How has the macro-economic imbalances procedure worked in practice to improve the resilience of the euro area? : euro area scrutiny.
Date: 2020-11-23
Institutions: Directorate-General for Internal Policies of the Union
Subjects: EU financial instrument, budgetary equilibrium, competitiveness, credit, debt, economic convergence, euro area, macroeconomics, performance measurement, private sector

IN-DEPTH ANALYSIS
Requested by the ECON committee

How has the macro-
economic imbalances
procedure worked in
practice to improve
the resilience of the
euro area?

Euro Area Scrutiny

External authors:

Alexander Kriwoluzky

Malte Rieth

Economic Governance Support Unit (EGOV)
Directorate-General for Internal Policies
PE 645.725 - April 2020

EN

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How has the MIP worked in practice to improve the resilience of the euro area?

How has the macro-
economic imbalances
procedure worked in
practice to improve
the resilience of the
euro area?

Euro Area Scrutiny

Abstract

(MIP)

Procedure 

Imbalance 

This  paper  analyses  the  effects  of  the  implementation  of  the
Macroeconomic 
the
macroeconomic performance of countries in the EU and the euro
area.  We  find  that  the introduction of  the  MIP  led  to  a decline  in
current  account  imbalances  and  private  sector  debt  and  credit
flows, but that the overall effects are limited. To strengthen the MIP,
we  support  the  introduction  of  the  Budgetary  Instrument  for
Convergence  and  Competitiveness,  i.e.  a  fund  that  pays  grants,
conditional on the implementation progress of economic reforms.

on 

This  document  was prepared  for the  Economic  Governance
Support Unit at the request of the ECON Committee.

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This document was requested by the European Parliament's Committee on Economic and Monetary
Affairs.

AUTHORS
Alexander Kriwoluzky, DIW Berlin and Freie Universität Berlin.
Malte Rieth, DIW Berlin.

ADMINISTRATOR RESPONSIBLE
Alice ZOPPÈ

EDITORIAL ASSISTANT
Ovidiu TURCU

LINGUISTIC VERSIONS
Original: EN

ABOUT THE EDITOR
The  Economic  Governance  Support  Unit provides in-house  and  external  expertise  to  support  EP
committees and other parliamentary bodies in shaping legislation and exercising democratic scrutiny
over EU internal policies.

To contact Economic Governance Support Unit or to subscribe to its newsletter please write to:
Economic Governance Support Unit
European Parliament
B-1047 Brussels
E-mail: egov@ep.europa.eu

Manuscript completed in April 2020
© European Union, 2020

This document and other supporting analyses are available on the internet at:
http://www.europarl.europa.eu/supporting-analyses

DISCLAIMER AND COPYRIGHT
The  opinions  expressed  in  this  document  are  the  sole  responsibility  of  the  authors  and  do  not
necessarily represent the official position of the European Parliament.
Reproduction  and  translation  for  non-commercial  purposes  are  authorised,  provided  the  source  is
acknowledged and the European Parliament is given prior notice and sent a copy.

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How has the MIP worked in practice to improve the resilience of the euro area?

CONTENTS

LIST OF ABBREVIATIONS

LIST OF FIGURES

LIST OF TABLES

EXECUTIVE SUMMARY

GENERAL INFORMATION

INTRODUCTION

LITERATURE REVIEW AND DESCRIPTIVES

2.1. The Macroeconomic Imbalance Procedure

2.2.

Literature review

2.3.

Evolution of scoreboard indicators

2.3.1. Scoreboard indicators

2.3.2. Country performances

EMPIRICAL MODEL AND RESULTS

3.1.

Empirical model

3.2.

Estimated impact of the macroeconomic imbalance procedure

3.3. The impact of the MIP on euro area countries

3.4.

Further sensitivity tests

3.5. Policy recommendations

CONCLUSIONS

REFERENCES

ANNEX

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LIST OF ABBREVIATIONS

AMR

BICC

CSR

ECA

EIP

EU

IDR

MIP

RSP

SGP

Alert Mechanism Report

Budgetary Instrument for Convergence and Competitiveness

Country-specific Recommendation

European Court of Auditors

Excessive Imbalance Procedure

European Union

In-depth Review

Macroeconomic Imbalances Procedure

Reform Support Programme

Stability and Growth Pact

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How has the MIP worked in practice to improve the resilience of the euro area?

LIST OF FIGURES

Figure 1: Evolution of external imbalances and competitiveness indicators.

Figure 2: Evolution of external imbalances and competitiveness indicators.

Figure 3: Evolution of labour market indicators.

Figure 4: Total number of threshold breaches by country.

Figure 5: Treatment and control group defined according to total number of hypothetical flags

prior to 2012 across 14 main scoreboard indicators.

LIST OF TABLES

Table 1: Estimated impact of MIP – baseline regression results.

Table 2: Estimated impact of MIP – controlling for country-specific time-trends.

Table 3: Estimated impact of MIP – fixed effects with lagged endogenous variable.

Table 4: Impact of MIP in euro area countries.

Table A1: Estimated impact of MIP – controlling for lagged other indicators.

Table A2: Estimated impact of MIP – controlling for lagged other indicators and interactions.

Table A3: Impact of MIP excluding program countries and years.

Table A4: Sensitivity analysis for current account balance and long-term unemployment rate.

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EXECUTIVE SUMMARY

This  paper  analyses  the  impact  of  the  Macroeconomic  Imbalance  Procedure  (MIP)  on  the
macroeconomic performance of the Member States of the European Union (EU) and of the euro area.
Specifically, the paper addresses three objectives. First and foremost, we assess whether the EU and
the euro area countries are now better equipped to prevent an economic crisis, due to the introduction
of the MIP. Secondly, we consider whether the framework would have helped to avoid the emergence
of imbalances in those six countries that required financial assistance during the global financial and
European crises. Third, based on our results, we propose recommendations to make the prevention of
unsustainable  polices  more  effective.  Part  of  the  recommendations  would  involve  a  supranational
fund.

Section 1 provides the introduction.

Section  2  outlines  the  Macroeconomic  Imbalance  Procedure  and  reviews  the  related  literature.
Afterwards, it describes the scoreboard indicators of the MIP in more detail.

Section 3 presents the empirical setup of the analysis. We analyse first the EU member countries, and
then the euro area countries only. Each time, we establish a treatment and a control group to analyse
whether the introduction of the MIP had an impact on the breach of the thresholds of the scoreboard
indicators.  We  find  that  the  introduction  of  the  MIP  led  to  fewer  breaches  of  those  scoreboard
thresholds that predict financial and economic crisis: the current account balance, and private sector
debt and credit flow. It is well established in the economic literature that the deterioration of these
indicators  worsens  a  crisis.  We  therefore  infer  that  the  improvement  in  these  indicators  put  the
countries in the EU and the euro area in better position to prevent a deep economic crisis. Since the
financial assistance during the financial and economic crisis provided to the six EU countries was in
three  cases  motivated  by the  need  to  recapitalise  the  banking  sector,  and in  one  case  to  assist in  a
balance of payment crisis, on the basis of our analysis we infer that parts of the financial assistance
could have been avoided, should the MIP have been in place and enforced at the time. However, due
to  the  existence  of  other imbalances, we  cannot  rule out  that  financial  assistance would have  been
necessary anyhow.

Nevertheless,  the  introduction  of  the  MIP  did  not  lead  to  fewer  breaches  of  the  threshold  for  the
majority of the 14 headline scoreboard indicators. One reason for this can be poor compliance with the
country-specific  recommendations  underpinned  by  the  MIP.  Recently,  in  its  “Economic  Governance
Review”,  the  European  Commission  also  reported that  the  degree  of  CSR’s  implementation  has
declined in last years. In order to increase CSRs compliance and the impact of the MIP, we therefore
support the introduction and the implementation of the Budgetary Instrument for Convergence and
Competitiveness  (BICC).  The  fund would  pay  grants  to  countries  that  meet  the  requirements  of  the
country-specific  recommendations,  adopted  by  the  Council.  This  fund  would  increase  both  the
implementation  and  the  importance  of  the  MIP  and  would  mitigate  the  potentially  adverse
consequences  of  structural  reforms  during  the  transition  period.  Furthermore,  we  advocate  to
incorporate “green” environmental indicators into the scoreboard.

Finally, Section 4 concludes.

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GENERAL INFORMATION

KEY FINDINGS

Based on an analysis limited to the indicators included in the “MIP scoreboard”, we find
that  the  introduction  of  the  Macroeconomic  Imbalance  Procedure  (MIP)  has  led  to
improvements in the current account balances and private sector financial indicators, in
the  euro  area  Member  States  and  of  the  European  Union.  Since  these  indicators  are
important  predictors  of  financial  and  economic  crises,  as  well  as  contributors  to  their
severity, the MIP contributes to a stable economic development, assuming that it is fully
implemented  and  enforced.  However,  the  introduction  of  the  MIP  did  not  lead  to  an
improvement  in  the  majority  of  the  14  headline  indicators.  In  order  to  increase
compliance  with  the  country-specific  recommendations,  meeting  their  requirements
could  be  rewarded  by  payments  from  a  recently  proposed  fund,  the  “Budgetary
Instrument for Convergence and Competitiveness”. Furthermore, we suggest adding to
the  scoreboard  indicators  that  can  detect  “green”  imbalances,  posing  a  risk  to  the
environment and thus to the economy.

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INTRODUCTION

The Global Financial Crisis of the years 2007/08 and the following deep economic crises in Europe had
severe economic consequences for most households in the European Union (EU). The crises have been
caused by imbalances in the financial sector, foremost in the US, the UK, Ireland, and the Netherlands.
The  financial  crisis  led  to  a  drastic  decline  in  external  credit  flows  and  strongly  affected  the  real
economy, not only in these countries, but in the whole EU. The following deep recession has worsened
public finances in EU Member States and led to a sovereign debt crisis in the euro area. Therefore, the
EU  introduced  the  Macroeconomic  Imbalance  Procedure  (MIP)  in  2011,  with  the  goal  to  carefully
monitor  economic  developments  in  Europe  and  to  detect  potential  imbalances  early,  in  order  to
prevent them from causing another deep recession. In this paper, we address the question whether
the  implementation  of  the  MIP  has  been  so  far  successful  in  detecting  imbalances  and  whether,
consequently,  the  countries  in  the  European  Union  are  better  equipped  to  prevent  unsustainable
economic developments in the future.

In order to address the question, we identify an empirical model for the EU. We build the model upon
the 14 headline indicators of the MIP. We assume that these indicators play a major role in determining
the policy responses by the EU and national policy makers under the MIP framework. While this is a
strong  assumption,  it  allows  to  focus  the  analysis  on  the  relevance  and  the  design  of  the  MIP
scoreboard.    In  order  to  estimate  the  impact  of  the  MIP  on  the  economic  performance  of  the  EU
countries,  we  adopt  a “difference-in-difference”  approach. The  construction of  the  treatment  group
follows  the  idea  that  macroeconomic  imbalances  are  persistent  and  that  countries  subject  to
macroeconomic imbalances prior to the introduction of the MIP are more likely to breach the threshold
afterwards than countries with fewer hypothetical breaches before 2012. Once we have established
the treatment and the control group, we estimate whether the introduction of the MIP had an impact
on  the  14  headline  indicators.  The  empirical  analysis  allows  us  to  test  whether  countries  in  the
treatment  group  performed  significantly  better  under  the  MIP,  compared  to  the  performances  of
countries in the control group. We conduct the analysis for the EU and for the euro area.

Our results reveal a mixed picture. We find on the one hand that the introduction of the MIP has had a
significant impact on the current account balance, private debt, and the labour market indicators in the
EU member states. On the other hand, we find that the MIP had no significant impact on the majority
of headline indicators. For the subsample of the euro area countries, we find that the current account
balance, private credit flow and the labour market indicators are affected by the introduction of the
MIP, while the majority of the indicators were, again, not significantly affected. For euro area countries,
the improvement of the current account balance and the private sector credit flow and debt indicators
came  at  a  cost  of  a  slight,  but  significant,  increase  in  unemployment.  This  result  suggests  that
improvements in the current account balance were accompanied by increases in unemployment.

The results are partly encouraging, because the MIP was successful in improving key indicators for a
financial  and  economic  crisis.  Current  account  deficits  are  undermining  macroeconomic  stability,
because importing more than exporting often, although not always, implies that the country has to
take on credit. In case the debt is denominated in a foreign currency (as it often happens for EU member
states that have not adopted the euro) , this foreign denominated debt makes the economy vulnerable
to possible exchange rate depreciations, which make the debt service for firms more expensive, leading
to high number of bankruptcies and thereby causing a deep economic contraction. In a seminal and
widely  cited  study, Schularick &  Taylor  (2012)  show  that  financial  crises  are  associated  with  credit
growth and that recessions are deeper if private debt is high. Given this background, our result that the
MIP has led to fewer breaches of the indicator thresholds related to private debt for the EU and private
credit  flow  for  euro  area  countries  is  important.  Next  to  these  indicators,  we  also  find  that  the

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introduction  of  the  MIP  led  to  declines  in  the  long-term  unemployment  rate  and  the  youth
unemployment rate. Both measures decreased in the EU and the euro area after their inclusion into the
MIP  main  indicators,  in  2015.  Our  results  therefore  indicate  that  the  reform  in  2015  was  a  useful
extension of the scoreboard indicators, because they measure further important characteristics of an
economy.

However,  the  results  are only  partly  encouraging,  because we  find  that  for  the majority  of  headline
indicators the MIP did not lead to fewer breaches of the thresholds. This is the case for the EU as well
for the euro area Member States. More precisely, for the external imbalances indicators, we do not find
an improvement in the change of export market shares, the real exchange rate, and unit labour costs.
Regarding the internal imbalances, we find that the treatment group of countries does not breach the
thresholds for change in housing prices, government debt and total financial sector liabilities less often
after the introduction of the MIP. In euro area countries, the threshold for the unemployment rate is
even more often breached after the introduction of the MIP than before.

Our results corroborate the analysis presented in the document “Economic Governance Review,” which
was  published  by  the Commission  in  February  2020.  The  document  states  that  current  account
balances  have  improved.  Here,  the  review  distinguishes  between  current  account  deficits  and
surpluses.  While  the  deficits  have  been  reduced,  the  surpluses,  especially  in  Germany,  still  exist.
However, it also states that the MIP “has not generated the political traction necessary to sustain reform
ambition in Member States where imbalances persist” (page 13). The argument is in line with the fact that
53  %  of  total  country-specific  recommendations  based  on  the  MIP  where  assessed  as  “limited/no
progress” implementation (Hagelstam et al. 2019).

We interpret our results in the following way. The improvement in some indicators implies that the MIP
would  have  helped  to  mitigate  the  effects  of  the  financial  crisis  in  2007/08,  if  it  were  in  place  and
enforced affectively. The persistence of further imbalances in the EU and in the euro area, together with
the high number of MIP-based country-specific recommendations which have not been implemented,
indicates  that  the  introduction  of  the  MIP  did  not  render  the  EU  and  euro  area  countries  immune
against the next economic and financial crisis. This assessment also applies to the question whether
the MIP would have prevented financial assistance which was given to Cyprus, Latvia, Greece, Ireland,
Spain, and Portugal in the aftermath of the financial crisis. In three countries the financial assistance
was motivated by and given to recapitalise the financial sector (Cyprus, Ireland, Spain) or to assist in a
balance-of-payment crisis (Latvia). Since the MIP seems to lead to an improvement in current account
imbalances, more specifically to smaller deficits that have to be financed by external credit, and to an
improvement in the private debt indicators, we conjecture that it would have mitigated the height of
the financial assistance to these countries. Due to the existence of other imbalances, we cannot rule
out that the financial assistance would have been necessary anyhow.

The results that the MIP works for some indicators, but not for the majority, point to potential ways to
improve the MIP. Firstly, in order to increase compliance, we suggest to couple country policy actions,
when they are in line with the recommendations, with grants from a fund such as the BICC. Importantly,
payments  from  the  fund  would  also  mitigate  the  potentially  painful  effects  of  structural  reforms.
Secondly, we endorse the suggestion made by the Commission in the Economic Government Review
to cover “green” environmental indicators. The economic crises in the future could well be associated
with environmental catastrophes, which disrupt the supply chains, trigger large migrations, or simply
destroy a part of the wealth and capital stock.

The paper is structured in the following way. Section 2 reviews the related literature and describes the
MIP and the 14 headline indicators of the scoreboard. In Section 3, we set up the empirical model and

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present  the  results.  Afterwards,  we  derive  our  policy  recommendations  from  the  analysis.  The  final
section concludes.

LITERATURE REVIEW AND DESCRIPTIVES

This  section  first  describes  the  setup  of  the  MIP  in  more  detail.  Afterwards,  we  review  some  of  the
literature about the MIP. Finally, we introduce the main indicators included in our empirical model, the
“MIP  scoreboard  indicators”,  and  discuss  the  evolution  of  the  scoreboard  indicators  for  the  EU
countries.

2.1.

The Macroeconomic Imbalance Procedure

The MIP has been introduced at the end of the year 2011, following the economic turmoil caused by
the financial crisis in 2007/2008 and its aftermath. It was motivated by one of the main lessons drawn
from  the  Global  Financial  Crisis,  whose  severe  repercussions  had  been  aggravated  by  pre-existing
financial  and  macroeconomic  imbalances.  The  underlying  idea  of  the  MIP  was  to  detect  such
imbalances and to correct them sufficiently early, in order to prevent and/ or mitigate future financial
and economic crises. The crisis has also shown that an economic downturn in one or a few countries in
the  EU  can  rapidly  spill  over  to  other  countries  and  has  the  potential  to  cause  a  recession  in  these
countries as well. Consequently, there is need for a supranational organisation as the EU to monitor the
financial and macroeconomic developments of its Member States, coupled with the right to intervene
in case the imbalances become unsustainable.

The  MIP  is  conducted  on  an  annual  basis  and  consists  of  the  following  parts.  It  begins  with  the
publication of the “Alert mechanism report” (AMR). The AMR is based on a scoreboard of 14 headline
indicators. These indicators cover three main areas: external imbalances and competitiveness, internal
imbalances,  and  labour  market  indicators.  The  first  area  comprises  indicators  such  as  the  current
account balance, the real effective exchange rate, and export market shares. To determine the internal
imbalances, the AMR examines the private sector credit flow and the private sector debt, as well as
government  debt.  In  order  to  determine  imbalances  in  the  labour  market,  the  AMR  monitors  the
activity rate, the long-term unemployment rate, and the youth unemployment rate. We will describe
all  indicators  in  detail in  Section  2.3.1.  In  the  case  a  member  State  breaches  the  indicator-specific
thresholds, it could be submitted to an “in-depth review” (IDR). The identification of macroeconomic
imbalances has been based on the likely magnitude of risks involved, their evolution over time, and
whether there are already planned or enacted policies to counter the imbalances.

The  IDR  is  a  country-specific  economic  analysis,  aimed  at  identifying  the  nature  and  severity  of  the
macroeconomic imbalances. The outcomes of the IDR range from “no imbalances” and “imbalances”
to “excessive imbalances” and “excessive imbalances with corrective action”. If the report asserts that
imbalances exist, it starts the process of “specific monitoring”, which involves an intensified dialogue
with national authorities.

In  case  the  IDR  attests  “excessive  imbalances  with  corrective  action”,  it  triggers  the  “Excessive
Imbalance  Procedure”  (EIP).  The  EIP  requires  that  countries  submit  a  corrective  action  plan,  which
should  set out  how  the  country  plans  to  correct  the imbalances.  The implementation  of  the  plan is
subsequently  monitored  by  the  Commission  and  the  Council.  Euro  area  Member  States  may  be
penalized  with  financial  sanctions.  So  far,  no  country  has  been  subject  to  the  EIP.  For  a  further
description, we refer the reader to a background note provided by the European Commission (2016).

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2.2.

Literature review

The literature on the MIP either investigates the quality of the scoreboard indicators, which are at the
basis of the AMR, or is concerned about the enforceability of the MIP, i.e. whether countries follow the
recommendations made in the Country-Specific Recommendations.

The quality of the scoreboard indicators was first criticized by Gros & Giovannini (2014). The authors
point out that most indicators are backward looking. Instead, in order to predict and prevent a crisis,
the indicators should be more forward looking. A forward-looking nature, the authors argue, would
also  make  it  easier  for  policymakers  to  address  them  directly. Miron  &  Alexe  (2014) consider  one
indicator, the current account balance. They argue that for Central and Eastern European countries the
indicator  should  allow  for  more  flexibility. Boysen-Hogrefe,  Jannsen,  Plödt,  &  Schwarzmüller  (2014)
analyse  the  early  warning  properties  of  the  scoreboard  indicators.  The  authors  find  that  especially
house prices, private debt and credit flow constitute good early warning indicators for a financial and
economic crisis.

Domonkos, Ostrihoň, Šikulová, & Širaňová (2017) also consider the relevance of the MIP scoreboard
indicators by analysing their predictive power for an economic crisis. They find that especially activity
rates,  youth  unemployment  rates  and  private  sector  debt  are  good  predictors  for  a  crisis. García  &
Bengoechea (2018) concentrate on predicting a sovereign debt crisis, or more precisely sovereign yield
spreads.  They  find  that  the  scoreboard  indicators can  indeed  explain  the  behaviour  of  sovereign
spreads two, three, and four quarters in advance. Siranova & Radvanský (2018) assess the quality of the
indicators and the corresponding thresholds for Central and Eastern European Countries. The authors
find that the external imbalance indicators exhibit a good predictive power. Given the results of the
other studies, Erhart, Becker, & Saisana (2018) consequently conclude that there is no agreement in the
literature on whether the indicators have a good predictive power and suggest several ways to improve
them.

Another  strand  of  research  is  concerned  with  the  effectiveness  of  the  MIP  on  national  policies.  A
foremost question is how the European Commission can enforce a change in national policies. So argue
Gros  &  Busse  (2013) that  the  MIP  has  no  effect  on  German  politics. Moschella  (2014) assesses  the
potential effectiveness of the EU relative to the IMF surveillance. The paper finds that the EU MIP has
advantages  over  the  IMF's  surveillance  in  terms  of  the  provision  of  clear  and  practical  advice,
knowledge of  domestic  politics  and  the introduction of mechanisms  that facilitate  the  activation  of
sanctions by the Commission. Neither the EU MIP nor the IMF, however, provide for mechanisms to
prevent  political  considerations,  e.g.  in  the  Council  or  the  Commission,  from  interfering  with  the
activation of sanctions and the distribution of the burden of adjustment within a country`s economy
and over time.

Bricongne,  Turrini,  &  Garcia  (2019)
investigate  whether  the  MIP  affect  the  country-specific-
recommendations compliance score. The authors consider both arms of the MIP, the preventive one
and the corrective one. The preventive arm helps countries to adopt good policies that would enhance
economic  development.  The  corrective  arm  aims  to  correct  policy  failures  and  address  major
macroeconomic risks. The finding of the paper is that the MIP imbalance category has a positive impact
on a country`s compliance score. The authors state that the pressure induced by the MIP classification
is the reason for the increased compliance. Thus, the MIP seems to have an effect on the national policy,
even without the EIP being put in place. A related result is established by Darvas & Leandro (2015). The
authors find a higher level of implementation for CSRs that are related to the SGP, followed by MIP-
related CSRs. However, in a recent paper, Efstathiou and Wolff (2018) show that the implementation of
recommendations  by  EU  countries  has  worsened  in  the  last  years.  This  finding  is  corroborated  by

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Hagelstam et al. (2019), who show that the share of Member States that make limited or no progress in
the implementation of the MIP-CSRs increased from 17% in 2012 to 53% in 2018.

The European Court of Auditors (ECA) published in 2018 a special report on the MIP (European Court
of  Auditors,  2018).  In  this  report,  the  ECA  points  out  that  the  classification  of  Member  States  lacks
transparency, that the IDR has become less visible, that the CSRs do not stem from specific identified
imbalances, and that there is a lack of public awareness. Consequently, the report suggests to link the
CSR  to  specific  imbalances,  to  clearly  characterize  the  severity  of  the  imbalances  and  to  trigger  an
excessive imbalance procedure if necessary, to include fiscal recommendations in the MIP, to separate
the  IDR  from  the  country  reports,  and  to  give  greater  prominence  to  the  MIP  by  improving
communication aspects.

In February 2020, the European Commission published the “Economic Governance Review”. The review
states  that  the  introduction  of  the  MIP was  important,  because it  complemented  other  surveillance
instruments, such as the SGP, and provided the basis for prioritising policies not dealt with by the SGP.
However,  the  MIP so  far  did  not  sufficiently  encompass  new  economic  challenges  related  to
environmental pressures. The review also sets out that the MIP did not generate the political traction
necessary  to  sustain  reform  ambition  in  EU  Member  States.  In  line  with Hagelstam  et  al.  (2019) the
review  states  that  the  implementation  of  MIP-related  recommendations  has  over  time  become
comparable to that of non MIP-related recommendations. Consequently, the review calls for incentives
in the future for member states to comply with the CSR and to implement reforms. One way could be
that the Commission makes full use of the MIP instruments, such as the EIP.

In  this  paper,  we  address  the  question  whether  the  implementation  of  the  MIP  has  improved  the
economic performance of the countries in the EU and the euro area, such that fewer thresholds of the
scoreboard indicators are breached. Although the indicators are not policy objectives, they are useful
in detecting macroeconomic imbalances early on, as the literature has shown.

2.3.

Evolution of scoreboard indicators

We now focus on the 14 main scoreboard indicators and investigate their average evolution over time
to obtain an impression of the main imbalances at the aggregate level. Then, we compute how often
countries breached the thresholds.

2.3.1.

Scoreboard indicators

The 14 main scoreboard indicators and their thresholds (in brackets) are:

External imbalances and competitiveness:

1. 3-year backward moving average of the current account balance as percent of GDP (- 4/6%);
2. net international investment position as percent of GDP (- 35%);
3.

3-year percentage change of the real effective exchange rates based on HICP/CPI deflators,
relative to 41 other industrial countries (± 5% - EA countries, ± 11% - non-EA countries);
4. 5-year percentage change of export market shares measured in values 3-year percentage

change;

5. nominal unit labour cost (9% - EA countries, 12% - non-EA countries).

14

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How has the MIP worked in practice to improve the resilience of the euro area?

Internal imbalances:

6. year-on-year changes in house prices relative to a Eurostat consumption deflator (6%)
7. private sector credit flow in % of GDP (14%)
8. private sector debt (consolidated) in % of GDP (133%)
9. general government sector debt in % of GDP (60%)
10. 3-year backward moving average of unemployment rate (10%)
11. year-on-year changes in total financial sector liabilities (16.5%)

Labour market indicators:

12. 3-year change in p.p. of the activity rate (-0.2 pp)
13. 3-year change in p.p. of the long-term unemployment rate (0.5 pp)
14. 3-year change in p.p. of the youth unemployment rate (2.0 pp).

Figure 1 shows the arithmetic means of the external imbalances and competitiveness indicators across
countries from 2000 until 2018. For most indicators, there is an increase. It mostly starts around the
introduction of the MIP in the year 2012, which is indicated by the vertical dashed line. This upward
movement since then is particularly evident for the real effective exchange rate, export market shares,
and  nominal unit  labour  cost indicators. For  the indicators of  current  account  balances  and  the  net
international investment position, the average trough was reached some years before 2012.

Figure 2 shows the arithmetic means of the internal imbalances’ indicators across countries since 2000.
Most  measures  improved  following  the  introduction  of  the  MIP.  Private  sector  credit  flows  are
substantially  lower  than  in  the  earlier  years,  and  private  as  well as  public  debt  ratios  are  on  a  clear
downward  trajectory.  The  unemployment  rate,  which  was  assigned  to  the  second  set  of  internal
indicators  at  the  instalment  of  the  MIP  and  which  was  complemented  later  on  by  a  third  set  of
indicators for labour markets, declines steadily since 2012, although the three-year moving average
shows this with some delay. Total financial sector liability growth ticks up slightly in recent years, but is
still far below its level in the years 2000.

Taken  together,  Figures  1  and  2  suggest  that  the  decline  in  the  current  account  and  the  net
international  investment  position  before  and  during  the  crisis’s  years  reflects  the  accumulation  of
private  debt.  The  subsequent  increase,  in  turn,  seems  to  be  first  driven  by  a  collapse  in  domestic
demand,  as  suggested  by  the  sharply  increasing  unemployment  rate,  and  then  later  on  by  a
moderation  of  private  and  public  debt  accumulation,  which  levelled  off  in  recent  years.  The
compression of domestic demand can also be seen from Figure 3, which shows the evolution of the
averages of the labour market indicators. They were added to the MIP in 2016. 2016 is indicated in the
figure by the dotted line, while the dashed line shows the introduction of the MIP with its initial 11
indicators in 2012, as before. All labour market indicators worsened during the crisis’s episodes. They
started to improve from 2011/12 onwards and continued on their paths towards higher activity and
lower unemployment rates in recent years.

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IPOL | Economic Governance Support Unit

Figure 1: Evolution of external imbalances and competitiveness indicators.

e
g
a
r
e
v
a

r
a
e
y

3

,

P
D
G

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2000 2005 2010 2015 2020
Year

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2000 2005 2010 2015 2020
Year

Source: Own calculations, Eurostat.

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2000 2005 2010 2015 2020
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2000 2005 2010 2015 2020
Year

Figure 2: Evolution of external imbalances and competitiveness indicators.

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2000 2005 2010 2015 2020
Year

Source: Own calculations, Eurostat.

16

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How has the MIP worked in practice to improve the resilience of the euro area?

Figure 3: Evolution of labour market indicators.

e
g
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h
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3

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2000

2005

2010
Year

2015

2020

Source: Own calculations, Eurostat.

2.3.2.

Country performances

While there is a general improvement in most indicators on average, countries have performed quite
differently  during  both  the  Global  Financial  Crisis  and  the  European  sovereign  debt  crisis.  This  is
evident from Figure 4, which shows the total number of threshold breaches (“flags”) across the 14 main
indicators over time by country. For the years prior to 2012 (and 2016 for the labour market indicators),
these breaches are hypothetical, because the MIP was not in place (or did not include these indicators).
Similar to the evolution of the macroeconomic indicators themselves (see Figures 1 to 3), there is a
general tendency of worsening during the two crises and an improvement afterwards.

However, there is also quite some country heterogeneity as regards the level of total flags. Austria, the
Netherlands, or Slovenia, for example, have a relatively low number of flags before the crises and the
introduction  of  the  MIP.  In  contrast,  Estonia,  Hungary  or  Portugal  consistently  breached  the
hypothetical  thresholds  for  three  or  more indicators  before  2012.  The  dynamics  post-2012  are  also
heterogeneous.  The  number  of  flags  declines  substantially  for  Portugal,  Spain,  and  Romania,  for
example,  whereas  it  remains  largely  constant  for  countries  such  as  Austria,  Germany  or  the  Czech
Republic.  All  in  all,  the  graph  suggests  that  the  number  of  (hypothetical)  threshold  breaches  has
declined with the introduction of the MIP. But the initial starting point and the speed of decline differ
markedly across countries.

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17

 
 
 
 
 
 
 
 
 
 
 
 
 
 
IPOL | Economic Governance Support Unit

Figure 4: Total number of threshold breaches by country.

AT

BE

BG

CY

CZ

DE

y
r
t
n
u
o
c

r
e
p
s
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a

l
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0
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1

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0
1

5

0

0
1

5

0

0
1

5

0

DK

EE

HR

HU

EL

IE

LV

MT

NL

ES

IT

PL

FI

LT

FR

LU

PT

RO

SE

SI

SK

UK

2000 2005 2010 2015 2020 2000 2005 2010 2015 2020

2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020 2000 2005 2010 2015 2020

Year

Graphs by Country identifier

Source: Own calculations, Eurostat.

EMPIRICAL MODEL AND RESULTS

In this section, we start by describing the empirical model used to evaluate the effectiveness of the MIP
framework. Then, we present the main estimation results, before conducting a sensitivity analysis. The
data used in the analysis are all 14 main scoreboard indicators, as defined above and downloaded from
Eurostat. Most of the time-series start in 1995. They all end in 2018. This yields a panel data set with
about 400–600 yearly observations, depending on the variable under consideration.

Empirical model

3.1.
We use a difference-in-difference strategy to identify the effect of the MIP. We compute the different
macroeconomic performance of countries with a high likelihood of being affect by the MIP before and
after  the  introduction  of  the  surveillance  mechanism  and  compare  it  to  the  macroeconomic
performance of countries with a low likelihood of being affect by the MIP over the same period. This
requires three ingredients. First, we require a definition of the treatment period. We use the years from
2012 onwards for the external and internal indicators and the years 2016 and subsequent ones for the
labour market indicators. Second, we need to measure macroeconomic performance. Here, we closely
follow the MIP framework and investigate the evolution of the 14 main scoreboard indicators, as they
are measured in the scoreboard. Although these indicators are not policy targets, they allow to shed
light on the economic dimensions along which the MIP works and along which it does not. Third, we
need  to  define  a  treatment  and  a  control  group.  For  this,  we  exploit  the  fact  that  macroeconomic
country  characteristics  are  persistent.  Hence,  countries  that perform  poorly  according  to  the  MIP
criteria before 2012, are likely to be affected most by the surveillance framework post 2012.

For  example,  a  country  with  high  unemployment  rates  in  the  period  1995-2011  is  likely  to  have
relatively high unemployment rates in the period 2012-2018 as well, compared to a country with lower

18

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How has the MIP worked in practice to improve the resilience of the euro area?

unemployment rate in prior to 2012. Unemployment rate consist of two components, a cyclical and a
structural  factor.  The  cyclical  factor  will  fluctuate  similarly  in  both  countries  and  periods,  due  to
common factors, such as a common monetary stance for euro area countries, the external value of the
currency  or  global  economic  cycles.  The  structural  component,  on  the  other  hand,  largely  reflects
labour market institutions, such as employment protection, unemployment benefits, or active labour
market  policies.  These  institutions  change  only  slowly  over  time.  Hence,  a  country  with  high
unemployment before 2012, is likely to have higher rate after 2012 as well, because the labour market
institutions are persistent.

The question then is whether the instalment of the MIP leads to a stronger decline in unemployment
rates  post  2012 in  the  country  with initially  higher unemployment  than in  the  country with  initially
lower unemployment rates. The effect can come to pass through several potential channels. First, the
country might (partially) adhere to the country-specific recommendations implied by the surveillance
framework and (partially) implement the suggested reforms. Second, the mere presence of the MIP
could induce countries to reform because they would like to reduce the likelihood that they breach the
thresholds,  become  classified  as  having  imbalances,  and  have  to  follow  the  country-specific
recommendations, which may not perfectly align with their preferred way of addressing the underlying
sources of misalignment. Third, political peer pressure and increased visibility of national policies at the
European level through the MIP could incentivise national governments to act pre-emptively.

At the heart of the identification strategy is the assumption that the evolution of the indicator under
consideration in both country groups would have been the same in the absence of the MIP. Then, any
differential between countries from before to after the introduction of the MIP can be attributed to the
MIP. In other words, the treatment in form of the MIP induces deviations of the affected countries from
the common trend. At the end of the analysis, we test this assumption. The level of the indicators under
consideration  can  differ  between  countries.  Econometrically,  this  difference  will  be  absorbed  by  a
country  specific  constant  that  capture  time-invariant  institutional  or  political  factors  of  individual
countries.

To construct the treatment group, that is the set of EU countries that is most affected by the MIP, we
compute the total number of hypothetical flags across all 14 main scoreboard indicators before 2012.
The idea is that this number reflects structural country characteristics, such as institutions or economic
structures  that  are  unlikely  to  change  within  a  few  years.  For  example,  countries  with  a  small
manufacturing sector might have persistently lower export market shares compared to countries with
a large manufacturing sector. Similarly, countries with rigid and potentially fragmented labour markets
are  likely  to  have  higher  general  and/or  youth  unemployment  rates  both  before  and  after  the
introduction of the MIP.

Moreover, these structural country characteristics can be expected to be positively correlated across
indicators. For example, countries with more rigid labour markets are typically less competitive and
have higher unemployment rates, lower activity rates and both lower export market shares and current
account  balances.  Moreover,  current  account  deficits  typically  go  hand-in-hand  with  other
maladjustments, such as excessive private or public debt accumulation, a negative net international
investment  position,  or  real  exchange  rate  misalignment.  This  is  corroborated  by  the  average
correlation among the total number of hypothetical flags per indicator before 2012, which is 0.2. Hence,
countries with a high total number of hypothetical flags across indicators in the period before 2012 are
likely to be affected most by the MIP regarding all 14 indicators of the scoreboard. As this correlation is
not  very  high,  we  show  in  the  Annex  that  the  main  results  are  robust  to  using  indicator-specific
definitions of treatment and control groups.

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Figure 5 shows the composition of the treatment and the control groups. They are identified according
to the total number of flags summed over the 14 main scoreboard indicators and the years prior to
2012.  The  median number  of  hypothetical  flags  is  62.5.  Thus,  the  14  EU  countries  with  63  or  more
hypothetical  flags  are  the  treatment  group.  They  are  shown  in  the  upper  panel.  The  remaining
countries, with 62 or fewer hypothetical flags, are the control group. They are shown in the bottom
panel.  The  treatment  group  mostly  coincides  with  the  general  notion  of  peripheral  countries  with
weaker  macroeconomic  fundamentals  and  less  efficient  political  institutions,  which  have  been  hit
hardest  by  the  global  and  the  European  crises.  The  control  group,  instead,  contains  many  Member
States that are often referred to as core countries, with competitive external sectors and more efficient
political and economic institutions. There are some exceptions to this general pattern. Luxembourg,
for example, is borderline in the treatment group and Italy borderline in the control group. The former
country,  however,  has  persistently  breached  current  account,  exchange  rate  and  private  credit  and
debt thresholds. In the latter, the threshold breaches cluster in the government debt and labour market
indicators.

In a sensitivity analysis, we define indicator-specific treatment and control groups. There, we compute
the number of hypothetical flags before 2012 (or alternatively 2008) per indicator and year and divide
the  country  sample  in  two  at  the  median  number  of  flags  for  each  indicator.  This  yields  different
treatment and control groups for each indicator. The average correlation between the treatment group
definitions  across indicators  is  0.25,  reflecting  that  the  number of  hypothetical  flags  per  indicator is
positively correlated within countries. In other words, countries with many hypothetical flags for one
indicator are more likely to have more hypothetical flags for another indicator as well, and are therefore
more  likely  to  be  in  several  of  the  indicator-specific  treatment  groups  and  in  the  treatment  group
defined by aggregating over all hypothetical flags across indicators.

Figure 5: Treatment and control group defined according to total number of hypothetical
flags prior to 2012 across 14 main scoreboard indicators.

Treatment group

HR

LU

RO

PL

LV

EL

CY

LT

IE

BG

EE

HU

ES

PT

Control group

0
0
1

0
8

0
6

0
4

0
2

0

0
6

0
4

0
2

2
1
0
2

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r
o

f

e
b
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a

l
f

l

a
c
i
t

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h

t

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H

2
1
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r
o

f

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b
s
g
a

l
f

l

a
c
i
t

e
h

t

o
p
y
H

0

AT

DE

CZ

FI

SI

FR

DK

BE

MT

NL

SE

UK

IT

SK

Source: Own calculations, Eurostat.

20

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How has the MIP worked in practice to improve the resilience of the euro area?

We construct a binary indicator
zero otherwise. The baseline estimation equation is:

that equals one if country i belongs to the treatment group, and

,

+

where

∗ ) +

= + + (

are year fixed effects,

is  a  set  of  control  variables.

is one of the 14 main scoreboard indicators for country i in year t,

are country fixed effects,
a dummy indicator equal to one for the years 2012-2018 when considering
the internal or the external indicators and equal to one for the years 2016-2018 when looking at the
is  a  parameter  to  be  estimated.  It
labour  market  indicators,  and
measures the impact of the MIP on the dependent variable. The variable
is an error term, which we
adjust for (common) autocorrelation and heteroskedasticity across panels using a feasible generalized
least  squares  estimator. The  control  variables  differ  across  specifications.  We  correct  for  country-
specific  trends,  for  all  lagged  scoreboard  indicators  that  are  not  within  the  same  category  as  the
indicator under consideration (for example, when
is the current account balance, which is part of
the external and competitiveness indicators, then the control variables include all external and labour
market indicators lagged by one year), or we maintain these indicator-set specific control variables and
additionally interact them with an indicator variable equal to one for the years 2012 onward, and zero
otherwise.

3.2.
Estimated impact of the macroeconomic imbalance procedure
In  this  section,  we  present  the  estimated  impact  of  the  MIP  based  on  the  difference-in-difference
approach outlined above. Panel (a) of Table 1 shows the baseline regression results for the five external
and competitiveness indicators. They are based on a simple model with time and year fixed effects and
no further control variables. The results suggest that the MIP has a significant impact on the current
account balance. The point estimate in column (1) is positive with value 0.78 and, given a standard error
of  0.36,  statistically  significant  at  the  five  percent level.  The  estimate  suggests  that,  on  average,  the
current  account  balance  is  0.8  percentage  points  of  GDP  higher  in  the  treatment  group  after  the
introduction of the MIP than before, relative to the change in the current account balance in the control
group over the same period. The relatively high Chi-squared statistic in column (1) at the bottom of the
panel of 701.81 indicates that the model describes the data well. The test statistic corresponds to the
null hypothesis that all parameters of the model are jointly equal to zero. This is strongly rejected by
the data, suggesting a good fit.1

For the other indicators, there is no improvement. At a first glance, column (4) suggests that the impact
of the MIP on the export market share is negative. However, this counterintuitive result is not robust,
as we show below, so that we will not interpret it further. Moreover, it needs to be kept in mind that
the real effective exchange rate has both an upper and a lower threshold. Hence, the non-significant
result  is  consistent  with  a  reduction  of  threshold  breaches  post  2012,  but  which  entails  both
appreciations and depreciations that cancel out on average. The same argument potentially holds for
the current account balance indicator, which also has two-sided thresholds. But the significant impact
implies that increases in the current account dominate the average effect. In a sensitivity analysis, we
account  for  this  two-sided  threshold  and  show  that  the  results  hold.  Nevertheless,  the  finding  of  a
positive impact might be of concern because it suggests that on average current account balances,
including those of excessive surplus countries, have increased.

1 The table does not show the more familiar R-squared statistic as in standard ordinary least squares,
as this measure of fit is not well-defined for generalized least squares estimation.

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IPOL | Economic Governance Support Unit

Panel (b) contains the results for the internal indicators. They suggest that the MIP has a significant
negative impact on private sector credit flow, private sector debt and financial sector liabilities. They
also  indicate  a  positive  impact  on  the  unemployment  rate.  As  we  show  below,  only  the  impact  on
private sector credit indicator and the unemployment rate are robust. The coefficients in column (3)
and (5) imply that the introduction of the MIP led to an average decline in the private sector debt level
of 4.6 percentage points of GDP in the treatment group from pre- to post-2012, relative to the decline
in that indicator in the control group over the same horizon, and to an increase of 0.5 percentage points
in  the unemployment  rate,  respectively.  Finally,  panel  (c)  presents  the  results  for  the  labour market
indicators.  They  suggest  strong  effects,  in  particular  for  long-term  and  youth  unemployment  rates,
which  drop  by  about  2  and  3  percentage  points  for  the  treatment  group  in  the  evaluation  period,
relative to the change in the control countries.

Table 1: Estimated impact of MIP – baseline regression results.

Panel (a): External and competitiveness indicators

Model

(1)

(2)

(3)

Dependent
variable

Impact MIP

Current
account
balance

0.78**

(0.36)

Observations

523

Chi2

701.87

International
investment
position

Real effective

exchange
rate

-1.90

(2.19)

536

938.43

-0.87

(0.95)

616

626.72

(4)

Export
market

share

-6.01***

(2.01)

463

724.23

(5)

Unit labour

costs

-4.18

(5.71)

597

39.04

Panel (b): Internal indicators

Model

(1)

(2)

(3)

(4)

(5)

Dependent
variable

House price
index

Private credit
flow

Private
sector debt

Impact MIP

1.31

(1.18)

Observations

467

Chi2

319.81

-3.07***

(1.02)

653

487.04

-4.60**

(1.87)

656

2275.04

1870.51

General
government
debt

1.26

(1.26)

660

Unempl. rate

0.51*

(0.29)

572

431.26

(6)

Financial
sector
liabilities

-6.36***

(1.58)

630

418.48

Panel (c): Labour market indicators

Model

(1)

(2)

(3)

Dependent
variable

Activity rate

Long-term
unempl. rate

Youth
unempl. rate

Impact MIP

0.65***

-1.88***

-2.88***

(0.25)

Observations

561

Chi2

126.75

(0.31)

437

227.49

(1.03)

586

306.64

Notes: The  table  shows  the  estimated  impact  of  the  macroeconomic  imbalance  procedure  on  the  14  main
indicators of the macroeconomic scoreboard. All models contain country and year fixed effects. Standard errors
adjusted for autocorrelation and heteroskedasticity are in parentheses (* p < 0.1, ** p < 0.05, *** p < 0.01).

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To  assess  the  robustness  of  the  results,  we  conduct  several  sensitivity  tests.  First,  we  add  country-
specific  time-trends  to  the model.  Although, in  theory,  the dependent  variables  are  all  stationary, it
could be that the data appear as if they had trends in our relatively short sample. Panel (a) of Table 2
shows that the impact of the MIP on the current account remains positive and statistically significant
at the 5% level. The point estimate actually increases from 0.8 to 1.8. In addition, the effect on the net
international investment position now appears as being significant. In contrast, the counterintuitive
negative effect on the export market share vanishes.

Table 2: Estimated impact of MIP – controlling for country-specific time-trends.
Panel (a): External and competitiveness indicators

Model

Dependent
variable

Impact MIP

(1)

Current
account
balance

1.80***

(0.42)

Country trends

Observations

yes

523

(2)

(3)

International
investment
position

Real
exchange
rate

7.95***

(2.15)

yes

536

-0.44

(1.06)

yes

616

(4)

Export
market

share

-1.83

(2.20)

yes

463

(5)

Unit labour

costs

4.85

(6.89)

yes

597

Chi2

2169.36

4600.16

906.65

1722.61

116.39

Panel (b): Internal indicators

Model

Dependent
variable

Impact MIP

Country trends

Observations

(1)

(2)

(3)

(4)

House price
index

Private
credit flow

Private
sector debt

General
government
debt

1.96

(1.68)

yes

467

-1.02

(1.33)

yes

653

-6.83***

(1.91)

yes

656

0.67

(1.24)

yes

660

(5)

Unempl.
rate

1.14***

(0.32)

yes

572

(6)

Financial
sector
liabilities

0.18

(2.26)

yes

630

Chi2

568.71

691.98

4867.00

6301.79

8800.84

594.21

Panel (c): Labour market indicators

Model

Dependent
variable

Impact MIP

Country trends

Observations

(1)

(2)

(3)

Activity rate

Long-term
unempl. rate

Youth
unempl. rate

0.36

(0.28)

yes

561

-1.96***

(0.41)

yes

437

-5.21***

(1.26)

yes

586

Chi2

303.26

267.90

364.68

Notes: The  table  shows  the  estimated  impact  of  the  macroeconomic  imbalance  procedure  on  the  14  main
indicators of the scoreboard when controlling for country-specific time trends. All models contain country and
year fixed effects. Standard errors adjusted for autocorrelation and heteroskedasticity are in parentheses (* p <
0.1, ** p < 0.05, *** p < 0.01).

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Panel (b) of Table 2 contains the estimates of the MIP impact on the internal indicators when controlling
for country-specific trends. Only the impacts on private sector debt and the unemployment rate remain
statistically significant. The effect on private credit flow and financial sector liabilities instead turns non-
significant. Panel (c) of Table 2 presents the findings for the labour market indicators as dependent
variables. They show the clearest pattern. The effect on both the long-term and youth unemployment
rates is highly statistically significant. It is also economically relevant.

Next, we remove the country trends and instead add the dependent variable lagged by one year as
additional regressor, to account for medium-run dynamics of the dependent variables. Table 3 contains
the results. The dependent variable of each model is given at the top of the panels. In column (1) of
panel (a), for example, the explanatory variables include country and year fixed effects, as before, and
the  current  account  balance  lagged  by  one  year  (denoted  by Dependent,  t-1).  Across  all  indicator
categories, the lagged dependent variables are estimated to be highly persistent. In column (1) of panel
(a),  for  example,  the  autocorrelation  is  0.89.  The  point  estimate  is  statistically  significant  at  the  one
percent level. The finding of generally high persistence is consistent with the argument above, used to
construct the treatment and control groups, that the macroeconomic performance of a country reflects
underlying structural economic and political factors which are moving only slowly over time.

The estimated effects of the MIP are similar to the previous results. There is a significant impact on the
current account, private sector debt as well as long-term and youth unemployment rates. The impact
on the current account is again positive, while for private sector debt, as well as long-term and youth
unemployment rates, the effect is estimated to be negative. The high explanatory power of the models,
as indicated by the large R-squared, is reassuring as it suggests that the models capture the dynamics
of the endogenous variables well. As we are using ordinary least squares now, the R-squared is well-
defined and used instead of the Chi-squared statistic.

Table 3: Estimated impact of MIP – fixed effects with lagged endogenous variable.
Panel (a): External and competitiveness indicators

Model

Dependent
variable

Impact MIP

(1)

Current
account
balance

0.66**

(0.28)

Dependent, t-1

0.89***

Observations

R2 within

(0.02)

495

0.87

(2)

(3)

International
investment
position

Real
exchange
rate

2.16

(1.85)

0.85***

(0.02)

508

0.84

-1.20

(0.92)

0.66***

(0.03)

588

0.59

(4)

Export
market

share

-0.27

(1.67)

0.77***

(0.03)

435

0.76

(5)

Unit labour

costs

-1.83

(7.05)

0.49***

(0.01)

569

0.74

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Panel (b): Internal indicators

(1)

(2)

(3)

(4)

House price
index

Private
credit flow

Private
sector debt

General
government
debt

0.90

(0.73)

0.92***

(0.01)

632

0.94

(5)

Unempl.
rate

-0.11

(0.18)

0.94***

(0.02)

544

0.89

(6)

Financial
sector
liabilities

-5.62**

(2.81)

0.16***

(0.04)

602

0.25

Model

Dependent
variable

Impact MIP

1.01

(1.16)

Dependent, t-1

0.40***

Observations

R2 within

(0.04)

439

0.52

Model

Dependent
variable

Impact MIP

-0.26

(0.27)

Dependent, t-1

-1.86***

Observations

R2 within

(0.16)

561

0.33

-6.19***

(1.47)

0.10**

(0.04)

625

0.36

-3.36**

(1.57)

0.86***

(0.02)

628

0.92

-1.41***

(0.39)

2.60***

(0.25)

437

0.54

-5.14***

(1.07)

6.82***

(0.56)

586

0.54

Panel (c): Labour market indicators

(1)

(2)

(3)

Activity rate

Long-term
unempl. rate

Youth
unempl. rate

Notes: The  table  shows  the  estimated  impact  of  the  macroeconomic  imbalance  procedure  on  the  14  main
indicators of the macroeconomic scoreboard when controlling for the lagged endogenous variable. All models
contain unreported country and year fixed effects. Standard errors are in parentheses (* p < 0.1, ** p < 0.05, *** p
< 0.01).

Finally, we conducted two additional sensitivity analyses which are presented in the Annex. First, we
control for all lagged scoreboard indicators that are not within the same category as the indicator under
is the current account balance, which is part of the external and
consideration. For example, when
competitiveness indicators, then the control variables include all external and labour market indicators
(lagged  by  one  year).  Second,  we  maintain  these  indicator-set  specific  control  variables  and
additionally interact them with an indicator variable equal to one for the years 2012 onward, and zero
otherwise. In this way, we allow for a change in the relationship between the indicator variables with
the  introduction  of  the MIP.  Both  specifications  reinforce  the  impression  from  the  main  results  (see
Table and Table A2). There is a significantly positive impact of the MIP on the current account balance
and the unemployment rate. We also again find a negative effect on the export market share, on private
credit flow as well as on long-term and youth unemployment rates.

The impact of the MIP on euro area countries

3.3.
In this subsection, we estimate the impact of the MIP on the subset of euro area countries. We employ
the same identification strategy as above but restrict the sample to those countries and years where a
country  is  member  of  the  common  currency  area,  for  both  the  construction  of  the treatment  and
control  groups  and  the  estimation.  The  assignment  of  the  euro  area  countries  to  each  group  are
essentially the same as before, with the exception of the Netherlands and Italy. The former is now in

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the  treatment  group  mainly  due  to  its  persistent  current  account  balance  breaches  and  the  latter
because of its permanently excessive government debt level.

By and large, the effect of the MIP on the scoreboard indicators in the subsample of euro area is similar
to the estimated impact in the full sample of all EU countries. Regarding the external indicators, the
effect  on  the  current  account  and  the  international  investment  position  is  positive  and  statistically
significant. For the internal indicators, we find a negative impact on private credit flow, but positive
effects  on  government  debt  and  the  unemployment  rate.  Finally,  concerning  labour  markets,  the
pattern is quite consistent with the evidence for the full sample. Long-term and youth unemployment
rates  are  significantly  lower  under  the  MIP than  before  2016,  compared  to  the  change  in  these
indicators in the control group over the same period.

Table 4: Impact of MIP in euro area countries.
Panel (a): External and competitiveness indicators

Model

Dependent
variable

Impact MIP

Observations

(1)

Current
account
balance

2.25***

(0.44)

297

(2)

(3)

International
investment
position

Real
exchange
rate

6.50*

(3.36)

291

0.20

(0.72)

321

(4)

Export
market

share

3.56

(2.23)

276

Chi2

1719.76

5580.95

2146.70

694.45

Panel (b): Internal indicators

Model

Dependent
variable

Impact MIP

Observations

Chi2

(1)

(2)

(3)

(4)

House price
index

Private
credit flow

Private
sector debt

-0.01

(1.45)

277

422.10

-4.57**

(1.86)

330

501.19

-2.27

(2.87)

333

7789.90

7650.72

General
government
debt

5.27***

(1.40)

338

(5)

Unit labour

costs

-1.00

(1.01)

319

503.43

(5)

Unempl.
rate

0.65**

(0.30)

320

872.34

(6)

Financial
sector
liabilities

0.47

(1.66)

321

605.54

Panel (c): Labour market indicators

Model

Dependent
variable

Impact MIP

Observations

Chi2

(1)

(2)

(3)

Activity rate

0.17

(0.36)

311

224.21

Long-term
unempl. rate

Youth
unempl. rate

-2.39***

-7.92***

(0.42)

243

355.08

(1.35)

339

344.49

Notes: The  table  shows  the  estimated  impact  of  the  macroeconomic  imbalance  procedure  on  the  14  main
indicators  of  the  scoreboard  in  the  euro  area  when  controlling  for  country-specific  time-trends.  All  models
contain  unreported  country  and  year  fixed  effects.  Standard  errors  adjusted  for  autocorrelation  and
heteroskedasticity are in parentheses (* p < 0.1, ** p < 0.05, *** p < 0.01).

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Further sensitivity tests

3.4.
In this subsection, we conduct further sensitivity tests. First, we control for the fact that formally the
MIP does not apply to countries under macro-economic adjustment programs. Therefore, we drop all
observations for countries and years when countries were subject to a program. In the years post-2012,
these are Cyprus, Greece, Ireland, Portugal, and Spain. The MIP could still influence the performance of
these countries when they are subject to a program, because they may want to avoid being classified
as having imbalances when re-entering the MIP formally, but the impact of the MIP can be expected to
be smaller, as the countries are not directly subject to the formal procedure during the program years.
Table  A3  in  the  Annex  shows  that  the  main  results  hold  when  excluding  the  program  country-year
observations.  In  particular,  there  is  a  statistically  significant  and  positive  impact  of  the  MIP  on  the
current account balance and statistically significant negative effects on private sector debt, long-term
and youth unemployment rates.

Given that throughout the specifications the impact of the MIP on the current account balance and the
long-term unemployment rate appears as most significant, we now focus on several tests for these two
indicators. For this, we include a large set of control variables, namely, the lagged indicators of the other
two categories and interaction terms of these variables with a post 2012 indicator variable. Moreover,
we  define  the  treatment  and  control  groups  using  only  data  until  2007,  thus  excluding  the  global
financial and European banking and sovereign crises. Furthermore, for the current account, we allow
for the two-sided threshold by using as dependent variable a dummy indicator that is one whenever
the  current  account  in  a  given  year  breaches  either  the  lower  or  the  upper  threshold,  and  zero
otherwise. Thereby, deficits and surpluses obtain similar weight.

Panel (a) of Table A4 in the Annex focuses on the current account. The dependent variable is the just-
described  flag  indicator.  Column  (1)  shows  that  the  MIP  has  a  significant  negative  impact  on  the
likelihood that a country breaches the threshold. The average number of current account flags in the
treatment group after the MIP is 22 percent lower than the change in that variable in the control group
over the same horizon. In column (2) we restrict the sample further to the years post-2008, to ensure
that the result is not driven by particularities of accession countries in the early 2000s. The result holds.
In column (3), we use the alternative definition of the treatment group, sketched above. We classify
countries not according to their overall number of hypothetical flags prior to the treatment, but only
according  to  their  hypothetical  current  account flags.  The  result  is  similar  to  columns  (1)  and  (2).  In
column  (4)  we  check  whether  there  are  significant  pre-treatment  trends.  For  this  we  construct  two
interaction variables with the treatment indicator and the year dummy 2010 and 2011, respectively.
The  insignificance  of  these  variables  suggests  that  there  are  no  strong  pre-treatment  trends  or
anticipation effects. Finally, in column (5), we conduct a placebo test by shifting the treatment further
to  the  year  2009.  We  find  no  significant  effect,  suggesting  that  the  significant  impact  of  the  MIP  in
columns (1) – (4) is no coincidence.

In panel (b) of Table A4 we conduct the same analyses with the long-term unemployment rate as the
dependent variable, instead of the current account flag. Here, we keep the original measurement of
the  dependent  variable,  that  is,  in  percentage  points,  as  this  indicator  does  not  have  two-sided
thresholds. The results are similar to those of panel (a). The sample for the definition of the treatment
group or the estimation sample (columns 1 and 2) does not change the main conclusion of a statistically
significant negative impact of the MIP. The point estimate drops considerably, however, compared to
the baseline estimates. The indicator-specific definition of the treatment group tends to strengthen the
estimated  impact.  Finally,  there  are  no  indications  of  significant  pre-trends  and  the  placebo  test  is
insignificant, as well.

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3.5.

Policy recommendations

Before we provide recommendations based on our estimation results, we summarize them briefly. Our
findings  show  on  the  one  hand  that  the  introduction  of  the  MIP  has  led  to  an  improvement  in  the
current  account deficits  and  private  sector  debt  in  the  EU.  For  the  euro  area,  we  find  that  the
introduction of the MIP has improved the current account deficits and private credit flow. In both cases,
total unemployment increased, but long-term unemployment and youth unemployment decreased.
On the other hand, we find for the majority of 14 headline scoreboard indicators that the introduction
of the MIP did not lead to a decrease in breaches of the thresholds. In addition, large current account
surpluses,  as  we  observe  them  in  Germany,  still  persist.  Our  finding  regarding  the  current  account
balances are thus foremost related to the reduction of current account deficits.

The result that the introduction of the MIP had an effect on the current account balances, as well as on
private  sector  credit  flows,  has  important  implications  for  the  question  whether  unsustainable
macroeconomic developments can be prevented in the future. Current account deficits, as well as high
private  sector  debt,  contribute  to  deep  financial  and  economic  crises.  Persistent  current  account
deficits or surpluses are a danger to macroeconomic stability, because importing more than exporting
often implies that the country has to take on credit. A sudden dry-up of external financing can generate
deep  economic  contraction,  as  it  requires  an  adjustment  of  internal  demand.  Persistently  running
surpluses goes hand in hand with increasing debt in other countries and also implies excessive savings
over  investment,  which  endangers  the  capital  stock  and  thus  long-term  growth  prospects  for  the
country.

Moderate levels of current account balances may also matter for countries in the euro area, where most
of the debt is denominated in Euro. Persistent current account imbalances can cause exit expectations
of a number of countries from the euro area (Bayer, Kim, and Kriwoluzky 2018). The exit expectations
from  a  currency  union  decrease  output  and  employment  in  countries  that  are  subject  to  them
(Kriwoluzky, Müller, and Wolf 2019).

Furthermore, in a seminal study, Schularick & Taylor (2012) show that financial crises are associated
with  high  credit  growth  and  that  recessions  are  more  deep  if  private  debt  is  high. Given  this
background, our result that the MIP has led to an improvement of private credit indicators is important.
It implies that if the MIP were in place and private credit growth were monitored and curbed at the
right time, the MIP would have helped to mitigate the effects of the financial crisis in 2007/08.

In the aftermath of the financial crisis, six countries received financial assistance: Cyprus, Latvia, Greece,
Ireland, Spain, and Portugal. In three countries, the financial assistance was motivated and given to
recapitalise the financial sector (Cyprus, Ireland and Spain) or to assist in a balance-of-payment crisis
(Latvia). Since the MIP leads to a reduction of current account deficits and to an improvement in the
private  debt  indicators,  we  conjecture  that  it  would  have  mitigated  the  height  of  the  financial
assistance to these countries. Due to the existence of other imbalances, we cannot rule out that the
financial assistance would have been necessary.

Next to the encouraging results, we also find that the introduction of the MIP did not lead to a decrease
in breaches of thresholds for the majority of the headline indicators. One reason for this finding has
been  set  out  in  the  Commission’s  document  “Economic  Governance  Review”:  the  degree  of  CSR
implementation has declined. Efstathiou and Wolff (2019) find that even Member States with excessive
imbalances do not implement significantly more reforms than other Member States. In their report, the
Five Presidents stated that the EIP “should be triggered as soon as excessive imbalances are identified and

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be  used  to  monitor  reform  implementation”.  We  suggest  a  different  road  to  obtain  a  higher  rate  of
compliance with country-specific recommendations that is the implementation of a fund such as the
BICC. The fund could provide grants (or incentives) to countries that implement the CSRs, by making
sufficient progress with the implementation of economic reforms and thereby increasing compliance.

Our  second  policy  recommendation  is  the  introduction  of  a  new  set  of  indicators.  These  indicators
should  cover  environmental  issues  and  reflect  the goals  formulated in  the  context of  the  European
Green  New  Deal.  While  there  exists  the  danger  of  having  too  many  objectives  and  indicators,
incorporating  environmental  indicators  into  the  MIP  would  have  the  advantage  of  having  a  precise
surveillance system that could also launch a corrective action. Economic crises in the future could well
be associated with environmental catastrophes, which disrupt supply chains, trigger large migrations,
or destroy a part of the capital stock.

CONCLUSIONS

In  this  paper,  we  have  investigated  whether  the  introduction  of  the  MIP  has  been  successful  in
detecting economic imbalances and whether the countries of the European Union, and especially in
the  euro  area,  are  better  equipped  today  to  prevent  unsustainable  economic  developments  in  the
future. Our analysis has been based on the idea that macroeconomic imbalances are persistent and
that  those  countries  that    have  hypothetically  been  breaching  thresholds  of  the  MIP  scoreboard
indicators  before  its  implementation  are  more  likely  to  be  in  breach  of  these  indicators  afterwards,
compared to countries with a sound macroeconomic environment before the introduction of the MIP.
We find that the MIP contributed to a more stable economic development. If it were in place in the run
up to the financial crisis, the crisis would probably not have been so severe.

We nevertheless find that the MIP did not lead to a decrease in breaches of thresholds for most of the
headline indicators. This finding corroborates recent observations that the implementation of country-
specific recommendations has been slowing down drastically. In order to improve the compliance with
country-specific recommendations, and thus the effectiveness of the MIP, we recommend to further
develop the MIP. First and most importantly, the MIP should have access to a fund that provide grants
(or subsidies) to countries that meet the requirements of the CSRs. This would not only increase the
enforceability of the recommendations but alleviate the costs of structural economic reforms and thus
make  countries  more  willing  to  implement  the  recommendations.  Furthermore,  additional  fiscal
indicators and especially environmental indicators should be monitored.

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Application: A Compendium.” Background Note. https://doi.org/10.2765/958247.

 García, Alfredo Arahuetes, and Gonzalo Gómez Bengoechea. 2018. “The Macroeconomic Imbalance
for  Predicting  Sovereign  Crises?”  Estudios  de  Economia.

Procedure:  A  Useful  Tool 
https://doi.org/10.4067/s0718-52862018000100079.

 Gros, Daniel, and Matthias Busse. 2013. “The Macroeconomic Imbalance Procedure and Germany:

When Is a Surplus an ‘Imbalance’?” CEPS Policy Brief.

 Gros, Daniel, and Alessandro Giovannini. 2014. “The ‘Relative’ Importance of EMU Macroeconomic
Imbalances  in  the  Macroeconomic  Imbalance  Procedure.”  Istituto  Affari  Internazionali  Working
Paper.

 Hagelstam, K, C Dias, J Angerer, and A. Zoppè. 2019. “The European Semester for Economic Policy

Coordination: A Reflection Paper.”



Kriwoluzky, Alexander, Gernot J. Müller, and Martin Wolf. 2019. “Exit Expectations and Debt Crises
in 
Economics.
https://doi.org/10.1016/j.jinteco.2019.103253.

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Unions.” 

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of 

 Miron, Dumitru, and Ileana Alexe. 2014. “Capital Flows and Income Convergence in the European
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Economics and Finance. https://doi.org/10.1016/s2212-5671(14)00058-6.

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 Moschella,  Manuela.  2014.  “Monitoring  Macroeconomic  Imbalances:  Is  EU  Surveillance  More
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https://doi.org/10.1111/jcms.12136.

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

Schularick, Moritz, and Alan M. Taylor. 2012. “Credit Booms Gone Bust
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IPOL | Economic Governance Support Unit

ANNEX

Table A1: Estimated impact of MIP – controlling for lagged other indicators.
Panel (a): External and competitiveness indicators

Model

Dependent
variable

Impact MIP

Observations

(1)

Current
account
balance

1.36***

(0.38)

371

(2)

(3)

International
investment
position

Real
exchange
rate

-0.04

(1.73)

374

-0.56

(0.66)

379

(4)

Export
market

share

-7.54***

(1.71)

367

(5)

Unit labour

costs

0.15

(0.86)

379

Chi2

2160.50

5598.70

1038.45

1095.65

724.19

Panel (b): Internal indicators

Model

Dependent
variable

Impact MIP

Observations

Chi2

(1)

(2)

(3)

(4)

House price
index

Private
credit flow

Private
sector debt

-0.92

(1.21)

367

441.73

-4.62***

(1.24)

379

706.37

0.58

(2.02)

379

General
government
debt

1.30

(1.36)

379

(5)

Unempl.
rate

0.64***

(0.25)

379

(6)

Financial
sector
liabilities

-1.24

(1.47)

379

6342.11

4472.70

1449.26

602.51

Panel (c): Labour market indicators

Model

Dependent
variable

Impact MIP

Observations

Chi2

(1)

(2)

(3)

Activity rate

0.18

(0.23)

387

218.39

Long-term
unempl. rate

Youth
unempl. rate

-1.66***

-4.12***

(0.31)

372

554.96

(0.99)

389

659.91

Notes: The  table  shows  the  estimated  impact  of  the  macroeconomic  imbalance  procedure  on  the  14  main
indicators  of  the  macroeconomic  scoreboard  when  controlling  for  the  lagged  indicators  of  the  other  two
categories.  All  models  contain  unreported  country  and  year fixed  effects.  Standard  errors  adjusted  for
autocorrelation and heteroskedasticity are in parentheses (* p < 0.1, ** p < 0.05, *** p < 0.01).

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How has the MIP worked in practice to improve the resilience of the euro area?

Table A2: Estimated impact of MIP – controlling for lagged other indicators and interactions.
Panel (a): External and competitiveness indicators

Model

Dependent
variable

Impact MIP

Observations

(1)

Current
account
balance

1.33***

(0.39)

371

(2)

(3)

Internationa
l investment
position

Real
exchange
rate

-2.38

(2.02)

374

-0.04

(0.68)

379

(4)

Export
market

share

-6.32***

(1.84)

367

(5)

Unit labour

costs

0.82

(0.86)

379

Chi2

2313.21

6520.80

1143.66

1179.16

826.15

Panel (b): Internal indicators

Model

Dependent
variable

Impact MIP

Observations

Chi2

(1)

(2)

(3)

(4)

House price
index

Private
credit flow

Private
sector debt

General
government
debt

1.92

(1.40)

379

(5)

Unempl.
rate

0.68**

(0.28)

379

(6)

Financial
sector
liabilities

-2.82*

(1.59)

379

674.90

6674.46

6412.03

1949.11

-1.78

(1.30)

367

485.12

-5.01***

(1.37)

379

738.27

0.59

(2.20)

379

Panel (c): Labour market indicators

Model

Dependent
variable

Impact MIP

Observations

Chi2

(1)

(2)

(3)

Activity rate

0.21

(0.24)

387

248.84

Long-term
unempl. rate

Youth
unempl. rate

-1.34***

-3.43***

(0.30)

372

775.41

(0.99)

389

783.94

Notes: The  table  shows  the  estimated  impact  of  the  macroeconomic  imbalance  procedure  on  the  14  main
indicators  of  the  macroeconomic  scoreboard  when  controlling  for  the  lagged  indicators  of  the  other  two
categories  and  interaction  terms  of  these  variables  with  a post  2012  indicator  variable.  All  models  contain
unreported country and year fixed effects. Standard errors adjusted for autocorrelation and heteroskedasticity
are in parentheses (* p < 0.1, ** p < 0.05, *** p < 0.01).

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IPOL | Economic Governance Support Unit

Table A3: Impact of MIP excluding program countries and years.
Panel (a): External and competitiveness indicators

Model

Dependent
variable

Impact MIP

Observations

(1)

Current
account
balance

1.53***

(0.44)

502

(2)

(3)

International
investment
position

Real
exchange
rate

10.36***

(2.29)

515

-0.03

(1.18)

595

(4)

Export
market

share

-1.09

(2.34)

442

(5)

Unit labour

costs

6.00

(7.38)

576

Chi2

1951.69

5342.21

891.08

1752.73

114.59

Panel (b): Internal indicators

Model

Dependent
variable

Impact MIP

Observations

Chi2

(1)

(2)

(3)

(4)

House price
index

Private
credit flow

Private
sector debt

General
government
debt

5.30***

(1.72)

446

661.26

-1.09

(1.38)

632

657.59

-10.59***

(2.03)

635

0.67

(1.28)

639

(5)

Unempl.
rate

0.85**

(0.34)

551

(6)

Financial
sector
liabilities

0.96

(2.55)

609

6178.86

6714.79

1164.18

554.51

Panel (c): Labour market indicators

Model

Dependent
variable

Impact MIP

Observations

Chi2

(1)

(2)

(3)

Activity rate

-0.23

(0.30)

540

321.96

Long-term
unempl. rate

Youth
unempl. rate

-1.09***

-4.30***

(0.41)

416

496.32

(1.29)

565

485.36

Notes: The  table  shows  the  estimated  impact  of  the  macroeconomic  imbalance  procedure  on  the  14  main
indicators  of  the  scoreboard  in  the  euro  area  when  excluding  country-year  observations  when  a  country
participates in an external macroeconomic adjustment program and when controlling for country-specific time-
trends. All models contain unreported country and year fixed effects. Standard errors adjusted for autocorrelation
and heteroskedasticity are in parentheses (* p < 0.1, ** p < 0.05, *** p < 0.01).

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How has the MIP worked in practice to improve the resilience of the euro area?

Table A4: Sensitivity analysis for current account balance and long-term unemployment rate.

Model

Panel (a)

(1)

(2)

(3)

(4)

(5)

Dependent variable: current account balance flag

Impact MIP common treatment

-0.22***

-0.31***

(0.06)

(0.06)

Impact MIP indicator-specific treatment

-0.19***

-0.18***

Placebo test year 2010

Placebo test year 2011

Placebo test treatment in 2009

Observations

Chi2

Panel (b)

(0.06)

(0.06)

0.02

(0.06)

0.05

(0.06)

371

305

305

305

-0.11

(0.07)

305

607.03

644.35

565.79

598.23

538.82

Dependent variable: long-term unemployment rate

Impact MIP common treatment

-0.56**

-0.52**

(0.25)

(0.26)

Impact MIP indicator-specific treatment

-0.65**

-0.64**

Placebo test year 2010

Placebo test year 2011

Placebo test treatment in 2009

Observations

chi2

(0.28)

(0.28)

-0.01

(0.30)

0.29

(0.30)

372

304

304

304

-0.04

(0.26)

304

720.64

752.97

742.71

735.43

730.38

Notes: The table shows the estimated impact of the macroeconomic imbalance procedure on current account
flags in panel a and on the long-term unemployment rate in panel b when using alternative samples, definitions
of treatment group and conducting pre-trend and placebo tests. All models contain unreported country and year
fixed effects and the set of control variables discussed in Table 6. Standard errors adjusted for autocorrelation and
heteroskedasticity are in parentheses (* p < 0.1, ** p < 0.05, *** p < 0.01).

PE 645.725

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This paper analyses the effects of the implementation of the Macroeconomic Imbalance Procedure
(MIP) on the macroeconomic performance of countries in the EU and the euro area. We find that the
introduction of the MIP led to a decline in current account imbalances and private sector debt and
credit  flows,  but  that  the  overall  effects  are  limited.  To  strengthen  the  MIP,  we  support  the
introduction  of  the  Budgetary  Instrument  for  Convergence  and  Competitiveness,  i.e.  a  fund  that
pays grants, conditional on the implementation progress of economic reforms.

This document was provided by the Economic Governance Support Unit at the request of the ECON
Committee).

PE 645.725
IP/A/ECON-ED/IC/2019-067

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