Ready or not? : constructing the monetary union readiness index.
Date: 2021-02-01
Institutions: Joint Research Centre
Subjects: European Monetary System, central bank, economic integration, macroeconomics, monetary agreement, monetary policy, monetary union, research report

Ready or Not? Constructing the Monetary Union 
Readiness Index 

Erhart, S. 

2021 

EUR 30578 EN

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Contact information
Name:  Szilárd Erhart 
Email:  szilard.erhart@ec.europa.eu 
Web:  https://ec.europa.eu/jrc/en/coin 

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https://ec.europa.eu/jrc 

JRC121303

EUR 30578 EN

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How to cite this report: Erhart, S., Ready or Not? Constructing the Monetary Union Readiness Index, EUR 30578 EN, Publications Office of 
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◦

Abstract

While all EU Member States can join the group’s monetary union, the eurozone, some mem-

bers are far more ready for the adoption and use of the single European currency. Here, we

construct a new Monetary Union Readiness Index (MURI) for EU Member States. The the-

oretical framework of the index is built on the economic theory of Optimal Currency Areas

and EU regulations such as the Treaty and the Maastricht criteria, and the Regulation on

the Macroeconomic Imbalance Procedure. The index measures (i) nominal convergence, (ii)

real convergence and (iii) macroeconomic stability. The MURI Index provides an easy to use

real-time policy tool to evaluate both candidate and current eurozone members. Hence, it

complements, aggregates and communicates key information in annual convergence reports

and in oﬃcial statistics. Our evaluation ﬁnds that Austria, Finland, Denmark, Sweden and

Germany showed the highest level of compliance with the diﬀerent eurozone criteria in 2018,

while Greece, Cyprus, Romania, Spain, and Italy the least.

JEL classiﬁcation: E42, E58, E61, F15, F33, F45

1

The author gratefully acknowledges the help, comments and suggestions received from an anony-

mous reviewer, from the participants of the Joint Research Centre COIN Seminar, 22 October

2020, from Csaba Balogh (Central Bank of Hungary), William E. Becker (Joint Research Centre

of the European Commission), Michael Fidora (European Central Bank), Balázs Forgó (European

Commission, DG ECFIN), Balázs Kóczián (Central Bank of Hungary), Isabelle Méjean (European

Economic Review), Judit Neményi (former Member of the Monetary Council in the Central Bank

of Hungary), Eric Ruscher (European Commission, DG ECFIN), István Székely (European Com-

mission, DG ECFIN) and from colleagues in the Euro Area External Sector and Euro Adoption

Division of the European Central Bank.

2

1

Introduction

The conceptualisation of monetary integration started as a fuelled debate on the Optimal Currency

Area (OCA) theory and on post-war economic integration of countries in Western Europe and in

North America, Scitovsky (1957), Meade (1957), Mundell (1961). Pioneering authors stressed

the importance of factors mobility, Mundell (1961), economic openness Mckinnon (1963) and the

diversity of production, Mckinnon (1969). Fleming (1971) showed that inﬂation rate similarity

helps keep the terms-of-trade rather stable. A fundamental contribution of the Mundell-Fleming

framework was the mapping of the so-called impossible trinity, which states that a country simul-

taneously may choose any two, but not all, of the following three goals: monetary independence,

exchange rate stability and ﬁnancial integration, Mundell (1961), Fleming (1962). Aizenman et al.

(2009) showed empirically the trade-oﬀs of monetary integration and why free lunch does not exist

in monetary integration. In the framework of impossible-trinity all options provide pros and cons

in terms of output volatility, inﬂation and debt burdens.

Monetary integration and the convergence of eurozone economies has become again a topic

of lively public and academic debates on the EU in repeated waves recently. The ﬁrst wave of

debate overlapped with the period when the single European currency was created at the end of

1990s, Frankel and Rose (1997). Discussions became heated again in the mid 2000s, when new

Member States joined the EU and a quick enlargement of the eurozone was scheduled, Csajbok

and Csermely (2001). The last wave of debate started after the eruption of the ﬁnancial crisis

and especially when the eurozne debt problem peaked in 2011-2012, Berti and Meyermans (2017),

Creel (2018).

del Hoyo et al. (2017), Zuk et al. (2018), Arestis (2016), Bibow (2016) concluded that the

temporary fulﬁlment of the numerical convergence criteria set in the Maastricht Treaty was not,

in itself, a guarantee of smooth membership in the euro area (see further details of the Mastricht

Treaty in Section 2.1). A holistic approach and the integration of all dimensions of sustainable

economic convergence – be these real, nominal or institutional in nature – is, therefore, required.

The president of the new European Commission conﬁrmed prioritising further the deepening of

the Economic and Monetary Union under the 2019-2024 European Commission, von der Leyen

(2019).

Here, we contribute to the discussions by constructing a new comprehensive index, which is built

on all relevant pillars of economic convergence and monetary integration. It reveals the various

ways in which monetary union readiness is measured. First, the European regulatory framework

(e.g. in the so-called Maastricht-criteria of nominal convergence and the European Commission’s

Macroeconomic Imbalance Procedure thresholds) used for the index construction. Second, further

requirements of successful monetary integration suggested in the theoretical literature (real con-

3

vergence and institutional criteria) were also built into the index. Comparing the nominal, real

and stability sub-indices of the MURI to each other help discuss the extent to which the oﬃcial

criteria are useful as normative criteria.

The MURI Index is compiled for all 2020 EU Member States (including the United Kingdom) for

14 years (between 2005 and 2018) and is based on oﬃcial statistics. The Monetary Union Readiness

Index (MURI) is derived from the normalized distance (i) to the Eurozone’s Maastricht thresholds,

(ii) to real convergence thresholds and ﬁnally (iii) to the European Commission’s Macroeconomic

Imbalance Procedure thresholds.

Readiness for euro in the majority of eurozone’s founding Members (Austria, Belgium, Finland,

France, Germany, Luxembourg, Netherlands) has varied in a range between 90 and 100, close to

the maximum MURI Index value since 2005. According to our index, Denmark and Sweden could

have considered adopting the euro throughout the entire sample period. Baltic and Central Eastern

European Member States of the EU showed continuous progress in euro readiness except for the

crisis years. According to our index, Cyprus and Greece seemed to be the least ready to use the

euro in 2018.

The convergence has been achieved in terms of all index pillars in the best performing countries.

The requirement of nominal convergence seems to be most fulﬁlled in most Member States, implying

that the European regulatory framework helped to anchor nominal convergence. Real convergence

is happening, although at a slower pace in new EU Member States than nominal convergence.

The usability of the MURI Index is investigated further by comparing it to public attitudes

towards the single European currency in the Eurobarometer survey. There is a positive, although

low (0.3) correlation in the eurozone between MURI Index value and the attitudes towards the euro

measured in the Eurobarometer survey. The correlation is negative and stronger if a subsample,

containing observations for Greece, is used. The Pearson correlation here is -0.75, implying that

the happiness with euro is sometimes U-shaped (which could be called the euro-smile).

In the

same vein, non-eurozone countries opinion on the euro is better in times when the country itself

is less prepared for the euro (correlation of -0.45). This phenomenon could also result in adverse

selection of members, suggesting that good performers may opt-out and less good ones could be

attracted more by monetary unions.

Understanding economic convergence at the twentieth anniversary of the European single cur-

rency may convey important policy messages. New candidates (Bulgaria and Croatia) announced

their intention in 2019 to enter the Exchange Rate Mechanism (ERM II), the so-called waiting

room of the European single currency. Also, the unity of the eurozone has been a serious concern

since the 2009 ﬁnancial crisis and may become again very pressing with the latest COVID19 de-

velopments in some Member States raising the issue of a possible exit from the euro area. Hence,

4

our results are useful to show the requirements of euro area membership for both existing members

and candidates thus provide incentives for the normative discussion on deepening the Economic

and Monetary Union (EMU).

The eurozone can also be interpreted as a contemporary model for forming currency unions.

Hence, the MURI index could serve as a possible benchmark for other planned currency unions in

the future (West African Monetary Zone, ASEAN, etc.).

The euro has been introduced in 19 EU Member States since January 1999, and the European

Central Bank and the European Commission regularly issues Annual Convergence Reports on

eurozone candidate Member States in terms of nominal convergence based on the Maastricht

Criteria. These assessments provide detailed information before eurozone entry but not afterwards,

once a Member State adopted the euro. Also, Denmark and the United Kingdom, gave notiﬁcation

that they would not participate in Stage Three of EMU. As a consequence, convergence reports are

not available for these countries. Furthermore, convergence reports do not explicitly evaluate real

convergence, which is an important factor for a successful monetary unions, Franks et al. (2018).

The contribution of our study to the current and future debate on the eurozone is threefold.

First, as a starting point we systematically investigate the available indicators, used for gauging

EU Member States readiness for euro adoption and use. We combine indicators stipulated both by

the European regulatory framework behind the euro and further real convergence and institutional

indicators suggested in the economic literature. Second, we construct an illustrative Monetary

Union Readiness Index, which could be considered as an augmented and instant form of the

convergence analysis. Third, we present methodological options to measure monetary readiness

and Monte Carlo simulation based conﬁdence intervals around the measurement. These can help

assess the optimality of the monetary integration and the certainty of the optimality. Finally, the

analysis constructs a novel dataset of key indicators which was not available earlier, and could be

used for later research of the EU28 Members States.

The rest of the paper is structured as follows. Section 2 describes the indicators of nominal

convergence, real convergence, and macroeconomic stability in the EU regulation and proposes

a methodology for the Monetary Union Readiness Index. Section 3 presents our data and its

treatment (winsorisation, distance-to-threshold transformation, normalisation). Section 4 discusses

changes in euro readiness in EU Member States based on the MURI Index. Section 5 presents an

analysis of the reliability and uncertainty of the MURI Index. Section 6 concludes.

5

2 Constructing the Monetary Union Readiness Index

The Monetary Union Readiness Index is a combination of three pillars and builds on economic

theories and legal requirements of eurzone membership in the European Union. The MURI Index

is constituted upon normalized indicators and is expressed on a scale between 0 and 100 (where 0

means the lack of readiness for the euro use/adoption and 100 perfect readiness).

The ﬁrst pillar of the MURI Index builds on nominal convergence criteria of monetary inte-

gration stipulated as the Maastricht Criteria and thresholds for Eurozone entrants (in Article 140

of the Treaty on the Functioning of the European Union). Nominal convergence criteria were also

suggested by authors of the OCA theory, Friedman (1953), Mckinnon (1963).

Table 1: The Monetary Readiness Index, pillars and indicators

Indicators

Nominal
convergence
(Maastricht
criteria)

Pillar 1

Pillar 2

Real
convergence

Pillar 3

Macroeconomic
Balance
(MIP
Indicators)

Pillar
weight

Indicators

1/3

1/3

1/3

Inﬂation
Long-term interest rates
Government debt
Government deﬁcit
Excessive Deﬁcit Procedure (EDP)
GDP per capita (PPP)
Correlation of real GDP growth with the eurozone
Share of trade with EU in total trade
Worldwide Governance Indicators
Current account balance
Net international investment position
Real eﬀective exchange rate
Export market shares
Nominal unit labour cost index
House price index, deﬂated
Private sector credit ﬂow
Private sector debt
General government sector debt
Unemployment rate
Total ﬁnancial sector liabilities

The second pillar of MURI Index uses further standard indicators of real economic conver-

gence in the economic literature on the Optimal Currency Area theory used for the evaluation

of candidates’ readiness for monetary union membership, including measures for income conver-

gence, synchronicity of macroeconomic cycles, trade integration and factors of institutional and

governance quality, Mundell (1961), Franks et al. (2018), del Hoyo et al. (2017).

The third pillar of the MURI is based on the Macroeconomic Imbalance Procedure (MIP)

scoreboard indicators and its background Regulation (No 1176/2011). This supra-national surveil-

lance framework in the EU monitors possible spill-over eﬀects of macroeconomic imbalances, which

could compromise the proper functioning of the monetary union, its functioning and institutions,

Commission (2017). The MIP scoreboard integrates several indicators for the better identiﬁcation

6

of internal and external problems in a monetary union ex-post of its creation and hence aims at

the establishment of an improved macroeconomic governance framework to optimally manage the

trade-oﬀs of monetary integration.

7

2.1 The Maastricht nominal convergence criteria

The Maastricht Treaty, entered into force in 1993, outlined the ﬁve convergence criteria, to measure

progress in Member States readiness to adopt the euro, Table 2. The Maastricht-criteria were

deﬁned as a set of macroeconomic indicators with thresholds, which focus on (i) price stability,

(ii) sound and sustainable public ﬁnances, (iii) exchange-rate stability, (iv) long-term interest rate

stability. Fulﬁlment of these criteria is mandatory before euro adoption.

Table 2: the Maastricht convergence criteria and their numeric thresholds

Price stability

Harmonised
consumer price
inﬂation

Sound and sustainable
public ﬁnances

Durability of
convergence

Government deﬁcit
and debt

Long-term
interest rate

=<average inﬂation
of three best
performing MSs
+ 1.5 percentage
points

3 % - Gov. deﬁcit
60% - Gov. debt
Not under excessive
deﬁcit procedure
at the time of
examination

=<average long-term
interest rate
of the three best
performing
MS in terms of price
stability + 2 perc.
points

Exchange rate
stability
Exchange rate
developments in
ERM II
Participation in
ERM II
for at least 2 years
without severe
tensions, in particular
without devaluing
against the euro

Our new index includes four of the ﬁve Maastricht criteria, except for Exchange-rate stability

criterion. The reason for omitting this criterion was that we wanted to design an index which

measures "euro readiness" both ex-ante and ex-post of euro adoption. As the exchange-rate criteria

can be evaluated only ex-ante and only for eurozone candidates but not ex-post for eurozone

members, the exchange rate stability criteria has not been integrated into the index. Nevertheless,

real-exchange rate stability assessment is part of the Macroeconomic Imbalance Procedure, and

MIP Scoreboard indicators are grouped into the third pillar of our index.

2.2 The real convergence criteria

Franks et al. (2018) and the Central Bank of Hungary, MNB (2020) argued recently that nominal

convergence criteria took center stage during EMU preparation. They criticize the Maastricht

criteria as only loosely linked to the actual economic conditions of a successful euro adoption.

Studies on monetary unions usually refer to Optimum Currency Area (OCA) theory which requires

real economic convergence that business cycles are synchronized, otherwise the optimal monetary

policy will diverge across countries, making the union less beneﬁcial for its members, Scitovsky

(1957), Meade (1957), Mundell (1961), Mckinnon (1963), Csajbok and Csermely (2001).

Hence, we added real convergence indicators, proposed and used in the literature, to our index

in Pillar 2 (Table 3).

Union-wide macroeconomic stability can be better achieved when there are no signiﬁcant cross-

8

country diﬀerences in income levels, Benczes and Szent-Ivanyi (2015), Franks et al. (2018). More-

over, income convergence may also be important for the macroeconomic stability after monetary

integration. Income convergence is a guarantee for higher level of social cohesion and hence can

help ensure that the beneﬁts from the monetary union are better distributed over the long run.

To measure income convergence, we calculated the ratio of the real GDP per capita of EU

Member States and that of the eurozone members on average. To assess the synchronicity of

business cycles, pairwise Pearson correlation coeﬃcients of Member States’ real GDP growth and

that of the eurozone was calculated (using a moving window of observations from the given year

[t] to the year preceding this year by 9 calendar years [t-9y]).

Table 3: Real economic convergence pillar and indicators

Income convergence

GDP
per capita (PPP)

in % of
eurozone
average

Synchronicity of
business cycles

Correlation of real
GDP growth

Pearson correlation
coeﬃcient of MS’s
growth
with EMU

Trade integration

Share of trade
with EU
in total trade

(exports to and
imports from EU)/
(total export +import)

Institutions and
Governance

Worldwide Governance
Indicators

Percentile rank

One of the key positive eﬀects of economic and monetary integration was assumed to be trade-

creation eﬀect and the higher-business cycle synchronicity that it may entail at the time when the

eurozone plan was rolled out, Frankel and Rose (1997), Frankel and Rose (1998). We used the

share of trade (exports plus imports to and from the EU) in the total value of exports and imports

to gauge trade integration over time.

The strength of the institutional environment is a further key factor in the analysis of the

sustainability of economic integration and convergence, ECB (2018), Buti et al. (2019). It is also

considered as relevant for maintaining an environment conducive to price stability after adoption of

the euro. Furthermore, the principle of institutional quality of National Central Banks is expressly

referred to in Article 130 of the Treaty and Article 7 of the Statute.

In our index, we used the Worldwide Governance Indicators (WGI), developed by the World

Bank, and used in numerous empirical studies as common measure of institutional quality. The

WGI reports governance indicators with a global coverage and with a long time-span over the

period from 1996 to 2018, for six dimensions of governance. We took the simple average of the

percentile rank for each of the six WGI Indicators: a. ’Rule of Law’; b. ’Control of Corruption’; c.

’Government Eﬀectiveness’; d. ’Voice and Accountability’; e. ’Regulatory Quality’ and f. ’Political

Stability and Absence of Violence’.

9

2.3 The Macroeconomic Imbalance Procedure criteria

Disappearing economic and social convergence between euro area Member States after the 2009

crisis have fuelled again policy discussions over the issue of convergence, Berti and Meyermans

(2017). To respond to the crisis, an augmented supra-national surveillance mechanism was stip-

ulated in the Macroeconomic Imbalance Procedure (MIP, REGULATION (EU) No 1176/2011).

The regulation sets out detailed rules for the detection of macroeconomic imbalances, Erhart et al.

(2019).

In our index we used the MIP Scoreboard which is a set of headline indicators aiming at the

assessment of macroeconomic imbalances, Table 4. It consists of 11 indicators currently, grouped

into (I) ﬁve ’external imbalance indicators’, and (II) six ’internal imbalance indicators’, which were

augmented by additional 3 employment indicators, Erhart (2019). However, these new employment

indicators do not play a direct role in the identiﬁcation of macro-ﬁnancial risks and do not trigger

by themselves steps in the MIP, hence our index does not cover them.

Table 4: The Macroeconomic Imbalance Procedure scoreboard and its indicators
(indicator thresholds are in parenthesis)

EXTERNAL IMBALANCE INDICATORS

INTERNAL IMBALANCE INDICATORS

Current account balance in % of GDP, 3 years
average (upper: +6% and lower - 4%)

House price index, deﬂated
1 year % change
(6%)

Net international investment position
in % of GDP
(-35%)

Private sector credit ﬂow
consolidated, in % of GDP
(14%)

Real eﬀective exchange rate
42 trade partners, 3 years % change
(-/+5)% for euro-area countries and
(- /+) 11% for non-euro-area countries)

Export market shares
5 years % change
(- 6%)

Nominal unit labour cost index
3 years % change
(+9% for euro-area countries and
+12% for non-euro area countries)

Private sector debt
consolidated, in % of GDP
(133%)

General government sector debt
in % of GDP
(60%)

Unemployment rate
3 years average
(10%)

Total ﬁnancial sector liabilities
non-consolidated - 1 year % change
(16.5%)

Many of the MIP indicators could be also related to the concepts explained in later papers on

the OCA theory focusing on the trade-oﬀs of monetary integration. For example, Mundell (1961),

Fleming (1962), Aizenman et al. (2009) showed in the well-known framework of the impossible-

10

trinity that all possible options (monetary independence, exchange rate stability and ﬁnancial

integration) provide pros and cons in terms of output volatility, inﬂation (MIP Internal imbalance

indicators) and debt burdens (MIP external imbalance indicators).

2.4 Measuring public opinions on the euro

The success of monetary integration is mainly an economic and partly a communication issue.

Hence, the Commission regularly measures attitudes towards the euro both in current and candi-

date Member States of the eurozone as part of the Eurobarometer surveys.

We used the Eurobarometer data to test the usability of the the Monetary Union Readiness

Index. We used the following questions in Table 5 of the Eurobarometer surveys, which are slightly

diﬀerent in current and candidate eurozone Member States. In the former group, questions request

opinion on actual experience with the euro, in the latter group the opinion on expectations is

requested. Finally, we calculated the diﬀerence between positive and negative answers.

Table 5: The Eurobarometer survey questions on the euro

Eurozone
countries
Generally speaking, do you think that having
the euro is a good or a bad thing
for your country?

Non-eurozone
countries
Do you think the introduction of the euro
would have positive or negative consequences
for your country?

2.5 Weighting

The EU’s regulatory framework on the eurozone does not diﬀerentiate between indicators required

for euro adoption in terms of importance. All ﬁve Maastrricht criteria and all 11 Macroeconomic

Imbalance Procedure criteria are considered as equally important.

Also, in practice, many composite indicators in every scientiﬁc and policy ﬁeld rely on equal

weighting schemes because this method is the easiest to understand and to communicate to the

wider public, OECD-JRC (2008).

Hence, we gave equal weights to all three pillars each having 1/3 weight in the index. We

applied the same equal weighting rule on the indicators in each pillar, meaning that each indicator

was given 1/5 weight in Pillar 1 and 1/4 weight in Pillar 2, and therefore each had 1/15 weight

(Pillar 1 indicators) and 1/12 weight (Pillar 2 indicators) weight in the aggregated index. The

number of indicators is higher in case of the MIP Scoreboard, each having 1/11 weight in Pillar 3

and 1/33 in the aggregated Monetary Union Readiness Index.

As the general government deﬁcit (in % of the GDP) and its distance from the 3% target

appears in both among the Maastricht criteria (Pillar 1) and in the Macroeconomic Imbalance

Procedure Scoreboard (Pillar 3) its weight is 1/12+1/33, e.g. approximately 11% in the overall

11

index. We think that this choice should not be considered as double counting, but as a reﬂection

of the importance of sound ﬁscal stance in the EU’s regulatory framework on the eurozone.

3 Data

The MURI Index is built on a rich panel dataset of three pillars and 20 indicators, Table 6.

Underlying indicators are downloaded from oﬃcial databases of the Eurostat, European Central

Bank and the World Bank, and all indicators are available for the period from 2005 and 2018.

Table 6: Data sources of the MURI Index (by indicators and index pillars)

Indicator
Inﬂation (%, year-on-year, HICP)
Long-term interest rates (%)
General Government Debt (% of GDP)
General government deﬁcit (% of GDP)
Excessive Deﬁcit Procedure (Y/N)
Real GDP growth rate (%)
Real GDP per capita (PPP)
Worldwide Governance
Index (percentile rank)
Share of exports to +
imports from EU (%in total export+import)
MIP Scoreboard
11 Headline indicators

Source
Pillar
Eurostat
Pillar 1
ECB
Pillar 1
Eurostat
Pillar 1
Eurostat
Pillar 1
Pillar 1
ECB
World Bank WDI Pillar 2
World Bank WDI Pillar 2

World Bank

Pillar 2

Eurostat

Eurostat

Pillar 2

Pillar 3

[The data can be downloaded as a Mendeley dataset: https://data.mendeley.com/datasets/

ymjbffrnrm/2].

3.1 Data treatment (winsorization, normalisation)

To gain indicators which are ready for building them into a meaningful index, we treated our

data in three steps as recommended by the OECD-JRC Handbook on Constructing composite

Indicators, OECD-JRC (2008).

In the ﬁrst step, most of our headline indicators were expressed as a distance to target indicator,

in other words the actual data was compared to thresholds. In case of the nominal convergence

pillar, the Maastricht thresholds (Table 2), in case of the Macroeconomic Balance pillar, the MIP

thresholds were used (Table 4). If a Member State met a given threshold the distance to target

was set to zero. Finally, we took the absolute value of the distance to threshold in case of lower

MIP thresholds.

As for the real convergence pillar, the real GDP per capita was compared to the average of the

eurozone. The correlation coeﬃcient was taken into account only in case of positive values, and

12

negative values were converted into zeros. The reason for this is that synchronicity of business

cycles could be assessed only in case of some sort of co-movement of economic growth cycles. In

other words, neither zero correlation nor negative correlation with the eurozone could be deemed

as adequate for eurozone members. The share of trade with the EU was expressed in comparison

to the total external trade of the given country. The Worldwide Governance Indicator is provided

as a percentile rank.

In the second step, we treated extreme values with the Winsorization technique. We set all

outliers at the 95th percentile above the 95th percentile and 5 percentile below the 5th percentile.

Such treatment was necessary in case of the minority of indicators, eg. for inﬂation, government

deﬁcit (both in Pillar 1) and private sector credit ﬂow (Pillar 3). As a matter of fact, in some

countries extreme levels of inﬂation were observed in the early years of the economic transition

reaching the levels of hundreds of percents annually (Bulgaria, Romania). Distance to threshold

was also extremely high (hundreds of percents) in in case of the government deﬁcit and private

sector credit ﬂow. Without such outlier treatment, distance-to-threshold metrics could become

biased and show an unrealistic picture of the country performance. The treatment of extreme

values with winsorization technique is a standard practice for building composite indicators and

recommended by the OECD-JRC (2008).

Furthermore, we only take into account unacceptable diﬀerence from the thresholds, values in

the accepted range were treated as indiﬀerent (replaced by zeros).

In the third step, we normalized all indicators with min-max normalisation, OECD-JRC (2008)

and reversed the scale for Pillar 1 and Pillar 3 indicators (where 0 means the lack of readiness for

euro use/adoption and 100 perfect readiness).

4 Monetary Union Readiness of EU Member States

Figure 1 plots the the Monetary Union Readiness Index by EU Member States, the higher the

score of a Member State on the 0-100 scale the more ready it is for euro adoption and use.

Eurozone readiness in the majority of eurozone’s founding Members (Austria, Belgium, Finland,

France, Germany, Luxembourg, Netherlands) varied in a range between 90 and 100, close to the

maximum MURI value. There is no signiﬁcant diﬀerence in terms of performance by pillars for

this group. Figures of all three pillars and their underlying indicators can be found in Appendix

A.

The index value has been lower, about 75-85 for some Mediterranean countries Cyprus, Italy,

Malta, Portugal, Spain and Ireland recently. Most members of this country group has suﬀered

likely the most from the crisis, which had reversed the direction of their convergence temporarily.

13

Figure 1: Evolution of the Monetary Union Readiness Index across EU Member States

According to our index, Denmark and Sweden could have considered adopting the euro through-

out the entire sample period.

Baltic and Central Eastern European Member States of the EU showed continuous progress in

euro readiness except for the crisis years. Countries in this group achieved a score of 80-90 in the

aftermath of the crisis. The Czech Republic was a forerunner of this group, achieving a MURI

value comparable to current eurozone members.

According to our index, Cyprus and Greece seemed to be the least ready to use the euro in the

early years of the single currency, and the situation somewhat worsened temporarily due to the

partly adverse eﬀect of the ﬁnancial crisis.

If one looks at all index pillars one by one, Figure 6, the convergence is high in terms of all index

pillars in the best performing countries (in Austria, Denmark, Finland, Germany, Luxembourg,

Sweden).

The requirement of nominal convergence seems to be most fulﬁlled in most Member States,

Figure 6. This implies that the European regulatory framework and institutions helped to anchor

nominal convergence. Furthermore, real convergence is happening, although at a slower pace in

new EU Member States than nominal convergence.

A simple cluster analysis was conducted to group countries by the MURI Index. Three groups

14

5010050100501005010050100200520102015202020052010201520202005201020152020200520102015202020052010201520202005201020152020AustriaBelgiumBulgariaCroatiaCyprusCzech RepublicDenmarkEstoniaFinlandFranceGermanyGreeceHungaryIrelandItalyLatviaLithuaniaLuxembourgMaltaNetherlandsPolandPortugalRomaniaSlovakiaSloveniaSpainSwedenUnited KingdomEuro Readiness IndexyearGraphs by countryFigure 2: Dendogram of the cluster analysis based on the MURI Indicators

of similar countries were identiﬁed. The ﬁrst group is consist of Austria, Denmark, Finland,

Sweden, Luxembourg, the Czech Republic, the Netherlands, Germany, Slovakia, Slovenia, France

and Belgium. The second group consists of Croatia, Ireland, Lithuania, Portugal, Poland, Latvia,

Italy, Spain, Bulgaria, the United Kingdom, Hungary, Malta and Estonia. Greece, Cyprus and

Romania is found to be not very similar to countries in the other two groups.

4.1 Association of the MURI Index with other eurozone indicators

Here we investigate further the usability of the MURI Index by comparing it to an important

metric of euro adoption, to the Eurobarometer survey on the public attitudes towards the single

European currency .

There is a positive, although low (0.3) correlation in the eurozone between the MURI Index

values and the attitudes towards the euro measured in the Eurobarometer survey, Figure 3 (a). The

correlation coeﬃcient becomes negative and bigger in absoulute value if a subsample, containing

observations for Greece, is used. The Pearson correlation is -0.75, implying that the happiness

with euro is sometimes U-shaped in some cases (which could be called the euro-smile).

In the

same vein, non-eurozone countries opinion on the euro is better in times when the country itself is

less prepared for the euro (correlation of -0.45, Figure 3 (b)). This phenomenon could also result

15

020406080L2 dissimilarity measureEstoniaMaltaHungaryUnited KingdomBulgariaSpainItalyLatviaPolandPortugalLithuaniaIrelandCroatiaGreeceRomaniaCyprusLuxembourgFinlandAustriaDenmarkSwedenCzech RepublicNetherlandsGermanySlovakiaSloveniaFranceBelgiumin adverse selection of members in monetary union, suggesting that good performers may opt-out

and less good ones attracted more by monetary unions.

Figure 3: The MURI Index and the Eurobarometer survey results

(a) Eurozone Member States

(b) Non-eurozone EU Member States

16

FRSIESLUBEDEPTELITIEATFINLELDENLBEMTIEATSIESCYITFRFILUPTELIEBEPTSKMTSINLCYLUITDEESFRATFISISKESBEATPTLUELMTFICYFRNLITDEIEELATIEPTFRSKESLUDEEEITCYSIBEFIMTNLMTFRDESICYBEATELITLUEESKPTNLESFIIEITEEPTESSIMTIEFRFICYELSKBENLDEATLUMTSKLUBEELFICYESATIEEEFRSIDELVNLITPTITIEESELLTATBEPTNLEEFILVSKFRCYMTDESILUFRCYELLUSKLTDEEELVFIATESSIIEMTPTITBENLFIESMTCYSKDEELLTBELVIEITEELUPTATSINLFREEELLUFIMTATSKLVFRLTIEITBECYPTSINLDEES20406080100Eurobarometer survey5060708090100Euro Readiness IndexLVSIEELTPLCYSKMTHUCZCYHUCZBGLTLVSKMTROPLEEBGROLVHUSKLTEECZPLEELTCZPLBGLVROHUCZBGEEROPLHULVLTHUBGPLLTLVCZROPLHUCZROBGLTLVHUPLLVROBGLTCZPLCZROLTBGHUHRHUCZBGROSEHRPLPLHUSEBGROCZHRROHUPLSECZBGHRBGCZROHUPLHRSE020406080Eurobarometer survey60708090100Euro Readiness Index5 Reliability and uncertainty of the MURI Index

The reliability of Monetary Union Readiness Index, measured by its components’ Cronbach-alpha

value is 0.74, above the 0.7 threshold for a reliable aggregate, recommended by the Handbook on

Constructing Composite Indicators, OECD-JRC (2008).

The development of an index, like any measurement, entails assumptions and subjective deci-

sions. Hence, this section also aims to test whether and to what extent some of these assumptions

inﬂuence the index values, within a range of plausible alternatives in an uncertainty analysis. Here,

we quantify the uncertainty in the Monetary Union Readiness Index, which can demonstrate the

extent to which countries can be diﬀerentiated by their Index scores.

There are many underlying assumptions of the MURI, which could be tested. Here, we ex-

amined two particularly important ones of these in our uncertainty analysis (a. the aggregation

method, b. weighting scheme). These were chosen as plausible alternative pathways in the con-

struction of the MURI in line with the literature on constructing composite indicators:

(a) the aggregation method [arithmetic or geometric mean]

(b) the weights [Gaussian noise on the weights, (cid:80) w = 1.]

To tackle the problem of zeros when using the geometric average formula, we replaced zeros by

0.01 after min-max normalisation.

The ﬁrst assumption which was varied was the aggregation method (arithmetic mean or ge-

ometric mean). In the MURI, the scores are aggregated into a single score using the arithmetic

average formula (see Subsection 2.5 on weighting). The geometric average was chosen as an al-

ternative approach, which is a non-compensatory aggregation method. In this way high scores in

one Pillar do not compensate low scores in another, which is an alternative way to look at euro

readiness. For instance, if a country scores high on nominal convergence indicators, can not oﬀset

its weak performance on real convergence ones. Second, nominal weights assigned at the pillar level

are all equal. The eﬀect of randomly varying these weights by +/-25% is also tested, to investigate

the eﬀect of modest variations in the importance of diﬀerent convergence pillars.

We performed a Monte Carlo experiment to test the above two assumptions, and re-builded

the Monetary Union Readiness Index 4000 times, each time with a randomly-selected combination

of assumptions (a) the aggregation formula and (b) the weights using the 2018 observations.

In general, the MURI Index is very robust, above and close to average values are invariant

to methodological choices. Mid-values can be stated to be within around 3-4 percentage points

of precision in terms of the MURI Index 0-100 scale. This ﬁnding could be used to guide the

conclusions that can be based on the index. For example, diﬀerences of two or three scores between

Member States cannot be deemed as highly signiﬁcant, whereas diﬀerences of 6-7 places upwards

17

Figure 4: Uncertainty of MURI Index scores (2018)

)
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80

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EU Member States

*The box plots show the 5%,30%,70% and 95% percentiles of the simulated score distribution, based on the 2018
observations of the MURI indicators.

or downwards can show a meaningful diﬀerence. The conﬁdence intervals are generally narrower

for top and mid-ranking Member States, and wider for some low-ranking ones. Still, it helps

identify the least ready Member States for the euro adoption and use. As a robustness check of

our uncertainty analysis we tested our index using observations from other years. There have been

no major diﬀerence in the results, scores are robust to choices.

The overall ﬁnding of our uncertainty analysis is that the uncertainty in the MURI Index is

manageable for most Member States. Hence, it allows meaningful conclusions to be drawn from the

MURI index. Monte Carlo simulations could also be used in the future as a meaningful measure

to judge the uncertainty about the eurozone readiness of a new candidate Member State. Finally,

the MURI Index may provide a practical tool to cross-check new euro adoption plans in the future.

6 Conclusions

While all EU Member States has the right to join the eurozone, some members are far more ready

for the use of the single European currency.

We constructed a novel Monetary Union Readiness Index (MURI) for EU Member States to

contribute constructively to the ongoing discussion on the monetary integration and economic

convergence of EU Member States. This discussion started with the conceptualisation of the

18

Optimal Currency Area (OCA) in the post-war era and continued with the creation of the eurozone

at the late 1990s. It attracted even broader attention at times of the eurozone enlargement and

after the 2008-9 ﬁnancial crisis. The European Commission published a reﬂection paper on the

deepening of the Economic and Monetary Union (EMU), conﬁrming that the single currency is

one of the most signiﬁcant and tangible achievements in the EU, Commission (2017).

We derive the Monetary Union Readiness Index (MURI) from the European Regulatory frame-

work on the Eurozone and from the economic theories on monetary unions. The MURI Index

combines the indicators and thresholds of its three equally weighted pillars in the ﬁelds of (i) nom-

inal convergence (e.g. the so-called Maastricht criteria), (ii) real convergence (e.g. the Optimal

Currency Area requirements) (iii) macroeconomic stability requirements (e.g. the Macroeconomic

Imbalance Procedure (MIP) indicators and thresholds). In total, the MURI Index has been built

upon 20 indicators (5 nominal convergence indicators, 4 real convergence indicators, and 11 MIP

indicators).

Readiness for euro in the majority of eurozone’s founding Members (Austria, Belgium, Finland,

France, Germany, Luxembourg, Netherlands) has varied in a range between 90 and 100, close to

the maximum MURI value since 2005. According to our index, Denmark and Sweden could have

considered adopting the euro throughout the entire sample period. Baltic and Central Eastern

European Member States of the EU showed continuous progress in euro readiness except for the

crisis years. According to our index, Cyprus and Greece seemed to be the least ready to adopt and

use the euro in the early years of the eurozone.

The convergence has been achieved in terms of all index pillars in the best performing countries.

The requirement of nominal convergence seems to be most fulﬁlled in most Member States, implying

that the European regulatory framework helped to anchor nominal convergence. Real convergence

is happening, although at a lower pace in new EU Member States than nominal convergence.

The development of an index, like any measurement, entails assumptions and subjective de-

cisions. We performed Monte Carlo simulations to test our key assumptions: a) the aggregation

formula and (b) the weights. We found that the MURI Index is very robust, index values above

and close to the average are invariant to methodological choices and mid-values can be stated to

be within around 3-4 percentage points of precision in terms of the 1-100 scale of the MURI Index.

Our simulation method could be also used to check the uncertainty around the readiness of future

eurozone candidate countries.

The contribution of our study to the debate on the eurozone is threefold. First, as a starting

point we systematically investigated the available indicators, used for gauging EU Member States

readiness for euro adoption and use. Second, we constructed an illustrative Monetary Union

Readiness Index. Third, we provided scores and visualized the results to support discussions and

19

decisions of policymakers.

Our results are relevant for current and candidate eurozone members, as they allow an estimate

of their euro readiness. Because the eurozone is a contemporary model for forming currency unions,

the MURI index could also serve as a possible benchmark for other planned currency unions in

the future (West African Monetary Zone, ASEAN, etc.). Bangake et al. (2007) concluded that

for the aim of a Maghreb monetary uniﬁcation, involved countries have to make more eﬀorts to

gather economic-institutional conditions, some of which are integral part of the improved regulatory

framework behind the euro.

6.1 Discussion

The majority of indicators in the MURI Index has been taken from the European Regulatory

framework on the euro. There are, however, indicators, mostly related to real convergence, which

have not been stipulated yet in the European legislation, but mentioned frequently in the economic

literature. The selection of the MURI indicators is, hence, to a certain degree subjective. Further

discussions may help to identify the indicators which ﬁt best the policy objectives. Our key goal was

to help operationalize an easy-to-use but robust measurement of eurozone readiness and support

formulate policy rules and monitor their eﬀectiveness in the future.

We did not attempt to set a threshold for MURI that candidates of euro adoptions should

reach before adopting the euro. Still, our index methodology could hopefully help future work on

evidence based policy-making related to the deepening of the Economic and Monetary Union.

20

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22

A Appendix

Figure 5: Evolution of the Monetary Union Readiness Index by Pillars

Figure 6: The MURI Index and Eurobarometer survey* results

*Normalized diﬀerence between positive and negative answers in the Eurobarometer Survey.

23

2040608010020406080100204060801002040608010020406080100199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020AustriaBelgiumBulgariaCroatiaCyprusCzech RepublicDenmarkEstoniaFinlandFranceGermanyGreeceHungaryIrelandItalyLatviaLithuaniaLuxembourgMaltaNetherlandsPolandPortugalRomaniaSlovakiaSloveniaSpainSwedenUnited KingdomPill1 (Maastricht nom. conv.)Pill2 (Real conv.)Pill 3 (Macroeconic stab., MIP)yearGraphs by country050100050100050100050100050100200520102015202020052010201520202005201020152020200520102015202020052010201520202005201020152020AustriaBelgiumBulgariaCroatiaCyprusCzech RepublicDenmarkEstoniaFinlandFranceGermanyGreeceHungaryIrelandItalyLatviaLithuaniaLuxembourgMaltaNetherlandsPolandPortugalRomaniaSlovakiaSloveniaSpainSwedenUnited KingdomEuro Readiness IndexEurobarometer surveyyearGraphs by countryFigure 7: The MURI Index and its pillars

(a) ERI Index

(b) Pillar 1 - Nominal convergence

(c) Pillar 2 - Real convergence

(d) Pillar 3 - Macroeconomic balance

The maps show the 2018 observations of the MURI Index and its pillars.

24

Figure 8: CPI - Compliance with the Maastricht criteria (Pillar 1 - nominal convergence)*

*100 full compliance, 0 non compliance

Figure 9: Long-term interest rates - Compliance with the Maastricht criteria (Pillar 1 - nominal
convergence)*

*100 full compliance, 0 non compliance

25

0.510.510.510.510.51199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020AustriaBelgiumBulgariaCroatiaCyprusCzech RepublicDenmarkEstoniaFinlandFranceGermanyGreeceHungaryIrelandItalyLatviaLithuaniaLuxembourgMaltaNetherlandsPolandPortugalRomaniaSlovakiaSloveniaSpainSwedenUnited KingdomInflation (PILL1)yearGraphs by country0.510.510.510.510.51199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020AustriaBelgiumBulgariaCroatiaCyprusCzech RepublicDenmarkEstoniaFinlandFranceGermanyGreeceHungaryIrelandItalyLatviaLithuaniaLuxembourgMaltaNetherlandsPolandPortugalRomaniaSlovakiaSloveniaSpainSwedenUnited KingdomLong−term interest rates (PILL1)yearGraphs by countryFigure 10: General government deﬁcit - Compliance with the Maastricht criteria score (Pillar 1 -
nominal convergence)*

*100 full compliance, 0 non compliance

Figure 11: General government debt - Compliance with the Maastricht criteria score (Pillar 1 -
nominal convergence)*

*100 full compliance, 0 non compliance

26

0.510.510.510.510.51199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020AustriaBelgiumBulgariaCroatiaCyprusCzech RepublicDenmarkEstoniaFinlandFranceGermanyGreeceHungaryIrelandItalyLatviaLithuaniaLuxembourgMaltaNetherlandsPolandPortugalRomaniaSlovakiaSloveniaSpainSwedenUnited KingdomGeneral government deficit (PILL1)yearGraphs by country0.510.510.510.510.51199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020AustriaBelgiumBulgariaCroatiaCyprusCzech RepublicDenmarkEstoniaFinlandFranceGermanyGreeceHungaryIrelandItalyLatviaLithuaniaLuxembourgMaltaNetherlandsPolandPortugalRomaniaSlovakiaSloveniaSpainSwedenUnited KingdomGeneral Government Debt (PILL1)yearGraphs by countryFigure 12: Excessive Deﬁcit Procedure (Pillar 1 - nominal convergence)*

*100 full compliance (no EDP), 0 no compliance (EDP)

Figure 13: GDP per capita (PPP) (Pillar 2 - real convergence)*

* Normalized values of GDP per capita compared to Eurozone average, 100 meaning the highest score in the EU, 0
the lowest in the EU.

27

.8.85.9.951.8.85.9.951.8.85.9.951.8.85.9.951.8.85.9.951199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020AustriaBelgiumBulgariaCroatiaCyprusCzech RepublicDenmarkEstoniaFinlandFranceGermanyGreeceHungaryIrelandItalyLatviaLithuaniaLuxembourgMaltaNetherlandsPolandPortugalRomaniaSlovakiaSloveniaSpainSwedenUnited KingdomExcessive Deficit Procedure (1 = no, 0 = yes, PILL1)yearGraphs by country0.510.510.510.510.51199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020AustriaBelgiumBulgariaCroatiaCyprusCzech RepublicDenmarkEstoniaFinlandFranceGermanyGreeceHungaryIrelandItalyLatviaLithuaniaLuxembourgMaltaNetherlandsPolandPortugalRomaniaSlovakiaSloveniaSpainSwedenUnited KingdomGDP per capita (ppp, PILL2)yearGraphs by countryFigure 14: Real GDP growth pairwise correlation (Pillar 2 - real convergence)*

*100 perfect correlation, 0 zero or negative correlation.

Figure 15: Share of trade with EU in total trade (Pillar 2 - real convergence)*

*(exports to and imports from EU)/(total export + import), 100 highest score, 0 lowest score

28

0.510.510.510.510.51199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020AustriaBelgiumBulgariaCroatiaCyprusCzech RepublicDenmarkEstoniaFinlandFranceGermanyGreeceHungaryIrelandItalyLatviaLithuaniaLuxembourgMaltaNetherlandsPolandPortugalRomaniaSlovakiaSloveniaSpainSwedenUnited KingdomReal GDP growth correlation (PILL2)yearGraphs by country0.510.510.510.510.51199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020AustriaBelgiumBulgariaCroatiaCyprusCzech RepublicDenmarkEstoniaFinlandFranceGermanyGreeceHungaryIrelandItalyLatviaLithuaniaLuxembourgMaltaNetherlandsPolandPortugalRomaniaSlovakiaSloveniaSpainSwedenUnited KingdomEx. from and Imp. to EU in total trade (PILL2)yearGraphs by countryFigure 16: Worldwide Governance Indicator (Pillar 2 - real convergence)*

* Percentile ranks, 100 highest score, 0 lowest score

Figure 17: Current Account Imbalance (Pillar 3 - Macroeconomic Balance)*

*100 full compliance with MIP, 0 lowest compliance

29

0.510.510.510.510.51199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020AustriaBelgiumBulgariaCroatiaCyprusCzech RepublicDenmarkEstoniaFinlandFranceGermanyGreeceHungaryIrelandItalyLatviaLithuaniaLuxembourgMaltaNetherlandsPolandPortugalRomaniaSlovakiaSloveniaSpainSwedenUnited KingdomWorldwide Governance Index (PILL2)yearGraphs by country0.510.510.510.510.51199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020AustriaBelgiumBulgariaCroatiaCyprusCzech RepublicDenmarkEstoniaFinlandFranceGermanyGreeceHungaryIrelandItalyLatviaLithuaniaLuxembourgMaltaNetherlandsPolandPortugalRomaniaSlovakiaSloveniaSpainSwedenUnited KingdomMIP Current account balance (PILL3)yearGraphs by countryFigure 18: Net International Investment Position Imbalance (Pillar 3 - Macroeconomic Balance)*

*100 full compliance with MIP, 0 lowest compliance

Figure 19: Real Eﬀective Exchange Rate Imbalance (Pillar 3 - Macroeconomic Balance)*

*100 full compliance with MIP, 0 lowest compliance

30

0.510.510.510.510.51199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020AustriaBelgiumBulgariaCroatiaCyprusCzech RepublicDenmarkEstoniaFinlandFranceGermanyGreeceHungaryIrelandItalyLatviaLithuaniaLuxembourgMaltaNetherlandsPolandPortugalRomaniaSlovakiaSloveniaSpainSwedenUnited KingdomMIP Net International Inv. Pos. (PILL3)yearGraphs by country0.510.510.510.510.51199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020AustriaBelgiumBulgariaCroatiaCyprusCzech RepublicDenmarkEstoniaFinlandFranceGermanyGreeceHungaryIrelandItalyLatviaLithuaniaLuxembourgMaltaNetherlandsPolandPortugalRomaniaSlovakiaSloveniaSpainSwedenUnited KingdomMIP Real Effective Exchange Rate (PILL3)yearGraphs by countryFigure 20: Export Market Shares Imbalance (Pillar 3 - Macroeconomic Balance)*

*100 full compliance with MIP, 0 lowest compliance

Figure 21: Nominal Unit labour cost index (Pillar 3 - Macroeconomic Balance)*

*100 highest score, 0 lowest score

31

0.510.510.510.510.51199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020AustriaBelgiumBulgariaCroatiaCyprusCzech RepublicDenmarkEstoniaFinlandFranceGermanyGreeceHungaryIrelandItalyLatviaLithuaniaLuxembourgMaltaNetherlandsPolandPortugalRomaniaSlovakiaSloveniaSpainSwedenUnited KingdomMIP Export market shares (PILL3)yearGraphs by country0.510.510.510.510.51199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020AustriaBelgiumBulgariaCroatiaCyprusCzech RepublicDenmarkEstoniaFinlandFranceGermanyGreeceHungaryIrelandItalyLatviaLithuaniaLuxembourgMaltaNetherlandsPolandPortugalRomaniaSlovakiaSloveniaSpainSwedenUnited KingdomMIP Nominal unit labour cost (PILL3)yearGraphs by countryFigure 22: Real house price index (Pillar 3 - Macroeconomic Balance)*

*100 full compliance with MIP, 0 lowest compliance

Figure 23: Private sector credit ﬂow (Pillar 3 - Macroeconomic Balance)*

*100 full compliance with MIP, 0 lowest compliance

32

0.510.510.510.510.51199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020AustriaBelgiumBulgariaCroatiaCyprusCzech RepublicDenmarkEstoniaFinlandFranceGermanyGreeceHungaryIrelandItalyLatviaLithuaniaLuxembourgMaltaNetherlandsPolandPortugalRomaniaSlovakiaSloveniaSpainSwedenUnited KingdomMIP Real house price (PILL3)yearGraphs by country0.510.510.510.510.51199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020AustriaBelgiumBulgariaCroatiaCyprusCzech RepublicDenmarkEstoniaFinlandFranceGermanyGreeceHungaryIrelandItalyLatviaLithuaniaLuxembourgMaltaNetherlandsPolandPortugalRomaniaSlovakiaSloveniaSpainSwedenUnited KingdomMIP Private sect. credit flow (PILL3)yearGraphs by countryFigure 24: Private sector debt (Pillar 3 - Macroeconomic Balance)*

*100 full compliance with MIP, 0 lowest compliance

Figure 25: General government sector debt (Pillar 3 - Macroeconomic Balance)*

*100 full compliance with MIP, 0 lowest compliance

33

0.510.510.510.510.51199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020AustriaBelgiumBulgariaCroatiaCyprusCzech RepublicDenmarkEstoniaFinlandFranceGermanyGreeceHungaryIrelandItalyLatviaLithuaniaLuxembourgMaltaNetherlandsPolandPortugalRomaniaSlovakiaSloveniaSpainSwedenUnited KingdomMIP Private sect. debt (PILL3)yearGraphs by country0.510.510.510.510.51199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020AustriaBelgiumBulgariaCroatiaCyprusCzech RepublicDenmarkEstoniaFinlandFranceGermanyGreeceHungaryIrelandItalyLatviaLithuaniaLuxembourgMaltaNetherlandsPolandPortugalRomaniaSlovakiaSloveniaSpainSwedenUnited KingdomMIP Gen. Gov. Debt (PILL3)yearGraphs by countryFigure 26: Unemployment rate (Pillar 3 - Macroeconomic Balance)*

*100 full compliance with MIP, 0 lowest compliance

Figure 27: Total ﬁnancial sector liabilities (Pillar 3 - Macroeconomic Balance)*

*100 full compliance with MIP, 0 lowest compliance

34

0.510.510.510.510.51199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020AustriaBelgiumBulgariaCroatiaCyprusCzech RepublicDenmarkEstoniaFinlandFranceGermanyGreeceHungaryIrelandItalyLatviaLithuaniaLuxembourgMaltaNetherlandsPolandPortugalRomaniaSlovakiaSloveniaSpainSwedenUnited Kingdomrev_norm_MIP_UR_DTTyearGraphs by country0.510.510.510.510.51199020002010202019902000201020201990200020102020199020002010202019902000201020201990200020102020AustriaBelgiumBulgariaCroatiaCyprusCzech RepublicDenmarkEstoniaFinlandFranceGermanyGreeceHungaryIrelandItalyLatviaLithuaniaLuxembourgMaltaNetherlandsPolandPortugalRomaniaSlovakiaSloveniaSpainSwedenUnited KingdomMIP Tot. fin. sect. liab (PILL3)yearGraphs by countryGETTING IN TOUCH WITH THE EU 

In person 

All over the European Union there are hundreds of Europe Direct information centres. You can find the address of the centre 
nearest you at: https://europa.eu/european-union/contact_en 

On the phone or by email 

Europe Direct is a service that answers your questions about the European Union. You can contact this service: 

- by freephone: 00 800 6 7 8 9 10 11 (certain operators may charge for these calls),

- at the following standard number: +32 22999696, or

- by electronic mail via: https://europa.eu/european-union/contact_en

FINDING INFORMATION ABOUT THE EU 

Online 

Information about the European Union in all the official languages of the EU is available on the Europa website at: 
https://europa.eu/european-union/index_en 

EU publications 
You can download or order free and priced EU publications from EU Bookshop at: https://publications.europa.eu/en/publications. 
Multiple copies of free publications may be obtained by contacting Europe Direct or your local information centre (see 
https://europa.eu/european-union/contact_en). 

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doi:10.2760/226646 

ISBN 978-92-76-29423-8