Estimation and modelling impacts of pillar 2 measures on the agricultural sector : workshop proceedings.
Date: 2018-06-27
Institutions: Joint Research Centre
Subjects: common agricultural policy, conference proceedings, farming sector, research report, rural development, utilised agricultural area

Estimation and Modelling Impacts 
of Pillar 2 Measures on the 
Agricultural Sector 

Workshop proceedings 

Dudu, H. 

Ferrari, E. 

2018 

EUR 29234 EN 

 
 
 
This publication is a Conference and Workshop report by the Joint Research Centre (JRC), the European 
Commission’s science and knowledge service. It aims to provide evidence-based scientific support to the 
European policymaking process. The scientific output expressed does not imply a policy position of the 
European Commission. Neither the European Commission nor any person acting on behalf of the Commission is 
responsible for the use that might be made of this publication. 

Contact information  
Name: Emanuele Ferrari 
Address: Calle Inca Garcilaso, 41092, Seville, Spain 
Email: emanuele.ferrari@ec.europa.eu 
Tel.:+34 954 488461 

JRC Science Hub 
https://ec.europa.eu/jrc 

JRC111756 

EUR 29234 EN 

PDF 

ISBN 978-92-79-85965-6 

ISSN 1831-9424 

doi:10.2760/884943 

Luxembourg: Publications Office of the European Union, 2018 

© European Union, 2018 

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How to cite this report: Dudu, H., E. Ferrari, Estimation and Modelling Impacts of Pillar 2 Measures on the 
Agricultural Sector. Workshop proceedings, EUR 29234 EN, European Union, Luxembourg, 2018, ISBN 978-92-
79-85965-6, doi:10.2760/884943, JRC111756. 

All images © European Union 2018, except: cover, Budimir Jevtic, People at harvest (114856539), Source: 
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Contents 

Acknowledgements ................................................................................................ 2 

Abstract ............................................................................................................... 3 

1  Introduction ...................................................................................................... 4 

2  Background ...................................................................................................... 6 

3  Impact of Pillar 2 measures: Estimation procedures ............................................... 7 

3.1.1  Overview of current literature ................................................................ 7 

3.1.2  CAP Subsidies and Productivity of EU Farms ............................................ 8 

3.1.3  Productivity effects of EU domestic support ............................................. 9 

3.1.4  Assessing CAP impact on growth and jobs ............................................. 10 

3.1.5  The Impact of Green Box on Productivity in FADN European Regions ........ 11 

3.1.6  Discussion ........................................................................................ 11 

4  Modelling Pillar 2 Measures ............................................................................... 13 

4.1.1  Overview of current literature .............................................................. 13 

4.1.2  Rural Development in Regional CGEs .................................................... 13 

4.1.3  Lessons learned from modelling of Pillar 2 in the CAPRI-RD project .......... 14 

4.1.4  The RD module in MAGNET ................................................................. 15 

4.1.5  Rural Development in farm models: IFM-CAP ........................................ 17 

4.1.6  Discussion ........................................................................................ 18 

5  Conclusions .................................................................................................... 20 

References ......................................................................................................... 21 

List of abbreviations and definitions ....................................................................... 24 

List of figures ...................................................................................................... 25 

Annexes ............................................................................................................. 26 

Annex 1. Workshop Agenda .............................................................................. 26 

Annex 2. List of participants .............................................................................. 28 

Annex 3. Presentations ..................................................................................... 29 

i 

 
Acknowledgements 

This  report  has  been  prepared  by  Hasan  Dudu,  Emanuele  Ferrari  from  the  European 
Commission's  Joint  Research  Centre  (JRC).  It  is  based  on  the  discussions  and  the 
outcome of a workshop organized by Hasan Dudu, Emanuele Ferrari, Simone Pieralli and 
Koen  Mondelaers  with  the  support  of  Sandra  Marcolini  and  held  at  the  premises  of  the 
JRC in Seville (Spain) on 14th September 2017. 

We  would  like  to  thank  all  the  workshop  participants  for  sharing  their  knowledge  and 
experiences  on  estimating  and  modelling  Rural  Development  measures  of  the  EU 
Common  Agricultural  Policy.  The  panel  of  speakers  and  participants included  a  group  of 
external experts from a broad range of organizations: Marian Rizov (Lincoln University), 
Zuzana Smeets Kristkova (Wageningen Economic Research), Maria Garrone (KU Leuven), 
Marta  Guth  (Poznań  University  of  Economics  and  Business),  Alex  Gohin  (INRA), 
Katarzyna  Zawalinska,  (Polish  Academy  of  Sciences),  Wolfgang  Britz  (Bonn  University), 
Jeroen  Buysse  (Ghent  University)  and  Hubertus  Gay  (Organisation  for  Economic  Co-
operation and Development - OECD). 

In addition, we are very grateful to our colleagues Giampiero Genovese, Head of the Unit 
Economics  of  Agriculture,  for  his  continuing  support  and  contribution  to  the  workshop, 
Simone  Pieralli,  Pavel  Ciaian,  Ignacio  Pérez-Dominguez,  Robert  M'barek,  Kamel  Elouichi 
for  animating  the  discussions,  Pierre  Boulanger  and  George  Philippidis  for  their 
contribution. 

Authors 

Hasan Dudu and Emanuele Ferrari are with the Joint Research Centre (JRC.D.4, Seville). 

2 

 
Abstract 

The  EU's  Rural  Development  Programme  is  worth  €100  billion  from  2014-2020  and 
leverages  a  further  €61  billion  of  public  funding  in  the  Member  States.  Their  possible 
impacts on farmers' behaviour and on farmers' productivity have long been discussed in 
the literature which shows a large knowledge gap regarding the role of Pillar 2 subsidies 
on agricultural productivity and on the methodology to estimate and model these effects. 

The  workshop  organised  by  the  JRC  aimed  at  individuating  the  needed  steps  to  fill  the 
existing gaps and create the necessary consensus between academia and policy makers 
to produce policy relevant results. 

The  workshop  highlighted  a  few  key  elements,  both  on  estimation  and  modelling  sides 
that will be useful to JRC to adopt the necessary steps to fill the above mentioned gaps. 

3 

 
1  Introduction 

Since  2005,  the  European  Commission's  Joint  Research  Centre  (JRC),  commissioned  by 
the Directorate-General for Agriculture and Rural Development (DG AGRI), maintains and 
applies  the  integrated  Modelling  Platform  for  Agro-economic  Commodity  and  Policy 
Analysis  (iMAP)  (M'barek,  Britz,  Burrell,  &  Delince,  2012)  (M'barek  &  Delincé,  2015), 
whose  aim  is  to  deliver  in-house  policy  support  to  the  European  Commission.  The 
platform  is  the  backbone  of  operational  data-  and  model-based  support  to  agricultural 
and  related  policy  analysis  composed  of  databases  and  large-scale  agro-economic 
models.  In  stand-alone  or  combined  mode,  the  tools  assess  policy  options  with  its 
impacts on markets (production, demand, trade and prices), land use, environment and 
farmers' income from a global to a farm level. The platform is centred on the challenges 
of  the  European  agricultural  sector  to  ensure  jobs  &  growth  in  highly  competitive 
globalised markets and an environmentally sustainable production. 

The  analysis  mainly  focusses  on  the  current  and  future  Common  Agricultural  Policy 
(CAP). The CAP has come a long way since its inception in 1962. The reforms at the end 
of  1990s  under  the  auspices  of  ‘Agenda  2000’  introduced  for  the  first  time  notions  of 
rural livelihoods, environmental responsibility and sustainability. As a result, the system 
of  agricultural  payments  was  delineated  into  market  support  Pillar  1  and  Rural 
Development  (RD)  programs  (Pillar  2),  which  would  form  the  basis  for  administering 
payments  to  the  present  day.  The  future  design  of  the  CAP  post-2020  is  again  under 
consultation  following  the  European  Commission  President's  commitment  “to  modernise 
&  simplify  the  CAP"  as  a  guiding  principle.  Ongoing  policy  discussions  and 
recommendations are contributing to the debate, which resulted in a Communication on 
the  future  of  the  CAP  after  2020  at  the  end  of  2017.  The  stakeholder  consultation  and 
the Inception Impact Assessment on the "Communication on Modernising and Simplifying 
the Common Agricultural Policy" are the most recent steps in this important endeavour. 

To support DG AGRI in this debate, the JRC prepared a series of studies among which the 
so-called  Scenar  2030  (M'Barek,  et  al.,  2017)  contributes  to  the  analysis  of  selected 
scenarios  and  provides  a  framework  for  further  exploration  along  the  process  of 
designing the future CAP and aims at identifying major future trends  and driving factors 
for European agriculture and rural regions and the perspectives and challenges resulting 
from  them  employing  a  set  of  economic  simulation  models  (MAGNET,  CAPRI  and  IFM-
CAP) included into iMAP. 

The  Scenar2030  preparatory  work  began  in  2015;  more  than  a  year  in  advance  of  the 
policy  options  announced  in  the  Inception  Impact  Assessment  (IIA).  One  of  the  most 
relevant efforts in preparing the report was the enhancement of the modelling of Pillar 2 
payments.  Indeed,  the  impact  of  agricultural  subsidies  on  productivity  has  long  been 
discussed  in  the  literature  without  any  clear  conclusions.  Depending  on  the  model 
specification,  statistical  method  and  data  source,  mixed  results  are  reported.  The 
empirical  evidence  shows  that  there  is  still  a  large  gap  in  the  literature  regarding  the 
understanding of the role of CAP Pillar 2 subsidies on agricultural productivity. 

Most of existing studies look at the effects on total factor productivity, whereas in reality, 
different  types  of  CAP  subsidies  might  provoke  a  factor-biased  technical  change  (for 
instance,  human  capital  subsidies  are  expected  to  stimulate  labour  productivity  more 
than  land  productivity).  None  of  the  studies  so  far  can  provide  reliable  inputs  for 
parametrization of economy-wide models (e.g. partial or general equilibrium models such 
as MAGNET or CAPRI) due to different use of functional forms and due to the prevalence 
of  micro-level  studies  which  causes  a  difficulty  of  generalization  of  the  results  on  the 
sector level. 

This  lack  of  understanding  is  both  a  constraining  factor  for  policy  makers  that  are 
interested in ex-post and/or ex-ante evaluation of the effectiveness of public investments 
and for modellers who need a reliable quantification of subsidies impact on productivity in 
their  ex-ante  exercises  such  as  Scenar  2020  (Nowicki,  Weeger,  &  H.v.  Meijl,  2007)  and 
(Nowicki, Goba, Knierim, Meijl, & M. Banse, 2009).  

4 

 
Latest published research by (Dudu & Smeets Kristkova, 2017) contributes to bridge this 
gap by providing a comprehensive empirical assessment of the role of CAP subsidies on 
productivity  across  EU-27  countries.  More  specifically,  the  contribution  of  this  work  is 
three-fold:  

i. 

ii. 

iii. 

the  study  uses  regional  (NUTS-2)  level  data  which  allows  us  to  capture  sector- 
rather than farm-level behaviour, 
the  effects  of  the  four  major  types  of  Pillar  2  subsidies  on  factor-augmenting 
technical change can be compared in a systematic way, 
The  adopted  methodological  framework  enables  to  simultaneously  estimate  both 
Constant  Elasticity  of  Substitution  (CES)  and  productivity  parameters,  which  can 
be readily used in impact-assessment models. 

Nevertheless,  combining  available  research  with  expert  opinions  helps  to  better  qualify 
some of the results coming from pure econometric estimation.  

The  econometric  estimations  and  the  application  of  different  productivity  rates  clearly 
showed  the  need  for  more  research  related  to  key  parameters  used  for  CAP  analysis, 
which  then  reveals  to  be  extremely  relevant  in  steering  the  final  simulation  results 
(Matthews,  Salvatici,  &  Scoppola,  2017).  Based  on  these  considerations,  the  JRC 
organised  a  workshop  in  Seville  on  the  14th  September  2017.  The  general  objective  of 
the  workshop  was  to  discuss,  with  European  academics  and  modellers,  current 
approaches  to  estimate  impacts  of  different  Rural  Development  measures  on  the 
agricultural  sector  and  ways  to  model  these  impacts  within  economic  simulation  models 
(partial  and  computable  general  equilibrium  models).The  specific  objectives  of  the 
workshop were to: 

  Discuss  the  current  literature  in  terms  of  estimation  and  modelling  of  Pillar  2 

payments. 

  Discuss the linkages between empirical evidence and modelling techniques. 
  Discuss the methods currently applied at JRC in sight of possible enhancements. 

This  report  presents  a  synthesis  of  the  workshop,  summarising  the  presentations  and 
discussions  in  the  different  sessions.  The  report  is  organized  following  the  structure  of 
the workshop. The first session of the workshop sets the scene and introduces the main 
topic  at  stake.  The  second  session  aims  at  providing  current  studies  estimating  the 
impacts  of  Pillar  2  measures  on  agricultural  productivity  and  other  main  factors  (e.g., 
growth,  employment).  The  third  session  provides  an  overview  of  existing  modelling 
approaches,  general  and  partial  equilibrium,  to  simulate  effects  of  shocks  in  Pillar  2 
payments  on the  EU agricultural sector. Finally, the  fourth session  concludes and draws 
some key messages on how the JRC shall proceed in future efforts on the topic. 

5 

 
 
2  Background 

The  workshop  started  with  an  introduction  by  Giampiero  Genovese,  Head  of  the 
Economics  of  Agriculture  Unit  with  the  Sustainable  Resources  Directorate  of  the  JRC  in 
which  all  participants  were  warmly  thanked  for  their  participation  and  the  key  topics  of 
the workshops highlighted. 

The  Economics  of  Agriculture  unit  of  the  he  Joint  Research  Centre  (JRC)  prepared  a 
study, which started at the end of 2015 and was published at the end of 2017 (M'Barek, 
et  al.,  2017),  aiming  to  analyse  the  impact  on  the  agricultural  sector  of  stylised 
scenarios, reflecting the main drivers of policy debate and thus providing a framework for 
further exploration of the process of designing the future CAP. In the frame of this study, 
three  main  economic  models,  from  the  iMAP  platform  (M'barek  &  Delincé,  2015)  were 
employed, the global multisector Computable General Equilibrium (CGE) model MAGNET 
(Woltjer & Kuiper, 2014) and the partial equilibrium (PE) models CAPRI and IFM-CAP. 

The JRC has improved the representation of the CAP in these models and in particularly 
the MAGNET model has been enhanced to capture the allocation of all CAP expenditures, 
the  level  of  coupling  of  Single  Farm  Payments  and  the  modelling  of  Rural  Development 
payments (Boulanger & Philippidis, 2014) and (Boulanger & Philippidis, 2015). The Rural 
Development  (or  Pillar  2)  payments  in  particular  might  have  impacts  on  productivity  of 
the  farmers  which  have  long  been  discussed  in  the  literature  without  any  clear 
conclusions.  Given  that  the  EU's  Rural  Development  Programme  is  worth  €100  billion 
from  2014-2020  and  leverage  a  further  €61  billion  of  public  funding  in  the  Member 
States,  understanding  their  impacts  on  farmers'  behaviour  remains  a  crucial  task  for 
agricultural economists.  

The  empirical  evidence  shows  that  there  is  still  a  large  knowledge  gap  in  the  literature 
regarding  the  understanding  of  the  role  of  CAP  Pillar  2  subsidies  on  agricultural 
productivity.  The  analysis  of  the  growing  literature  on  the  topic  shows  no  clear 
conclusion: results are typically dependent on crop, farm type, region, time, aggregation 
level  etc.  so  that  no  clear  policy  message  can  be  deducted.  So  far,  there  is  no 
comprehensive  study  at  NUTS  II  level  for  the  EU,  and  in  many  cases  subsidies  are 
generally  treated  ad-hoc  and  mostly  as  a  uniform  category.  Additionally,  there  is  no 
consensus  on  the  methodology:  form  of  production  function,  estimation  approach, 
parametrization etc. 

The  importance  of  producing  reliable  results  has  been  underlined  by  Koen  Mondelaers 
(DG AGRI). The credibility of the results produced by scientists and applied researchers is 
crucial.  All  policy  DGs,  and  DG  AGRI  in  particular,  need  evidence  to  analyse  different 
policy options and model results to quantify these options in the frame of the upcoming 
Impact  Assessment  (IA)  of  the  Common  Agricultural  Policy  (CAP).  The  results  of  these 
models  will  be  even  more  relevant  in  the  discussion  of  the  next  Multiannual  Financial 
Framework  (MFF)  of  the  European  Commission  (EC)  which  will  have  to  deal  with  the 
Brexit case and almost unavoidably with budget cuts. In addition, the shift from Pillar 1 
to Pillar 2 is, since some time, one of the possible reforms of the CAP, making modelling 
of  Rural  Development  always  a  more  important  task  for  agricultural  economists  and 
modellers. For the policy makers in DG AGRI, the workshop was relevant to enhance the 
model  credibility  and  to  convince  policy  makers  of  the  usability  of  model  results.  For 
these  reasons,  the  workshop  was  relevant  to  discuss  between  academics  and  policy 
makers  about  techniques  to  estimate  and  model  the  impact  of  Rural  Development 
payments  in  economic  models.  Additionally,  it  was  a  good  forum  to  discuss  the 
importance  of  having  good  estimates  of  the  parameters  and  to  explain  results  which, 
according to policy makers, looked as counterintuitive in some of the analysed studies.  

For  all  these  reasons,  the  JRC  decided  to  organise  a  workshop  gathering  some  of  the 
most  relevant  experts  in  the  topic  with  the  aim  of  individuating  the  needed  steps  to  fill 
the  existing  gaps  and  create  the  necessary  consensus  between  academia  and  policy 
makers to produce policy relevant results. 

6 

 
3  Impact of Pillar 2 measures: Estimation procedures 

3.1.1  Overview of current literature 

The first session of the workshop was dedicated to the approaches used to estimate the 
impacts  of  CAP  measures  on  productivity.  The  session  started  with  the  presentation  of 
Hasan  Dudu  (JRC)  on  a  brief  survey  of  the  existing  literature.  The  presentation  started 
with  the  discussion  of  the  difference  between  the  Technical  Efficiency  (TE)  and  Total 
Factor Productivity (TFP). Total factor productivity explains the change in the production 
that  cannot  be  attributed  to  the  changes  in  the  production  factors.  On  the  other  hand, 
technical efficiency explains ability of a firm (or any economic agent) to produce maximal 
output by using a given amount of factor  (Kumbhakar & Lovell, 2000). TFP explains the 
deviation  of  the  economic  agents  from  an  estimated  production  function  while  TE is  the 
distance  of  firms  to  the  most  efficient  production  function  (i.e.  the  production  frontier).  
In  other  words,  former  measures  the  distance  to  an  average  production  function  while 
the  latter  measures  the  distance  to  a  frontier  (i.e.  most  efficient)  production  function. 
These two are not necessarily same, if the average and the frontier production functions 
are  not  parallel  to  each  other  (Figure  1).  Since  TFP  and  TE  measures  different  things, 
impacts of subsidies on these are not necessarily same. This difference needs to be taken 
into account while considering the findings in the literature.  

Figure 1: Average production function vs. production frontier 

Another  way  to  reflect  the  differences  of  TFP  and  TE  is  to  look  at  their  composition.  A 
change  in  TFP  can  be  attributed  to  technical  efficiency  change,  technical  change  (i.e. 
change  in  production  technology),  scale  efficiency  change,  allocative  efficiency  change 
etc… (Key, McBride, & Mosheim, 2008). Hence TFP and TE does not necessarily move in 
the same direction, as shown by (Key, McBride, & Mosheim, 2008) for US hog production 
where TE is negative but TFP is positive.  

In  general,  there  is  a  tendency  in  the  literature  to  link  agricultural  subsidies  to  lower 
technical  efficiency  or  productivity.  However  empirical  findings  are  contradicting.  Some 
authors  report  negative  findings  (Latruffe,  Bravo-Ureta,  Moreira,  Desjeux,  &  Dupraz, 
2011); (Zhu & Lansink, 2012); (Cechura, Grau, Hockmann, Levkovych, & Zdenka, 2016) 
while others find a positive relationship (Kumbhakar & Lien, 2010); (Latruffe & Desjeux, 
2016).  There  are  also  studies  that  find  ambiguous  relationship:  either  no  impact  of 
subsidies (Pufahl & Weiss, 2009); (Ratinger, Medonos, & Hruška, 2014) or different sign 
of impacts on TE and TFP (Fogarasi & Latruffe, 2009). 

Indeed,  only  a  few  studies  comprehensively  examine  the  impacts  of  CAP  subsidies  at 
NUTS II level for the EU member states and compare the productivity effects across the 
different CAP subsidy categories. Furthermore, most studies use farm-level data, mostly 
from Farm Accountancy Data Network (FADN), whilst capturing private returns, does not 

7 

 
 
 
consider the public or social returns that are obtained from public investment. Moreover, 
in  most  of  the  studies,  agricultural  subsidies  are  treated  ad-hoc  and  as  a  uniform 
category. However, when separating the individual subsidy groups, productivity effects of 
subsidies might change direction. 

Examining  the  relevant  literature,  a  significant  proportion  of  the  empirical  evidence  is 
based on the use of a parametric Stochastic Frontier Approach (SFA) or a non-parametric 
Data  Envelopment  Analysis  (DEA)  method.  The  general  consensus  emerging  from  these 
studies is that a negative technical efficiency effect from subsidies is observed. However, 
as  shown  in  various  papers,  this  negative  impact  on  technical  efficiency  is  not 
incompatible  with  positive  effect  on  productivity.  Depending  on  the  scope  of  the  study, 
the results differs: (Mary, 2013) and (Kumbhakar & Lien, 2010) reports negative impacts 
on  TFP  (although  the  latter  reports  positive  impact  on  TE);  (Serra,  Zilberman,  &  Gil, 
2008)  report  ambiguous  impact  on  TFP  and  (Rizov,  Pokrivcak,  &  Ciaian,  2013)  and 
(Czyżewski, Guth, & Matuszczak, 2018) find mainly negative but improving impacts after 
decoupling or depending on the type of the support programme.  

There is no comprehensive study in the literature that analyse the productivity at NUTS 2 
level  for  the  whole  Europe.  Lastly,  no  study  estimates  a  CES  production  frontier  or 
production  function  to  analyse  the  impact  of  CAP  payments  on  factor  augmenting 
productivity. 

3.1.2  CAP Subsidies and Productivity of EU Farms 

The second presentation of the session, by Dr. Smeets-Kristkova, presented the findings 
of  (Dudu  &  Smeets  Kristkova,  2017).  The  aim  of  the  study  was  to  fill  the  gap  in  the 
literature  by  taking  into  account  the  fact  that  different  CAP  subsidies  are  likely  to  have 
different productivity effects. In addition, the study copes with the need for an estimation 
approach  that  is  consistent  with  economic  simulations  models  used  for  impact 
assessment. To this end, the study uses a novel theoretical approach by linking subsidies 
to  factor-augmenting  technical  change  in  the  CES  framework  and  employs  an 
econometric  model  that  enables  to  test  explicitly  the  endogeneity  problem  i.e.,  the 
Generalized  Method  of  Moments  (GMM).  Further  the  study  relies  on  a  data  set  that  is 
conceptually in-line  with  the  CGE  data  following  the  System  of  National Accounts  (SNA) 
conventions. 

The  study  relies  on  several  assumptions  to  be  able  to  estimate  the  impacts  of  the  CAP 
subsidies in a consistent way. First of all, the model estimates a CES production function 
which necessitates the assumption of constant returns to scale1. This assumption is also 
made  by  the  CGE  models  used  for  the  impact  assessment  and  does  not  contradict  with 
the general impact assessment framework.  

A second set of assumptions are necessitated by the dataset employed. The study mainly 
relies  on  at  NUTS  II  level  Economic  Accounts  for  Agriculture  Rev  1.1  (EAA97)  data  set 
which  is  consistent  with  SNA  definitions  and  is  consistent  with  the  assumptions  and 
definitions used in the CGE modelling. The main assumptions while preparing the dataset 
are as follows:  

  Value added price change is same for all NUTS regions within in a country 

  Total area of rented land in a NUTS2 region does not change significantly over the 

study period (2007-2013).  

  Change in the price of capital is same for all NUTS2 regions in a country.  

Other minor assumptions and details of the calculations done to estimate the model can 
be  found  in  (Dudu  &  Smeets  Kristkova,  2017).  After  the  data  cleaning  process,  the 
resulted  dataset  was  complete  only  for  10  countries  (Austria,  Bulgaria,  Czech  Republic, 

1 CES production function can be generalized by adding a scale parameter to the exponential. However, we did 
not  use  this  specification  as  it  is  not  very  prevalent  in  the  CGE  literature.  See  (Kmenta,  1967)  for  the 
estimation of the generalized CES function.  

8 

 
                                           
Denmark,  Greece,  Finland  France,  Hungary,  Netherlands  and  Portugal).  The  CAP  data 
was taken from (Boulanger & Philippidis, 2014) which is also used by JRC to supply GTAP 
centre with EU agricultural support data.  

The  results  of  the  study  suggested  that  exogenous  productivity  growth  is  zero 
(statistically  insignificant)  which  is  consistent  with  the  finding  in  the  literature.    The 
difference between new and old member states is significant only  at 10% level and new 
member  states  have  a  higher  exogenous  productivity  growth.  Human  capital,  physical 
capital  and  agro-environmental  CAP  payments 
improve  the  productivity.  Rural 
Development related CAP payments do not affect the agricultural productivity. 

The descriptive analysis of the relationship between input and land use growth and ratio 
of agro-environmental payments to output revealed an important fact: Land use declines 
with  increasing  agro-environmental  payments  while  intermediate  input  use  do  not 
change  significantly2.  That  is,  the  higher  the  share  of  agro-environmental  payments  in 
the output of a region, the more input they use without changing the land use. Thus, the 
positive impact of agro-environmental payments in productivity can be attributed to the 
higher  intermediate  input  use  by  the  regions  that  receive  relatively  higher  agro-
environmental subsidies.  

The  study  suffers  from  several  deficiencies:  It  includes  only  10  countries  (due  to  data 
availability);  many  variables  are  missing  and  substituted  by  proxies;  Pillar  1  is  not 
included;  relies  on  constant  returns  to  scale  and  CES  assumptions;  do  not  take  into 
account different nesting structures (due to missing data on the share of different factors 
in payments); and lastly excludes intermediate inputs.  

3.1.3  Productivity effects of EU domestic support 

Professor  Marian  Rizov  presented  their  study  on  the  impact  of  CAP  subsidies  on 
productivity  of  EU  farms.  Rizov  et  al.  (2013)  use  FADN  dataset  and  modified  Olley  and 
Pakes  (1996)  semi-parametric  TFP  estimation  methodology  to  directly  introduce  the 
effect  of  subsidies  in  a  model  of  unobserved  productivity.  This  way  they  estimate 
consistent  production  function  coefficients  within  sectors  and  countries  and  obtain 
unbiased farm-specific TFP measures. Further they verify the impact of subsidies on TFP 
by the means of correlation analysis and GMM regressions.  

After  mentioning  the  mixed  findings  in  the  literature  and  theoretical  explanations  given 
by several authors, Professor Rizov introduced the theoretical framework underlying the 
estimation  algorithm.  Rizov  et  al.  (2013)  estimate  a  Cobb-Douglas  production  function 
with  unobserved  productivity  component  applying  extensions  to  the  Olley  and  Pakes 
(1996)  estimation  approach  suggested  by  Ackerberg  et  al.  (2007).  The  estimation 
methodology  deals  well  with  simultaneity  and  selection  biases  as  well  as  directly 
incorporates the impact of subsidies in a model of unobserved productivity utilised in the 
estimated  specifications.  The  sample  consists  of  farm  level  production  data  from  FADN 
data set for EU15 for the period 1995-2008. The sample represents the 90% of land use 
in EU15.  

The  findings  of  the  study  suggest  that  most  member  states  exhibit  constant  or  slightly 
increasing  returns  to  scale.  The  farm  productivity  measures  aggregated  by  country  and 
farm  type  present  evidence  that  productivity  level  and  growth  systematically  differs 
between  the  north  and  south  European  countries.  The  study  finds  negative  correlation 
between  subsidies  and  farm  productivity  in  the  period  prior  to  the  decoupling  reform; 
after  decoupling,  in  2005/2006,  the  correlation  between  subsidies  and  productivity  is 
more nuanced as in several countries it turns positive. 

2 Note that, it was not possible to include the intermediate inputs in the estimations as a factor of production 
due  to  limitations  in  the  data  set.  Hence,  the  relationship  between  agro-environmental  payments  and 
intermediate input is analysed descriptively.  

9 

 
                                           
3.1.4  Assessing CAP impact on growth and jobs 

Dr.  Garrone  presented  preliminary  results  of  two  joint  studies  on  the  impact  of  CAP  on 
growth  jobs  and  regional  GDP  growth.  Garrone  et  al.,  (2017)  and  Olper  et  al.  (2017) 
estimate  the  ex-post  impacts  of  CAP  as  a  treatment  by  employing  panel  data 
econometrics.  In  the  empirical  estimation  they  differentiate  between  coupled  vs. 
decoupled  support  and  CAP  Pillar  1  vs.  CAP  Pillar  2  payments..  The  study  uses  the 
Clearance Audit Trail System (CATS) database for 220 NUTS2 regions for EU28 over the 
period  2004-2014.  They  disaggregate  CAP  Pillar  1  payments  as  coupled  and  decoupled 
and CAP Pillar 2 payments.  

There  are  few  studies  in  the  literature  about  the  impact  of  CAP  on  GDP  per-capita 
growth. Esposti (2007) finds a positive CAP effect on regional GDP growth during 1990-
2000,  but  also  reports  that  the  share  of  agriculture  in  employment  has  a  negative  GDP 
growth effect. On the other hand, (Crescenzi & Giua, 2016) report insignificant impact of 
CAP on GDP growth, although both Pillar 1 and Pillar 2 payments have a positive effect in 
areas with high endowments of infrastructure and R&D. 

The preliminary findings of (Olper, Emmers, Garrone, & Swinnen, 2017) suggests that for 
EU28,  overall  CAP  subsidies  has  no  effect  on  per-worker  GDP  growth,  while  similar  to 
Esposti  (2007),  they  find  that  GDP  growth  is  lower  in  regions  with  a  higher  share  of 
agricultural  employment.  In  addition,  they  find  that  decoupled  CAP  payments  are 
associated  positively  with  agricultural  labour  productivity  growth  (see  results  below). 
Thus,  the  last  effect  seems  not  strong  enough  to  overcome  the  productivity  gap  with 
other  sectors,  so  the  association  of  a  region’s  share  of  agricultural  employment  with 
regional growth was negative. 

The  analysis  of  the  relationship  between  CAP  and  Agricultural  productivity  focuses  on 
agricultural  value  added  per  work.  The  preliminary  results  suggest  a  positive  impact  of 
Pillar  I  and  Pillar  II  payments.  The  positive  effect  comes  almost  exclusively  from 
decoupled  Pillar  I  and  Pillar  II  subsidies.  The  CAP  seems  to  be  effective  in  increasing 
farmers’  investments  in  agricultural  productivity.  In  theory,  CAP  payments  could  reduce 
farmers’ credit constraints, allowing them to invest more. This should matter most in the 
NMS.  In  addition,  CAP  support  would  reduce  farmers’  exposure  to  income  risk,  which, 
again,  would  lead  them  to  invest  more.  The  statistical  results  are  consistent  with  this 
theory. The shift from “coupled” towards “decoupled” payments and Pillar II is associated 
with higher agricultural productivity growth. In theory, the coupled CAP payments could 
lead  to  a  distortion  of  farmers’  resource  allocation,  which  over  time  would  reduce 
productivity growth3. A shift away from these coupled payments would have the opposite 
effect.  This  is  consistent  with  what  the  statistical  analysis  finds,  and  with  findings  of 
several other studies showing that agricultural productivity in the EU benefited from the 
shift from “coupled” to “decoupled” subsidies4 

Lastly  the  preliminary  results  for  the  impact  of  CAP  on  the  agricultural  employment 
suggest  that  for  EU28  there  is  a  significant  negative  association  between  the  outflow  of 
labour  from  agriculture  and  the  decoupled  Pillar  I  and  Pillar  II  payments.  Hence,  on 
average,  decoupled  CAP  subsidies  maintain  employment  in  EU  agriculture.  No  such 
association was found for the  coupled payments. For EU15, effects  of  Pillar 1  decoupled 
and  Pillar  2  are  both  strongly  negative  while  coupled  payments  have  a  positive  and 
significant effect. Lastly for EU13 the coupled and decoupled components have negative 
impact, but barely significant (10% level). 

Overall, the results suggest that there may not be a trade-off between employing people 
in  agriculture  and  supporting  increases  in  agricultural  productivity.  Decoupled,  non-
distortionary  payments  stimulate  higher  productivity  agriculture.  They  also  have  a 
positive  association  with  agricultural  employment.  In  this  way  productivity  growth  and 
employment can go hand in hand. 

3 Some earlier studies also find that the higher the share of subsidies in total farm income, the more negative 
the impact, e.g. Zhu and Lansink (2010); Bojnec and Latruffe (2013); and Zhu et al. (2012) 
4 Rizov et al. (2013); Kazukauskas et al (2014) 

10 

 
                                           
3.1.5  The  Impact  of  Green  Box  on  Productivity  in  FADN  European 

Regions 

Dr.  Guth  has  presented  their  joint  work,  (Czyżewski,  Guth,  &  Matuszczak,  2018)  where 
they test the possible impact of CAP programmes on the ‘greening’ trend, which have a 
positive  impact  on  productivity  in  FADN  regions.  They  use  a  two  stage  approach  where 
they first identify the clusters of EU-FADN regions differ significantly in terms of farming 
models.  In  the  second  step  they  estimate  the  impact  of  CAP  "green  programmes"  on 
productivity.  

For  the  first  stage  of  the  study,  (Czyżewski,  Guth,  &  Matuszczak,  2018)  use 
agglomerative cluster analysis (Ward's method) classifies the FADN regions according to 
the  criterion  of  percentage  contributions  to  the  different  “boxes”  of  subsidies.  In  the 
second stage they analyse the productivity of each cluster with panel data regression. 

The  cluster  analysis  identified  three  clusters  of  regions  with  different  farming  models 
according to the support structure: 

  moderately sustainable,  

  Weakly  sustainable,  where  the  contribution  of  single  farm  and  area  payments  to 

the political rent was markedly higher than in the others, at close to 80%.  

  Strongly sustainable, combining various forms of assistance to farms. 

Only  models  A  and  C  were  to  a  greater  or  lesser  extent  aligned  with  the  development 
priorities of the European agricultural model emphasised in the new financial perspective 
of 2014–2020. 

Panel  data  analysis  shows  that  three  clusters  of  regions  in  the  EU28  countries  differs 
significantly in terms  of  the  structure  of  CAP  schemes.  In  the  most  numerous  group,  of 
the  EU28  regions,  the  moderately  sustainable  model  A  operated,  primarily  combining 
direct support with payments for public goods. The second most numerously represented 
was  the  weakly  sustainable  model  B,  in  which  support  consisted  chiefly  of  single  farm 
and  area  payments.  The  smallest  group  of  regions  featured  a  highly  sustainable  model, 
combining  various  forms  of  support  for  farms  at  similar  levels  (both  through  direct  and 
production  subsidies,  and  through  payments  for  the  supply  of  public  goods  and  to  a 
lesser  degree  the  subsidisation  of  investment).  An  agricultural  support  model  which 
reflects  structural  farming  differences  is  a  significant  factor  in  determining  the 
productivity of intermediate consumption over the whole studied period. 

The  direction  of  the  influence  of  studied  schemes  depends  on  the  sustainability  level  of 
farming  in  the  respective  regions.  Hence,  the  single  payments  might  have  a  positive 
influence  on  productivity  only  in  the  old  member  countries  included  in  the  most 
sustainable  model,  while  the  environmental  subsidies  positively  contributed  to 
productivity only in moderately sustainable model of farming. 

Although  there  is  evidence  for  a  negative  general  impact  of  CAP  subsidies  on 
productivity,  in  each  cluster  we  can  observe  CAP  programmes  which  positively  affected 
the productivity of intermediate consumption. Cluster A (moderately sustainable model of 
farming), which encompasses the majority of new member  states, was characterised by 
the highest number of such schemes. 

3.1.6  Discussion  

Alex  Gohin  noted  that  many  drivers  on  the  productivity  makes  identification  a  difficult 
task especially if the price volatility is taken into account. He  criticized  (Dudu & Smeets 
Kristkova, 2017) for:  

11 

 
 
 
 

 

Ignoring the winners and losers from the productivity gains due to CAP payments. 
He  pointed  that  Constant  returns  to  scale  assumptions  implies  that  there  are  no 
profits.  

Ignoring the family labour in the study which is an important factor of production 
and  has  different  characteristics  then  the  paid  labour.  He  defended  the  fact  that 
without taking into account the ownership of the factors, the study cannot derive 
conclusions about important policy questions.  

  The exclusion of variable inputs 

 

 

 

Importance  of  the  relationship  between  agri-environmental  measures  and 
productivity.  He  pointed  that  the  mentioned  relationship  is  now  only  correlation 
and not causality. 

Ignoring  the  relative  price  effects  which  can  play  an  important  role  in  the 
production  decision  of  the  farmers.  The  prices  of  outputs  and  inputs,  excluding 
land, are likely exogenous but only at the local level.  

Ignoring  the  value  of  public  goods  which  are  difficult  to  include  in  such  models. 
The value added calculations only capture private effects but not public goods. 

  Not  splitting  the  agro-environmental  payments  for  contract  and  non-contract 
farmers.  Since  these  payments  are  for  a  limited  period  he  suggested  taking  this 
fact into account?  

From the econometric point of view Dr. Gohin raised the following issues:  

  The  identification  of  causality:  Is  CAP  causing  higher  productivity  or  more 

productive regions attracting more subsidies?  

  A  series  of  instruments  for  the  endogenous  prices,  different  than  lagged  prices, 
should  be  used  to  account  for  endogenous  prices.  A  sensitivity  analysis  needs  to 
be run to check the robustness of the results against the lag selection.  

  What about the cross effects? These need to be taken into account.  

  The model ignores the  benefits of the provision of the public goods.  How  can we 

include them? 

Koen Mondelaers (DG AGRI) criticised the constant returns to  scale assumption and the 
CES  assumption,  leading  to  a  constant  elasticity  of  substitution  among  inputs.  He  also 
stressed that differences among Member States' responses to  different expenditures are 
considered  only  via  a  dummy  variable  for  all  New  Member  States,  while  one  for  each 
state  might  be  more  appropriate.  Mr  Mondelaers  also  expressed  his  concern  about 
considering with marginal methods' differences a big change such as a no-CAP scenario. 
Finally, he said that also Pillar 1 subsidies should be considered in the analysis. 

Dr Dudu clarified that the approach followed is based on the system of national accounts 
and that family labour is already included in the labour statistics used (from EUROSTAT). 
In the same way, environmental benefits as public goods, because are not in the system 
of  national  accounts,  are  also  ignored  in  this  contribution  for  the  moment.  He  also 
mentioned  that  variable  dummies  for  specific  member  states  have  been  used  without 
particular change of the results. 

The study by Ms Guth sparked a question on the way of clustering the regions in the EU. 

12 

 
4  Modelling Pillar 2 Measures 

In  this  section,  after  a  brief  literature  review  of  models  dealing  with  impacts  of  Pillar  2 
payments  on  the  EU  agriculture,  a  selection  of  available  examples  drawn  from  PE,  CGE 
and farm-level model is presented. 

4.1.1  Overview of current literature 

The  session  was  dedicated  to  the  approaches  used  to  model  Rural  Development 
payments  in  economic  simulation  models.  The  session  was  opened  by  a  presentation  of 
Emanuele Ferrari on a brief survey of the existing modelling approaches. 

The  first  consideration  is  that  in  general,  RD  measures  are  difficult  to  be  taken  into 
account  by  these  models  for  several  reasons:  First  of  all,  due  to  their  complexity  and 
number.  There  are  118  different  Rural  Development  Programmes  (RDP)  in  the  28 
Member States, with 20 single national programmes and 8 Member States opting to have 
two  or  more  (regional)  programmes.  Member  States  and  regions  draw  up  their  Rural 
Development programmes based on the needs of their territories and addressing at least 
four of the six common EU priorities. 

Secondly,  it  is  somehow  complicated  to  translate  into  modelling  what  policy  makers 
thinks the impact of each policy should be on the economy. First of all, all measures are 
notified in green-box to WTO so they should likely have very small impacts on production 
and  trade  via  productivity  and  other  (land,  labour  use)  changes.  Ana  additional 
complexity  is  given  by  the  difficulty  to  link  the  reduced  and  sparse,  but  growing, 
empirical evidence to economic modelling to improve the model parametrisation. 

The main attempts to take into consideration Pillar 2 Payments in economic modelling are 
presented below and include the IFM-CAP, CAPRI RD and AMGNET models from the iMAP 
platform and other regional CGE approaches. 

In conclusions, the analysed literature concludes that a CGE approach might be required 
for a comprehensive ex-ante analysis of Pillar 2 payments as many measures have direct 
and indirect outside the agriculture sector (factor markets, construction, tourism…). 

Secondly,  most  of  the  modelling  attempts  have  a  regional  (NUTS2)  dimensions  it 
becomes very  complicate to deal with Pillar 2  payments at MS  or EU28 averages, given 
the  extreme  differences  in  which  the  projects  are  designed  and  implemented  by 
European regions. 

Finally, as a clear link to the first session of the workshop and the estimation of impacts 
of  these  policies,  the  parametrisation  of  the  models  (and  the  way  in  which  different  RD 
measures  are  mapped  into  more  general  categories  to  be  more  easily  modelled) 
essentially determines the outcome of any modelling attempts. 

4.1.2  Rural Development in Regional CGEs 

Katarzyna Zawalińska from the–  Institute of Rural and Agricultural Development (IRWiR 
PAN)  of  the  Polish  Academy  of  Sciences  presented  the  approach  of  modelling  Rural 
Development in regional CGE models, with focus on Poland. 

She first underlined a few concerns about the use of these models. 

In  the  first  place  they  are  related  to  the  representation  of  "rurality"  in  regional  CGE 
models. When models take into account NUTS3 region, the OECD/Eurostat categorization 
of rural vs urban regions hold. On the other hand, when models work at NUTS2 level, this 
distinction is less precise. This spatial distinction is crucial to model correctly households 
(rural  households  might  have  different  consumption  patterns  than  urban  households), 
land (e.g., land under LFA can have different parameters than "normal" land). 

Secondly,  when  taking  into  account  RD  measures  new  challenges  are  merging  with 
reference to technological and environmental impact of these measures. 

13 

 
In  more  practical  terms,  when  modelling  RD  measures  the  first  task  is  to  group  them 
according  to  a  chosen  approach.  Grouping  can  be  made  following  the  design  of  them 
measures or the use  of the money. For example, analysing a survey  on the direction of 
spending  of  RDP  measures  by  purpose  in  Poland  it  emerges  that  from  most  Polish 
farmers Single Farm Payments (SFP) and LFA payments are equivalent. 

Katarzyna  Zawalińska  presented  an  example  of  modelling  LFA  with  the  POLTERM  model 
for  Poland.  POLTERM  is  an  implementation  of  the  TERM  model  (Horridge,  2012)  to  the 
Polish  economy  (Zawalinska,  Giesecke,  &  Horridge,  2013).  The  model  includes  20 
agricultural  sectors  and  8  processed  food  commodities,  a  distinction  between  LFA  and 
non-LFA land 2 representative households (rural and urban) and 16 NUTS2 regions. The 
model  simulates  the introduction  of  LFA  payments  modelled  as land  rental  subsidy.  The 
main results report an increase of the Poland GDP by 0.07%. The owner price of LFA land 
increased  by  11.2%  and  at  the  same  time  user  price  of  LFA  land  declined  by  6.06%. 
Since the price increased the LFA farmers supply more land (by 2.26%). The increase in 
land supply makes marginal product of labour to raise, this accounts for rise of real wage 
0.16%.  Notice, however, that real consumption increase is substantially higher than that 
in  GDP.  Real  consumption  increases  by  0.2%  while  GDP  by  0.07%.  This  reflects  two 
things. Firstly, the terms of trade improves (0.06%), secondly and more importantly the 
LFA  funds  are  financed  almost  entirely  by  the  rest  of  the  EU.  This  transfer  to  Poland 
follows  the  Polish  real  balance  of  trade  to  move  towards  deficit  (real  exports  falls  by 
0.23% and real imports increases by 0.07%. It is this movement towards the deficit and 
the resulting decrease in exports that accounts for improvement in terms of trade. 

In  regional  terms,  the  winners  are  the  most  agricultural  and  rural  regions  while  main 
losers  are  the  most  urban  ones.  Employment  shifts  from  most  urban  to  more  rural 
regions while the increase in land supply is due to land that would have otherwise been 
abandoned. 

In  conclusion  regional  CGE  models,  stand  alone,  has  a  problem  with  precise 
representation  of  rurality.  The  results  of  modelling  of  RDP  measures  in  regional  CGE 
depend very much on the interpretation of the modellers of the purpose of the measures 
(what were designed for or how the measures were actually spent by beneficiaries). 

In order to overcome some of the limitation listed in the presentation a few proposals are 
made  to  move  forward  modelling  RD  measures  in  a  regional  CGE  frame.  The  measures 
related  to  increase  in  productivity,  knowledge-transfer,  Research  &  Development  would 
require  an  endogenous  growth  theory  behind  in  order  to  be  implemented  in  such  a 
model. To grasp the effects on the environment of the measures, environmental aspects 
should be included and integrated in the CGE database. Lastly, some  of the deficiencies 
in  regional  CGE  approach  (e.g.  more  precise  implementation  of  the  measures)  could  be 
overcome  by  linking  CGE  with  more  precise  and  detailed  agricultural  partial  equilibrium 
models. 

As  a  final  remark,  Katarzyna  Zawalińska,  reminded  that  a  good  modelling  of  RD  should 
look at how farmer spend the money they receive and which effects this money have on 
farmers and  in order to improve the quality of modelling asked to make public data on 
RD expenditure through official EC channels. 

4.1.3  Lessons learned from modelling of Pillar 2 in the CAPRI-RD project 

Wolfgang  Britz  from  the  Bonn  University  presented  Lessons  learned  from  modelling  of 
Pillar 2 in the CAPRI-RD project within the 7th framework program, 2009-2013, focusing 
on modelling the Pillar 2 of the CAP, not only for agriculture. 

The main challenges to model RD measures are due to the wide range of diverse national 
or even regionalised measures which form Pillar 2. In addition, the measures are  of the 
opt-in  type  so  that  farmers  are  not  obliged  to  adopt  them  and  finally  there  is  no 
harmonized  data  base  on  Pillar  2  programs  which  is  directly  suitable  for  modelling.  In 
terms  of  data  availability,  it  should  also  be  underlined  that  available  data  focus  on  how 
the  money  are  spent  (planned  or  actual)  while  limited  information  are  available  on  the 

14 

 
real  use  of  the  money  and  data  are  generally  classified  by  political  aim  rather    than  by 
detailed implementation of the expenditures. 

The  approach  of  the  CAPRI  RD  project  in  modelling  RD  measures  was  to  develop  a 
standardized "impact pathway" matching the classification in the available data bases. In 
other  word,  the  main  idea  was  to  "follow  the  money"  instead  of  focusing  on  the  logic 
behind  the  intervention.  As  an  example,  any  expenditure  linked  to  "village  renewable" 
measures  causes  a  shift  of  government  demand  towards  the  construction  sector. 
Following this approach, all measures were grouped in to different categories with same 
expenditure logic. 

During  the  project  life  time  a  couple  of  test  were  performed  with  the  newly  developed 
model tool: an ex-ante application for Slovenia to assess general options (not details) in 
terms  of  RD  measures  and  an  ex-post  assessment  for  Germany  (Schroeder,  Gocht,  & 
Britz, 2015) to compare the evaluation of a few Rd programmes with model results. 

The CARPI RD projects produced a functioning set of regional CGE models with matching 
SAMs (based on 2005 data, by now rather old). The CAPRI code was modified to model 
selected  Pillar  2  instruments  in  the  regional/farm  type  models  of  CAPRI.  Overall  the 
results  was  an  even  more  complex  CAPRI  modelling  tool  with  higher  data  demands, 
asking for experts in PE/CGE and bio-physical modelling, the first and second pillar of the 
CAP. 

The main conclusions drawn by Wolfgang Britz are the following: 

Modelling Pillar 2 is far more complex compared to Pillar  1 (but recent change in 

Pillar 1 are challenging as well); 

Looking  at  Pillar  2  the  data  need  is  a  concrete  bottleneck  which  is  slowing  down 

the process of model improvement. 

A  serious  modelling  of  these  measures  would  require  regional  expertise  to  guide 

the modeller on the implementation logic to associate to each measure/programme. 

In addition, one of the main gaps of the current literature is the lack of consideration of 
administrative  costs  linked  to  planning,  implementation,  controlling,  monitoring  and  ex-
post assessment of these programmes. These  are potentially very high costs and so far 
are barely taken into consideration by models. 

4.1.4  The RD module in MAGNET 

Emanuele  Ferrari  (JRC)  presented  the  approaches  taken  by  the  global  CGE  MAGNEt  to 
model  Rural  Development  payments.  The  Modular  Agricultural  GeNeral  Equilibrium  Tool 
(MAGNET) (Woltjer & Kuiper, 2014) is a multi-region CGE model which is a derivative of 
the well-known Global Trade Analysis Project (GTAP) model. It is developed and applied 
at  Wageningen  Economic  Research  (WECR)  at  the  University  of  Wageningen  and is  also 
employed by the Thünen Institute (TI) and the JRC. 

In  a  first  version  of  the  model,  Pillar  2  measures  were  aggregated  into  five  groups 
according to the similarities in the economic mechanisms which underlie them: 

1.  Investment  in  human  capital  (e.g.,  vocational  training,  setting  up  of  young 

farmers, use of advisory services, etc.);  

2.  Investment  in  physical  capital  (e.g.,  modernisation  of  agricultural  holdings, 
infrastructure  investments,  adding  value  to  agricultural  and  forestry  products, 
etc.); 

3.  Agri-environmental  payments  (e.g.,  Natura  2000  payments,  forest-environment 

payments, etc.);  

4.  Least favoured areas (e.g., payments to farmers in mountainous areas); 

15 

 
 
 
 
 
5.  Wider  RD  schemes  (e.g.,  diversification 

into  non-agricultural  activities; 

encouragement of rural tourism; village renewal and development, etc.). 

Payments of classes (i), (ii), (iii), and (v) were then assumed to incur endogenous output 
or input productivity effects. Investments in physical capital were leading to increases in 
output  productivity  only  within  agricultural  sectors.  Estimates  of  vintage  effects  of 
investment  in  physical capital  on  output  productivity  suggested  a  rate  of  return  of  30% 
(Nowicki et al., 2009). Investments in human capital were modelling as increasing output 
productivity  in  agriculture  through  greater  awareness  of  farming  practise,  better  use  of 
machinery,  improved  fertiliser,  pesticide  and  feed  application,  and  more  efficient  land 
use. The productivity parameter of 0.4 (Nowicki et al., 2009) indicates an internal rate of 
return of 40% for the OECD countries. Accordingly, an investment of one euro per unit of 
physical capital stock increases output productivity by 40%. Agri-environmental schemes 
and LFA were directly tied to the land factor. Payments were modelled as compensation 
of farmers in return for a more extensive (and consequently less productive) production 
system.  This  approach  reduced  land  productivity  indirectly  due  to  less  commercial 
technologies  (i.e.,  more  extensive  production  techniques,  and/or  an  insurance  effect 
which  makes  people  work  less).  Due  to  agri-environmental  schemes,  labour  and  capital 
productivity  in  agricultural  sectors  decreases  by  5%  for  every  euro  of  expenditure  on 
agri-environmental schemes. Wider RD payments were modelled as initiatives to reverse 
the  economic  and  social  decline  in  rural  areas  such  as  promoting  innovation,  creating 
employment opportunities and thereby output productivity change not only in agriculture 
but also in the wider rural economy. In this case, the model assumed same rate of return 
used for physical capital investments (i.e., 30%). 

By  their  nature,  ‘agri-environmental  schemes’  and  ‘least  favoured  areas’  are 
almost  completely  tied  to  the  land  factor,  whilst  remaining  Pillar  2  measures  are  linked 
(in varying degrees) to land, capital, both labour types and intermediate inputs based on 
the aims of the policies and discussions with experts. 

Output  augmenting  or  factor  augmenting  technical  changes  multiply  a  ‘response 
parameter’ by the ratio of payments to the specific total cost-price value (based on GTAP 
data).  This  yields  endogenous  augmenting  technical  change.  In  the  agriculture  sectors 
the total output augmenting effect is the sum of human capital physical capital and wider 
rural measures. The response parameters reflect the literature estimates (or best guess) 
of  rates  of  return  of  these  investments.  For  example,  looking  at  human  capital,  if  the 
investment  in  human  capital  equals  1%  of  total  output  value,  output  productivity 
increases by 0.40*1%= 0.40% (40% return). In non-agriculture sectors, the total output 
augmenting  effect  is  the  sum  of  output  augmenting  due  to  wider  rural  measures 
investments. A factor augmenting change is due to agro-environmental measures which 
affect non-land agriculture factors (i.e., labour and capital). 

An application of this approach to model Rural Development payments showed a 
limited  impacts  of  CAP  cuts  on  EU  agricultural  (production  neutral  behaviour  SFP). 
Changes  in  output  linked  to  productivity  effects  arise  from  changing  in  Pillar  2 
expenditures  (Boulanger  and  Philippidis,  2015).  Reductions  in  Pillar  2  (human  and 
physical  capital  and  wider  rural  measures)  generate  productivity  losses  in  agricultural 
and  (to  a  lesser  extent)  non-agricultural  sectors  which  are  particularly  pronounced  in 
Poland.  The  UK  and  Austria  witness  small  positive  technological  gains  as  60%  of  their 
Pillar 2 expenditure (including co-financed support) is assigned to (productivity reducing) 
agri-environmental measures. 

A  new  approach  has  been  recently  applied  with  the  MAGNET  model.  In  this 
approach  each  payment  type  has  a  direct  relation  with  the  factor  to  which  they 
correspond  and  the  focus  switches  from  total  to  single  Factor  productivity.  To 
parametrise  it,  a  response  parameter  has  to  be  econometrically  estimated.  This 
parameter  links  the  change  in  the  payments  to  the  increase  in  productivity  of  a  given 
factor.  For  instance,  it  the  response  parameter  is  equal  to  0.05,  a  100%  increase  in  a 
given  payment  would  be  equal  to  a  5%  increase  in  the  productivity  of  the  given  factor 
linked to that payment. Under this new approach, human capital investments are linked 

16 

 
to  the  labour  factor  productivity;  physical  capital  investments  are  linked  to  the  capital 
factor  productivity;  agro-environmental  investments  are  linked  to  the  land  factor 
productivity  while  wider  rural  payments  are  stilll  linked  to  the  total  factor  productivity 
parameter. 

In  M'barek  et  al.,  (2017),  the  MAGNET  model  has  performed  the  CAP  scenario 

analysis with the following parameters: 

 

 

 

 

100% increases in human capital investments produce labour factor productivity 
improvements of 1.6%.  

100% 
productivity improvements of 2.5%.  

in  physical  capital 

increases 

investments  produce  capital 

factor 

100% 
productivity improvements of 0%. 

increases 

in  agro-environmental 

investments  produce 

land 

factor 

100% increases in wider rural payments increase productivity with 0.2% 

An  additional  test  with  parameter  increased  by  50  and  100  per  cent  and  Agri-
environmental  payments  equal  to  5%  was  also  performed  as  sensitivity  analysis  of  the 
parametrisation  of  the  model.  The  results  of  the  analysis  show  a  negative  effect  of 
abolishment  of  the  Pillar  2  payments  on  agricultural  production.  The  magnitude  of  the 
shocks  varies  according  to  the  parameter  used  in  the  scenarios.  The  bigger  is  the 
response  parameter,  the  higher  is  the  loss  in  terms  of  production  and  the  bigger  is  the 
increase  in  price.  On  the  other  hand,  the  impact  on  the  employment  level  has  to  be 
better  explained  as,  the  bigger  the  response  parameter,  the  less  the  job  lost  in 
agricultural  sector.  Pillar  2  payments  in  human  capital  causes  an  increase  of  labour 
productivity  and  consequently  a  decrease  of  labour  demand,  which  being  more 
productivity  can  produce  more  with  less  inputs.  Removing  the  subsidies,  cause  job  to 
become less productive (less paid and of minor qualities too) so that demand for labour 
is decreasing less compared to the baseline. 

As  conclusion  it  should  be  underlined  that  parametric  uncertainty  (i.e.,  more 
econometrics  definitely  needed  to  take  this  further)  remains  a  key  drawback  of  any 
approach  to  model  RD  payments.  In  addition,  MAGNET  treatment  is  necessarily  very 
stylized  due  to  this  uncertainty,  but  at  very  least  goes  deeper  than  other  global  CGE 
treatments of this issue. 

The  discussion  following  the  presentation  focused  on  some  of  the  hypotheses 
(including  the  functional  form  behind  the  labour  market  and  the  hypothesis  of  constant 
return to scale) behind the model and on the interpretation results presented. 

4.1.5  Rural Development in farm models: IFM-CAP 

Jeroen  Buysse  (University  of  Gent)  presented  a  feasibility  study  to  incorporate 

Rural Development policy modelling in IFM-CAP. 

The approach proposed is based on an adoption modelling, estimated with a two-
step  econometric  Heckman  approach.  In  the  first  step  a  probit  regression  model  is 
performed to to predict adoption of a farm. In the second step, the “level” of adoption is 
estimated  including  only  adopting  farms  with  the  same  explanatory  variables  as  in  the 
first step 

The  results  of  the  econometric  estimation  are  then  implemented  into  the  model. 
The  econometric  model  selects  the  farms  with  highest  probability  of  adopting  an  RD 
measure. The model then calculates the intensity of adoption (2nd step Heckman). In the 
following  step,  subsidies  are  multiplied  by  the  sampling  weight.  The  steps  1-3  are 
replicated until the total budget is allocated and exhausted. Finally, coupling coefficients 
are assigned to farms with new adopting behaviour. 

This  approach  has  a  few  implications  for  the  modelling  strategy.  First  of  all,  the 
adoption of RDP is calculated outside the IFM-CAP model. Secondly, budget allocation to 

17 

 
different  RDP  measures  will  be  exactly  reflected  in  the  IFM-CAP  model.  Thirdly,  no 
simulations  of  behaviour  of  farms  that  would  strategically  change  land  allocation  of 
livestock production to satisfy with the requirements of certain RD measures are needed. 
The level of data detail finally will define modelling details. 

The main drawback of this approach is that FADN is not representative in terms of 

RD adoption.  

4.1.6  Discussion 

Hubertus Gay (OECD) facilitated and chaired the discussion on the modelling. He started 
his presentation reminding the  challenges linked to the  modelling of the RDmeasures in 
any kind of economic simulation model. 

The  first  challenge  is  due  to  the  diversity  of  the  six  common  EU  priorities  which  lies 
behind the RD payments: 

• 

fostering knowledge transfer and innovation in agriculture, forestry and rural 
areas 

•  enhancing  the  viability  and  competitiveness  of  all  types  of  agriculture,  and 

promoting innovative farm technologies and sustainable forest management 

•  promoting food chain organisation, animal welfare and risk management in 

agriculture 

• 

restoring,  preserving  and  enhancing  ecosystems  related  to  agriculture  and 
forestry 

•  promoting resource efficiency and supporting the shift toward a low-carbon and 

climate-resilient economy in the agriculture, food and forestry sectors 

•  promoting  social  inclusion,  poverty  reduction  and  economic  development 

in rural areas. 

The second challenge is due to the specificity of these policy measures which worth €100 
billion from 2014-2020 and leverage a further €61 billion of public funding in the Member 
States. 

1.  All the measures are co-financed by Member States 

2.  They target specific RD objectives 

3.  They are based on multi-annual commitments 

4.  Plus: opt-in and not mandatory 

The OECD is approaching these challenges collecting as much data as possible to allows 
researchers  and  policy  makers  to  compare  between  countries  and  over  time  (Agri-
www.oecd.org/tad/sustainable-agriculture/agri-
environmental 
environmentalindicators.htm,  Innovation,  agricultural  productivity  and  Sustainability 
www.oecd.org/tad/agricultural-policies/innovation-food-agriculture.htm). 

indicators 

The OECD has also recently published an evaluation of Agricultural Policy Reforms in the 
2014-20 
European 
(http://dx.doi.org/10.1787/9789264278783-en)  paying  special  attention 
risk 
to 
management instruments and environmental measures. 

Agricultural 

Common 

Union: 

Policy 

The 

From the previous presentation a few key messages emerged: 

  Pillar 2 modelling is far more complex compared to Pillar 1. 

  Modellers  need  to  better  understand  the  logic  behind  Pillar  2  measures.  For 
instance, why are they notified as green-box measures to the WTO by the EU? 

  More specific data are needed; this is often a huge bottleneck. 

18 

 
 

“Implementation”  logic  in  models  of  specific  programs  needs  national  /  regional 
expertise 

  One  of  the  main  gap  so  far  is  represented  by  the  lack  of  modelling  of 
administration  costs  (planning,  implementation,  controlling,  monitoring,  ex-post 
assessment  ..)  of  Pillar  2  which  are  rarely  included  into  models  but  potentially 
quite high. 

A  few  indications  were  mentioned  as  possible  way  forward.  Estimation  of 
parameters has to be consistent with the type of economic model which will be used for 
performing  the  simulations.  The  difference  between  the  micro  and  macro  level  has  also 
to  be  accounted  for,  the  assessment  of  a  single  measure  has  to  necessary  be  a  micro 
impact  study  while  only  the  assessment  of  many  or  all  RD  measures  can  be  performed 
with  a  more  macro  model.  Models  have  to  be  able  to  differentiate  between  intervention 
logic  and  implementation  logic  (possibly  with  insights  from  the  behavioural  economics) 
and to account for administration costs to implement the measures..  

In  the  end,  multiple  approaches  will  be  needed  to  perform  a  comprehensive 

analysis. 

19 

 
 
5  Conclusions 

The  workshop  concluded  with  a  summarising  session  where  the  key  inputs  from  the 
previous sessions were highlighted. 

From the JRC side some key elements were underlined. 

On the estimation side, several improvements can be achieved: 

  Collection of more detailed, more frequent and spatial disaggregated data 

  A  few  assumptions  used  by  last  JRC  estimation  process  can  help  to  improve  the 
estimates.  Among  them,  the  inclusion  of  intermediate  inputs,  family  labour  and 
the possibility to use an improved database to provide MS estimates. 

On model: 

  The  impact  of  abolishing  Pillar  2  measures  on  the  labour  market.  How  can  it  be 

better communicated and how the methodology can be improved? 

  One  of  the  main  gaps  highlighted  by  all  presenters  in  reference  to  the  models  is 
the  absence  of  deadweight  losses  and  administrative  costs  related  to  RD 
measures. 

  A thoughtful reflection on why RD measures are notified as green box ones to the 
WTO  and  how  can  this  be  taken  into  account  when  they  are  implemented  in 
economic models. 

  Again  in  terms  of  modelling,  the  approach  of  following  the  money  (in  terms  of 
understanding  how  money  allocated  to  each  measure  is  spent)  should  be  better 
analysed. 

 

IFM-CAP approach looks promising 

  CGE different approaches are currently in use. JRC should perform a review of the 
current approach and highlights pros and cons of the different methodologies. 

  The  treatment  of  the  labour  market  is  a  key  part  of  the  rural  development 
payments  and  models  (CGE  in  particular)  should  look  at  improved  approach  to 
model this key market. 

  Sensitivity analysis is always a very powerful tool to show how much models are 

sensitive to changes in key parameters. 

In  addition  to  these  conclusions,  from  DG-  AGRI  and  policy  making  side  a  few 
statements concluded the workshop: 

  The methodology proposed by Dudu & Smeets Kristkova is appreciated for the 
effort that went into the development of the approach, the data 'manipulation' 
and  the  estimations.  DG  AGRI  has  full  confidence  that  JRC  pursues  the  best 
way  possible  to  improve  their  models  and  provide  sound  analyses.  However, 
important  drawbacks  were  highlighted  before  and  during  this  workshop  in 
relation to the current estimation and modelling techniques. These drawbacks 
should be addressed in the near future by the JRC.. 

  The  methodology  would  remain  rigorous  even  when  combined  with  expertise 
providing specific knowledge of given territories or regions. The combination of 
expert knowledge and a sound econometric estimation will make results more 
easily interpretable and credible. 

  One  of  the  key  to  make  results  more  credible  is  related  to  how  these  results 
improving 

are  communicated.  Scientists  should  spend  more  time 
communication towards policy makers. 

in 

20 

 
  
 
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23 

 
 
 
List of abbreviations and definitions 

CAP 

CAPRI 

CATS 

CES 

CGE 

DEA 

Common Agricultural Policy 

Common Agricultural Policy Regional Impact model 

Clearance Audit Trail System 

Constant Elasticity of Substitution 

Computable General Equilibrium 

Data Envelopment Analysis  

DG AGRI 

Directorate-General for Agriculture and Rural Development 

EC 

FADN 

GDP 

GMM 

GTAP 

European Commission 

Farm Accountancy Data Network 

Gross Domestic Product 

Generalized Method of Moments 

Global Trade Analysis Project 

IFM-CAP 

Individual Farm Model for Common Agricultural Policy Analysis 

IIA 

iMAP 

JRC 

LFA 

Inception Impact Assessment 

integrated  Modelling  Platform  for  Agro-economic  Commodity  and  Policy 

Analysis 

Joint Research Centre 

Least Favoured Area 

MAGNET 

Modular Applied GeNeral Equilibrium Tool 

MFF 

PE 

RD 

RDP 

SFA 

SNA 

SFP 

TI 

TE 

TFP 

Multiannual Financial Framework 

Partial Equilibrium 

Rural Development 

Rural Development Programmes 

Stochastic Frontier Approach 

System of National Accounts 

Single Farm Payments 

Thünen Institute 

Technical Efficiency 

Total Factor Productivity 

WECR 

Wageningen Economic Research at the University of Wageningen  

24 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
List of figures 

Figure 1: Average production function vs. production frontier ...................................... 7 

25 

 
 
 
Annexes 

Annex 1. Workshop Agenda 

WORKSHOP ON ESTIMATION AND MODELLING IMPACTS 
OF PILLAR 2 MEASURES ON THE AGRICULTURAL SECTOR 
Seville, 14 September 2017 

Venue 

European Commission – Joint Research Centre (JRC) 
 Edificio Expo, Calle Inca Garcilaso 3, 41092 Seville, Spain - Room A41 

Organisers 

Emanuele Ferrari 
Hasan Dudu 
Koen Mondelaers 
Sandra Marcolini 

JRC - scientist 
JRC - scientist 
DG AGRI 
JRC - logistics 

AGENDA  

09:30 – 10:00  Welcome and background 

Giampiero Genovese (JRC) 
Koen Mondelaers (AGRI) 

Session 1: Impact of Pillar 2 measures: Estimation procedures  
Session 1a - Chair: Simone Pieralli (JRC) 

10.00-10.20 

Overview of current literature 

Hasan Dudu (JRC) 

10.20-10.40 

10.40-11.00 

11.00-11.30 

CAP Subsidies and Productivity of EU 
Farms 
Productivity  effects  of  EU  domestic 
support 
Coffee break 

Marian Rizov (Lincoln University) 
(VC) 
Zuzana Smeets Kristkova (WECR) 

Session 1b - Chair: Emanuele Ferrari  

11.30-11.50 

11.50-12.10 

12.10.12.30 

Assessing CAP impact on growth and 
jobs 
The Impact Of The CAP Green Box On 
Productivity In FADN European 
Regions 
Discussant 

12.30.13.00 

Open Discussion 

13.00-14:00 

Networking lunch 

Maria Garrone (KUL)  

Marta Guth (Poznań University of 
Economics and Business) 

Alex Gohin (INRA) 

All participants 

14.00-14.20 

Session 2a: Modelling Pillar 2 Measures - Chair: Hasan Dudu (JRC) 
Overview of current literature 

Emanuele Ferrari (JRC) 

14.20-14.40 

Rural Development in Regional CGEs 

14.40-15.00 

15.00-15.30 

Lessons learned from modelling of 
Pillar II in the CAPRI-RD project 
Coffee break 

Katarzyna Zawalinska  
(Polish Academy of Sciences) (VC) 
Wolfgang Britz (Bonn University) (VC) 

15.30-15.50 

The RD module in MAGNET 

Emanuele Ferrari (JRC) 

Session 2b – Chair: Koen Mondelaers (AGRI) 

15.50-16.10 

16.10.16.30 

Rural  Development  in  farm  models: 
IFM-CAP 
Discussant  

16.30.17.00 

Open Discussion 

Jeroen Buysse (Ghent University) 

Hubertus Gay (OECD) 

All participants 

26 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
17.00-17.10  Way forward – estimation point of 

Hasan Dudu (JRC) 

Session 3 - Conclusions 

view 

17.10-17.20  Way forward – modelling point of 

Emanuele Ferrari (JRC) 

view 

17.20-17.30  Way forward and conclusion 

Koen Mondelaers (AGRI) 

21.00   

Dinner 

27 

 
 
 
 
 
Annex 2. List of participants 

IN SEVILLE 

BY VIDEOCONFERENCE 

BUYSSE Jeroen 
Ghent University 

GARRONE Maria 
University of Leuven 
GAY Stephan Hubertus OECD 

GOHIN Alexandre INRA 
GUTH Marta 
Poznań University of Economics and Business  

SMEETS KRISTKOVA Zuzana  
Wageningen University and Research Centre 

BRITZ Wolfgang 
University of Bonn  

RIZOV Marian  
University of Lincoln 
ZAWALIŃSKA Katarzyna  
Polish Academy of Sciences 

IN SEVILLE 

BY VIDEOCONFERENCE 

CIAIAN Pavel 

DUDU Hasan 

FERRARI Emanuele 

GENOVESE Giampiero 

HIMICS Mihaly 

LOUHICHI Kamel 

MONDELAERS Koen 

PIERALLI Simone 

JRC 

JRC 

JRC 

JRC 

JRC 

JRC 

AGRI 

JRC 

BIELZA Maria 

CARMONA GARCIA Gema 

HELAINE Sophie 

LONDERO Pierluigi 

JRC 

JRC 

AGRI 

AGRI 

28 

 
 
 
 
 
 
 
 
 
 
 
 
Annex 3. Presentations 

Introduction – Giampiero Genovese (JRC)   

29 

 
 
30 

 
 
 
Impact of CAP Pillar II Payments on Agricultural Productivity– Hasan Dudu (JRC) 

 
 
 
 
 
 
 
Do CAP subsidies affect productivity of the EU farm?– Marian Rizov (Lincoln International 
Business School) 

 
 
 
 
 
 
 
 
 
Impact  of  CAP  Pillar  II  Payments  on  Agricultural  Productivity–  Zuzana  Smeets  Kristkova 
(WECR) 

 
 
 
 
 
 
 
 
 
 
CAP Impact on Growth and Jobs– Maria Garrone  

 
 
 
 
 
 
 
 
 
 
 
 
 
The impact of the CAP green box on productivity in FADN European Regions– Marta Guth 
(Poznań University of Economics and Business) 

 
 
 
 
 
 
 
 
CAP  and  Productivity:  Critical  econometric  identification  before  policy  simulation– 
Alexandre Gohin (INRA Rennes) 

 
 
 
 
Modelling Pillar 2 Measures Overview of the current Literature– Emanuele Ferrari (JRC) 

 
 
 
 
 
 
 
Rural development in regional CGEs - Katarzyna Zawalińska (IRWiR PAN) 

 
 
 
 
 
 
Lessons learned from modelling of Pillar II in the CAPRI-RD project- Wolfgang Britz (Bonn 
University) 

 
 
 
 
 
 
 
 
MAGNET - Pillar 2 payments and productivity effects- Emanuele Ferrari (JRC) 

 
 
 
 
 
 
 
Feasibility 
in IFM-CAP- Jeroen Buysse (Gent University) 

study 

Rural 

on 

Development 

policy 

modelling 

 
 
 
 
 
 
 
 
 
Assessment of rural Development – Hubertus Gay (OECD) 

 
 
 
 
 
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doi:10.2760/884943 

ISBN 978-92-79-85965-6