Moving the frontier of macroeconomic modelling of research and innovation policy.
Date: 2020-09-08
Institutions: Directorate-General for Research and Innovation
Subjects: economic model, innovation, investment, macroeconomics, policymaking, report, research and development, sustainable development

Moving the Frontier of 
Macroeconomic Modelling 
of Research and 
Innovation Policy 

 
 
 
 
 
 
 
Moving the Frontier of Macroeconomic Modelling of Research and Innovation Policy 

European Commission 
Directorate-General for Research and Innovation 
Directorate A — Policy & Programming Centre 
Unit A.1 — Chief Economist - R&I Strategy & Foresight 

Contact   Román Arjona, Chief Economist and Head of Unit A1 

Roberto Martino, Coordinator, Macroeconomic Modelling of R&I 

Email 

RTD-ECONOMIC-ANALYSIS@ec.europa.eu 
Roman.ARJONA-GRACIA@ec.europa.eu   
Roberto.MARTINO@ec.europa.eu  
RTD-PUBLICATIONS@ec.europa.eu

European Commission 
B-1049 Brussels 

Manuscript completed in September 2020 

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doi:10.2777/34199  KI-03-20-515-EN-N 

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EUROPEAN COMMISSION 

Moving the Frontier of 
Macroeconomic Modelling 
of Research and 
Innovation Policy 

AUTHORS 

Barbara Annicchiarico (University of Rome Tor Vergata) 

Omar Licandro (University of Nottingham)  

Pierre Mohnen (UNU-Merit)  

Eva Ortega (Bank of Spain) 

Reinhilde Veugelers (University KU Leuven) 

2020 

 Directorate-General for Research and Innovation 

Table of Contents 

EXECUTIVE SUMMARY 

1. INTRODUCTION

2. MAIN FEATURES AND POLICIES OF R&I

3. THE MACROECONOMIC MODELS CURRENTLY USED

FOR R&I POLICY, AND HOW THEY CAN BE IMPROVED

4. CONCLUSIONS AND RECOMMENDATIONS

5. REFERENCES

APPENDIX 

 3 

13 

14 

58 

90 

95 

 120 

2 

EXECUTIVE SUMMARY 

The  Directorate  General  for  Research  and  Innovation  of  the  European  Commission 
(DG R&I) has engaged since 2014 in a medium to long-term agenda to improve the 
treatment of research and innovation (R&I) in macroeconomic models used to assist 
policy making.   

Since December 2018, an Expert Group has supported the European Commission in 
the further development of its modelling activities. The Group is chaired by Reinhilde 
Veugelers  (University  KU  Leuven),  with  Pierre  Mohnen  (UNU-Merit)  as  rapporteur, 
and  Barbara  Annicchiarico  (University  of  Rome  Tor  Vergata),  Giovanni  Dosi 
(Sant’Anna  School)1,  Omar  Licandro  (University  of  Nottingham)  and  Eva  Ortega 
(Bank of Spain) as members. 

MAIN FEATURES AND POLICIES OF R&I FOR MACRO-MODELLING 

Based on the existing literature on R&I and innovation policies, mostly the academic 
macroeconomic literature, Section 2 of this report describes the critical features and 
enablers  of  the  innovation  process  needed  to  appropriately  account  for  its  role  in 
economic  growth  and  welfare,  as  well  as  the  main  economic  policies  oriented  to 
promote innovation, boosting  growth and  enhancing sustainability. This description 
of R&I features, enablers and policies can be used to assess applied macro-models 
on their capacity to study and evaluate innovation and innovation policy.    

R&I features 

Technological  knowledge  is  the  sum  of  methods  and  techniques  employed  in  the 
production of goods and services. It results from the cumulative aggregation of new 
technological ideas or innovations, resulting from a systematic effort of research and 
development  (R&D)  benefiting  from  multiple  forms  of  knowledge  spillovers.  It  is 
non-rival  -it  can  be  used  by  many  agents  at  the  same  time-  and  suffers  from 
economic obsolescence -it depreciates in value due to the emergence of subsequent 
innovations.  Non-rivalry  and  obsolescence  prevent  private  R&D  returns  from 
equalizing  the  corresponding  social  returns.  Technological  knowledge  is  indivisible, 
tacit and path dependent. 

Innovations  or  new  technological ideas  are  the  direct  result  of  R&D  activities.  R&D 
benefits from knowledge spillovers, i.e., unintended knowledge transfers that cannot 
be  appropriated  by  the  innovator.  Negative  rent  or  market  spillovers  are  also 
associated  with  R&D,  i.e.,  the  transfer  of  monopoly  rents  between  firms  resulting 
from  the  introduction  of  innovations.  They  can  take  the  form  of  business  stealing 
when  a  new,  better  quality  product  displaces  an  existing  one,  or  more  generally 
obsolescence referring to the loss in value of an existing product as a result of the 
reduction in demand that follows from the introduction of innovations in competitive 
products.  The  empirical  literature  finds  that  knowledge  spillovers  quantitatively 
dominate market spillovers making the social return to R&D larger than the private 
return, which provides a justification for government-supported innovation policy.  

1   Giovanni Dosi is a member of the Expert Group but not an author of this report. 

3 

 
                                                
Firm heterogeneity in products and production processes naturally emerge from the 
stochastic  nature  of  the  innovation  process.  Well  beyond  the  representative  agent 
model,  the  literature  on  endogenous  growth  and  firm  dynamics  builds  on  the  fact 
that  R&I  shapes  the  dynamics  of  heterogeneous  firm-level  productivity,  and  the 
associated  creation  and  destruction  of  jobs,  eventually  affecting  the  evolution  of 
aggregate  productivity,  GDP  growth  and  people’s  welfare.  In  this  process,  R&D 
activities are addressed to create new products, improve the quality of existing ones 
as  well  as  to  reduce  the  associated  production  costs.  This  approach  provides  a 
sound  basis  for  modern  quantitative  macro-models  of  innovation  and  innovation 
policy, using macro and micro data to discipline the analysis.  Heterogeneity is also 
an  essential  feature  in  the  evolutionary  view  of  technological  progress,  and  in  the 
literature  on  vintage  technologies,  studying  the  embodied  nature  of  technical 
progress. Other relevant dimensions of heterogeneity like granularity, the existing of 
superstar  firms  and  the  network  structure  of  the  production  process  are  also  of 
critical relevance. 

Different  types  of  research  and  innovation  can  be  distinguished.    Basic  research  is 
fundamentally addressed to move the frontier of knowledge, while  applied research 
aims  at  providing  solutions  to  technological  problems.  Basic  research  is  at  the 
highest level of knowledge spillovers, motivating public support. Product innovations 
result in the introduction of new goods or services. Process innovations result in cost 
reductions.  From  the  perspective  of  a  product,  productivity  has  at  least  two 
conceptually  different  components:  the  quality  of  the  product  and  the  technical 
efficiency of the production process. The former is related to product innovation and 
the latter to process innovation. Innovation may at different degrees be incremental 
or drastic, of a general-purpose nature or product/sector specific.  

New technological ideas, after discovery, diffuse throughout the economy, affecting 
the  design  of  products  and  production  processes  in  different  sectors  and  locations. 
Diffusion  may  be  modelled  as  an  adoption  process  aiming  to  catch-up  the  frontier 
technology  or  as  the  result  of  adoption  delays.  In  the  former  approach,  adoption 
rates positively depend on the distance between the frontier and the actual state of 
technology.  In  the  latter,  innovations  belong  to  different  technological  vintages 
susceptive of long adoption delays. More recently, the literature stresses the role of 
production and innovation networks in the diffusion of firm level innovations, making 
idiosyncratic shocks to firm’s productivity lead to aggregate outcomes. 

The  report  also  describes  the  most  commonly  used  measures  of  R&I  inputs  and 
outputs, and illustrates how these measures are  being  used in the macroeconomic 
literature of innovation and growth. It includes a description of the measurement of 
R&D and intangible capital, the estimation of knowledge and market spillovers,  the 
use  of  patents  information,  as  well  as  the  measurement  of  process  and  product 
innovation, and firm level productivity. 

R&I enablers 

Market  size.  Firms  have  more  incentives  to  do  R&D  when  there  is  a  large  market 
willingness to pay to recover the fruits of their R&D investment.  

Competition  and  patents.  Patents  systems  grant  temporarily  monopoly  rights  to 
innovators  in  order  to  protect  them  from  being  copied  or  imitated.  However,  the 
relationship  between  competition  and  innovation  is  inverted  U-shaped.  When 
competition is low, incumbents do not need to protect their monopoly rents, having 

4 

 
little incentives to innovate. When markets become more competitive, technological 
leaders innovate to escape competition, followers and potential entrants to catch-up 
or  leapfrog  the  leader.  However,  when  there  is  too  much  competition,  the  gains 
from  innovation  dilute  to  the  point  that  neither  incumbents  nor  potential  entrants 
have incentives to innovate. Moreover, mark-up dispersion affects the misallocation 
of  resources,  affecting 
innovate.  When  mark-ups  are 
heterogeneous, relative prices are distorted and resources misallocated, the degree 
of misallocation increasing with mark-up dispersion. 

incentives 

the 

to 

Human  capital.  Leading  the  innovation  process  requires  a  highly  performing 
education system able to generate a large body of high human capital scientists and 
applied researchers dominating and moving the frontier of knowledge.  

R&I  financing  suffers  from  high  risk  and  large  asymmetrical  information  problems 
between  the  innovator  and  the  fund  provider.  For  this  reason,  small  and  new 
innovative  firms  experience  high  financial  costs  and  credit  scarcity,  and  large 
established firms prefer internal funds for financing their R&D investments. Financial 
frictions have an important negative effect on R&D investment.  

R&I policy 

Macro-models  aimed  to  evaluate  innovation  policy  should  include  the  main  R&I 
policy instruments used in practice, the distortions they are expected to correct and 
the relevant channels through which they are presumed to operate.  In order to be a 
good  laboratory  for  the  quantitative  evaluation  of  R&I  policy,  macroeconomic 
models  of  R&I  need  to  be  disaggregated  enough  and  disciplined  by  the  available 
macro  and  micro  data  on  those  dimensions  in  which  policies  are  expected  to 
operate. 

R&D  tax  incentives  aim  at  reducing  the  gap  between  private  and  social  returns  to 
R&D,  and  alleviating  the  financial  problems  faced  by  R&I  performers.  The  use  of 
R&D tax incentives is worldwide spread. In principle, R&D tax incentives are neutral 
-can  be  claimed  automatically  as  long  as  some  general  criteria  are  fulfilled.  But 
governments  can  give  special  tax  incentives  to  specific  areas  like  health  or 
environmental protection or targeted firms, like SMEs.  

Direct subsidies. Research grants are another main R&I policy instrument. They are 
aimed  to  promote  research  excellence  by  supporting  projects  with  the  highest 
expected social return, expected to move the frontier of scientific and technological 
knowledge.  Contrary  to  tax  incentives,  research  grants  are  non-neutral.  They  are 
granted  generally  through  a  competitive  process  where  the  best  projects  are 
selected by experts.  

Public  financial  support  for  innovation  can  also  be  given  via  loans,  guarantees  and 
venture  capital,  including  the  support  to  startups  and  highly  innovative  small  and 
medium  size  firms.  The  governments  can  also  directly  perform  R&D  in  public 
universities  or  research  labs  or  engage  in  public  private  partnerships.  Instead  of 
stimulating  innovation  by  lowering  R&D  costs,  public  procurement  can  provide  a 
demand for new products, facilitating their diffusion. 

Patents  are  used  to  protect  innovation  from  copy  and  imitation,  by  creating 
temporary monopoly rents. It does so in exchange for the disclosure of the relevant 

5 

 
information  behind  the  innovation,  which  favours  the  spread  of  knowledge 
spillovers. 

Other  policy  instruments  affecting  innovation  include  corporate  taxes.  Regulations 
and environmental policies may direct technical change towards the achievement of 
socio-economic and environmental challenges, introducing incentives to innovate in 
the desired direction.  

Dimensions of macroeconomic impact of R&I 

Several  macroeconomic  dimensions  can  be  impacted  by  R&I  and  R&I  policies.  A 
main  impact  dimension  of  R&I  policies  studied  with  macro-models  is  GDP  and  its 
growth rate. As GDP is a common anchor metric used for targeting macroeconomic 
performance,  macroeconomic  models  are  a prime  tool  to  assess  the  impact of  R&I 
policy interventions on GDP at various horizons.   

R&I policies need to be evaluated also by their potential effects on competitiveness, 
i.e.,  the  ability  of  a  firm,  sector  or  country  to  successfully  compete  in  global 
markets. Multi-country macro-models of R&I should include channels through which 
competition  in  global  markets  shapes  the  incentives  of  firms  to  innovate,  building 
countries’ comparative advantages. 

Since  innovation  creates  and  destroys  jobs  and  skills,  R&I  policy  needs  also  to  be 
evaluated  by  its  labour  market  effects,  addressing  important  issues  like  the  skill 
premium,  job  polarization  and  automation,  skill  obsolescence  and  structural 
transformation.   

Social  exclusion and inequality have  emerged  as  major  concerns.  Inequality  across 
countries  or  regions  requires  asymmetric  multi-country-region  models  with 
heterogeneity in  firms’  innovative  capabilities.    Inequalities  among  individuals  or 
across socio-economic groups require the explicit modelling of different categories of 
workers having different skills.   

Macro-models  aiming  at  studying  the  effects  of  R&I  on  health  should  include 
individual health statuses, life-expectancy and population aging, explicitly modelling 
health technology.   

The  importance  of  the  environment  has  been  gaining  momentum  in  both  policy-
making  and  academic  circles.  Macroeconomic  models  for  R&I  policies  evaluation 
should  embody  environmental  externalities,  account  for  the  damage  of  pollutants, 
deal  with  the  issue  of  resource  scarcity  as  well  as  the  creation  of  new  clean 
technologies. 

THE  MACROECONOMIC  MODELS  CURRENTLY  USED  BY  DG  R&I  FOR 
R&I POLICY  

Section  3  assesses  for  the  3  macro-models  currently  used  by  the  European 
Commission,  DG  R&I,  namely  QUEST,  RHOMOLO  and  NEMESIS,  whether  they 
contain  the  basic  features,  enablers  and  policies  thought  to  be  important  for 
modelling R&I and assessing R&I policy interventions, and, if not, how they could be 
included. 

6 

 
QUEST  III  is  a  large-scale  Dynamic  Stochastic  General  Equilibrium  (DSGE)  model 
designed  as  a  laboratory  for  policy  analysis  of  the  EU  economy.  It  is  a  multi-
countrymodel  calibrated  for  EU  countries  and  the  rest  of  the  world.  The  explicit 
modelling  of  knowledge  creation  and  of  technology  adoption  allows  QUEST  III  to 
support  questions  related  to  the  evaluation  of  R&I  policies.    QUEST  III  adopts  a 
semi-endogenous  growth  framework  with  expanding  varieties  and  incorporates  an 
endogenous diffusion process of new ideas. Given the general equilibrium structure 
of  the  model  several  agents  are  involved  in  the  innovation  process.  R&D  activities 
take  place  in  research  institutes  employing  skilled  labour  and  making  use  of  the 
commonly available domestic and trade related international knowledge (knowledge 
spillovers).  Technology  adopters  use  skilled  labour  to  transform  non-adopted 
technologies (or ideas) into technologies that  are then used by intermediate goods 
producers to produce new varieties. In this sector firms produce intermediate goods 
using  physical  capital  and  the  designs  of  adopted  technologies  licensed  from 
households. In the final good sector, firms produce horizontally differentiated goods 
employing  three  types  of  labour  inputs  of  different  skill  levels  and  a  composite  of 
intermediate goods.  

QUEST III includes several mechanisms and features related to the creation and the 
diffusion  of  new  technologies  (spillovers,  basic  and  applied  research,  absorptive 
capacity, non-rivalry, obsolescence, congestion externalities) and to the enablers for 
R&I creation, adoption and diffusion (market size, competition, intellectual property 
rights  and  the  skill  composition  of  the  labour  force).  The  general  equilibrium 
framework allows to capture the effects of R&I policies on the main macroeconomic 
aggregates,  inequalities,  labour  markets,  external  competitiveness  and  to  address 
the  budgetary  and  financing  issues  related  to  costly  R&I  policies.  The  model 
presents a rich set of tools and channels through which R&I activities and adoption 
can be promoted that range from more direct interventions, such as tax incentives, 
subsidies  and  patent  policies  to  policy  initiatives  that  involve  the  overall  economy, 
such as public procurement, regulation, education policies. 

RHOMOLO is a Spatial Computable General Equilibrium (SCGE) model covering all 
the  European  Union  NUTS  2  regions  and  disaggregating  each  regional  block  in  ten 
sectors. The model is based on a large system of simultaneous equations where all 
sectors are interlinked through input-output relations and interactions are mediated 
by  markets  and  prices.    RHOMOLO  is  used  extensively  for  impact  assessments  of 
regional policies and provides sector- and region- specific simulations to support the 
EU  policy  making  on  a  wide  range  of  structural  reforms,  investment  policies, 
including R&I support programs and of initiatives reinforcing the internal market.  

In  RHOMOLO  V3  in  each  region  scientific  and  technical  activities  take  place  in  a 
wider  sector  also  embodying  other  professional  services  and  administrative  service 
activities. In this sector imperfectly competitive firms make use of physical capital, 
intermediate  inputs  and  labour  inputs  of  different  skill  levels.  An  increase  in  R&D 
investments generates two direct effects on the economy. The first effect is related 
to  the  positive  effect  of  innovation  on  total  factor  productivity  (productivity 
channel). R&D investment increases the stock of knowledge which through spillovers 
generates  a  structural  positive  impact  on  all  the  other  sectors  and  regions  of  the 
economy.  The  strengths  of  these  spillovers  are  sector-  and  region-  specific  and 
depend on estimated elasticities. A higher R&D intensity at regional level is a sign of 
greater  absorptive capacity  of firms located  in that  region  and  is associated  with a 
larger elasticity of total factor productivity to R&D investments. The second effect of 
R&D  investment  refers  to  the  direct  temporary  impact  on  aggregate  demand 

7 

 
(demand  channel).  R&I  spending  is  introduced  as  private  investment,  so  that 
increases in innovation activities give rise to a higher demand for capital goods.  

RHOMOLO presents several mechanisms and features related to the creation and the 
diffusion  of  new  technologies,  and  to  the  enablers  for  R&I  creation,  adoption  and 
diffusion.  Its  extreme  granularity  allows  to  evaluate  the  impact  of  innovation 
activities  on  a  vast  range  of  indicators  at  regional  and  sectoral  level,  and  to  keep 
track of the potential effects on the geographic distribution of economic activity and 
population.  RHOMOLO  can  simulate  a  vast  range  of  policies  directly  affecting 
innovation  activities,  especially  the  impact  of  R&I  policy  initiatives  undertaken  at 
different levels (EU funding, national and regional) and targeting specific sectors. 

NEMESIS  is  a  large  scale  multisector  macroeconometric  model  covering  all  the 
European  Union  countries.  NEMESIS  is  mainly  used  for  the  impact  assessment  of 
research  and  innovation  policies  carried  out  at  country  and  EU  level.  The  model 
belongs to the neo-Keynesian tradition and is based on national accounting with the 
demand side predominant in the short to medium run, and the supply size driven by 
technological  change  predominant  in  the  long  run.  Sectors  and  countries  are 
interlinked  through  trade  and  international  knowledge  spillovers.    The  main 
behavioural  equations  describing  consumption,  factor  demand  by  firms,  wage  and 
price formation, import and export decision are subject to reaction delays reflecting 
the existence of adjustment costs and of adaptive expectations.   

In NEMESIS  innovation activities  take place  in each country/sector  and are  related 
to  investments  in  three  categories  of  intangible  services:  R&D,  ICT  and  other 
intangibles.  The  production  of  each  innovation  component  is  a  positive  function  of 
the country/sector specific stock of knowledge and of the absorption capabilities that 
depend on innovation intensity.  Each country/sector specific stock of knowledge in 
turn  builds  on  the  stocks  of  intangible  assets  (R&D,  ICT  or  other  intangibles) 
belonging  to  all  sectors  and  countries  of  the  model  economy.  This  feature  allows 
capturing the existence  of  knowledge externalities  across sectors and countries.  In 
each sector services produced by the stock of knowledge increase the productivity of 
all  the  other  production 
in  a  Hicks-neutral  productivity 
improvement.  In  addition,  innovation  services  improve  the  quality  of  products 
increasing market demand.  

factors,  resulting 

NEMESIS  can  represent  many  aspects  related  to  the  creation  and  the  diffusion  of 
new technologies, and to the enablers for knowledge creation. The multi-sector and 
multi-country  structure  of  the  model  permits  to  evaluate  the  impact  of  innovation 
activities and of the related R&I policies for several key economic indicators. 

Comparing  the  strengths  and  weaknesses  of  QUEST  III,  RHOMOLO  and  NEMESIS 
informs that all the three models are able to account for several features related to 
R&I and address a vast range of policy initiatives and reforms falling in the domain 
of  research  and  innovation  or  in  other  areas  of  interventions  that  may  have  an 
impact  on  knowledge  creation  and  diffusion.  The  presence  of  knowledge  spillovers 
and  of  several  sources  of  market  imperfections  shapes  the  scope  for  public 
interventions  in  R&I.  The  modelling  of  the  labour  market  is  differentiated  among 
different  skill  categories  of  workers,  therefore  the  three  models  can  capture  the 
social dimension of technological progress and account for income inequalities.  The 
three  models  have  a  multi-country  structure  and  can  be  used  to  address  issues 
related  to  the  interactions  between  national  and  international  R&I  policies,  and  to 

8 

 
 
 
 
evaluate  the  impact  of  technological  advancements  on  trade  performance  and 
external competitiveness. 

The  distinct  strengths  of  QUEST  III,  RHOMOLO  and  NEMESIS  are  related  to  the 
specific  modelling  approach  they  are  based  on.    The  main  strengths  of  QUEST  III 
refer to its theoretically consistent micro-founded approach based on the optimising 
behaviour of rational agents. Its general equilibrium structure allows to disentangle 
clearly  the  different  transmission  channels  of  R&I  policies  over  time  and  to  fully 
account  for  reallocation  effects  and  the  possibility  of  policy  trade-offs.  The  main 
strengths of RHOMOLO  pertain to its  granular structure  and  the detailed  modelling 
of spatial interactions across regions. With these features RHOMOLO can effectively 
address  questions  related  to  the  geographic  concentration  of  innovation  activities 
and to the interactions between specific R&I policy initiatives and cohesion policies. 
The main strengths of NEMESIS are related to its ability to fit the observed data and 
to  its  flexibility,  allowed  by  its  departure  from  the  general  equilibrium  framework. 
Also,  NEMESIS  presents  a  rich  modelling  of  innovation  activities  that  allows  to 
account  for  several  direct  links  between  concrete  R&I  policy  interventions  and 
market outcomes. 

Although all three models present a vast range of mechanisms and features through 
which  R&I  policies  can  be  simulated,  all  of  them  have  some  important  limitations 
and  need  further  developments  to  better  cover  key  features  of  the  technological 
process.    A  first  weakness  is  related  to  the  lack  of  firm  heterogeneity  and  the 
associated  dynamics  of  products,  firms  and  markets.  None  of  the  models  can 
capture the complex process of creation and destruction of products and production 
processes, plants and firms  through  which  technological progress  occurs.  A second 
weakness  regards  the  absence  of  endogenous  financial  frictions.  Firms  operate 
under  very  different  market  conditions  and  especially  small  and  innovative  firms 
may  have  limited  access  to  credit.      A  third  weaknesses  is  that  none  of  the  three 
models can capture the risk associated to R&I activities and the randomness in the 
outcome of the R&I process. A fourth weakness is that the role of human capital is 
limited  in  all  three  models.  There  is  not  any  explicit  link  between  human  capital 
formation,  the  investments  in  the  education  system  and  the  individual  educational 
choices  and  training.  Finally,  although  all  the  three  models  have  already  been 
extended  or  used  to  address  environmental  sustainability  issues,  the  relationship 
between  environmental  regulation  and  technical  change  as  well  as  the  effects  of 
technological  progress  on  the  environment  are  all  aspects  that  have  not  yet  been 
systematically incorporated in the core models.  

Applying  the  check-list  developed  in  this  report  of  the  key  ingredients  a  macro-
model requires to deliver a sound macroeconomic evaluation of innovation policy to 
the three macroeconomic models, QUEST III, RHOMOLO and NEMESIS, reveals the 
following lessons: 

  All three models present a rich set of mechanisms and parameters through 

which R&I policies can be simulated. 

 

Yet,  none  of  the  models  covers  all  of  the  key  characteristics  of  innovation 
and innovation policy.  This implies that none of them can be considered as 
dominantly  better  for  the  modelling  of  innovation  and  innovation  policies.   
Each has its own strength and weaknesses.  Which model to choose for R&I 
policy analysis will depend on what the major objectives are of the modelers 
and their users. 

9 

 
  All  models  require  further  developments  to  better  cover  key  features  of 
innovation and innovation policy.   Some key features can be included with 
minor changes to the models, others would require bigger adjustments.     

  All the models struggle with a lack of  sufficiently recent and disaggregated 
data to calibrate/estimate critical parameters.    All models can be improved 
with better data. 

CONCLUSIONS AND RECOMMENDATIONS FOR MOVING FORWARD 

  No  single  macro-model  can simultaneously  account for all features, enablers 
and policy instruments in the checklist provided and used in this report, and still 
consistently  produce  the  accurate  quantitative  figures  needed  for  the  macro-
evaluation  of  all  current  R&I  policies.    Choices  have  to  be  made  about  which 
features  and  policies  are  more  relevant,  depending  on  the  objectives  and  the 
innovation  policies  being  evaluated.    In  making  these  choices,  a  few 
recommendations should be kept in mind: 

o  A  good  macroeconomic  model  designed  to  evaluate  innovation 
firms  and 
policies  should 
ideally 
heterogeneous  production  and  R&I 
technologies.  This 
requires,  in  particular,  an  important  disaggregation  at  the  level  of 
firms,  including  innovation  incentives  and  performance,  as  well  as 
the  associated  dynamics,  including  the  entry  of  new  innovative 
firms, their development and eventual exit. 

include  heterogeneous 

o  When  the  different  features  of  the  R&I  process,  their  diffusion 
channels  (the  enablers),  the  different  policy  instruments  and  the 
different  dimensions  of  macroeconomic  impact  are  studied  in 
different  models  (or  sub-models),  or  at  different  levels  of 
disaggregation, their results should nevertheless remain comparable 
with a common core.  

o  For the geographic dimension in the case of the European Union, a 
macro model designed to evaluate innovation policies ideally has to 
mimic a multi-country, multi-region Europe trading  with  the  rest  of 
the  world,  the  degree  of  disaggregation  depending  on  the  policy 
under evaluation.  

o 

Innovation,  by  creating  and  destroying  jobs  and  the  value  of  the 
associated  skills,  impacts  social  inequality,  deeply  affecting  the 
distribution  of  income  and  wealth.  To  evaluate  this  dimension  of 
R&I,  macroeconomic  models  must  be  designed  to  properly  account 
for  heterogeneity  across  workers  and  households  and  the 
associated dynamics. Modelling skill and education heterogeneity is 
of  fundamental  importance  to  evaluate  the  labour  market  and  the 
distributional implications of innovation policies. 

o  Environmental  issues,  such  as  those  falling  in  the  domain  of 
climate  change,  have  become  pressing  in  the  recent  decade. 
Environmental  policies  direct 
the 
development  of  environmental-friendly  technologies.  However,  the 
advancement  of  green  and  clean  energy  technologies  and  the 
development of abatement technologies require large investments in 

technical  change 

towards 

10 

 
 
 
 
 
 
R&I. These two dimensions should be addressed together in models 
integrating environmental aspects with endogenous technical change 
mechanisms.    

 

There will never be one perfect or right model.  “All models are wrong, but 
some  are  useful”.      The  agenda  should  thus  be  to  help  incumbent  models 
becoming more useful for innovation and innovation policy analysis, as well 
as helping the development of new models which  are  better able to  assess 
innovation and innovation policy analysis.   

  A  lot  of  progress  could  already  be  made  from  sharing  and  learning 

among the currently used models.   

  When  contemplating  bigger  adjustments  to  the  models,  a  more 
modular  approach  should  be  considered,  with  a  stable  “core”  model 
that  is  capable  and  flexible  to  link  to  (new)  submodules.    Modules  can 
address in more detail key features of innovation or different dimensions 
of macroeconomic performance and can be activated when needed.   

  Commonly  missing  features  in  the  current  models  (such  as  the 
formation of human capital for the creation and adoption of innovation; 
the modelling of risk and uncertainty, role of the public R&D sector, the 
heterogeneity  and dynamics of  the innovative  firms population)  require 
bigger  adjustments  through  new  submodules.      As  the  features  are 
missing  in  all  of  the  currently  used  applied  macro-models,  efforts  to 
develop new submodules for these features could be shared among the 
models.    

 

To  achieve  the  objectives  mentioned  above  in  the  development  of  R&I 
macro-models  for  the  evaluation  of  R&I  policies  the  following  families 
of  models  should be seriously considered: endogenous growth models 
with firm dynamics,  models of vintage technologies, models of directed 
technical  change,  models  of  human  capital  formation  and  occupational 
choice, and models of search in the labour market, among others. 

  Beyond further developing currently used  models,  new  models  should 
be  on  the  radar  as  well,  such  as,  models  of  networks,  models  of 
structural  transformation,  models  of  bounded  rationality,  evolutionary 
game theory and agent-based computational models.  New models offer 
new perspectives for modelling currently missing features.    

  Macroeconomic  models can only be a good  laboratory for  the  evaluation of 
economic  policy,  if  they  are  as  close  as  possible  to  the  available  data  on 
those dimensions in which the policy is supposed to operate. This is a basic 
requirement for any macroeconomic model to provide accurate quantitative 
figures that help evaluating the contribution of innovation policies.   High on 
the  to-do  list  should  be  to  improve  the  data  availability  for  modelling  of 
the key R&I features and key R&I policy interventions, in particular data on 
innovation policies in Europe. 

  Modern macroeconomic models based on microeconomic foundations should 
be  designed  consistently  with  microeconomic  data  and  the  institutional 
framework  in  which  economies  operate.    This  is  generally  done  by  an 
exercise  of  calibration  and/or  estimation  of  behavioral  parameters  of  the 
model  that  are  reasonable  (i.e.  consistent  with  empirical  evidence  from 

11 

 
micro studies on different dimensions of the model) but that also allow the 
replication of the observed data.  

  Transparency  should  be  improved  for  all  models  to  enable  a  more 
informed  use  by  policy  makers  of  the  results  and  to  enable  a  more 
interactive  mode  of  continuous  development  of  the  models.  Ideally,  a  full 
description  of  the  model,  code  and  dataset  should  be  available  on 
open access. 

Within  this  overall  agenda  for  moving  the  macro-modelling  of  innovation  policies 
forward, DG R&I can play a pivotal role through the following actions: 

 

 

 

 

 

Providing  grants  for  excellent  research  at  the  knowledge  frontier  of 
macroeconomic models contributing to the development of the modelling of 
R&I and R&I policy as well research into new models; 

Procuring  research  into  further  developments  of  currently  used  models  by 
the EC to improve the modelling of their R&I policy evaluation, with a close 
involvement of policy modellers and users; 

Full transparency should be a condition (a full description of the model, code 
and dataset should be open access); 

Improving  the  provision  of  common  data  inputs  to  macro-models,  most 
notably data on innovation policies; 

Providing  a  platform  for  macro-models  where  research  results  and  best 
practices  can  be  exchanged,  databases  can  be  shared  and  where  open 
access  to  models  and  codes  can  instigate  further  (co-)development  of 
existing macro-models or inspire the research on new models; 

  Regularly  bringing  together  producers  and  users  of  macro-models, 
improving  the  transparency  of  models  to  users  and  improving  the  user 
friendliness of models for model-users. 

12 

 
 
 
 
 
 
 
 
1. 

INTRODUCTION 

The  Directorate  General  for  Research  and  Innovation  of  the  European  Commission 
(DG R&I) has engaged since 2014 in a medium to long-term agenda to improve the 
treatment of research and innovation (R&I) in macroeconomic models used to assist 
policy making.  

In  December  2018,  an  Expert  Group  was  set  up  to  produce  a  report  that  policy 
makers and practitioners can use as guideline for this agenda and to help identifying 
potential future strands of work in its support.  

The  aim  of  the  report  is  double.  First,  it  aims  to  identify  those  characteristics  and 
properties  of  R&I  that  should  ideally  be  included  in  macro-models,  i.e.,  those  that 
would  help  to  understand  the  behaviour  of  innovation  and  to  produce  sound 
evaluations of R&I policies. Second, it aims at identifying the main R&I policies used 
in  practice,  those  that  macro-models  need  to  include  in  order  to  produce  well-
founded assessments.  

Section  2  identifies  those  critical  properties  and  policies  of  the  R&I  process  that 
ought to be included in macro-models. Since our focus is on moving the frontier of 
macro-modelling R&I, this section will rely mostly on  the state-of-the-art academic 
literature  on  macro-modelling  R&I  and  the  current  state  of  experience  with 
quantitative  macro-models,  leaving  some  relevant  contributions  of  the  R&I 
literature, especially from the micro literature, left uncovered.  

The  critical  properties and policies  of the R&I process  may affect  the incentives on 
doing research and development (R&D) and the probability of innovating, how much 
is  or  ought  to  be  spent  on  R&I  and  the  effects  of  R&I  investments  on  various 
measures  of  economic  performance,  especially  productivity  and  economic  growth, 
but  also  other  dimensions  of  relevance  for  policy  makers,  like  environmental  and 
socio-economic  sustainability.  For  each  of  these  properties  and  policies,  the  report 
aims  at  explaining  why  it  would  be  worth  to  include  them  in  the  modelling,  what 
effects their inclusion is expected to have on the performance of the model and the 
scope for R&I policy evaluation. The report will also discuss what is required in order 
to  include  these  properties  and  policies  in  the  modelling,  both  in  terms  of  model 
design and empirical implementation.  

In Section 3, we check whether and how such properties and policies are accounted 
for  in  the  macro-models  used  by  the  European  Commission.  In  particular,  we  will 
look at the three models currently most commonly used by DG-R&I, namely QUEST, 
RHOMOLO  and  NEMESIS.  We  assess  whether  they  contain  the  basic  features  and 
policies  identified  in  Section  2  as  important  for  modelling  R&I  and  assessing  R&I 
policy interventions, and, if not, how they could be included. 

The  report  closes  with  some  conclusions  and  recommendations  on  how  to  move 
forward  the  medium-long  term  agenda  on  the  development  and  use  of 
macroeconomic models to inform and advice research and innovation policy making. 

13 

 
 
 
2.  MAIN FEATURES AND POLICIES OF R&I 

Based on the existing literature on R&I and innovation policies, mostly the academic 
macroeconomic literature, this section describes and analyses the critical features of 
the  innovation  process  needed  to  appropriately  account  for  its  role  on  economic 
growth  and  welfare,  as  well  as  the  main  policies  to  promote  innovation,  boosting 
growth  and  enhancing  sustainability.  This  description  and  analysis  of  innovation 
features  and  policies  is  addressed  to  illuminate  the  design  of  macro-models  for 
evaluating  innovation  and  innovation  policy.  This  is  clearly  not  an  exhaustive 
account of all there is to know  about R&I  and  innovation policy, as the focus is on 
what we can learn for the macroeconomic assessment of R&I policies 

2.1. Main features of R&I 

Our journey starts by describing the main features of technological knowledge, 
the  process  of  knowledge  creation,  the  innovation  process  and  its  diffusion,  to 
finally refer to its measurement. 

2.1.1. Technological knowledge  

Technological  knowledge  is  the  sum  of  techniques  employed  in  the 
production of goods and services. It results from the cumulative aggregation of 
new  technological  ideas  or  innovations,  resulting  from  a  systematic  effort  of 
research  and  development  (R&D)  benefiting  from  multiple  forms  of  knowledge 
spillovers.  Like  for  physical  and  human  capital,  R&D  is  a  form  of  investment  that 
cumulates in the  stock  of  technological knowledge.  In macro-models, technological 
knowledge is generally represented by a state variable that cumulates previous R&D 
effort,2  but  can  also  include  other  mechanisms  such  as  learning-by-doing3  and 
adoption of technologies developed by others4. 

The stock of technological knowledge also relates to intangible capital, which 
includes  computerised 
innovative  property  and  economic 
competencies  5  Technological  knowledge  has  some  specific  properties  that  to  a 
great degree differentiate it from other forms of capital. The specific features of 

information, 

2   See the seminal contributions of Romer (1990), Aghion and Howitt (1992) and Grossman 

and Helpman (1992). 

3   See  the  seminal  contribution  of  Arrow  (1962).  Irwin  and  Klenow  (1994),  among  others, 
provide empirical evidence supporting the existence of learning-by-doing in the innovation 
process. 

4   See Comin and Gertler (2006) and the documents supporting the FRAME (2019) project. 

5   Corrado  et  al.  (2005)  and  (2009)  extend,  in  the  framework  of  the  economic  growth 
literature,  the  definition  of  a  production  technology  to  include  the  stock  of  intangible 
capital.  They  find  for  the  US  that  the  growth  rate  of  output  per  worker  increases  more 
rapidly  when  intangible  capital  is  included,  that  capital  deepening  (on  physical  and 
intangible  capital)  becomes  the  unambiguously  dominant  source  of  growth,  diminishing 
the role of total factor productivity, and find that the labour income share has significantly 
decreased over the last 50 years due to the rise of intangible capital 

14 

 
                                                
technological knowledge include, first of all, its non-rival nature, i.e. it can be 
used  by  many  agents  at  the  same  time.6  In  this  sense,  it  entails  high 
generation  costs  but  can  be  easily  reproduced  by  those  that  have  the  needed 
absorptive  capacity.7  To  some  extent,  it  is  indivisible.8  Last  but  not  least,  it 
suffers  from  economic  obsolescence:  it  may  depreciate  in  value  but  not  in 
essence, due to the emergence of subsequent innovations.9  

Technological  knowledge  typically  will  have  a  social  value  that  is  different  from 
its private value. On one hand, since innovations are non-rival, part of their return 
is  appropriated  by  others.  On  the  other  hand,  the  negative  effect  that  innovations 
have on existing technologies is not paid by the innovator, but by the owners of the 
existing technologies. 

However,  technological  knowledge  does  not  get  transmitted  as  easily  as  sharing  a 
simple  piece  of  information  such  as  the  latest  result  of  a  soccer  match.  First, 
technological knowledge is partially tacit, that is, it cannot be entirely explained in a 
manual by means of words,  symbols or graphs,  as opposed to codified knowledge. 
Second,  it  is  cumulative,  meaning  that  it  cannot  be  understood  without  grasping 
prior  knowledge.  Third,  it  cannot  be  assimilated,  adopted  and  reproduced  without 
incurring  substantial  costs,  those  related  to  building  the  needed  absorptive 
capacity. It bears the cost of learning, adaptation and reproduction.  

Technological  knowledge  is  therefore  not  a  free  good  that  falls  like  manna  from 
heaven  for economic actors wishing  to  use  it, but it  requires a deliberate effort on 
the  part  of  the  actors  to  generate,  adopt  and  use  it.  We  can  distinguish  different 
stages  in  this  process,  starting  from  the  Schumpeterian  distinction  between 
invention,  innovation,  adoption  and  diffusion,  although  some  scholars  would 
consider  this  subdivision  to  be  already  reductionist  because  it  looks  like  a  linear 
process without possible feedbacks.10 

6   The  fundamental  role of  non-rivalry  on  innovation  and  economic  growth  was pointed  out 
by the seminal contribution of Romer (1990). Referring to Romer, Jones (2019) says “he 
pinpointed  that  the  nonrivalry  of  ideas  is  ultimately  responsible  for  the  rise  in  living 
standards over time.” 

7   For Cohen and Levinthal (1990), absorptive capacity is the “firm’s ability to recognize the 
value  of  new,  external  information,  assimilate  it,  and  apply  it  to  commercial  ends.”  In 
more  precise  terms,  Leahy  and  Neary  (2007)  define  firm’s  absorptive  capacity  as  “its 
ability  to  absorb  spillovers  from  other  firms.”  Cohen  and  Levinthal  (1989)  “suggest  that 
R&D not only generates new information, but also enhances the firm's ability to assimilate 
and exploit existing information.” 

8   To quote Dosi and Nelson (2010) “half a statement about whatever property of the world 

or of a technology is not worth half of the full one; most likely it is worth zero.” 

9   The  business  stealing  effect  in  Aghion  and  Howitt  (1992)  is  a  form  of  economic 

obsolescence. See also Boucekkine et al. (2009). 

10   See Schumpeter (1934). For a reminder of the basics of the economics of information and 

references to the prior literature, see Dosi and Nelson (2010). 

15 

 
 
                                                
2.1.2. Innovations and the creation of new technological knowledge 

Innovations  represent  additions  to  the  stock  of  technological  knowledge  which 
result  from  the  use  of  R&D  (internal  or  external  to  the  firm)  as  an  input  in  the 
innovation  process.  Innovations  modify  the  economic  environment  by  introducing 
novel  final  consumption  goods  (like  the  washing  machine,  the  TV  or  the 
smartphone),  by  developing  new  materials  (like  plastics  or  graphene)  and  drugs 
(like penicillin, aspirin or AIDS drugs), new sources of energy (like electricity, solar 
panels or hydrogen), new ways of processing information (like the printing press, or 
quantum  computers),  new  means  of  transportation  (the  sailing  boat  or  electric 
cars), new services (like on-line dating) among many others. 

Creating  new  technological  knowledge  requires  a  systematic  effort  of  research  and 
development  (R&D),  and  benefits  from  multiple  forms  of  knowledge  spillovers.  In 
this  sense,  R&D  can  be  seen  as  the  main  input  in  the  generation  of  those 
innovations that cumulate into the stock of technological knowledge. 

In  this  subsection  we  review  the  features  of  R&D  and  knowledge  spillovers  in 
creating  innovations,  while  considering  firm  heterogeneity,  uncertainty  and  path-
dependency,  as  well  as  different  classifications  of  innovations:  basic  vs  applied, 
product vs process, and drastic vs incremental. 

R&D 

R&D  refers  to  those  economic  activities  undertaken  with  the  purpose  of 
improving the actual state of technology. In the Frascati manual (OECD, 2015), 
R&D  efforts  are  subdivided  into  basic,  applied  and  experimental  developmental. 
Basic  research  is  addressed  to  the  advancement  of  knowledge  without  any  explicit 
intention  of  solving  a  particular  practical  problem.  Applied  research,  by  contrast, 
aims to solve technological problems. Experimental development is that stage in the 
R&D  process  where  experiments  are  made  to  adjust  the  initial  idea  for  a  more 
successful  product  or  process.  Invention  can  come  from  the  efforts  of  individual 
researchers  doing  informal  R&D,  or  from  R&D  efforts  done  in  an  organized  way  in 
scientific research labs of the private or public sectors. In line with Griliches (1979) 
and Mansfield (1980), the cumulative aggregation of past R&D effort into basic and 
applied  research  (including  experimental  developments)  gives  rise  to  a  stock  of 
basic or scientific knowledge and a  stock of applied knowledge, respectively.11  The 
relation between basic and applied research, and the associated market failures, are 
discussed in more detail in Section 2.1.3. 

In  a  world  of  competition,  firms  may  imitate  each  other  to  the  extent  that 
intellectual property rights allow it. The imitation effort usually enters as part of the 
diffusion process of technologies 

11   Comin  and  Gertler  (2006)  develop  a  macro-model  of  innovation  and  adoption  aimed  to 
better understand the relation between the stock of basic and applied knowledge, and the 
associated  spillovers,  as  well as  their  effects  on  output growth  in  the  short,  medium  and 
long  run.  The  FRAME  (2019)  project  uses  Comin  and  Gertler  (2006)’s  framework  to 
evaluate  the  economic  and  socio-economic  effects  of  basic  and  applied  research,  as  well 
as 
See 
https://www.h2020frame.eu/frame/home.html 

framework. 

innovation 

policy 

role 

this 

the 

of 

in 

16 

 
                                                
In  a  world  of  open  innovation,  firms  may  collaborate  in  their  research  efforts, 
exploiting  knowledge  complementarities.12  Firms  may  also  benefit  from  scientific 
progress,  coming  from  universities  or  public  research  labs.  Instead  of  searching 
themselves for new ideas, firms may prefer to buy technological knowledge on the 
market  for  technology,  by  investing  in  new  machines  that  incorporate  the  latest 
technology,  or  by  licensing.  Finally,  an  important  element  in  knowledge  building  is 
learning-by-doing and learning-by-using.13 

The  recent  macroeconomic  literature  on  R&I  follows  the  pioneer  work  by  Romer 
(1990),  Aghion  and  Howitt  (1992)  and  Grossman  and  Helpman  (1992).  In  this 
literature,  R&D  activities  are  undertaken  with  the  purpose  of  improving  the  actual 
state of technology, which in Romer (1990) takes the form a new product and in the 
Schumpeterian  framework  a  better-quality  version  of  an  existing  product  -see 
Aghion and Howitt (1992) and in Grossman and Helpman (1992). 

Spillovers 

The  process  of  building  technological  knowledge  considerably  benefits  from 
knowledge spillovers, i.e., unintended knowledge transfers. Knowledge spillovers 
result  from  the  non-rivalry  nature  of  technological knowledge  and  it  is  constrained 
by  partial  excludability.14  The  creation  of  new  technological  knowledge  helps  the 
development  of  subsequent  inventions  through  knowledge  spillovers.  As  stated  by 
Bloom  et  al.  (2019)  “the  literature  on  spillovers  has  consistently  estimated  that 
social returns to R&D are much higher than private returns.” 

In order to benefit from R&D spillovers, firms have to develop their own absorptive 
capacity. The absorptive capacity of a firm positively depends on the accumulation 
of  its  previous  R&D  investments.  It  helps  understanding  why  private  R&D  and 
knowledge  spillovers  are  likely  to  be  complements  in  the  creation  of  new 
technologies. Griffith et  al. (2003) implement empirically the concept of absorptive 
capacity  in  the  framework  of  the  Schumpeterian  model  to  conclude  that  previous 
studies underestimate the social return to R&D by neglecting absorptive capacity.15  

12   Belderbos et al. (2004) study the determinants of firms to engage in R&D cooperation. 

13  

The role of learning-by-doing in the growth process was first studied by Arrow (1962). 

14   Walsh et al. (2007) provide an interesting example of partial excludability in relation with 

academic biomedical research. 

15   See also Aghion and Jaravel (2015). 

17 

 
 
 
                                                
Besides knowledge spillovers, the literature also identifies different forms of rent or 
market  spillovers,  referring  to  the  transfer  of  monopoly  rents  between  firms  or 
sectors.16  Innovations  have  the  negative  side  effect  of  destroying  value  of  current 
technologies,  which  is  the  case  of  a  new  product  reducing  the  market  share  of  an 
old  product.  Rent  spillovers  may  take  the  form  of  business  stealing  or,  more 
generally,  technological  obsolescence.  In  the  framework  of  the  Schumpeterian 
model of Aghion and  Howitt (1992),  business stealing  refers to  the case  of  a  new, 
better  quality  product  displacing  partially  or  completely  an  existing  one,  as 
typewriters  were  substituted  by  computers.  Obsolescence  or  product  market 
spillovers refer to the loss in value of an existing product as a result of the reduction 
in  demand  resulting  from  the  emergence  of  new  products  or  better  versions  of 
existing products.17  

Bloom et al. (2013) develop a general framework to empirically evaluate the relative 
importance  of  the  two  countervailing  spillovers  referred  above:  the  positive  effect 
emerging 
from  knowledge  spillovers  and  the  negative  effect  of  business 
stealing/obsolescence.  They  find,  using  US  firm-level  panel  data,  that  knowledge 
spillovers quantitatively dominate market spillovers, “so that the gross social returns 
to R&D are at least twice as high as the private returns.”18  

Whether  spillovers  are  localized  remains  a  contentious  issue.  Bottazzi  and  Peri 
(2003) use regional R&D and patent data for Europe to find that “spillovers are very 
localized and exist only within a distance of 300 km.” Bottazzi and Peri (2007) use 
OECD data to study the dynamic relationship between R&D employment and patent 
applications. They report large spillover effects: “A 1% positive shock to R&D in US 
increases the knowledge creation in other countries by an average of 0.35% within 
ten  years.  The  same  shock  generates  a  maximum  6%  effect  on  the  US  stock  of 
knowledge after five to ten years.” 

Firm heterogeneity and uncertainty 

Modelling  the  innovation  process  has  to  consider  firm  heterogeneity  and  the 
associated  differences  in  the  dynamics  of  products,  firms  and  markets  induced  by 
R&I.  Heterogeneity  of  individual  firms  can  be  reflected  in  many  dimensions: 
differences  in  their  production  and  R&D  technologies,  vintages  of  capital,  their 

16   Comin  and  Gertler  (2006)  develop  a  macro-model  of  innovation  and  adoption  aimed  to 
better understand the relation between the stock of basic and applied knowledge, and the 
associated  spillovers,  as  well as  their  effects  on  output growth  in  the  short,  medium  and 
long  run.  The  FRAME  (2019)  project  uses  Comin  and  Gertler  (2006)’s  framework  to 
evaluate  the  economic  and  socio-economic  effects  of  basic  and  applied  research,  as  well 
as 
See 
of 
https://www.h2020frame.eu/frame/home.html 

framework. 

innovation 

policy 

role 

this 

the 

in 

17   The  issue  of  measuring  the  obsolescence  rate  of  technological  knowledge  was  raised  in 
70’s by Bosworth (1978) and Pakes and Schankerman (1984). The macroeconomic effects 
of  obsolescence  have  been  studied  in  the  vintage  capital  literature  by  Boucekkine  et  al. 
(1997) and Bambi et al. (2014), among others. See also Chapter 3 of Aghion and Howitt 
(1998) for a discussion of the effects of rent spillovers in endogenous growth models. 

18   In  the  same  line,  Jones  and  Williams  (1998)  claim  that  conservative  estimates  suggest 
that optimal R&D investment is at least two to four times actual investment, which implies 
that in fact there is too little R&D. 

18 

 
 
                                                
expectations and information sets, and their market situation  among others. These 
differences  will  not  only  matter  ex  ante,  but  they  will  also  shape  the  dynamics. 
Given  some  initial  states  (e.g.  technology  and  its  distribution),  at  each  period 
heterogeneous agents update their choices, depending on their own history and the 
state of the whole  economy, and interact through markets to determine  the  states 
for  the  subsequent  period.  Heterogeneity  is  therefore  an  essential  feature  of 
macroeconomic  models,  particularly  those  using  quantitative  and  computational 
methods. 

It  is  also  important to  emphasize  the  stochastic  nature  of  technological  progress. 
The probability of making and succeeding in the right investments may be different 
across  firms,  even  drawn  from  the  same  probability  distributions.  There  may  be 
even uncertainty and asymmetric information on the exact shape of the distribution 
of  R&D  returns,  since  firms  face  different  technological  trajectories  and  past 
experience.  

The  literature  on  endogenous  growth,  following  the  seminal  contributions  by 
Aghion  and  Howitt  (1992)  and  Grossman  and  Helpman  (1992)  among  others, 
stresses  the  fact  that  firms  produce  differentiated  products  by  means  of 
heterogeneous  technologies  with  R&I  shaping  the  dynamics  of  firm’s  productivity. 
Following the seminal contributions of Hopenhayn (1992) and Jovanovic (1982), the 
literature on firm dynamics represents firm heterogeneity by assuming that firms' 
productivity  evolves  over  time  following  some  random  processes  with  high 
persistence.19 These random processes are affected by the R&D activities of firms.20 
An  equilibrium  is  then  characterised  by  a  distribution  of  productivity  across 
products,  plants  and  firms.21  Endogenous  growth  theory  and  the  theory  of  firm 
dynamics converged to a common framework of R&I and  heterogeneous firms well 
beyond the representative agent model -see Klette and Kortum (2004).  

19   See  Aghion  et  al  (2015)  for  a  discussion  of  the  relation  between  the  Schumpeterian 
approach and the theory and empirics of firm dynamics (including the size distribution of 
firms, the relation between size and age, and the behaviour of small firms). 

20   It  is  important  to  notice  that  the  literature  on  firm  dynamics  in  models  of  monopolistic 
competition, inspired by Melitz (2003), implicitly requires a notion of innovation too. New 
firms  in  Melitz  produce  a  new  differentiated  good,  meaning  that  the  fixed  entry  cost  in 
Melitz (2003) may be interpreted as the research cost of producing a new variety. See the 
survey by Licandro (2018) published in the forthcoming book by Akcigit et al. (2020). 

21   Under some general aggregation conditions, general equilibrium models of firm dynamics 
share common properties with the Neoclassical growth model. Collard and Licandro (2020) 
provided  a  simplified  version  of  the  Hopenhayn  model  which  dynamics  collapses  to  the 
dynamics  of  the  Neoclassical  growth  model.  The  same  aggregation  conditions  apply  to 
models of endogenous growth. 

19 

 
 
 
                                                
 
All this to point out that every firm is in a different position and may act differently 
depending on its characteristics, its history, its particular needs and its expectations. 
This  individual  heterogeneity  is  an  essential  feature  in  the  evolutionary  view  of 
technological  progress  and  the  associated  agent  based  computational  models 
(ACE).22  In  this  strand  of  the  literature,  heterogeneity  shows  up  at  all  levels  of 
aggregation  (Dosi  1988)23.  Dosi  et  al.  (2010)  is  a  representative  example  of  this 
literature. Their agent-based model has a machine-producing sector where firms do 
R&D  and/or  imitate  in  order  to  produce  more  advanced  machines  to  the  customer 
goods  sector.  Their  success  in  coming  up  with  better  machines  depends  partly  on 
luck, but partly on their heterogeneous search capabilities. 

Neary  (2010)  stresses  the  importance  of  considering  granularity  when  modelling 
firm heterogeneity. He claims that “it is unsatisfactory … to view large firms as more 
productive clones of small ones.” Consequently, he suggests three alternative ways 
of modelling imperfect competition: heterogeneous industries, natural oligopoly, and 
superstar firms.24 

Path dependency 

During  the  R&D  stage,  researchers  benefit  from  their  past  experience  and  are 
conditioned by their state of knowledge and their past technological choices. This is 
where  path  dependency  or  technological  cumulativeness  comes  into  the 
picture.  To  quote  Dosi  and  Nelson  (2010),  “…  advances  are  likely  to  occur  in  the 
neighbourhood of the techniques already in use within the firm.” Following Griliches 
(1979),  one  way  of  modelling  the  cumulative  nature  of  R&D  is  to  consider  not  the 
flow  of  R&D  expenditure  but  the  accumulated  stock  of  past  and  present  R&D 
expenditure as the appropriate variable to affect productivity or to enter as an input 
in an extended production function. But it is not only the total past R&D but also of 
the direction in which past research has been conducted that determines the future 
technological trajectory. 

Path dependency may trap countries into bad outcomes. Models of path dependency 
and poverty traps are surveyed by Azariadis and Stachurski (2005). An example is 
Acemoglu  (1997)  where  multiplicity  of  steady  state  equilibria  results  from  labour 
market frictions. In this framework, poor technology adoption and low investment in 
human  capital  are  self-reinforcing,  generating  a  shortage  of  skilled  labour  that 
makes  technology  adoption  unprofitable.  Economies  with  strong  labour  market 

22   The evolutionary approach follows Nelson and Winter (1982). See also Nelson (2009) and 

Dosi and Nelson (1994) and (2010). 

23   Agent-based  computational  economics  (ACE),  defined  as  the  computational  study  of 
economies  considered  as  complex  evolving  systems  ACE  provides  an  alternative 
methodology to the representative agent models to build macroeconomic models from the 
bottom  up  based  on  assumptions  about  the  way  agents  behave  and  interact,  which  are 
rooted  in  the  actual  empirical  microeconomic  evidence  (Tesfatsion  2006).  According  to 
Tesfatsion (2006), complex systems are characterized by interacting agents who through 
their  interactions  lead  to  emergent  properties  of  the  system  that  are  not  those  of  the 
agents. 

24   See Gabaix (2011) on the role of granularity for the aggregate effect of idiosyncratic firm 

shocks, and Dorn et al. (2020) and Veugelers (2018) on superstar firms. 

20 

 
                                                
frictions  and  a  history  of  low  human  capital  are  trapped  in  an  equilibrium  of  low 
technology  adoption  and  low  human  capital.  More  recently,  Benigno  and  Fornaro 
(2018)  rationalize  the  Great  Recession  that  followed  the  2007  financial  crisis  as  a 
stagnation  trap,  i.e.,  an  equilibrium  with  high  unemployment  and  weak  growth. 
They  generate  this  result  in  a  Schumpeterian  growth  model  interacting  with  a 
Keynesian  growth  theory  in  which  pessimistic  expectations  can  lead  to  very 
persistent, or even permanent stagnation trap.  

Path  dependency  is  also  a  fundamental  property  of  macro-models  with  vintage 
technology.  In  these  models,  medium  term  fluctuations  critically  depend  on  the 
past  of  R&D  investments  following  the  so-called  echoes  principle.  Bambi  et  al. 
(2014)  claim  that  the  type  of  wave-like  fluctuations  previously  described  by 
Schumpeter  (1934)  help  to  replicate  the  medium-term  fluctuations  of  the  US 
economy studied by Comin and Gertler (2006).25  

2.1.3. Innovation typology 

Basic and applied research 

Basic  research  is  fundamentally  addressed  to  move  the  frontier  of  knowledge, 
without  necessarily  intended  to  solve  any  practical  problem.  In  Nelson  (1959)‘s 
words “scientific research is the human activity directed toward the advancement of 
knowledge”, while applied research is more likely to provide solutions to practical 
problems (see also Oslo Manual (OECD, 2018)).  

It is difficult to imagine  the current  state of technology  and welfare  in  our modern 
society  without  the  development  of  modern  sciences.  As  a  fundamental  principle, 
the  scope  of  applied  research  is  limited  by  the  development  of  basic  research.  As 
pointed  out  above,  innovation,  in  particular  drastic  innovation,  requires  new 
knowledge  which  critically  depends  on  new  scientific  knowledge.  This  makes  basic 
research  to  be  a  high  source  of  knowledge  spillovers.  One  of  the  main  policy 
implications  in  Nelson  (1959)  is  that  if  applied  research  may  stimulate  private 
industry to spend close to the social optimum, “the social benefits of basic research 
are  not adequately  reflected in  opportunities for private profits.”  Public support  for 
basic research is consequently critical.  

Most  of  the  literature  on  endogenous  growth  is  addressed  to  model  applied 
research. The study of the economic impact of basic research, pioneered by Nelson 
(1959)  and  Arrow  (1972),  has  been  recently  embodied  in  the  endogenous  growth 
literature by Akcigit et al. (2020). 

25   In Bambi et al. (2014) “waves of innovations arrive  en masse, moving the economy to a 
boom;  the  associated  increase  in  productivity  raises  purchasing  power  all  over  the 
business sphere, inducing research activities to flourish; but, the new products will take a 
while to develop; when the new wave of innovations is eventually implemented, the new 
products  enter  the market producing  a second  boom,  which  will  generate  a  third,  then a 
fourth and so on and so for.” 

21 

 
 
 
                                                
Process and product innovation  

The  latest  version  of  the  Oslo  Manual  (OECD,  2018)  recognizes  two  kinds  of 
innovation.  On  the  one  hand,  you  have  product  innovations,  which  could  be 
completely  new  goods  or  services  or  goods  and  services  that  have  undergone 
significant improvements in one or the other functional characteristic, be it quality, 
affordability,  or  durability.  On  the  other  hand,  you  have  process  innovations, 
which refer to improvements in the business functions, such as increased efficiency, 
meeting  regulatory  requirements  or  cost  reductions.  Process  innovations  are 
generally modelled as cost-reducing whereas product innovations are modelled as 
demand  shifting.26  Some  innovations  may  play  dual  roles,  being  a  product 
innovation for some firms but a process innovation for others (a new robot will be a 
product  innovation  for  the  firm  producing  it,  but  a  process  innovation  for  the  firm 
using it in its production process).  

From  the  perspective  of  a  product,  productivity  has  at  least  two  conceptually 
different  components:  the  quality  of  the  product  (as  perceived  by  consumers)  and 
the  technical  efficiency  of  the  production  process  (in  transforming  inputs  into 
output).  The  former  is  related  to  product  innovation  and  the  latter  to  process 
innovation. The empirical literature on firm level productivity, following the seminal 
contribution  of  Foster  et  al.  (2008),  developed  methods  to  decompose  these  two 
components of firm’s productivity (see Section 2.1.5 below).  

Romer (1990), Aghion and Howitt (1992) and Grossman and Helpman (1992), and 
the large literature that followed, developed macro-models of product innovation, in 
which researchers succeed in generating new products or in improving the quality of 
the  existing  ones.  They  feature  well  the  type  of  innovation  generated  by  startups, 
but  also  by  incumbent  firms,  that  undertake  product  innovation.27  Klette  and 
Kortum (2004) extend the Schumpeterian model to allow for multiproduct firms, in 
which incumbent firms invest in R&D to generate better quality versions of existing 
products  produced  by  other  firms.  In  Acemoglu  and  Cao  (2015)  or  Impullitti  and 
Licandro  (2018),  firms  invest  in  process  innovation  aiming  at  reducing  their 
production  costs.  Akcigit  and  Kerr  (2018)  combine  both  product  and  process 
innovation in line with the literature just cited above. In the vintage capital literature 
new  technologies  are  embodied  in  new  capital  (tangible  or  intangible).  Dosi  et  al. 
(2010)  model  innovation  in  terms  of  new  machines  that  will  be  used  by  the 
consumption goods sector. 

26   Huergo  and  Jamandreu  (2004)  use  Spanish  data  to  study  the  relation  between  process 
innovation  and  productivity  growth  concluding  that  process  innovation  lead  to  extra 
productivity growth. 

27   This  is  the  case  in  the  model  of  step-by-step  innovation  suggested  by  Aghion  et  al. 

(2001). 

22 

 
 
 
 
                                                
Drastic versus incremental innovations 

Innovation  may  be  at  different  degrees  incremental  or  drastic,28  and  they  may 
vary  in  their  general-purpose  nature.  At  the  very  extreme  of  the  innovation 
process  are  those  innovations  that  drastically  change  the  environment  by 
introducing  new  technological  paradigms  that  affect  production  and  innovation  in 
several sectors. These innovations are referred in the growth  literature as general-
purpose  technologies  (GPT).  Examples  of  general-purpose  technologies  are  the 
introduction of the steam engine, electricity power, information and communication 
technology.  Bresnahan  and  Trajtenberg  (1995)  stressed  the 
fact  that  a 
decentralized economy will have difficulty in fully exploiting the growth opportunities 
of GPT's, lowering the rate of technical progress.29  

In the first family of endogenous growth models, innovation is the intended result of 
applied  R&D  activities  undertaken  by  potential  entrants  (or  inventors),  which  is 
more  likely  but  not  necessarily  of  the  drastic  type.  Subsequent  research  also 
explored  the  effects  on  growth  of  R&D  activities  and  innovation  developed  by 
existing firms, being referred by the notion of innovation-by-incumbents, which is 
more likely but not necessarily to be of the incremental type 

2.1.4. Diffusion of new technologies  

New  technological  ideas,  after  discovery,  diffuse  throughout  the  economy  by 
affecting  the  design  of  products  and  production  process  of  firms  in  different 
industrial  sectors  and  geographical  locations.  This  process  is  referred  as  the 
diffusion  of  new  technologies.30  Extensive  empirical  work  was  undertaken  by 
Comin  et  al.  (2008)  and  Comin  and  Hobijn  (2004)  and  (2010),  among  others, 
describing  the  long  process  of  diffusion  of  new  technologies  across  time  and 
countries.  

The  diffusion  of  technologies  can  take  place  through  the  various  mechanisms  by 
which  knowledge  spills  over  across  space  and  time:  trade  relationships,  foreign 
direct  investment,  mergers  and  acquisitions,  movement  of  personnel,  patents, 
patent citations,  publications,  research collaborations, and  networks.31  The process 
of  diffusion  is  itself  subject  to  path  dependency,  network  externalities  and  the 
presence of complementary products or services. A new product or process is more 

28   In  the  Community  Innovation  Surveys  (Eurostat),  there  is  the  distinction  between  a 
product  new  to  the  firm  and  a  product  new  to  the  market,  the  latter  being  more  drastic 
than the former. 

29   See also Jovanovic and Rousseau (2005). 

30   For more on diffusion see Adams (1990), Jovanovich and Lach (1997) and Stoneman and 

Battisti (2010), a.o. 

31   See Cai and Li (2019) and Zacchia (2019), Acemoglu et al.  (2012b), Carvalho and Draca 

(2017) on networks, Keller (2004) on trade.  

23 

 
                                                
likely to be widely and/or faster adopted if it uses existing or familiar technologies, 
or if complementary goods or services already exist.32  

Nelson  and  Phelps  (1966)  suggested  two  alternative  approaches  of  technological 
diffusion  highly  suitable  for  macroeconomic  modelling.  In  their  framework,  the 
technological  frontier  exogenously  moves  at  a  constant  positive  rate.  In  the  first 
approach,  adoption  rates  are  assumed  to  positively  depend  on  the  distance  from 
the technological frontier  of the actual state of technology: the further a firm is 
from  the technological frontier,  the  larger  its  margin to  improve,  the more likely it 
will adopt.  The second is a  vintage  technology  approach,  where discoveries  are 
adopted with a delay.33  

Comin  and  Gertler  (2006)  embody  the  first  approach  in  a  dynamic  stochastic 
general  equilibrium  (DSGE)  model  with  both  innovation  and  adoption.  It  has  the 
additional  feature,  when  compared  to  Nelson  and  Phelps  (1966),  that  innovation 
moves the technological frontier instead of it being exogenously driven. As pointed 
out  by  Comin  (2009),  this  adoption  mechanism  is  pro-cyclical,  which  allows  the 
Comin and Gertler (2006) model to match the observed pattern of short-, medium- 
and  long-term  output  fluctuations  of  developed  economies.  Using  the  same 
framework,  Anzoategui  et  al.  (2019)  find  that  the  slowdown  in  productivity  that 
followed  the  Great  Recession  was  an  endogenous  response  of  innovation  and 
adoption  to  the  contraction  in  demand  induced  by  the  downturn,  explaining  a 
significant fraction of the fall in productivity that followed. Recoveries from financial 
crises are slow due to the long delays associated to the adoption process. 

In the framework of the endogenous growth theory and based on Boucekkine et al. 
(1997),  Bambi  et  al.  (2014)  adopt  the  vintage  technology  approach.  From  the 
Schumpeterian perspective, they stress the  fundamental role of the history of R&D 
investments  (path  dependency)  on  the  endogeneity  of  medium-term  fluctuations 
according  to  the  so-called  echoes  effect  principle.  More  recently,  Sedlacek  (2020) 
uses a DSGE model of vintage technologies to study the counter-cyclical behaviour 
of uncertainty, measured as the dispersion of firm-level productivity shocks, relating 
the diffusion of innovation with the business cycles.  

More  recently,  a  new  literature  emerged  stressing  the  role  of  production  and 
innovation  networks  in  the  propagation  of  firm  level  productivity  shocks. 
Acemoglu  et  al.  (2012b)  show  that  under  some  general  conditions  the  network 
structure  of  production  and  the  associated  input-output  links  make  idiosyncratic 
shocks to firm’s productivity lead to aggregate fluctuations. The occurrence of such 
an  event  depends  on  the  structure  of  the  production  network  with  upstream  and 
downstream interconnections capturing the possibility  of “cascade effects” affecting 
not only immediate  downstream  customers, but also  the rest of the economy.  The 
diffusion  of  micro  innovations  is  expected  to  propagate  through  the  production 
network,  depending  on  its  structure,  to  eventually  affect  the  structure  of  the 
network itself. Acemoglu et al. (2016b) use U.S. patents and their citations to map 
the  American  innovation  network.  They  study  the  evolution  of  citation  patterns 

32   It is not always the superior technology that gets adopted. See David’s (1985) story of the 

adoption of the QWERTY typewriter as an example. 

33   Endogenous adoption delays in vintage capital models and their business cycle properties 
are studied by Caballero and Hammour (1994), Boucekkine et al. (1997) and Gilchrist and 
Williams (2000), among others.  

24 

 
                                                
across  technology  classes  to  measure  the  structure  of  the  innovation  network  and 
find  that  the  interaction  of  pre-existing  network  structures  with  patent  growth  has 
strong predictive power on future innovation. 

The dynamics which  drives the rate of diffusion  of capital-embodied innovation  are 
described  in  Dosi  et  al  (2010).  Their  model  includes  firms  producing  consumption 
goods employing capital (composed of different vintages) and labour under constant 
returns  to  scale.  Desired  production  is  fixed  according  to  adaptive  demand 
expectations.  Given  the  actual  stock  of  inventories,  if  the  capital  stock  is  not 
sufficient to produce the desired production level, consumption-good firms invest in 
new  machines  in  order  to  expand  their  production  capacity.  They  also  invest  to 
replace old and obsolete machines according to a payback period rule and according 
to  the  prices  of  new  machines.  As  new  machines  embed  state-of-the-art 
technologies, the labour productivity of consumption-good firms increases over time 
according to the vintages of machine present in their capital stock.  

2.1.5. Measuring inputs and outputs 

As  a  general  principle,  a  macroeconomic  model  is  a  good  laboratory  for  the 
evaluation of economic policy if it is as close as possible to the available macro and 
micro data on those dimensions in which the policy is supposed to operate. In other 
words,  models  designed  to  evaluate  innovation  policy  in  practice  have  to  be 
quantitatively relevant, i.e. subject to quantitative evaluation consistently with both 
macro and micro evidence, and providing quantitative figures concerning the effects 
of innovation policy. 

This section describes the most commonly used measures of R&I inputs and outputs 
in  the  macroeconomic  and  applied  microeconomic  literature  of  innovation  and 
growth.  The  references  to  the  literature  here  are  not  exhaustive.  They  just  aim  to 
provide  examples  of  use  of  these  measures  in  macro-models.  For  more  elaborate 
discussions on measuring innovative inputs and outputs, see a.o. Gault (2013), Hall 
and Jaffe (2018) and Mohnen (2019). 

Measurement of R&D  

R&D statistics are available annually for most countries and for many years and are 
collected in a harmonized way. They are supposed to cover all R&D performers, i.e., 
the business enterprise sector, government, higher education and private non-profit 
organizations, but are unequally able to capture the whole population of performers 
across  countries  (different  response  rates,  different  sampling  below  a  certain  size 
cut-off point).34 When stocks rather than flows of R&D are used as building blocks, 
they may differ depending on the assumed depreciation rate and construction of the 
initial  capital  stock.  These  depreciation  rates  may  vary  across  sectors  -see  Li  and 
Hall  (2018).  Moreover,  some  firms  might  conduct  informal  R&D,  which  is  not 
reported in the R&D surveys. That  R&D statistics may differ depending on whether 
they are extracted from R&D surveys or from Innovation surveys is indicative of the 
measurement problems. 

34   See the Frascati Manual (OECD, 2015). 

25 

 
                                                
R&D  data  are  frequently  used  in  the  macroeconomic  literature  of  innovation.  The 
approach adopted by the FRAME (2019) project, partially based on Anzoategui et al. 
(2019),  is  an  example35.  Another  interesting  example  is  in  Akcigit  et  al.  (2020). 
They  use  firm  level  French  data  on  basic  and  applied  R&D  to  measure  the 
basic/applied  research  intensity  of  firms  that  they  use  to  estimate  a  macro-model 
with basic and applied R&D. 

Measurement of spillovers  

As  reported  by  Bloom  et  al.  (2019),  two  main  methods  have  been  used  in  the 
empirical literature on R&I to measure knowledge spillovers: the production function 
approach  and  the  patent  citation  approach.  In  the  first  approach,  knowledge 
spillovers are measured as the effect of some aggregate (industry, regional, national 
or international) R&D stock on the productivity of the firm -see Griliches (1979). In 
the  second  approach,  patent  citations  are  used  to  relate  an  innovation  to  the 
preceding  ones.36  R&D  stocks  are  generally  calculated  as  the  weighted  sum  of 
various  industry  specific  R&D  stocks  measured  at  the  regional,  sectorial,  country 
specific  (domestic  and/or  foreign)  levels.  The  weights  can  be  proportional  to 
potential inter-industry knowledge flows (such as purchases or sales of intermediate 
inputs, investment goods, flows of labour, or patent citations) or proportional to the 
proximity of locations in the geographical or technological space (e.g. the space  of 
patent  classes,  research  areas,  product  classes,  qualifications  of  R&D  personnel). 
Foreign  spillovers  can  be  proportional  to  international  trade,  foreign  direct 
investment, trade in technology, patent citations, international collaborations, etc.37  

Patent  citations  have  the  advantage  of  directly  identifying  the  path  that  spillovers 
follow  from  one  innovator  to  the  next,  but  are  based  on  patent  counts  with  all 
patents  having  the  same  weight  independently  of  their  economic  value.  The 
production  function  method,  in  contrast,  measures  spillovers  in  terms  of  their 
economic  value  as  measured  by  their  contribution  to  firm’s  productivity,  but  is 
uninformative on the channels through which spillovers operate. Different measures 
of  distance  have  been  used  in  the  production  function  approach  to  help 
disentangling  different  channels,  like  geographical  proximity,  sector/industry, 
product class and technological distances, among others.  

As discussed above, firms may also face a negative externality generally referred as 
product  market  spillovers.  Bloom  et  al.  (2013)  use  firm  level  information  on  the 
distribution  of  patenting  across  technology  fields,  and  sales  across  four-digit 
industries to determine the firm’s position in technology and product market spaces, 
which  allows  them  to  construct  distinct  measures  of  the  distance  between  firms  in 

35   Anzoategui  et  al.  (2019)  use  US  R&D  data  to  estimate  the  elasticity  of  R&D  in  the 
innovation  technology,  getting  an  estimated  value  in  line  with  the  estimates  reported  by 
Griliches (1990). 

36   See  Trajtenberg  (1990)  and  Jaffe  et  al.  (1993).  The  importance  of  this  measure  is 
highlighted  by  Hall  et  al.  (2005).  To  give  an  idea  of  the  magnitude  of  knowledge 
spillovers, they find that an extra citation per patent increase the market value of the firm 
by 3%. 

37   For  a  discussion  on  the  measurement  of  domestic  and  international  R&D  spillovers,  see 

Belderbos and Mohnen (2013) 

26 

 
                                                
the  technology  and  product  market  dimensions.  They  use  this  information  to 
distinguish empirically between technology and product market spillovers. 

Measurement of intangibles 

Since the pathbreaking work by Corrado et al. (2005), statistical offices have made 
considerable  efforts  to  incorporate  intangible  investments  to  the  measurement  of 
Gross Domestic Product (GDP) in National Accounts. Investment in intangible capital 
covers  three  types  of  assets:  computerized  information  (software,  databases), 
innovative  property  (R&D,  mineral  exploitation,  copyright  and  license  costs  and 
other  product  development,  design  and  research  expenses),  and  economic 
competences  (brand  equity, 
firm-specific  human  capital  and  organizational 
structure).  Since  then,  intangible  investments  have  been  added  to  Gross  Fixed 
Capital Formation, instead of being considered as intermediate inputs (as it was the 
case  in  the  past).  Some  data  on  these  intangibles  have  been  collected  in  the 
Innodrive and Intan projects funded by the European Commission, but they are not 
systematically updated -see for instance Corrado et al. (2013).  

Measurement of patenting 

Many  studies  have  resorted  to  patent  data  as  a  mean  to  measuring  innovation 
output  -see  Griliches  (1990).  Patents  however  are  well  known  to  be  an  imperfect 
measure for innovation output.  Nevertheless, patents are widely used as measures 
of the output of innovation. 

Patent  data  have  the  advantage  that  they  are  easily  available,  for  long  periods  of 
time,  and  that  they  contain  lots  of  information:  content  of  the  patented  invention, 
timing  of  introduction,  renewals  and  termination,  name  and  the  location  of  the 
assignee and references  to  prior knowledge.  All  these  pieces of information can be 
useful to infer the private and social value of a patent.  

The weakness of patent data is the unequal propensity of patenting across firms and 
sectors,  and  the  difficulty  of  merging  patent  data  with  other  firm-  or  sector-level 
data  (technology  classification  versus  industry  classifications,  disambiguation  for 
matching  on  the  basis  of  firm  names).  It  is  also  well  known  that  patents  are  of 
unequal  value,  rendering  the  simple  counting  of  patents  an  imperfect  measure  for 
innovation output. One way to correct for that is to weigh patents by their forward 
citations.38  As  patents  are  essentially  formal  ways  to  protect  intellectual  property, 
they  can  also  be  used  as  strategic  weapons  of  competition  (patent  thickets),  as 
signals  to  attract  venture  capital  or  as  means  to  acquire  new  technology  through 
cross-licensing. If they are not used to protect the patented idea, but used more as 
a strategic tool of competition, patents are not necessarily realizations of innovation. 

An example of a macro-model of heterogenous firms using patent data is Akcigit et 
al.  (2020),  which  is  aimed  to  evaluate  the  public  policy  support  to  both  basic  and 
applied R&D. 

Measurement of licensing  

38   For  a  more  detailed  discussion  of  patents  as  an  innovation  indicator,  see  Nagaoka  et  al. 

(2010) 

27 

 
                                                
Data on licensing of patents are scarce. The European and Japanese Patent Offices 
(EPO  and  JPO)  organised  a  survey  of  licensing  among  patent  holders  in  2007 
(Zuniga  and  Guellec,  2009).  They  observe  that  licensing  is  quite  common  among 
patenting firms, as one company in five (four) in Europe (Japan) licenses patents to 
non-affiliated  partners,  and  the  relationship  between  firm  size  and  probability  to 
licensing is U-shaped. Arqué-Castells and Spulber (2018) use data on patent trades 
from  USPTO,  licensing  deals  from  the  Securities  and  Exchange  Commission  filings 
and  cross-licensing  data  from  the  SEC  forms.  Abrams  et  al.  (2019)  use  data  on 
licensing  revenues  from  a  large  non-practicing  entity  to  estimate  some  key  micro 
parameters of their macro-model. 

Measurement of innovation expenditure (other than R&D) 

Besides  R&D,  innovating  firms  must  incur  a  range  of  other  expenditure  to  bring  a 
new product on  the market  or  to  introduce  a new  process. The innovation surveys 
that  follow  the  Oslo  Manual  (OECD,  2018)  collect  the  following  costs:  engineering, 
design  and  other  creative  activities,  marketing  and  brand  equity  activities,  IP-
related activities, employee training activities, software developments and database 
activities,  activities  related  to  the  acquisition  or  lease  of  tangible  assets,  and 
innovation management activities. Unfortunately, many of these items are not (yet) 
collected regularly by all firms, and are therefore difficult to quantify and very likely 
subject  to  substantial  measurement  errors.  For  instance,  firms  report  training 
activities in general but not necessarily specifically those for the production of new 
products or the use of new machines. 

Measurement of innovation output 

Innovation  output  measures  are  provided  in  the  innovation  surveys,  which  by  and 
large follow the guidelines of the Oslo Manual (OECD, 2018) in the form of product 
and  process  innovations.  Innovation  surveys  are  supposed  to  be  representative 
regarding  size,  industry,  and  in  some  countries  regional  distribution,  based  on 
stratified  random  sampling,  above  a  certain  minimal  size  threshold.  They  are 
currently conducted every two years in EU countries and on a more irregular basis in 
many  other  countries.  A  few  countries  have  yearly  data  (Germany  since  1993,39 
Spain since 1990,40 U.S. since 2009). As opposed to patent data, innovation output 
data  are  to  a  large  extent  subjective  (hence  subject  to  measurement  errors), 
qualitative  (binary  or  categorical  variables),  refer  to  a  three-year  interval  (the 
Spanish  data  are  annual),  they  are  biased  towards  large  firms,  less  internationally 
comparable than patents, and not as easily accessible for reasons of confidentiality. 
It  is  therefore  difficult  to merge  innovation  survey  data  from  different  countries  to 
conduct  international  comparisons,  unlike  what  can  be  done  with  business  register 
data  like  ORBIS/AMADEUS  from  Bureau  van  Dijk,  the  Business  Environment  and 
Enterprise  Performance  Survey  (BEEPS)  database  from  the  World  Bank  and  the 
European  Bank  for  Reconstruction  and  Development  or  the  EU  Industrial  R&D 

39   The  German  Mannheim  Innovation  Panel  is  managed  by  the  ZEW-Leibniz  Centre  for 

European Economic Research. 

40   The  Spanish  ESEE  (Encuesta  sobre  Estrategias  Empresariales)  Survey  on  Business 
Strategies  has  been  conducted  since  1990  by  the  Ministry  of  Industry  and  the  SEPI 
Foundation. https://www.fundacionsepi.es/investigacion/esee/en/spresentacion.asp 

28 

 
                                                
Investment Scoreboard database managed by the DG Joint Research Centre of the 
European Commission.41  

A critical issue on the measurement of innovation output relates to the contribution 
of  new  products  to  GDP  and  GDP  growth.  Alternative  methodologies  have  been 
recently suggested to estimate the gains from increasing product variety. Broda and 
Weinstein  (2006)  use  customs  data  to  measure  the  contribution  of  the  raise  in 
product variety of imported goods to US output and welfare.  

Measurement of productivity 

The productivity of a firm has at  least  three conceptually different dimensions: the 
quality  of  those  products  offered  by  the  firm  or  product  value  (as  perceived  by 
consumers  or  by  firms  using  them  as  inputs  in  their  production  process),  the 
technical efficiency of firm's production process (in the transformation of inputs into 
output), usually referred as total factor productivity (TFP), and the quality and price 
of inputs (including labour) used in the production process. The literature has been 
mainly  concentrated  in  measuring  firm's  total  factor  or  multi-factor  productivity. 
Different  techniques  have  been  developed  in  recent  years  to  estimate  the  product 
value of firms (usually referred as demand shocks in the literature, since an increase 
in product value raises the demand of the firm) the production efficiency of the firm 
(usually  referred  as  total  factor  productivity  measured  in  physical  quantities  or 
TFPQ)  and  the  quality  adjustments  of  the  inputs.  For  a  recent  survey  of  the 
interpretation and the measurement of productivity, see Syverson (2011).42  

For a description of the aggregate and sectorial measurement of productivity see the 
OECD (2011) manual. 

Other measures for innovation output 

Some other technological indicators exist but are not available on a systematic basis 
and  therefore  not  often  used  in  macro-models.  As  an  alternative  to  questionnaire-
based  surveys  or  patents,  other  measures  of  innovation  output  have  been 
constructed based on literature-based announcements of new products, scientific or 
technical  journals,  invention  counts,  and  user  innovations.  Big  data  allow  for  the 
examination  of  innovation  from  different  angles  and  may  encompass  the  limits 
related  to  the  lack  of  granularity,  coverage  and  timeliness  of  other  sources.  For 
instance,  web  mining  allows  to  extract  innovation-related  information  from  the 
websites  of  a  large  number  of  businesses,  differentiating  between  sectors  and 
regions.  On  the  use  of  web  mining  techniques  for  the  measurement  of  innovation 
see e.g. Katz and Cothey (2006), Gök et al. (2015) and Kinne and Axenbeck (2018). 
With  progress  in  digital  technologies  the  literature  on  text  and  web  mining  search 
methods is growing rapidly in different macroeconomic research areas. Aggregated 
news indexes  and indicators of economic  policy  uncertainty,  market sentiment and 

41   For  a  critical  discussion  of  the  quality  and  the  use  that  has  been  made  of  data  from  the 

innovation surveys, see Mairesse and Mohnen (2010). 

42   The main methodological issues on estimating TFP at the firm level has been discussed by 
Olley and Pakes (1996), Levinsohn and Petrin (2003), De Loecker (2011) and Ackerberg, 
Caves and Frazer (2015). 

29 

 
                                                
communication  transparency  are  often  used  in  vector  autoregressive  models  to 
study  macroeconomic  fluctuations.  Examples  in  this  direction  include  Baker  et  al. 
(2016),  Hansen  and  McMahon  (2016),  and  Larsen  and  Thorsrud  (2019),  among 
others.43 An early example of web data used for policy analysis is the  Billion Prices 
Project.44 

2.1.6. Linking inputs and output: knowledge production functions 

In macro-models of R&I, innovation is assumed to be produced by means of an R&D 
technology  using  as  inputs  labour,  capital  (tangible  and  intangible,  like  research 
infrastructure, computers and software) and other intermediate inputs (like research 
materials such as protein structures). R&D labour is to a large extent comprised of 
scientists and technicians, i.e.  highly skilled and specialized labour, where research 
skills are acquired by specific R&D education (typically PhD degrees) and training. 

The  productivity  of  firm  specific  R&D  technologies benefit  from  multiple  knowledge 
spillovers, the assimilation of which may require some form of absorptive capacity 

Variable returns to R&D45 

Empirical studies estimating  knowledge  production  functions typically obtain higher 
rates  of  social  return  on  basic  R&D  than  on  applied  R&D,  the  reason  being  the 
longer  lasting  effect  of  basic  R&D.  Griliches  (1986),  for  example,  reports  a 
significant premium to basic research. Social returns are generally found to be lower 
for  public  R&D  (be  it  financed  or  executed)  than  for  private  R&D,  due  to  the  so-
called  ivory  tower  effect  -see  a.o.  Aghion  et  al.  (2008).  Yet  public  R&D  might  be 
justified  for  reasons  of  high  externalities,  indivisibilities,  high  uncertainty,  gigantic 
costs and low immediate returns, as for instance for national defence, atomic energy 
and  generally  for  mission-oriented  R&D,  areas  where  the  private  sector  typically 
does not find it profitable to invest46. Akcigit et al. (2020) use a quantitative macro-
model  of  endogenous  growth  with  both  basic  vs  applied  research  and  public  vs 
private research. They find that standard innovation policies (like a uniform R&D tax 
credit)  can  accentuate  dynamic  misallocation  by  oversubsidising  applied  research, 
concluding  that  “Policies  geared  towards  public  basic  research  and  its  interaction 
with the private sector are significantly welfare-improving.” 

The  returns  to  R&D  may  also  depend  on  the  existence  of  complementarities, 
between  R&D  and  innovation  and  communication  technologies  (ICT)  that  allow 

43   Big  data  are  used  for  business  cycle  analysis  and  forecasting.  See  e.g.  Choi  and  Varian 
(2012)  and  D’Amuri  and  Marcucci  (2017).  For  a  survey  on  nowcasting,  see  Buono  et  al. 
(2017). 

44   The 

project 

was 

launched 

by 

the 

MIT 

in 

2008. 

See 

http://www.thebillionpricesproject.com/. 

45   For more details on the literature of returns to R&D, see Hall et al. (2010). Donselaar and 

Koopmans (2016) provide a meta-analysis of the rate of return to R&D. 

46   See also Archibugi and Philippetti (2018) and Mazzucato (2013). 

30 

 
 
 
                                                
productivity gains in doing research -see Mohnen et al. (2018), or between in-house 
research  and  purchased  technology  –see  Cassiman  and  Veugelers  (2006),  or 
between process and organizational innovation -see Bresnahan et al. (2002). 

There  is  an  old  debate  among  macroeconomists  whether  returns  to  R&D  are 
constant  or  not.  Endogenous  growth  theory  assumes  that  returns  to  R&D  are 
constant,  which  in  the  original  Romer  (1990)  model  generates  the  undesirable 
property  known  as  scale  effect:  since  returns  are  constant,  the  growth  rate  of  the 
economy  positively  depends  on  the  economy  size.  Jones  (1999),  among  others, 
argues  that  scale  effects  are  counterfactual  since  large  countries  don’t  grow  on 
average  faster  than  small  countries.  This  controversy  gave  rise  to  the  so-called 
semi-endogenous growth models, under the assumption of  decreasing returns to 
R&D. Any form of congestion externality that makes spillovers operate in per capita 
terms,  easily  destroys  scale  effects  under  constant  returns.  These  congestion 
externalities may be related to adoption and learning. Many mechanisms have been 
proposed  in  the  literature  to  remove  scale  effects  in  fully  endogenous  growth 
models.  Early  works  include  e.g.  Smulders  and  Van  de  Klundert  (1995),  Peretto 
(1998),  Young  (1998),  Dinopoulos  and  Thompson  (1999)  and  Howitt  (1999). 
Consistent  with  the  evidence  reported  in  Griliches  (1990),  the  creation  of 
technologies  seems  to  be  subject  to  diminishing  returns  at  the  aggregate  and 
sectorial  level.  More  recently,  Bloom  et  al.  (2017)  claim  that  ideas  are  getting 
harder  to  get,  pointing  out  that  research  effort  is  rising  sharply  while  research 
productivity is declining substantially in a wide range of sectors. By contrast, returns 
may  also  increase  because  of  intertemporal  R&D  spillovers  which  again  may  differ 
across  sectors;  the  so-called  standing  on  the  giant’s  shoulders  argument  -see 
Scotchmer  (1991)  and  Caballero  and  Jaffe  (1993).  Knowledge  accumulated  today 
may lead to new inventions in the future. 

2.2. Enablers for R&I creation, adoption and diffusion 

2.2.1. Size of the market 

Firms  may  be  more  willing  to  do  R&D  when  there  is  a  large  market  in  order  to 
quickly  be  able  to  recover  their  R&D  investment  expenditure.  The  market  can  be 
national  or  international  depending  on  the  presence  or  not  of  trade  and  non-trade 
barriers.  Acemoglu  and  Linn  (2004)  find  for  the  pharmaceutical  industry  a  large 
effect of potential market size on the entry of non-generic drugs and new molecular 
entities.  Moreover,  as  pointed  out  by  Impullitti  and  Licandro  (2018),  when 
innovation  is  addressed  to  reduce  production  costs,  the  incentives  for  investing  in 
R&D  are  larger  the  larger  the  size  of  the  market.  Trade  openness  enhances 
innovation  and  productivity  growth  by  increasing  the  size  of  the  market  faced  by 
exporters.  See  the  discussion  on  Competitiveness  and  globalization,  Section  2.4.3. 
As  discussed  in  Section  2.3.1,  public  procurement  operates  through  this  channel 
too. 

2.2.2. Competition and intellectual property right protection 

Patents systems grant temporarily monopoly rights to innovators in order to protect 
them from being copied or imitated. By restricting competition, the patent system 
aims at solving the market distortion generated by the non-rivalry feature of 
technological knowledge; patents are expected to promote innovation restoring the 
incentives to innovate. Consequently, patents systems relate to the argument 
invoked by Schumpeter (1942) that monopoly rents are the desired reward for 

31 

 
innovation. Since Romer (1990), Aghion and Howitt (1992) and Grossman and 
Helpman (1992), macroeconomic models of R&I assume that innovations are 
patented, crediting monopoly rents to innovators. 

Competition, however, can be beneficial to innovation too, since in competitive 
environments firms innovate with  the hope of  escaping  competition  -see Aghion et 
al. (2001). This argument relates to the so-called Arrow replacement effect: entrant 
firms  have  more  incentives  to  innovate  than  incumbents  since  innovation  allows 
them  to  steal  at  least  partially  the  monopoly  rents  of  incumbents  -see  Arrow 
(1962b).  In  the  extreme  case  when  the  prospect  of  entry  is  very  low,  incumbents 
have  little  incentives  to  innovate.  However,  under  the  prospect  of  their  monopoly 
rents  being  stolen,  incumbents  innovate  to  escape  competition  from  potential 
entrants. Competition may also promote innovation through the type of market size 
effect described supra, if it results in increasing the size of the market  by lowering 
prices.  

Theoretical  and  empirical  research  has  been  developed  asserting  that  the 
relationship  between  competition  and  innovation  follows  an  inverted  U-shape. 
When competition is low, incumbents do not need to protect their monopoly rents, 
having  little  incentives  to  innovate  (replacement  effect).  When  markets  become 
more  competitive,  incumbent  firms  innovate  more  to  escape  competition  (escape 
competition  effect).  However,  when  there  is  too  much  competition,  the  gains  from 
innovation  dilute  to  the  point  that  neither  incumbents  nor  potential  entrants  have 
incentives  to  innovate  (Schumpeter’s  argument).  Aghion  et  al.  (2005)  find  strong 
evidence  of  an  inverted  U-shape  relation  between  product  market  competition  and 
innovation, and develop an endogenous growth model to understand this evidence. 
Aghion  et  al.  (2009b),  using  micro  data,  find  that  “the  threat  of  technologically 
advanced  entry  spurs  innovation  incentives  in  sectors  close  to  the  technology 
frontier,  where  successful  innovation  allows  incumbents  to  survive  the  threat,  but 
discourages  innovation  in  laggard  sectors,  where  the  threat  reduces  incumbents' 
expected rents from innovating.” 

It is not just competition within the industry that matters for innovation, but lack of 
competition in the upstream industries may also harm innovation in the downstream 
industries, as shown by Bourles et al. (2013). 

Besides the incentives of firms to innovate to escape competition or to catch up with 
the industry leaders, there is an indirect aspect of competition that is emphasized by 
evolutionary economists  -see Dosi et al.  (2010). Market shares  expand  (shrink) as 
firms  are  more  (less)  competitive  than  the  sector  average.  Hence  markup  pricing 
increases  profits  but  may  reduce  the  market  share.  Exogenous  limits  on  the 
achievable  market  shares  (antitrust  policies)  give  newcomers  the  chance  to  enter 
the market via a search process for new technologies, the success of which depends 
on their search capabilities and the level of technological opportunities. 

Different measures may be used to identify competition. Markups is one of them, 
since  the  degree  of  competition  of  a  market  negatively  relates  to  the  average 
markup that firms charge over their marginal costs. The literature on misallocation, 
indeed,  stresses  the  importance  of  markup  dispersion.  In  a  framework  of 
heterogeneous, oligopolistic competitive firms, markups are heterogeneous implying 
that  relative  prices  are  distorted,  and  allocations  are  not  efficient.  The  degree  of 
misallocation  increases  with  the  variance  of  markups.  Edmond  et  al.  (2015)  study 
the  procompetitive  gains  from  international  trade  in  a  quantitative  macro-model 

32 

 
with  endogenous  variable  markups.  Using  Taiwan  data,  they  find  that  trade 
openness  reduces  markup  distortions  significantly  lowering  productivity  losses  due 
to misallocation.  

2.2.3. Human Capital 

Innovation  requires  an  education  system  able  to  generate  a  large  enough  body  of 
scientists and applied researchers capable of moving the frontier of knowledge. The 
adoption of new technologies also requires a large body of highly qualified workers 
able  to  easily  understand  and  operate  the  frontier  technology,  as  well  as  an 
important effort of making the frontier technology user-friendly. 

Human  capital  and  skills  formation  are  endogenously  determined  by  educational 
choices  and  training.  The  role  of  human  capital  formation  for  economic 
development has been emphasized by Lucas (1988), based on the theory of human 
capital  first  developed  by  Becker  (1962)  and  Schultz  (1961).47  In  this  view, 
individual education cumulates into a stock of individual human capital, that makes 
different  cohorts  of  workers  more  productive  over  time.  Like  intangible  capital, 
human capital benefits from knowledge spillovers. 

Human  capital,  like  tangible  and  intangible  capital,  is  heterogeneous  inter-  and 
intra-generationally.  Phenomena  like  the  skill  premium  and  job  polarization  are 
analysed  by  means  of  occupational  choice  models  stressing  the  role  of  intra-
generational  heterogeneity  and  heterogenous  education  -see  Acemoglu  and 
Autor  (2011)  and  Autor  and  Dorn  (2013).  Inter-generational  heterogeneity  is 
studied  in  models of  vintage  human  capital  -see  Chari  and  Hopenhayn  (1991)  and 
Boucekkine et al. (2002). 

2.2.4. Financing of R&D 

Innovation  financing  faces  in  a  world  of  imperfect  information  a  problem  of 
asymmetric information between the innovator and the fund provider. Because of 
the non-rival nature of knowledge, the innovator has no interest in sharing with the 
fund provider some of the information the latter would need to justify the funding.48 
Therefore,  R&D  is  as  much  as  possible  financed  through  internal  funds.  In  the 
absence  of  sufficient  self-funding  possibilities,  external  funding  will  have  to  be 
accessed.  This  external  funding  can  be  private  or  public.  Major  sources  of  private 
funding include bank financing, capital markets and venture capital. Major sources 
of  public  funding  include  grants,  subsidies,  tax  incentives  and  public  venture 
capital. 

In  their  survey  of  empirical  evidence,  Hall  and  Lerner  (2010)  conclude  “that  while 
small and new innovative firms experience high  costs of capital … the evidence 
for  high  costs  of  R&D  capital  for  large  firms  is  mixed.  Nevertheless,  large 
established  firms  do  appear  to  prefer  internal  funds  for  financing  such 
investments and they manage their cashflow to ensure this.”  

47   Lucas  (1988)  acknowledges  the  previous  contribution  of  Uzawa  (1965).  See  also  Barro 

(2001). 

48   See Lee et al (2015) on the difficulties of using intangible collateral for risky projects. 

33 

 
 
                                                
In the macroeconomic literature, there has been a surge of research on the role of 
financial  constraints  in  the  aftermath  of  the  2007  financial  crisis.  In  a  multi-
country  Schumpeterian  growth  model,  Aghion  et  al.  (2005b)  introduce  imperfect 
creditor protection. They show that there is a critical level of financial development 
below  which  countries  converge  to  a  low  level  long-run  growth  rate,  compared  to 
the  growth  rate  of  countries  with  a  level  of  financial  development  larger  than  the 
critical  one.  They  provide  crosscountry  evidence  supporting  their  main  finding. 
Aghion et al. (2012), using French microdata, find strong evidence on the negative 
effect of credit constraints on R&D. They conclude that firm R&D investment is pro-
cyclical  for  firms  that  face  tight  financial  constraints,  particularly  in  sectors  that 
strongly  depend  upon  external  finance.  Buera  et  al.  (2011)  develop  a  quantitative 
framework  to  explain  the  relationship  between  total  factor  productivity  (TFP)  and 
financial  development.  Financial  frictions  distort  the  allocation  of  capital  and 
entrepreneurial talent across firms, adversely affecting aggregate productivity. Their 
quantitative  analysis  shows  that  financial  frictions  account  for  a  substantial  part  of 
the observed crosscountry differences in output per worker, aggregate TFP, sector-
level  relative  productivity,  and  capital-to-output  ratios.  Christiano  et  al.  (2014) 
introduce  financial constraints in a standard  DSGE  model  and  find  that fluctuations 
in  entrepreneurial  idiosyncratic  risk  are  the  main  source  of  business  cycle 
fluctuations.  In  this  framework,  Vinci  and  Licandro  (2020)  relate  the  Great 
Recession  with  the  destruction  of  the  assets’  value  (tangible  and  intangible) 
generated  by  a  surge  of  entrepreneurial  risk  during  the  financial  crisis,  and  the 
associated reduction in the incentives to invest on these assets. In the K+S model -
see  Dosi  et  al.  (2010),  firms  need  external  financing  and  capital  markets  are 
imperfect,  implying  that  the  firms  may  be  rationed,  cutting  their  investment  and 
then downscale their production plans. 

2.3. R&I Policy 

Following  the  academic  literature  on  R&I,  this  section  reviews  and  assesses  the 
main  R&I  policy  instruments  used  in  practice,  the  distortions  they  are  expected  to 
correct and the relevant channels through which they are presumed to operate. The 
main  features  of  R&I  identified  in  Section  2.1  serve  to  identify  the  channels  and 
frictions that macro-models should ideally include to better understand and evaluate 
the working of R&I policies.  This is singularly true when the aim  is to evaluate the 
different  programmes,  legislations,  and  policies  that  constitute  the  EU  innovation 
strategy,  since  particular  attention  should  be  paid  to  the  different  types  of  market 
failures and channels that policy makers and experts had in mind when designing it. 
Box 1 in Appendix on “R&I policies in the EU and its member states” provides a brief 
description  of  the  main  innovation  policy  instruments  deployed  in  the  EU.  Finally, 
particular  attention  will  be  paid  to  the  empirical  evidence  on  the  effects  of 
innovation policies, and the macroeconomic modelling of these policies, frictions and 
channels. 

Innovation policies are usually designed to address the potential market failures and 
distortions  discussed  supra,  such  as  non-rivalry  of  ideas,  knowledge  and  market 
spillovers  (positive  and  negative),  asymmetric  information  between  innovators  and 
providers  of  finance,  coordination  failures,  and  uncertainty,  among  others. 
Innovation  policies  can  also  lead  to  direct  technological  change  to  meet  societal 
challenges like climate change, health or population aging.49 Innovation policies also 

49   See Mazzucato (2013). 

34 

 
                                                
come  at  a  cost,  including  the  cost  of  administering  the  policies  and  the  cost  of 
failure  to  reach  the  aimed  targets.  Some  of  the  policy  instruments  designed  to 
protect  and  promote  innovation  may  generate  other  distortions.  Patents,  being 
designed to solve the non-rivalry problem, promote non-competitive behaviour. Tax 
incentives may cause deadweight losses.50 

In  a  recent  survey  of  the  empirical  literature  on  R&I  policies,  Bloom  et  al. 
(2019) synthetize the evidence into what they refer as a toolkit for innovation policy 
makers. They rank  R&I policies in terms of  their overall impact from  a social cost-
benefit  perspective  and  in  terms  of  their  distributional  effects,  conditional  on  the 
strength and quality of the evidence and the magnitude of the estimated effects. In 
their view, “In the short run, research and development tax credits and direct public 
funding  seem  the  most  effective,  whereas  increasing  the  supply  of  human  capital 
(for  example,  through  expanding  university  admissions  in  the  areas  of  science, 
technology,  engineering,  and  mathematics)  is  more  effective  in  the  long  run.” 
Competition and trade policies seem to have small benefits for innovation but they 
are  inexpensive  for  the  public  budget.  R&D  tax  credits  and  trade  policies  tend  to 
increase  inequality,  as  they  boost  the  relative  demand  of  skilled  labour,  while 
human capital policies have the opposite effect.  

Macroeconomic  models  aimed  to  evaluate  the  EU  innovation  policy  have  to 
include  these  key  channels,  in  order  to  provide  figures  that  help  to  quantitatively 
assess  their  contribution  to  innovation,  productivity  and  economic  growth.  The 
innovation  process  needs  then  to  be  detailed  enough  for  a  model  to  be  able  to 
evaluate the different policies, their objectives and the channels through which they 
are  expected  to  operate.  Correctly  representing  these  policies  and  the  channels 
through which they are expected to affect productivity and GDP growth, as well as 
other  socio-economic  and  environmental  variables,  will  be  critical.  In  order  to 
identify  the  key  aggregate  channels  through  which  innovation  policy  may  affect 
productivity  and  growth,  the  use  of  relevant  micro  and  macro  data  is  paramount. 
Such a quantitative effort has to be undertaken in the framework of macroeconomic 
models of innovation and growth with heterogeneous agents.  

2.3.1. Major R&I policy instruments 

This  section  discusses  the  major  R&I  policy  instruments  used  in  practice,  which 
include  R&D  tax  incentives  (like  R&D  tax  credits),  R&D  subsidies  (like  research 
grants),  research  loans  and  public  venture  capital,  public  R&D  and  public-private 
partnership, public procurement and patents. 

R&D tax incentives 

R&D tax incentives are designed to promote innovation, aiming at reducing the gap 
between  private  and  social  returns  to  R&D,  and  alleviating  the  financial  problems 
faced by R&I performers. They are directed towards lowering R&D costs by means of 
R&D  tax-credits,  R&D  tax  allowances,  accelerated  depreciation  of  investment  in 
research  equipment,  or  reduced  tax  rates  on  corporate  revenues  from  R&D, 

50   For  a  more  comprehensive  discussion  of  various  innovation  policies  and  an  analysis  of 
their  effectiveness,  see  Edler  et  al.  (2016).  See  Feldstein  (1999)  for  an  analysis  of  the 
deadweight losses associated to income taxes. 

35 

 
                                                
innovation or patents (patent box/innovation box).51 The use of R&D tax incentives 
is worldwide spread and represents a sensible contribution to the reduction of R&D 
costs incurred by firms. The  OECD  (2018c) report is a comprehensive study of  the 
extent and deepness of this type of policies52. 

In their design, R&D  tax-credits can be in  proportion of  R&D  levels or  in excess of 
R&D  relative  to  a  certain  base.  As  small  firms  and  startups  suffer  more  from  the 
asymmetric information related to R&D financing, they benefit generally from higher 
R&D tax credit rates. In principle, R&D tax incentives are neutral letting the private 
sector choose the projects in which to invest. But government can also take a more 
directive  approach  and  give  special  tax  incentives  to  areas  considered  of  prime 
importance  like  health,  environmental  protection  or  defence,  or  incentives  to  do 
R&D in collaboration with universities. To allow firms benefit from the tax incentives 
even if they have no taxes to pay (absence of profits) the tax-credits can be made 
refundable,  carried  back  or  forward,  or  they  can  be  deducted  from  social  security 
contributions. 

Mohnen  (2018)  discusses  the  effectiveness  of  R&D  tax  incentives.  Besides  the 
stimulating marginal effect, tax incentives, especially when they are proportional to 
R&D  volume  and  benefit  mainly  large  R&D  performers,  may  lead  to  deadweight 
losses by supporting R&D that would be done anyway. The R&D tax-credits involve 
administration  costs  for  government  and  implementation  costs  for  receivers,  in 
addition  to  the  tax  distortions  related  to  raising  tax  revenues.  Besides  the 
cost/benefit calculations, it is also important to keep in mind that R&D tax policies, if 
they  are  stable  and  reliable,  reduce  the  cost  and  the  uncertainty  related  to  R&D. 
Some studies have found that they can also raise the wages of R&D labour if there 
is a lack of researchers with the required qualifications. And finally, it must be kept 
in  mind  that  the  effectiveness  of  R&D  tax-credits  can  be  substantially  reduced  by 
R&D tax competition. 

Most empirical studies show that R&D tax incentives are quite effective in reaching 
the  objective  of  raising  R&D  expenditure.  However,  no  firm  conclusion  can  yet  be 
reached  on  the  degree  of  effectiveness:  the  estimated  elasticities  are  below  or 
above  1  depending  on  the  time  horizon  considered  and  the  presence  or  not  of 
negative externalities, and the bang for the buck is often lower than 1  -see Ientile 
and Mairesse (2009), European Commission (2014), Bloom et al. (2019). 

Concerning the heterogeneity of R&D tax credits, Busom et al. (2014) report that in 
Spain  financially  constrained  firms  and  new  entrants  prefer  direct  subsidies  over 
R&D tax credits because they are not able to fully benefit from R&D tax credits, and 

51   Bloom  et  al.  (2019)  strongly  argue  against  patent  boxes,  i.e.,  special  tax  regimes  that 
apply  lower  taxes  to  patent  revenues.  Patent  boxes  give  to  multinationals  considerable 
freedom in deciding where to book taxable income from patents, but have little effect on 
the  real  location  of  R&D.  In  this  sense,  they  claim  “patent  boxes  are  an  example  of  a 
harmful  form  of  tax  competition  that  distorts  the  tax  system  under  the  guise  of  being  a 
pro-innovation policy.” 

52   It does assert that “As of 2017, 30 of the 35 OECD countries, 21 of 28 EU countries and a 
number  of  non-OECD  economies  provide  tax  relief  on  R&D  expenditures.”  It  does  also 
report  that  in  countries  like  France,  Portugal  and  Spain,  R&D  tax  incentives  reduce  the 
R&D costs in at least 30%. See also Appelt et al. (2019). 

36 

 
                                                
that  small  firms  contrary  to  large  firms  prefer  tax  credits  over  direct  support 
because  they are easier to  get  without  having to reveal any information about the 
amount  and  the  kind  of  research  that  is  being  performed.  Many  countries  have 
introduced ways to recover the R&D tax credits even in the absence of profits, the 
so-called  refundability  of  R&D  tax  credits.  As  shown  by  Agarwal  et  al.  (2020),  in 
Canada small firms without tax liability are more responsive to R&D tax credits than 
small  firms  with  tax  liability.  In  the  same  vein,  Dechezleprêtre  et  al.  (2016)  show 
that in the UK the young firms among the small firms are more responsive to R&D 
tax credits because they are credit constrained. A higher additionality for small firms 
than  for  large  firms  is  also  reported  in  Lokshin  and  Mohnen  (2012)  for  the 
Netherlands  and  Hægeland  and  Moen  (2007)  for  Norway.  Not  only  do  small  firms 
receive  higher  R&D  tax  credits,  but  they  are  also  more  responsive  to  the  tax 
incentives. 

In  the  macroeconomic  literature,  the  aggregate  effects  of  non-neutral  (size 
dependent)  R&D  tax  credits  is  an  important,  but  still  open  question.53  Benedetti-
Fasil  et  al.  (2020)  find  that  R&D  tax  credits  directly  increase  technology  adoption 
and  productivity  of  incumbent  firms  (intensive  margin),  while  the  general 
equilibrium  effects  raise  firms’  survival  rates  (extensive  margin),  which  has  the 
counterbalancing  effect  of  reducing  adoption  and  the  average  productivity  of  the 
economy. 

At the EU, R&D tax incentives are under national jurisdiction -see Box 1. R&I policies 
in the EU and its member states. It raises the issue of tax competition within the 
Union.  An  ideal  macro-model  designed  to  evaluate  this  dimension  of  the  EU 
innovation strategy should ideally include knowledge spillovers and be multi-country 
with  heterogenous  tax  credit  policies  able  to  analyse,  among  other  issues,  tax 
competition. 

Direct subsidies 

Another way of lowering the cost of R&D, reduce the gap between private and social 
returns  to  R&D  and  alleviate  financial  frictions  is  through  direct  support  in  the 
form  of  grants  and  subsidies.  Certain  types  of  R&D,  collaboration  partners  or 
research  areas  are  targeted  to  receive  (partial)  support  from  the  government 
usually  in  the  form  of  grants.  The  idea  is  to  support  the  projects  with  the  highest 
estimated  social  return.  It  also  goes  in  the  direction  of  addressing  specific  social 
challenges  like  environmental  sustainability  and  digital  transformation.  Generally, 
direct  subsidies  are  aimed  to  move  the  frontier  of  knowledge  by  promoting 
excellence  in  basic  research;  promote  the  emergency  of  new  technological 
paradigms 
top 
universities/research  centres  and  the  leaders  in  the  private  sector;  promote 
research in areas of fundamental relevance  for society (environment, digitalization, 
health) through mission-oriented objectives. 

research  cooperation  between 

robotics);  promoting 

(like 

The  major  conceptual  difference  between  direct  R&D  support  (like  grants)  and 
indirect  support  (like  tax-credits)  is  with  respect  to  the  neutrality  of  policy 
instruments.  Whereas  tax  incentives  can  be  claimed  automatically  as  long  as  a 

53   Gunner  et  al.  (2008)  study  the  macroeconomic  effects  of  size-dependent  policies  in 

models of exogenous growth. 

37 

 
                                                
firm does R&D (sometimes under some additional restrictions), grants and subsidies 
are  granted generally  through a  competitive  process where the  “best” projects are 
selected  by  experts.  The  EC  Framework  Programme  grants  are  examples  of  direct 
R&D support. Direct subsidies are used by every single country in the EU as one of 
their major innovation policy instruments -see Box 1. R&I policies in the EU and its 
member states. 

The issue of additionality is as the heart of the justification for R&I policies and for 
government  intervention.  Additionality  has  to  do  with  how  much  a  policy  can 
generate in addition to what  would  have been  the  case  without the policy.54  There 
are  different  dimensions  of  additionality,  namely  input  additionality,  output 
additionality  and  behavioural  additionality.  Input  additionality  refers  to  the  effects 
that  R&I  policy  interventions  may  have  on  private  R&D  expenditure.  Output 
additionality  is  related  to  increases  in  the  proportion  of  innovation  outputs  that 
would  not  have  been  achieved  without  the  public  intervention  (e.g.  number  of 
patents, new products, enhanced productivity etc..) as a result of the policy. Finally, 
behavioural  additionality  refers  to  the  changes  that  occur  in  firms’  behaviour  and 
strategy  as  a  result  of  a  policy.  In  the  presence  of  behavioural  additionalities,  the 
traditional input- and output additionality concepts may not adequately capture the 
impact of public R&I policies on the innovation process itself.55 

Acemoglu et al. (2018), in the framework of a macro-model with firm heterogeneity, 
find  that  in  the  US  economy  there  is  large  room  for  policy  intervention,  but  that 
neutral R&D subsidies are not very effective.56 Subsidies should be non-neutral but 
oriented to support research projects with the highest social return. As pointed out 
by  Bloom  et  al.  (2019):  “A  disadvantage  of  tax-based  support  for  research  and 
development  is  that  tax  policies  are  difficult  to  target  at  the  R&D  that  creates  the 
most  knowledge  spillovers  and  avoids  business-stealing.  In  contrast,  government-
directed  grants  can  more  naturally  do  this  type  of  targeting.”  In  this  direction, 
Akcigit et al. (2016) suggest a methodology to study the optimal design of corporate 
taxation  and  R&D  subsidies  in  a  macroeconomic  framework  of  heterogeneous 
innovation  capacity  and  private  information.  They  find  that  linear  corporate  taxes 
combined  with  a  nonlinear  R&D  subsidy  can  do  almost  as  well  as  full  unrestricted 
optimal  policies.  To  provide  a  sound  evaluation  of  R&D  direct  subsidies, 
macroeconomic  models  are  expected  to  deal  with  the  complex  structure  of  R&D 
grants, the associated selection process, its efficiency on selecting the best projects 
and  the  associated  effects  on  the  variance  of  the  productivity  distribution  of  firms. 
The  FRAME  (2019)  project  developed  a  framework  to  study  the  role  of  R&D 
subsidies  in  DSGE  models  with  homogeneous  firms,  being  able  to  identify  the 

54   For examples of studies evaluating the additionality of subsidies, see Lach (2002), Takalo 
et  al.  (2013)  and  Gonzalez  et  al.  (2005),  among  others.  Lach  (2002)  finds  evidence  of 
partial additionality. For Israel manufacturing in the 90’s, he estimates that an extra dollar 
of  R&D  subsidies  increases  long-run  company  financed  R&D  expenditures  by  41  cents 
(total  R&D  expenditures  increase  by 1.41  dollars).  He  claims  that  “the  magnitude of  this 
effect is large enough to justify the existence of the subsidy program.” 

55   See for example Buisseret et al. (1995). 

56   See also Benedetti-Fasil et al. (2020). 

38 

 
                                                
distinct  effects  of  supporting  basic  and  applied  R&D  on  the  diffusion  of  innovation, 
productivity and output, and some relevant redistribute effects. 

Loans and public venture capital 

Public  financial  support  for  innovation  can  also  be  given  via  cheap  loans,  loan 
guarantees  or  a  guaranteed  financing  from  government.  Such  government 
financial support schemes to R&D vary along three main dimensions: (i) the interest 
rate  charged  on  loans;  (ii)  whether  repayment  is  conditional  on  the  project’s 
outcome; (iii) the co-financing requirements applicants must comply with. 

An alternative way is for the public sector to provide financing by participating in the 
capital of  startup  firms,  the  so-called  public  venture  capital.  In  that  case,  not  only 
financing  is  provided  but  also  management  guidance  and  network  connections  to 
give  the  innovative  projects  the  best  chances  to  succeed.  After  a  few  years, 
typically, the public sector sells its participation, sometimes with a gain, sometimes 
with a loss. 

Public  financial  support  for  innovation  is  a  dimension  of  innovation  policy  that 
macro-models  should  incorporate.  Particular  attention  should  be  given  to  the 
relevant  dimensions  of  firm  heterogeneity,  including  the  support  to  startups  and 
highly innovative small and medium size firms, and the treatment of zombie firms, 
among others. 

Public R&D and public-private partnerships 

Instead of just subsidising or participating in the financing of R&D, governments can 
also decide to perform the R&D itself in public universities or public research 
labs. This would be the case for projects too basic, too large, expensive or risky to 
be  undertaken  by  a  private  company,  like  space  exploration  or  the  production  of 
nuclear  energy.  Examples  of  publicly  funded  research  labs  would  be  the  German 
Max  Planck  Institute,  the  CNRS  in  France,  the  US  Manhattan  project.  Bloom  et  al. 
(2019) in their toolkit for innovation policy makers rank direct public funding at the 
very top.  

Public  R&D  can  also  be  done  joint  with  the  private  sector,  where  both  sides  co-
finance projects, share knowledge and research facilities.  The Fraunhofer Institutes 
in  Germany  or  TNO  in  the  Netherlands  are  examples  of  such  public-private 
partnerships, where  the private and the public sector join forces  in research topics 
in the public and private interest.57 Akcigit et al. (2020) find that policies promoting 
basic research and its interaction with the private sector are welfare-improving. 

Public procurement 

Instead  of  stimulating  innovation  on  the  supply  side  by  lowering  the  cost  of 
innovation,  an  alternative  way  for  the  government  is  to  provide  demand  for 
innovations.  Through  innovative  public  procurement,  innovators  can  more  quickly 
recover  the  investment  costs  and  at  the  same  time  increase  the  diffusion  of 

57   See Comin et al. (2019b) for a recent study of the contribution of Fraunhofer Institute to 

innovation in Germany. 

39 

 
                                                
innovations.  Public  procurement  can  also  be  used  to  define  the  functional 
requirements of innovations. Shaping markets for innovations decreases the risk of 
investing  in  R&D.  Demand  can  also  be  encouraged  by  giving  subsidies  to  private 
consumers  of  new  products  (e.g.  photovoltaic  panels)  or  by  encouraging  the 
adoption of new products through information campaigns or by regulations. 

Cozzi and Impullitti (2010) argue that government spending played a significant role 
in stimulating the wave of innovation in U.S. economy in the late 1970s and 1980s. 
Using  a  quality-ladders  endogenous  growth  model  with  heterogeneous  industries 
and  endogenous  supply  of  skills,  they  show  that  a  change  towards  a  larger 
technological content of public spending  stimulates R&D, raises  the wage of skilled 
workers and stimulates human capital accumulation. 

Carvalho  and  Draca  (2017)  stress  the  role  of  the  US  Department  of  Defense 
spending  in  funding  corporate  innovation  in  the  US.  They  estimated  that  defence 
procurement  had  a  positive  direct  and  indirect  market  size  effect  on  private 
patenting and R&D investment.  

Patents 

Patents  are  the  instrument  generally  used  to  protect  innovation  from  copy  and 
imitation,  thus  enhancing  the  incentives  to  innovate.  Patents  provide  temporary 
monopoly rents that are expected to let firms recover their R&D investments. By 
doing this, patents distort the static allocation of resources, eventually affecting the 
diffusion  of  innovations  and  knowledge.  Given  the  complexity  of  the  problem, 
various dimensions of patents can be adjusted to make monopoly rents close to its 
optimal level (length, breadth, height, renewal fees, etc…). This monopoly position, 
which  conflicts  with  competition  policy,  is  seen  as  the  price  to  pay  to  stimulate 
private  R&D.58  Patents  can  also  be  more  or  less  strongly  implemented,  depending 
on how much patent infringement can be defended by the patent holders. Moreover, 
various strategies exist to  make  patents even  more anti-competitive (patent trolls, 
patent thickets).59 

An  important  property  of  patents  is  that  they  grant  property  rights  protection  to 
innovators  in  exchange  for  the  disclosure  of  the  relevant  information  behind  the 
innovation being patented. Disclosure favours the spread of knowledge spillovers.60 
Secrecy  is  a  mutually  exclusive  alternative  strategy  to  patents,  also  creating 
monopoly  rents  when  successful,  with  the  additional  social  cost  of  reducing 

58   For a historical analysis of patent protection, see Lerner (2002). Boldrin and Levine (2013) 
argue  against  patent  protection. In  the same direction,  Bessen  and  Maskin  (2009)  argue 
that  when  innovations  are  sequential  (so  that  each  successive  invention  builds  in  an 
essential  way  on  its  predecessors)  and  complementary  (so  that  each  potential  innovator 
benefits from the discoveries of others), the prospective profits of inventors may actually 
be enhanced by competition and imitation rather than patent protection. 

59   See Abrams et al. (2019b) on patent trolls. 

60   Kultti  et  al.  (2006)  discuss  the  virtues  of  patents  vs  secrecy  by  means  of  disclosure 
theory. The paper rationalises the optimal use of patents in a framework of simultaneous 
innovations. 

40 

 
                                                
knowledge spillovers. Arundel (2001) studies the relative importance of secrecy vs. 
patents using the European Community Innovation Survey (CIS).  He finds that the 
probability  that  a  firm  rates  secrecy  as  more  valuable  than  patents  declines  with 
firm  size  for  product  innovation,  while  there  is  no  relationship  for  process 
innovations.  

Most  macro  models  of  innovation  work  under  the  assumption  that  innovation  is 
protected  by  patent  law.  Grossman  and  Lai  (2004)  analyse  strategic  intellectual 
property rights policy in a multi-country endogenous growth model. 

2.3.2. Other policy areas affecting R&I 

A whole range  of policy  interventions may affect  the  incentives to R&D  investment 
and  can  therefore  impact  R&I.  This  section  looks  at  the  effect  on  R&I  of  corporate 
taxes, regulations and environmental policies.  

Corporate taxes 

An  excessive  burden  of  the  tax  system  may  impede  innovation.  From  a  macro-
modelling perspective,  Ferraro et al. (2011) take  Peretto (2003) and (2007) to the 
data and find that cutting taxes on capital gains increases long-run growth. Cutting 
corporate income and dividends has the opposite effect, since these policies induce 
more entry, reducing the per firm innovation rate. More recently, Benedetti-Fasil et 
al.  (2020)  study  corporate  tax  reforms  in  a  macro-model  with  firm  heterogeneity 
and business cycle. They find that corporate taxation not only affect the number but 
also  the  size  distribution  of  firms  having  important  implications  for  innovation. 
Finally,  Ates  and  Saffie  (2018a)  analyse  corporate  taxation  in  a  Schumpeterian 
model  with  financial  frictions  and  heterogeneity  in  R&D  technology.  In  this 
framework,  high  corporate  taxes  are  associated  with  low  entry,  which  implies  low 
growth.  

Regulations  

The  relationship  between  regulation  and  innovation  is  multi-faceted,  depending  on 
the  nature  and  the  quality  of  the  regulation  itself,  on  the  sectors  involved  and  the 
time  horizon  considered61.  At  times  tight  regulations  tend  to  exert  pressure  on 
companies  forcing  them  to  innovate.  Specific  regulations  addressing  negative 
environmental externalities or dealing with health and safety of citizens may affect 
the  direction  of  technical  change  and  act  as  a  powerful  stimulus  to  innovation.  On 
the other hand, more prescriptive regulations envisaging high compliance costs and 
red-tape burdens may hinder innovation activities.  

Environmental policies 

Macroeconomic  models  integrating  environmental  constraints  and  the  negative 
externalities  of  pollution  may  be  used  to  study  the  impact  of  a  wide  range  of 
environmental  policies  on  economic  performances  and  innovation  activities.  A  key 
insight  from  the  environmental  economics  literature  is  that  tightened  pollution 
targets  induce  environmental  innovation:  appropriately  designed  environmental 

61   See Pelkmans and Renda (2014) and Blind (2016). 

41 

 
                                                
policies  can  promote  the  development  and  the  adoption  of  advanced  abatement 
technology,  and  can  affect  technological  progress  and  the  diffusion  of  clean 
technologies.62  Models  with  directed  technical  change  can  capture  the  transition 
towards  green  technologies  induced  by  these  policies.63  As  shown  by  Aghion  et  al. 
(2016)  firms  in  a  laissez-faire  economy  may  be  induced  to  innovate  in  the  “wrong 
direction”,  because  of  a  strong  “path  dependence”  phenomenon  in  favour  of  dirty 
innovations.64  However, the link between  innovation and  environmental policies can 
be  bi-directional.  Environmental  policies  may  not  only  push  and  direct  innovation, 
environmental policies may also respond to innovation.65 

In  modelling  environmental  policies  economists  distinguish  between  two  classes  of 
pollution control instruments: command & control versus market-based instruments. 
The command & control approach is essentially a regulatory approach whereby a 
regulatory  authority  “commands”  emission  reduction  by  directly  prescribing 
emission  ceilings  (e.g.  firms  are  bound  by  an  upper  emission  level  or  by  a  cap  on 
the  ratio  of  emissions  per  output  or  per  hour;)  or  by  imposing  technological 
standards (e.g., firms must adopt the best available technology). The “control” step 
involves  verifying  how  pollution  reductions  are  achieved,  standards  are  met,  and 
enforcement tools such as fines are specified. Market based instruments instead 
consist of incentives to reduce pollutant emissions. The most common market-based 
instruments  are  emissions  taxes  (e.g.  carbon  tax),  cap-and-trade  schemes, 
abatement subsidies, subsidies for the adoption of clean technology and for the use 
of renewable energy. 

For  a  discussion  on  the  effectiveness  of  different  environmental  policy  instruments 
for  the  adoption  and  the  development  of  clean  technology,  see  e.g.  Requate  and 
Unold  (2003)  and  Requate  (2005).  Moreover,  public  authorities  can  directly  steer 
the  development  of  green  technologies,  either  through  public  R&D  or  through 
targeted  financing  of  private  R&D.  For  further  discussion  on  the  macroeconomic 
modelling of environmental externalities, see Section 2.4.6. 

2.3.3. Policy mix 

Some  innovation  policies  may  work  well  in  some  situations  and  less  in  others. 
Governments  must  also  be  careful  to  avoid  that  some  of  the  policies  stand  in  the 
way  of  each  other.  As  already  mentioned,  intellectual  property  rights  may  collide 

62   See e.g. Jaffe et al. (2002) for a survey. 

63   See e.g. Peretto (2009), Acemoglu et al. (2012) and Hémous (2016). 

64   The  economic  literature  points  to  an  overestimation  of  the  costs  of  environmental 
regulation  when  directed  technical  change,  R&D  and  learning-by-doing  mechanisms  are 
not  considered.  See  e.g.  Buonanno  et  al.  (2003)  and  Manne  and  Richles  (2004).  On  the 
role of learning-by-doing mechanism in the abatement process of pollution see Brock and 
Taylor (2003). 

65   See e.g. Carrión-Flores and Innes (2010). 

42 

 
                                                
 
with  antitrust  policies.  Budget-neutral  expansionary  R&I  policies  may  entail  higher 
taxes and/or undesirable spending cuts on other  items. When  lumpsum  taxation is 
not available, financing public R&D and other expansionary R&D policies may require 
the  increase  of  distortionary  taxation  thereby  raising  the  opportunity  cost  of  using 
public  funds  for  R&D.  Debt  financing  of  public  R&D  may  be  limited  by  balanced 
budget  constraints.  Innovation  policy  is  often  intertwined  with  industrial  policy  i.e. 
supporting certain champion sectors, firms or technologies. 

The  potential  interactions  between  policies  implemented  at  different  governance 
levels  represent  another  important  dimension  that  needs  to  be  considered. 
Nationwide  innovation  policies  are  likely  to  influence  the  performance  of  self-
contained  regional  innovation  plans  and  of  R&D  policies  targeted  at  regional 
strengths, such as cluster policies, smart specialization strategies or cohesion funds. 
Similarly, the goals pursed by supranational R&I  policies and the instruments used 
for  these  purposes  may  not  always  be  consistent  with  national  and  regional 
innovation policies.  

Building  further  on  Klette  and  Kortum  (2004),66  Acemoglu  et  al.  (2018) 
quantitatively  study  the  role  of  innovation  policy.  They  compare  optimal  social 
allocations with second-best policy allocations (those resulting from an optimal use 
of  the  individual  different  instruments  available  for  innovation  policy).  In  this 
literature, innovation  policy is modelled by means of simple policy instruments like 
R&D  subsidy or R&D tax-credits, which  can be uniform  (a common  subsidy or tax-
credit  rate  for  all  firms)  or  state-dependent  (i.e.  depending  on  observable 
characteristics,  like  firm  size  or  past  R&I  performance).  These  innovation  policy 
instruments  are  also  compared  with  other,  generic  policy  instruments  that  also 
affect  innovation,  like  corporate  taxes.  While  this  approach  allows  to  compare 
various instruments, it does not look at policy mixes. 

How  then  study  the  policy  mix  in  macro-models?  Akcigit  et  al.  (2016)  give  an 
important step in this direction by studying the optimal design of corporate taxation 
and R&D subsidies in an endogenous growth framework of heterogeneous firms with 
heterogeneous  innovation  capacity,  knowledge  spillovers  and  private  information. 
The  optimal  policy-mix  here  is  modelled  as  a  dynamic  mechanism  design  problem 
with  spillovers.  The  model  is  estimated  using  firm-level  data  matched  to  patent 
data.  In  this  framework,  they  show  that  very  simple  innovation  policies,  such  as 
linear corporate taxes combined with a nonlinear R&D subsidy – that provides lower 
marginal subsidies at higher R&D levels – can do almost as well as full unrestricted 
optimal policies. 

Aghion et al. (2001), in the context of a Schumpeterian growth model with step-by-
step  innovations  and  where  laggards  imitate  to  catch-up  the  leader,  point  out  the 
complementary  role  for  antitrust  policy  and  patent  policy.  In  this  context,  (a) 
product market competition has a positive effect on growth, and (b) some imitation 
is growth enhancing but too much imitation is growth reducing. 

66   Klette  and  Kortum  (2004)  develop  a  general  equilibrium  model  of  innovation  and 
technological change that replicates the dynamics of heterogeneous firms, including entry 
and exit. Within this framework, they are able to account for the persistence of firms’ R&D 
investment,  the  concentration  of  R&D  among  incumbents,  the  link  between  R&D  and 
patenting,  and  the  positive  correlation  between  the  R&D  to  revenue  ratio  and  firm’s 
productivity but not with firm size or growth. 

43 

 
                                                
2.3.4. Costs of policy interventions: administrative burdens 

An  excessive  complexity  of  regulatory  procedures  and/or  a  heavy  administrative 
burden  on  existing  businesses  and  on  startups  may  constitute  severe  barriers  to 
entry,  hinder  firms’  expansion  and  undermine  R&D  incentives.  In  models  with 
endogenous  growth,  for  instance,  impediments  introduced  by  administrative 
burdens  may  be  seen  as  increasing  the  cost  of  introducing  an  innovation.  Clearly, 
the  reduction  of  administrative  burden  through  administrative  simplification  is 
beneficial for firms since it reduces average production costs.  

However,  when  it  comes  to  understand  the  relationship  between  administrative 
burdens and innovation, there  are  contrasting forces that  should be  considered. In 
models  where  monopolistic  profits  represent  the  crucial  incentives  for  innovation  a 
reduction  of  rents  generated,  for  instance,  by  an  alleviation  of  the  administrative 
burden on new businesses, may, in  principle, reduce the pace of innovation. Rents 
of  the  incumbents  may  in  fact  be  eroded  by  the  entry  of  new  firms.  On  the  other 
hand, in models exhibiting scale effects, administrative simplification provisions may 
induce faster growth.  

In macroeconomic models for R&I policy evaluation, administrative burdens may be 
introduced  in  the  form  of  overhead  labour,  entry  barriers  or  fixed  costs  (see  e.g. 
Roeger et al. 2008). 

2.4. Dimensions of macroeconomic impact of R&I 

Innovation and innovation policies affect the evolution of technology, which reflects 
in  aggregate  productivity  gains,  economic  growth,  developing  comparative 
advantages and improving  competitiveness in a globalized world. Despite its direct 
effect  on  people’s  income  and  consumption/investment  opportunities,  technical 
progress  also  shapes  human  life  in  other  dimensions  by  transforming  the  labour 
market  (creating  and  destroying  jobs  and  skills),  modifying  the  (un)equal 
distribution of income and wealth, potentially degrading the environment, but also a 
source of new clean technologies, affecting people’s health. 

2.4.1. GDP 

A main impact dimension of R&I policies is GDP and its growth rate (GDP level, GDP 
per  worker,  GDP  per  capita).  GDP  is  a  common  anchor  metric  used  for  targeting 
macroeconomic performance.   As such, macroeconomic models are a prime tool  to 
assess  the  impact  of  R&I  policy  interventions  on  GDP  at  various  horizons  (short-, 
medium- and long-term).  

As a discipline device, macroeconomic models are targeted to replicate the observed 
behaviour of GDP and its main components as measured by National Accounts. The 
bases of modern macroeconomics were developed in the fifties and sixties, with the 
so-called  Neoclassical  growth  theory,  by  assuming  a  single  final  good,  directly 
associated  to  GDP  in  the  data,  a  representative  firm  operating  an  aggregate 
technology, and a representative household taking consumption/ saving decisions -
see  Solow  (1956).  This  small  set  of  assumption  was  enough  to  replicate  the  so-

44 

 
 
called  Kaldor  facts  that  emerged  from  the  recently  created  national  accounts  -see 
Kaldor (1961).67  

68  Following  the  Neoclassical  tradition,  the  dynamic  and  stochastic  general 
equilibrium  (DSGE)  model,  inspired  in  the  seminal  contribution  by  Kydland  and 
Prescott  (1982),  developed  in  the  nineties  aiming  at  the  study  of  macroeconomic 
fluctuations  and  stabilization  policies  -see  Clarida  et  al.  (1999)  and  Woodford 
(2011).  In  our  days,  the  DSGE  framework  is  the  standard  instrument  for  macro 
policy  evaluation  used  by  most  Central  Banks  and  Ministries  of  Finance  all  around 
the globe. QUEST, developed by the DG  ECFIN for the EU, is a good example  -see 
Section 3.2. 

In the Neoclassical growth framework, and the subsequent developments that gave 
rise  to  the  DSGE  model,  the  growth  rate  of  productivity  is  assumed  to  be 
exogenous. The introduction of R&I in models of endogenous growth, pioneered 
by Romer (1990) and Aghion and Howitt (1992), among others, improved over the 
Neoclassical growth model by endogenizing  the  growth rate of productivity. This is 
an  evolution  of  the  macroeconomic  literature  of  fundamental  importance  for  the 
macro-modelling of R&I and R&I policy evaluation. 

Macro-models  of  heterogeneous  firms  and  households  were  developed  during  the 
last two decades,  allowing DSGE  models to go beyond the representative firm and 
household  assumptions  -see  the  discussion  on  firm  heterogeneity  at  Section  2.1.2 
and all along Section 2.2. Macro-models of heterogenous firms follow the traditions 
initiated  by  the  firm  dynamics’  framework  -see  Jovanovic  (1982)  and  Hopenhayn 
(1992), and the Schumpeterian framework -Aghion and Howitt (1992). Instead of a 
representative firm, the  production side of the economy is populated  by  a mass of 
heterogenous  perfectly  competitive  (Hopenhayn,  1992)  or  monopolistically 
competitive  (Aghion  and  Howitt,  1992)  firms.69  In  the  tradition  of  Bewley  (1977) 
and Aiyagari (1994), Krusell and Smith (1998) develop a stochastic growth model of 
heterogeneous  households  with  partially  uninsurable  risk  to  study  the  interaction 
between  income  and  wealth  inequality  with  the  main  macroeconomic  variables. 
Models  of  heterogeneous  firms  and  households  use  micro  data,  on  top  of 

67   In  the  US,  National  and  Income  and  Product  Accounts  (NIPA)  were  developed  in  the 
thirties  and  forties  -see  Kuznets  (1934)  and  Carson  (1975).  The  publication  of  the  NIPA 
officially statistics started in 1947. 

68   Kaldor  (1957)  states  that,  over  long  periods  of  time,  the  following  variables  are  roughly 
constant,  1)  the  labour  and  capital  shares  of national  income;  2)  the  rate  of  growth  of 
the capital  stock per  worker;  3)  the  rate  of  growth  of  output  per  worker;  4)  the 
capital/output ratio; 5) the rate of return on investment. 

69   In Hopenhayn (1992), firm's technology is assumed to have decreasing returns to labour, 
like  in  the  span  of  control  model  suggested  by  Lucas  (1978).  Following  the  seminal 
contribution by Dixit and Stiglitz (1977), models of monopolistic competition alternatively 
assume that heterogeneous firms produce a large variety of differentiated products under 
constant  returns.  This  framework  has  been  extended  to  alternative  forms  of  imperfect 
competition like in Melitz and Ottaviano (2008) and Impullitti and Licandro (2018). Collard 
and  Licandro  (2020)  study  the  general  conditions  for  aggregation  that  makes 
heterogeneous firm economies to share similar aggregation properties as the Neoclassical 
growth model. 

45 

 
                                                
macro  data,  to  discipline  the  analysis.  A  critical  aspect  remains:  How  do 
heterogenous  behaviours  aggregate  in  GDP  and  its  components?  Consistently  with 
the  aggregation  theory  in  Solow  (1962),  Collard  and  Licandro  (2020),  show  that 
macro-models based on Hopenhayn (1992) show aggregate properties similar to the 
Neoclassical  model,  making  the  selection  process  play  a  fundamental  role  in 
determining aggregate productivity.70  

Agent-based  models  (ABM)  provide  an  alternative  based  on  assumptions  about 
the  way  agents  behave  and  interact,  which  are  rooted  in  the  actual  empirical 
microeconomic  evidence  -see  Simon  (1977)  and  Kirman  (2016).  Evolution  and  the 
emergence of novelty are at the heart of these models -see David (2006), Dosi and 
Winter  (2002),  Dosi  and  Virgillito  (2017).      ABM  or  ACE  focus  explicitly  on  the 
heterogeneity of agents, their interactions and believes, the randomness and path-
dependency of technological change, the non-linearity of economic phenomena and 
the resulting out-of-equilibrium dynamics, raising their potential to explain short-run 
fluctuations,  long-run  trends  as  well  as sharp  discontinuities  and  the  possibility  of 
crises. Yet they suffer, as most models, from the curse of dimensionality. The more 
agents  in  a  model  and  the  more  complex  their  interrelationships,  the  more  time  it 
takes to run the model, to achieve convergence and to understand the driving forces 
and  locations  of  tension  in  the  model.   While  ABM  models  are  able  to  replicate 
observed  stylized  facts  of  industrial  dynamics  and  macroeconomic  fluctuations  in 
artificial economies, they have so far not been used to replicate the current stage of 
a particular economy and then predict its subsequent evolution. 

Stabilization  policies.  There  is  a  growing,  recent  literature  bridging  endogenous 
growth  and  business  cycle  approaches  to  macroeconomic  analysis71.  The  FRAME 
(2019) project, based on Comin and Gertler (2006) and Anzoategui et al. (2019) is 
an example of this approach. As stated by Bianchi et al. (2019): “New insights from 
the FRAME project show that fiscal austerity has severe negative consequences for 
productivity  and  economic  growth  in  the  medium-run  and  can  lead  to  slow 
recoveries.”  Benigno  and  Fornaro  (2018),  referred  when  discussing  about  path 
dependency in Section 2.1.2, is another relevant example. More recently, Vinci and 
Licandro  (2020)  suggest  an  endogenous  growth  DSGE  model  that  replicates  the 
behaviour of the US economy in both normal times and the Great Recession. In this 
framework,  monetary  policy  not  only  favours  economic  recoveries,  but  due  to 
knowledge spillovers, promotes productivity growth, giving rise to important welfare 
gains. In other words, by favouring a faster recovery, monetary policy not only has 
stabilization effects, but medium- and long-term effects on productivity and GDP per 
capita. 

2.4.2. Competitiveness and globalization 

Macro-models can also be used to evaluate R&I policies on their potential effects on 
competitiveness, i.e., the ability of a firm, sector or country to successfully compete 

70   The economic theory of index numbers gives support to aggregation in the measurement 
of GDP and GDP growth in National Accounts. See Caves et al. (1982) and more recently 
Duran and Licandro (2015). 

71   See  Fatás  (2000),  Comin  and  Gertler  (2006),  Aghion  et  al.  (2009),  (2010)  and  (2014), 

Sedlacek (2020), Anzoategui et al. (2019) and Benedetti-Fasil et al. (2020). 

46 

 
                                                
in  global  markets.72  Structural  competitiveness  problems  are  typically  associated 
with  low  productivity  growth,  while  significant  competitiveness  divergences  among 
risky.73 
deeply 
integrated  economies  may  be  perceived  as  potentially 
Competitiveness is, however, a multifaceted concept that can have several different 
meanings and measures, whether the focus is on firms, sectors or countries. At the 
macroeconomic level the focus is on country-level competitiveness, but in multi-
sector  macroeconomic  models  it  is  also  possible  to  measure  the  impact  of  R&I 
policies on competitiveness at a  sectoral level and  provide insights into what may 
drive  countries  competitiveness  at  large.  Unit  Labour  Costs  and  Real  Effective 
Exchange  Rates  are  the  most  established  measures  of  competitiveness.  Both 
measures  are  usually  evaluated  at  economy-wide  level,  but  in  multi-sector 
macroeconomic models they can be assessed at more disaggregated levels. 

Growth  models  with  heterogeneity  in  firm-level  productivity  can  account  for  the 
effects of R&I policies on firm-level competitiveness and for the rise of superstar 
firms. In general, more granular models and/or models with heterogenous firms are 
better  suited  for  the  evaluation  of  vertical  targeting  policies,  aimed  at  promoting 
innovation  and  technological  adoption  in  a  selection  of  sectors  and/or  regions.  In 
Baldwin  and  Robert-Nicoud  (2008),  the  potential  effect  of  innovation  policies  on 
firm-level productivity does not extend to national comparative advantage, as firms 
across countries compete horizontally  as in  Krugman  (1980) and  new technologies 
complement  existing  ones  as  in  Romer  (1990).  Impullitti  and  Licandro  (2018) 
incorporate  an  additional  mechanism  through  which  industrial  policies  (e.g.  trade 
liberalization,  R&D  subsidies,  bureaucratic barriers  to  entry)  may  affect  firm-level 
and  aggregate  productivity,  namely  the  competition  channel.  By  affecting  the 
profitability  of  new  firms  successfully  entering  export  markets,  trade  policies  have 
an  impact  on  market  concentration  and  product  market  competition,  which  feeds 
back 
to  national 
to 
competitiveness.  

thereby  contributing 

incentives 

innovate, 

firm 

to 

Within the framework of endogenous growth theory, Grossman and Helpman (2015) 
discuss  the  classic  channels  linking  trade  and  growth:  International  knowledge 
spillovers, market size and competition, relative prices, and technology diffusion. In 
macro-models  of  firm  heterogeneity,  the  link  between  trade  openness  and 
productivity  growth  is  also  shaped  by  these  classical  channels,  but  they  can  be 
magnified  by  a  rich  set  of  selection  effects.  Impullitti  and  Licandro  (2018)  study 
productivity  and  welfare  gains  from  trade  in  a  macro-model  of  innovation-driven 
growth with firm heterogeneity and variable markups. They find that accounting for 
firms’  innovation  responses  generates  dynamic  gains  that  double  the  traditional 
static  gains  from  trade.  Sampson  (2016)  studies  welfare  gains  in  a  macro-model 
where  productivity  growth  is  driven  by  knowledge  diffusion  at  the  entry  stage. 
Trade-induced selection accelerates knowledge diffusion and growth, thereby tripling 
the  gains  from  trade  relative  to  heterogeneous  firms’  economies  with  static 
productivity at the firm level. Along similar lines, Perla et al. (2019) set up a macro-
model where growth is driven by knowledge diffusion between incumbent firms, and 
show that trade accelerates technology diffusion and growth.  

72   See  Fagerberg  (1988).  Among  the  stylised  facts  of  modern  growth,  Jones  and  Romer 

(2010) report that globalization has increased the size of the market. 

73   See Gros (2012) on the Alert Mechanism of the EU Macroeconomic Imbalance Procedure.  

47 

 
                                                
Modelling  R&I  in  a  multi-country,  open  economy  macro-model  with  heterogeneous 
firms  would  be  needed  to  properly  evaluate  the  effect  of  R&I  policies  on 
competitiveness  in  a  globalized  world.  Akcigit  et  al.  (2018)  argue  that  policies 
promoting R&I (like R&D tax credits or R&D subsides) dominate protectionism. They 
quantitatively  assess  the  introduction  in  the  US  in  the  eighties  of  R&D  tax-credits 
and  find  that  this  policy  instrument  was  an  effective  response  to  the  increasing 
Japanese  and  European  competition,  leading  to  more  innovation  both  in  the  short 
and in the long run, and substantial welfare gains. 

2.4.3. Labour markets 

The creation of new technologies is intrinsically related to the destruction of old ones 
and  the  associated  skills.  R&I  policies  have  to  consider  the  effects  that  technical 
progress  has  in  the  evolution  of  employment  and  wages  across  industries  and 
occupations. 

The  by  now  abundant  literatures  on  skill  premia,  job  polarization,  automation  and 
skill  obsolescence  show  the  important  the  labour  market  effects  of  innovation  and 
technical progress, which develops differently across sectors, affecting unevenly the 
dynamics of jobs and occupations. 

There  is  an  abundant  empirical  literature  on  the  firm  level  relationship  between 
productivity  and  employment,  documenting  the  extent  of  labour  reallocation 
associated to differences in productivity across firms.74 Lentz and Mortensen (2008) 
study  the  labour  market  effects  of  such  productivity  differences  in  an  extended 
version  of  the  Schumpeterian  model  suggested  by  Klette  and  Kortum  (2004)  with 
firm  heterogeneity  and  R&I.  On  a  panel  of  Danish  firms,  they  find  that  53%  of 
aggregate growth is due to selection as more productive firms grow faster, crowding 
out less productive firms. The labour market effects of R&I are somehow disrupted 
by the associated misallocation of labour across firms, as documented by Hsieh and 
Klenow (2009), among others. 

The  increase  of  the  skill  premium,  defined  as  the  relative  wage  of  college  vs  non-
college graduates, observed in  the  seventies and  eighties in the US  was attributed 
by Katz and Murphy (1992) to the fact that technical progress was biased in favour 
of skilled workers,  i.e., the so-called  skill  bias  technical  change  hypothesis.75  In 
their view, the observed increase in both employment and wages of skilled workers, 
relative  to  unskilled  workers,  was  governed  by  a  rise  in  the  demand  for  skills 
induced by skill bias technical change, which also induced an increase in the relative 
supply  of  skilled  workers.76  Acemoglu  (1998)  argues  that  during  this  period  R&I 

74   See Bartelsman and Doms (2000) for a survey. Some interesting measurement issues are 

in Foster et al. (2008). 

75   Acemoglu (2003) reports a little increase in the skill premium in Europe. 

76   Krusell et al. (2000) attribute this bias in technical progress to a combination of two facts: 
capital embodied technical progress and capital-skill complementarity. Autor et al. (2003) 
argue that the rise in automation observed during the last decades of the 20th century lead 
to the observed decline in routine jobs and the consequent increase in nonroutine jobs. In 
their  view,  computers  substitute  for  workers  performing  routine  tasks,  but  complement 
workers performing nonroutine tasks (a particular form of capital-skill complementarity). 

48 

 
                                                
efforts were mainly directed to increase the performance of skilled jobs, giving rise 
to the bias in technical progress reported by Katz and Murphy (1992). Acemoglu and 
Autor  (2011)  and  Autor  and  Dorn  (2013)  subsequently  observed  that  the 
simultaneous  rise  of  the  skill  premium  and  the  relative  employment  of  skilled 
workers  was  part  of  a  more  complex  phenomenon  referred  in  the  literature  as  job 
polarization,  in  which  losses  of  wages  and  employment  concentrated in  the  middle 
of the skill distribution.77 They relate this phenomenon to a rise in automation  and 
the destruction of what they call routine jobs, that concentrate in the middle of the 
skill distribution.78  

technology  across  sectors, 

Another  strand  of  the  literature  analyses  the  labour  market  implications  of  the 
diverse  evolution  of 
the  so-called  structural 
transformation.  This  literature  aims  at  understanding  the  observed  unbalanced 
patterns  of  sectorial  evolution 
in  modern  economies  between  agriculture, 
manufacturing and services.79 The unbalanced evolution of industries is mimicked by 
an  unbalance  evolution  of  occupations,  as  pointed  out  by  Duernecker  and 
Herrendorf  (2019),  consistent  with  the  polarization  of  wages  and  employment 
observed in the data.  

Innovation and the progress in technology in a world of heterogeneous workers, not 
only has an impact on employment but also on education and occupational choices. 
In  Acemoglu  and  Autor  (2011)  and  Autor  and  Dorn  (2013),  for  example,  workers 
have  a  choice  between  being  skilled  or  unskilled  which  depends  on  the  expected 
equilibrium wages.80 As in Katz and Murphy (1992), the supply of skills follows the 
evolution of technology. Modelling the joint evolution of technology and occupations, 
and the associated evolution  of  skills is of fundamental importance  to evaluate  the 
labour market effect of R&I policies. 

Macroeconomic models designed to understand R&I and evaluate the impact of R&I 
policies have to consider their effects on the labour market, ideally in a framework 
of heterogeneous firms and workers, and endogenous skills. 

77   Job polarization in Europe was studied by Goos et al (2009). 

78   More  recently,  Acemoglu  and  Restrepo  (2018)  and  Graetz  and  Michaels  (2018),  among 
others,  studied  the  economic  contributions  of  modern  industrial  robots,  showing  large 
gains  in  productivity.  Basso  and  Jimeno  (2020)  stress  the  role  of  demographics  in  the 
interplay with robotization. In a Comin and Gertler (2006) endogenous growth framework, 
they find  that  low fertility  and population  ageing  lead  to reductions  in  the  growth rate of 
GDP and the labour income share. 

79   See  Duarte  and  Restuccia  (2010),  Kongsamut  et  al.  (2001),  Ngai  and  Pissarides  (2007), 
Acemoglu  and  Guerrieri  (2008),  Boppart  (2014)  and  Comin  et  al.  (2019).  See  also 
Herrendorf et al. (2014) for a survey on this literature. 

80   A  useful  model  of  occupational  choice  was  suggested  by  Roy  (1951).  Autor  et  al  (2003) 
use  the  Roy  model  to  understand  how  computers  changed  the  demand  and  supply  of 
skills. 

49 

 
 
                                                
2.4.4. Inequality 

Social exclusion and inequality have emerged as major societal concerns, motivating 
the  need  for  macro-models  to  include  the  relationship  between  technology 
advancement and inequality.81 

There are different types of inequality that may be reduced or magnified by growth 
and  technological  progress.  Inequality  across  countries  requires  a  multi-country 
modelling structure with heterogeneity in innovative capabilities. Regional inequality 
can be studied in regional models where imbalances in the distribution of economic 
activities (i.e. core-periphery patterns)82 emerge endogenously. Inequalities among 
individuals  or  across  socio-economic  groups  require  the  explicit  modelling  of 
different categories of workers having different skills.  

Acemoglu et al. (2006) develop a macro-model of innovation and adoption to better 
understand the relation between technical progress and  crosscountry differences in 
development.  Technological  progress  may  favour  some  specific  social  groups  and 
sectors.  In  models  where  growth  is  driven  only  by  factor  accumulation  and  where 
technological  progress  is  neutral,  inequalities  among  individuals  or  across  socio-
economic  groups  may  emerge  because  different  individuals  and  socio-economic 
groups  have  different  propensities  to  accumulate  factors  and  because  factor 
accumulation, in turn, may affect the remuneration of the specific productive factors 
owned  by  individuals.  In  this  context  inequalities  across  workers  employed  in 
different  sectors  may  emerge  as  a  result  of  structural  changes.  Notably,  structural 
changes  may  be  generated  by  different  channels:  preference-driven  channels 
generated by differences in the income elasticity of demand across goods (see e.g. 
Duarte and Restuccia 2010; Boppart 2014), or supply-driven channels generated by 
sectoral  differences  in  factor  proportions,  sectoral  differences  in  the  degree  of 
capital–labour substitutability or by sector-specific technical progress (see e.g. Ngai 
and Pissarides 2007, Acemoglu and Guerrieri 2008, Alvarez-Cuadrado et al. 2017). 
Multi-sector  macroeconomic  models  can  capture  these  aspects.  However,  the 
evidence  on  the  unbalanced  evolution  of  industries  and  occupations  and  on  the 
polarization of wages (cf. supra) suggests the need  of macroeconomic models able 
to  capture  aspects  related  to  skill-based  technical  changes,  skill  obsolescence  and 
the impact of robotization.  

2.4.5. Health 

Health is something that people value for themselves. In addition, there is also the 
productive  side  of  health,  since  healthier  people  can  work  harder  and  longer.  With 
an ageing population promoting a healthy ageing lifecycle approach is a societal and 
economic challenge shared by all European countries.  

81   Aghion et al. (1999) survey the literature on the relation between inequality and economic 
growth.  On one side,  they  stress  the role  of  capital  market  imperfections  on  shaping  the 
trade-off  between  equity  and  efficiency.  On  the  other  side,  they  point  out  that  technical 
change has been a crucial factor in explaining the recent upsurge in wage inequality. See 
Aghion et al (2019) on the relation between innovation and top income inequality. 

82   See Forslid and Ottaviano (2003) and Baldwin (2001). See also Rodriguez-Pose (2018). 

50 

 
                                                
R&I can affect health through different channels.83 A direct channel is through R&I in 
the  medical  and  pharmaceutical  sector.84  An  indirect  channel  is  through  R&I 
affecting  environmental  degradation  and  the  related  negative  effects  on  human 
health.85  Life expectancy is  an important dimension  of  health that is also expected 
to interact with human capital and growth. Acemoglu and Johnson (2007) study the 
relation  between  life  expectancy  and  growth  concluding  that  “there  is  no  evidence 
that  the  large  increase  in  life  expectancy  raised  income  per  capita.”  However, 
Cervelatti and Sunde (2011) challenge this view showing that life expectancy played 
an important role on the outset of the demographic transition.86 

In  macroeconomic  models,  since  the  seminal  contribution  of  Grossmann  (1972), 
health has been modelled  as a durable  good (health  capital) that enters the  utility 
function  of  individuals  directly  or  indirectly  through  the  effects  that  health  has  on 
the time that can be spent on activities. Health is subject to deterioration and may 
vary with the age of individuals in models of heterogeneous agents. 

The  assessment  of  health  impacts  of  R&I  poses  several  measurement  issues 
requiring  inputs  from  epidemiological  studies  on  the  health  effects  of  different 
medical treatments, lifestyles, pollutants and several other factors.  

2.4.6. Environment, non-renewable and waste disposal 

Following  the  growing  concern  of  society  for  a  sustainable  environment,  the 
importance of environment has been gaining momentum in both policy-making and 
academic circles. This is particularly the case for the relationship between economic 
growth, environmental quality and natural resources. Macroeconomic models for R&I 
policies  evaluation  should  embody  environmental  externalities,  account  for  the 
damage of pollutants and deal with the issue of  resource scarcity. The shift to 
new or improved low-emission and high-efficiency technologies in key sectors such 
as transport, power generation and energy may play a major role in tackling climate 
change.  Technological  progress  can  also  help  reduce  pollutant  emissions  from 
industry, agriculture and other sources, limit the depletion of exhaustible resources, 
improve waste disposal and recycling, reduce the abatement costs and stimulate the 
developments  of  a  broad  portfolio  of  green  technologies.  In  this  process  economic 
policies play a major role in fostering changes and in directing technical change. In 
this respect, general equilibrium models allow to capture direct and indirect effects 
of  technological  progress  on  the  environment,  and  to  highlight  the  possible  trade-
offs and the emergence of rebound effects.  

83   See Aghion et al. (2010) and Barro (2013). More recently, Bloom et al (2019). 

84   Bloom  et  al.  (2017)  argue  that  research  productivity  has  been  declining  sharply  in  the 
pharmaceutical  sector.  In  particular,  Di  Masi  et  al.  (2016)  estimate  that  the  real  cost  of 
R&D  in  the  pharmaceutical  industry  has  been  raising  at  8.5%  annually  in  the  last  two 
decades. 

85   See Science for Environment Policy (2018). 

86   See also Kalemli-Ozcan et al. (2000) and Boucekkine et al. (2003). 

51 

 
                                                
The  directed  technical  change  literature  stresses  the  importance  of  path 
dependency  in  R&D  efforts  and  of  policy  actions  in  promoting  the  shift  towards 
sustainable  technologies.  Because  of  path  dependency,  in  a  laissez-faire  economy, 
firms  failing  to  internalize  environmental  externalities  may  be  induced  to  keep 
innovating in the  “wrong  direction”.  On directed technical change,  see  e.g.  Aghion  et  al. 
(2016),  Acemoglu  et  al.  (2012),  while  for  models  with  endogenous  innovations  in 
abatement  technologies,  see  e.g.  Bovenberg  et  al.  (1995)  and  Goulder  and 
Schneider (1999). 

Since  the  seminal  studies  of  Nordhaus  (1974),  Grossman  and  Krueger  (1991)  and 
Stokey  (1998)  on  the  relationship  between  growth  and  the  environment, 
macroeconomic  models  have  been  addressing  environmental  constraints  and  the 
role of R&I along different, but complementary dimensions. 

A first dimension is related to the emissions and/or the abatement of all global and 
local  pollutants  involving  air,  water,  soil,  light  and  noise.  Greenhouse-gas 
emissions  and  climate  change  (global  pollutants),  among  them,  are  of  a  first 
importance. In this domain central  issues of  concern are  the development of clean 
technologies  not  involving  fossil  fuels  and  the  improvement  of  the  available 
abatement technologies. Policies against global pollutants and climate actions (both 
price  and  quantity  regulations)  can  be  introduced  in  all  macroeconomic  models 
based on general equilibrium. However, cap-and-trade schemes set at international 
level require the use of multi-country frameworks. Similarly, the study of the effects 
of  unilateral  environmental  policies  and  of  possible  carbon  leakages  requires  open 
economy  models  with  multiple  countries.  For  a  throughout  discussion  on  climate 
change  from  the  perspective  of  macroeconomic  modelling  and  quantitative 
evaluation,  see  Hassler  et  al.  (2016).  Concerning  local  pollutants,  regional  and 
sectoral models may represent the main tool of analysis. 

At  present  CGE  models  and  Integrated  Assessment  Models  (IAMs)  are  the  main 
policy-analysis  tools  used  to  estimate  the  economic  costs  and  benefits  of  different 
actions  against  climate  change.  See  e.g.  the  DICE  and  RICE  models  of  Nordhaus 
(2007, 2008), the EPPA  model  (Paltsev et  al. 2005), the CIM-EARTH model (Elliott 
2010)  and  the  REMIND  model  used  in  Rauner  et  al.  (2020).87  Multi-sector 
econometric  models  integrating  macroeconomic  activity  with  key  climate  variables 
are also used to evaluate short- and long-term impacts of climate actions. In recent 
years,  dynamic  stochastic  general  equilibrium  models  have  been  employed  for  the 
study  of  energy  and  environmental  policies.  For  variants  of  dynamic  general 
equilibrium models of the economy and climate, see Golosov et al. (2014) and Van 
der Ploeg and Rezai (2020). For an extension of the QUEST model in this direction, 
see  Conte  et  al.  (2010).  Overall  DSGE  models  can  capture  important  factors  for 
environmental policy analysis, such as physical and transition risks, short-run trade-
offs and uncertainty.88 In this respect, also environmental agent-based models with 

87   In an extended version of the DICE model of climate change with endogenous innovation 
in  the  energy  sector,  Popp  (2004)  shows  how  neglecting  induced  technological  change 
overestimates the welfare costs of an optimal carbon tax policy by about 8 percent. 

88   For other examples of environmental DSGE models focusing on the short-run trade-offs of 
climate actions, see e.g. Fischer and Springborn (2011), Heutel (2012), Annicchiarico and 
Di Dio (2015), Annicchiarico et al.  (2018) and Annicchiarico and Diluiso (2019). 

52 

 
                                                
climate  risk  can  be  used  to  construct  mitigation  scenarios  under  uncertainty  and 
capture the dynamic adjustment towards a greener economy.89 

A second dimension is that related to resource scarcity and the management of 
non-renewable  resources.  Multi-sector  models  can  capture  the  economic 
mechanisms  that  may  lead  to  save  scarce  resources  and  induce  endogenous 
energysaving technical change. This aspect can be easily introduced in CGE models 
or  in  dynamic  general  equilibrium  models  with  directed  technical  change  or  in 
econometric  models  -see  e.g.  Bergman  (2005),  Peretto  (2009),  Acemoglu  et  al. 
(2012), Peretto and Valente (2015). 

A third dimension regards the role of innovation on the efficiency and the safety of 
waste  management.  Waste  disposal  aspects  have  been  integrated  in  CGE  and 
macro-econometric models, but in principle they may be explicitly introduced in any 
class of macroeconomic models. 

Finally, innovation can favour the transition towards a circular production system 
and  the  implementation  of  cost-effective  recycling  programmes  able  to  close  the 
loop.  Given  their  input-output  structure,  CGE  models  are  natural  candidates  to 
represent this dimension related to environmental  constraints, although all general 
equilibrium models can easily capture circular features. See  McCarthy et al. (2018) 
for  an  overview  on  the  potential  modelling  approaches  to  the  transition  towards  a 
circular economy. 

2.5. A concluding checklist of mean features, enablers, policy 

instruments and dimensions to evaluate R&I and R&I policies in macro-

models 

Based  on  the  existing  academic  macroeconomic  literature  on  R&I  and 
innovation  policies,  this  section  described  the  critical  features  of  the  innovation 
process which macro-models need to include to appropriately account for its role on 
economic  growth  and  welfare.    It  also  identified  the  main  policies  to  promote 
innovation,  boosting  growth  and  enhancing  sustainability.  In  this  section,  these 
main  features,  enablers  and  main  policy  instruments  are  summarized  in  the 
following set of tables. 

Starting  from  the  notion  that  technological  knowledge  is  the  sum  of  methods  and 
techniques  employed  in  the  production  of  goods  and  services,    resulting  from  the 
cumulative  aggregation  of  new  technological  ideas or  innovations,  resulting  from  a 
systematic effort of research and development (R&D) benefiting from multiple forms 
of  knowledge  spillovers,  the  main  features  characterizing  this  R&D  and  innovation 
process  as  well  as  its  main  enablers  can  be  derived  which  applied-macro-models 
should cover when used for evaluating innovation and innovation policy. 

89   See e.g. Lamperti et al. (2018). 

53 

 
 
 
 
                                                
Table 2.1 - Main features of R&I 

Technological 
Knowledge 

Stock of knowledge 
(endogenously 
determined) 

Innovation 
Typology 

Absorptive Capacity 

Non-Rivalry 

Obsolescence  

R&D 

Knowledge Spillovers 

Rent Spillovers 

Firm Heterogeneity 

Path Dependency 

Knowledge Spillovers 

Absorptive Capacity 

Innovation & 
Creation of New 
Technologies 

Diffusion of New 
Technologies 

Basic and applied 
research 

Process and Product 
Innovation 

Drastic and 
Incremental 
Innovations;  GPTs 

Linking inputs and 
outputs:  the 
knowledge 
production function 

R&D labour inputs 

R&D equipment 

“Standing on the 
shoulders of giants” 
effects 

Congestion 
externalities 

54 

 
 
 
 
 
 
 
 
 
 
 
Table 2.2 - Main R&I enablers 

Size of the Market 

Competition 

Intellectual property rights 

Human capital 

Stock (endogenously determined) 

Heterogeneity: different labour skills 

Financing of R&D 

Financial frictions (endogeneous) 

Risk Premia 

55 

 
 
 
 
 
 
 
Table 2.3 - Main R&I policy instruments 

Section 2 also reviewed and assessed R&I policy, the distortions they are expected 
to correct and the relevant channels through  which  they are  presumed to operate.   
Macro-models  aimed  to  evaluate  innovation  policy  should  cover  these  main  R&I 
policy instruments and other policies that directly or indirectly affect innovation, as 
well as the design of the appropriate policy mix.   

R&D tax incentives 

Direct subsidies/grants 

Loans and public venture capital 

Public R&D 

Public-Private Partnerships 

Public Procurement 

Patents 

Other policy instruments 

Regulation 

Corporate tax 

Environmental policies 

Competition Policy 

Education Policy 

Labour market policy 

Policy Mix 

Budget spent 

Administrative Burden 

Interactions between national  and 
supranational level policies 

Interactions between regional and national 
level policies 

Sector or technology specific policy 
interventions 

56 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Although macroeconomic models are used mostly to assess the impact of R&I policy 
interventions  on  GDP  at  various  horizons  (short-,  medium-  and  long-term),  there 
are  also other dimensions of  societal  welfare  that can be assessed through macro-
models.  

Table 2.4 - Dimensions of macroeconomic impact of R&I 

GDP (growth) 

Competitiveness  

Labour markets 

Inequality 

Health 

Environment 

Firm; country; region 

Employment & wages 

Across workers 

Across countries 

Across regions 

Section  3  will  use  this  set  of  tables  to  assess  the  applied  macro-models  currently 
used by the European Commission for assessing their use for R&I policy evaluation.  
More particularly, these models are assessed on their inclusion of the basic features, 
enablers  and  policies  as  important  for  modelling  R&I  and  assessing  R&I  policy 
interventions, and, if not, how they could be included.     

It should be remarked that this summary only provides a synthetic representation of 
the  main  features  related  to  the  modelling  of  knowledge  creation  and  diffusion  of 
technologies, the main policy instruments and the main dimensions of impact.  The 
list is not meant to be exhaustive and the presence or absence of individual features 
or instruments or dimensions of impacts should not be interpreted by itself as a sign 
of strength or weakness of the models. 

57 

 
 
 
 
 
 
 
 
 
 
3.  THE  MACROECONOMIC  MODELS  CURRENTLY  USED  FOR  R&I 

POLICY, AND HOW THEY CAN BE IMPROVED  

Section 3 checks whether and how the main R&I properties and policies discussed in 
Section  2  are  accounted  for  in  the  macroeconomic  models  used  by  the  European 
Commission and, if not, how they could be included. Three macroeconomic models 
are presently used by the Commission’s DG for Research and Innovation (DG R&I): 
QUEST III, RHOMOLO and NEMESIS. 90  

 

The  QUEST  III  model  is  a  dynamic  macroeconomic  model  of  the  European 
Commission designed to be a laboratory for economic policy analysis for the 
EU countries. It is developed by DG for  Economic and Financial Affairs (DG 
ECFIN) with support from the Joint Research Centre (JRC) in Ispra.  

  RHOMOLO 

is  a  spatial  computable  general  equilibrium  model  of 
the European  Commission.  The  model  is  developed  by  the  Regional 
Economic  Modelling  team  at  the JRC  in  Seville  in  cooperation  with  DG  for 
Regional and Urban Policy (DG REGIO).  

  NEMESIS 

(New  Econometric  Model 

for  Evaluation  by  Sectoral 
Interdependency  and  Supply)  is  a  European  macro-sectoral  econometric 
model initially developed by a consortium led by ERASME  (a research team 
common to the University of Paris1 Sorbonne and the Ecole Centrale Paris), 
and including the  CCIP (Chamber  of  Commerce  of  Paris), NTUA (University 
of  Athens),  the  Federal  Planning  Bureau  (Brussels)  and  the  University  of 
Maastricht. At present the model is managed by SEURECO (a spin- off from 
the  ERASME  team).  It  is  not  owned  by  the  European  Commission,  but 
regularly procured by DG for Research and Innovation (DG R&I). 

This  section  describes  these  three  models,  their  main  characteristics,  their 
treatment of R&I and R&I policies, evaluates their strengths and limitations to do so 
and suggests how missing features could be included. 

Macroeconomic models designed for policy evaluation are continuously updated, as 
new  data  become  available,  better  estimates  of  the  parameters  are  provided  and 
improved  theoretical  foundations  are  introduced.  In  this  respect  this  section 
presents a snapshot of the current version in use of the three models.  

90   A comparison of QUEST III with R&D, RHOMOLO and NEMESIS that looks especially at the 
R&D and innovation features of such models is also available in Akcigit et al. (2020). The 
authors discuss also some applications of the models among which the impact assessment 
of Horizon Europe carried out by the European Commission in 2008. 

58 

 
 
 
 
 
 
                                                
3.1. The QUEST III model  

3.1.1. Main features  

QUEST  III  is  a  New-Keynesian  dynamic  stochastic  general  equilibrium 
model (DSGE). DSGE models have a long tradition in macroeconomics as a useful 
instrument  to  study  the  long-,  medium-  and  short-term  performance  of  modern 
economies,  as  well  as  to  evaluate  the  effects  of  macroeconomic  policies  in  these 
dimensions. The use of dynamic general equilibrium models to study the long-term 
behaviour  of  modern  economies  and  to  evaluate  the  effects  of  macroeconomic 
policies  can  be  traced  back  to  the  fifties  and  sixties,  with  the  development  of 
modern growth theory, inspired by the seminal work of Ramsey (1928) and initially 
developed  by  Solow  (1956,  1957),  among  many  others.  Innovation  in  general 
equilibrium models was first introduced by Romer (1986, 1990), Aghion and Howitt 
(1992) and Grossman and Helpman (1991), and extended later by many others  to 
produce  a  framework  of  critical  importance  for  the  study  of  innovation  and 
innovation policy. The use of dynamic general equilibrium models to the analysis of 
business  cycles  fluctuations  was  pioneered  by  Kydland  and  Prescott  (1982) 
influential  work,  giving  rise  to  a  powerful  methodology  used  to  relate  models  with 
the  data,  to  then  test  the  accuracy  of  model  predictions  with  the  behaviour  of 
macroeconomic aggregates at different frequencies. Inspired by this methodology, a 
huge  effort  has  been  done  during  the  past  four  decades  by  the  so-called  New 
Keynesians,  Clarida  et  al  (1999)  and  Woodford  (2011),  among  others,  to  both 
render the DSGE model close  to  the  behaviour  of  most aggregate observables and 
give  a  critical  role  to  the  working  of  monetary  authorities.  The  New  Keynesian 
approach  has  been  adopted  by  monetary  authorities  and  finance  ministries  of 
developed  and  developing  countries  to  discuss  the  state  of  the  economy  and  to 
evaluate the working of macroeconomic policy. 

There exist different versions of the QUEST III model, each of which with a focus 
on specific sectors, countries and policy areas. The variant discussed here is the one 
that enriches the QUEST III model described and estimated in Ratto et al. (2009) 
by  explicitly  incorporating  an  R&D  sector  and  a  semi-endogenous  growth 
mechanism  à  la  Jones  (1995),  adapting  the  Romer  (1990)  model  with  expanding 
varieties. Roeger et al. (2008) and Varga and in't Veld (2011) provide details of the 
QUEST model. 

In  QUEST  III,  consistently  with  the  standard  DSGE  modelling  approach, 
equilibrium  conditions  for  the  main  macroeconomic  variables  are  derived  from  the 
optimising  behaviour  of  agents,  given  technological,  institutional  and  budgetary 
constraints.  The  model  includes  standard  features  useful  for  capturing  data, 
including  habit  persistence,  variable  capacity  utilisation,  real  frictions,  such  as 
imperfectly competitive markets, labour and investment adjustment costs, variable 
and  nominal  frictions  on  wages  and  prices.  The  model  economy  is  populated  by 
households,  final  and  intermediate  good  producers,  research  labs,  technology 
adopters, a fiscal authority and a central bank.  

In  the  final  good  sector,  monopolistically  competitive  producers  produce 
horizontally differentiated goods using a composite of intermediate goods and three 
types  of  labour  inputs  of  different  skill  levels.  In  the  intermediate  good  sector, 
monopolistically competitive firms produce intermediate goods using capital, rented 
from  households,  and  the  designs  of  adopted  technologies  licensed  from 
households.  The  process  for  creating  new  technologies  takes  place  in  research 

59 

 
institutes  that  use  high-skilled  labour  inputs  along  with  the  existing  stock  of 
domestic  and  foreign  ideas.  Technology  adopters  employ  skilled  labour  to  convert 
non-adopted  technologies  into  technologies  that  can  be  used  for  intermediate 
production. There are two types of households: the non-liquidity constrained, and 
the  liquidity  constrained.  The  non-liquidity  constrained  households  have  full  access 
to financial and capital markets, buy the patents of adopted technologies and licence 
them  to  producers  operating  in  the  intermediate  good  sector.  As  in  Galí  et  al. 
(2004),  the  liquidity  constrained  households  do  not  have  access  to  financial  and 
capital  markets,  therefore  they  consume  all  their  after-tax  labour  income  in  each 
period.  The  fiscal  authority  faces  a  budget  constraint,  collects  social  security 
contributions,  taxes  on  labour  income,  on  consumption  and  capital  income,  net  of 
tax  credits  and  tax  allowances,  pays  transfers  and  unemployment  benefits  to 
households and subsidies that incentivize  R&D, and  spends on goods, services  and 
public  capital.  The  behaviour  of  the  central  bank  is  described  by  an  interest  rate 
rule of the Taylor type. 

QUEST III is a multi-countrymodel calibrated for EU countries and the rest of the 
world.  Individual  country  blocks  are  interdependent  with  one  another  through 
international  trade,  financial  flows  and  knowledge  spillovers.  Euro  area  countries 
have in common the monetary authority. 

3.1.2. Innovation and creation of new technological knowledge  

In QUEST III innovation and the creation of new technologies take place in the R&D 
sector,  which  is  operated  by  research  institutes,  according  to  a  semi-endogenous 
growth mechanism in the spirit of Jones (1995). This implies that in the long run the 
growth rate of output per capita depends on population growth and on technological 
parameters. 

The  production  of  new  ideas  depends  on  the  number  of  high  skilled  workers 
employed  in  the  R&D  sector,  LRD,  and  on  the  domestic  and  the  international 
aggregate stocks of knowledge (labelled as Z and Z∗ respectively), measured as the 
existing stock of patents. New ideas are accumulated and produced according to the 
following knowledge production function: 

(1) 

(2) 

 Zt+1 = vtLRD,t + (1 − δZ)Zt,   

  vt = vZt

∗ωZt

ϕLRD,t

ρZ−1,                   

where  Zt  is  the  stock  of  knowledge  available  at  the  beginning  of  period  t, 

denotes  total  productivity  of  the  R&D  process, 

  and 

  capture  the  international 

and  the  domestic  spillover  effects  of  existing  knowledge  (i.e.  the  standing  on  the 
shoulders of giants effect), while ρZ ∈ (0,1) measures the contribution of high-skilled 
labour  inputs  to  the  R&D  activity.  In  each  country  block  the  aggregate  stock  of 
international  knowledge  is  the  weighted  average  of  the  trading  partners  stock  of 
knowledge,  where  weights  depend  on  bilateral  trade  flows.  The  decreasing  returns 
of research activity is meant to represent congestion externalities (i.e. the  stepping 
on  toes  effect)  and  is  also  related  to  the  risk  of  duplication  of  new  ideas.  Ideas 
become obsolete at rate δZ ∈ (0,1).  The two elasticities 

  and    are positive and 

60 

0v 
 
 
such  that  their  sum  is  less  than  one  so  as  to  account  for  potential  diminishing 
returns to scale with respect to the existing stock of ideas.91 The obsolescence rate 
captures  the  concept  referring  to  the  loss  in  value  of  existing  technologies  as  a 
result of the emergence of new technologies. To benefit from international spillovers 
domestic  firms  must  develop  their  own  absorptive  capacity  which  depends  on  the 
accumulation of their own knowledge.  

The model presents a final good sector, an intermediate good sector, an R&D sector 
and  a  technological  adoption  sector,  but  it  does  not  disaggregate  business 
economies  by  industries  and  services.  In  each  sector  of  QUEST  III  firms  share  the 
same  technology  and  have  the  same  size.  In  the  two  monopolistically  competitive 
sectors  firm  are  different  in  the  sense  that  they  produce  different  varieties  of  the 
same  good  according  to  the  Dixit-Stiglitz  setup.  The  number  of  intermediate-good 
firms  that  employ  adopted  technologies  to  produce  marketable  goods 
is 
endogenous.  Firm  entry  is  related  to  the  expansion  of  adopted  technologies,  while 
exit is exogenous and depends on the obsolescence rate. 

QUEST  III  distinguishes  between  basic  (innovation)  and  applied  (adoption) 
research.  Innovation  is  incremental  and  there’s  only  product  innovation.  The 
development  of  general-purpose  technologies  is  not  modelled.  However,  different 
types  of  innovations  can  be  mapped  by  changing  exogenously  the  productivity  of 
the  three  sectors  using  existing  estimates  on  the  relevant  elasticities.92 
Complementarities  with  ICT,  the  effects  of  automation  and  robotization  can  be 
introduced  exogenously  through  shocks  affecting  the  total  factor  efficiency  of 
knowledge production. 

91   This assumption prevents the model from delivering endogenous growth. The growth rate 

of innovation does not depend on the scale of production.  

92   In  a  “green”  variant  of  the  model  there  is  a  distinction  between  clean  and  dirty 

technologies. See Conte et al. (2011). 

61 

 
 
 
 
 
 
 
 
 
 
                                                
3.1.3. Diffusion of new technologies  

In  the  latest  version  of  QUEST  III,  the  diffusion  process  of  new  ideas  is 
modelled endogenously following Comin and Gertler (2006) and Anzoategui et al. 
(2016).93  Adopters  convert  non-adopted technologies  (or  ideas) into ones  that can 
be  used  by  the  intermediate  goods  producers.  The  stock  of  adopted  technologies, 
A,94 evolves accordingly to the following law of motion: 

(3) 

𝐴𝑡+1 = (1 − 𝛿𝑍)[𝜆𝑡(𝑍𝑡 − 𝐴𝑡) + 𝐴𝑡], 

where  𝑍𝑡 − 𝐴𝑡  indicates  the  stock  of  unadopted  technologies  and  λt  measures  the 
pace  of  adoption  (i.e.  the  probability  that  a  typical  adopter  succeeds  in  converting 
an  idea  into  a  usable  technology)  which,  in  turn,  depends  on  the  amount  of  high-
skilled labour employed Lad,t: 

(4) 

 λt = λ̅(ZtLad,t)ρλ, 

where λ̅ measures the productivity of the adoption process and  ρλ ∈ (0,1). According 
to (4), then, there is a positive spillover effect from the total stock of existing ideas 
Zt, so capturing the idea that economies that are on the frontier of innovation tend 
to be more effective in the conversion of new inventions in usable technologies. The 
inverse  of  λ  measures  the  diffusion  lag  in  the  adoption  process.  As  mentioned  in 
Section  3.1.2,  QUEST  III  can  distinguish  between  scientific  knowledge  (related  to 
basic  research  and  captured  by  Z)  and  technological knowledge  (related to  applied 
research and captured by A). 

Non-liquidity  constrained  households  own  the  patents  of  adopted  technologies  and 
license  them  to  intermediate-good  procedures.  The  number  of  intermediate-good 
producers expands with that of the adopted technologies. 

Complementarities with ICT can be introduced exogenously through changes in the 
productivity  of  the  adoption  process.  The  model  does  not  differentiate  between 
adoption and adaptation of technologies, and does not explicitly model the diffusion 
of new paradigms or of general-purpose technologies. 

Technology  transfers  or  exchanging  patents  and  knowhow,  and 
imitation 
phenomena  are  not  modelled  explicitly,  however  they  can  be  mapped  indirectly 
through  the  international  and  domestic  knowledge  spillovers.  In  each  country  the 

93   Comin  and  Gertler  (2006)  suggest  a  methodology  to  decompose  aggregate  macro  series 
into short-, medium- and long-term frequencies, and extend the DSGE model to replicate 
all  these  dimensions  of  the  data.  This  feature  has  been  introduced  in  QUEST  following  a 
project financed by the Directorate General for Research and Innovation of the European 
Commission  in  the  context  of  the  Horizon  2020  Policy  Support  Facility.  For  details,  see 
https://rio.jrc.ec.europa.eu/library/research-innovation-and-economic-growth-technology-
diffusion. The FRAME program is also inspired in the Comin and Gertler (2006) approach. 

94   The  stock of  adopted  technologies determines  the  number  of  intermediate good varieties 

to be used in the final-good sector. See Roeger et al. (2008).  

62 

 
 
 
                                                
creation of new ideas depends on the trade-weighted knowledge stock of all trading 
partners, thus innovations propagate across countries through international trade. 

3.1.4. Enablers for R&I creation, adoption and diffusion  

In  QUEST  III,  the  size  of  national  and  international  markets  determines  the 
market  size  for  innovative  goods  but  cannot  permanently  affect  the  rate  of 
technological  progress.  An  expansion  of  the  final  good  sector,  triggered  by  an 
expansion  of  domestic  and/or  foreign  demand,  gives  rise  to  an  increase  in  the 
demand  for  intermediate  goods,  so  temporarily  stimulating  the  entry  of  new  firms 
and  favouring  adoption  and  R&D  activity.95  The  size  of  the  market  for  innovation 
also depends on the degree of openness of an economy. In QUEST III there are 
barriers  to  trade  in  the  form  of  import  adjustment  costs  and  the  structure  of 
preferences is such that there is a limit to the diversification in international trade. 
Changes in these features have an impact on innovation and adoption. In addition, 
the intensity of the international spillovers of knowledge is related to the trade-
weighted stock of knowledge of all trading partners. 

Product  market  reforms  that  enhance  competition  in  goods  markets  may  have 
different  effects  on  R&D  activity.  Markets  are  monopolistically  competitive  as  in 
Dixit-Stiglitz, therefore markups do not depend on the number of firms but may be 
changed  in  the  simulation  scenarios  to  represent  pro-competitive  effects  of  shocks 
or  policies.  More  competition  in  the  final  good  sector  tends  to  increase  R&D 
intensity.  By  expanding  the  size  of  this  sector,  more  competition  generates  an 
increase  in  the  demand  for  intermediate  goods  that  stimulates  entry  of  new  firms 
and increases R&D (positive market size effect). However, reform policies fostering 
competition  in  the  intermediate  good  sector  deters  firm  entry,  thus  slowing  down 
technological  progress  and  adoption.96  Besides  the  existence  of  positive  spillovers 
(representing  the  non-appropriable  component  of  the  R&D  activity),  knowledge 
creation and adoption are modelled as markets where labs, adopters and firms sell 
and buy technologies, similarly to any other commodity of the economy.  

Intellectual  property  rights  are  introduced  into  the  model  via  a  patent  system. 
Firms operating in the intermediate good sector acquire licences of existing patents 
when  entering  the  market,  that  is  why  pro-competitive  policies,  by  reducing 
markups in this sector, may have a detrimental impact on innovation and adoption, 
and so growth may temporarily slow down. 

The  model  presents  different  types  of  labour  inputs  (low,  medium  and  high 
skilled).  High  skilled  workers  (scientists  and  engineers)  are  employed  in  the  final 
good  sector,  in  the  R&D  sector  and  in  the  adoption  process.  Therefore,  allocating 
more  high-skilled  workers  to  R&D  and  adoption  processes  decreases  the  share  of 
high-skilled available for final goods production. This explores the potential crowding 
out  effects  between  production  and  R&D  activities.  The  supply  of  labour  is 

95   The semi-endogenous framework used in QUEST III delivers only a scale effect  in levels, 
implying  that  the  stock  of  knowledge/ideas  is  increasing  in  the  size  of  the  market,  while 
the  rate  of  innovation  and  adoption  can  only  be  temporarily  affected  during  the 
adjustment process towards the balanced growth path.  

96   See Roeger et al. (2008). 

63 

 
                                                
endogenous,  therefore  changes  in  the  labour  market  conditions  may  induce  skilled 
workers  to  work  more  and/or  ask  for  a  higher  skill  premium.  However,  in  the 
version  of  QUEST  currently  in  use,  human  capital  formation  is  not  endogenous. 
Nevertheless, it is possible to exogenously change the skill composition of the labour 
force and simulate the effects of policy interventions aimed at improving the quality 
of education, also accounting for the costs that these interventions may entail. For 
instance,  in  Varga  and  in’t  Veld  (2011)  the  evolution  over  time  of  skill-specific 
human  capital  depends  on  the  number  of  school  years  and  on  the  participation  in 
training  activities.  The  skill  upgrading  is  modelled  as  a  slow  change  in  the  skill 
composition  of  the  labour  force,  as  education  policies  only  affect  the  youngest 
cohorts before their passing through the labour market.  

In  QUEST  III  with  R&D  there  is  no  financial  intermediation,  hence  it  is  not 
possible to differentiate between internal and external financing of the R&D activity. 
However, there are exogenous risk premia on intangible and tangible assets that 
may  reflect  the  existence  of  financial  frictions  (e.g.  collateral  constraints, 
asymmetric  information)  and  of  underdeveloped  venture  capital  markets.  By 
changing the risk premium on intangible assets, QUEST III can simulate the effects 
of  policy  interventions  aimed  at  facilitating  access  to  credit  for  firms  transforming 
new  ideas  into  marketable  products  and  services.  A  streamlined  version  of  QUEST 
with  no  R&D  has  been  extended  to  include  financial  frictions  in  Kollmann  et  al. 
(2014). 

3.1.5. R&I Policy 

In  QUEST  R&I  activities  are  carried  out  in  the  private  sector.  There  is  no  public 
R&D  and  the  public  sector  has  no  direct  role  in  the  diffusion  of  knowledge. 
However,  the  public  sector  has  a  rich  set  of  tools  and  channels  through  which 
innovation  and  adoption  can  be  promoted.  The  existence  of  knowledge  spillovers 
justifies  government  interventions  aimed  at  promoting  R&I.  Moreover,  QUEST 
displays  several  sources  of  inefficiencies  and  market  distortions  (such  as  imperfect 
competition,  overhead  labour  costs,  adjustment  costs  etc…)  typical  of  New 
Keynesian DSGE models, that provide a rationale for public intervention.  

The  government  can  grant  subsidies  on  the  wage  of  skilled  workers  to  the 
R&D and the adoption sectors. Given the multi-country structure of the model, it 
is possible to distinguish between interventions at different levels (supranational and 
national). Specific regional policy measures can be simulated by accounting for the 
relative size of the  regions involved. The productivity of firms is positively affected 
by the stock of public  capital which is provided by the government through  public 
procurement.  Through  this  channel  the  model  can  simulate the  effects  of  several 
public interventions, such as those related to transport, energy, telecommunication 
and  ICT  infrastructure.  By  increasing  factor  productivity  public  procurement 
stimulates the  demand  for  intermediate  goods  and,  therefore,  technology  adoption 
and R&D  activities.  In addition,  government investments have a transitory positive 
demand-side  effect  during  the  period  of  implementation,  further  stimulating 
adoption  and  R&D.  The  government  can  expand  the  production  possibilities  of  the 
economy  through  reforms  that  increase  productivity.  As  an  example,  see  the 
simulation exercise of Lorenzani and Varga (2014), who study the economic impact 
of digital structural reforms in the QUEST III model. 

Governments  can  provide  tax  credits  on  the  income  from  intangible  capital 
(patents on adopted technology)  in favour of  non-liquidity constrained  households. 

64 

 
Through this channel the cost paid by intermediate goods producing firms for using 
blueprints  decreases,  so  raising  the  demand  for  new  technologies  and  stimulating 
the  entry  of  new  firms.  By  this  channel  the  speed  of  technology  diffusion  is 
enhanced, and knowledge creation is incentivised. 

Public  funding  of  R&I  activities  can  be  simulated  by  changing  the  exogenous  risk 
premium  on  intangible  assets.  In  this  way  it  is  possible  to  evaluate  the  effects  of 
public  loans  and  public  venture  capital.  See  for  instance  Roeger  et  al.  (2008) 
and D’Auria et al. (2009).  

In  QUEST  there  are  several  tax  instruments,  such  as  taxes  on  consumption, 
labour and capital income. The model also includes social security contribution rates, 
differentiating the share borne by firms from that paid by workers. In addition, there 
are  tax  credits  on  tangible  and  intangible  capital  income,  depreciation  allowances 
and unemployment benefits. Through these policies the government can also affect 
innovation activities.  Interventions  alleviating  the  administrative  burden  and 
simplifying  regulation  can  be  simulated  by  reducing  the  overhead  labour 
component in the final good sector. Overhead labour, in fact, includes the fraction of 
time  that  workers  spend  on  administrative  tasks  and  bureaucracy.  Another  policy 
instrument  is  represented  by  the  fixed  entry  cost  faced  by  new  entrants  in  the 
intermediate-goods  sector.  Through  this  channel  the  model  can  simulate  the 
potential effects of changes in entry regulation on R&I activities. 97 

Product  market  reforms  that  enhance  competition  in  goods  markets  and  that 
change  the  patent  policy  can  be  simulated  as  shocks  that  decrease  the  markup  of 
prices  over  marginal  costs.  Public  procurement  related  to  missions  and  sector-
specific industrial policies can be simulated indirectly by changing the aggregate 
policy variables of the model in proportion with size of the sectors involved. Several 
environmental  policy  tools  are  available  in  the  “green”  version  of  QUEST 
described  in  Conte  et  al.  (2011).  In  the  main  version  of  the  model  it  is,  however, 
possible to simulate the macroeconomic effects of an increase of the excise taxes on 
fuel by properly changing the consumption tax rate. 

The  public  financing  problem  is  addressed  in  terms  of  budget  spent,  cost  of 
borrowing, and effects on public debt. General equilibrium effects also allow to 
keep  track  of  crowding-out  and  crowding-in  phenomena,  to  directly  evaluate  the 
effects  of  policies  on  R&I  activities  and  observe  the  complex  interactions  between 
short-run  stabilization  policies  (monetary  policy,  automatic  stabilizers,  fiscal  rules) 
and the long-run performances of the economy. Several simulation scenarios can be 
constructed under different hypotheses regarding monetary and fiscal policies.  

Finally,  the  multi-country  structure  allows  to  differentiate  between  national  and 
supranational  policies,  and  to  account  for  policy  interdependence  and  trade 
barriers.  

97   See for instance the contribution of Benedetti-Fasil et. al (2020). 

65 

 
 
                                                
 
3.1.6. Dimensions of macroeconomic impact of R&I 

By virtue of its general equilibrium structure with the QUEST III model it is possible 
to  evaluate  the  potential  effects  of  innovation  on  GDP,  employment  and 
productivity.  Country-blocks  are  open  to  international  trade  and  technological 
advancements improve their external  competitiveness.  The  differentiation  among 
different categories of workers by  skill  allows to study the  impact of innovation on 
inequalities.  Labour  supply  is  endogenous,  and  households  offer  low-,  medium- 
and  high-skilled  labour  services.  It  is  then  possible  to  study  income  inequalities 
across  different  categories  of  workers  and  to  measure  skill  premia  as  in  Roeger  et 
al. (2019). Creation of new ideas requires the employment of high-skilled workers, 
and more accumulated knowledge increases the productivity of high-skilled workers 
in the adoption sector. A large stock of adopted technologies, in turn, increases the 
productivity of the final good sector, but it does not alter the structural parameters 
of  the  labour  aggregate  (composed  by  low-skilled,  medium-skilled  and  high-skilled 
labour inputs). Finally, in the model there is involuntary unemployment because of 
the market power of trade unions that charge a markup over the reservation wage 
of each category of workers. 

The implications for the quality of the environment and environmental externalities 
are introduced in the “green” version of QUEST described in Conte et al.  (2011). 
Non-economic  effects,  such  as  the  implications  of  innovation  on  health  are  not 
observable. 

3.1.7. Strengths and limitations  

QUEST  is  a  dynamic  general  equilibrium  model  based  on  microfoundations, 
where  the  behaviour  of  agents  is  the  outcome  of  an  intertemporal  optimisation 
problem  given  technological,  institutional  and  budgetary  constraints.  Since  agents’ 
decisions  account  for  expectations  about  the  future,  anticipation  effects  of  current 
and future policies are fully captured by the model. The model has a well-defined 
welfare  function,  which  allows  for  performing  welfare  evaluation  of  policies  in 
addition to the positive analysis of comprehensive reform packages.  

The  model  embodies  several  imperfections  and  frictions  in  goods  and  labour 
markets  that  are  useful  to  match  data.  QUEST  III  can  provide  a  direct  link 
between  concrete  policy  interventions  and  market  outcomes,  also  in  the 
area  of  R&I.  The  explicit  modelling  of  knowledge  creation  and  of  technology 
adoption can be used for  policy analysis. The model,  in  fact, presents a rich set of 
mechanisms  and  parameters  through  which  R&I  policies  can  be  simulated.  The 
differentiation  of  workers  by  skill  allows  to  study  the  effects  of  education  policies 
and their impact on technological progress and growth. QUEST’s general equilibrium 
structure allows to account for different transmission channels of innovation policies 
and  to  evaluate  their  impact  on  the  main  macroeconomic  aggregates.  For  costly 
policies budgetary and financing issues are fully addressed. Due to its multi-country 
setup  there  are  interlinkages  across  countries  via  trade,  financial  flows  and 
knowledge  spillovers,  it  is  possible  to  differentiate  between  supranational  and 
national  innovation  policies  and  there  is  policy  interdependence.  Finally,  by 
integrating New Keynesian elements, QUEST III can be used to assess the short-run 
trade-offs of R&I policies and their interactions with fiscal and monetary policies.  

Despite its sophisticated structure  the  current  version  of QUEST is  not  designed to 
factor in several important aspects related to R&I activities and policies.  

66 

 
The most important limitation of the present version of QUST III is the absence of 
firm heterogeneity. Firms operate under different market conditions, differ in their 
size  and  have  access  to  different  technologies.  The  gains  from  R&I  policies  cannot 
be  evaluated  properly  without  such  distinctions.  Similarly,  firms  may  have  limited 
access to credit, and this might be the case especially for small and new innovative 
firms.  Accounting  for  financial  frictions  would  further  improve  the  ability  of  the 
model to evaluate the margins through which R&I policy may operate.98  

A  second  limitation  of  QUEST  is  that  the  model  does  not  allow  to  differentiate 
between public and private R&I and adoption activities. In the present version these 
activities are carried out only by the private sector. Therefore, it is not possible to 
distinguish the nature of  public research  conducted by public institutions from  that 
conducted  by  private  companies.  In  addition,  complementarity  and  substitutability 
issues between public and private efforts in R&I activities cannot be addressed99.  

A third limitation is related to the fact that the accumulation of human capital is 
not endogenous. The introduction of endogenous skill accumulation may improve 
the  ability  of  the  model  to  simulate  the  effects  of  education  policies  on  innovation 
activities  and  growth.  Moreover,  when  R&D  activities  increase  in  QUEST  there  is  a 
shortage of skilled workers in the final good sector, so restricting the expansionary 
effects of innovation policies.  

fourth 

A 
limitation  concerns  the  absence  of  any  environment-related 
dimensions in the main version of the model. Given its multi-country and dynamic 
general equilibrium structure, QUEST III is well suited to incorporate aspects related 
to climate change and to assess the macroeconomic implications of climate actions 
(e.g. physical and transition risks). 

Finally, there are some other limitations referring to the fact that in QUEST cyclical 
fluctuations  emerge  as  a  result  of  external  shocks  and  waves  of  optimism  and 
pessimism do not emerge endogenously. In QUEST there are no features related to 
randomness in the outcome of the R&I process, however the value of ideas and so 
the  incentives  to  innovate  depend  on  external  shocks  and  on  policy  interventions. 
Imperfect  and  asymmetric  information  features  may  be  introduced  into  QUEST  by 
solving  the  model  under  different  assumptions  regarding  the  way  in  which  agents 
formulate expectations or regarding the set of information their decisions are based 
on. 

98   The modelling framework developed by Benedetti-Fasil et al. (2020) constitutes an effort 

in this direction.   

99   An  extension  of  the  model  in  this  direction  can  be  made  along  the  lines  of  Akcigit  et  al. 

(2020).  

67 

 
 
 
 
                                                
 
3.2. The RHOMOLO model 

3.2.1. Main features  

RHOMOLO  is  a  spatial  computable  general  equilibrium  (SCGE)  model.  A 
detailed description of RHOMOLO V3 is available in Lecca et al. (2018).  

CGE  models  have  a  long  tradition  with  roots  in  the  input-output  models  à  la 
Leontief, but where the interactions among sectors and factor inputs, and so output 
and  demand  depend  on  prices.  Since  Johansen  (1960)’s  Norway  model,  several 
computable  general  equilibrium  models  have  been  developed  as  a  tool  for  policy 
analysis  in  various  domains  that  range  from  fiscal  policies,  trade  liberalization  and 
development and regional market integration to climate change100.  

The structure of RHOMOLO is based on a large system of simultaneous equations 
with  multiple  interacting  agents.  Consistently  with  the  tradition  of  the 
computable  general  equilibrium  (CGE)  class  of  models,  RHOMOLO  is  based  in  a 
framework à la Arrow-Debreu where agents’ interactions (along supply and demand) 
are mediated by markets and prices. However, as most CGE models, RHOMOLO has 
some  features  that  do  not  strictly  adhere  to  the  theoretical  general  equilibrium 
paradigm.  The  model,  in  fact,  allows  for  imperfectly  competitive  product 
markets and labour market imperfections resulting in unemployment.  

Individual  behaviour  is  based  on  (static)  optimisation,  given  technological, 
institutional  and  budgetary  constraints.  Expectations  are  assumed  to  be  backward 
looking, although it is possible to switch towards a forward-looking approach.  

Policy  interventions,  reforms  and  other  changes  in  external  factors  are 
introduced  as  shocks  to  the  model  that  is  solved  sequentially  period  after  period 
with stock variables being upgraded at the beginning of each period according to a 
recursive-dynamic modelling strategy. 

RHOMOLO  has  a  regional  structure  covering  268  block-economies  of  which  267 
correspond  to  the  EU  NUTS2  regions  and  1  region  corresponds  to  the  rest  of  the 
world.  In  each  region  the  business  activity  is  disaggregated  into  10  NACE  rev.  2 
economic sectors (also called industries). A subset of these sectors is characterised 
by  a  monopolistically-competitive  market  structure  à  la  Dixit-Stiglitz  in  which 
identical firms produce horizontally differentiated varieties. In each of these sectors 
there is free entry, so the number of firms is endogenous and is pinned down by a 
zero-profit equilibrium condition (i.e. break-even condition).101 The other sectors are 
instead  characterized  by  perfect  competition.  All  sectors  are  interlinked  through 
input-output  relations.  In each sector  firms use capital, labour and intermediate 
inputs from other sectors.  

100  See  e.g.  Shoven  and  Whalley  (1984)  Robinson  and  Thierfelder  (2002)  and  Bouët  (2008) 
for  surveys  on  CGE  models  and  Eliott  et  al.  (2010)  for  an  example  of  CGE  models 
integrating environmental features. 

101  The 

imperfectly  competitive  sectors  of  RHOMOLO  are 

'Mining  and  Utilities', 
'Manufacturing', 
and 
'Information 
Communication',  ‘Financial,  Insurance  and  Real  Estate  Activities'  and  'Professional, 
Scientific and Technical Activities’.  

Transportation', 

'Construction' 

'Retail 

and 

68 

 
                                                
In  the  baseline  version  of  the  model  markups  depend  on  the  elasticity  of 
substitution  between  varieties  of  the  same  good.  However,  alternative  markup 
definitions of the oligopolistic type can be  assumed (such as  Bertrand and Cournot 
solutions).  In  such  a  way  markups  become  endogenous  and  depend  on  the  firm 
market share.  

A region may sell goods and services to all the other regions at prices that depend 
on the local market shares. Each region is populated by a representative household 
consuming  all  varieties  of  final  goods  available  in  the  economy.  Households  derive 
their income from  labour (in the form  of  wages net of tax payments), government 
transfers  and  the  ownership  of  physical  capital  (in  the  form  of  rents  net  of  taxes) 
and  of  other  financial  assets.  In  each  region  a  fraction  of  income  is  saved.  To 
account  for  capital  flows  across  regions,  RHOMOLO  separates  investment  from 
saving decisions. Current investments depend on the distance between the desired 
stock of capital and its current available stock consistently with an accelerator model 
à la Jorgenson and Stephenson (1969). 

Labour services are differentiated by skill levels in low, medium and high. The 
government  collects  taxes  from  labour  and  capital  income  and  from  production 
activities. Fiscal revenues are used to finance transfers to the various agents in the 
economy and current spending on goods, services and capital.  

RHOMOLO  explicitly  models  spatial  linkages  among  regions.  Regions  are 
connected  through  trade  flows  of  goods  and  services,  labour  and  capital  mobility. 
Spatial  frictions,  such  as  transportation  and  migration  costs,  limit  and  make  costly 
trade  and  factor  mobility.  Transport  costs  are  specific  to  trading  partners  and  to 
sectors.  RHOMOLO  includes  backward  and  forward  linkages  between  firms  and 
workers/consumers.  Regional  goods  are  produced  by  combing  labour  and  capital 
with  bundles  of  domestic  and  imported  intermediate  goods.  This  technology  gives 
rise to vertical linkages between firms. As a result of these features, the model 
can account for the existence of agglomeration and dispersion forces, and for the 
emergence  of  complex  core-periphery  patterns  in  the  distribution  of  economic 
activity across regions.  

3.2.2. Innovation and creation of new technological knowledge  

In RHOMOLO V3 R&I activities occur in a separate sector, which is a broad 
sector “Professional, Scientific and Technical Activities, Administrative and Support 
Service  Activities”  (NACE  REV  2  M-N  sectors)  characterized  by  imperfect 
competition.  In  this  version  of  the  model  there  is  not  any  specific  mechanism  by 
which new knowledge is  created (such as e.g. an endogenous growth mechanism). 
Yet,  in  the  policy  simulation  scenarios  innovation  activities  can  affect  the 
economy  through  two  main  channels: the productivity  channel giving rise to  a 
permanent  structural  effect  and  the  demand  channel,  giving  rise  to  a  temporary 
effect. 

Innovation has positive effects on total factor productivity (productivity channel). 
The  idea  behind  this  mechanism  is  that  R&I  investments  give  rise  to  knowledge 
accumulation that, in turn, positively affects productivity of the sectors and regions 
involved.  Innovation  is  translated  into  the  model  as  an  increase  in  total  factor 
productivity  of  the  sectors  involved  using  existing  estimates  on  elasticities  that,  in 
turn,  depend  on  the  R&D  intensity  of  the  regions  and  on  R&D  value  added. 
Elasticities are region- and sector-specific. A higher R&D intensity of a region is 

69 

 
associated  with  a  higher  elasticity  of  total  factor  productivity  to  R&D 
efforts.  The  rationale  for  this  assumption  that  firms  located  in  regions  already 
characterized  by  high  R&D  spending  are  more  prone  to  generate  value  from 
innovation activities. 102 This feature is meant to capture the existence of knowledge 
spillovers. The R&D spillover elasticities are taken from the estimates by Kancs and 
Siliverstovs (2016).  

Through  the  productivity  channel  RHOMOLO  can  simulate  indirectly  a  vast 
range of policies in support of R&D activities and the effects of different types of 
R&I  (e.g.  general-purpose  technologies).  The  regional  and  sectoral  disaggregation 
of  the  model  allows  to  map  the  effects  of  innovation  activities  and  policies  on 
specific  sectors  and  areas,  and  to  evaluate  the  reallocation  of  factor  inputs  across 
sectors  and  regions  that  may  follow.  Increases  in  total  factor  productivity  reduce 
production  costs  and  make  firms  more  competitive,  so  expanding  production 
capacity and trade, and attracting more labour and capital. However, in the absence 
of  an  explicit  modelling  of  a  sector  where  new  ideas  are  created  RHOMOLO  can 
hardly  capture  other  aspects  of  the  R&I  process.  Aspects  related  to  path 
dependency  are  not  modelled.  No  distinction  can  be  made  between  product  and 
process innovation, and between basic and applied research. TFP is a combination of 
both, and the same aggregate technology may emerge from a model of product or 
process innovation. 

The  demand  channel  refers  to  direct  and  temporary  effects  that  innovation 
activities  may  have  on  aggregate  demand.  R&I  spending  is  introduced  as  private 
investments, so that a higher expenditure gives rise to a higher  demand for capital 
goods In particular, the rise of public spending in support of innovation induces an 
increase  in  private  R&I  investments,  diverting  resources  from  public  investments 
and reducing the public demand for capital goods. As a result of this policy there will 
be a decline of public procurement and an increase in private R&I investments. The 
net effect on aggregate demand and on the level of economic activity deriving from 
this channel will depend on the input composition and on the import penetration of 
the sectors involved. 

In  the  current  version  of  RHOMOLO  in  each  sector  and  region  firms  share  the 
same  technology  and  have  the  same  size.  In  imperfectly  competitive  sectors 
firms  produce  different  varieties  of  the  same  good  according  to  the  Dixit-Stiglitz 
logic  of  horizontal  differentiation.  However,  the  economic  activity  of  each  region-
block is disaggregated into 10 NACE rev. 2 economic sectors a subset of which are 
treated  as  imperfectly  competitive  sectors.  The  calibration  of  the  structural 
parameters reflects region and sector heterogeneity. 

3.2.3. Diffusion of new technologies  

In RHOMOLO V3 the spatial diffusion of innovation and the adoption lags inherent 
to  the  diffusion  of  new  technologies  and  ideas  is  simulated  indirectly  by 
constructing  specific  simulation  scenarios  using  the  productivity  channel  and 
exploiting the regional and sectoral disaggregation of the model.  

102  This characteristic of RHOMOLO is consistent with the logic of endogenous growth models 

without scale effects. 

70 

 
                                                
Technology  transfers,  possibilities  or  exchanging  patents  and  knowhow,  imitation 
phenomena and  spatial knowledge spillovers (in  neighbouring  regions for instance) 
can be mapped through exogenous changes in the total factor productivity involving 
specific regions and sectors. The granularity of the model confers extreme flexibility 
in the design of these policy experiments. Regions are interlinked via trade, capital 
and  labour  flows,  therefore  R&I  policies,  fiscal  policy  interventions  and  structural 
reforms occurring in one region-block affect all the other regions via these channels 
(policy interdependence). 

International  technology  spillovers  and  international  diffusion  are  not  explicitly 
modelled in the current version of RHOMOLO.  

3.2.4. Enablers for R&I creation, adoption and diffusion 

In  RHOMOLO  V3  R&I  the  effects  of  the  incentives  to  the  creation  and  adoption  of 
new  ideas  can  be  simulated  by  constructing  specific  simulation  scenarios  based  on 
existing estimates of the relevant elasticities relating to the intensity of competition, 
product  market  regulation,  trade  barriers  and  market  size  to  R&I  spending  and  to 
total factor productivity.  

There are barriers to trade in the form of bilateral transportation costs of the iceberg 
type103.  A  subset  of  sectors  is  characterized  by  imperfect  competition  in  which 
alternative  markup  definitions  can  be  used  (according  to  Bertrand  and  Cournot 
pricing  assumptions  or  monopolistic  competition).  These  features  allow  the 
construction of comprehensive reform scenarios in which the production capacity of 
regions  and  their  size  may  be  affected  directly  and  indirectly  through  the  dynamic 
effects that reforms may have on R&I.  

In  general,  the  granularity  of  the  model  allows  for  the  simulation  of  specific  pro-
competitive policies and of changes in the entry conditions involving specific sectors 
and  regions.  In  addition,  the  available  pricing  options  permit  the  analysis  of  the 
effects  of  firm  entry  and  of  market  size  on  markups.  The  strength  of  intellectual 
property rights may reflect on the size of the markups. 

RHOMOLO  considers  three  different  labour  inputs,  low,  medium  and  high, 
corresponding  to  different  skill  or education  level. Labour supply is fixed and 
human  capital  formation  is  not  endogenous;104  however,  the  human  capital  of  a 
region can change through migration of workers from other regions (e.g. Brandsma 
et  al.  2014).  In  addition,  the  skill  level  of  the  labour  force  can  be  exogenously 
increased through education policies. The model can simulate interventions aimed at 
increasing  the  quality  of  education  and  account  for  the  additional  costs  that  these 
measures  may  entail  for  the  public  sector.  The  changes  in  the  skill  composition  of 
the workforce is anchored to the number of school years as done in Varga and in’t 

103  See Persyn et al. (2019) for details on the estimation of the iceberg-cost matrix. 

104  Only recently a new labour market setting has been introduced in which the labour supply 
can  change  as  a  result  of  individual  participation  decisions,  both  at  the  intensive  and 
extensive margins. See Christensen and Persyn (2020). 

71 

 
                                                
 
Veld (2011) for the QUEST model. Human capital projects increase the productivity 
of labour differentiated by skill level and so the returns on investment. Through this 
channel  it  is  therefore  possible  to  increase  the  production  capacity  of  specific 
regions.  

The  financing  problem  of  the  R&D  activity  is  not  directly  addressed.  However, 
RHOMOLO  models  explicitly  a  wide  imperfectly  competitive  sector  including 
financial services, along with insurance and real estate activities (NACE REV 2 K-L 
sectors). These services are used by firms of the other sectors as production inputs. 
RHOMOLO  can  thus  simulate  policies  that  facilitate  the  access  to  credit  and/or 
promote venture capital. Given the input-output structure of the economy any policy 
that expands the financial sector and renders easier the access to financial services 
may  have  expansionary  effects  on  all  the  other  sectors  of  the  economy.  It  is  also 
possible  to  change  the  user  cost  of  capital  through  an  exogenous  risk  premium 
and  mimic  the  macroeconomic  effects  of  financial  frictions,  such  as  collateral 
constraints, asymmetric information and underdeveloped venture capital markets. 

3.2.5. R&I policy  

In RHOMOLO policy shocks in support of R&I expenditure (subsidies, tax incentives 
or direct pubic spending on R&I) are introduced as a reduction in the user cost of 
capital  which  in  turn  gives  rise  to  an  increase  in  R&I  investments  in  the  wide 
macro-sector in which innovation activities take place. The increase in investments 
generates  the  demand  channel.  The  impact  of  R&I  expenditure  on  total  factor 
productivity  is  captured  by  a  set  of  regional  spillover  elasticities.  This  is  the 
productivity  channel.  By  changing  the  user  cost  of  capital,  it  is  also  possible  to 
simulate public funding interventions such as loans and public venture capital. For a 
recent  ex  ante  assessment  of  the  Horizon  Europe  2021-2027  programme,  see 
Christensen (2018) and Christensen et al. (2019).  

The  sectoral  and  the  spatial  disaggregation  allows  for  the  simulation  of 
comprehensive  industrial  policy  packages  targeting  specific  sectors  and  geographic 
areas.  The  geographic  granularity  of  the  model  allows  for  the  simulation  of  policy 
interventions  at  different  levels  (EU  funding,  national  and  regional.  Christensen 
(2018) uses RHOMOLO to assess ex ante the potential economic impact of Horizon 
Europe  for  the  programming  period  2021-2027  under  three  different  scenarios,  (i) 
continuation, where the new programme is designed along the lines of the previous 
one, (ii) centralisation, in which a major share of funds is centralised at the EU level, 
(iii) decentralisation, in which the programme is decentralised at national level.  

The  government  consumes  goods  and  services  produced  by  all  sectors  of  the 
economy.  This  implies  that  RHOMOLO  can  simulate  the  effects  of  changes  in  the 
composition  of  public  spending.  Moreover,  the  productivity  of  firms  depends 
positively  on  the  stock  of  public  capital  that  is  accumulated  by  the  government. 
Public capital is not modelled as a pure public good, but is characterized by a certain 
degree of congestion, therefore an increase in production reduces the benefits of the 
existing public capital stock.   

Through the public capital channel RHOMOLO can simulate the effects of several 
public interventions, such as those related to transport, energy, telecommunication 

72 

 
and  ICT  infrastructure.105  Higher  government  procurement,  in  turn,  implies 
transitory positive demand-side effects during the period of implementation, further 
stimulating the level of economic activity. In addition, with the transport policy tool 
it is possible to study transport infrastructure investments in specific regions,  as in 
Persyn et al. (2019).  

Several  sectors  are  characterized  by  the  existence  of  fixed  costs  and  by  imperfect 
competition.  Through  these  channels  RHOMOLO  can  simulate  product  market 
reforms,  such  as  those  falling  in  the  domain  of  regulation,  administrative  burden, 
patents and competition. Policy initiatives encouraging the entry of innovative firms 
can be simulated by fixed entry cost reductions.  

Thanks  to  its  spatial  structure  RHOMOLO  can  account  for  the  effects  of  policy 
initiatives  facilitating  the  completion  of  the  EU  market  promoting  capital  and 
labour  mobility. Mobility improves the allocation of factor inputs across European 
countries and favours the matching between skill demand and skill supply.106  

The general equilibrium structure allows to address the public financing problem 
of  any  costly  policy  interventions  and  to  account  for  potential  crowding-out  and 
crowding-in  phenomena.  This  is  particularly  important  for  comparing  alternative 
innovation  policy  options  and  computing  the  fiscal  multipliers  of  public  spending. 
Costly  policies  interventions,  such  as  subsidies  incentivising  R&I  activities,  require 
additional  revenues  that  must  be  collected  through  higher  taxes  (or  by  spending 
cuts  on  other  items).  Higher  taxes,  in  turn,  may  affect  consumption,  investment, 
employment and production, resizing the effects of the initial policy interventions.  

In  its  present  version  RHOMOLO  does  not  distinguish  between  clean  and  dirty 
technologies and does not embody climate action instruments or Pigouvian taxes or 
subsidies.  However,  the  model  presents  a  broad  sector  including  electricity,  gas, 
steam and air conditioning supply, water supply, sewerage, waste management and 
remediation  activities  (NACE  REV  2  B,  D,  E  sectors).  Any  direct  or  indirect  policy 
interventions falling in the domain of environmental sustainability can be translated 
into  the  model  through  this  sector.  For  a  simulation  analysis  of  policies  promoting 
the shift toward clean energy in RHOMOLO, see Di Comite et al. (2018). 107  

105  For  an  example  of  simulation  of  modernization  of  the  existent  transport  network,  see  Di 

Comite et al. (2019).  

106  See  e.g.  Ciaian  and  Kancs  (2015)  for  a  simulation  of  the  European  Qualifications 
Framework initiative aiming at improving the recognition of skills and qualifications across 
EU countries. 

107  The  input-output  and  regional  data  of  RHOMOLO-MRIO  can  be  used  as  a  separate 
laboratory for policy analysis based on a Leontief model. In this respect, for a study of the 
impact of coal phase-out plans, see Mandras et al. (2019).  

73 

 
 
 
                                                
 
3.2.6. Dimensions of macroeconomic impact of R&I 

The  effects  of  R&I  policies  are  assessed  through  the  effects  that  higher  R&I 
expenditure  have  on  demand  and  productivity.  With  RHOMOLO  it  is  possible  to 
assess the potential effects of R&I on GDP and employment for each region-block. 
Technological  progress  is  modelled  as  an  increase  in  total  factor  productivity 
using  existing  estimates  on  elasticities.  Therefore,  changes  in  the  innovation  effort 
affect  productivity  by  construction.  Increases  in  total  factor  productivity  lower  unit 
costs  and  make  firms  more  competitive.  The  productivity  improvement,  in  turn, 
expands trade and production capacity of a region, and increases the remuneration 
of factor inputs, so attracting more capital and labour from other regions.  

Labour  dynamics  depend  on  a  wage  setting  mechanism  according  to  which  the 
bargaining  power  of  workers  depends  on  market  conditions.  A  higher  level  of 
unemployment  reduces  the  market  power  of  wage  setters.  The  representation  of 
the labour markets is such that involuntary  unemployment may emerge. However, 
the  modelling  of  the  labour  markets  is  flexible  and  allows  switching  from  a  wage 
setting  mechanism  to  a  Phillips  curve  or  to  a  competitive  equilibrium  with  no 
involuntary  unemployment.  With  the  differentiation  of  workers  by  skill  RHOMOLO 
allows  studying  the  impact  of  technological  progress  and  of  innovation  policies  on 
inequalities.  The  effects  of  skill  biased  technical  changes  can  be  mapped  by 
changing  exogenously  the  skill  composition  of  the  labour  demand  in  response  to 
technological progress consistently with data. For a recent study with RHOMOLO on 
the  regional  socio-economic  impact  of  the  European  R&I  programme,  see 
Christensen (2018). 

The  current  version  of  the  model  does  not  account  for  environmental  externalities 
and climate change effects. However, as discussed in Section 3.2.5, RHOMOLO can 
be used to simulate the economic impact of energy and environmental policies. 
Thanks  to  its  granularity  and  flexibility  RHOMOLO  can  be  integrated  with  other 
models  and  be  used  in  different  areas  related  to  sustainability.108  Non-economic 
dimensions such as health effects of R&I are not considered.  

3.2.7. Strengths and weaknesses  

RHOMOLO  is  a  spatial  CGE  model  designed  for  the  EU  regions.  All  behavioural 
equations  reflect  the  (static)  optimising  behaviour  of  agents  that  formulate  their 
decisions given the budgetary, the technological and the institutional constraints of 
the  economy.  The  general  equilibrium  structure  of  the  model  allows  to  fully 
capture  direct  and  indirect  effects  of  alternative  R&I  policy  options  on  the  main 
macroeconomic variables of each region-block. The economic activity of each region 
is  disaggregated  into  sectors.  All  sectors  are  interlinked  through  input-output 
relations.  With  these  features  it  is  possible  to  simulate  R&I  policies  that  target 
specific sectors and assess the spillover effects on the rest of the economy.  

The  economy  is  composed  by  268  blocks  representing  267  EU  NUTS2  regions  and 
the rest of the world. With this geographic disaggregation the model can capture 

108  In  this  respect,  see  the  discussion  in  Lavalle  et  al.  (2017)  which  explores  the  potential 

integration of RHOMOLO with the territorial modelling platform LUISA. 

74 

 
                                                
country  and  region-specific  aspects  of  the  economies  and  to  simulate  policy 
interventions and exogenous shocks involving targeted areas.  

Regions are interlinked through trade, labour and capital mobility. Thanks to 
this spatial dimension RHOMOLO can model geographical spillovers, migration flows 
and  the  emergence  of  core-periphery  patterns  and  of  cross-region  inequalities. 
Moreover,  due  to  its  detailed  spatial  dimension  the  model  can  effectively  address 
questions  related  to  different  levels  of  policy  interventions  (EU,  national  and 
regional) and to the geographic concentration of innovation policies.  

Another important strength of RHOMOLO is its flexibility. The model has a rich set 
of  channels  and  parameters  through  which  it  is  possible  to  design  complex 
scenarios and simulate innovation policy interventions. However, RHOMOLO is 
a  deterministic  model  able  to  provide  only  point  estimates  of  the  effects  of 
exogenous  shocks  to  the  economy.  Therefore,  risk  and  uncertainty  cannot  be 
modelled explicitly. However,  ad hoc sensitivity analyses are usually carried out 
to  account  for  uncertainty  related  to  the  size  of  the  parameters,  and  different 
policy scenarios are constructed to account for policy uncertainty.  

Agents take their decisions by solving static optimisation problem. The wage setting 
process is based on a backward-looking mechanism, so that wage decisions depend 
on  the  lagged  wage  value.  Labour  migration  decisions  are  modelled  in  a  discrete-
choice  framework  and  are  also  based  on  backward-looking  expectations.  Waves  of 
optimism or pessimism of agents cannot be simulated. 

The current version of RHOMOLO does not model R&I activities as a separate sector 
of  the  economy,  where  innovations  are  created.  The  effects  of  R&I  policies  are 
assessed  through  the  effects  that  higher  R&I  expenditure  have  on 
productivity and demand. Assessing the impacts of R&I in RHOMOLO consists of 
three main steps. The first step regards the quantification of the policy changes that 
are  usually  related  to  the  amount  of  resources  at  disposal  for  each  initiative.  The 
second  step  consists  of  the  econometric  estimation  of  the  elasticities  that  relate 
innovation  policies  to  productivity  of  specific  regions  and  sectors  targeted  by  the 
interventions.  Elasticities  may  also  be  taken  from  previous  literature.  In  the  third 
step  the  estimated  policy  impact  on  productivity  is  used  as  input  in  RHOMOLO  to 
construct  the  simulation  scenario  and  to  evaluate  the  effects  on  regional 
macroeconomic  variables  such  as  GDP,  employment,  consumption,  investments, 
inequalities, trade etc…  

In some simulations,  the direct effects of  policies, such  as those  favouring venture 
capital or facilitating access to credit or consisting of public funding of private R&D, 
are modelled as reductions of the risk premia on capital. As QUEST III, the current 
version  of  RHOMOLO  does  not  feature  endogenous  financial  frictions.  In  other 
simulations, such as those concerning the quality of education, training and human 
capital, the direct effects of  policy measures are  introduced as exogenous changes 
in  the  skill  composition  of  the  labour  force  of  the  regions  involved.  Other 
interventions,  such  as  those  encouraging  the  entry  of  innovative  firms,  may  be 
mapped through changes in the fixed cost. Also, in these cases the simulation of the 
policy  requires  a  preventive  empirical  assessment  of  the  link  between  the  policy 
intervention and the model variables to be used in the simulation.  

This  simulation  strategy  confers  extreme  flexibility  in  the  design  of  the  policy 
scenarios and of reforms in the  R&D  system  but requires data that are  not always 

75 

 
available  at  regional  level.  Therefore,  the  lack  of  data  availability  for  specific 
indicators  and  variables  at  regional  level  may  represent  an  impediment  for  the 
effective  design  of  some  simulation  experiments  and  for  the  translation  of  policies 
into model variables.  

The introduction  of an endogenous growth  mechanism and of  an explicit modelling 
of technology diffusion and adoption in RHOMOLO would also require data necessary 
for  the  estimation  of  the  deep  structural  parameters  at  regional  level.  These  data 
are not always available at regional level.  

Other limitations of RHOMOLO can be addressed in future developments to improve 
its ability to simulate R&I policies. For instance,  the structure of RHOMOLO is  well 
suited  to  incorporate  aspects  related  to  the  environment  and  resource 
efficiency,  such  as  those  falling  in  the  domain  of  clean  v.  dirty  energy,  resource 
efficiency,  renewables,  waste  prevention  and  management,  eco-tourism,  eco-
innovation  etc…  Extensions  of  the  model  in  this  direction  and/or  integrations  with 
other models could open up to the possibility of simulating more effectively several 
policy  measures  promoting  green  growth,  energy  efficiency  and  the  transition 
towards a circular economy.  

Finally,  the  skill  composition  of  the  labour  force  could  in  principle  be  made 
endogenous  to  improve  the  ability  of  the  model  to  simulate  the  effects  of 
education policies and technological progress.  

3.3. The NEMESIS model 

3.3.1. Main features  

NEMESIS  is  a  large-scale,  partly  estimated,  partly  calibrated,  econometric 
model of the neo-Keynesian tradition with demand predominant in the short run 
and  supply  pulled  by  technological  change  predominant  in  the  long  run.  NEMESIS 
distinguishes  27  European  countries,  30  sectors  covering  the  whole  economy,  and 
27  commodities.  The  rest  of  the  world  areas  are  represented  in  less  detail.  For  a 
detailed  explanation  of  the  NEMESIS  model,  see  Boitier  et  al.  (2015),  SEURECO 
(2018) and Le Mouël (2019). 

There  are  four  main  categories  of  agents  in  the  model:  firms  (financial  and  non-
financial corporations), households, general government and the rest of the world.  

The main  behavioural  equations  (consumption  by  households, factor demand  by 
firms,  wage  and  price  formation,  imports  and  exports)  are  subject  to  reaction 
delays,  reflecting  the  existence  of  adjustment  costs  and  of  adaptive  expectations. 
Therefore,  the  economy  takes  some  time  to  reach  its  long-run  equilibrium  where 
factors of production and consumption goods are at their desired level, while in the 
short run, exogenous shocks lead to cyclical price and quantity variations.  

On  the  supply  side  in  each  sector  firms  use  variable  and  quasi-fixed  production 
inputs  that  are  combined  in  a  multilevel  nested  constant  elasticity  of  substitution 
(CES) production function. The variable inputs are energy, materials, high skilled 
labour  and  low  skilled  labour,  while  the  quasi-fixed  inputs  are  physical  capital  and 
innovation  services  (i.e.  state  variables  that  in  the  current  period  depend  on  past 

76 

 
investment decisions of the firm).109 Innovation services enter the first level of  the 
nested  CES  production  function  and  affect  directly  the  productivity  of  “ordinary” 
factor inputs (labour, physical capital, energy and material). The typical firm of each 
sector  is  a  short-run  cost  minimizer  with  respect  to  the  variable  inputs  (energy, 
intermediate  inputs  and  the  two  types  of  labour)  and  an  intertemporal  value 
optimizer with respect to quasi-fixed inputs (capital stock and innovation services). 
The  quasi-fixed  inputs  follow  an  error-correction  model  to  reflect  the  existence  of 
adjustment  costs  and  to  reproduce  the  gradual  adjustment  towards  the  long-run 
equilibrium. 

Technical  change  is  endogenized  via  investments  in  three  categories  of 
intangibles:  R&D,  ICT  and  other  intangibles  (OI)  (including  training  and  software). 
Both  process  and  product  innovations  are  jointly  determined  by  the  same 
investment  effort.  Services  produced  by  the  stock  of  knowledge  intervene  at  the 
first  level  of  the  nested  CES  production  function  and  reduce  the  cost  of  variable 
inputs (where the impact of innovations on production is Hicks-neutral). In addition, 
innovation  services  increase  the  quality  of  the  goods  produced.  Innovations  can 
then  simultaneously  lower  the  average  cost  of  production  and  shift  out  the 
consumer’s demand curve.  

The  sectors  of  the  model  are  interlinked  through  two  main  channels:  (i)  the 
exchange of intermediate goods and services, that  is also related to demand flows 
of  energy,  investment  goods  and  materials;  and  (ii)  spillovers  that  can  occur 
through  knowledge  spillovers  and  through  the  diffusion  of  enabling  technologies, 
such as ICT, that enlarge the technological opportunities of all sectors. 

The price setting reflects a monopolistic competitive market structure with constant 
price markups specific to each sector. 

Labour  demand  is  determined  by  (static)  firms’  optimising  behaviour,  while  wage 
dynamics  evolve  consistently  with  a  wage  bargaining  process  following  an 
augmented  wage  Phillips  curve.110  As  a  result,  real  wages  change  in  response  to 
productivity shocks, and to changes in the unemployment rate evolution and in the 
wages  outside  the  sector.  The  excess  of  labour  demand  over  supply  can  create 
inflationary  pressures  through  the  augmented  price  Phillips  curve  channel.  The 
structure  of  the  labour  force,  in  turn,  depends  the  education  level  and  the 
participation  rate  by  qualification.  Labour  is  mobile between  sectors,  but  there  are 
adjustment delays.  

On  the  demand  side  aggregate  consumption  depends  mainly  on  disposable 
income, wealth and on the demographic structure of the economy. Consumption is 
also  a  function  of  interest  rates,  price  dynamics  and  unemployment  rate.  This  last 
variable is taken as a proxy for uncertainty. Disposable income comes from wages, 
revenues  from  capital  as  well  as  transfers  less  taxes.  Savings  are  given  by  the 
difference between real disposable income and consumption.  

109  At  each  level  of  the  nested CES  production function  there are compound  inputs resulting 

from the use of variable and quasi-fixed inputs.  

110  The wage Phillips curve has coefficients that are country and labour-type specific, but not 

sector-specific. 

77 

 
                                                
Aggregate  consumption  is  disaggregated  into  27  sub-consumption  components, 
which  are  grouped  in  durable  and  non-durable  goods.  The  allocation  of  aggregate 
consumption  in  these  components  depends  on  price  elasticities,  degree  of 
substitution,  relative  prices,  total  income  and  demographic  changes,  following  an 
adaptation of the Almost Ideal Demand System proposed by Deaton and Muelbauer 
(1980).  

Government  spending  is  split  into  education,  health,  defence,  infrastructures, 
investments and  other items. The model features a rich set of taxes and subsidies 
such  as  taxes  on  production  and  imports,  taxes  on  income  and  wealth,  taxes  on 
capital  income,  social  security  contributions,  subsidies  on  production,  investment 
grants, excises duties and other energy tax rates. 

International  trade  occurs  within  Europe  and  with  the  rest  of  the  world.  Exports 
and imports depend on foreign relative to domestic prices and income. The stock of 
innovations is also included as a determinant of trade flows in order to capture the 
role of innovation on international competitiveness.111   

3.3.2. Innovation and creation of new technological knowledge  

In  NEMESIS  the  modelling  of  R&I  is  to  a  certain  extent  inspired  by  the 
endogenous growth theories. Innovation takes place in each country/sector and 
stems  from  investments  in  three  types  of  innovation  components:  R&D,  ICT 
and  other  intangibles  (OI).  This  three-dimensional  innovation  process  allows  to 
account  for  a  large  variety  of  innovative  activities  and  the  related  knowledge 
spillovers.  While  R&D  activities  mainly  pertain  to  industrial  sectors,  the  explicit 
modelling  of  intangible  innovations  allows  to  endogenize  innovation  in  the  services 
industry too. 

When  modelling  R&I,  NEMESIS  uses  as  unit  of  analysis  a  country-sector  pair. 
Intangibles  for  each  country/sector  are  built  in  four  stages.  First,  in  a 
particular country/sector, for each of the three technological components (R&D, ICT 
and  OI),  a  stock  of  intangible  capital  cumulates  past  investments  conveniently 
depreciated  at  an  exogenous  rate.  E.g.,  there  is  a  stock  of  intangible  R&D  (ICT  or 
OI) that cumulates past R&D (ICT or OI) investments in the country/sector. Second, 
the  stock  of  technological  knowledge  for  each  of  the  three  components,  in  each 
country/sector, builds on the stock of intangibles of the rest of countries/industries, 
benefiting  from  knowledge  spillovers  from  the  stock  of  intangibles  of  all  other 
countries  and  industries.  E.g.,  the  stock  of  R&D  knowledge  in  a  particular 
country/sector  depends  on  the  stock  of  intangible  R&D  of  this  country/sector  and 
benefits from  spillovers from  the  stock of intangible  R&D  of all other  countries and 
sectors. Specifically, stocks of intangibles from other sectors and other countries are 
aggregated  by  a  weighting  scheme  that  depends  on  patent  citations.112  These 
sectoral  interlinkages  reflect  the  existence  of  knowledge  spillovers  and  of 
strategic  complementarities  between  innovation  services.  Third,  for  each  of  the 
three components (R&D, ICT and OI), the corresponding stock of knowledge is used 

111  For further details, see Boitier et al. (2015) and documentation of the SIMPATIC project. 

112  The OECD concordance table is used to allocate citations across sectors and countries. See 

Johnson (2002).  

78 

 
                                                
to  produce  country/sector  specific  technological  services.  At  this  stage,  knowledge 
externalities can be transformed into marketable innovations provided that there is 
a sufficient degree of absorption capacity. The absorption capacity is modelled as 
a linear function of the innovation investment intensity (relative to production). This 
feature allows to capture the idea of an increasing complexity of knowledge creation 
process and removes scale effects from the model. For each innovation component 
these  knowledge  functions  are  also  country/sector  specific.  Finally,  for  each 
country/sector,  R&D,  ICT  and  OI  services  aggregate  in  country/sector  specific 
technological services. 

two  channels.  The 

first  channel  captures  process 

Technological  services  affect  as  a  by-product  the  economic  performance  of  firms 
through 
innovation. 
Technological services enter the production function and increase the productivity 
of  other  production  factors,  resulting  in  a  Hicks-neutral  productivity  improvement 
(TFP  effect).  The  second  channel  reflects  product  innovation.  In  this  case, 
innovation  services  increase  the  quality  of  products  and  shift  out  demand.  In 
each  sector  the  demand  function  for  products  reflects  a  taste  for  quality  (quality 
effect).  

While  the  model  features  sector  heterogeneity,  firms  in  each  sector  are 
homogenous. No distinction is made between incumbents and entrants or  between 
large  and  small  firms.  Since  in  each  sector  firms  invest  in  innovation  there  is 
neither  entry  of  innovative  firms,  nor  exit  of  obsolete  products.  As  a  result  of 
obsolescence  of  technology,  if  the  increase  in  research  effort  is  not  maintained  in 
the long run the positive effects of innovations will fade away progressively. 

Public  investments  in  R&D  are  exogenously  determined  and  their  externalities 
are  allocated  to  different  sectors  according  to  a  grand-fathering  distribution  in 
proportion  to  the  share  of  each  sector  in  the  overall  business  R&D  expenditure. 
Public R&D enters the knowledge stock, but not the absorption capacity. Therefore, 
the  impact  that  private  and  public  R&D  activities  have  on  the  production  of 
innovations is asymmetric. In addition, public R&D takes a longer time than private 
R&D to enter the knowledge capital stock. This feature may allow to capture the fact 
that public investments in R&D may be more oriented to basic research.  

3.3.3. Diffusion of new technologies  

In  NEMESIS,  the  diffusion  process  has  a  serendipitous  nature,  even  though  the 
propagation is mediated by the absorption capacity.  

Diffusion of technologies is represented through interlinkages across all sectors and 
countries  described  in  the  previous  section.  For  the  three  innovation  components 
(R&D, ICT and OI), the knowledge variable is modelled as the sum of the intangible 
capital  stocks  in  all  countries  and  sectors,  weighted  by  coefficients  of  diffusion 
reflecting  the  relative  propensity  of  innovations  to  propagate  and  be  adopted 
elsewhere.  

The  diffusion  coefficients  are  calibrated  using  patent  citations  between 
countries  and  sectors.  A  system  of  matrices,  based  on  the  OECD  concordance 
table  (see  Johnson,  2002),  allocates  patent  citations  by  country  and  technology 

79 

 
class.113  In  particular,  the  construction  of  R&D  spillovers  uses  the  concordance 
between  patent  classes  and  the  sectors  that  produce  the  technology  covered  by 
patents  (Industry  of  Manufacture)  to  weigh  the  patent  citations.  The  other  two 
technology indicators (ICT and OI) use the concordance between patent classes and 
the  sectors  that  use  technology  (Sector  of  Use)  to  weigh  patent  citations.  The 
spillovers  related  to  R&D  reflect  pure  knowledge  externalities  between  sectors  as 
Industry  of  Manufacture.  The  spillovers  related  to  ICT  and  OI  refer  to  network 
externalities  and  the  diffusion  of  organizational  innovations.  Innovations  propagate 
across countries through the same channels.  

The model does not differentiate between adoption and adaptation of technologies, 
while  it  can  account  indirectly  for  the  diffusion  of  new  paradigms  or  of  general-
purpose technologies.  

3.3.4. Enablers for R&I creation, adoption and diffusion  

There is an optimal level of investments in R&D and in the other two intangibles, ICT 
and  OI,  which  is  given  by  the  equality  at  the  margin  of  the  user  cost  and  the 
marginal benefit from investments in innovation in terms of cost reduction and sales 
expansion.  Innovation  occurs  in  house.  Investments  depend  on  the  market 
conditions  faced  by  firms,  on  their  available  technology  and  on  the  tax-
subsidy mix they are subject to. The optimal demand for innovation investments 
is a positive function of expected demand (or output) and a negative function of the 
ratio between the user cost and the production price. Therefore, all policy measures, 
reforms  and  shocks  that  modify  the  user  costs  or  benefits  change  the  innovation 
investment  decisions  of  firms.  The  endogeneity  of  the  innovation  investment 
decisions  implies  that  an  expansion  of  domestic  and/or  foreign  demand  may 
incentivize  investments  in  R&D,  ICT  and  OI.  Changes  in  the  fixed  costs  faced  by 
firms may free up resources to be used in innovation activities. The user cost can be 
lowered by giving firms R&D tax incentives. 

The intensity of the three different innovation components increases with the level 
of  innovation  services  they  provide  but  tends  to  decrease  if  the  growth  rate  of 
knowledge  spillovers  increases.  As  more  R&D  is  done  the  R&D/output  ratio 
increases, but as the productivity of R&D rises because of spillovers, the output rises 
as well, so that the R&D/output ratio can decrease. 

Markets  are  imperfectly  competitive,  but  markups  are  constant  and  do  not 
depend on the number of firms. However, the effects of pro-competitive policies on 
innovation activities may be simulated by changing these markups.  

Innovation  takes  place  at  firm  level,  as  discussed  in  the  previous  section,  patent 
citations across sectors and countries are the basis of the mechanism of knowledge 
spillovers and propagation.  

113  These  matrices  were  developed  in  the  context  of  the  DEMETER  and  SIMPATIC  projects. 

See Meijers and Verspagen (2010) and Belderbos and Mohnen (2013). 

80 

 
                                                
 
There  are  two  types  of  workers:  low-skilled  and  high-skilled.  Both  skill 
categories are used as production inputs entering different levels of the nested CES 
framework describing production of each sector. Human capital is exogenous and 
reflects  the  skill  composition  of  the  labour  force  of  the  countries,  based  on  the 
structure  of  the  population  and  on  investment  in  education.  According  to  the 
International  Standard  Classification  of  Education (ISCED)  levels,  low-skilled 
workers regroup the categories from 0 to 4, while high skilled workers regroup the 
categories  5  and  6.  In  NEMESIS,  human  capital  can  be  changed  exogenously 
through  the  participation  rates  of  the  working  age  population  by  gender,  age  and 
skill.  

The  multidimensional  nature  of  innovation,  combining  investment  in  R&D,  in  ICT 
and in OI, allows for an  endogenous  representation  of  the  investments of  private 
firms  in  training.  Investment  in  training  is  explicitly  modelled  through  the  OI 
component.  

In  NEMESIS  firms  invest  in  innovation  using  internal  resources.  The  financial 
market is not modelled and interest rates are exogenous. However, risk premia 
paid  to  capital  owners  may  be  changed  exogenously,  affecting  the  user  cost  of 
capital and agents’ accounts. Different scenarios can then be constructed accounting 
for  the  possibility  of  financial  frictions  and  limited  access  to  capital  markets.  For 
instance,  the  model  can  account  for  variations  in  access  to  financial  capital  via 
cross-border  venture  capital  funds,  financing  facilities  from  European  or  national 
institutions, or ease of financing via R&D tax credits or direct subsidies. 

3.3.5. R&I policy  

The presence of knowledge spillovers at the basis of the analytical  framework used 
in NEMESIS to endogenize growth justifies the need for public interventions in R&I. 
The social returns of innovation activities are higher than the private returns. Hence, 
in the absence of an ad hoc policy mix, private sector’s R&I activities are below the 
optimal social level.  

NEMESIS  can  be  used  to  simulate  the  macroeconomic  and  sectoral  impacts  of 
R&I  policy  instruments  such  as  public  R&D,  EU  or  National  public  funding,  tax 
incentives and subsidies. This positive approach in the evaluation of the innovation 
policies is based on three key channels of the model. The first channel regards the 
effects  of  the  policies  on  R&I  investment  decisions  and  of  the  related  additionality 
effects. The second channel refers to the effects that different policies may have on 
the size and the direction of knowledge spillovers. The third channel is related to the 
diffusion of knowledge across sectors.  

In the context of the I3U project (Investigating the Impact of the Innovation Union), 
the  following  main  policy  areas  were  incorporated  in  NEMESIS:  (i)  human  capital; 
(2) finance; (3) action to market. The first area incorporates measures that increase 
the  productivity  of  public  research  by  influencing  the  strength  and  direction  of 
knowledge spillovers. This area includes all policy interventions aimed at improving 
the  quality  of  human  capital  and  the  allocation  of  skills  and  qualifications.  The 
second  area  on  finance  incorporates  government  measures  aimed  at  easing  the 
financing  of  R&D,  be  it  direct  grants,  financial  incentives  to  attract  private  capital, 
special  measures  for  SMEs,  access  to  venture  capital,  matching  private  and  public 
funds  to  finance  R&D  or  access  to  structural  funds.  Action  to  market  regroups 

81 

 
commitments  that  foster  market  conditions  for  innovation  such  as  the  European 
Design Leadership Board and European Creative Industries.114 

Other  policy  related  aspects  can  be  simulated  in  NEMESIS  such  as  environmental 
and energy policies, with the Environment Module (NEEM) that delivers indicators on 
energy  demand  and  supply,  distinguishing  between  different  types  of  energy 
sources  and  technologies.  NEEM  allows  to  consider  the  effects  of  environmental 
policies, such as carbon tax, emission permits, Pigouvian subsidies etc… 

In NEMESIS there are several tax and subsidy instruments, such as VAT, tax on 
labour  and  capital  income,  excises  duties  and  consumption  taxes,  subsidies  on 
production,  social  security  contributions.  Through  these  policy  tools  the  model  can 
be  used  to  construct  complex  policy  scenarios,  also  accounting  for  public  financing 
issues of R&I policies and for the possible emergence of crowding-out and crowding-
in phenomena. 

The multi-country structure of NEMESIS allows to differentiate between national 
and supranational policies, while its multi-sectoral structure enables the study of 
the  impact  of  sector-specific  policy  measures,  as  well  as  the  effects  on  the 
production structure of broad interventions.  

Finally,  the  NEMESIS  Agriculture  and  Land-Use  module  (NALU)  can  be  used  to 
simulate several structural interventions in all the main land-use intensive sectors. 

3.3.6. Dimensions of macroeconomic impact of R&I 

NEMESIS can evaluate the impact of innovation activities and of the related policies 
for  key  economic  indicators.  There  are  three  layers  of  indicators.  The  first  layer 
refers  to  the  main  macroeconomic  indicators,  such  as  GDP,  consumption, 
investments, imports, exports, unemployment rates etc… The second layer refers to 
the sectoral variables, such as value added and employment per NACE sector. The 
third  layer  refers  to  the  accounts  of  the  different  categories  of  agents 
(households,  financial  and  non-financial  corporations,  general  government  and  the 
rest of the world). 

The  demographic  structure  of  NEMESIS  and  its  detailed  workforce  allows  to  keep 
track  of  social  indicators,  such  as  unemployment,  participation  rates  by  age, 
gender and qualifications, inequality index for wages and earnings across countries 
and regions. 

Firms employ low and high skilled workers. On the demand side households are not 
heterogenous,  however  it  is  possible  to  study  the  impact  of  R&I  policies  on  the 
distribution of income by looking at wage dynamics by skill. In each sector wage 
is  determined  according  to  the  model  developed  by  Chagny  et  al.  (2002), 
accounting  for  the  distinction  between  high  and  low  skilled  labour  inputs  and  the 
related market and institutional factors. 

Innovations  deliver  Hicks  neutral  productivity  improvements,  but  by  changing  the 
quality of goods and given the non-homotheticity of preferences, they may give rise 

114  See Le Mouël et al. (2017). 

82 

 
                                                
to change in the  relative demand of goods and services. The effects of skill-biased 
technical  change  can  be  studied  by  considering  the  impact  of  major  innovation 
efforts occurring in skill-intensive sectors. 

The  NEMESIS  Energy  Environment  Module  (NEEM)  accounts  for  the  negative 
environmental  externalities  of  economic  activities  providing  detailed  results  on 
different pollutant emissions (CF6, CO2, HFC, NOX, PFC and SO2).  

3.3.7. Strengths and weaknesses  

NEMESIS  is  a  macro-econometric  model  belonging  to  the  neo-Keynesian 
tradition.  The  equations  describing  agents’  decisions  are  empirically  specified, 
partly estimated and partly calibrated. This feature ensures that the model can 
mimic the observed dynamics for the main macroeconomic indicators. The model 
can  capture  the  cyclical  variations  of  prices  and  quantities  in  response  to  policy 
shocks  and  describe  the  adjustment  dynamics  towards  the  medium  and  long-run 
equilibrium.  

The presence of three types of innovation activities (R&D, ICT and OI) allows 
to  simulate  different  policies  and  to  account  for  the  effects  of  knowledge  and 
network  externalities.  R&I  leads  to  both  cost  reductions  (via  Hicks  neutral 
productivity  improvements)  and  demand  expansions  (via  quality  upgrades). 
Moreover, NEMESIS distinguishes between private  and public R&D investments 
with different effects on knowledge formation and on economic performance.  

Countries  and  sectors  are  interlinked  via  exchange  of  goods  and  knowledge 
externalities.  With  this  feature  NEMESIS  can  study  the  direct  and  indirect  effects 
R&I policies at national or supranational level and/or targeting specific sectors.  

The  NEMESIS  model,  although  inspired  by  endogenous  growth  models,  is  less 
theoretically  grounded,  but  empirically  richer,  as  it  allows  for  sector 
heterogeneity, various sources of technological change and spillovers across sectors 
and countries.  

The additional module NEEM can be used to study the impact of R&I policies on the 
environment (emissions and land use) and to construct complex scenarios of policies 
for smart and sustainable growth.  

The  detailed  modelling  of  population  and  working  force  allows  for  the  study  of  the 
impact of R&I policies on unemployment, participation rates and inequalities across 
different categories of workers (by skill, age and gender) and across countries.  

Despite  all  the  above  strengths  NEMESIS  has  some  limitations,  most  of  them 
typical of models based on an econometric approach.  

The  model  does  not  feature  firm  heterogeneity.  Each  firm  undertakes  R&I 
activities  and  there  is  neither  entry  of  innovative  firms,  nor  exit  of  less  efficient 
firms.   

The overall effects of innovation activities are amplified by cross sector and country 
spillovers.  In each country/sector block both process and product  innovations are 
jointly determined by the same investment effort, while the functional specification 
of  the  three  components  of  innovation  services  is  based  on  cross-sector  and 

83 

 
international  spillover  effects,  and  the  diffusion  process  of  innovation  has  a 
serendipitous  nature,  even  though  propagation  is  related  to  the  country/sector 
absorption capacity.  These  features run  the  risk  of  overestimating  the  positive 
impact of R&I policies.    

NEMESIS  does  not  capture  the  intrinsic  risks  associated  and  randomness  in  the 
outcome  of  the  R&I  process  and  does  not  feature  endogenous  financial  frictions 
and risk premia are exogenous.   

The representation and the role of human capital are limited in NEMESIS. The skill 
composition of the labour force is not endogenously determined.  

The simulation strategy adopted is extremely flexible, but it may require data 
that are not always available, or their quality may not be as good. In addition, the 
use of external empirical studies may be necessary to introduce the shocks into the 
model.  These  aspects  may  represent  an  obstacle  for  the  design  of  simulation 
scenarios and for the translations of policy shocks into model variables. 

The model lacks microfoundations, therefore the mechanism of transmission from 
policy shocks to endogenous variables is based on reduced form equations. It is not 
possible  to  disentangle  direct  from  indirect  effects  of  policy  actions.  In  addition, 
several  assumptions  have  to  be  made  about  the  empirical  specifications  of  agents’ 
behavioural  equations,  their  dynamic  structure  and  the  way  in  which  expectations 
are formulated.  

In  NEMESIS,  consistently  with  the  other  econometric  models,  agents  formulate 
adaptive  expectations.  This  way  of  formulating  expectation  introduces  further 
lagged  variables  into  the  behavioural  equations,  so  that  it  is  not  possible  to 
disentangle  the  partial  adjustment  effects  from  the  expectation  effects.  From  this 
perspective, the transition from theory to data is not straightforward.  

Finally,  another  important  limitation  is  that  agents’  behaviour  is  described  by 
equations that do not adjust to policy changes; therefore, NEMESIS is subject to the 
“Lucas critique” and cannot account for the fact that agents change their decisions 
when faced with a change in the policy regime. The lack of forward-looking decision 
making can then be a source of bias in the simulation results. 

3.4.  Evaluating  the  modelling  of  R&I  and  R&I  policies  in  QUEST  III, 

RHOMOLO and NEMESIS  

This final section will assess the three models, QUEST, RHOMOLO and NEMESIS on 
the  basic  R&I  features  and  R&I  policies  identified  in  Section  2  as  important  for 
macro-modelling  R&I  and  assessing  R&I  policy  interventions,  using  the  series  of 
tables presented in below.  

Tables  3.1-3.4  show  how  QUEST  III,  RHOMOLO  and  NEMESIS  score  on  the  critical 
properties and characteristics described in Section 2. The tables are not meant to be 
an exhaustive and precise representation of the three models. They only provide a 
synthetic  representation  of  the  main  features  related  to  the  detailed  modelling  of 
knowledge creation, diffusion, enablers of innovation, impact dimensions and of the 
relevant policy tools. Some features can be present either in an external module or 
in other variants of the core model, while other features are captured indirectly (or 

84 

 
exogenously)  by  the  models,  meaning  that  the  impact  evaluation  requires  an  ex 
ante empirical assessment of the link between specific policy interventions and the 
model  variables  to  be  used  in  the  simulation.  Therefore,  an  indirect  or  exogenous 
representation  of  a  specific  feature  is  not  a  mark  of  weakness.  Similarly,  the 
presence or absence of a feature is not by itself a sign of strength or weakness. 

85 

 
 
 
Table 3.1 - Main features of R&I in QUEST III, RHOMOLO and NEMESIS 

QUEST 
III 

RHOMO
LO 

NEME
SIS 

Technological Knowledge 

Stock of knowledge 

Absorptive capacity 

Non-rivalry 

Obsolescence 

Innovation and the creation of new technologies 

Innovation typology 

Diffusion of new 
technologies 

R&D 

Knowledge spillovers 

Rent spillovers  

Firm heterogeneity 

Path dependency 

Basic and applied research 

Process and product innovation 
Drastic versus incremental 
innovations 

Endogenous diffusion of 
technologies 

Knowledge spillovers 

Absorptive capacity  

Linking inputs and output: the knowledge production function 

R&D labour inputs 

R&D equipment 

Standing on the shoulders of giants 
effects 

Congestion externalities 

V 

V 

V 

V 

V 

V 

X 

X 

X 

V 

X 

X 

V 

V 

V 

V 

X 

V 

V 

VX 

VX 

VX 

X 

VX 

VX 

X 

X 

X 

X 

X 

X 

X 

VX 

VX 

VX 

VX 

X 

X 

V 

V 

V 

V 

V 

V 

X 

X 

X 

V 

V 

X 

V 

V 

V 

V 

V 

V 

V 

Note: this table only provides a synthetic representation of the main features of the 
models  related  to  the  detailed  modelling  of  knowledge  creation  and  diffusion  of 
technologies.    V  (X)  indicates  that  the  feature  is  (not)  present  in  the  model.  VX 
indicates  that  the  feature  is  currently  modelled  indirectly  (or  exogenously)  or  that 
has been modelled in other variants (or external modules) of the main model.  The 
presence or absence of a feature is not by itself a sign of strength or weakness. 

86 

 
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
Table 3.2 - Enablers for R&I creation, adoption and diffusion in QUEST III, 
RHOMOLO and NEMESIS 

QUEST 
III 

RHOMOL
O 

NEMESI
S 

Size of the market 

Competition 
Intellectual property 
rights 

Human capital 

Financing of R&D 

V 

V 

V 

X 

V 

Stock endogenously 
determined 

Different labour skills 

Endogenous financial frictions  X 

Exogenous risk premia 

V 

V 

V 

VX 

X 

V 

X 

V 

V 

V 

X 

X 

V 

X 

V 

Note: this table only provides a synthetic representation of the main features of the 
models  related  to  the  enablers  for  R&I  creation,  adoption  and  diffusion.  V  (X) 
indicates that the feature is (not) present in the model. VX indicates that the feature 
is currently modelled indirectly (or exogenously) or that has been modelled in other 
variants  (or  external  modules)  of  the  main  model.  The  presence  or  absence  of  a 
feature is not by itself a sign of strength or weakness. 

87 

 
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
Table 3.3 - R&I policy in QUEST III, RHOMOLO and NEMESIS 

R&D tax incentives 

Direct subsidies 

Loans and public venture capital 
Public  R&D  and  public-private 
partnerships 

Public procurement  

Patents 

Regulations  

Corporate taxes 

Environmental policies 

Policy mix 

Budget spent 

Antitrust policies 

Education policies 
General taxes or 
subsidies/support  

QUEST 
III 

RHOMOL
O 

NEMESI
S 

V 

V 

VX 

VX 

V 

V 

V 

V 

VX 

VX 

VX 

VX 

V 

VX 

V 

V 

VX 

VX 

V 

V 

V 

V 

V 

V 

VX 

V 

V 

VX 

VX 

V 

VX 

V 

V 

VX 

VX 

VX 

V 

V 

V 

V 

V 

V 

V 

V 

X 

V 

V 

Interactions between national 
and supranational level policies   V 

Interactions between regional 
and national level policies  
Sector specific policy 
interventions 

X 

VX 

V 

Administrative burdens 

Note:  this  table  only  provides  a  synthetic  representation  of  the  main 
features of the models related to R&I policy. V (X) indicates that the feature is 
(not)  present  in  the  model.  VX  indicates  that  the  feature  is  currently  modelled 
indirectly (or exogenously) or that has been modelled in other variants (or external 
modules) of the main model. The presence or absence of a feature is not by itself a 
sign of strength or weakness. 

88 

 
 
  
  
 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
 
 
Table  3.4  -  Dimensions  of  macroeconomic  impact  of  R&I  in  QUEST  III, 
RHOMOLO and NEMESIS 

QUEST III  RHOMOLO  NEMESIS 

GDP 

Labour markets 

Competitiveness and globalization 

Inequality 

V 

V 

V 

Across workers  V 

Across countries  V 

Health 

Environment, non-renewable and waste disposal  

Across regions 

X 

X 

VX 

V 

V 

V 

V 

V 

V 

X 

V 

V 

V 

V 

V 

X 

X 

VX 

VX 

Note: this table only provides a synthetic representation of the main features of the 
models  related  to  the  dimensions  of  the  macroeconomic  impact  of  R&I.  V  (X) 
indicates that the feature is (not) present in the model. VX indicates that the feature 
is currently modelled indirectly (or exogenously) or that has been modelled in other 
variants  (or  external  modules)  of  the  main  model.  The  presence  or  absence  of  a 
feature is not by itself a sign of strength or weakness. 

89 

 
  
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4.  CONCLUSIONS AND RECOMMENDATIONS 

This  section  closes  with  some  conclusions  and  recommendations  on  how  to  move 
forward  in  the  medium-long  term  agenda  on  the  development  and  use  of 
macroeconomic models for R&I policy analysis. 

Before we set out  the recommendations which  follow from  our analysis in  Sections 
4.1-4.4, it is important to first recall some general principles on why and how to 
use quantitative macroeconomic models for innovation policy analysis.   

  Quantitative  macro  models  are  an  important  tool  to  have  in  the  toolkit  for 
innovation  policy  analysis  because  they  offer  the  benefit  of  taking  the 
interdependence  between  markets,  sectors,  regions  and  countries  into 
account  and  thus  help  to  identify  the  critical  enablers  of  R&I  and  any 
possible weak points (like frictions in labour, financial and product markets) 
that could prevent the effectiveness of innovation policy.      

  As 

innovation 

long-term 
macroeconomic  performance,  macroeconomic  models  should  include  a 
proper modelling of R&I and R&I policies.    

for  medium  and 

important  driver 

is  an 

 

 

Institutions using macro-models on a regular basis typically have a portfolio 
of  different  models  useful  for  different  purposes.  A  single  encompassing 
model  will  be  frustratingly  simple  for  some  purposes  and  unnecessarily 
complicated  for  others.    Analysis  of  the  macroeconomic  implications  of 
innovation  and  of  innovation  policies  will  typically  be  done  inside  or  in 
interaction  with  macroeconomic  models  which  are  commonly  used  for  the 
main purposes of the macroeconomist.    

In  order  to  be  a  good  laboratory  for  the  quantitative  evaluation  of  R&I 
policy,  macroeconomic  models  of  R&I  need  to  be  disciplined  by  the 
available  macro  and  micro  data  on  those  dimensions  in  which  policies 
are expected to operate.  Limited availability of macro and micro data may 
seriously jeopardize the full modelling of R&I and R&I policies.   

  Continuous  interactions  among  modellers,  users,  outside  experts  and 
policymakers is essential to ensure relevant models. Any model needs to be 
transparent and sufficiently understandable for decision makers to work with 
it.  Users’  adoption  requires  a  strong  effort  to  facilitate  comprehensibility  of 
the  model  functioning  and  of  its  results,  which  requires  good  and  frequent 
communication between the groups.   

  Models  need  not  necessarily  be  utterly  complicated.  As  models  are  already 
complex,  it  should  be  avoided  to  make  them  intractable  black  boxes  by 
trying  to  enlarge  them  with  too  many  new  features.  Models  cannot  have 
everything nor should they. 

 

Yet any simplification should be  analysed on its impact on the dynamics of 
the  model,  and  sensitivity  tests  need  to  be  conducted  to  predict  at  least 
upper  and  lower  bounds  for  the  variables  of  interest  depending  on  the 
assumptions made. 

90 

 
4.1. Recommendations on how to include R&I and R&I policies in 

macro-models 

Macroeconomic  models  designed  to  evaluate  innovation  policies  have  to  include  a 
fair,  even  if  highly  simplified,  description  of  the  key  features  that  describe  the 
working  of  innovation  and  the  main  policies  at  hand  to  promote  innovation  and 
maximize  its  impact  on  the  macro  economy.  By  omitting  the  relevant  channels 
through  which  innovation  policies  are  expected  to  affect  the  macro  economy,  no 
model can be of good advice for innovation policy. This report reviewed in Section 2 
the  key  features  that  describe  the  workings  of  innovation  and  the  main  policies  at 
hand to promote innovation. 

 

The key R&I features are: 1) Well-defined technologies transforming  R&D 
inputs  into  innovation  outputs,  dynamically  cumulating  into  an  aggregate 
stock  of  technological  knowledge  (sectorial,  regional,  national);  2)  R&D 
technologies benefiting from positive knowledge spillovers (geographical and 
sectorial),  and  (oligopolistic)  market  structures  allowing  for  negative  rent 
spillovers  (in  the 
form  of  business  stealing  or  obsolescence);  3) 
heterogeneous and firm-specific production and R&D technologies, explicitly 
modelling  the  creation  of  new  products,  and  shaping  the  endogenous 
random process of firm-specific productivity (in terms of product quality and 
efficiency  of  the  production  process);  4)  firm  heterogeneity  and  market 
dynamics  (entry,  exit  and  firm  life-cycles)  endogenously  emerge;  5)  when 
needed,  the  distinction  between  basic  and  applied  research,  product  and 
process innovation, incremental and drastic innovations should be made; 6) 
an explicit modelling of the diffusion process (aiming to catch-up the frontier 
technology or in the form of vintage technologies).  

 

The  following  enablers  are  key  to  design  the  mechanisms  of  the  R&I 
process:  a  well-defined  market  size  effect,  an  inverse  U-shaped  relation 
between  competition  and  innovation,  markup  dispersion,  an  education 
system  providing  the  needed  human  capital,  and  an  explicit  modelling  of 
R&I financing. 

  Macro-models  should  be  aimed  to  evaluate  the  main  R&I  policy 
instruments used in practice: 1) R&D tax incentives, in particular R&D tax-
credits; 2) direct subsidies, in particular research grants; 3) public research 
loans  and  public  venture  capital;  4)  public  R&D  and  public-private 
partnership; 5) public procurement; 6) a well-defined patent system. Other 
policy  instruments  include  corporate  taxes  and  regulations  addressed  to 
protect the environment and the health of citizens. 

 

In  the  evaluation  of  innovation  policies,  macro-models  should  reflect  the 
existence  of  trade-offs,  of  general  equilibrium  effects  and  of  budget 
constraints,  and  account  for  the  fact  that  agents  may  react  to  changes  in 
policy by modifying their behaviour consistently. 

  Macroeconomic models should provide a good quantitative evaluation of the 
macroeconomic  impact  of  R&I  in  dimensions  like:  1)  GDP  and  its  main 
components  (level  and  growth);  2)  labour  market  outcomes,  including  the 
effect  on  the  creation  and  destruction  of  jobs  and  skills  (job  polarization, 
skill  obsolescence,  structural  transformation);  3)  competitiveness  and 
globalization,  including  the  channels  through  which  innovation  strengthens 

91 

 
 
 
competitiveness and builds comparative advantages; 4) social exclusion and 
inequality,  in  a  framework  of  heterogeneous  households  with  different 
education  and  skills;  5)  heterogeneous  states  of  health,  population  aging 
and  life-expectancy;  6)  the  relation  between  economic  growth,  the 
environment and sustainability. 

In  order  to  implement  these  key  innovation  (policy)  ingredients  in  macroeconomic 
models, it will not be possible to include all of them. No single macro-model can 
simultaneously  account  for  all  these  features  of  the  R&I  process,  and  still 
consistently  produce  the  accurate  quantitative  figures  needed  for  the  macro-
evaluation  of  all  current  R&I  policies.  Choices  will  have  to  be  made  about  which 
features  and  policies  are  more  relevant  for  the  economy.    Depending  on  the 
objectives  and  the  innovation  policies  being  evaluated  as  well  as  the  channels  and 
the  dimensions  of  impact  considered,  some  features  may  be  more  crucial  than 
others.  In making these choices, a few principles should be kept in mind: 

ideally 

  A  good  macroeconomic  model  designed  to  evaluate  innovation  policies 
should 
include  heterogeneous  firms  and  heterogeneous 
production  and  R&I  technologies.  This  requires,  in  particular,  an 
important  disaggregation  at  the  level  of  firms,  including  innovation 
incentives  and  performance,  as  well  as  the  associated  dynamics,  including 
the entry of new innovative firms, their development and eventual exit. 

  When the different features of the R&I process, their diffusion channels (the 
enablers),  the  different  policy  instruments  and  the  different  dimensions  of 
macroeconomic impact are studied in different models (or sub-models), 
or  at  different  levels  of  disaggregation,  their  results  should  nevertheless 
remain comparable with a common core.  

 

 

For the geographic dimension in the case of the European Union, a macro 
model designed to evaluate innovation policies ideally has to mimic a multi-
country, multi-region Europe trading with the rest of the world, the degree 
of disaggregation depending on the policy under evaluation.  

Innovation, by creating and destroying jobs and the value of the associated 
skills, impacts social inequality, deeply affecting the distribution of income 
and wealth. To evaluate this dimension of R&I, macroeconomic models must 
be  designed  to  properly  account  for  heterogeneity  across  workers  and 
households  and  the  associated  dynamics.  Modelling  skill  and  education 
heterogeneity  is  of  fundamental  importance  to  evaluate  the  labour  market 
and the distributional implications of innovation policies. 

  Environmental  issues,  such  as  those  falling  in  the  domain  of  climate 
change, have become pressing in the recent decade. Environmental policies 
direct  technical  change  towards  the  development  of  environmental-friendly 
technologies.  However,  the  advancement  of  green  and  clean  energy 
technologies  and  the  development  of  abatement  technologies  require  large 
investments in R&I. These two dimensions should be addressed together in 
models integrating environmental aspects with endogenous technical change 
mechanisms. 

92 

 
 
 
 
 
4.2. Lessons learned from macroeconomic models currently used in DG R&I  

Applying  the  checklist  developed  in  this  report  of  the  key  ingredients  required  to 
deliver  a  sound  macroeconomic  evaluation  of  innovation  policy  to  the  three 
macroeconomic models presently used by the Commission’s Directorate-General for 
Research  and  Innovation  -  QUEST  III,  RHOMOLO  and  NEMESIS  -  the  following 
lessons stand out: 

  All three models present a rich set of mechanisms and parameters through 

which R&I policies can be simulated. 

 

Yet,  none  of  the  models  covers  all  of  the  key  characteristics  of  innovation 
and innovation policy.  This implies that none of them can be considered as 
dominantly  better  for  the  modelling  of  innovation  and  innovation  policies. 
Each  has  its  own  strengths  and  weaknesses.    Which  model  to  choose  will 
depend on what the major objectives and preferences of the users are.  

  All  models  require  further  developments  to  better  cover  key  features  of 
innovation  and  innovation  policy.  Some  key  features  can  be  included  with 
minor changes to the models, others will require bigger adjustments.     

 

The  following  key  features  are  poorly  included  in  all  three  models:  the 
heterogeneity  and  dynamics  of  the  population  of  innovative  firms,  the 
modelling  of  financial  frictions,  the  modelling  of  risk  and  uncertainty,  the 
formation of human capital for the creation and the adoption of innovations, 
and the environment-related aspects.   

  All  of  the  models  struggle  with  a  lack  of  sufficiently  recent  and 
disaggregated data to calibrate/estimate critical parameters. All models can 
be improved with better data. 

4.3. Some suggestions for the way forward 

 

There  will  never  be  one  perfect  or  right  model.  All  models  are  wrong,  but 
some are more useful than others.  

  More  progress  could  be  made  in  sharing  and  mutual  learning  among  the 

currently used models.   

  Addressing  the  commonly  missing  features  in  the  current  models  will 
require  bigger  adjustments.  When  contemplating  big  adjustments  to  the 
models,  a  more  modular  approach  should  be  considered,  with  a  stable 
“core” model that is capable and flexible to link to (new) submodules, which 
address in more detail key features of innovation or different dimensions of 
macroeconomic performance, and which can be activated when needed.  

 

To  achieve  the  objectives  mentioned  above  in  the  development  of  R&I 
macro-models  for  the  evaluation  of  R&I  policies  the  following  families  of 
models  should  be  seriously  considered:  endogenous  growth  models  with 
firm dynamics, models of vintage technologies, models of directed technical 
change,  models  of  human  capital  formation  and  occupational  choice,  and 
models of search in the labour market, among others.  

93 

 
  Beyond  further  developing  currently  used  models,  new  models  should  be 
on  the  radar  as  well,  such  as,  models  of  networks,  models  of  structural 
transformation,  models  of  bounded  rationality,  evolutionary  game  theory, 
and agent-based computational models.  New models offer new perspectives 
for modelling currently missing features. 

  Macroeconomic models can only be a good  laboratory for the evaluation of 
economic  policy,  if  they  are  as  close  as  possible  to  the  available  data  on 
those dimensions in which the policy is supposed to operate. This is a basic 
requirement for any macroeconomic model to provide accurate quantitative 
figures that help  evaluating  the  contribution  of  innovation policies. High on 
the  to-do  list  should  be  to  improve  the  data  availability  for  modelling  of 
the key R&I features and key R&I policy interventions, in particular data on 
innovation policies in Europe. 

  Modern macroeconomic models based on microeconomic foundations should 
be  designed  consistently  with  microeconomic  data  and  the  institutional 
framework  in  which  economies  operate.    This  is  generally  done  by  an 
exercise  of  calibration  and/or  estimation  of  behavioural  parameters  of  the 
model  that  are  reasonable  (i.e.  consistent  with  empirical  evidence  from 
microstudies  on  different  dimensions  of  the  model)  but  that  also  allow  the 
replication of the observed data.  

 

Improve transparency of any model being used to enable a more informed 
use  by  policy  makers  of  the  results.  Ideally,  a  full  description  of  the 
model, code and dataset should not only be transparent to the users, but 
also  be  open  access,  to  enable  a  more  interactive  mode  of  continuous 
development of the models.  

4.4. Some suggestions for moving the EC-R&I agenda moving forward 

Within  this  overall  agenda  for  moving  the  macro-modelling  of  innovation  policies 
forward, DG R&I can play a pivotal role through the following actions: 

 

 

 

 

 

Providing  grants  for  excellent  research  at  the  knowledge  frontier  of 
macroeconomic models contributing to the developments of the modelling of 
R&I and R&I policy as well research into new models. 
Procuring  research  into  further  developments  of  currently  used  models  by 
the EC to improve the modelling of their R&I policy evaluation, with a close 
involvement of policy modellers and users; 
Full transparency should be a condition (a full description of the model, code 
and dataset should be open access); 
Improving  the  provision  of  common  data  inputs  to  macro-models,  most 
notably data on innovation policies; 
Providing  a  platform  for  macro-models  where  research  results  and  best 
practices  can  be  exchanged,  databases  can  be  shared  and  where  open 
access  to  models  and  codes  can  instigate  further  (co-)development  of 
existing macro-models or inspire the research on new models;     

  Regularly  bringing  together  producers  and  users  of  macro-models, 
improving  the  transparency  of  models  to  users  and  improving  the  user 
friendliness of models for model-users.  

94 

 
 
 
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Acemoglu  D.  (2003)  Patterns  of  Skill  Premia,  Review  of  Economic  Studies  70(2), 
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APPENDIX 

Box 1: R&I POLICIES IN THE EU and its MEMBER STATES 

The EU  landscape of research and innovation (R&I)  policies is characterised by the 
interplay  of  different  levels  of  governance,  with  most  policy  initiatives  being 
undertaken at the national and the European level.115 This box describes the overall 
R&I policy framework in Europe, focusing on the main tools and objectives at the EU 
level,  for  then  analysing  the  main  policy  instruments  from  a  modelling  perspective 
consistently with Section 2 of this report. It also provides some insights on national 
policy actions that complement the EU instruments or are specific to member states 
(e.g. tax incentives). 

R&I  policy  has  historically  aimed  at  fixing  market  failures,  as  to  incentivise  private 
R&D  investments  towards  the  social  optimum,  as  well  as  at  addressing  systemic 
failures  that  prevent  the  diffusion  and  take-up  of  scientific  and  technological 
knowledge.  More  recently,  a  new  frame  has  emerged  calling  for  a  transformative 
R&I  policy  providing  the  direction  to  tackle  contemporary  economic,  social  and 
environmental  challenges  and  to  achieve  the  Sustainable  Development  Goals  (see 
Schot and Steinmueller, 2018; Biggeri and Ferrannini, 2020).  

The  EU  R&I  policy  has  embraced  the  transformative  framework,  most  notably  to 
deliver  on  the  Green  Deal,  as  to  set  the  direction  of  investments,  reforms  and 
regulation (European Commission, 2020). This direction complements and provides 
a new frame for existing tools, e.g. tax incentives, grants, direct public investments, 
partnerships, etc. Within the framework of the sustainability transition, R&I policies 
at the EU level aim to promote excellence in research and convergence across 
regions.  

Horizon Europe. The current main policy action is Horizon 2020, the Framework 
Programme,  which  will  be  followed  by  its  successor,  Horizon  Europe,  the 
Framework  Programme  9.  These  are  the  main  EU  instrument  for  stepping  up 
investment  in  R&I  capacity  and  they  cover  the  period  2014-2020  and  2021-2027 
respectively.  Competitive,  mission-oriented  grants  are  the  main  policy  instrument 
used  in  this  programme  aimed  to  promote  excellence  in  research,  knowledge 
diffusion and collaboration between universities and private firms.  

115  Regional  policy  is  also  a  crucial  dimension  of  the  R&I  framework  in  Europe,  since  huge 
differences  do  persist  across  regions  and  within  countries  in  terms  of  economic 
development, R&I investments and performance. Furthermore, regional governments and 
stakeholders  are  key  actors  when  it  comes  to  R&I  policy  interventions.  Therefore, 
consistently  with  the  acknowledgement  of  the  importance  of  place-based  policies  that 
account for and embrace the specificities of regional ecosystems, EU regions are required 
to develop their Smart Specialisation Strategies. These are conceived within the Cohesion 
policy  of  the  European  Commission  and  constitute  a  place-based  approach  to  policy, 
identifying  strategic  areas  for  intervention  based  on  an  assessment  of  strengths  and 
potential  of  regional  economies  and  their  R&I  ecosystems.  For  further  information  see 
https://s3platform.jrc.ec.europa.eu/what-is-smart-specialisation- .  

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In  particular,  Horizon  Europe  will  be  the  largest  ever  Framework  Programme, 
including novelties compare to its predecessor, Horizon 2020. These include EU-wide 
missions, i.e. time-bound and specific goals on issues (e.g. cancer)  where Europe 
needs  to  deliver,  the  European  Innovation  Council  (EIC),  as  a  major  tool  to 
support  Europe  transition  into  the  next  wave  of  innovations  across  digital/AI  and 
deep  tech,  and  partnerships  responding  to  strategic  priorities  of  Member  states 
and stakeholders, including industry and civil society. Overall, at the time of writing, 
Horizon  Europe  is  expected  to  have  a  budget  of  94.1  billion  EUR  for  the  period 
2021-2027, of which: 

  Open  Science  Pillar  (25.8  billion  EUR):  Supports  research  through 
fellowship,  exchanges  and  others  activities  financed  by  European  Research 
Council grants (16.6 billion EUR), Marie Curie Fellowships (6.8 billion EUR), 
and  invests  in  world-class  research  infrastructures  (2.4  billion  EUR). 
(Emphasis  on  Open  Access  knowledge,  reproducibility  and  reusability,  i.e. 
mostly basic research). 

  Global  Challenge  Pillar  and  Industrial  competitiveness  (52.7  billion 
EUR):  directly  supports  research  related  to  societal  challenges  reinforcing 
technological and industrial capabilities116.  

  Open  Innovation  Pillar  (13.5  billion  EUR):  Supports  market-creating 
innovation,  breakthrough  ideas,  and  scaling-up  innovative  enterprises 
through  the  European  Innovation  Council  (10.5  billion  EUR)  and  the 
European Institute of Innovation and Technology to foster the integration of 
business, research, high education and entrepreneurship (3 billion EUR), i.e. 
mostly applied research. These are "bottom-up" activities that will be often 
co-founded by national programmes.  

Invest  EU  Fund.  Furthermore,  the  European  Commission  aims  to  guarantee 
affordable finance and mobilise private funds for R&I investments through different 
instruments. These include the dedicated window under the  InvestEU Fund117, for 
which  the  European  Commission  has  proposed  a  mobilization  of  about  16.6  billion 
EUR  through  market-based  instruments  (as  financial  instruments  and  budgetary 
guarantees) that are expected to leverage 200 billion EUR in the private sector. 

Structural Funds. Direct subsidies that go mainly, but not only, to lagging Member 
States  or  regions  under  the  European  Cohesion  policy.  The  period  2021-2027 
foresees 1279.4 billion EUR, of which 187.4 for research and innovation, together 
with  European  Strategic  Investments,  Single  Market  and  Space.  Furthermore, 
Smart  specialization  strategies  under  the  EU  Structural  Funds  are  a  key 
initiative  supporting  R&I  investments,  contributing with  more  than  40  billion  EUR 
to  the  development  of  R&I  strategies  across  regions  and  Member  States  in  the 
period 2014-2020. 

116  Most notably, 7.7 billion EUR are foreseen for Health, 2.8 for Inclusive and Secure Society, 
15  for  Digital  and  Industry,  15  for  Climate,  Energy  and  Mobility  and  10  for  Food  and 
Natural  Resources.  Finally,  2.2  billion  EUR  go  to  non-nuclear  direct  actions  of  the  Joint 
Research Centre (JRC) of the European Commission. 

117  See  https://ec.europa.eu/commission/priorities/jobs-growth-and-investment/investment-

plan-europe-juncker-plan/whats-next-investeu-programme-2021-2027_en. 

121 

 
 
                                                
Major R&I Policy instruments 

Following the outline of Section 2.3, a non-exhaustive summary of the major policy 
instruments in the EU and its member states is presented below. To be noted that 
the policy landscape is quite complex  and  diverse.  Table X reports some  examples 
of  such  policies  across  Member  States  and  in  Figure  X  the  distribution  of  indirect 
(tax incentives) and direct (grants and subsidies) R&I support. 

Although overall R&D  expenditure in  the  EU  has been increasing annually by  1.3% 
since 2010, it remains lower than the 3% target and the gap with main international 
competitors persists. Business R&D investment is particularly subdued and relatively 
low  in  international  comparison,  being  equal  to  1.45%  of  GDP  in  2017  against 
2.04%  in  the  US,  2.52%  in  Japan,  1.65%  in  China  and  3.62%  in  South  Korea 
(European Commission, 2020). 

Tax incentives 

Tax incentives is a  national policy  instrument, widely  used in the  EU  to reduce  the 
cost  of  R&D  activities  and  to  leverage  private  resources.  The  amount  of  foregone 
revenue  varies  across  member  states,  ranging  from  0.30%  of  GDP  in  countries 
strongly relying on tax incentives, e.g. France and Belgium, to shares below 0.01%, 
e.g. Sweden and Poland. The average level of foregone tax revenues in the EU had 
a  threefold  increase  in  the  EU  since  2007,  being  equal  to  0.11%  in  2017.  While 
some  countries  do  not  make  use  of  R&D  tax  incentives,  the  number  of  Member 
States  providing  them  increased  from  12  in  2000  to  21  in  2018  (Appelt  et  al., 
2019).  (see Figure X). 

Grants and subsidies 

As  it  can  be  observed  in  Figure  A.1,  direct  support  in  the  form  of  grants  and 
subsidies  account  for  around  45%  of  total  public  support  to  business  R&D 
investments  in  the  EU  in  2017.  As  for  the  use  tax  incentives,  there  is  large 
heterogeneity across Member States but they are used in every and single country, 
being the most prominent (in a few cases the only one) support scheme in countries 
such as Spain, Poland, Sweden and Germany. To be noted that direct support tools 
allow  for  a  more  narrowed  targeting  of  researchprojects,  providing  more 
directionality  to  public  funding  compared  to  tax  incentives  that  tend  to  be  mostly 
horizontal tools. 118 

Direct supportinstruments are also provided directly at the European level. The most 
notable case is Horizon 2020 and its successor Horizon Europe, providing grants on 
a competitive basis to applicants from EU Member States and Associated Countries. 
Horizon  2020  also  includes  tools  aimed  at  financing  innovative  projects  and 
breakthrough  innovations.  Overall,  over  8  billion  EUR  are  expected  to  directly 
finance R&I activities performed by SMEs, with 4.13 billion EUR already financed at 
the end of 2017. While the EIC will be one of the main tools of the next Framework 

118  This does not mean that tax incentives cannot be somehow targeted. For instance, the tax 
incentives provisions in the most recent Industry 4.0 policy package in Italy are “targeted” 
to innovative activities and investments, while keeping their horizontal dimension. 

122 

 
                                                
Programme  starting  in  2021,  an  EIC  pilot  has  been  activated  as  part  of  Horizon 
2020 with a budget of 3 billion EUR. 

Figure A.1 – Direct and indirect public support to business R&D (in % of GDP) 

Source.  European  Commission  (2020),  based  on  OECD  data  (online  data  code: 
rd_e_gerdfund) 

Direct public R&D and public-private partnerships  

As discussed elsewhere in this report, there may be several reasons why the public 
sector may decide to directly perform R&D investments, rather than leaving it to or 
financing  business  efforts119.  This  is  done  through  public  research  centres, 
universities  and  public  administrations.  In  the  EU,  direct  public  R&D  accounts  for 
around 33% of total R&D expenditure in 2018, of which  2/3 are performed in the 
higher education sector. Public efforts have remained substantially unchanged in the 
last two decades in the EU, public R&D intensity remaining close to 0.7% of GDP in 
2018.120 Substantial heterogeneity can be observed across Member States. 

119  The  quality  of  public  science,  the  characteristics  and  scope  of  public  research  and  the 
creation  of  a  public  pool  of  knowledge  are  usually  cited  as  specific  traits  justifying  direct 
public R&D (Archibugi and Filippetti, 2018). 

120   There is no “official accounting” for public R&D directly performed by the European Union 
as an institution. It is worth noting that, as mentioned above, Horizon Europe foresees a 
budget  of  2.2  billion  EUR  for  the  Joint  Research  Centre  of  the  European  Commission  for 
the period 2021-2027 

123 

 
 
                                                
At  the  EU  level,  European  Partnerships  provide  a  framework  for  programme  level 
collaboration  in  the  EU.  It  allows  to  translate  common  EU  priorities  into  concrete 
roadmaps  and  coordinated  implementation  of  activities.  European  Partnerships  are 
specific collaborative research instruments involving a broad range of public and/or 
private  actors,  such  as  research  funders  and  organisations,  universities,  industry, 
bodies with a public service remit at local, regional, national or international level or 
civil society.  

Public procurement 

Public  procurement  aims  at  directly  creating  demand  for  innovation.  While  14%  of 
EU  GDP  is  spent  every  year  on  public  procurement  (European  Commission,  2020), 
several  Member  States  have  in  place  different  public  procurement  programmes  for 
research  and  innovation,  as  for  instance  Belgium,  France,  Austria  and  Italy  (see 
Table  X).    According  to  a  report  by  the  OECD  (2017),  around  80%  of  OECD 
countries  have  developed  a  strategy  to  support  innovative  outcomes  through  the 
use of public procurement.121 

Loans and venture capital 

Loans  and  venture  capital  funds  address  the  issue  of  inadequate  access  to  finance 
for innovative projects. Countries can set up agencies or schemes that may reduce 
the  cost  of  loans  or  by  providing  public  guarantees for  the  access  to  credit,  as  for 
instance  in  Italy  (Cassa  Depositi  e  Prestiti).  There  are  also  instances  of  public 
venture  capital,  e.g.  Banque  Publique  d’Investissement  in  France  and  Invitalia  in 
Italy.  Public  venture  capital  has  proven  to  be  crucial  in  Europe  in  the  last  decade. 
Indeed,  while  private  sources  have  been  volatile  during  macroeconomic  shocks  as 
the  last  economic  crisis  in  2008,  public  venture  capital  has  been  resilient  overtime 
and has increased its share on total fund from 13% in 2007 to 22% in 2018. Some 
examples are reported in Table A.1. 

The European Union provides programmes to ease the access to venture capital for 
innovative SMEs and midcaps. They include: 

  VentureEU  aims  at  reducing  Europe’s  equity  gap  relatively  to  peer 
economies,  reducing  the  fragmentation  of  the  European  Venture  Capital 
markets and aims to attract additional  private  investors.  Under VentureEU, 
the EU will provide funds for 410 million EUR for the period 2021-2027 (200 
from  Horizon  2020)  aiming  to  raise  up  to  2.1  billion  EUR  of  public  and 
private investments. 
The  Single  EU  Equity  Financial  Instrument,  supporting  businesses’ 
growth and R&I activities at different stages.  

 

 

The European Fund for  Strategic Investments  (EFSI)  Equity  Instrument. 
In particular, the InnovFin Equity instrument mobiles 4-5 billion EUR to be 
invested  in  companies  operating  in  innovative  sectors  covered  Horizon 
2020.  

121   Survey data based on 35 respondents among OECD countries. 

124 

 
 
 
 
                                                
Regulation 

Framework  conditions  are  considered  as  enablers  of  investments,  most  notably  in 
risky  innovative  activities.  Regulation  in  the  labour,  product  and  goods  market  is 
country  specific122.    At  the  European  level,  two  main  policy  tools  related  to 
regulation and innovation should be mentioned: 

- 

The EU Single Market  Programme (SMP),  which aim  is to create a territory 
without  any  internal  borders  or  other  regulatory  obstacles  to  the  free 
movement  of  goods  and  services.  While  not  specific  to  research  and 
innovation,  the  SMP  contributes  to  the  diffusion  of  knowledge  and 
technology and their take-up. Furthermore, free mobility of researchers as a 
key  priority  of  the  European  Research  Area  contributes  to  research 
circulation between EU countries. 

-  The  Innovation  Principle,  regulatory  tool  conceived  to  help  policy  makers 
achieving  EU  policy  objectives  by  ensuring  that  legislation  is  designed  in  a 
way  that  creates  the  best  possible  conditions  for  innovation  to  flourish.  In 
particular,  the  innovation  principle  implies  that  future  initiative  undertaken 
by  the  European  Commission,  e.g.  policy  or  regulations,  will  consider  the 
effect  on  innovation.  The  purpose  is  to  set  up  an  innovation-friendly 
regulatory framework. 

122  Data on the strictness of regulation in each domain is produced by several organisations, 

e.g. the OECD, the World Bank and the World Economic Forum among others. 

125 

 
 
 
 
                                                
 Budget 

p.a. 

 1-5M€ 

 <1M€ 

Table A.1 – A sample of policy instruments in EU Member States 

Policy 

Policies 

instruments 

Country examples 

Direct and 

indirect 

financing 

Grants and 
matching 
grants for 
innovation 
and/or R&D 
projects 

Vouchers for 

innovation and 
collaboration 
Loans & 
guarantees 
for innovation 

Tax incentives for 

R&D (e.g. tax 
credits) 

- AT: Impact Innovation Programme 

- MT: R&D Feasibility Studies 

- NL: SME Innovation Support to Top Sectors 

 20-50M€ 

FI:  Business  Finland  Programme  for  young 
innovative companies 

- 
- ES: Grants for innovative clusters 
- IE: Enterprise Ireland R&D&I Fund 

 20-50M€ 
 5-20M€ 
 20-50M€ 

- EE: Innovation vouchers 
- LT: Innovation vouchers 
- PT: Innovation Vouchers 

 <1M€ 
 1-5M€ 
 5-20M€ 

- 

- 

SI:  Slovenia  enterprise  fund  -  bank  loans 
guarantees / interest rate subsidy. 
PT:  Incentive  scheme  for  the  qualification  of 
SMEs 
LV:  Corporate  income  tax  incentive  for  R&D 
investments 
PT:  Fiscal  incentives  for  the  employment  of 
- 
doctorate holders 
- NL: Innovation Box 

- 

 50-100M€ 

 20-50M€ 

Equity finance for  - MT: Business Startup Funding Programme 
innovative 
enterprises 

- FR: Digital Ambition Fund 
- FI: European Angels Fund Finland 
- LU: Luxembourg Future Fund 

 100-500M€ 
 20-50M€ 
 5-20M€ 

Demand pull 
instruments  R&D 

Public 
procurement for 

- 

innovative 

for  startup 

SI:  Seed  capital 
enterprises 
CY:  Financial  support  to  business  R&D  and 
innovation: procurement 

- 
  grants for R&D and innovation 
BE(Flanders):  Programme 
public procurement 
FR: Online  platform  for public  procurement  of 
innovation 
AT:  Action  plan  on  public  procurement 
promoting innovation 

innovative 

for 

- 

- 

- 

 1-5M€ 

 20-50M€ 

 5-20M€ 

Pre-commercial 
procurement 
Supplier 
development 
programmes 
Corporate open 
innovation 

- LT: Pre-commercial public procurement 
- IT: Pre-commercial Procurement 

 20-50M€ 

- PT: INTERFACE Programme- Suppliers’ Clubs    

- AT: Open Innovation- A strategy for Austria 

 1-5M€ 

- 

EL:  Special  call  on  aquaculture,  industrial 
materials and open innovation 
culture   

 5-20M€ 

Business advisory  - FI: Activating Finnish Living Labs 

 <1M€ 

Technology 
adoption and  services 

generation 

instruments 

Technology 
extension 
services 

Technology 

- CY: Innovation habitats 

ES:  International  Network  (Red  Exterior) 
programme 

- 

- IE: Knowledge Transfer Ireland 
- EE: Technology Competence Centers 
- EE: Product Development Masterclass 
- DE: Innovative Hochschule 

 >500M€ 
 1-5M€ 
 <1M€ 
 50-100M€ 

126 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                       
             
 
   
 
 
 
 
 
 
 
 
                       
             
 
    
 
 
 
 
 
 
 
 
                       
             
 
    
 
 
 
 
 
 
 
 
                       
             
 
    
 
 
 
 
 
 
 
 
 
 
                       
             
 
    
 
 
 
 
 
 
 
 
 
 
                       
             
 
    
 
 
 
 
 
 
 
 
 
 
                     
             
 
    
 
 
 
 
 
 
 
 
 
                     
             
 
    
 
 
 
 
 
 
 
 
 
                     
             
 
    
 
 
 
 
 
 
 
 
 
                     
            
 
    
 
 
 
 
 
 
 
 
 
                    
             
 
    
 
 
 
 
 
 
 
 
                    
             
 
    
 
  
 
 
 
 
 
 
 
 
                     
            
 
    
 
  
 
 
 
 
 
 
 
 
                     
            
 
    
 
 
  
 
 
 
 
 
 
 
 
                 
 
          
 
    
 
  
 
 
 
 
 
 
 
                     
            
 
    
 
 
 
 
 
 
 
 
                 
 
            
 
    
 
 
 
 
 
 
 
 
 
                  
 
            
 
    
 
 
 
 
 
 
 
 
 
                 
 
            
 
    
 
 
 
 
 
 
 
 
 
                 
 
     
    
    
 
 
 
 
 
 
 
                  
 
          
 
    
  
 
 
 
 
 
 
 
                 
 
          
    
 
 
  
 
 
 
 
 
 
 
                  
 
          
    
 
 
 
 
 
 
 
 
 
                  
 
          
    
 
 
  
 
 
 
 
 
 
 
               
 
   
    
    
 
 
 
 
 
 
 
 
                  
 
   
    
    
 
  
 
 
 
 
 
 
 
                
 
   
    
    
 
 
 
 
 
 
 
 
                       
             
 
    
 
 
 
 
 
 
 
 
                
 
   
    
    
 
 
 
 
 
 
 
 
                
   
    
    
 
 
 
  
 
 
 
 
 
 
 
             
   
    
  
 
 
 
 
 
 
 
               
   
    
  
  
 
 
 
 
 
 
 
               
   
    
  
 
  
 
 
 
 
 
 
 
              
   
    
  
 
 
 
 
 
 
 
             
   
    
  
 
 
 
 
 
 
 
 
             
   
    
  
 
 
 
 
 
 
 
 
 
             
   
    
  
 
Early-stage 

support for 
innovative 
ventures 

Cooperation 

Framework 

conditions 

centres 
Incubators 

- LU: Technoport 

Accelerators 

Supporting 
clusters 
and networks for 

innovation 

Inducement 
(incentive 
setting); 
recognition 
awards; 
appropriate 
IPR; standard 
setting; 
quality 
infrastructure; 
investing in 
education 
/ skills; ‘green 
cards’ 
for highly skilled 
immigrants 

BE 
(Flanders): 
acceleration programme 

- 
- PT: Collaborative laboratories 

IMEC.ISTART- 

Business 

- DE: Innovation Forums SME 

 1-5M€ 

DE:  WIR!  Change  through  innovation  within 
the region 

- 
- PT: INTERFACE Program 
- PT: Competitiveness Clusters 
- IE: Innovation 4 Growth 

CY:  National  policy  statement  on  enhancing 
the entrepreneurial ecosystem 

- 

- PT: Startup Visa 

DE:  Technology  Transfer  with  Norms  and 
Standards 

- 

- PT: SME Leader and SME Excellence 

- 

- 

for 

the  Development  of 

LV:  Guidelines 
Education: STEM Skills 
LV:  Structural  Funds  Programme  for  the 
development of STEM 
infrastructure 
Colleges 

in 

- NL: 2020 National Technology Pact 
- IE: National Skills Strategy 2025 
- SI: Co-financing of Research Equipment 

Source: European Commission (2019). Selection based on the information available 
at the EC-OECD STIP Compass portal. 

127 

 
 
 
 
 
 
 
 
             
   
   
  
  
 
 
 
 
 
 
 
             
   
    
  
 
  
 
 
 
 
 
 
 
             
   
    
  
  
 
 
 
 
 
 
 
                       
             
 
    
 
 
 
 
 
 
 
             
   
   
  
 
  
 
 
 
 
 
 
            
   
   
  
 
  
 
 
 
 
 
 
            
 
   
  
 
 
  
 
 
 
 
 
 
           
 
   
  
 
 
  
 
 
 
 
 
 
  
  
 
 
 
 
 
 
 
       
 
   
  
  
 
 
 
 
 
 
 
       
 
   
  
 
  
 
 
 
 
 
 
       
 
   
  
 
  
 
 
 
 
 
 
       
   
  
 
  
 
 
 
 
 
 
   
 
   
  
 
  
 
 
 
 
 
 
   
 
 
  
 
 
 
  
 
  
 
 
 
 
 
   
 
  
 
  
 
 
 
 
 
 
 
  
 
 
  
 
 
 
 
 
 
 
 
                       
             
 
    
 
 
Box  2:  GUIDE  TO  MACRO-MODELLING  INNOVATION  AND 
INNOVATION POLICY 

Innovation attributes, the policy instruments addressed to correct 
distortions, and the key features a macro-model needs to include. 

NON-RIVALRY 

Issue:  Like  ideas,  technological  knowledge  can  be  used  by  many  agents  at  the 
same  time.  Innovators  cannot  fully  appropriate  the  social  return  of  their 
investments. 

Policy  modelling:  Non-rivalry  justifies  the  use  of  patents  to  protect  innovation 
(property-right-protection). But, patents generate monopoly rents. 

Macro-modelling implications: Patent protection and imperfect competition. 

KNOWLEDGE SPILLOVERS 

Issue:  Innovation  benefits  from  multiple  forms  of  (unintended)  knowledge 
transfers. 

Policy  modelling:  Tax-credits  and  subsidies  increase  the  private  return  to  R&D, 
closing the gap with the social return. 

Macro-modelling 
Networks. 

implications:  Different 

forms  of  knowledge  spillovers. 

MARKET SPILLOVERS 

Issue:  Market  spillovers  emerge  from  creative-destruction  and  designate  the 
transfer  of  monopoly  rents  between  firms  resulting  from  the  introduction  of 
innovations. Obsolescence; loss of value of current business due to the emergence 
of new technologies. Business stealing is a particular case. 

Policy  modelling:  Social  policies  designed 
redistributive effects of creative-destruction.  Competition policy. 

to  compensate 

the  negative 

Macro-modelling  implications:  Creative-destruction.  Schumpeterian  growth. 
Vintage technologies. Heterogenous firms and selection.  Market competition.  

FIRM HETEROGENEITY 

Issue:  Firm  heterogeneity  emerges  from  the  stochastic  nature  of  the  innovation 
process.  Misallocation;  Markup  dispersion  and  financial  constraints,  among  others, 
make  R&D  to  be  inefficiently  allocated  among  heterogeneous  firms,  negatively 
affecting efficiency.  

Policy  modelling:  Non-neutral  tax-credits,  research  subsidies  and  research  loans 
should  help  correcting  misallocation  by  supporting  promising  startups,  innovative 
firms and excellence in research.  

128 

 
Macro-modelling  implications:  Firm  heterogeneity,  entry  and  exit,  startups, 
firm’s age profiles. Non-neutral innovation policies. 

ABSORPTIVE CAPACITY 

Issue:  Limits  to  create  absorptive  capacity;  the  ability  of  a  firm  to  absorb 
knowledge spillovers generated by other firms; which depends on past R&D. 

Policy modelling: Policies addressed to promote absorptive capacity.  

Macro-modelling  implications:  Spillovers,  R&D  and  absorptive  capacity  in  a 
framework of heterogeneous firms. 

BASIC and APPLIED RESEARCH 

Issue:  Basic  research  is  addressed  to  the  advancement  of  scientific  knowledge. 
Applied  research  uses  scientific  knowledge  to  solve  technological  problems.  Basic 
research is at highest level of knowledge spillovers. 

Policy  modelling:  Subsidies  addressed  to  support  basic  research  and  the 
collaboration between scientists and firms. Public research centres and universities, 
public-private partnership. 

Macro-modelling  implications:  Basic  and  applied  research.  Innovation  and 
adoption. Technological diffusion. 

COMPETITION and MARKUP DISPERSION  

Issue:  Misallocation;  Innovation  generates  natural  and  patent  induced  market 
power,  heterogeneous  across  firms;  markup  dispersion  distort  the  allocation  of 
resources (relative prices are different from relative marginal productivities).  

Policy  modelling:  Non-neutral  tax-credit,  research  grants  and  research  loans 
designed to reduce misallocation, complemented with the regulation of competition.  

Macro-modelling  implications:  Heterogenous  firms;  heterogeneous  markups; 
market competition. 

UNCERTAINTY and FINANCIAL FRICTIONS 

Issue:  R&I  faces  large  idiosyncratic  uncertainty  and  asymmetric  information, 
making  financial  restrictions  to  be  particularly  high  due  to  adverse  selection  and 
moral hazard, particularly affecting startups and innovative SME. 

Policy  modelling:  No-neutral  public  loans  and  public  venture  capital  oriented  to 
finance highly innovative research projects. Financial regulation in general.  

Macro-modelling 
heterogeneous firms including startups; entry and exit. 

implications:  Financial  restrictions 

in  a 

framework  of 

129 

 
 
MARKET SIZE 

Issue:  Firms  are  more  willing  to  do  R&D  when  there  is  a  large  market  to  recover 
the fruits of their R&D investment. 

Policy modelling: Public procurement. 

Macro-modelling  implications:  Firm  heterogeneity,  innovation-by-incumbents, 
granularity. 

ENVIRONMENTAL SUSTAINABILITY 

Issue:  Market  forces  may  direct  technical  change  towards  an  unsustainable  path; 
failure to internalize the social cost of environmental degradation. 

Policy  modelling:  Innovation  policy  should  be  oriented  towards  the  protection  of 
the environment and the progress of society.  

Macro-modelling  implications:  Negative  externalities,  like  pollution,  should  be 
included. 

HUMAN CAPITAL, SKILLS AND INEQUALITIES 

Issue:  A  large  body  of  researchers  with  high  human  capital  is  needed  to  support 
the R&I sector. Skill obsolescence: Innovation and the diffusion of new technologies 
destroy  skill,  affect  the  wage  distribution,  employment  and  the  occupational 
structure.  

Policy modelling: A mix of innovation, education and social policies. In particular, 
a highly performant education system. 

Macro-modelling 
implications:  Heterogeneous  households;  heterogeneous 
workers;  skill-biased  technical  change.  Vintage  technologies,  occupational  choice, 
overlapping generations. Redistributive policies. 

COMPETITIVINESS AND GLOBALIZATION 

Issue:  knowledge  spillovers  diffuse  through  trade,  which  may  be  constrained  by 
trade  barriers  and  barriers  to  competitiveness  (the  ability  of  a  firm,  sector  or 
country to successfully compete in global markets). 

Policy  modelling:  Trade  policy  combined  with  pro-innovation  policies  aimed  at 
promoting competitiveness and developing comparative advantages in technological 
industries.  

Macro-modelling  implications:  Multi-country,  open  economy;  heterogeneous 
firms  with  endogenous  market  competition  and  concentration;  endogenous 
comparative advantages. 

130 

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The aim of the report is double. First, it aims to identify those 
characteristics  and  properties  of  research  and  innovation 
(R&I)  that  should  ideally  be  included  in  macro-models,  i.e., 
those  that  would  help  to  understand  the  behaviour  of 
innovation  and  to  produce  sound  evaluations  of  R&I  policies 
The  critical  properties  and  policies  of  the  R&I  process  may 
affect  the  incentives  on  doing  research  and  development 
(R&D)  and  the  probability  of  innovating,  how  much  is  or 
ought to be spent on R&I and the effects of R&I investments 
on  various  measures  of  economic  performance  and  other 
dimensions of relevance for policy makers, like environmental 
and  socio-economic  sustainability.  For  each  of 
these 
properties  and  policies,  the  report  aims  at  explaining  why  it 
would be worth to include them in the modelling, what effects 
their inclusion is expected to have on the performance of the 
model and the scope for R&I policy evaluation. The report will 
also  discuss  what  is  required  in  order  to  include  these 
properties  and  policies  in  the  modelling,  both  in  terms  of 
model  design  and  empirical  implementation.  Second,  it  aims 
at  identifying  the  main  R&I  policies  used  in  practice,  those 
that  macro-models  need  to  include  in  order  to  produce  well-
founded  assessments.  The 
report  closes  with  some 
conclusions  and  recommendations  on  how  to  move  forward 
the medium-long term agenda on the development and use of 
macroeconomic  models  to  inform  and  advise  research  and 
innovation policy making 

Studies and reports