Risk-sharing among European countries.
Date: 2018-01-17
Institutions: Joint Research Centre
Subjects: capital market, credit, economic policy, economic recession, public debt, research report, savings

Risk-sharing among        
European Countries 

M. Nardo, F. Pericoli, P.Poncela 

2017

EUR 28934 EN

This publication is a Technical 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. 

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

JRC109598 

EUR 28934 EN 

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ISBN   978-92-79-77063-0 

ISSN 1831-9424 

doi: 10.2760/028526 

Luxembourg: Publications Office of the European Union, 2017  

© European Union, 2017 

Reuse is authorised provided the source is acknowledged. The reuse policy of European Commission documents 
is regulated by Decision 2011/833/EU (OJ L 330, 14.12.2011, p. 39). 

For any use or reproduction of photos or other material that is not under the EU copyright, permission must be 
sought directly from the copyright holders. 

How to cite this report: Nardo, M., Pericoli, F. and Poncela, P., Risk-sharing among European Countries, EUR 
28934 EN, Publications Office of the European Union, Luxembourg, 2017, ISBN 978-92-79-77063-0, doi 
10.2760/028526, PUBSY No JRC109598.  

All images © European Union 2017. 

 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
Contents 

Foreword ........................................................................................................... 2 

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

1  Executive summary ......................................................................................... 4 

2  Introduction ................................................................................................... 7 

3  The channels of risk-sharing ............................................................................. 9 

3.1  The estimated model ................................................................................. 11 

3.2  The data ................................................................................................. 13 

4  Results ......................................................................................................... 14 

4.1  Target group: EU15 .................................................................................. 14 

4.2  Target group EU15: sub-sample analysis ..................................................... 17 

4.3  A closer look to Ireland .............................................................................. 19 

4.4  Target group: EU28 .................................................................................. 21 

5  Relative performance of countries ..................................................................... 24 

6  Conclusions ................................................................................................... 27 

References ........................................................................................................ 29 

List of figures .................................................................................................... 30 

List of tables ...................................................................................................... 31 

Annex: additional tables and figures ..................................................................... 33 

i 

Foreword 

This  report  details  the  final  estimations  about  the  degree  of  risk  sharing  that  European 
countries actually displayed both before and after the 2008 crisis. It is the final delivery of 
the Administrative Arrangement FISMA/2016/086/B2/ST/AAR. 

Authors 

Michela Nardo, Joint Research Centre, European Commission. 

Filippo Pericoli, Joint Research Centre, European Commission. 

Pilar Poncela, Joint Research Centre, European Commission. 

2 

 
 
Abstract 

This technical report details the results of risk sharing in the EU country by country. The 
great  recession  and  the  subsequent  sovereign  debt  crisis  in  Europe  have  shown  an 
asymmetric behavior of the different member countries of the EU, also with regards of risk 
sharing. We provide country specific measures decomposing risk sharing as that obtained 
via the capital markets, international transfers and savings or the credit markets channel. 
Afterwords, we use a mean group estimator to measure average risk sharing for the group 
of  countries.  This  can  help  to  identify  where  risk  sharing  is  working  and  through  which 
channels. 

3 

 
1  Executive summary1 

The Five  Presidents’  Reports  (2015),  and more recently  the Investment Plan  for  Europe 

(2016) claim the need for mechanisms and institutions capable of absorbing the impact of 

economic  shocks  across  states  and  regions  and  achieve  an  effective  risk-sharing  across 

European  countries.  One  of  these  mechanisms  is  the  Capital  Markets  Union  aimed  at 

promoting cross-border links and cross-ownership of productive assets. The possibility to 

receive  interests  or  dividends  from  investments  made  abroad  or  to  earn  profits  from 

activities  undertaken  in  other  countries  allows,  in  hard  times,  to  reduce  the  volatility  in 

disposable income that would otherwise occur, hereby, sustaining domestic consumption 

and investments. International risk sharing therefore focuses on the cross border channels 

at work in smoothing income and consumption when a country is hit by an output shock.  

We  follow  the  international  literature  and  use  the  term  risk-sharing  to  identify  the  3 

channels  through  which  shocks  to  GDP  are  cushioned:  1)  the  capital  markets  channel, 

capturing private cross-border links; (2) the government channel, capturing international 

public  transfers;  and  (3)  the  credit  markets  channel  also  affected  by  domestic  (fiscal) 

policies.  A  disclaimer  is  needed:  2  of  these  channels  (the  capital  market  and  the 

government channel) imply only cross border links and, therefore, the possibility to share 

domestic  risks  with  other  countries.  The  third  channel  (credit  market)  captures  all  the 

policies  affecting  gross  savings  (e.g.  domestic  fiscal  policies,  and  international  capital 

transfers) mixing pure domestic smoothing with cross-border effects taking place through 

net borrowing and lending from the rest of the world. This decomposition comes from the 

system of National Accounts and is limited by its structure.  

In  an  ideal  world  of  perfect  risk  sharing,  countries  are  completely  insured  against  bad 

events, and domestic consumption growth is independent from domestic output growth. 

In practice, this is hardly the case. If in the US three quarters of shocks to per capita GDP 

of individual states are absorbed (smoothed), Europe is lagging far behind and in spite of 

the recent recuperation that can be measured including data up to 2016, still more than 

60% of GDP idiosyncratic shocks remain unsmoothed, i.e. directly transmitted to domestic 

consumption.  This  very  high  average  figure  yet  can  hide  highly  heterogeneous  patterns 

across countries. In this report we provide country measures of risk sharing for all the 

EU countries. Our measures are based on estimations using a variety of models, from those 

customarily  used  in  the  literature  to  the  more  recent  dynamic  approach  to  take  into 

account, among other things, the interdependence of the channels. 

1   The content of this report does not reflect the official opinion of the European Union. Responsibility for the 

information and views expressed therein lies entirely with the authors. 

4 

 
 
                                           
Our findings are the following: 

• 

In line with the literature, the bulk of risk sharing takes place through the credit 

markets (saving) channel in all EU28 countries. The credit markets channel 

smooths more than 50% of the shocks in some countries, being an important 

buffer  for  output  shocks  especially  in  Ireland,  Sweden,  Finland,  Greece  and 

Belgium (sample 1960-2016). A subsample analysis (1999-2016) reveals that 

this channel has had a reduced incidence in several countries (e.g. Sweden and 

Belgium) after the introduction of the euro.  

•  For several countries (among them Italy and Portugal), we find that the credit 

market  channel  acted  as  shock  amplifier  during  the  recent  2008  crisis  and 

subsequent sovereign debt crisis, provoking a certain degree of dis-smoothing 

(the  shock  not  only  is  transmitted  to  consumption  but  that  channel  induces 

additional consumption drop). . 

•  Risk sharing has greater effects in small open economies: Sweden is able to 

smooth about 3 times more than Germany and UK in the first subsample. 

•  Risk sharing through international public transfers is almost non-existent 

for all periods and countries analysed. 

•  Capital markets risk sharing remains very low for all subsets of countries and 

periods considered. Nevertheless, it increased its role in recent times, especially 

in the Baltic countries and Ireland. 

•  Luxembourg  and  Malta  also  emerge  as  achieving  high  quotas  of  risk  sharing, 

especially  through  the  capital  and  credit  markets  channels.  This  reflects  their 

roles of financial hubs.  

Our findings suggest a number of general issues relevant for policy making: 

1.  Both, the interactions of the channels as well as their dynamic behavior should be 

taken  into  account  for  policy  recommendations.  As  for  the  first,  the  analysis  we 

provide relies on estimates of risk sharing on impact, that is, contemporaneous to the 

shock. This is when most of risk sharing takes place. However, analysing the dynamic 

aspects  of  risk  sharing,  the  picture  may  change:  after  an  initial  positive  smoothing 

following  a  GDP  shock,  we  find,  for  some  countries,  a  dis-smoothing  in  the  credit 

channel in the subsequent years. 

2.  The  interaction  between  channels  needs  to  be  considered  when  designing  policies 

targeted  to  a  single  channel.  Our  analysis  shows  that  when  there  is  an  interaction 

between channels they act mainly as substitutes: policies positively affecting the 

5 

 
 
 
capital  channel  may  produce  negative  (dis-smoothing)  effects  in  the  credit  channel. 

Whether  the  substitution  effects  also  hold  for  the  government  channel  needs  to  be 

analysed. 

3.  From  the  country-specific  analysis  emerges  that in  Europe  risk  sharing  is mainly 

confined  to  small  and  open  economies  as  in  larger  countries  the  scale  of  cross-

border risk sharing is relatively small as compared with the size of the economy.  

4.  The highest positive figures for the capital markets channel are obtained in Ireland and 

the  Baltic  countries.  In  spite  of  having  the  same  label,  risk-sharing,  the  situation  is 

totally different in these two examples. In the Baltic countries, the banking union was 

the mechanism at work to smooth the shocks: by transferring funds to their subsidiaries 

in  Lithuania,  Latvia  and  Estonia,  foreign  owned  banks  (mainly  Finnish  and  Swedish) 

acted  as  shock  absorbers  allowing  the  Baltic  citizens  to  smooth  their  consumption 

during the negative GDP shocks of the latest crisis. On the contrary, in Ireland, we find 

smoothing  of  positive  GDP  shocks:  foreign  owned  companies  that  moved  their 

headquarters to Ireland for tax purposes, contributed to high positive GDP growth. A 

substantial part of this growth, however, was redirected back in the form of outflows 

of equity income provoking the smoothing of positive GDP growth.  Risk sharing is 

therefore a stabilizer that works both ways: for negative as well as for positive 

shocks.  

5.  The  size  of  the  shocks  experienced  by  each  country  is  very  different  and  deserves 

careful evaluation. The volatility of idiosyncratic GDP for Ireland and Greece is 10 and 

8  times,  respectively,  that  of  more  stable  countries,  like  France,  Belgium  or  the 

Netherlands. This implies that not all the countries had the same needs in terms 

of  risk  sharing.  We  find  that,  for  European  countries,  the  size  of  the  shock  is  not 

related to the size of the economy while the degree of risk-sharing is. Our findings point 

to an effective role of risk sharing in small countries only: being small and open though 

necessary is not a sufficient condition to profit from risk sharing.  

6 

2  Introduction 

International  risk  sharing  focuses  on  the  cross-border  channels  at  work  for  smoothing 

income and consumption when a country is hit by an output shock. In hard (good) times, 

the possibility to receive or distribute interests or dividends from investments made abroad 

or  to  earn  profits  form  activities  undertaken  in  other  countries  allows  reducing  the 

fluctuations in disposable income that would otherwise occur, hereby, stabilizing domestic 

consumption and investments and, finally, supporting growth. 

Clearly,  the  capability  of  a  system  of  countries  to  share  risks  not  only  depends  on  the 

cross-border mechanisms which attenuate fluctuations of disposable  income but also on 

the domestic pattern of savings influenced, among other things, by country specific fiscal 

policies.  The  literature  proposes  three channels for sharing risks  in case  of  idiosyncratic 

GDP shocks: the capital markets channel, the government or fiscal channel (also known as 

public  risk  sharing)  and  the  credit  markets channel.  Whereas  the  first  two  channels  are 

only dealing with cross-border transactions, the third mixes cross-border transactions with 

domestic  smoothing  (see  next  section)  and  can  be  seen  as  the  part  of  consumption 

smoothing  achieved  through  public  and  private  dis-saving.  We  are  fully  aware  of  the 

ambiguity of these definitions rooted in the categorization of national accounts items, but 

we stick to this labelling following a well-established literature and denote the channels as 

capital markets, government and credit markets channels. 

In an ideal world of perfect risk sharing, where countries are completely insured against 

output shocks, domestic consumption growth will be orthogonal (hence, independent) to 

idiosyncratic or country specific output growth. In practice, this is rarely the case. Even in 

well-functioning monetary unions like the US, evidence suggests that one quarter of shocks 

to  per capita  GDP of individual  states remains unsmoothed (Asdrubali  et al.,  1996). We 

find that in the Euro Area and the European Union more than 60% of GDP idiosyncratic 

shocks  remain  unsmoothed.  This  very  high  average  figure,  however,  can  hide  highly 

heterogeneous  patterns  across  countries  that  deserve  further  investigation.  Anecdotic 

evidence,  in  fact,  suggests  that  the  2008  recession  and  the  subsequent  sovereign  debt 

crisis  in  Europe  led  to  an  asymmetric  risk  sharing  of  the  different  EU  member  states. 

Kalemli-Ozcan  et  al.  (2014),  for  instance,  link  the  recent  downfall  in  risk  sharing  of 

peripheral European Union (EU) countries (Portugal, Italy, Ireland, Greece and Spain) to 

the  fact  that  their  governments  did  not  save  during  the  expansionary  phases  of  the 

business cycle and were not able to borrow on the international markets during the crisis 

due to the high levels of outstanding public debt. 

7 

 
 
The idea of country specific risk sharing was first introduced by Mélitz and Zumer (1999) 

and  further  developed  in Sørensen,  Wu,  Yosha  and Zhu  (2007). However,  as Asdrubali, 

Kim, Pericoli and Poncela (2017) point out, these authors focus on interaction effects with 

country specific explanatory variables and not on country specific estimates per-se of risk 

sharing.  Our  aim,  therefore,  is  to  fill this  gap providing  an  individual  characterization of 

risk sharing for each of the channels and each country of the EU and several subsets within 

it. As in Asdrubali et al. (2017), we measure country specific risk sharing, but we focus on 

the countries in the EU, instead of the set of OECD countries. We also capture the change 

in  risk  sharing  due  to  more  recent  turbulent  times,  providing  a  subsample  analysis, 

whenever data availability allows. We estimate a variety of models, generalizing the results 

shown in the policy report “Risk sharing in Europe” by Poncela, Pericoli, Manca and Nardo 

(2016). The interested reader should refer to it for the general setup to measure static and 

dynamic risk sharing, the notation, the exact definitions, the estimation methods and the 

pros and cons of each estimation strategy. However, instead of considering the countries 

as homogeneous, we allow for country heterogeneity as in Asdrubali et al. (2017), where 

the reader is referred for the general set up of country heterogeneity in risk sharing. 

8 

3  The channels of risk-sharing 

Following  the  structure  of  national  accounts,  Asdrubali  et  al.  (1996)  defined  three 

channels for risk sharing (or, equivalently, consumption smoothing): the capital markets 

channel,  the  government  channel  and  the  credit  markets  channel.  They  start  from  the 

following identity:2  

(cid:1833)(cid:1830)(cid:1842) =

(cid:1833)(cid:1830)(cid:1842)
(cid:1833)(cid:1840)(cid:1835)

(cid:1833)(cid:1840)(cid:1835)
(cid:1833)(cid:1830)(cid:1835)

(cid:1833)(cid:1830)(cid:1835)
(cid:1829)

(cid:1829) 

where  GDP  stands  for  Gross  Domestic  Product,  GNI  for  Gross  National  Income,  GDI  for 

Gross  Disposable  Income  and  C  for  Consumption.  Manipulating  the  identity  (for  details, 

see Poncela et al. 2016) one obtains workable expressions for the three channels: 

Δlog(GDP) − 	Δlog(GNI) = (cid:2010)(cid:2868),(cid:3012) + (cid:2010)(cid:3012)Δlog(GDP) + (cid:1873)(cid:3012) 

Δlog(GNI) − 	Δlog(GDI) = (cid:2010)(cid:2868),(cid:3007) + (cid:2010)(cid:3007)Δlog(GDP) + (cid:1873)(cid:3007) 

Δlog(GDI) − 	Δlog(C) = (cid:2010)(cid:2868),(cid:3004) + (cid:2010)(cid:3004)Δlog(GDP) + (cid:1873)(cid:3004)   

Δlog(C) = (cid:2010)(cid:2868),(cid:3022) + (cid:2010)(cid:3022)Δlog(GDP) + (cid:1873)(cid:3022) 

(1) 

(2) 

(3) 

(4) 

Equation (1) is employed to identify the capital markets channel and is based on the 

difference between Gross Domestic Product and Gross National Income. It corresponds to 

national  accounts’  Net  Factor  Income  (NFI)  category  and  accounts  for  two  types  of 

transactions between residents and non-residents:  

• 

• 

Income  from  work:  compensations  to  national  employees  working  abroad. 

Migrants are excluded if living in the foreign country for more than one year. 

Income  from  property  or  investments:  rents,  interests  and  profits,  including 

retained earnings. It includes factor income from abroad gained from properties 

(e.g. buildings, shops, factories, financial assets like bonds and shares in foreign 

countries)  and  earning,  thereby,  rents  and  interests.  The  item  also  includes 

2 From the System of National Accounts the following identities hold: GNI = GDP + primary incomes receivable 
from  the  rest  of  the  world  -  primary  incomes  payable  to  the  rest  of  the  world.  GDI=GNI  +  current  transfers 
receivable  from  the  rest  of  the  world  -  current  transfers  payable  to  the  rest  of  the  world.  S=GDI  -  final 
consumption expenditure. 

9 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
                                           
profits earned from  entrepreneurial activities of goods and services production. 

It covers income from foreign direct investments, portfolio investment incomes 

and  other  investment  incomes,  and  includes  payments  on  debt  securities 

(interests) and on equity securities (dividends). Notice that capital gains and 

losses coming from buying or selling activities/securities do not pertain to this 

channel since they are classified as part of the value of the investments (and 

included in the credit channel). 

To get a flavour of the importance of each type of transaction on cross border smoothing 

through  the  capital  markets  channel,  The  Quarterly  Report  on  the  Euro  Area  (2016) 

estimated for a group of 13 countries3 belonging to the euro area, that only 0.2% of shocks 

were smoothed through cross-border labour compensation, out of the 5.6% of total shocks 

smoothed through this channel, indicating that most of the risk sharing achieved through 

this channel was due to income from property or investments. 

Equation  (2)  is  used  to  identify  the  fiscal  or  government  channel  (or  public  risk-

sharing)  and  is  based  on  the  difference  between  Gross  Disposable  Income  and  Gross 

National  Income,  i.e.  Net  International  Transfers  (NIT).  It  includes  transfers  made  by  a 

resident  entity  to  a  non-resident  entity  without  an  economic  counterpart.  It  includes 

general  government  transfers  (transfers  between  governments  and  international 

cooperation  entities)  such  as  cash  transfers  between  governments  in  order  to  finance 

current expenditures; gifts of food, international aid for earthquakes or natural disasters; 

gifts on military equipment and regular contributions paid by governments to international 

organizations and vice versa. Included here are also transfers between governments 

and non-residents other than governments and international organizations. For instance, 

current taxes on income or social security contributions between a government and non-

residents  are  included  here.  Certain  classes  of  cross-border  transfers  made  between 

private sectors are also recorded in this category and include workers’ remittances by 

migrants (staying in the foreign country for more than one year) and international transfers 

between  private  entities  aimed  to  alleviate  poverty  and  the  consequences  of  natural 

disasters. 

Equation  (3)  represents  the  credit  markets  channel  and  is  based  on  the  difference 

between Gross Disposable Income and Consumption. This difference is the balancing item 

in the system of national accounts that corresponds to gross savings. It comprises not only 

household savings, but also corporate and government savings. This category includes net 

lending/borrowing to/from the rest of the world plus gross capital formation and net capital 

transfers to the rest of the world. Capital transfers are defined as transfer of the ownership 

3 The list of countries included in the sample was DE, EE, ES, FI, FR, IE, IT, LV, NL, PT, SK and SL. 

10 

 
                                           
of an asset (other than inventories and cash), or the cancellation of a liability by a creditor, 

without  any  counterpart  being  received  in  return.  They  cover  capital  taxes,  investment 

grants and other capital transfers. Notice that this channel has also a domestic connotation, 

through the gross capital formation, since agents can smooth consumption by borrowing 

and lending in international markets but also, for instance, by investing less. This channel 

therefore covers both national and international smoothing effects.  

Finally,  equation  (4),  relating  consumption  to  GDP,  measures  the  fraction  of  domestic 

shocks that is directly transmitted to domestic consumption, hence, remains unsmoothed 

and, therefore 1-(cid:2010)(cid:3022) measures the total amount of smoothed shocks. If (cid:2010)(cid:3022) = 0, there will be 

full risk sharing, whereas if (cid:2010)(cid:3022) > 0, domestic output shocks are at least partially passed to 

consumption.  In  the  extreme  case  of  (cid:2010)(cid:3022) > 1,  GDP  shocks  are  amplified  rather  than 

smoothed. 

Each  of  the  estimated  parameters  (cid:2010)(cid:3012), (cid:2010)(cid:3007), 	(cid:2010)(cid:3004)  in  the  equations  (1)  to  (3)  represents  the 

amount  of  risk  sharing  (in  percentage  to  1)  that  takes  place  through  the  capital, 

government and credit channels, respectively. Alternatively, 1-(cid:2010)(cid:3022), the total amount of risk 

sharing can also be given by the sum of percentage smoothed through each one of the 

channels,  that  is,  (cid:2010)(cid:3012) + (cid:2010)(cid:3007) + (cid:2010)(cid:3004).  Notice  that  we  could  have  negative  estimated  betas, 

meaning that the associated channel does not contribute to consumption smoothing but 

rather amplifies consumption volatility in response to GDP shocks. 

3.1  The estimated model 

We  have  estimated  equations  (1)  to  (3)  for  the  whole  data  set  as  well  as  country  by 

country. The estimation with the whole data set will give an idea of the average risk sharing 

for the set of countries for homogeneous countries. The estimation, country by country, 

allows  capturing  the  heterogeneity  across  the  different  economies  and  will  give  a 

characterization of the structure of risk sharing across the set of countries considered. We 

have used a variety of models, from the most popular  ones used in the  literature up to 

most sophisticated versions that take into account the dynamic nature of the problem as 

well as the possible issue of bidirectionality (both, Y causes X and X causes Y) in equations 

(1) to (3).4  

We have estimated equations (1) to (3) by Least Squares (LS) methods both from a static 

and  a  dynamic  perspective.  We  have  done  this  as  a  robustness  check  since  the  static 

approach is the usual in the literature. We have run our estimations for the full sample of 

countries with the largest available time span. However, for long samples and/or sets of 

4 Bidirectional causation can result in biased estimates. 

11 

 
 
                                           
heterogeneous  countries,  the  hypothesis  of  constant  parameters  could  be  difficult  to 

maintain, so we have also estimated country specific parameters. We have considered each 

equation in isolation as well as the estimation of equations (1) to (3) as a system (SURE) 

using panel  techniques  with  time fixed  effects and  errors following an autoregression  of 

order 1 to capture the main dynamics in the data. Alternatively, we have estimated a fully-

fledged dynamic model which is a variation of the basic set up described in Poncela et al. 

(2016).  It  is  based  on  a  dynamic  panel  approach  where,  instead  of  pooling  all  the 

information  relative  to  the  countries,  we  estimate  the  following  system  of  equations  for 

each country: 

(cid:1850)(cid:3036),(cid:3047) = (cid:1827)(cid:2868),(cid:3036) + (cid:1827)(cid:2869),(cid:2919)(cid:1850)(cid:3036),(cid:3047)(cid:2879)(cid:2869) + (cid:1827)(cid:2870),(cid:2919)(cid:1850)(cid:3036),(cid:3047)(cid:2879)(cid:2870) + ⋯ + (cid:1827)(cid:3043),(cid:3036)(cid:1850)(cid:3036),(cid:3047)(cid:2879)(cid:3043) + (cid:1847)(cid:3036),(cid:3047) 

(5) 

For each country i and each time period t, (cid:1850)(cid:3036),(cid:3047) is the 4 × 1 vector  

(cid:1850)(cid:3036),(cid:3047) =









i

GDP
t
−
i
GNI
−
GDI
−
i
C
t

GDP
t
i
GNI
t
GDI

i
t

i
t
i
t









, 

(cid:1827)(cid:2868),(cid:3036) is the 4x1 vector of intercepts, (cid:1827)(cid:3037),(cid:3036), j=1,…,p; i=1,…,N are 4 × 4 matrices of coefficients, 

and  (cid:1847)(cid:3036),(cid:3047)  is  multivariate  white  noise.  In  this  setting  and  according  to  the  literature  (i.e., 

Asdrubali and Kim, 2004) the shock is originated via the error term (cid:1847)(cid:3036),(cid:3047) and transmitted to 

the whole system. 

Notice that equation (5) is analogous, in compact notation, to the system of equations (1) 

to (3) plus an equation describing GDP dynamics and the addition of a certain number of 

past  values of  the dependent variable (cid:1850)(cid:3036),(cid:3047).  Past values are  inserted to  capture  the  long-

lasting  effects  of  each  channel,  i.e. effects that  could  take  place some periods  after  the 

shock  actually  hits  the  country.  In  so  doing  we  are  able  to  see  when  a  given  channel 

acts/stops smoothing consumption, if a channel is activated immediately after the shock 

or if it affects the economy only with some delay. Within this methodology we compute the 

effect of the GDP shock to the system.  

12 

 
 
 
 
 
3.2  The data 

The annual data used for the country-specific analysis are coming from National Accounts 

statistics  (AMECO5)  covering  the  timespan  1960-2016.  Data  on  GDP,  net  factor  income 

(NFI=GDP-GNI),  net  international  transfers  (NIT=GNI-GDI)  and  savings  (S=GDI-C)  are 

transformed in growth rates. 

Idiosyncratic GDP reflects that the behaviour of each country’s domestic output can be 

different  from  the  average6  of  a  group  of  countries,  the  target  group.  We  compute 

idiosyncratic values for all the variables in the analysis. We analyse two groups of target 

countries: 

1. 

European Union 157 (EU15). This choice is only due to data availability, larger 

for  this  set  of  countries.  As  robustness  check  we  repeat  the  estimation 

eliminating Luxembourg due to its very volatile behavior. We estimate the model 

for the whole time span as well as for the two sub-samples 1960-1998 and 1999-

2016, to highlight the effects of the recent sub-prime and sovereign crises. 

2.  European  Union  countries  (EU28).  As  a  robustness  check,  we  also  repeat  the 

estimations  excluding  from  the  analysis  Croatia  that  entered  the  EU  in  2013, 

Luxembourg,  which  exhibits  a  very  volatile  behavior  and  the  smallest  EU 

countries (Slovenia, Malta, and Cyprus). Lack of data prevents the estimation of 

sub-samples and we limit our sample size to the period 1995-2016. 

To interpret the results, we set to 100 the contemporaneous effect of a shock on GDP, and 

report the fraction smoothed through each channel. Notice that this normalization is done 

for  each  country.  Then,  the  numbers  that  appear  in  the  tables  should  be  taken  as  the 

percentage of idiosyncratic shocks that each country is able to smooth through the different 

channels. 

5 The annual macro-economic database is compiled by DG ECFIN (https://ec.europa.eu/info/business-economy-
euro/indicators-statistics/economic-databases/macro-economic-database-ameco_en).  
6 The average is weighted to reflect the importance of each county in the target group (see, Beyer, Doornik, and 
Hendry, 2001, for the exact method of calculating the aggregates). 
7  Austria,  Belgium,  Denmark,  France,  Germany,  Greece,  Ireland,  Italy,  Netherlands,  Portugal,  Spain,  Sweden 
and United Kingdom. 

13 

 
 
                                           
4  Results  

4.1  Target group: EU15 

Table 1 displays the average risk sharing achieved by each country in the period 1960-

2016. We present the results from 2 estimation methods for risk sharing, considering the 

channels  jointly  in  a  regression  framework  (SURE  estimation)  and  adding  the  dynamic 

behavior to the joint estimation of risk sharing through the 3 channels (VAR estimation). 

SURE estimation, common in the  literature, constitutes the benchmark when comparing 

the fully dynamic VAR approach. In the ideal case of full risk sharing among the countries 

in  the  sample,  the  shock  to  idiosyncratic  GDP  should  not  be  transmitted  to  domestic 

consumption  thanks  to  cross-border  smoothing.  The  column  Total  represents  the 

percentage of GDP shocks that is overall smoothed or, in other terms, not transmitted to 

domestic  consumption  (e.g.  for  Ireland  is  63%,  with  the  SURE  estimation),  while  the 

remaining columns detail the percentage of total risk sharing smoothed through each of 

the  channels  (e.g.  in  Ireland  14%  of  the  shocks  are  smoothed  via  the  capital  markets 

channel, SURE estimation). Negative percentages indicate “dis-smoothing”: the shock not 

only is transmitted to consumption but that channel induces further reductions. From now 

on, all the tables are read in the same way. As an additional robustness check, we also 

include in the Annex the estimation of risk sharing through each channel when considering 

them  in  isolation.  The  results  are  very  close  to  those  of  the  static  system  (SURE 

estimation).  Table  A5  in  the  Annex  shows  the  benchmark  estimation  for  this  group  of 

countries and the following tables those for the remaining groups analysed. 

14 

 
 
 
 
 
 
 
 
 
 
 
 
 
Table  1.  Percentage  of  risk  sharing  in  case  of  shocks  to  domestic  output.  Analysis  per 

country, target group EU15 excluding LU. Total refers to the percentage of total risk sharing 

(%  of  domestic  consumption  smoothed).  Capital,  Gov  and  Credit  refer  to  risk  sharing 

obtained via capital markets, government and credit channels, respectively. Sample: 1960-

2016. 

 1960-2016 

SURE estimation 

VAR estimation 

Country 

Total 

Capital 

Gov 

Credit 

Total 

Capital  Gov 

Credit 

Austria 

Belgium 

Denmark 

Finland 

France 

Germany 

Greece 

Ireland 

Italy 

Netherlands 

Portugal 

Spain 

Sweden 

UK 

25 

68 

14 

52 

24 

25 

32 

63 

31 

54 

13 

23 

47 

10 

-1 

7 

-5 

-6*** 

3 

0 

3 

14*** 

4 

10 

-4 

2 

-6 

-1 

2 

-4 

2 

0 

-1 

1 

1 

0 

-2 

-1 

4 

1 

0 

2 

24**

65***

17**

58***

22

25***

27***

49***

28***

46***

12

21***

53***

9

3 

46 

13 

43 

9 

23 

42 

79 

26 

31 

15 

27 

63 

18 

-3 

0 

-2 

-1 

1 

-1 

0 

17 

5 

0 

-3 

3 

-8 

2 

1 

-3 

1 

0 

2 

2 

-2 

3 

-1 

1 

-1 

3 

0 

3 

4 

49*** 

14 

45*** 

6 

22** 

44*** 

59*** 

21** 

31 

19 

21** 

72*** 

14 

Note: data source AMECO, JRC estimations.  

The symbols ** and *** indicate significant at 5 and 1% level. 

The  analysis  for  the  whole  sample  indicates  that  the  credit  markets  channel  (or  gross 

savings) is the most important channel for consumption smoothing. The importance of this 

channel is however different across countries, being quite high and statistically significant 

at  1%  with  all  estimation  procedures  for  Belgium,  Finland,  Sweden,  Ireland  and  Greece 

(the particular ordering depends on the estimation method). For instance, no matter the 

estimation  method,  we  can  conclude  that  Finland  smooths  approximately  half  of  GDP 

shocks, basically, through the credit markets channel.  

The  remaining  channels  do  not  show  too  much  capacity  of  consumption  smoothing.  A 

notable exception is Ireland, where the capital markets channel can smooth about 15% of 

15 

 
 
 
 
GDP  shocks.  As  regards  international  transfers,  we  do  not  detect  significant  smoothing 

through this channel as the numbers that appear in the table are much smaller and hardly 

significant. Notice that there are two countries, France and UK, where we do not detect 

any significant smoothing effect through any of the channels by any estimation method. 

Table 2 shows the volatility (measured through the standard deviation) of idiosyncratic 

GDP.  Leaving out  the case  of  Ireland that  we will  explain  later  on,  it turns out  that,  for 

instance, for the sample analysed, the volatility of idiosyncratic GDP in Greece is 8 times 

that of France, meaning that the shocks Greece has faced are 8 times bigger, on average, 

than those of more stable countries, like France, Belgium or the Netherlands, so not all the 

countries had the same needs for risk sharing.  

We repeat the analysis including Luxembourg in the set of countries. Table A1 in the Annex 

shows the results. Overall, Luxembourg has the highest shares of consumption smoothing, 

being  close  to  full  risk  sharing.  This  is  achieved  through  the  capital  and  credit  markets 

channels. 

Table  2.  Standard  deviation  of  idiosyncratic  GDP,  target  group  EU15  excluding  LU. 

Sample: 1960-2016. 

Country 

Std Dev 

Ireland 

Greece 

Portugal 

Finland 

Spain 

Sweden 

UK 

Denmark 

Germany 

Italy 

Austria 

Netherlands 

Belgium 

France 

0.053 

0.042 

0.023 

0.016 

0.013 

0.012 

0.011 

0.010 

0.007 

0.007 

0.007 

0.005 

0.005 

0.005 

Note: data source AMECO, JRC estimations.  

16 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4.2  Target group EU15: sub-sample analysis 

In order to get a flavour on how sharing risks has worked in recent times, we divide the 

sample  in  two  periods  1960-1998  and  1999-2016.  The  values  within  each  subsample 

should  be  taken  as  average  figures  for  the  years  covered  in  each  particular  subsample. 

Notice that the second subsample is much shorter and, therefore, uncertainty around the 

estimations should be higher leading to less significant results. Table 3A shows the results 

for the first subsample (1960-1998) and Table 3B for the second one (1999-2016). In a 

similar way to the whole sample, the credit markets channel seems to be predominant to 

achieve consumption smoothing during the 1960-1998 period. The 3 methodologies used 

confirm that risk sharing through the credit markets channel was quite high in Belgium, 

Finland, Greece, Ireland, Italy and Sweden. This result is quite robust since no matter the 

estimation and/or model we used, the result is always statistically significant. There could 

be also some evidence that the capital markets channel was not working for some countries 

(Finland, Portugal, and Sweden). 

Table 3A. Percentage of risk sharing in case of shocks to domestic output. Analysis per 

country, target group EU15 excluding LU. Total refers to the percentage of total risk sharing 

(%  of  domestic  consumption  smoothed).  Capital,  Gov  and  Credit  refer  to  risk  sharing 

obtained  via  capital  markets,  government  and  credit  channels,  respectively.  Estimation 

sample: 1960-1998. 

1960-1998 

SURE estimation 

VAR estimation 

Country 

Total 

Capital 

Gov 

Credit 

Total 

Capital  Gov 

Credit 

Austria 

Belgium 

Denmark 

Finland 

France 

Germany 

Greece 

Ireland 

Italy 

Netherlands 

Portugal 

Spain 

Sweden 

UK 

29 

86 

10 

51 

24 

28 

47 

39 

42 

59 

14 

20 

57 

13 

-3 

1 

-2 

-7*** 

-1 

-1 

2 

4 

2 

-4 

3 

0 

-3 

1 

0 

1 

29*** 

89*** 

9 

58*** 

28 

28*** 

45*** 

33*** 

5** 

-4*** 

41*** 

8 

-5** 

0 

-7*** 

-2 

-1 

4 

-1 

0 

3 

52*** 

15 

21*** 

64*** 

12 

3 

75 

8 

57 

25 

20 

55 

40 

36 

50 

28 

20 

88 

25 

Note: data source AMECO, JRC estimations.  

The symbols ** and *** indicate significant at 5 and 1% level. 

17 

-4 

1 

0 

-5 

-4 

-5 

3 

-2 

6 

12 

-5 

-3 

-7 

0 

5** 

2 

-3 

77*** 

2 

0 

0 

4** 

1 

-3 

-2 

2 

-6 

1 

0 

4 

7 

62*** 

29 

20 

51*** 

44*** 

32*** 

36 

39*** 

23** 

95*** 

21 

 
When we look at the second subsample (1999-2016), for the first time the capital markets 

channel  appears  statistically  significant  with  all  the  procedures  used  to  estimate  the 

amount of risk sharing for a country, Ireland. The next section will describe in more detail 

the  specific  characteristics  of  the  Irish  economy.  There  is  also  strong  evidence  that  the 

credit  market  channel  was  working  for  Finland  and  Ireland  although,  for  the  first  time, 

there is evidence that this channel produced dis-smoothing in some countries (especially, 

Portugal  and  Italy),  indicating  their  inability  to  put  in  place  short-term  measures  to 

counteract the effects of the 2008 recession and the subsequent sovereign debt crisis. The 

credit  channel  has  stopped  to  work  also  in  Belgium  and  Sweden,  compared  to  the  first 

subsample. Recall that in Sweden the dot.com crisis was very severe and the credit channel 

(the usual way in this country to smooth consumption) was not able soften this shock. 

Table 3B. Percentage of risk sharing in case of shocks to domestic output. Analysis per 

country, target group EU15 excluding LU. Total refers to the percentage of total risk sharing 

(%  of  domestic  consumption  smoothed).  Capital,  Gov  and  Credit  refer  to  risk  sharing 

obtained  via  capital  markets,  government  and  credit  channels,  respectively.  Estimation 

sample: 1999-2016. 

1999-2016 

SURE estimation 

VAR estimation 

Country 

Total  Capital  Gov  Credit

Total  Capital  Gov  Credit

Austria 

Belgium 

Denmark 

Finland 

France 

Germany 

Greece 

Ireland 

Italy 

Netherlands 

Portugal 

Spain 

Sweden 

UK 

8 

21 

42 

73 

23 

17 

9 

77 

2 

32 

2 

33 

43 

5 

4 

23 

-1 

-4 

5

2

-16 

-5  63***

13 

14 

4 

7 

-3 

63**

4 

0 

2 

4

13

1

20** 

-1  58***

-1 

17 

5 

6 

13 

-3 

4 

-1 

2 

4 

1 

-1 

-1

16

-6

23

29

9

18 

16 

-9 

47 

6 

40 

24 

85 

-18 

37 

-21 

58 

32 

0 

-7 

14 

-10 

1 

-9 

7 

6 

37*** 

4 

18 

-3 

9 

13 

-6 

-3 

-7 

2 

-2 

-2 

-1 

-3 

1 

0 

-9 

17 

6 

2*** 

3 

28 

9 

-1 

48*** 

16 

33** 

21 

46*** 

-23** 

28 

-35** 

43 

17 

3 

Note: data source AMECO, JRC estimations.  

The symbols ** and *** indicate significant at 5 and 1% level. 

18 

 
 
We also repeated the subsample analysis including Luxembourg. Tables A2 and A3 in the 

Annex show the results. The conclusions for the first subsample (1960-2016) are the same 

as for the whole sample, being Luxembourg the country with the highest figures for risk 

sharing,  specially,  through  the  capital  and  credit  markets.  Curious  enough,  for  the  first 

time there is evidence of some risk sharing through the credit markets channel in the UK, 

although it totally disappears in the second subsample (see, Table A3). 

4.3  A closer look to Ireland 

Not only the credit market channel works quite well in Ireland, but also does the capital 

markets channel. Another fact that catches one’s eye is the high volatility of idiosyncratic 

GDP (10 times that of France, Belgium or the Netherlands). In fact, real GDP growth rate 

in Ireland for 2015 was over 26%! A closer look to Ireland shows two features.  

The first is due to the 2008 crisis. After 2008 Ireland experienced a huge housing bubble 

together with excessive credit expansion, the subsequent burst of this bubble generated 

loan losses for banks, a decline of aggregate demand, an increase in unemployment and a 

deterioration of public finances; as a consequence, the sovereign debt crisis came along 

and the Irish authorities requested assistance from the EU and IMF in November, 2010. 

The total financial assistance program amounted to €85 billion.  

In spite of the crisis and its effects Ireland has experienced a positive growth in the years 

before the Great Recession and the subsequent sovereign debt crisis. Figure 1 plots real 

GDP growth rate per capita computed employing the usual risk sharing deflator (CPI). With 

the exception of 2008 and 2009, per capita real GDP growth rates in Ireland were always 

positive, as Figure 1 shows. Average Irish real GDP growth rate (per capita) has been 7.6% 

for the sample 1960-2016. 

19 

 
 
 
 
 
 
 
 
 
 
 
 
 
Figure 1. Ireland’s real per capita GDP growth rates. Sample: 1960-2016. GDP deflator 
is CPI. 

gdp

.4

.3

.2

.1

.0

-.1

-.2

65

70

75

80

85

90

95

00

05

10

15

Note: data source AMECO. 

The Net Factor Income (NFI) in Ireland has been negative since 1976,8 indicating steady 

capital  outflows.  This  is  mainly  due  to  the  activities  of  foreign-owned  multinational 

corporations  and  the  repatriation  of  their  profits  from  Ireland.  Among  EU15  (excluding 

Luxemburg) Ireland has the highest (negative) ratio of NFI to GDP. Due to low corporation 

tax  rates,  some  large  multinational  enterprises  have  reallocated  their  headquarters  to 

Ireland. As the Irish Central Statistical Office points out,9 especially in recent years, the 

net negative flows recorded in the balance of payments are mainly due to outflows of equity 

income  (both,  in  direct  investment  income  as  well  as  in  portfolio  investment  income, 

although  the  first  one  is  more  pronounced),  that  are  much  higher  than  their  inflows 

counterparts. Other entries in the net factor income, as the compensation to employees 

and other net investment incomes are insignificant. This means that Ireland was actually 

smoothing positive GDP growth. This was mainly due to the repatriation of profits of foreign 

owned multinationals settled in Ireland. 

8 This means that the Gross National Income (GNI) has been lower than the Gross Domestic Product (GDP) during 
all those years. 
9  http://cso.ie/en/media/csoie/methods/balanceofinternationalpayments/trendsnetfactorincome.pdf 

20 

  
 
 
 
 
 
                                           
Figure 2. Irish ratio NFI/GDP (in percentage). Sample: 1960-2016 

Share of NFI/GDP

.04

.00

-.04

-.08

-.12

-.16

-.20

-.24

65

70

75

80

85

90

95

00

05

10

15

Note: data source AMECO. 

The second peculiar feature of Ireland is a real GDP growth rate for 2015 at 26% with a 

negative net factor income close to 21% of GDP. As pointed out by the OECD,10 some large 

multinationals  have  recently  domiciled  their  headquarters  in  Ireland  moving  there  their 

intangible assets (intellectual property rights) because of Irish low corporate tax regime. 

This implies that the sales associated to these assets are accounted for in the Irish GDP, 

pushing  up  the  value  of  the  state’s  balance  sheet.  In  2016,  GDP  growth  rate  in  Ireland 

went  down  to  around  5%,  more  in  line  with  average  historical  Irish  growth  rates.  We 

included the latest available data up to 2016 in our estimations. 

4.4  Target group: EU28 

Due  to  data  availability,  the  sample  used  for  the  estimation  of  EU28  covers  the  period 

1995-2016. We perform the analysis excluding Croatia, Cyprus, Luxembourg, Malta, and 

Slovenia: Croatia recently joined the EU, Malta, Cyprus and Slovenia are very small and 

Luxembourg displays a volatile behaviour that could condition estimations. Including those 

countries, however, does not change the findings (see Annex 1 for the results). Table 4 

reports the  results. Due to the turbulent times included  in the sample, we  find  cases of 

positive and negative short term smoothing through any of the channels. The conclusions 

for the  countries  in the  EU15 group practically remain  unaltered  when  a  larger  group  is 

10 https://www.oecd.org/std/na/Irish-GDP-up-in-2015-OECD.pdf 

21 

 
 
 
 
 
                                           
considered,  being  Ireland  and  Finland  those  with  the  higher  percentage  of  domestic 

consumption smoothing. 

Cross-border risk sharing via capital markets works quite well for Ireland and the Baltic 

countries (Lithuania, Latvia and Estonia). It is worth noting the case of Latvia where 

risk  sharing  achieved  through  the  capital  markets  channel  (the  highest  among  all  the 

countries in the sample) is offset by dis-smoothing through the credit channel. Figures A1 

and  A2  offer  an  intuitive  graphical  explanation.  In  Figure  A1,  idiosyncratic  GDP  and 

consumption growth rates go hand in hand indicating the total absence of smoothing, while 

in  Figure  A2  idiosyncratic  GDP  is  compared  with  the  three  channels,  showing  that  the 

capital markets channel acts as shock absorber. Instead, the credit or savings channel acts 

counter-cyclically to GDP, offsetting the smoothing achieved through the capital markets 

channel. A closer look to the Baltic countries reveals that there was a strong presence of 

foreign banks (mainly Nordic) in all 3 countries (Latvia, Lithuania and Estonia), although 

in Latvia domestic banks also have a considerable presence. After the bust in construction, 

activity fell from 10 to 4% of GDP and housing prices collapsed. In fact, GDP growth rates 

fell more than 14% in the 3 Baltic countries in 2009. However, the foreign banks absorbed 

most of the shocks and the three countries were growing at 6% or more by 2011. The only 

country  that  needed  international  financial  assistance  was  Latvia,11  which  has  a  larger 

share of domestic banks. The bailout in Latvia imposed austerity measures to the national 

government that jointly with the high unemployment rate (around 20% during the crisis) 

turn out in a severe dis-smoothing behavior in consumption.  

11https://ec.europa.eu/info/business-economy-euro/economic-and-fiscal-policy-coordination/eu-financial-
assistance/which-eu-countries-have-received-assistance/financial-assistance-latvia_en 

22 

 
 
 
 
 
 
 
 
 
 
                                           
Table  4.  Percentage  of  risk  sharing  to  shocks  to  domestic  output.  Target  group  EU28 

excluding LU, CY, MT, HR and SI. Total refers to the percentage of total risk sharing (% of 

domestic consumption smoothed). Capital, Gov and Credit refer to risk sharing obtained 

via capital markets, government and credit channels, respectively. Sample: 1995-2016. 

1995-2016 

SURE estimation 

VAR estimation 

Country 

Total 

Capital  Gov. 

Credit 

Total 

Capital  Gov. 

Credit 

9 

16 

-13 

2 

-4 

4 

1 

5*** 

-3

0

49

30

-3 

-5 

48**

Austria 

Belgium 

Bulgaria 

Czech Republic 

Denmark 

Estonia 

Finland 

France 

Germany 

Greece 

Hungary 

Ireland 

Italy 

Latvia 

Lithuania 

Netherlands 

Poland 

Portugal 

Romania 

Slovakia 

Spain 

Sweden 

UK 

8 

13 

41 

35 

40 

17 

55 

24 

8 

12 

-4 

67 

-1 

6 

18 

18 

29 

10 

13 

19 

29 

18 

12 

13** 

3 

11 

12 

6 

10 

17** 

-9 

16*** 

2 

0 

2 

0 

2 

0 

0 

3 

4 

16*** 

-1 

13 

9 

8 

-2 

-3 

5 

10 

-10 

0 

1 

2 

2 

3 

3 

2 

0 

21 

5 

49 

8 

13 

28 

59 

2 

38 

19 

9 

73 

0 

-3 

25** 

16 

-5 

13** 

8 

15 

-2 

0 

1 

38*** 

2

53***

11

-4

3

-14

50***

5

-16 

-2 

-14

3

5

19

1

12

19

22

5

22

-2 

21 

27 

38 

10 

17 

33 

58 

11 

12 

25*** 

20*** 

18 

10 

18** 

-1 

-1 

4 

12 

1 

1 

-2 

9 

3 

-7* 

2 

20 

10 

15** 

-11 

25 

13 

-3** 

54*** 

-3 

0 

0 

0 

1 

1 

4 

-1 

-2 

8 

3 

2 

-9 

40 

19* 

8 

34** 

-15 

-31*** 

2 

11 

20 

-10 

16 

7** 

27*** 

2 

4 

0 

52** 

-4 

11 

Note: data source AMECO, JRC estimations. The symbols ** and *** indicate significant at 5 and 1% level. 

We repeat the analysis including all the countries within the EU28. Due to data availability, 

the sample covers the time span 1995-2016. Table A4 in the Annex shows the results that 

remain unaltered although placing also Luxembourg and Malta among the first places in 

risk sharing, as expected (being them international financial hubs). 

23 

 
5  Relative performance of countries  

The static estimation of the first two models allows decomposing the effect of risk sharing 

for  each  country  in  two  parts:  a  common  and  an  idiosyncratic  one.  During  crisis  period 

nearly  all  countries  have  displayed  some  smoothing  through  the  analysed  channels. 

However, to better understand the differences across countries we look at each country’s 

performance on top of the average (common) performance for each of the channels. This 

is the idiosyncratic effect. For this purpose, we re-write the basic equations for estimating 

risk sharing (1) to (3) as: 

Δlog(GDP) − 	Δlog(GNI) = (cid:2010)(cid:2868),(cid:3012) + ((cid:2010)(cid:3012),(cid:3014)(cid:3008) + (cid:2010)(cid:3012),(cid:3036))	Δlog(GDP) + (cid:1873)(cid:3012) 

Δlog(GNI) − 	Δlog(GDI) = (cid:2010)(cid:2868),(cid:3007) + ((cid:2010)(cid:3007),(cid:3014)(cid:3008) + (cid:2010)(cid:3007),(cid:3036))	Δlog(GDP) + (cid:1873)(cid:3007) 

Δlog(GDI) − 	Δlog(C) = (cid:2010)(cid:2868),(cid:3004) + ((cid:2010)(cid:3004),(cid:3014)(cid:3008) + (cid:2010)(cid:3004),(cid:3036))	Δlog(GDP) + (cid:1873)(cid:3004) 

(1’) 

(2’) 

(3’) 

where  we  have  decomposed  the  parameter  that  represents  risk  sharing  through  each 

channel  as  the  sum  of  two  parts,  a  common  or  average,  and  an  idiosyncratic  one.  To 

estimate  the  common  part  of  risk  sharing  through  each  channel,  (cid:2010)(cid:3012),(cid:3014)(cid:3008)  for  the  capital 

markets, (cid:2010)(cid:3007),(cid:3014)(cid:3008) for the fiscal or government channel and (cid:2010)(cid:3004),(cid:3014)(cid:3008)	for the credit market channel, 

we  use  the  Mean  Group  estimator  of  Pesaran  and  Smith  (1995),  averaging  the  country 

estimates for each channel. For each channel and each country, the idiosyncratic part is 

given  by  the  amount  of  risk  sharing  estimated  on  top  of  the  common  or  average  part. 

Table  5  gives  the  results  for  the  target  group  EU15  excluding  Luxembourg  for  the  full 

sample (1960-2016). 

The  bottom  line  (MG  estimator)  represents the  average or  common  risk  sharing  for  the 

group of countries considered, while for each country we report the idiosyncratic behaviour 

i.e. the share of additional consumption smoothing through each one of the channels. If 

the  previous  sections  gave  a  picture  of  risk sharing  in  absolute  terms  for  each  country, 

Table 5 focuses on the relative performance of the countries giving a comparative analysis. 

As  expected,  allowing  for  heterogeneity  results  in  different  average  estimates  of  risk 

sharing than considering all the countries as homogeneous and estimating a unique model 

for all of them as usually reported in the literature. Notice, as well, the case of UK (followed 

by Portugal) pointing out that risk sharing was way behind average figures for the whole 

set of years, especially through the credit market channel.  

24 

 
  
 
 
 
 
 
 
 
 
 
Table  5.  Percentage  of  common  and  idiosyncratic  risk  sharing  to  shocks  to  domestic 

output.  Target  group  EU15  excluding  LU.  Capital,  Gov  and  Credit  refer  to  risk  sharing 

obtained via capital markets, government and credit channels, respectively. Sample: 1995-

2016. 

 1961-2016 

SURE estimation 

Country 

Total 

Capital 

Gov 

Credit

Austria 

Belgium 

Denmark 

Finland 

France 

Germany 

Greece 

Ireland 

Italy 

Netherlands 

Portugal 

Spain 

Sweden 

UK 

MG estimator 

-9 

34 

-20 

18 

-10 

-9 

-2 

29 

-3 

20 

-21 

-11 

13 

-24 

34 

-2 

6 

-6 

-7 

2 

-1 

2 

13 

3 

9 

-5 

1 

-7 

-2 

1 

2 

-4 

2 

0 

-1 

1 

1 

0 

-2 

-1 

4 

1 

0 

2 

0 

-9 

32 

-16 

25 

-11 

-8 

-6 

16 

-5 

13 

-21 

-12 

20 

-24 

33

Note: data source AMECO, JRC estimations.  

Table 6 gives the results for the set of EU28 for the sample 1995-2016, excluding the very 

small  and  volatile  economies.  Focusing  now  on  the  capital  markets  channel,  it  is  worth 

mentioning  the  poor  behaviour  of  this  channel  in  Bulgaria  and  Italy.  However,  while  in 

Bulgaria  the  credit  markets  channel  compensates  the  dis-smoothing  that  took  place 

through the capital markets channel, in Italy the credit channel was additional to the capital 

channel in the collapse of risk sharing in this country in last 20 years.  

25 

 
 
 
 
 
 
 
Table  6.  Percentage  of  common  and  idiosyncratic  risk  sharing  to  shocks  to  domestic 

output. Target group EU28 excluding LU, CY, MT, HR and SI. Capital, Gov and Credit refer 

to risk sharing obtained via capital markets, government and credit channels, respectively. 

Sample: 1995-2016. 

1995-2016 

SURE estimation 

Country 

Total 

Capital  Gov 

Credit

Austria 

Belgium 

Bulgaria 

Czech Republic 

Denmark 

Estonia 

Finland 

France 

Germany 

Greece 

Hungary 

Ireland 

Italy 

Latvia 

Lithuania 

Netherlands 

Poland 

Portugal 

Romania 

Slovakia 

Spain 

Sweden 

United Kingdom 

MG estimator 

-13 

-8 

20 

14 

19 

-4 

34 

3 

-13 

-9 

-25 

46 

-22 

-15 

-3 

-3 

8 

-11 

-8 

-2 

8 

-3 

-9 

21 

3 

10 

-19 

-5 

-9 

7 

-3 

5 

6 

0 

4 

11 

-15 

10 

10 

7 

3 

2 

-8 

-9 

-1 

4 

-16 

6 

1 

-5 

3 

4 

-6 

1 

-1 

1 

-1 

1 

-1 

-1 

2 

3 

-2 

-1 

0 

1 

1 

2 

2 

1 

-1 

1 

-17

-14

35

16

34

-12

39

-3

-18

-11

-28

36

-9

-28

-11

-9

5

-13

-2

5

8

-9

8

14

Note: data source AMECO, JRC estimations.  

26 

 
6  Conclusions 

We characterize risk sharing (i.e. consumption smoothing) across the EU28 countries and 

derive estimates of risk sharing for each channel – credit, capital and government channels. 

To  check  the  robustness  of  our  results,  we  provide  estimations  using  several  models 

(considering the channels individually, as a system taking into account possible interactions 

among them, and as a dynamic system to add time-dependent effects). 

Following the literature we define GDP shocks as deviations from the cross-country average 

GDP growth rates of a set of target countries. The first target group is composed by 15 EU 

countries  for  which  data  availability  is  the  largest  (overall  1960-2016).  This  enables 

supplementary  analysis  on  the  effects  of  the  financial  and  the  sovereign  crisis  on  risk 

sharing by splitting the sample size in two periods. In our sample not all countries had the 

same needs for risk sharing as the size of the idiosyncratic shocks is country-dependent. 

For instance, the volatility of idiosyncratic GDP for Ireland and Greece is 10 and 8 times 

higher than that of more stable countries, like France, Belgium or the Netherlands.  

Looking at the set of countries in the EU15 group, results show that the credit markets 

channel  is  the  first  source  of  consumption  smoothing.  However,  the  consumption 

smoothing ability of this channel depends on the sample analysed. In most recent times, 

shaped by the turbulences of the 2008 and the sovereign crisis, this channel became less 

important  and  occasionally,  for  certain  countries  (notably,  Italy  and  Portugal),  even 

displayed negative values, indicating a certain degree of dis-smoothing (in other terms the 

credit channel was not able to absorb GDP shocks). Notice that this channel acts, not only 

through cross border lending and borrowing, but it also has a national connotation, as it 

reflects the decreased investments due to the shock. Although some countries appear as 

having higher shares of consumption smoothing through this channel in the sample 1960-

1998  (i.e.,  Belgium  and  Sweden),  analysing  all  samples,  overall  Ireland  and  Finland 

appear achieving the greatest shares of risk sharing through the credit markets channel. 

The  capital  markets  channel,  languishing  in  the  older  sample,  becomes  much  more 

active in the post Euro sample, especially in Ireland where huge outflows in the net factor 

income (mainly in the form of income on equities) have worked to smooth positive GDP 

shocks in later times due to the repatriation of profits earned by multinationals settled in 

Ireland, and attracted by low corporation tax rates.  

27 

 
 
 
 
 
The government channel (risk sharing achieved through public international transfers) 

shows to be practically inexistent for all target groups and sample sizes. 

Looking at the whole set of EU countries, though with a smaller sample size (1995-2016), 

results  hardly  change  with  respect  to  the  EU15  countries.  Surprisingly,  the  capital 

markets  channel  as  a  source  of  risk  sharing  seems  working  efficiently  in  the  Baltic 

countries  (Lithuania,  Latvia  and  Estonia).  This  is  due  to  strong  presence  of  foreign 

banks that acted as shock absorbers avoiding the collapse of the banking system during 

the breakdown of the housing market. However, while in Lithuania and Estonia the banking 

systems  had  a  predominant  presence  of  Finnish  and  Swedish  banks,  Latvia  had  a 

substantial  share  of  national  banks  that  were  nationalized  following  the  international 

bailout (Latvia was the only Baltic country to be intervened). During this period the savings 

channel in Latvia acted counter cyclically offsetting the benefits from the capital markets 

channel. Luxembourg and Malta also emerge as achieving high percentages of risk sharing, 

especially through the capital and credit markets channels. As an exception, Luxembourg 

seems  to  experience  a  considerable  quota  of  dis-smoothing  through  the  government 

channel when we consider all the countries (the time span, in this case, is 1995-2016). 

Our  analysis  suggests  that  different  countries  have  difference  needs  in  terms  of  risk 

sharing. Furthermore, channels work differently in each country depending on the size and 

openness of the countries. This might have implications for one-size-fits-all type of policies 

fostering risk sharing. 

28 

References 

Asdrubali, P. and Kim, S. (2004). “Dynamic Risk Sharing in the United States and Europe.” 
Journal of Monetary Economics, 51(4):809–836. 

Asdrubali,  P.,  Kim,  S.,  Pericoli,  F.,  and  Poncela,  P.  (2017).    “Shock  Absorption  in  OECD 
Countries: a Heterogeneous Panel VAR Approach”. Mimeo.  

Asdrubali, P., Sørensen, B., and Yosha, O. (1996).   “Channels of Interstate Risk Sharing: 
United States 1963-1990.” The Quarterly Journal  of Economics, 111(4):1081–1110. 

Balli,  F.,  Pericoli,  F.,  and  Pierucci,  E.  (2014).   “Foreign  portfolio  diversification  and  risk-
sharing.” Economics Letters, 125(2):187–190. 

Beyer,  A.,  Doornik,  J.A.  and  Hendry,  D.H.  (2001).  “Constructing  Historical  Euro-zone 
Data.” Economic Journal, vol. 111(469), 102-121 

European Commission (2016). “Quarterly Report on the Euro Area.” Institutional papers. 

European Commission (2015). “The Five President Report. Completing Europe’s Economic 
and Monetary Union”. 

Investment  Plan  for  Europe  (2016)  https://ec.europa.eu/commission/priorities/jobs-
growth-and-investment/investment-plan-europe-juncker-plan_en 

Kalemli-Ozcan, S.,  Luttini,  E.,  and  Sørensen, B. (2014). “Debt Crises and Risk-Sharing: 
The Role of Markets versus Sovereigns.” Scandinavian Journal  of Economics, 116(1):253–
276. 

Mélitz, J., Zumer, F. (1999). “Interregional and international risk sharing and lessons for 
EMU.”  Carnegie-Rochester Conference Series on Public Policy 51 (0), 149-188. 

Pesaran, M.H.,  and  Smith,  R.  (1995).  “Estimating  long-run  relationships  from  dynamic 
heterogeneous panels.” Journal of Econometrics 68, 79-113. 

Poncela, P. Pericoli, F., Manca, A. And Nardo, M. (2016). “Risk Sharing in Europe” (2016). 
European Commission, Joint Research Centre, Policy Report. 

Sørensen, B. E., Wu, Y.-T., Yosha, O., and Zhu, Y. (2007). “Home Bias and International 
Risk  Sharing:  Twin  Puzzles  Separated  at  Birth.”    Journal  of  International  Money  and 
Finance, 26(4):587–605. 

29 

 
 
List of figures 

Figure 1. Ireland’s real per capita GDP growth rates. Sample: 1960-2016. GDP deflator 
is CPI. 

Figure 2. Irish ratio NFI/GDP (in percentage). Sample: 1960-2016. 

Figure  A1.  Idiosyncratic  GDP  and  consumption  growth  rates  in  Latvia.  Sample  1995-
2016. 

Figure  A2.  Idiosyncratic  GDP  growth  rates  and  the  3  channels  (capital  markets, 
government and credit markets channel) in Latvia. Sample 1995-2015. 

30 

 
 
 
List of tables 

Table  1.  Percentage  of  risk  sharing  in  case  of  shocks  to  domestic  output.  Analysis  per 
country, target group EU15 excluding LU. Total refers to the percentage of total risk sharing 
(%  of  domestic  consumption  smoothed).  Capital,  Gov  and  Credit  refer  to  risk  sharing 
obtained via capital markets, government and credit channels, respectively. Sample: 1960-
2016. 

Table  2.  Standard  deviation  of  idiosyncratic  GDP,  target  group  EU15  excluding  LU. 
Sample: 1960-2016. 

Table 3A. Percentage of risk sharing in case of shocks to domestic output. Analysis per 
country, target group EU15 excluding LU. Total refers to the percentage of total risk sharing 
(%  of  domestic  consumption  smoothed).  Capital,  Gov  and  Credit  refer  to  risk  sharing 
obtained  via  capital  markets,  government  and  credit  channels,  respectively.  Estimation 
sample: 1960-1998. 

Table 3B. Percentage of risk sharing in case of shocks to domestic output. Analysis per 
country, target group EU15 excluding LU. Total refers to the percentage of total risk sharing 
(%  of  domestic  consumption  smoothed).  Capital,  Gov  and  Credit  refer  to  risk  sharing 
obtained  via  capital  markets,  government  and  credit  channels,  respectively.  Estimation 
sample: 1999-2016. 

Table  4.  Percentage  of  risk  sharing  to  shocks  to  domestic  output.  Target  group  EU28 
excluding LU, CY, MT, HR and SI. Total refers to the percentage of total risk sharing (% of 
domestic consumption smoothed). Capital, Gov and Credit refer to risk sharing obtained 
via capital markets, government and credit channels, respectively. Sample: 1995-2016. 

Table  5.  Percentage  of  common  and  idiosyncratic  risk  sharing  to  shocks  to  domestic 
output.  Target  group  EU15  excluding  LU.  Capital,  Gov  and  Credit  refer  to  risk  sharing 
obtained via capital markets, government and credit channels, respectively. Sample: 1995-
2016. 

Table  6.  Percentage  of  common  and  idiosyncratic  risk  sharing  to  shocks  to  domestic 
output. Target group EU28 excluding LU, CY, MT, HR and SI. Capital, Gov and Credit refer 
to risk sharing obtained via capital markets, government and credit channels, respectively. 
Sample: 1995-2016. 

Table A1. Percentage of risk sharing in case of shocks to domestic output. Analysis per 
country,  target  group  EU15.  Total  refers  to  the  percentage  of  total  risk  sharing  (%  of 
domestic consumption smoothed). Capital, Gov and Credit refer to risk sharing obtained 
via capital markets, government and credit channels, respectively. Sample: 1960-2016. 

Table A2. Percentage of risk sharing in case of shocks to domestic output. Analysis per 
country,  target  group  EU15.  Total  refers  to  the  percentage  of  total  risk  sharing  (%  of 
domestic consumption smoothed). Capital, Gov and Credit refer to risk sharing obtained 
via capital markets, government and credit channels, respectively. Sample: 1961-1998. 

Table A3. Percentage of risk sharing in case of shocks to domestic output. Analysis per 
country,  target  group  EU15.  Total  refers  to  the  percentage  of  total  risk  sharing  (%  of 
domestic consumption smoothed). Capital, Gov and Credit refer to risk sharing obtained 
via capital markets, government and credit channels, respectively. Sample: 1999-2016. 

Table A4. Percentage of risk sharing in case of shocks to domestic output. Analysis per 
country,  target  group  EU28.  Total  refers  to  the  percentage  of  total  risk  sharing  (%  of 
domestic consumption smoothed). Capital, Gov and Credit refer to risk sharing obtained 
via capital markets, government and credit channels, respectively. Sample: 1996-2016. 

Table A5. Percentage of risk sharing in case of shocks to domestic output. Benchmark 
analysis: uniequational estimation. Analysis per country, target group EU15 excluding LU. 
Total  refers  to  the  percentage  of  total  risk  sharing  (%  of  domestic  consumption 
smoothed).  Capital,  Gov  and  Credit  refer  to  risk  sharing  obtained  via  capital  markets, 
government and credit channels, respectively. Sample: 1960-2016. 

31 

Table A6. Percentage of risk sharing in case of shocks to domestic output. Benchmark 
analysis: uniequational estimation. Analysis per country, target group EU15 excluding LU. 
Total  refers  to  the  percentage  of  total  risk  sharing  (%  of  domestic  consumption 
smoothed).  Capital,  Gov  and  Credit  refer  to  risk  sharing  obtained  via  capital  markets, 
government and credit channels, respectively. Estimation sample: 1960-1998 

Table A7. Percentage of risk sharing in case of shocks to domestic output. Benchmark 
analysis: uniequational estimation. Analysis per country, target group EU15 excluding LU. 
Total  refers  to  the  percentage  of  total  risk  sharing  (%  of  domestic  consumption 
smoothed).  Capital,  Gov  and  Credit  refer  to  risk  sharing  obtained  via  capital  markets, 
government and credit channels, respectively. Estimation sample: 1999-2016. 

Table A8. Percentage of risk sharing to shocks to domestic output. Benchmark analysis: 
uniequational estimation. Analysis per country, target group EU28 excluding LU, CY, MT, 
HR and SI. Total refers to the percentage of total risk sharing (% of domestic consumption 
smoothed).  Capital,  Gov  and  Credit  refer  to  risk  sharing  obtained  via  capital  markets, 
government and credit channels, respectively. Sample: 1995-2016. 

Table  A9.  Percentage  of  common  and  idiosyncratic  risk  sharing  to  shocks  to  domestic 
output. Benchmark analysis: uniequational estimation. Target group EU15 excluding LU. 
Capital, Gov and Credit refer to risk sharing obtained via capital markets, government and 
credit channels, respectively. Sample: 1995-2016. 

Table A10. Percentage of common and idiosyncratic risk sharing to shocks to domestic 
output. Target group EU28 excluding excluding LU, CY, MT, HR and SI. Capital, Gov and 
Credit refer to risk sharing obtained via capital markets, government and credit channels, 
respectively. Sample: 1995-2016. 

32 

 
Annex: additional tables and figures 

Analysis for EU15, including Luxembourg 

Table A1. Percentage of risk sharing in case of shocks to domestic output. Analysis per 
country,  target  group  EU15.  Total  refers  to  the  percentage  of  total  risk  sharing  (%  of 
domestic consumption smoothed). Capital, Gov and Credit refer to risk sharing obtained 
via capital markets, government and credit channels, respectively. Sample: 1960-2016. 

EU15 1960-2016 

Uniequational estimation 

SURE estimation 

VAR estimation 

Country 

Total 

Capital 

Gov 

Credit 

Total 

Capital 

Gov 

Credit 

Total 

Capital  Gov 

Credit 

Austria 

Belgium 

Denmark 

Finland 

France 

Germany 

Greece 

Ireland 

Italy 

Luxembourg 

Netherlands 

Portugal 

Spain 

Sweden 

United Kingdom 

26 

63 

16 

49 

30 

28 

34 

65 

28 

91 

54 

10 

24 

47 

6 

-2 

6 

-2 

-6 

5 

2 

2 

14*** 

7 

21*** 

10 

-4 

1 

-8 

-1 

3 

-1 

1 

0 

1 

0 

1 

0 

-2 

-4 

0 

3 

0 

0 

4 

24

58***

18

55***

23

27**

30***

51***

23

74***

44***

11

22**

55***

4

25 

68 

14 

52 

24 

25 

32 

64 

31 

92 

54 

13 

23 

47 

10 

-1 

7 

-5 

-6*** 

3 

0 

3 

14*** 

4 

19 

10 

-4 

2 

-6 

-1 

2 

-4* 

2 

0 

-1 

0 

1 

0 

-2 

-4 

-1 

4 

1 

0 

2 

24** 

65*** 

17** 

58*** 

22 

25*** 

27*** 

49*** 

28*** 

3 

45 

13 

43 

9 

23 

42 

79 

25 

-3 

0 

-2 

-1 

1 

-1 

0 

17*** 

5 

1 

-3 

1 

0 

2 

2 

-2 

3 

-1 

4 

49*** 

14 

44*** 

6 

22** 

44*** 

60*** 

21** 

78*** 

100 

39** 

-16 

76*** 

45*** 

13 

21*** 

53*** 

9 

31 

15 

27 

63 

19 

-1 

-3 

3 

-9 

2 

1 

-1 

3 

0 

3 

31 

19 

21** 

72*** 

14 

Note: data source AMECO, JRC estimations.  

The symbols ** and *** indicate significant at 5 and 1% level. 

33 

 
 
 
 
 
 
 
 
 
 
 
 
Table A2. Percentage of risk sharing in case of shocks to domestic output. Analysis per 
country,  target  group  EU15.  Total  refers  to  the  percentage  of  total  risk  sharing  (%  of 
domestic consumption smoothed). Capital, Gov and Credit refer to risk sharing obtained 
via capital markets, government and credit channels, respectively. Sample: 1961-1998. 

Uniequational estimation 

SURE estimation 

VAR estimation 

Country 

Total 

Capital  Gov  Credit 

Total 

Capital  Gov     

Credit 

Total 

Capital  Gov 

Credit 

Austria 

Belgium 

Denmark 

Finland 

France 

Germany 

Greece 

Ireland 

Italy 

Luxembourg 

Netherlands 

Portugal 

Spain 

Sweden 

UK 

28 

74 

13 

48 

30 

34 

49 

43 

33 

91 

58 

10 

20 

53 

6 

-1 

3 

-3 

-8** 

4 

0 

1 

5 

6 

10*** 

8 

-5 

1 

-8 

-3 

2 

-2 

2 

1 

-1 

1 

0 

0 

-2 

0 

0 

3 

-1 

0 

4 

27 

73*** 

14 

56*** 

27 

33** 

48*** 

38*** 

29 

81*** 

50*** 

13 

20 

61*** 

5 

29 

85 

10 

51 

24 

27 

47 

39 

42 

92 

59 

14 

20 

57 

13 

-3 

1 

-2 

-7*** 

-1 

-1 

2 

4 

2 

-4 

3 

0 

-3 

1 

0 

1 

5** 

-4** 

10 

8 

-5** 

0 

-7*** 

-2 

0 

-1 

4 

-1 

0 

3 

29*** 

89*** 

9 

58*** 

27* 

28*** 

45*** 

33*** 

41*** 

83*** 

52*** 

15 

21*** 

64*** 

12 

-1 

84 

9 

47 

21 

18 

59 

47 

38 

116 

38 

20 

21 

78 

26 

-4 

1 

0 

-5 

-2 

-3 

5 

-10 

2 

-1 

1 

1 

2 

2 

0 

5 

2 

85*** 

8 

51*** 

21 

18 

53*** 

52*** 

8*** 

-2 

32*** 

-2 

10 

-6 

-1 

-7** 

1 

1 

3 

-2 

-2 

0 

5 

117*** 

26 

28 

24** 

85*** 

20** 

Note: data source AMECO, JRC estimations.  

The symbols ** and *** indicate significant at 5 and 1% level. 

34 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Table A3. Percentage of risk sharing in case of shocks to domestic output. Analysis per 
country,  target  group  EU15.  Total  refers  to  the  percentage  of  total  risk  sharing  (%  of 
domestic consumption smoothed). Capital, Gov and Credit refer to risk sharing obtained 
via capital markets, government and credit channels, respectively. Sample: 1999-2016. 

EU15 1999-2016 

Uniequational estimation 

SURE estimation 

VAR estimation 

Country 

Total 

Capital 

Gov 

Credit 

Total 

Capital 

Gov 

Credit 

Total 

Capital  Gov 

Credit 

-1 

-4 

-5 

5 

2 

63*** 

-4** 

63** 

12 

-2 

19 

53 

2 

42 

16 

79 

-8 

4 

13 

1 

58*** 

-1 

49* 

105 

16 

-5 

24 

29 

9 

16 

5 

41 

29 

1 

-6 

-4 

-7 

7 

-11 

6 

-2 

26*** 

4 

49 

30 

14 

2 

-4 

0 

-1 

-2 

3 

19 

4 

22 

-3** 

49*** 

0 

0 

13 

37** 

-4** 

22** 

0 

0 

-7 

-3 

6 

4 

0 

1 

53*** 

-13 

64** 

-10 

-15 

35 

33** 

1 

4 

0 

2 

0 

4 

-24 

-1 

2 

4 

1 

-1 

Austria 

Belgium 

Denmark 

Finland 

France 

Germany 

Greece 

Ireland 

Italy 

Luxembourg 

Netherlands 

Portugal 

Spain 

Sweden 

United Kingdom 

16 

25 

43 

54 

32 

20 

10 

76 

0 

92 

35 

0 

31 

26 

5 

-5 

13 

3 

24 

10 

7 

3 

19*** 

0 

8 

5 

-10 

-15 

10 

-3 

3 

0 

-1 

13

6

50*

45

13

16

4

57***

1

84*** 

-27** 

36***

21 

3 

0 

2 

8 

-3 

1 

5 

-3 

2 

17

-3

26

26

-5

8 

21 

42 

72 

23 

17 

9 

77 

3 

90 

32 

2 

33 

43 

5 

4 

23 

-16 

13 

14 

4 

7 

20** 

0 

65 

17 

5 

6 

13 

-3 

Note: data source AMECO, JRC estimations.  

The symbols ** and *** indicate significant at 5 and 1% level. 

35 

 
 
 
  
 
 
 
 
 
 
 
 
 
Table A4. Percentage of risk sharing in case of shocks to domestic output. Analysis per 
country,  target  group  EU28.  Total  refers  to  the  percentage  of  total  risk  sharing  (%  of 
domestic consumption smoothed). Capital, Gov and Credit refer to risk sharing obtained 
via capital markets, government and credit channels, respectively. Sample: 1996-2016. 

EU28 1995-2016 

Uniequational estimation 

SURE estimation 

VAR estimation 

Country 

Total 

Capital 

Gov 

Credit 

Total 

Capital  Gov 

Credit 

Total 

Capital  Gov 

Credit 

9 

16 

-13 

-1 

-28 

2 

-4 

4 

3 

-2 

1  5*** 

-3 

0 

49 

16 

29 

30 

-5 

48** 

Austria 

Belgium 

Bulgaria 

Croatia 

Cyprus 

Czech Republic 

Denmark 

Estonia 

Finland 

France 

Germany 

Greece 

Hungary 

Ireland 

Italy 

Latvia 

Lithuania 

Luxembourg 

Malta 

Netherlands 

Poland 

Portugal 

Romania 

Slovakia 

Slovenia 

Spain 

Sweden 

UK 

12 

22 

42 

26 

6 

28 

40 

16 

51 

31 

11 

13 

9 

67 

0 

9 

18 

87 

67 

25 

20 

12 

11 

17 

42 

32 

23 

7 

13 

4 

-15** 

3 

-31 

8 

7 

8 

2 

16 

16 

5 

9 

16*** 

-2 

12** 

13** 

4 

0 

4 

3 

-4 

2 

-7 

1 

-2 

3 

-1 

3 

1 

0 

0 

3 

-2 

-5

19

53***

20

41

18

40

7

51**

12

-3

6

-2

51***

2

-5

7

69*** 

-23*** 

41**

18 

19 

7 

10 

1 

4 

9 

3 

-3 

-2 

-6 

-1 

1 

1 

1 

1 

-2 

3 

1 

0 

55***

7

13

1

9

11

35

27

26

9

8 

13 

41 

18 

-1 

35 

40 

17 

56 

24 

8 

12 

-4 

67 

-1 

6 

18 

81 

73 

18 

29 

10 

13 

20 

35 

29 

18 

12 

-3 

13 

3 

12 

12 

6 

10 

17** 

-9 

16*** 

16*** 

58 

13 

13 

9 

8 

-2 

-3 

4 

4 

11 

-10 

2 

0 

2 

0 

2 

0 

0 

3 

4 

-1 

-22 

-3 

0 

1 

2 

2 

3 

0 

3 

2 

0 

Note: data source AMECO, JRC estimations.  

The symbols ** and *** indicate significant at 5 and 1% level. 

36 

3 

4 

38 

6 

-2 

46 

12 

32 

58 

-3 

40 

17 

6 

80 

2 

53*** 

10 

-4 

3 

-14 

50*** 

-14 

3 

45 

63** 

5 

19 

1 

12 

19 

31 

22 

5 

22 

3 

31 

82 

92 

14 

47 

8 

6 

37 

46 

39 

29 

8 

-2 

1 

17 

-1 

22 

-3 

-3 

18*** 

-1 

-1 

8 

-5 

3 

-1 

-2 

1 

7

4

13

12

-26

49**

17

13

10** 

-3** 

51***

0 

4 

1 

0 

-4

37

-1 

-4** 

22**

-13 

-6 

25

27*** 

42*** 

0 

2 

6 

53***

-20

-46**

24*** 

-2 

8

64 

28 

9 

21 

16** 

5 

-11 

12 

3 

-8 

5 

-28 

46**

-6 

-3 

10 

5 

1 

69

8

15

-13

0

11 

37**

-3 

3 

-2 

0 

37**

33

39**

3

5 

-14 

3 

 
 
 
Table  A5.  Percentage  of  risk  sharing  in  case  of  shocks  to  domestic  output.  Benchmark 
analysis: uniequational estimation. Analysis per country, target group EU15 excluding LU. 
Total refers to the percentage of total risk sharing (% of domestic consumption smoothed). 
Capital, Gov and Credit refer to risk sharing obtained via capital markets, government and 
credit channels, respectively. Sample: 1960-2016. 

 1960-2016 

Uniequational estimation 

Country 

Total 

Capital  Gov 

Credit 

Austria 

Belgium 

Denmark 

Finland 

France 

Germany 

Greece 

Ireland 

Italy 

Netherlands 

Portugal 

Spain 

Sweden 

UK 

24 

61 

17 

49 

29 

27 

34 

65 

27 

54 

9 

24 

46 

5 

-2 

5 

-3 

-6** 

1 

1 

3 

15*** 

2 

23

-3 

58***

1 

0 

1 

1 

0 

0 

18

56***

28

25**

31***

50***

2 

10 

-4 

2 

-6 

0 

-1 

26

0 

44***

3** 

10

0 

0 

3 

22**

53***

3

Note: data source AMECO, JRC estimations.  

The symbols ** and *** indicate significant at 5 and 1% level. 

37 

 
 
 
 
Table  A6.  Percentage  of  risk  sharing  in  case  of  shocks  to  domestic  output.  Benchmark 
analysis: uniequational estimation. Analysis per country, target group EU15 excluding LU. 
Total refers to the percentage of total risk sharing (% of domestic consumption smoothed). 
Capital, Gov and Credit refer to risk sharing obtained via capital markets, government and 
credit channels, respectively. Estimation sample: 1960-1998. 

1960-1998 

Uniequational estimation 

Country 

Total 

Capital  Gov 

Credit 

Austria 

Belgium 

Denmark 

Finland 

France 

Germany 

Greece 

Ireland 

Italy 

Netherlands 

Portugal 

Spain 

Sweden 

UK 

26 

72 

13 

48 

31 

31 

50 

43 

32 

57 

10 

20 

52 

4 

-2 

2 

-2 

-7*** 

-1 

0 

1 

7*** 

4 

8 

3 

-2 

2 

0 

-1 

1 

0 

1 

-1 

1 

-5** 

3** 

1 

-8** 

-2 

-1 

0 

3 

25 

71*** 

13 

56*** 

33 

30** 

49*** 

36*** 

29** 

49*** 

11 

20 

60*** 

3 

Note: data source AMECO, JRC estimations.  

The symbols ** and *** indicate significant at 5 and 1% level. 

38 

 
 
 
 
Table  A7.  Percentage  of  risk  sharing  in  case  of  shocks  to  domestic  output.  Benchmark 
analysis: uniequational estimation. Analysis per country, target group EU15 excluding LU. 
Total refers to the percentage of total risk sharing (% of domestic consumption smoothed). 
Capital, Gov and Credit refer to risk sharing obtained via capital markets, government and 
credit channels, respectively. Estimation sample: 1999-2016. 

1999-2016 

Uniequational estimation 

Country 

Total  Capital  Gov  Credit

Austria 

Belgium 

Denmark 

Finland 

France 

Germany 

Greece 

Ireland 

Italy 

Netherlands 

Portugal 

Spain 

Sweden 

UK 

13 

21 

45 

56 

27 

20 

10 

76 

1 

38 

1 

32 

27 

4 

0 

15 

-9 

8 

7 

3 

6 

-1 

-5 

-4 

-3 

5 

1 

1 

14

10

58**

51**

15

16

3

20*** 

0  57***

-5 

18 

3 

5 

1 

10 

2 

-1 

2 

3 

-1 

-1 

4

21

-4

24

27

-4

Note: data source AMECO, JRC estimations.  

The symbols ** and *** indicate significant at 5 and 1% level. 

39 

 
 
 
Table A8. Percentage of risk sharing to shocks to domestic output. Benchmark analysis: 
uniequational estimation. Analysis per country, target group EU28 excluding LU, CY, MT, 
HR and SI. Total refers to the percentage of total risk sharing (% of domestic consumption 
smoothed).  Capital,  Gov  and  Credit  refer  to  risk  sharing  obtained  via  capital  markets, 
government and credit channels, respectively. Sample: 1995-2016. 

1995-2016 

Uniequational estimation 

Country 

Total 

Capital  Gov. 

Credit 

Austria 

Belgium 

Bulgaria 

Czech Republic 

Denmark 

Estonia 

Finland 

France 

Germany 

Greece 

Hungary 

Ireland 

Italy 

Latvia 

Lithuania 

Netherlands 

Poland 

Portugal 

Romania 

Slovakia 

Spain 

Sweden 

UK 

3 

14 

42 

29 

35 

16 

47 

20 

4 

14 

9 

67 

-5 

9 

18 

20 

18 

10 

12 

16 

32 

21 

5 

14 

10 

2 

-1 

-14

5

-16***  4*** 

54***

12 

4 

9** 

0 

14 

7 

7 

9 

17*** 

3 

-5 

2 

1 

5 

1 

2 

1 

0 

15

35

5

47**

1

-4

5

-1

50***

-4  2*** 

12*** 

12*** 

18 

8 

11 

1 

1 

7 

-2 

3 

3 

-1 

0 

0 

3 

1 

3 

3 

2 

0 

-3

-6

6

2

10

-3

11

12

22

20

2

Note: data source AMECO, JRC estimations.  

The symbols ** and *** indicate significant at 5 and 1% level. 

40 

 
 
 
 
 
Table  A9.  Percentage  of  common  and  idiosyncratic  risk  sharing  to  shocks  to  domestic 
output. Benchmark analysis: uniequational estimation. Target group EU15 excluding LU. 
Capital, Gov and Credit refer to risk sharing obtained via capital markets, government and 
credit channels, respectively. Sample: 1995-2016. 

 1961-2016 

Uniequational estimation 

Country 

Total 

Capital  Gov 

Credit

Austria 

Belgium 

Denmark 

Finland 

France 

Germany 

Greece 

Ireland 

Italy 

Netherlands 

Portugal 

Spain 

Sweden 

UK 

MG estimator 

-10 

27 

-17 

15 

-5 

-7 

0 

31 

-7 

20 

-25 

-10 

12 

-29 

34 

-3 

4 

-4 

-7 

0 

0 

2 

14 

1 

9 

-5 

1 

-7 

-1 

1 

1 

-4 

0 

-1 

0 

0 

-1 

-1 

-2 

-1 

2 

-1 

-1 

2 

1 

-9 

26 

-14 

24 

-4 

-7 

-1 

18 

-6 

12 

-22 

-10 

21 

-29 

32

41 

 
 
 
 
Table A10. Percentage of common and idiosyncratic risk sharing to shocks to domestic 
output.  Benchmark  analysis:  uniequational  estimation.  Target  group  EU28  excluding 
excluding LU, CY, MT, HR and SI. Capital, Gov and Credit refer to risk sharing obtained via 
capital markets, government and credit channels, respectively. Sample: 1995-2016. 

1995-2016 

Uniequational estimation 

Country 

Total 

Capital  Gov 

Credit 

Austria 

Belgium 

Bulgaria 

Czech Republic 

Denmark 

Estonia 

Finland 

France 

Germany 

Greece 

Hungary 

Ireland 

Italy 

Latvia 

Lithuania 

Netherlands 

Poland 

Portugal 

Romania 

Slovakia 

Spain 

Sweden 

United Kingdom 

MG estimator 

Note: data source AMECO, JRC estimations.  

7 

3 

-23 

5 

-3 

2 

-7 

7 

0 

0 

2 

10 

-11 

5 

5 

11 

1 

4 

-6 

-6 

0 

-9 

-4 

7 

1 

-2 

3 

2 

-6 

1 

0 

4 

0 

1 

0 

-1 

1 

2 

-2 

-1 

-1 

2 

0 

2 

2 

1 

-1 

1 

-26

-7

42

3

23

-7

35

-11

-16

-7

-13

38

-15

-18

-6

-10

-2

-15

-1

0

10

8

-10

12

-17 

-6 

22 

9 

15 

-4 

27 

0 

-16 

-6 

-11 

47 

-25 

-11 

-2 

0 

-2 

-10 

-8 

-4 

12 

1 

-15 

20 

42 

 
 
 
 
Latvia 

Figure  A1.  Idiosyncratic  GDP  and  consumption  growth  rates  in  Latvia.  Sample  1995-
2016. 

.12

.08

.04

.00

-.04

-.08

-.12

-.16

96

98

00

02

04

06

08

10

12

14

16

gdp_i

c_i

Figure  A2.  Idiosyncratic  GDP  growth  rates  and  the  3  channels  (capital  markets, 
government and credit markets channel) in Latvia. Sample 1995-2016. 

.12

.08

.04

.00

-.04

-.08

-.12

96

98

00

02

04

06

08

10

12

14

16

gdp_i

kap

gov

cre

43 

 
 
 
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doi:10.2760/028526 

ISBN 978-92-79-77063-0