands associated with the 
impulse  responses,  and  thus  it  is  entirely  possi 
ble  that  the  differences  are  not  that  statistically 
significant. 

More important for the Commission's  purposes is 
the  question  of  the  economic  importance  of  the 
differences. Figure 15f makes the differences look 
large.  However  they  need  to  be  looked  at  in 
comparison  to  the  overall  estimated  impact  of 
the project, and here they seem rather less large. 
The  maximum  excess  response  of  the  spillover 
model's  estimate  of  the  movements  in  relative 
employment  over the Pissarides-Wasmer  model's 
estmate  of the same variable  is 0.43  percentage 
points (in 1997). Since a 0.1%  change in employ 
ment  in  Madrid  equates  to  about  1.6  thousand 
jobs,  this  translates  into  a  maximum  excess re 
sponse  of  the  spillover  model  over  Pissarides-
Wasmer  of just  under  7  thousand  jobs  in  1997, 
while  for  most  of  the  time  the  difference  is very 
much less. This is in comparison to an estimated 
total  employment  response  in  Madrid  in  1997 of 
nearly 18 thousand, and 60 thousand in Spain as 
a whole. 

As far as private investment is concerned, the dif 
ferences  turn  out  to  be  somewhat  more  signifi 
cant. The maximum difference  between the  esti 
mates  from  the  two  models  is  0.68  percentage 
points  (again in  1997). This  is equivalent to  a dif 
ference in the annual rate of private investment in 
Madrid of 5bn  1990 ptas, whereas the estimated 
change in the annual rate of investment  in Madrid 
as  a  result  of  the  project  is also  in the  region  of 
5bn  1990 ptas  in 1997. However the annual rate 
of  investment  in  Spain  as  a  whole  is  higher  by 
63bn  1990  ptas  in  1997  as  a  result  of  the  pro 
ject,  so when  compared  to  the estimated  overall 
impact  of the  project,  the difference  between the 
two models is again relatively small. 

What  should  be  of  more  importance  to  the 
Commssion  than  any  differences  between  the 
estimates  from  the  two  models  of  the  relative 
employment  and  private  investment  move 
ments,  is whether  the  overall economy-wide  re 
sponse  is reasonable, and  about  which the Pis 
sarides-Wasmer  model  says  nothing.  In fact  the 
multipliers  seem  somewhat  on  the  large  side. 
For instance  by  1997  public  capital  in the whole 
of  Spain  has  risen  by  56bn  1990  ptas,  while 

174 

Regional  Output  and  Employment  in the  Presence  of  Interregional  Spillovers 

Spanish  output  is  estimated  to  be  379bn  ptas 
higher,  a  ratio  of  1:7  which  seems  intuitively 
somewhat  high.  In  part  this  high  "bang  per 
ECU"  is  due  to  the  project  being  in  Madrid 
where  the  present  ratio  of  public  capital  to  out 
put  is rather  low, and the  marginal  effectiveness 
of extra public  spending therefore  high (see also 
Section  7.4  below). For the simulations  below of 
the  Rlas-Bajas  Motorway,  where  the  marginal 
effectiveness  of public  infrastructure  spending is 
lower, the ratio is a more reasonable  1:3.5. Even 
so  it  is  probably  a  bit  bigger  than  one  might 
have  expected.  Further  work  on  this  model 
would  best  be  directed  at  exploring  the  robust 
ness of these sort of numbers. 

7.2. Simulations of selected  road 

transport  projects: 
Rias-Bajas  Motorway 

Henceforth  we just  report  simulations  using  the 
full model with  interregional  spillovers, as the re 
sults  are  more  plausible.  The  ECU  value  of 
spending  for  the  Rias-Bajas  Motorway  as  sup 
plied  by  the  Commission  is  also  given  in Table 
8,  together  with  the  current  and  constant  price 
peseta equivalents.  In evaluating this  project  we 
have  had  to  make  assumptions  both  about  the 
initial  allocation  of  public  and  private  capital 
across  NUTS III regions (recall that we only have 
data  for  these  variables  at  the  NUTS  II  level), 
and  as  to  how  the  spending  on  the  Rias-Bajas 
Motorway  is  broken  down  across  those  same 
regions.  As  far  as  the  first  question  goes,  we 
have simply assumed that the public  and private 
capital  stocks  are  distriubuted  equally  across 
the  constituent  NUTS  III  regions  within  any 
NUTS  II  region.  As  to  the  breakdown  of  the 
spending  on  the  motorway  itself,  we  have  as 
sumed that the spending  on the  Orense-Porrino 
leg  ¡s  allocated  equally  between  Orense  and 
Pontevedra,  and  that  spending  on  the  Ca-
marzana-Orense  leg  is  allocated  equally  be 
tween Orense and Zamora. 

Once again we do not give the full detailed simu 
lation  results.  However,  Table  11  reports  the  re 
sults  for  the  three  regions  in  which  the  project 
takes  place,  i.e.  Zamora,  Orense  and  Ponteve 
dra, together with the impact  on the whole econ 
omy, while Figure 16 reports the impact  on these 
three regions as a whole, together with the whole 
economy  impact.  Figures  17a to  17e then  graph 

the  impact  on each of the five variables  in all the 
50 NUTS III regions. 

Once  again  the  important  role  of  interregional 
spillovers  is illustrated, with a total economy  out 
put  effect  some four times  greater  than the  local 
effect.  The  peak  effect  on  aggregate  output  oc 
curs  in 1999 and corresponds to a %%  increase 
in Spanish GDP. Employment  in the whole econ 
omy  rises by a maximum of  60 thousand, corre 
sponding  to  1/2% of  the  employed  labour  force. 
The  regional  employment  effect  represents  only 
a  quarter  of  this.  Some  features  of  the  interre 
gional spillovers are worth  remarking  upon. Most 
notable  is  the  large  local  spillover  effect  onto 
output  in  the  adjoining  regions  of  Lugo  and  La 
Coruna.These  powerful  local  effects  reflect  the 
"peninsula"  character  of  Galicia, so that  spillover 
effects  get  "bottled  up". This is in contrast  to the 
results  for  the  Madrid  Ring  Road,  where  the 
large  number  of  adjoining  regions  means  that 
the effect  is diffused over a wider area. A second 
feature  worth  noting  is  that  private  investment 
actually  falls  in the  three  target  regions  (remem 
ber  that  for  the  Madrid  Ring  Road,  private  in 
vestment  in  Madrid  rose,  albeit  somewhat  less 
than in the rest of the country). Again this reflects 
the low direct  response of local investment to lo 
cal  public  spending, coupled  with  idiosyncrasies 
in  the  way  that  the  spillovers  work  themselves 
through. 

7.3 Simulation  of  environmental 

projects 

We  have  also  used  the  full  model  with  interre 
gional  spillovers  to  simulate  a  large  number  of 
environmental  projects  (water,  urban,  etc.) 
These  cover  15  of  the  17  NUTS  II  regions,  the 
exceptions  being  Cantabria  and  La  Rioja  where 
no  Cohesion  projects  were  specified1.  Because 
we  were  unable  to  find  any  statistically  robust 
differences  between  the  impact  of  different 
types  of  public  sector  projects  (other  than  mo 
torways)  we  have  not  tried  to  draw  any  distinc 
tion  between  the  different  sorts  of  projects,  and 
simply  reported  the  estimated  impact  of  aggre 
gate  spending  in  the  region.  The  ECU  value  of 
spending  on these  projects  is given  in Table 12. 
In  some  cases  one  might  have  knowledge 

The  Information  supplied  to  us also listed  a number  of  projects  that 
were not explicitly tied to a specific  NUTS II region. These were omit 
ted from the simulation. 

Cohesion  spending  simulations 

175 

about  the  characteristics  of  a  particular  project, 
for  instance  from  microeconomic  cost-benefit 
analysis, that  leads one to expect the effects will 
be  larger  or  smaller  than  that  for  the  "typical" 
public  sector  project  to which the  model simula 
tions  refer.  In that  case  it would  be perfectly  ap 
propriate  to  magnify  or  attentuate  the  simulated 
effects  appropriately,  using  this  ancillary  knowl 
edge.  However,  since  we  do  not  presently  have 
information  about  the  precise  nature  of the  pro 
jects, we did not feel it was appropriate to  make 
such  ad  hoc  adjustments  here. As with the  sim 
ulation of the  Rias-Bajas  Motorway,  we have as 
sumed  that  private  and  public  capital  is distrib 
uted  evenly  throughout  any  given  NUTS  II  re 
gion, and that  the  urban and environmental  pro 
jects  are  likewise  distributed  evenly  within  the 
NUTS  II  region,  so  that  the  size  of  the  public 
spending  shock  is  the  same  ¡n all  NUTS  III re 
gions  within  a given  NUTS  II region.  More  detail 
on  the  individual  projects  would  enable  this  as 
sumption to  be refined. 

Again  given the  large amount  of  information, we 
do  not  give  details  for  every  region.  However 
Table  13  gives  the  effect  on  the  aggregate 
economy,  while  Figure  18 gives  a graphical  pre 
sentation  of  the  same  information.  The  detailed 
responses  by  NUTS  III  region,  for  each  of  the 
variables,  is  given  in  Figures  19a  to  19e.  As 
there  is  rather  a  lot  of  output,  we  shall  not  dis 
cuss  the  results  for  each  region  in  detail.  How 
ever,  generically  the  time  profile  of  the  inputs  is 
rather  similar  across  regions,  being  concentrat 
ed  over  the  three-year  period  1993-5.  Conse 
quently  the  general  characteristics  of  the  dy 
namic  responses  are  also  extremely  similar 
across  regions,  and  replicate  the  gérerai  fea 
tures  discussed  above  in  connection  with  the 
transport  projects.  Where  they  do  differ  across 
regions  is  in  the  absolute  magnitude  of  the  re 
sponses.  In part this  is due to the different  sizes 
of  the  projects  in  the  different  regions,  as  evi 
denced  by  Table  12.  However,  they  also  differ 
because  of  the  initial  positions  of  regions  differ, 
with  some  regions  having  lower  levels  of  public 
capital  relative to  output,  and therefore  a higher 
marginal  product  of  public  capital,  than  others. 
This  leads  us  naturally  to  the  question  of  where 
the  Commission  should  direct  its  funds  if  it 
wishes  to  get  the  maximum  "bang-per-buck"  in 
terms of output  per  ECU expended. This is best 
done  by  looking  at  how  the  effects  of  a  "stan 

dard"  project  of  fixed  size  vary  according  to 
which  region it is located in. 

7.4 Simulations  of a "standard" 

project 

What  we  have  therefore  done  is  use  the  model 
to  estimate  the  impact  on  the  level  of  regional 
gross  value  added  of  a  public  capital  project  of 
specified size for each region in turn.  Because of 
lack  of  time,  we  have  not  been  able  to  do  this 
for  the  full  model  (which  requires  running  50 
separate  simulations).  However,  the  results  on 
the  base  model  on  the  17  NUTS  II  regions  are 
indicative of the sort of results one might  expect. 
The  project  size  is  set  at  7480  million  ECU, 
which  is  roughly  equivalent  to  one  billion  1990 
pesetas.  The  impact  after  12  years,  under  both 
the  endogenous  and  exogenous  public  capital 
assumptions  is given in Table 14 (the rank order 
ing  after  other  simulation  periods  ¡s  Identical). 
There  is  a  wide  range  of  effects.  For  instance, 
under  the  endogenous  public  capital  assump 
tion,  the  smallest  effect  on  output,  ¡n  Ex 
tremadura,  is  only  330  million  pesetas.  By  con 
trast  the  largest  effect,  in  Madrid,  comes  in  at 
1.67  billion  pesetas.  In  case  this  seems  like 
rather  a  wide  spread,  it  should  be  pointed  out 
that  the  differences  in  the  figures  are  a  direct 
consequence  of  the  differences  in  output-public 
capital  ratios  across  regions, since with the  par 
ticular  production  technology  assumed  here the 
marginal  return  to  public  investment  will  be di 
rectly  proportional  to  the  output-public  capital 
ratio.  The  return  to  a  public  project  is  so  much 
higher  in Madrid than  in Extremadura  simply be 
cause the output-capital  ratio is five times higher 
in  Madrid  than  ¡n  Extremadura.  Clearly  it  would 
be  desirable  to  work  with  more  flexible  produc 
tion functions that  did not  necessarily  have quite 
such  a  close  link  between  the  output-public 
capital  ratio  and  the  marginal  return  to  public 
sector  investment.  However,  it  is  unlikely  that 
there  is enough  information  in the available  data 
to  pin  down  the  relationship  between  the  out 
put-public  capital  ratio  and  the  marginal  return 
to  public sector  investment  much more precisely 
than  here,  and  using  the  existing  public  capital 
to  output  ratio  as a guide to  the  location  of  Co 
hesion  projects  seems  as  good  a rule of  thumb 
to use as any. 

176 

Regional Output and Employment  in the Presence of Interregional Spillovers 

8.  Concluding Remarks 

This chapter  has extended the work  of  Pissarides 
and Wasmer  in  developing  a  more  complex VAR 
model of the Spanish regions. The key features ar 
eas  follows.  First,  regional  output  is  incorporated 
into the model, as well as public and private invest 
ment,  and  employment  and  unemployment.  Sec 
ond, the structure is generalised to allow feedback 
from  output  and the  labour  market  to  public  and 
private investment decisions, with economic theory 
being  used to  identify  the  model  structure. Third, 
by  modelling  the  levels  of  each  of  the variables, 
rather than defining them relative to the economy-
wide  average  or  aggregate  as  in  Pissarides-Was 
mer, we can say something about absolute, as well 
as  relative  movements  in the  variables  of  interest. 
Fourth,  we  extend  our  Base  Model  to  allow  for 
economy-wide  and  local  Interregional  spillovers, 
which  are found  to  be  both  statistically  and eco 
nomically  important.  These  interregional  spillovers 
are also an important feature of the general equilib 
rium  modelling  of  transport  networks  undertaken 
by Venables elsewhere in this study. 

From an economic  perspective, the main findings 
are as follows: 

(1) Allowing  for  the  associated  implications  for 
employment  and  private investment, the elas 
ticity of regional output to net productive pub 
lic  investment  is around  a third to  a half,  that 
is  a  3%  increase  in  the  local  public  capital 
stock  raises  output  by around  1-1.5%  (this is 
true  in  both  the  Base  Model  and  the  model 
with  interregional  spillovers,  although  in  the 
latter  case  there  are  also  significant  positive 
effects  on  activity  outside  the  region  in which 
the investment takes place); 

(2) There  is  evidence  of  important  interregional 
spillovers,  not  only  from  aggregate  economic 
activity, but also from activity in geographically 

adjacent  provinces.  Simulations  of  specimen 
Cohesion  Fund  projects  Indicate  that  these 
greatly magnify the impact of a typical  project. 
For  instance  more  than  two-thirds  of  the  in 
creased  output  due to  the  Madrid  Ring  Road 
takes  place  outside  the  Madrid  region,  while 
only a quarter  of the Increase in output  due to 
the  Rias-Bajas  Motorway  extension  occurs  in 
Zamora-Orense-  Pontevedra.  The  presence 
of  these  interregional  spillovers  is also  impor 
tant  in  reconciling  the  results  obtained  here 
with  those  obtained  by  Pissarides  and  Was 
mer for the same projects; 

(3) The estimated  marginal  return from  public  in 
vestment  varies  widely  across  Spanish  re 
gions.  We  estimate  it to  be five times  greater 
in  Madrid  (where  the  return  is  the  highest) 
than  in  Extremadura  (where  the  return  is the 
lowest),  although  at  the  present  time  this  re 
sult is provisional  being based only on simula 
tions  with  the  base  model,  rather  than  with 
the full model with interregional spillovers. 

In conclusion, it should be noted that this model is 
still  somewhat  in the  developmental  stage, having 
grown  out  of  the  earlier  work  of  Pissarides  and 
Wasmer. We have seen that their work, while highly 
suggestive,  misses  the  economy-wide  conse 
quences of public sector investment projects by fo 
cussing on movements relative to the representative 
region; these appear to be quantitatively significant. 
The first objective in extending this work should be 
exploring the  robustness  of  the  estimated magni 
tude  of  these  spillover  effects  onto the  rest  of the 
economy. Subsidiary  issues needing further investi 
gation  include the  best  way to  incorporate  motor 
ways and further exploration of the differences with 
Pissarides-Wasmer.  Finally the  model  needs to  be 
extended to the other Cohesion countries. 

Concluding  Remarks 

177 

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lem", NBER Macroeconomics Annual, 1, 15-77. 

Davidson,  Russell, and James  McKinnon  (1981),  "Several tests  for  model specification  in the  presence 
of alternative hypotheses", Econometrica,  49, 781-793. 

Holtz-Eakin,  Douglas  (1992),  "Public  sector  capital  and  the  productivity  puzzle",  Working  paper  4144, 
National Bureau of Economic Research. 

Hulten,  Charles  R, and  Robert  M. Schwab  (1993),  "Endogenous  growth,  public  capital, and the  conver 
gence of regional manufacturing industries", Working paper 4538, National Bureau of Economic Research. 

Layard,  P. Richard  G., and  Charles  R.  Bean  (1989),  "Why  does  unemployment  persist?",  Scandinavian 
Journal of Economics,  91(2), 371-396. 

Layard, P. Richard G.; Stephen Nickell, and Richard A. Jackman (1991), Unemployment: Macroeconom 
ic Performance and the Labour Market.  Oxford University Press. 

Mas, Matilde; Joaquin Maudos; Francisco Perez and Ezequiel Uriel (1995), "Public capital and productiv 
ity in the Spanish regions (1964-1991)", mimeo, Instituto Valenciano de Investigaciones Económicas. 

Mas,  Matilde; Joaquin  Maudos;  Francisco  Perez and  Ezequiel  Uriel (1996),  "Infrastructuresand  produc 
tivity in the Spanish regions", Regional Studies, 30 (forthcoming). 

Phelps, Edmund S. (1994) Structural Slumps, Harvard University Press. 

Sims, Christopher,  (1980), "Macroeconomics and Reality", Econometrica,  48,  1-48. 

Bibliography 

179 

TABLE 1 : Simple Production  Functions 

(Dependent variable 

is  f.nY) 

Independent Variable 

Equation 1 

Equation 2 

Trend 

/πΚ 

iriG 

rnN 

Standard  error 

Durbin-Watson 

Note: t-statistics  in parentheses 

.0315 
(7.21) 

.477 
(15.25) 

.105 
(2.84) 

.598 
(11.19) 

.055 

1.15 

TABLE 2:  Unit Root Tests 

Variable 

k 

9 
u 

n 

y 

d 

-.279 

- . 3 77 

-.801 

- . 8 74 

- . 70 

Region-
specific 

.497 
(14.55) 

.156 
(3.14) 

.405 
(6.44) 

.046 

1.57 

t-statistic 

9.02 

6.52 

11.56 

10.76 

11.03 

TABLE 3 

:  Estimate of Base VAR Model 

Dependent Variable: 

Private Capital (k) 

Public Capital (g) 

Unemployment  (u) 

Employment (n) 

Output (y) 

k,-, 

kt_2 

9t-i 

9.-2 

Ul-1 

U,-2 

n,-i 

Π|-2 

Yt-1 

y.-2 

Standard  Error 

1.09 
(14.85) 

- 0 . 44 
(6.82) 

- 0 . 04 
(0.65) 

0.06 
(1.26) 

- 0 . 14 
(0.95) 

- 0 . 48 
(2.89) 

0.05 
(0.70) 

-0.28) 
(4.15 

0.17 
(4.11) 

- 0 . 08 
(1.73) 

0.022 

- 0 . 03 
(0.29) 

- 0 . 23 
(2.34) 

0.91 
(11.17) 

- 0 . 22 
(2.97) 

0.04 
(0.19) 

0.06 
(0.22) 

0.10 
(1.03) 

- 0 . 05 
(0.53) 

0.18 
(2.77) 

- 0 . 04 
(0.58) 

0.033 

- 0 . 01 
(0.18) 

0.16 
(3.19) 

- 0 . 06 
(1.50) 

0.03) 
(0.68 

0.79 
(6.66) 

- 0 . 26 
(1.99) 

- 0 . 01 
(0.14) 

0.05 
(0.87) 

- . 09 
(2.61) 

- 0 . 02 
(0.44) 

0.017 

- 0 . 24 
(1.78) 

- 0 . 11 
(0.91) 

0.11 
(1.12) 

- 0 . 02 
(0.27) 

0.36 
(1.29) 

0.10 
(0.34) 

0.61 
(5.03) 

- 0 . 23 
(1.85) 

0.11 
(1.37) 

0.10 
(1.18) 

0.040 

- 0 . 03 
(0.17) 

0.02 
(0.13) 

0.08 
(0.69) 

0.14 
(1.31) 

0.55 
(1.67) 

- 0 . 31 
(0.84) 

0.38 
(2.64) 

- 0 . 23 
(1.56) 

0.54 
(5.82) 

- 0 . 16 
(1.67) 

0.048 

Note: Region 

specific constants and trends omi 

ted: t-statistics in 

parentheses. 

180 

Regional Output and Employment  in the Presence of Interregional Spillovers 

TABLE 4:  F-Tests of Joint Significance of Regressors in Table 5 

Dependent Variable: 

Private Capital (k) 

Public Capital (g) 

Unemployment (u) 

Employment (n) 

Output (y) 

k 

9 
u 
n 

y 

138.85 
0.87 
8.02 
10.13 
8.97 

8.42 
92.73 
0.08 
0.53 
3.85 

11.77 
1.33 
23.41 
0.44 
3.84 

8.34 
0.97 
0.89 
13.15 
2.00 

0.01 
3.88 
1.40 
3.51 
17.16 

Note: All statistics are 

distributed as F(2,160). 

TABLE 5:  Estimates of Β Matrix 

Uy  = 

0.67uk  +  0.17ug  +  0.33un  +  ed 
(4.39) 

(2.76) 

(1.88) 

St.error: 0.042 

Uu  = 

-  0.32uk -  0.09ug  -  0.05ed  +  eu 

St.error:  0.014 

(6.33) 

(2.60) 

(1.73) 

Un  = 

-  1.78uu + 
(17.18) 

e, 

St.error: 0.023 

Test of overidentifying  restrictions: χ2(3) = 

5.73 

Tables 

181 

TABLE 6: 

Estimate of VAR Model with Spil 

overs 

Dependent Variable: 

Private Capital (k 

Public Capital (g) 

Unemployment (u) 

Employment (n) 

Output (y) 

k,-, 

ki-2 

9 t -i 

9t-2 

U|-1 

U|-2 

nt-i 

n,_2 

yt-i 

yt-2 

yt 

yt-i 

yt-2 

y*t 

yVi 

y*t-2 

Standard Error 

1.05 
(25.67) 
-0.47 
(13.48) 
0.03 
(1.14) 
0.01 
(0.46) 
-0.11) 
(2.21 
-0.14 
(2.71) 
-0.01 
(0.31) 
-0.07 
(3.12) 
0.00 
(0.06) 
-0.05 
(3.01) 
0.37 
(9.34) 
-0.10 
(1.65) 
0.22 
(4.60) 
-0.04 
(1.53) 
0.03 
(1.12) 
0.08 
(2.93) 
0.016 

0.12 
(1.68) 
-0.32 
(5.43) 
0.92 
(22.16) 
-0.31 
(7.76) 
-0.01 
(0.17) 
0.04 
(0.45) 
0.02 
(0.56) 
0.10 
(2.52) 
0.01 
(0.27) 
0.00 
(0.16) 
0.11 
(1.61) 
0.20 
(1.99) 
-0.23 
(2.76) 
0.01 
(0.22) 
-0.12 
(2.44) 
0.06 
(1.32) 
0.028 

0.02 
(0.48) 
0.24 
(6.18) 
-0.06 
(2.21) 
0.02 
(0.95) 
0.57 
(10.37) 
-0.28 
(4.79) 
0.00 
(0.16) 
-0.03 
(1.20) 
-0.01 
(0.40) 
-0.01 
(0.41) 
-0.15 
(3.48) 
0.07 
(1.05) 
-0.16 
(3.05) 
-0.08 
(2.49) 
0.02 
(0.68) 
-0.02 
(0.64) 
0.018 

-0.01 
(0.11) 
0.32 
(3.61) 
0.13 
(2.14) 
-0.12 
(1.97) 
-0.12 
(0.96) 
0.34 
(2.48) 
0.56 
(10.35) 
0.04 
(0.68) 
-0.08 
(1.82) 
0.12 
(2.85) 
0.43 
(4.24) 
-0.33 
(2.23) 
0.12 
(0.96) 
0.21 
(2.89) 
-0.08 
(1.10) 
0.05 
(0.69) 
0.042 

0.04 
(0.33) 
0.00 
(0.00) 
0.15 
(2.39) 
-0.02 
(0.40) 
0.03 
(0.25) 
-0.07 
(0.50) 
0.09 
(1.56) 
0.11 
(1.84) 
0.40 
(9.35) 
-0.18 
(4.22) 
0.56 
(5.43) 
-0.39 
(2.62) 
0.14 
(1.17) 
0.51 
(6.84) 
-0.33 
(4.37) 
0.08 
(1.13) 
0.042 

Note: y, Is economy-wide  output; y", is average of output  (y,) In adjoining  provinces;  region-specific  constants and trends  omitted 
for brevity; t-statistics  in parentheses. 

TABLE 7:  Estimates of Β Matrix for Model with Spillovers 

uy=  0.21 uk  +  0.06Ug  +  0.29un  +  ed 
(0.97) 

(2.03) 

(0.61) 

uu  = - 0 . 3 9 uk-  0.07ug  -  0.04ed  +  eu 
(1.55) 

(2.21) 

(1.46) 

St.error: 0.037 

St.error:  0.016 

un =  -  1.26uu + 

e, 

St.error:  0.031 

(15.76) 

Test of overidentifying restrictions: χ2(3)  =  5.40 

182 

Regional Output and Employment  in the Presence of Interregional Spillovers 

TABLE 8:  Values of  Road Transport  Projects 

1993 

1994 

1995 

1996 

1997 

1998 

168 

26874 

22432 

72 

11501 

9215 

Madrid  Ring  Road 

Eligible  costs  (M.  ECU) 

Current  prices  (M.  pesetas) 

1990  prices  (M.  pesetas) 

Rias-Bajas  (Galicia) 

Eligible  costs  (M.  ECU) 

Current  prices  (M.  pesetas) 

1990  prices  (M.  pesetas) 

Rias-Bajas  (Cas.&Leon) 

Eligible  costs  (M.  ECU) 

Current  prices  (M.  pesetas) 

1990  prices  (M.  pesetas) 

Source: Data provided by Cohesion Fund 

52 

8264 

6318 

133 

25080 

19174 

12 

1981 

1450 

65 

12318 

9017 

212 

40239 

29458 

15 

2741 

1919 

170 

32119 

22492 

53 

9909 

6641 

Tables 

183 

co 
4^ 

IQ 
O' 
n 
tu 

m 
3 

CD 

"0 

GO 

Table 9: Simulations  of Madrid Ring Road with Base  Model 

1995 

1999 

1997 

1993 

2003 

2001 

2005 

2011 
These simulations treat the public capital stock as exogenous and assume 4% per annum replacement for depreciation. 
Private  Capital  (%) 
2.10 
Public Capital  (%) 
5.25 
Unemployment  (%) 
-1.07 
Employment  (%) 
2.19 
Output  (%) 
3.79 
Private Capital (B.1990 ptas) 
143.43 
Public Capital (B.1990 ptas) 
39.44 
Unemployment  (Thousands) 
-19.90 
Employment  (Thousands) 
35.89 
Output  (B.1990 ptas) 
319.97 

2.69 
5.25 
-1.76 
3.20 
3.87 
183.88 
39.44 
-32.70 
52.40 
326.98 

2.18 
5.25 
-1.91 
3.47 
3.92 
149.29 
39.44 
-35.53 
56.75 
330.88 

1.36 
5.25 
-1.83 
3.33 
3.69 
93.02 
39.44 
-34.09 
54.47 
311.45 

0.52 
5.25 
-1.49 
2.67 
2.92 
35.83 
39.44 
-27.82 
43.67 
246.75 

2.82 
5.25 
-1.48 
2.75 
3.79 
192.69 
39.44 
-27.52 
45.01 
319.56 

2.38 
5.25 
-1.07 
2.16 
3.76 
162.74 
39.44 
-20.01 
35.35 
317.13 

0.07 
5.06 
-0.89 
1.55 
1.77 
4.62 
38.01 
-16.62 
25.41 
149.64 

2.67 
5.25 
-1.22 
2.35 
3.75 
182.26 
39.44 
-22.67 
38.51 
316.28 

0.00 
2.99 
-0.26 
0.46 
0.67 
0.00 
22.46 
-4.79 
7.50 
56.46 

2009 

2007 

2013 

2015 

1.91 
5.25 
-1.16 
2.37 
3.83 
130.32 
39.44 
-21.54 
38.84 
322.97 

1.83 
5.25 
-1.28 
2.59 
3.85 
125.35 
39.44 
-23.75 
42.43 
325.09 

These simulations treat the public capital stock as 
0.07 
Private  Capital  (%) 
Public Capital  (%) 
5.20 
Unemployment  (%) 
-0.66 
Employment  (%) 
1.13 
1.16 
Output  (%) 
Private  Capital (B.1990 ptas) 
4.62 
Public Capital (B.1990 ptas) 
39.05 
Unemployment  (Thousands) 
-12.21 
Employment  (Thousands) 
18.51 
Output  (B.1990 ptas) 
97.71 

0.00 
2.99 
-0.26 
0.46 
0.67 
0.00 
22.46 
-4.79 
7.50 
56.46 

endogeno 
0.36 
4.93 
-0.86 
1.56 
1.53 
24.53 
37.00 
-15.99 
25.56 
129.11 

us and assume 4% per annum replacement for depreciation. 

0.87 
4.03 
-0.83 
1.60 
1.91 
59.36 
30.29 
-15.45 
26.18 
160.96 

1.32 
3.21 
-0.68 
1.32 
1.92 
90.46 
24.13 
-12.67 
21.61 
162.25 

1.49 
2.49 
-0.47 
0.89 
1.63 
101.62 
18.70 
-8.66 
14.62 
137.82 

1.35 
1.83 
-0.22 
0.46 
1.28 
92.03 
13.76 
-4.12 
7.47 
108.02 

1.02 
1.30 
-0.01 
0.12 
0.99 
69.97 
9.78 
-0.26 
1.94 
83.55 

0.65 
0.98 
0.10 
-0.05 
0.77 
44.38 
7.33 
1.80 
-0.83 
65.34 

0.32 
0.87 
0.10 
-0.04 
0.62 
21.79 
6.51 
1.82 
-0.71 
52.39 

0.09 
0.93 
0.02 
0.09 
0.53 
6.21 
6.99 
0.37 
1.48 
44.75 

-0.01 
1.10 
-0.09 
0.27 
0.50 
-0.74 
8.24 
-1.69 
4.50 
42.48 

ÇJ 
CD 

CO 
en 

Table 10: Simulations of Madrid Ring Road with Full Model with Interegional  Spillovers 

2009 

2011 

2013 

2015 

1999 

1993 

2003 

1995 

2005 

2007 

2001 

0.07 
3.02 
-0.16 
0.28 
0.41 
4.81 
21.15 
-3.04 
4.58 
34.19 

0.32 
4.57 
-0.62 
1.07 
1.63 
21.83 
32.05 
-11.56 
17.52 
137.36 

0.19 
5.18 
-0.48 
0.96 
1.25 
13.25 
36.37 
-8.90 
15.63 
105.58 

1997 
These simulations treat the public capital stock as endogenous and assume 4% per annum replacement for depreciation. 
Madrid 
Private Capital (%) 
Public Capital (%) 
Unemployment  (%) 
Employment  (%) 
Output  (%) 
Private Capital (B.1990 ptas) 
Public Capital (B.1990 ptas) 
Unemployment  (Thousands) 
Employment (Thousands) 
Output (B.1990 ptas) 
Whole Economy 
Private Capital (%) 
Public Capital (%) 
Unemployment  (%) 
Employment  (%) 
Output (%) 
Private Capital (B.1990 ptas) 
Public Capital (B.1990 ptas) 
Unemployment  (Thousands) 
Employment (Thousands) 
Output (B.1990 ptas) 

0.56 
1.98 
-0.42 
0.42 
1.28 
38.56 
13.90 
-7.82 
6.95 
107.98 

0.46 
3.15 
-0.58 
0.78 
1.60 
31.43 
22.07 
-10.75 
12.76 
135.07 

0.26 
0.47 
-0.22 
0.43 
0.66 
147.28 
48.60 
-27.47 
53.63 
336.46 

0.46 
0.54 
-0.30 
0.48 
0.74 
260.74 
55.82 
-37.52 
59.91 
378.78 

0.15 
-0.41 
0.26 
-0.48 
-0.25 
83.60 
-42.39 
32.41 
-60.76 
-124.97 

0.71 
0.27 
-0.25 
0.30 
0.70 
402.25 
27.87 
-31.33 
37.37 
357.52 

0.65 
0.05 
-0.09 
0.04 
0.45 
370.96 
4.94 
-11.32 
4.71 
231.30 

0.45 
-0.20 
0.09 
-0.24 
0.11 
256.46 
-20.20 
11.87 
-30.75 
55.65 

0.63 
0.45 
-0.32 
0.45 
0.77 
354.93 
46.33 
-40.73 
56.13 
393.05 

0.07 
0.23 
-0.07 
0.15 
0.24 
40.43 
23.56 
-9.21 
18.89 
123.21 

0.56 
1.25 
-0.22 
0.09 
0.79 
38.21 
8.75 
-4.15 
1.53 
66.82 

0.41 
0.81 
-0.04 
-0.19 
0.32 
28.13 
5.71 
-0.67 
-3.06 
26.86 

0.15 
0.57 
0.12 
-0.41 
-0.07 
9.95 
3.97 
2.19 
-6.76 
-5.81 

-0.16 
0.46 
0.21 
-0.54 
-0.33 
-11.26 
3.20 
3.93 
-8.76 
-27.64 

-0.18 
-0.54 
0.35 
-0.61 
-0.51 
-100.50 
-55.22 
44.46 
-76.36 
-261.67 

-0.43 
0.48 
0.22 
-0.52 
-0.44 
-29.22 
3.37 
4.14 
-8.45 
-37.08 

-0.43 
-0.53 
0.35 
-0.58 
-0.64 
-245.53 
-54.13 
44.58 
-73.09 
-325.35 

-0.57 
0.63 
0.15 
-0.35 
-0.39 
-38.71 
4.44 
2.77 
-5.73 
-33.02 

-0.55 
-0.38 
0.26 
-0.41 
-0.59 
-314.11 
-38.78 
32.79 
-51.63 
-302.84 

-0.54 
0.88 
0.01 
-0.08 
-0.19 
-37.15 
6.20 
0.24 
-1.31 
-16.16 

-0.51 
-0.13 
0.10 
-0.14 
-0.39 
-291.20 
-13.09 
12.67 
-17.91 
-199.88 

co 
CD 

u 
CD 
CD 
O' 

"O 
O 
3 
CD 

"Ό 
3 

cg 
o' 

CO 
"D 

-0.64 
23.16 
-2.76 
4.62 
7.40 
-5.85 
34.92 
-4.96 
7.59 
26.11 

-0.28 
28.78 
-2.24 
4.25 
5.41 
-2.54 
43.38 
-4.01 
6.98 
19.10 

-0.77 
15.02 
-2.30 
2.83 
6.49 
-7.09 
22.64 
-4.13 
4.65 
22.92 

0.04 
18.21 
-0.79 
1.13 
1.61 
0.39 
27.46 
-1.41 
1.85 
5.68 

2005 
-0.31 
6.40 
-0.97 
0.59 
2.46 
-1.39 
7.02 
-0.74 
0.37 
5.00 

2003 
-0.49 
9.33 
-1.48 
1.23 
4.19 
-2.21 
10.25 
-1.12 
0.77 
8.51 

2001 
-0.56 
14.98 
-2.12 
2.64 
5.94 
-2.52 
16.45 
-1.61 
1.65 
12.08 

1995 
0.05 
12.94 
-0.56 
0.82 
1.16 
0.24 
14.21 
-0.43 
0.51 
2.35 

1999 
-0.45 
22.63 
-2.49 
4.30 
6.36 
-2.03 
24.85 
-1.88 
2.69 
12.93 

Table  11:  Simulations  of  Rias-Bajas  Motorway  with  Full  Model with  Interegional  Spillovers 
Zamora 
1997 
Private  Capital  (%) 
-0.11 
Public Capital  (%) 
26.58 
Unemployment  (%) 
-1.84 
Employment  (%) 
3.35 
Output  (%) 
4.23 
Private  Capital (B.1990  ptas) 
-0.50 
Public Capital (B.1990  ptas) 
29.18 
Unemployment  (Thousands) 
-1.39 
Employment  (Thousands) 
2.09 
Output  (B.1990 ptas) 
8.60 
Oense 
Private Capital  (%) 
Public Capital  (%) 
Unemployment  (%) 
Employment  (%) 
Output  (%) 
Private Capital  (B.1990 ptas) 
Public Capital (B.1990  ptas) 
Unemployment  (Thousands) 
Employment  (Thousands) 
Output  (B.1990 ptas) 
Pontevedra 
Private Capital  (%) 
Public Capital  (%) 
Unemployment  (%) 
Employment  (%) 
Output  (%) 
Private Capital (B.1990 ptas) 
Public  Capital  (B.1990  ptas) 
Unemployment  (Thousands) 
Employment  (Thousands) 
Output  (B.1990 ptas) 
Whole  Economy 
Private  Capital  (%) 
Public  Capital  (%) 
Unemployment  (%) 
Employment  (%) 
Output  (%) 
Private  Capital  (B.1990  ptas) 
Public Capital (B.1990  ptas) 
Unemployment  (Thousands) 
Employment  (Thousands) 
Output  (B.1990  ptas) 

0.07 
0.56 
-0.08 
0.16 
0.24 
37.33 
57.32 
-10.12 
20.14 
121.18 

0.23 
0.96 
-0.23 
0.46 
0.64 
130.74 
98.55 
-29.45 
57.53 
323.98 

0.39 
0.89 
-0.30 
0.48 
0.66 
220.05 
91.40 
-38.16 
60.48 
333.90 

0.49 
0.63 
-0.30 
0.40 
0.58 
275.79 
64.35 
-37.62 
50.45 
296.68 

0.50 
0.34 
-0.20 
0.24 
0.48 
285.36 
35.06 
-25.74 
30.62 
246.77 

0.43 
0.11 
-0.06 
0.04 
0.32 
243.74 
11.14 
-7.79 
4.95 
161.87 

-0.14 
4.12 
-1.04 
1.50 
2.86 
-1.31 
6.21 
-3.85 
4.74 
28.59 

-0.06 
9.49 
-1.09 
2.27 
3.19 
-0.56 
14.30 
-4.01 
7.20 
31.84 

0.04 
8.81 
-0.43 
0.69 
1.07 
0.35 
13.28 
-1.60 
2.18 
10.70 

-0.09 
6.75 
-1.22 
2.06 
3.68 
-0.84 
10.17 
-4.51 
6.53 
36.70 

-0.20 
2.58 
-0.76 
1.07 
1.81 
-1.82 
3.89 
-2.83 
3.40 
18.05 

-0.52 
6.69 
-1.11 
0.90 
2.56 
-4.77 
10.08 
-2.00 
1.47 
9.04 

-0.72 
9.41 
-1.62 
1.48 
4.37 
-6.61 
14.19 
-2.90 
2.42 
15.43 

-0.21 
1.98 
-0.54 
0.81 
1.11 
-1.92 
2.98 
-2.00 
2.57 
11.09 

2007 
-0.13 
5.30 
-0.69 
0.41 
1.44 
-0.57 
5.82 
-0.52 
0.26 
2.93 

-0.29 
5.79 
-0.84 
0.75 
1.63 
-2.64 
8.73 
-1.51 
1.24 
5.77 

-0.19 
1.87 
-0.42 
0.66 
0.81 
-1.72 
2.82 
-1.54 
2.09 
8.04 

2009 
-0.04 
5.08 
-0.53 
0.37 
1.07 
-0.18 
5.58 
-0.40 
0.23 
2.18 

-0.14 
5.70 
-0.70 
0.72 
1.37 
-1.32 
8.59 
-1.26 
1.18 
4.83 

-0.18 
1.92 
-0.35 
0.57 
0.70 
-1.69 
2.89 
-1.31 
1.79 
7.02 

2011 
-0.08 
5.16 
-0.44 
0.34 
1.04 
-0.35 
5.66 
-0.33 
0.22 
2.12 

-0.15 
5.81 
-0.60 
0.67 
1.39 
-1.34 
8.75 
-1.08 
1.10 
4.90 

-0.23 
1.96 
-0.31 
0.50 
0.68 
-2.12 
2.96 
-1.13 
1.59 
6.77 

2013 
-0.20 
5.27 
-0.39 
0.34 
1.11 
-0.90 
5.79 
-0.29 
0.21 
2.26 

-0.26 
5.90 
-0.53 
0.63 
1.46 
-2.41 
8.90 
-0.96 
1.04 
5.15 

-0.31 
1.98 
-0.27 
0.47 
0.63 
-2.86 
2.99 
-1.00 
1.50 
6.80 

2015 
-0.33 
5.38 
-0.40 
0.40 
1.19 
-1.47 
5.90 
-0.30 
0.25 
2.42 

-0.40 
5.96 
-0.52 
0.65 
1.51 
-3.71 
8.98 
-0.93 
1.07 
5.33 

-0.38 
2.01 
-0.27 
0.50 
0.70 
-3.49 
3.03 
-0.99 
1.57 
7.02 

2017 
-0.39 
5.48 
-0.47 
0.52 
1.27 
-1.75 
6.02 
-0.35 
0.33 
2.58 

-0.49 
6.03 
-0.58 
0.75 
1.56 
-4.46 
9.09 
-1.03 
1.24 
5.50 

-0.39 
2.07 
-0.31 
0.58 
0.75 
-3.60 
3.13 
-1.14 
1.82 
7.50 

0.28 
-0.07 
0.07 
-0.14 
0.10 
158.19 
-6.68 
8.38 
-18.21 
50.40 

0.09 
-0.18 
0.15 
-0.26 
-0.11 
50.09 
-18.41 
18.68 
-33.14 
-56.16 

-0.09 
-0.22 
0.17 
-0.29 
-0.24 
-49.83 
-22.59 
21.83 
-36.71 
-122.93 

-0.20 
-0.18 
0.14 
-0.23 
-0.27 
-114.50 
-18.01 
18.13 
-29.31 
-137.58 

-0.23 
-0.06 
0.07 
-0.11 
-0.21 
-130.29 
-5.86 
9.10 
-13.91 
-105.75 

-0.18 
0.10 
-0.02 
0.04 
-0.08 
-99.45 
10.18 
-2.37 
4.63 
-42.13 

TABLE 12: 

Values of the  Environmental Projects (M. ECU) 

Andalucía 

Aragon 

Asturias 

Baleares 

Canarias 

Cantabria 

Castilla y Leon 

Castilla-La Mancha 

Cataluna 

Com.Valenciana 

Extremadura 

Galicia 

Madrid 

Murcia 

Navarra 

Pais Vasco 

La Rioja 

1993 

18.16 

0 

7.89 

0 

0 

0 

0 

0 

0 

0 

0 

0 

61.32 

0 

0 

2.46 

0 

1994 

41.00 

0 

15.32 

0 

16.21 

0 

0 

0 

24.23 

7.12 

0 

0 

22.52 

21.42 

0 

0 

0 

1995 

86.82 

19.14 

41.60 

20.55 

0 

0 

1.78 

18.51 

140.15 

38.52 

9.61 

85.46 

22.44 

33.60 

23.64 

6.14 

0 

Tables 

187 

CO 
co 

CQ 
O' 

Table 13: Simulation of Environmental Projects with Full Model with Interregional Spillovers 

1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015 

Whole Economy 
Private Capital (%) 0.05 0.27 0.58 0.81 0.92 0.86 0.64 0.31 -0 03 -0 29 -0 40 -0 35 
Public Capital (%) 0.36 1.07 1.15 0.95 0.59 0.21 -0.13 -0.38 -0.51 -0.49 -0 33 -006 
Unemployment (%) -0.06 -0.27 -0.46 -0.49 -0.40 -0.18 0.07 0.26 0 34 0 32 0 21 0 05 
Employment (%) 0.12 0.52 0.81 0.67 0.48 0.13 -0.21 -0.47 -0.58 -0 53 -0 34 -0 08 
Output (%) 0.19 0.80 1.22 1.09 0.94 0.67 0.32 -0.07 -0 36 -0 49 -0 44 -0 25 
Private Capital (B.1990 ptas) 29.81 150.97 327.58 462.20 523.99 488.84 362.21 175.61 -17 97 -163 59 -226 18 -200 15 
Public Capital (B.1990 ptas) 37.38 109.61 117.91 97.46 60.28 21.06 -13.36 -39.15 -52.68 -50.75 -33 66 -6 61 

P. Unemployment (Thousands) -7.78 -33.87 -58.03 -61.45 -50.65 -22.45 8.53 32.11 42.81 40 01 26 20 6 28 

Employment (Thousands) 14.94 65.70 101.45 84.40 59.78 16.79 -26.41 -59.46 -73.53 -66 67 -42 91 -10 39 
Otìtput (B.1990 ptas) 95.39 406.17 622.96 553.09 481.06 341.96 161.62 -33.83 -183.94 -248.25 -222.66 -128^32 

CD 
T) 
CD 
CO 
CD 
13 
O 
Φ 
O 

CO 
η 

Table 14:  Effect  on  Regional  Output  (Millions of 1990  Pesetas) 
of a One Billion  Peseta Project  (1990 Prices) after 12 Years 

Endogenous Public 
Capital 

Exogenous Public 
Capital 

Andalucía 

Aragon 

Asturias 

Baleares 

Canarias 

Cantabria 

Castilla y Leon 

Castilla-La Mancha 

Cataluna 

Com.Valenciana 

Extremadura 

Galicia 

Madrid 

Murcia 

Navarra 

Pais Vasco 

La Rioja 

465 

456 

512 

989 

546 

484 

438 

356 

1148 

796 

329 

650 

1669 

530 

599 

610 

633 

696 

683 

767 

1482 

818 

725 

656 

533 

1720 

1192 

493 

974 

2500 

794 

897 

914 

949 

Tables 

189 

Fig. 1 : Private Capital  Stock 

Andalucía 

Castilla  y Leon 

Madrid 

Aragon 

Castilla-La  Mancha 

Murcia 

Asturias 

Cataluna 

Navarra 

Baleares 

Com.Valenciana 

Pais  Vasco 

Canarias 

Extremadura 

La  Rioja 

Cantabria 

Galicia 

190 

Regional Output and Employment  in the Presence of Interregional Spillovers 

Fig.2: Public Capital Stock (dashed is with motorways) 

Andalucía 

Castilla ν Leon 

Madrid 

Aragon 

Castilla-La Mancha 

Murcia 

Asturias 

Cataluna 

Navarra 

*>* // 

-
./, 

i/ 

Baleares 

Com 

.Valenciana 

Pais Vasco 

/ , 

-

/ 
Λ/ 

/ 

Canarias 

Extremadura 

La Rioja 

Cantabria 

Galicia 

/ 

κ 

y 

•-

/ 

/ 

~ i,n 

, HS 

<M 

Figures 

191 

Fig.3: Unemployment Rate 

Andalucía 

Castilla  y Leon 

Madrid 

Aragon 

Castilla-La  Mancha 

Murcia 

Asturias 

Cataluna 

Navarra 

Baleares 

Com.Valenciana 

Pais  Vasco 

Canarias 

Extremadura 

La  Rioja 

Cantabria 

Galicia 

192 

Regional Output and Employment  in the Presence of Interregional Spillovers 

Fig.4: Labour Force (Dashed) and Employment (Solid) 

Andalucía 

Castilla y Leon 

Madrid 

s 

k / " 

χ^^-/ 

V 

A r a g on 

Castilla-La Mancha 

Murcia 

y 

/ 

/ 

/ 

y ^ -" Γ~ 

Ij \ / 

yl \ / 

Asturias 

Cataluna 

Nas-arra 

Baleares 

Com. Valenciana 

Pais Vasco 

Canarias 

Extremadura 

La Rjoja 

Cantabria 

Galicia 

Λ 
'/ 

— *ly 

\ / 

Figures 

193 

Fig.5: Output 

Andalucía 

Castilla  y Leon 

Madrid 

Arapon 

Castilla-La  Mancha 

Murcia 

Asturias 

Cataluna 

Navarra 

Baleares 

Com.Valenciana 

Pais  Vasco 

Canarias 

Extremadura 

La  Rioja 

Cantabria 

Galicia 

194 

Regional Output and Employment  in the Presence of Interregional Spillovers 

Fig 6: impulse Responses for Base Model 
Shock to: 

Private  Investment 

Public Investment 

Natural  Rate 

Labour  Supply 

Demand 

Private  Capital 

Public  Capital 

Unemployment 

Employment 

" 

'  '  '  '  '  '  ' 

χ 

'  '  '  '  '  '  '  '  * " "  " 

'  '  '  '  * '  ' 

'  " 

1 

I 

1 

l 

1 

1 

t 

1 

I 

11 

.. 

.J 

Output 

— _

_^ 

CO 
cn 

CD 
CD 

o' 

O 

"O 
o 

Τ) 
CD 

D 

Fig 7: Impulse Responses for Model including Motorways 
Shock to: 

Private Investment 

Pu )lic Investment 

Motorway Inv. 

Natural Rate 

Labour Supply 

Demand 

Private Capital 

A 

.

.

.

.

.. t .

.

.

.. ., 

Public Capital 

\^yZ 

Motorway Capital 

^ ~~ 

. - I 

^ " 

" "* 

\ 

^ ' 

'

'

■

·

·

·

'

■

·

*

"" 

' '

'

*

'

'

'

*

■" " 

I Jncmploymcnt 

^ "\ 

y "" 

^ 

' ' ' ' ■ " " 

' ' 

>

.

.

.

.

.

.

., 

I employment 

x _ 

^-^Z 

* ■■ " 

W-r-

; : ■ ; -— 

( I m p i li 

\ 

CD 

CD 

Fig 8: Impulse Responses for Model with Disaggregated  Public Capital 
Shock to: 

Private Investment 

Water&Urban  Investment 

Transport  Investment 

Natural  Rate 

Labour  Supply 

Demand 

Private  Capital 

Water&Urban  Capital 

Transport  Capital 

Unemployment 

Employment 

Output 

\ 

, 

CD 

CD 
co 

co 
o' 

ΓΠ 

"O 
o 
■< 
3 
CD 

CD 

CD 
O' 

Fig 9: Impulse Responses for Base Model (NUTS III) 
Shock to: 

Private Investment 

Public Investment 

Natural Rate 

Labour Supply 

Demand 

on · 

0 01 

Private Capital ββ0 

4 01 

^\ 

v_­^ 

1 Î 1 * i 4 7 1 I |0 11 13 

1 J * ) i τ I 1 

1 ι τ I » 

Public Capital ββ0 

Unemployment 

Employment 

Output 

11 1 « > * 7 t t IB tl IS 

: ι * i 

> ( 7 I t ID II 11 

I I 10 11 II 

] I « 1 < 

1 7 ) i 7 I » |0 n υ 

Fig 10: Impulse Responses for Model with Spillovers (NUTS III) 
Shock to: 

Privale Investment 

Public Investment 

Natural Rate 

Labour Supply 

Demand 

1 » t I I I» II I] 

ι ι ι ' ι ι J ι ι ie 

■1 11 

1

1

.

1

.

71 

13 1

.

1* 

» I« 11 II 

O il 

ODI 

3 0! ■ 

ao? 

o oi ■ 

301 

001 ■ 

s o i: 

30«) 

O t ti 

4 MB 

0 C I5 

J O ÏÎ 

; 

0 01 ' 

va ■ 

« 01 ­

­OBI 

0 01 ■ 

301 

.... 

I 1 1 4 1 i 1 I I 10 II 11 

< 1 1 

«

1

1

11 

io n il 

^r- ———._ 

1

1

1

*

1

(

3

11 

10 II 11 

L i 1 1 1 t 7 1 t 10 11 |J 

1 1 1 " S ( 1 1 t 

ie n υ 

I 1 

« J t 1 I 

io a n 

1

1

1

4

1

1

7

t! 

IO M 11 

ι : ι « J ι ι ι t io u fi 

^~^_ 

\ 

\ 

^ w* 

Private Capital 

Public Capital 

Unemployment 

Employment 

Output 

co 
CD 

1

1

1

(

1

.

71 

1

1

.

1

.

71 

ι « i i ι ι k io n ¡t 

1 ì I * 1 t 1 t ι 

IO 11 11 

1 I . 1 t I I I 

Fig 11: Impulse Responses to Output Spillovers 

Shock to: 

Total Output 

Local Output 

Private Capital 

Public Capital 

Unemployment 

Employment 

\ 

0 01 -

0.01 

0.00 ■ 

-0 01 -

o o: 

0 01 -

-0 Ol 

W 

-002 

" i 

4 

¿ 

á 

IO 

n 

-oca 

2 i 

i 

t 

10 

ή 

Output 

\ 

\ 

0.01  -

C.Ol 

-0  01 

i 

' 

i 

-0  07 

: ■ . i 

1 10 

ii 

200 

Regional Output and Employment ¡η the Presence of Interregional Spillovers 

Fig  12: Madrid  Ring Road,Base Model,Endogenous  Public  Capital 

Public  Capital  (Billions of  1990 Ptas) 

Private Capital  (Billions of  1990  Ptas) 

1992 

2000 

2008 

2016 

1992 

2000 

2008 

2016 

Employment  (Thousands) 

Unemployment  (Thousands) 

28.0 

1992 

2000 

2008 

2016 

1992 

2000 

2008 

2016 

Output  (Billions of  1990 Ptas) 

1992 

2000 

2008 

2016 

Figures 

201 

Fig  13: Madrid  Ring Road,Base Model,Exogenous  Public  Capital 

Public Capital (Billions of 1990 Ptas) 

Private Capital (Billions of 1990 Ptas) 

1992 

2000 

2008 

2016 

1992 

2000 

2008 

2016 

Employment (Thousands) 

Unemployment (Thousands) 

1992 

2000 

2008 

2016 

1992 

2000 

2008 

2016 

Output  (Billions of  1990 Ptas) 

350-

300 -

250 -

150-

/ 

100  -

sn -

1992 

f 

1 

2000 

2008 

2016 

202 

Regional Output and Employment in the Presence of Interregional Spillovers 

Fig  14: Madrid  Ring Road,Full  Model,Endogenous  Public  Capital 

Public Capital (Billions of 1990 Ptas) 

Private Capital (Billions of 1990 Ptas) 

480 

1990 

2000 

2010 

1990 

2000 

2010 

-320 

Employment (Thousands) 

Unemployment  (Thousands) 

-80 

1990 

2000 

2010 

1990 

2000 

2010 

Output (Billions of 1990 Ptas) 

400 

-400 

1990 

2000 

2010 

Figures 

203 

Fig. 15a: Madrid Ring Road,Full Model.Private Capital (%) 

Zirigia 

β .4 / \ 

­0.4 

■0 

991 20CI 

A5U1TÍ13 

/ ~\ 

□ I 

0.4 

0.0 

­0.4 

973 2001 

' 

B i i i r as 

04 

/\ 

­0 4 

m looi 
1 

V 

V 

/Λ, 

0 4 

­0.4 

Lis Piimii 

/

"\ 

0.4 

0.0 

«4 

V 

V 

w: 200* 
1 

19 

92 200* 

M u c hi 

Tenerife 

/ ~\ 

D 4 

00 

«4 

/ ~\ 

04 

0.0 

0.4 

V 

V 

1 

»92 2001 

1992 2001 

J i ai 

C i n t i b r ii 

ru 

/

"\ 

4 

/ ~\ 

V 

V 

0.4 

19 

91 2001 

S t i l i ci 

/ ^\ 

4 

0 

4 

19­

» 200« 

■' 

Sevill» X~\ 

V 

.4 ■ 

1991 IODI 

, .. Α ν θι 

.4 

/ ~\ 

B u r p» 

4 ■ 

1992 JOO* 

A 

/\ 

4 

V 

V 

V 

ι »! mot 

1992 200* 

C i t t h na 

Leoa 

/

"\ 

4 

4­

A 

' 

V 

1991 lOOt 

1992 lOOt 

. H u e sa 

Pal e n dt 

4 

/\ 

' 

' 

ν 

204 

Regional Output and Employment in the Presence of Interregional Spillovers 

Fig. 15b: Madrid Ring Road,Full Model Public Capital (%) 

­rh 

Zaragoza 

■013 

1992 

OJO 

OD 

0.00 

­ 0 13 

/\ 

O JO 

0 13 

0 00 

­ 0 13 

Bal arcas 

\ / 

J 

J 

Barcelona 

1"«° 

YJ 

2001 

Gorma 

0 13 

0.00 

■0.23 

1992 

2 0 0* 

Segovñ 

1 

OU 

y 

OJO 

0 13 

­ 0Ü 

1992 

OJO 

0 13 

ooo 

­ ou 

V/ 

2 0 01 

1 
»2 

2 0 01 

y 

Sora 

Lenaa 

1 

OJO 

0 13 

­ 0 13 

1992 

y 

\y 

2 0 0* 

1972 

2 0 01 

OJO 

0 13 

0.00 

­ 0 13 

19 

92 

2 0 01 

OJO 

OU 

SOO 

­ OU 

1992 

y 

\j 

am 

Z a m o ra 

A l i a r ti 

Grana di 

V d l a d o l id 

Tampona 

± 

JO 

0 13 

0 00 

­ 0 13 

" 

OJO 

0 13 

° 

­ 0 13 

\ y 

2 0 01 

Albacete 

y 

1? 

2 0 0* 

OJO 

ou 

0 00 

­ OU 

19 

91 

2 0 01 

y 

OJO 

ou 

■ou 

IV 

n 

Castellan 

\ J 

2 0 01 

M a j a» 

C k i d a d R e i] 

V il m ai 

/ 

0 13 

­ 0 13 

19­

η 

^Λ 
\ 
\ y 

2 0 0* 

OJO 

0 13 

0~00 

­ O lí 

195 

η 

2004 

y 

1 

OJO 

ou 

­ou 

195 

2 

2 0 01 

y 

Sevilla 

Biagoi 

Cuenca 

Badajoz 

Guipúzcoa 

\ y 

aoot 

CaiaJum 

\ y 

2 0 0* 

O lí 

­ O lí 

UK 

ΓΪ 

tu 

■ t lí 

OU 

/Λ 

­ ou ■ 

1 9! 

1 

\ y 

2 0« 

\y 

2 0 0* 

Guadal afara 

C a c e ra 

Vizcaya 

OJO 

•u ■ 

ΟΛΟ 

­ OU 

/ 

199 

2 

2 0 0* 

199 

1 

2 0 0* 

y 

Huesca 

Pal encía 

Toledo 

La C o r u na 

La Rioja 

0 13 

-Olí 

\ y 

O lí 

ΧΛ 

■025 

OU 

­ou 

\  y 

\ / 

\ J 

\ J 

2 0 01 

Cadiz 

\ y 

2 0 0* 

Cordoba 

\ J 

ZOO* 

\ J 2 0 01 

H u e l va 

| 

\ y 

2 0 0* 

J i cn 

\ y 

2 0 0* 

OD 

0.00 

­ OD 

1992 

OJO 

OU 

0 00 

­ OD 

1992 

OJO 

OU 

om 

­ OD 

1992 

OJO 

OD 

0 00 

­0­2J 

1992 

OJO 

QU 

o« 

■ 0 1) 

1992 

OJO 

0 1} 

' 

■0.23 

1992 

0 13 

' 

­ 0 13 

1992 

OJO 

0 1 3­

ΟΛΟ 

­ 0 13 

1992 

O JO 

0 13 

0 , 00 

­ O lí 

Figures 

205 

Fig. 15c: Madrid  Ring Road,Full  Model,Unemployment  (%) 

206 

Regional Output and Employment  in the Presence of Interregional Spillovers 

Fig. 15d: Madrid  Ring Road,Full Model,Employment  (%) 

Figures 

207 

Fig. 15e: Madrid Ring Road,Full Model,Output (%) 

Gerona 

Γ\ 

O.TO 

OD 

-OU 

\y 

ατο 

ou 

Alicante 

^^—-
/ \ 

■013 

\ v^ 

Badajoz 

Guipúzcoa 

Vizcaya 

La Rioja 

208 

Regional Output and Employment in the Presence of Interregional Spillovers 

Fig 15f: Comparison of Models for Madrid Ring Road 

Relative Employment (Per Cent) 

1990 

1995 

2000 

2005 

2010 

2015 

Relative Private Investment (Per Cent) 

r. - il » » J 1 

Piss.-Wasmer 

y \ 

' \ 

' \ 
1 \ 
> \ 

/ \ 
/ N 

' N / ^\ 

\ / 

\ / ^v 
Λ ^\ 
/ ^ -v 

/ ^ ~~ Χ 

1.00 -

0.75 -

0.50 -

0.25 -ï 

-015 -

-0.50 -

-0.75 -

1990 

1
1995 

1

2000 

)1 

2005 

2010 

2015 

Figures 

209 

' Fig  16: Rias-Bajas  Motorway,  Full  Model 

Public Capital (Billions of 1990 Ptas) 

Private Capital (Billions of 1990 Ptas) 

Whole  Economy 
Z-O-P 

300 

100 

75 -

50 

25  -

1990 

2000 

2010 

-150 4 

1990 

2000 

2010 

Employment  (Thousands) 

Unemployment  (Thousands) 

1990 

2000 

2010 

1990 

2000 

2010 

Output (Billions of 1990 Ptas) 

r\ 
\ 
ƒ 

/ 

\ 

V/ 

350-

280 -

210-

140 -

7 0-

-70 -

140 -

1990 

2000 

2010 

210 

Regional Output and Employment in the Presence of Interregional Spillovers 

Fig. 17a: Rias-Bajas  Motorway, Private  Capital  (%) 

I77Î 

ÌOOC 

TOM 

MU 

17*1 

TOCO 

TO« 

MU 

1777 

TOC« 

ï f t ft 

TOH 

1777 

70CC 

» et 

7711 

1777 

TOOO 

ìrat 

7011 

Figures 

211 

Fig. 17b: Rias-Bajas Motorway, Public Capital (%) 

ι»« »oo ro« ï'il 

xa* ici« 

212 

Regional Output and Employment in the Presence of Interregional Spillovers 

Fig. 17c: Rias-Bajas  Motorway,  Unemployment  (%) 

Figures 

213 

Fig. 17d: Rias-Bajas Motorway, Employment (%) 

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Regional Output and Employment  in the Presence of Interregional Spillovers 

Fig. 17e: Rias-Bajas  Motorway,  Output  (% 

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Figures 

215 

Fig 18: Environmental Projects Full Model 

Public Capital (Billions of 1990 Ptas) 

Private Capital (Billions of 1990 Ptas) 

Whole Economy 

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1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 

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1990 1995 2000 2005 2010 2015 1990 1995 2000 2005 2010 2015 

Output (Billions of 1990 Ptas) 

750 

500 

250 

-250 

1990 1995 2000 2005 2010 2015 

216 

Regional Output and Employment in the Presence of Interregional Spillovers 

Fig. 19a: Environmental  Projects,  Private  Capital  (%) 

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217 

Fig. 19b: Environmental Projects, Public Capital  (%) 

218 

Regional Output and Employment  in the Presence of Interregional Spillovers 

Fig. 19c: Environmental  Projects,  Unemployment  (%) 

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219 

Fig. 19d: Environmental  Projects, Employment  (%) 

220 

Regional Output and Employment  in the Presence of Interregional Spillovers 

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Fig. 19e: Environmental  Project,  Output  (%) 

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» II 

European  Commission 

The socio-economic  impact of projects financed by the Cohesion  Fund — A modelling  approach 
Volume  1: Introduction and summary — Literature review —Vector  auto-regression  models 

Luxembourg: Office for Official  Publications of the European  Communities 

1999 — 221 pp. — 21 χ 29.7 cm 

ISBN 92-828-3064-0  (Volume 1) 

ISBN 92-828-3065-9  (Volume 2) 
ISBN 92-828-3066-7  (Volume 3) 
ISBN 92-828-7238-6  (Volumes  1, 2 and 3) 

Price (excluding VAT) in Luxembourg: EUR 86 (Volumes  1, 2 and 3) 

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