Portuguese crisis: was it predictable?

What next?

A. Calvaresi, L. Maggi

International Financial Management - 2013

Table of Content

Introduction .................................................................................................. 2

Portuguese Crisis: the main steps ................................................................. 3

Alert Mechanism Report .............................................................................. 4

The AMR Scoreboard for Portugal .............................................................. 8

Dataset ........................................................................................................ 10

Classification Models ................................................................................. 14

What next? .................................................................................................. 18

Bibliography ............................................................................................... 20

2

Introduction

The incidence of the Euro zone crisis has highlighted the need to correct the imbalances between

the Member States. These imbalances, notably abundant credit, large and persistent current account

deficits and surpluses, losses of competitiveness and accumulation of debt, have been accumulated

for a decade, contributing and aggravating the crisis (European Commissionb, 2013).

The goal of this work is to understand if the Portuguese crisis was predictable. In doing this we will

make use of imbalance indicators involved in the Alert Mechanism Report which is the first step of

Macroeconomic Imbalance Procedure introduced by the European Union (EU) in 2011 in order to

implement a surveillance procedure for the prevention and correction of macroeconomics

imbalances (European Commission, 2012).

Our research question is situated in the long line of studies of early warning indicators but it doesn’t

aim to investigate which are the specific leading indicators that were able to predict the Portuguese

crisis. It would be a good theoretical exercise but the contingent factors that characterize the

Portuguese crisis would make the result not generalizable. The origin of 2010–2013 Portuguese

financial crisis due to the bankrupt of two important banks in the country is a separate question

from our. We would like to verify if the mechanism developed by the EU would have been able to

predict a growing increase in vulnerability of Portugal.

In doing this, is not sufficient look at the measures of the imbalance indices in the years before the

crisis. There are some countries that showed good performances in the years before the crisis (i.e.

Ireland) that needed a bailout from international institutions, while countries with bad performances

(i.e. Malta) didn’t need international aid. Therefore we will make use of several classification

models in order to verify the capability of the imbalance indicators (measured from 2001 to 2009)

to predict which countries requested access to international financing (from 2010 until now). In

addition, we will look at the performances of Portugal in the indicators that would have been more

powerful in the prediction of the impact of the crisis.

In the first chapter we provide an overview of the Portuguese crisis and the main steps that led the

country to request access to international financing. The second chapter describes the scoreboard of

the Alert Mechanism Report, prepared by the European Commission, made up of eleven indicators

that monitor external imbalances and competitiveness, as well as internal imbalances. In the third

step we give an overview of Portugal’s performances from 2001, in terms of the eleven imbalance

indicators. The chapter four describes the dataset we use and the chapter five presents the

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classification models we perform, including the analysis of the results. In conclusion we describe

briefly the challenges that Portugal has to engage to exit the crisis.

Portuguese Crisis: the main steps

After the repercussion of 2007-08 crisis, in November 2008, in an environment of increasing

surveillance of the banking sector’s performance, the government decided to nationalize the Banco

Português de Negócios (BPN), a medium size bank that was facing significant liquidity constraints

and in which the Bank of Portugal had found a number of irregularities (Pereira and Wemans,

Banco Privado Português (BPP), which had accumulated losses for years due to 2012). Also the

bad investments and accounting fraud, received a national bailout. The slippery economic growth

rate and the banking sector crisis brought rapidly the country to be unable to refinance itself on the

financial market and to repay back the previous debt.

In march 2011, the first minister José Sócrates was forced to resign from his position after the

fourth rejection of the austerity measures (arranged with the European Commission) from the

Portuguese parliament. At that time, treasury bills (T-bills) with a six and twelve month deadline,

for a total offer of EUR 550 and 450 million respectively, had been assigned with an higher interest

rate than the previous year: from 2,984% to 5,117% for the six month T-bills and from 4,331% to

5,902% for the others (Bufacchi, 2011). But in front of the impossibility to repay back EUR 9

billion due to the bonds on short deadline between the months of April and June, while the public

savings were estimated to be only EUR 3 billion, on 6 April the Portuguese government notified an

official help request to the European Commission (Merli, Bufacchi and Da Rold, 2011).

On 16 May 2011, was officially approved a EUR 78 billion bailout package for Portugal, which

became the third Eurozone country, after Ireland and Greece, to receive emergency funds. The

bailout loan was equally split between the members of the Troika, namely the European Financial

Stabilisation Mechanism (EFSM), the European Financial Stability Facility (EFSF), and the

International Monetary Fund (IMF). the Troika negotiated with the Portuguese authorities an

Economic Adjustment Programme which seeks to restore confidence, put public finances on

sustainable footing, enable the economy to return to balanced growth and safeguard financial

stability (European Commissionc, 2013)

On 5 July 2011, Portuguese long term T-bills rating was downgraded by Moody’s from Baa1 to

Ba2. In order to improve the State's financial situation, the Portuguese government, now headed by

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Pedro Passos Coelho, implemented measures as the tax increase, the freeze of civil service,

privatizations and the reduction of higher wages. (Wiki).

Alert Mechanism Report1

The Stability and Growth Pact (SGP), signed in 1997 by the Member States of the EU, leads the

European governance and represents the main juridical fundament for the regulation of the fiscal

policies. It involves a preventive arm and a corrective arm, defining with a major level of detail the

way in which the rules have to be actualized. The rules are inspired to the principle of “sound fiscal

policy”, aiming the achievement of the stability on the short term and the sustainability on the long

term for the fiscal policy in order to comply the financial constraints fixed by the European

Commission, or rather each country has to stay within the limits on government deficit (3% of

GDP) and debt (60% of GDP). A fiscal policy which is far from these principles can negatively

influence on the economic growth perspective and inflation of a country and also of the whole

union.

In order to realize an efficient preventive arm, the EU introduced in 2011 the Macroeconomic

Imbalances Procedure (MIP) as part of the Sixpack legislation which reformed the SGP. The first

step of each round of the MIP is the draft of the Alert Mechanism Report (AMR): it identifies which

of the Member States show indicators with values that point out a potential macroeconomic

imbalances in progress, evaluating the need of a further in-depth review.

The AMR is based on a scoreboard, consisting of eleven headline indicators covering the major

sources of macroeconomic imbalances. The scoreboard data are primarily derived from data

compiled by the European Statistical System and the European System of Central Banks. For each

indicator are provided threshold alarm values, which can detect both upper and lower bounds of the

variable. The scoreboard, coupled with an economic analysis, is published into an annual report in

the month of November.

The goal of the AMR is to discover potential imbalances at the first stage to allow preventive

actions which aim to avoid their evolution. The coupled economic analysis suggests the absence of

automatism in the procedure: the crossing of the threshold value for a specific indicator doesn’t

necessarily imply the presence of a macroeconomic imbalance.

If a further in-depth review is carried out, the European Commission has three possibilities:

1 The information related to MIP and AMR come from Senate Budget Department (2013).

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if it considers the situation as not problematic, any additional action is undertaken;

if it considers the situation as the starting point of a macroeconomic imbalance, it proposes

appropriate policy recommendations to the Member State;

if it considers the presence of a significant macroeconomic imbalance, it proposes to the

European Council to open an Excessive Imbalance Procedure (EIP).

In the last case, the EIP is triggered by the corrective arm and requires a corrective action plan to

the Member State. In order to ensure enforcement of the EIP, financial sanctions can be imposed on

euro area Member States.

The eleven headline indicators covering which constitute the MIP scoreboard are the following:

a) 3 year backward moving average of the Current Account Balance in % of GDP

b) Net International Investment Position in % of GDP

c) 3 years percentage change of the Real Effective Exchange Rate (42 industrial countries)

based on HICP deflators

d) 5 years percentage change of Export Market Share (share of world exports)

e) 3 years percentage change in Nominal Unit Labour Cost

f) year-on-year change in Deflated House Prices

g) Private Sector Credit Flow in % of GDP

h) Private Sector Debt in % of GDP

i) General Government Sector Debt in % of GDP

j) 3 year backward moving average of Unemployment Rate

k) year-on-year change of Total Financial Sector Liabilities

a) 3 year backward moving average of the Current Account Balance in % of GDP (thresholds

between -4% and +6% of GDP)

It is the driving factor for the net debt/credit of a country economy toward the rest of the world and

provides important information about the international economic relations. On one hand, a high

deficit of the current account balance can be over the limit if it reflects an unsustainable debt to

foreign countries because of excessive importations not compensated by adequate exportations. On

the other hand, a surplus can be an alarm if reflects a huge weakness of the domestic demand.

b) Net International Investment Position in % of GDP (threshold of -35% of GDP)

It registers the net financial position (activities minus liabilities) of domestic economic sectors,

providing an overview of the financial relations of a country economy toward the rest of the world

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and giving information about the financial vulnerability. It is the cumulated value of the current

account balance. Therefore many considerations on the capability of current account balance to

point out macroeconomic imbalances can be also apply to the net international position.

c) 3 years percentage change of the Real Effective Exchange Rate (REER) based on HICP

deflators (volatility thresholds between -5% and +5)

The REER is the ratio between foreign and domestic prices calculated in the same currency. It is the

nominal effective exchange rate explicit in real terms on the base of a consumer price index, while

the nominal effective exchange rate is calculated as a weighted mean of nominal exchange rate

between the currency of a country and the currency of its main trading partner, using the

correspondent export volumes as weights. The European Commission will consider extending the

basket of trading partners further when data of sufficient quality for additional emerging countries

become available. The REER underlines the competitiveness-based-on-prices of a country in the

international trade and the medium-long-term pressure on the national companies caused by the

price variations. The REER takes also into account the inflation based on the HICP deflators

(Harmonized Index of Consumer Prices), which measure the changes over time in the prices of

consumer goods and services acquired by households.

A significant deviation from a reference value assesses an excessive growth of the internal prices

toward the external prices without an opportune adjustment of the nominal exchange value. The

divergence of the competitiveness-based-on-prices between different economies can be seriously

risky for a monetary union such as the Europe.

d) 5 years percentage change of Export Market Share (volatility threshold of -6%)

It is the ratio between the growth of the export volume of a country and the growth of the

exportations in the world. A country can lose its market shares not only if its exportations decrease,

but also if they increase less than the exportations in the world, negatively impacting on the

international competitiveness of the country.

e) 3 years percentage change in Nominal Unit labour Cost (volatility threshold of +9%)

It measures the average working cost for product unity. An increase means that the working cost is

raised more than its productivity, compromising the competiveness of a country economy if the

increase is not compensated by negative variation of other costing factors, as an example the capital

cost.

7

f) year-on-year change in Deflated House Prices (threshold of +6%)

According to the European Commission, a huge positive variation of the house prices is usually

linked to several financial crisis episodes, as happened in the last years. In addition, the cyclic

variation of the house prices impacts on different levels of the economy, as the richness of owning

family and the profitability of the building sector.

g) Private Sector Credit Flow in % of GDP (threshold of 14% of GDP)

It is measured by the flow of the loans and the purchase of financial activities (capital stocks are not

included) as a percentage of GDP. A big expansion of the credit in the private sector is associated

with an increasing probability of financial crisis, both in the advanced and emerging countries,

making a country more sensible to economic shocks.

h) Private Sector Debt in % of GDP (threshold of 133% of GDP)

It is the ratio between the sum of loans and purchase of financial activities (capital stocks are not

included) and the GDP. It is the equivalent of the previous indicator in terms of cumulated value.

i) General Government Sector Debt in % of GDP (threshold of 60% of GDP)

It is the total debt accumulated by the public administration as a percentage of GDP. The respect of

threshold is one of the two balance rules requested by the Treaty of Maastricht to the Member

States of the EU. According to the European Commission, its relevance for the macroeconomic

surveillance is due to its strong connection with the private sector. Financial and sovereign risks are

hugely linked each other: the government interventions to save private financial institutions have

caused considerable gains of the public debt levels, negatively impacting on the solvability and

liquidity of the governments.

j) 3 year backward moving average of Unemployment Rate (threshold of 60% of GDP)

High and persisting unemployment rates point out the presence of structural problems in the

allocation of the economic resources and an overall incapability of adjustment.

k) year-on-year change of Total Financial Sector Liabilities (threshold of 16,5% of GDP)

This indicator has been added in the AMR 2013 (published in November 2012) after technical

evaluations executed by the European Commission: a wide expansion of the financial sector debt

has usually preceded financial crises.

8

The AMR Scoreboard for Portugal

The figure shows the AMR scoreboard for Portugal. For each indicator the shading identifies the values beyond the threshold.

9

A persistent deficit in the current account balance stands out. The negative net investment position

is the result of substantial and prolonged current account deficit. According to the leading indicators

literature the current account balance has been often found to help predict crisis incidence (Frankel

and Saravelos, 2011). A specific current account balance is not necessarily a source of problems as

it reflects the natural economic structure of a country or the dynamics of a particular economic

cycle. It becomes unsustainable if is persistent over the years and leads to a worrying private and

public debt. In Portugal the current account shows imbalance for more than 40 years (European

Commissionc, 2013) and the scoreboard doesn’t show that the Net External Debt as % GDP has

nearly tripled in the last 10 years (European Commissiona, 2013).

Portugal’s competitiveness in the years before the crisis didn’t show a particularly alarming

situation, however the occurrence of the crisis has led to a strongly decelerating net exports.

Changes in world export market shares of goods and services point to potentially important

structural losses in overall competitiveness in the global economy.

About the private sector credit flow and the private sector debt, Sachs, Tornell and Velasco (1996),

who were among the first to popularize this measure, argue that it is a good proxy for banking

system vulnerability, as rapid credit growth is likely associated with a decline in lending standards.

The banking crisis in Portugal and the consequent sovereign debt crisis are linked to excessive

exposure of banks over the years. Moreover a high level of private sector debt increases the

exposure of the private sector to changes in the business cycle, inflation and interest rates.

(European Commission, 2012).

The relationship between crisis incidence and public debt has been examined less frequently

(Frankel and Saravelos, 2011). In the course of the financial crisis, it has become clear that the

overall indebtedness of a Member State is important and that there are strong linkages between

private sector and general government debt. Therefore, private sector debt needs to be considered

together with general government debt because the leveraging in the private sector could be

magnified by the sovereign debt crisis and by the fiscal pressure exerted on highly-indebted public

sectors. This is particularly true in Portugal which is characterised by highly indebted public and

private sectors. Moreover, when denominated in a foreign currency, debt may raise additional

vulnerability concerns arising from exchange rate fluctuations (European Commission, 2012).

10

Dataset

The dataset is primarily derived from data compiled by the European Statistical System and

European System of Central Banks.

The dataset is composed by:

11 independent variables. They correspond to the AMR indicators covering the major

sources of macroeconomic imbalances. The independent variables are measured from 2001

to 2009 period.

Code Description

a 3 year backward moving average of the current account balance in % of GDP

b net international investment position in % of GDP

c 3 years percentage change of the real effective exchange rate

d 5 years percentage change of export market share

e 3 years percentage change in nominal unit labour cost

f year-on-year change in deflated1 house prices

g private sector credit flow in % of GDP

h private sector debt in % of GDP

i general government sector debt in % of GDP

j 3 year backward moving average of unemployment rate

k year-on-year change of total financial sector liabilities

1 dependent variable. This variable is a binary crisis indicator, taking the value 1 if a

country requires access to financing from the so-called Troika and 0 otherwise. Even if the

country has not received the loan, it’s sufficient the starting of the negotiation to take the

value 1. The dependent variable is measured from 2010 to 2013 period.

Code Description

out Access request to international financing

The variables refer to a sample of 10 countries, which are shown in the following table.

Portugal Italy

Spain Belgium

Greece United Kingdom

Cyprus Germany

Ireland France

Out=1 (Crisis country) Out=0 (Not crisis country)

The choice of the countries is driven by the scope to balance the countries that take out=1 and

countries that take out=0. The presence of countries like Italy in the group with out=0, though it

doesn’t represent the “bests in class”, makes the analysis more difficult. Although it had not good

11

performances in the years before the crisis, it never requested access to Troika founds and this

makes it a “not crisis country”.

The independent variables are annual indicators measured in the period from 2001 to 2009. The

choice is driven by the intention of minimizing endogeneity issues, in fact all the “crisis countries”

required international financing in 2010 or later. In this way we can be sure that the independent

variables are causes of crisis and not consequences.

Every instance of the dataset corresponds to the performance, in terms of the 11 indicators, of a

single country in a single year. The dependent variable associated to the instance is 0 or 1 on base of

the country. We obtain a dataset with 86 instances and 11 features, plus a column that identifies the

dependent variable. Some countries, like Cyprus and Belgium, have missing values for 2001 and

2002, in that cases we delete the related instances.

We perform the correlation matrix in order to anlyze the correlations within the independent

variables and between each independent variable and the dependent variable.

An high correlation stands out between the 3 years backward moving average of current account

balance (a) and the net international investment position (b). This means that the net international

investment position stays at high negative levels in countries characterized by persistent current

account deficits leading to weak growth rates. The potential vulnerability of a country from external

deficits can be reduced if these are financed through foreign investments; the cases where this does

not happen are not sustainable in the long term.

The correlation matrix shows as the current account deficit and the negative net international

investment position affect the incidence of crisis more than the others variables. It’s also possible to

obverse this looking at the distribution of 0 and 1 in the below graph. Where a and b are both high,

the dependent variable takes value 0.

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Furthermore, one can observe an high positive correlation between the credit growth (g) and an high

level of private sector debt (h) and the occurrence of the crisis. The graph below shows the relation

between the private sector debt and the dependent variable. One can observe that all the countries

with high private sector debt in the period before the crisis have required access to international

financing during the crisis period.

13

The figure below shows the distribution of 0 and 1 related to the range of values of each independent variables

14

Classification Models

The scope of this empirical exercise is to build a classification model in order to verify the

capability of the imbalance indicators to predict the Portugal crisis which is measured by the binary

dependent variable. Through the building of a classification model it’s also possible to observe

which are the indicators that would have been more powerful in the prediction of the impact of the

crisis.

The binary classifier tries to predict and describe the dependent variable in terms of the independent

variables. In the terminology of machine learning, classification is considered an instance of

supervised learning, this means that in the training set (the dataset described in the previous chapter)

the values of the dependent variables are known. Therefore we need to know a posterior which are

the countries that have requested access to international financing. This is an information that was

not possible to know in the years before the crisis, however our goal is not to build a model that is

able to predict the crisis but verify the predictive capability of the indicators involved in the AMR.

Moreover, analyzing the different predictive powers of the independent variables, it’s possible to

observe the performances of Portugal for the indicators that most influence the impact of the crisis.

We use several tools and algorithms of WEKA software to carry out this work. WEKA (Waikato

Environment for Knowledge Analysis) is a popular open source suite of machine learning software

written in Java and developed at the University of Waikato (New Zealand). It contains a collection

of visualization tools and algorithms for data analysis and predictive modeling (Bouckaert et al.,

2013).

First we perform on the dataset the J48 algorithm which generates a decision tree.

15

The result is encouraging, the classifier is able to classify correctly all the instances of the dataset

utilizing only two features. We could expected that an high deficit in the current account is a signal

that precedes the impact of the crisis. The contruintive result is related to the unemployment rate,

indeed the model considers a low level of unemployment as a leading indicator of crisis incidence.

In order to explain this result, would be necessary to carry out a more detailed analysis on the level

of employment in a country: in nations like Ireland, Portugal and Cyprus the unemployment rate

was always below the threshold of 10% in the years before the crisis and it has increased during the

crisis proving that the unemployment is a consequence of the crisis and not a cause. In some cases,

as the Portugal, the low level of the unemployment rate was due to the high number of employees in

the public sector. In 2005, the number of public employees per thousand inhabitants in the

Portuguese Administration (70,8) was above the community average (62,4 per thousand

inhabitants) (Ministry of the Presidency, 2010). In these cases the low level of unemployment can

be effectively a leading indicator of crisis incidence.

The decision tree classifies correctly all the instances related to Portugal, since its level of current

account balance is each year below the threshold of -3.1%.

In order to analyze which are the indicators that more affect the incidence of the crisis, we use the

OneRAttributeEval method that ranks the features according to their capacity to predict the

dependent variable. It evaluates the worth of each attribute by means of the OneR classifier. This

uses only one attribute for the prediction of the binary variable. The ranking below shows the

percentage of the correctly classified instances related to the attribute utilized for the prediction.

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The result is consistent with what has been observed by analyzing the correlation matrix: a negative

trend in the current account balance (a) and a negative net international investment position (b) are

risk factors more than the other indicators. For these indicators, OneR identifies the following

thresholds:

In both cases Portugal is classified as a country which will need international aid.

Another way to identify the most important leading indicators is building linear logistic regression

models. For this purpose we use the SimpleLogistic alghoritm after normalizing the dataset to avoid

that the value of the parameters is affected by the different scales of the features.

A negative trend in the current account balance in the pre-crisis period is once again the factor that

pushed the country toward the request of access to international financing. The appreciation of the

REER (c) also stands out as a leading indicator of crisis incidence. The same for the private sector

credit flow (g). One can observe that all the variables considered in the logistic regressions have the

consistent sign, except for the 5 years percentage change of export market share (d). According to

this model the increase of the export market share in the years before the crisis has a negative

impact on the crisis incidence. For the emerging countries the increase of the export market share

often goes hand in hand with a weakness in the domestic demand, but it’s not the case of the

17

countries in the sample. Probably this result is due to the high number of countries that have not

needed help until now, despite their low level of exports in the pre-crisis years; accordingly the

dependent variable chosen as crisis incidence doesn’t appropriately reflect the negative trend of the

export market shares.

This analysis shows how is possible to build classification models, based on the imbalance

indicators of the AMR, that are able to predict the recourse to international financing with high

probability. This means that the imbalance indicators are useful leading indicators of crisis

incidence. Therefore if AMR had been used before the impact of the crisis, it could predict which

countries would have been the most vulnerable.

In the specific case of Portugal the worse performances in the years before the crisis were in the

current account balance (a), net international investment position (b), private sector credit flow (g),

private sector debt (h) and general government sector debt (i). Observing the features ranking

(defined by OneRAttributeEval), four out of these five indicators appear in the first five place of the

ranking. This means that the Portugal had bad performances in the indicators that most affect the

crisis incidence. This is one more reason to argue that the Portuguese crisis was predictable.

The models considered have four main limitations:

Each instance, that corresponds to the set of indicator measurements for a single country in a

single year, is independent to the others. This means that the models don’t consider any

information about the trends of the values and each set of indicator measurement has the

same weigh, so that the instances that refer to years near the crisis are indistinguishable from

those that are more distant in time.

The models lose information about the set of instances that belong to the same country. For

the models exist only two kinds of instances: one with out=0 and the other with out=1. They

have not information about the country which the instance refers to.

The models don’t identify some important aspects. For instance they consider always good

the growth of the current account balance and the depreciation of REER. They are not able

to identify the double threshold for these indicators, this is due to the fact that in all the

“crisis country” is possible to observe high deficit of the current account balance and

overvaluation of the currency in the years before the crisis.

The dependent variable that we have chosen points out only the countries where there has

been a strong incidence of crisis, bringing them to request access to international financing.

If a country didn’t need aid from the Troika doesn’t mean that it has not been hit by the

crisis. Only one crisis measure may be limitative to catch the several effects of the crisis.

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What next?2

According to the last Assessment of the national reform programme and stability programme for

Portugal (European Commissionc, 2013) defined by the European Commission, “significant fiscal

consolidation has been achieved since the start of the programme, the stabilisation of the banking

sector is progressing and major structural reforms are being implemented. Nevertheless, significant

challenges remain, and the authorities will need to engage in further fiscal consolidation to put

public finances on a sustainable footing and tackle the rapid increase unemployment”.

The recapitalisation of the banking sector has been completed and Portuguese banks have

significantly improved their capital ratios and available liquidity, although they experienced

negative net profits in the first half of 2013 and high funding costs. However, the access to credit

remains costly and difficult compared to the euro area average, in particular for small and medium-

sized enterprises.

Although a surprising growth of the economic activity in the second quarter of this year, GDP is

forecast to contract by 1.8% in 2013, before expanding by 0.8% in 2014. The source of major

concern is the labour-market conditions, especially with regard to youth unemployment. A

comprehensive labour market reform was adopted and entered into force in 2012. The rise of the

unemployment rate during the years has implications on poverty and inequality, with around a

quarter of the population at risk of poverty and social exclusion in 2012.

The fiscal target to reduce the deficit of 5.5% of GDP is within reach, thanks to an improved

expenditure control, a reinforced fight against tax evasion and an increased efficiency of the health

sector. A further adjustment will continue in 2014 to achieve the 4% of GDP deficit target, as

established by the Seventh Review of the Economic Adjustment Programme for Portugal (European

Commissiond, 2013). Modernising public administration, improving the sustainability of the

pension system and achieving cost savings across ministries will be the main leverages to comply

the objectives under the programme and a rigorous implementation of the draft budget will be a

decisive step. There also needs to be faster progress to reduce the administrative burden for

businesses and attract investments.

The combined Eighth and Ninth Review (European Commissione, 2013) confirms the sustainability

of the public debt which is expected to peak at 127.8% in 2013 and to decline thereafter. Portugal

2 All the data cited in this chapter come from the following sources: European Commission (2012), European

Commissiond (2013) and European Commission

e (2013).

19

successfully sold 10-year bonds in May 2013, the first long term bond sale since the programme's

start. Having piked toward 8% following the political crisis in July, the current yield on 10-year

bonds is around 6%. In order to resume regular bond issuance, it’s necessary a strong political

stability and the compliance to the adjustment programme agreed with the European Union.

20

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