Economic Computation and Economic Cyberneti cs Studies and Research, Issue 22018 Vol. 52 [626415]

Economic Computation and Economic Cyberneti cs Studies and Research, Issue 2/2018; Vol. 52
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DOI: 10.24818/18423264/52.2.18.09

Associate Professor Oana -Ramona LOBON T, PhD
West University of Timis oara
Professor Nicoleta -Claudia MOLDOVAN ,PhD
West University of Timis oara
Alexandru BOCIU , PhD Student (Corresponding author)
West University of Timis oara
Associate Professor Codrut a CHIS ,PhD
Banat University of Agricultural Sciences and Veterinary Medicine
Lectures Daniel BRÎNDESCU OLARIU , PhD
West University of Timis oara

A FACTOR ANALYSIS OF THE PUBLIC SECTOR
PERFORMANCE. SIGNIFICANT DIFFERENCES BETWEEN OLD
AND NEW EU COUNTRIES

Abstract: This paper revisits public sector performance analysis for EU states
during 1995 -2014 using principal component analysis technique. The following
approaches are considered: (i) analysis of public sector performance and (ii)
identification of the most important subsector. In order to compare the EU
countries public sector performance , we considered seven subsectors, namely,
administratio n, health, education, infrastructure, income distribution, economic
stabili ty and economic performance. R esults emphasize that administration
subsector has a major contribution in achieving public sector performance. The
results indicate that EU old countr ies have the best competitive potential for
prospectively furthering sustainable development. Only four EU states register a
discord ant behavior. Greece and Italy r epresent old EU states that aren’ t
performant and Malta and Cyprus represent the ne w EU states that are
performant . These findings are relevant to national policy sustainable development
agendas, at different levels, considering development as one of the most important
issue for policy -makers and public managers.
Keywords: public sector perfo rmance , composite index, EU countries ,
Principal Component Analysis, development.
JEL Classification: C38, H11, O52
1. Introduction
This paper reexamines public sector performance for the EU Member States. In
this process, the most important subsectors of the public environment are
considered, namely, administration, health, education, infrastructure, income
distribution, economic stability and economic performance. In 2015, the EU Council

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adopted a new set of integrated guidelines in the Europe 2020 strategy. The strategy
offers directions for the economic policies of the Member States to ensure
sustainable economic growth by 2020. A careful analysis of the development
process in European countries highlights that the economic dimension no longer
needs to be marginalized in mains tream approaches. Promoting growth and
development, political stability, social or environmental sustainable strategies are of
particular importance. Therefore, the macrostructural policy framework should be a
more consistent contribution from European pol icies to the objectives of the Europe
2020 strategy . Sound public policies are a key for growth; national strategies should
prioritize growth -enhancing expenditures within social areas such as education and
health, infrastructure, income distribution, econ omic stability or innovation. It is
also essential not to ignore the importance of a performant and sustainable public
sector, especially on the public administration level, due to its effect on the
competitiveness of Member States. The EU's growth strateg y emphasizes the
importance of effective and transparent public administration, whose modernization
is a key issue in restoring competitiveness in a number of Member States. Both
structure and scope of the public sector, though public institutions and publ ic
policies are specific for each country, and their architecture are occurring in
competency management at the national level of government. Thus, it is important
to determine the opportunities and challenges for a government as a promoter for
development .
This paper contributes to the literature by a quantitative analysis of the
European Union Member Countries’ performance. In this sense, a ranking of the EU
states is made to homogeneous units (clusters) according to performance results and
their status as old and new EU countries. The achievements in different public
sectors areas for EU old and new countries will be a reference to identify the best
competitive potential and perspective to further sustainable development.
In this paper, the overall ass umption behind the assessment of public sector
performance is that , the observed and expected outcome indicators , explain the
results of public spending policies’ impact. Performance evaluation should be
integral parts of a national agenda for reform.
Hereinafter this paper is organized as follows: Section 2 considers a brief
review of the related literature in terms of public sector performance. We also
provide the methodology that aims to explain the performance of public institutions
and public policies in the light of their key characteristics and the described dataset.
Finally, section 3 reports the robustness of our empirical results and section 4
concludes with recommendations.
2.Literature review
The enquiry of public sector performance is useful for the national policy
agenda. It offers recommendations for improving the performance of various public

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fields by adopting the best practices of other countries or by responding to different
socioeconomi c, political or environmental challenges.
Many authors and int ernational organizations compute both public sector
performance (PSP) and public sector efficiency indicators (PSE) for various public
sectors as a whole and for its core functions. The assess ment of public sector
efficiency and public sector performance requires different input and output data.
Most studies use input and output data to measure efficiency by reference to the
production possibility frontier. The suitable research methodology see ms to be
related to parametric and non -parametric frameworks.
Nevertheless, the consensus regarding the measurement of the public sector
performance and efficiency is still very limited. This paper provides a proxy for
measuring public sector performance and efficiency by using composite indices
built on account of different subsectors of the public sector. Divergent views have
been considered to define the relevant subsectors of the public sector. Afonso et al.
(2003, 2006, 2013) define seven significant s ub-indicators, namely, “process” or
“opportunity” indicators, such as administration, education, health and public
infrastructure outcomes, and the “Musgravian” tasks for the government considering
income distribution, economic stability and economic perfo rmance.
Using the methodology developed by Afonso et al. (2006 ) to measure the
efficiency of the public sector, Angelopoulos et al. (2008) performed the
construction of composite indices referring only to four dimensions specific to the
public sector, name ly, administration, stability, infrastructure and education. In
addition, the mentioned authors estimate technical efficiency by applying stochastic
production frontier analysis, incorporating the two measures, both Public Sector
Efficiency and Technical E fficiency into an econometric model.
Therefore, we note that studies in this field highlight two types of performance
indicators of the public sector, specifically (i) process or opportunity indicators; and
(ii) traditional or Musgravian indicators. Thus, based on the s tudy by Afonso et al.
(2006) and Rouag and Stejskal (2014) , we can distinguish between these indicators
using cluster analysis methodology to develop two composite sub -indicators that
measure performance.
It is important to mention that ide ntifying several indicators that measure the
efficiency and performance of the public sector has been a concern not only for the
academic environment but also for many international bodies. Thus, a robust
framework for comparison between states regarding t he public sector is relevant in
terms of institutional, decision -making perspective and the quality of public
policies. In this regard, we consider the perspective offered by Kaufmann et al.
(1999a), which considers the three dimensions of bureaucratic qu ality, the rule of
law and graft necessary for the assessment of governance, to be significant.
Currently, the set of governance measuring indicators named The Worldwide
Governance Indicators, devel oped by Kaufmann et al. (2010 ) is available for
approximat ely 200 countries and highlights the following six governance

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dimensions:” i) voice and accountability, ii) political stability and absence of
violence or terrorism, iii) government effectiveness, iv) regulatory quality, v) rule of
law and vi) control of corruption”. The OECD (2007) supports the development of a
robust comparison framework by classifying data on the following four levels:
outcomes (sub -central public revenue), inputs (general mix of inputs and work),
processes (budgetary procedures and prac tices, human resource management,
integrated E -government systems, governance centers, quality management) and
outputs (central government). Transparency International, through the studies of
Lambsdorff (2005) , who created the Corruption Perceptions Index (CPI), considers
the public sector to be highlighted not only by corruption, but also by the quality of
governance.
The World Bank proposes to measure the quality of public policies in line with
economic growth and poverty reduction through the Country Po licy and
Institutional Assessment index (CPIA). This index is obtained by aggregating, in
equal weight, twenty criteria clustered in four basic areas, namely, i) economic
management, ii) structural policies, iii) equity and iv) public sector management and
institutions.
The World Economic Forum uses the Growth Competitiveness Index (GCI),
which consists of three basic dimensions both in the process of economic growth as
well as for the measurement of a country's general performance, i.e., i) the quality of
the macroeconomic environment, ii) the state of a country's public institutions and,
given the increasing importance of technology in the development process, iii) a
country's technological readiness. Additionally, to highlight a country's overall
perform ance from a sustainable economic growth perspective, the Institute for
Management Development (IMD) has developed the World Competitiveness
Yearbook (WCY) indicator. This is achieved by aggregating twenty sub -indicators
through four important areas, namely , i) economic performance, ii) government
efficiency, ii) business efficiency and iv) infrastructure.
Summarizing the wide range of possibilities for sizing different dimensions of
public sector performance and efficiency through aggregated indicators, we
appreciate the importance of distinguishing between indices a nd parameters.
Tampieri (2005) shows that the parameters are an integral part of the index
construction, in accordance with the objectives, resources and indices associated
with the indices. Ind ices of performance measurement are obtained by considering
some weighted averaged parameters, allocating to each one a weight or relevance in
the index construction.

3.Methodology and data

There are some very rigorous parametric and non -parametric research methods
on data quality in support of methodological approaches related to construction and
use of public sector performance indicators in cross -country and over -time

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comparison studies. T he parametric are generally applied to data intervals, with a
Gaussian normal distribution, and the non -parametric ones apply to nominal, ordinal
or interval data. Therefore, we consider non -parametric methods more appropriate
for social sciences. Although non-parametric tests are less sensitive than parametric
tests, we still retain our attention to a parametric method, such as the stochastic
frontier analysis originally developed by Aigner et al. (1977) , which estimate the
so-called Technical Efficiency o r Inefficiency of the public sector.
Nonparametric methods assume the existence of a convex production frontier,
particularly through the developed by Farrell (1957) and the Free Disposal Hull
proposed by Deprins et al. (1984) in previous studies. These methods have become
the predominant approach to assess the relative efficiency of public spending across
countries and within sectors. Exceeding the mode l proposed by Farell (1957) ,
Charnes, Cooper & Rhodes (1 978) develop the Data Envelopment Analysis (DEA)
methodology based on a linear programming mathematical technique. DEA is
capable of determining the efficiency of a decision -making unit (DMU). DEA
identifies efficient and inefficient units on the efficiency pr oduction frontier. Most
importantly, DEA identifies ways to improve inefficient units by considering
efficient units to be a good practice pattern. At least one organizational unit will be
located on the efficiency frontier, and the others will be envelope d by it. The main
DEA models are i) input oriented (CCR model with constant returns -to-scale, BCC
model with variable returns -to-scale and NIRS model non -increasing
returns -to-scale); ii) output oriented (CCR model with constant returns -to-scale,
BCC model with variable returns -to-scale and NIRS model non -increasing
returns -to-scale); iii) lacking orientation (non -oriented model with constant
returns -to-scale, multiplicative model with variable returns -to-scale and additive
model with variable returns -to-scale).
Unlike DEA, Free Disposal Hull (FDH) does not require the convexity
hypothesis and is recommended as a powerful tool for analyzing the efficiency of the
public sector. From a technical and empirical point of view, the FDH involves a
small number of assumptions about the production technology of a unit when it
determines the technical efficiency.
Principal component analysis (PCA), also called factor analysis, is a
non-parametric analysis widely used as an effective method for the construction of
comp osite indicators. Mathematically eloquence of PCA is the orthogonal -linear
transposing data in a system of coordinates, so that the greatest variance of
projection data becomes the first coordinated and the second largest variance
becomes the second main c omponent, etc. Cross -country analysis or sector level
analyses are important in highlighting best practices for public policies
implementation. Non -parametric analysis has the advantage that estimates the
relationship between inputs and outputs with minima l assumptions.
We follow the methodology of Afonso et al. , (2006) to measure public sector
performance. We assume a typology of welfare states, considering the degree to

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which a country guarantees certain basic rights for citizens and stable conditions for
growth and development.
The general approach to design a composite index is to select and prepare the
variables to be included, to weight and aggregate these variables and finally, to
review the aggregation pro cess robustness (OECD, 2007) . The factoria l analysis,
mainly the principal component analysis proves its valences in the process of
constructing aggregated indices because it is a mathematical technique developed to
connect a set of observed variables to a smaller number of latent dimensions. It a lso
allows the application of more variables for a concept operationalization. Principal
component analysis is a widely used and effective method of constructing composite
indicators. Mathematically speaking, PCA is defined as an orthogonal -linear
transfor mation transposing data in a system of coordinates so that the greatest
variance of projection data becomes the first coordinated and the second largest
variance become s the second main component . Considering a matrix of data, X, with
n rows and p columns, PCA transforms a p -dimensional set of weight vectors
𝐰(𝐤)= (𝐰𝟏,…..𝐰𝐩)(𝐤) into a new set of vectors of main components 𝐭𝐢=
(𝐭𝟏,….𝐭𝐦)(𝐢) with 𝒕𝒌(𝒊)=𝒙(𝒊)∙𝒘(𝒌) so that individual variables from t of the data
set comprise the maximum variation fr om x with each w vector, unit vector.
First vector w(1) satisfies the relation:
w(1)= {∑(t1)(i)2
i }||w||=1arg max= {∑(x(i)∙w)2
i }
||w||=1arg max
(1)
Equivalent as matrix:
w(1)= {||Xw||2}= {wTXTXw}||w||=1arg max
||w||=1arg max (2)
And w(1) is defined as unit vector; it results the relation:
w(1)=argmax {wTXTXw
wTw} (3)
The k component is determined by the removal of the first k -1 main
components of X:
Xk̂=X− ∑ Xw(s)w(s)T k−1
s=1 (4)
Then, it identifies the vector that removes the maximum variation from the new
data matrix:
w(k)= {||Xk̂w||2}||w||=1arg max=argmax {wTX̂Xk̂wkT
wTw} (5)
XTXis proportional with the covariance matrix of the X data set, and the
covariance Q between the two main components is:

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Q(PC(j),PC(k))∝(Xw(j))T∙(Xw(k)) (6)
= w(j)TXTXw(k)
= w(j)Tλ(k)w(k)
= λ(k)w(j)Twk
Eigenvectors wi and wk that co rrespond to the eigenvalues symmetric matrix
are orthogonal.
The covariance matrix of the original variables can be thus written:
Q∝ XTX=WΛWT (7)
The covariance matrix between the two main components becomes:
WT QW ∝ WTΛWTW= Λ (8)
Where Λ is the eigenvalues orthogonal matrix λ(k)of
𝐗𝐓𝐗 și 𝛌(𝐤)= ∑𝐭𝐤(𝐢)𝟐=∑(𝐱(𝐢)∙𝐰(𝐤))𝟐 (9)
As we already state, the model used by Afonso et al. (2006) is a reference for
our empirical approach . By using the technique of composite indicators, the authors
have considered the public sector efficiency as the performance of public sector in
relationship to the relevant category of public expenditures; therefore, it is also
possible to highlight the opportunity costs generated by achieving performance.
However, this topic it was not considered for this paper.
We summed up our analysis to the performance composite index construction
according to the mentioned reference. Multivariate statistical methods can be used to
weight and aggregate variables in a composite index. An advantage of these methods
is that they require no a priori assumptions about the weights of the different
dimensions. From the multivariate statistical technique , principal component
analysis (PCA) is useful for reducing and interpreting large multivariate data sets
with underlying linear struc tures and for discovering previously unsuspected
relationships and it was consider ed in this paper (Tabachnick and Finell 2012) .
Data represent a strongly balan ced panel for 28 European Union states, divided
into “old” and “new” Member States and cover a time span between 1995 and 2014.
The question addressed was to determine the EU membership status as “old” and
“new”, following the two enlargement waves in 2004 and 2007. Because we were
concerned with identifying structural changes in public sector perf ormance and not
so much annual fluctuations, we employ observations for a period of at least 10
years. We find 2004 to be the year with the largest wave of enlargement in the
history of the EU. Ten new countries, with a population of more than 100 million,

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joined the European Union, including the following: Cyprus, Estonia, Latvia,
Lithuania, Malta, Poland, the Czech Republic, Slovakia, Slovenia and Hungary.
In this paper, the EU membership status notes the following as “old” Member
States: Austria, Belgium, Germany, Denmark, Spain, Finland, France, Greece,
Ireland, Italy, Luxembourg, Netherlands, Portugal, Sweden and United Kingdom;
this paper notes the following as “new” Member States: Bulgaria, Cyprus, Czech
Republic, Estonia, Hungary, Lithuania, L atvia, Malta, Poland, Romania, Slovenia,
Slovakia and Croatia.
Seven relevant sub -sector indicators for constructing public sector performance
index were considered as follows: 1) Administration , measured through good
governance indicators , namely, politi cal stability and absence of violence, control of
corruption, government effectiveness, regulatory quality and rule of law; 2)
Education as early leavers from education and training, school enrolment lower
secondary and quality of technology and science; 3 ) Health highlighted by life
expectancy and infant mortality rate; 4) Infrastructure as electricity sources and
water sources; 5) Distribution through the Gini index; 6) Stability measured through
inflation and general government gross debt; and 7) Economic Performance
captured by GDP growth rate, unemployment and GDP per capita rate. The data
sources used in the computation of these indices consider the perceptions of various
respondents such as citizens, companies, country analysis, international a gencies
and non -governmental organizations. The data also consider official information
providers, of which we evaluate the Worldwide Governance Indicators for
Administration dimension of public sector, Eurostat for Education, Health, Stability
and Economi c Performance dimensions of public sector and finally , the World Bank
for the Distribution and Infrastructure domains.
4. Results
As we pointed out in the methodology section, factor analysis was applied for
the considered variables as Public S ector governance sub -indicators. T he descriptive
analysis of the considered variables is presented below, with N = 243 (Table 1).
Table 1 . Descriptive analysis of variables
Main
variables Public sector performance
sub-indicators Mean
Std. Deviation

Administration A1_Political Stability and Absence of Violence 71.76 14.948
A2_Control of Corruption 77.76 15.742
A3_Government Effectiveness 80.69 14.031
A4_Regulatory Quality 84.44 10.112
A5_Rule of Law 79.57 14.98

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Main
variables Public sector performance
sub-indicators Mean
Std. Deviation

Education E1_Early leavers from education and training 12.8148 6.81563
E2_School enrolment lower secondary 29.4049 11.5159
E3_Quality of technology and science 7.94 2.825
Health H1_Life expectancy 76.9317 3.39332
H2_Infant mortality rate 5.028 2.90398
Infrastructure I1_Electricity sources 51.1408 28.23364
I2_Water sources 98.7967 2.49925
Distribution D Gini index 31.39 3.514
Stability S1_Inflation 3.66 5.294
S2_General government gross debt 51.0391 30.7427
Economic
Performance EP1_GDP growth 2.1058 4.25355
EP2_Unemployment 8.8819 3.97917
EP3_Rate of GDP per capita 1.9317 4.47151
Source: Authors` processing
Within the table Communalities, the column Extraction shows the communality
corresponding to each variable after drawing the factors (Table 2). Thus, the higher
the communality of a variable, the more it tends to be suitable for the chosen model.

Table 2 . Communalities
Communalities
Raw Rescaled
Initial Extraction Initial Extraction
Political Stability and Absence
of Violence 223.453 120.846 1 0.541
Control of Corruption 247.818 229.509 1 0.926
Government Effectiveness 196.861 185.439 1 0.942
Regulatory Quality 102.243 86.356 1 0.845
Rule of Law 224.415 210.235 1 0.937
Early leavers from education
and training 46.453 7.429 1 0.16
School enrolment lower
secondary 132.616 43.999 1 0.332
Quality of technology and
science 7.983 1.845 1 0.231
Life expectancy 11.515 7.832 1 0.68
Infant mortality rate 8.433 5.175 1 0.614

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Communalities
Raw Rescaled
Initial Extraction Initial Extraction
Electricity sources 797.138 793.803 1 0.996
Water sources 6.246 2.339 1 0.374
Gini index 12.347 2.389 1 0.194
Inflation 28.027 6.488 1 0.231
General government gross debt 945.114 939.201 1 0.994
GDP growth 18.093 2.905 1 0.161
Unemployment 15.834 3.149 1 0.199
Rate of GDP per capita 19.994 4.113 1 0.206
Source: Authors` processing and Extraction Method: PCA
Likewise, own values were calculated, including percentages of explained
version for each extracted factor and percentages of cumulative version, explained
by all extracted factors before and after rotation (the used extraction method was
Varimax, accordin g to Figure 1 ).

Source: Authors’ processing

Figure 1 . Total Variance Explained

There were four eigenvalues greater than 1, 𝜆1=6.155, 𝜆2=2.874, 3=2.442 and
𝜆4=1.678; therefore, the model will contain 4 main components (Figure 1). Factor
no. 1 explains 34.195% of the variance of variables, factor no. 2 explains 15.967%,
Total% of
VarianceCumulative
% Total% of
VarianceCumulative
%
1 6.568 36.486 36.486 6.155 34.195 34.195
2 3.367 18.704 55.190 2.874 15.967 50.162
3 1.794 9.966 65.156 2.442 13.565 63.727
4 1.420 7.891 73.047 1.678 9.320 73.047
5 0.995 5.528 78.574
6 0.941 5.226 83.800
7 0.720 4.002 87.802
8 0.604 3.355 91.157
9 0.419 2.329 93.486
10 0.317 1.761 95.247
11 0.260 1.447 96.694
12 0.241 1.338 98.033
13 0.124 0.690 98.723
14 0.095 0.529 99.251
15 0.056 0.309 99.560
16 0.044 0.246 99.807
17 0.028 0.155 99.962
18 0.007 0.038 100.000Total Variance Explained
ComponentInitial Eigenvalues Rotation Sums of Squared Loadings
Extraction Method: Principal Component Analysis.

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factor no. 3 explains 13.565% and factor no. 4 explains 9.32%. The total variance
explained by the four factors is 73.047%, and some of these factors are
representatives for the Administration domain. Administration domain refers to all
public activities directed at policymaking, legislation and management of the public
sector, as well as civil s ervices directed towards the legal participation of citizens in
society.
Figure 2 presents the Reproduced Correlations resulting from of the model`s
adequacy analysis by the four factors. The percentage of non redundant residue
greater than 0.05 is 40% ( <50%) .

Source: Authors’ processing
Figure 2 . Reproduced Correlations
Political
Stability
and
Absence of
ViolenceControl of
CorruptionGovernment
EffectivenessRegulatory
Quality Rule of LawEarly
leavers
from
education
and trainingSchool
enrolment
lower
secondaryQuality of
technolgy
and
scienceLife
expectancyInfant
mortality
rateElectricity
sourcesWater
sourcesGini
index InflationGeneral
governmenet
gross debtGDP
growth UnemploymentRate of
GDP per
capita
Political
Stability and
Absence of
Violence0.647a0.537 0.592 0.572 0.560 -0.487 -0.411 0.131 0.182 -0.351 -0.305 0.272 -0.523 -0.190 -0.211 0.114 -0.429 0.059
Control of
Corruption0.537 0.897a0.890 0.848 0.898 -0.095 0.092 0.482 0.692 -0.720 -0.007 0.581 -0.255 -0.444 0.224 -0.063 -0.421 -0.185
Government
Effectiveness0.592 0.890 0.913a0.863 0.895 -0.228 -0.041 0.461 0.656 -0.766 -0.093 0.633 -0.311 -0.522 0.186 -0.052 -0.366 -0.165
Regulatory
Quality0.572 0.848 0.863 0.822a0.849 -0.196 -0.030 0.432 0.598 -0.693 -0.085 0.567 -0.291 -0.462 0.140 0.009 -0.387 -0.101
Rule of Law 0.560 0.898 0.895 0.849 0.908a-0.138 0.059 0.470 0.701 -0.728 -0.024 0.585 -0.308 -0.431 0.228 -0.125 -0.434 -0.244
Early leavers
from education
and training-0.487 -0.095 -0.228 -0.196 -0.138 0.764a0.746 0.127 0.150 0.183 0.480 -0.176 0.545 0.202 0.297 0.005 0.040 -0.019
School
enrolment
lower
secondary-0.411 0.092 -0.041 -0.030 0.059 0.746 0.797a0.246 0.371 -0.023 0.507 -0.006 0.485 0.082 0.445 -0.168 0.003 -0.216
Quality of
technolgy and
science0.131 0.482 0.461 0.432 0.470 0.127 0.246 0.343a0.485 -0.464 0.121 0.390 0.056 -0.337 0.296 -0.098 -0.089 -0.169
Life expectancy0.182 0.692 0.656 0.598 0.701 0.150 0.371 0.485 0.799a-0.696 0.206 0.571 -0.030 -0.418 0.539 -0.443 -0.151 -0.543
Infant mortality
rate-0.351 -0.720 -0.766 -0.693 -0.728 0.183 -0.023 -0.464 -0.696 0.845a0.031 -0.737 0.128 0.687 -0.406 0.275 0.009 0.360
Electricity
sources-0.305 -0.007 -0.093 -0.085 -0.024 0.480 0.507 0.121 0.206 0.031 0.331a-0.043 0.311 0.095 0.290 -0.158 0.030 -0.179
Water sources 0.272 0.581 0.633 0.567 0.585 -0.176 -0.006 0.390 0.571 -0.737 -0.043 0.654a-0.073 -0.642 0.352 -0.216 0.070 -0.281
Gini index -0.523 -0.255 -0.311 -0.291 -0.308 0.545 0.485 0.056 -0.030 0.128 0.311 -0.073 0.617a-0.051 0.224 0.148 0.368 0.161
Inflation -0.190 -0.444 -0.522 -0.462 -0.431 0.202 0.082 -0.337 -0.418 0.687 0.095 -0.642 -0.051 0.730a-0.284 0.042 -0.252 0.085
General
governmenet
gross debt-0.211 0.224 0.186 0.140 0.228 0.297 0.445 0.296 0.539 -0.406 0.290 0.352 0.224 -0.284 0.576a-0.511 0.195 -0.549
GDP growth 0.114 -0.063 -0.052 0.009 -0.125 0.005 -0.168 -0.098 -0.443 0.275 -0.158 -0.216 0.148 0.042 -0.511 0.913a-0.117 0.915
Unemployment -0.429 -0.421 -0.366 -0.387 -0.434 0.040 0.003 -0.089 -0.151 0.009 0.030 0.070 0.368 -0.252 0.195 -0.117 0.658a-0.050
Rate of GDP
per capita0.059 -0.185 -0.165 -0.101 -0.244 -0.019 -0.216 -0.169 -0.543 0.360 -0.179 -0.281 0.161 0.085 -0.549 0.915 -0.050 0.935a
Political
Stability and
Absence of
Violence-0.028 -0.014 -0.050 -0.031 0.007 0.129 -0.133 -0.039 -0.029 0.044 -0.049 0.055 -0.049 0.091 -0.008 0.054 0.004
Control of
Corruption-0.028 0.038 0.020 0.047 0.012 -0.004 -0.056 -0.009 0.055 -0.017 -0.060 0.021 0.012 -0.043 -0.025 0.108 -0.015
Government
Effectiveness-0.014 0.038 0.021 0.042 0.006 -0.003 -0.034 -0.043 0.030 -0.012 -0.051 0.036 0.037 -0.016 -0.023 0.088 -0.015
Regulatory
Quality-0.050 0.020 0.021 0.042 0.040 -0.073 0.034 -0.101 0.066 0.011 -0.041 0.101 0.011 -0.093 -0.074 0.065 -0.061
Rule of Law -0.031 0.047 0.042 0.042 0.025 -0.030 0.001 -0.040 0.026 -0.059 -0.074 0.064 0.056 -0.037 -0.038 0.107 -0.020
Early leavers
from education
and training0.007 0.012 0.006 0.040 0.025 0.096 -0.123 -0.064 0.001 -0.250 0.070 -0.061 -0.049 -0.123 -0.060 0.052 -0.042
School
enrolment
lower
secondary0.129 -0.004 -0.003 -0.073 -0.030 0.096 -0.204 -0.003 -0.052 -0.139 0.055 -0.108 -0.065 0.008 0.012 0.020 0.014
Quality of
technolgy and
science-0.133 -0.056 -0.034 0.034 0.001 -0.123 -0.204 0.031 0.068 -0.021 -0.126 0.067 0.143 -0.012 0.003 0.016 -0.020
Life expectancy-0.039 -0.009 -0.043 -0.101 -0.040 -0.064 -0.003 0.031 -0.032 -0.012 -0.019 -0.097 0.028 0.063 0.087 -0.018 0.062
Infant mortality
rate-0.029 0.055 0.030 0.066 0.026 0.001 -0.052 0.068 -0.032 0.017 -0.027 0.014 -0.019 0.003 -0.029 0.069 -0.017
Electricity
sources0.044 -0.017 -0.012 0.011 -0.059 -0.250 -0.139 -0.021 -0.012 0.017 -0.003 -0.010 -0.086 0.041 0.054 -0.069 0.028
Water sources -0.049 -0.060 -0.051 -0.041 -0.074 0.070 0.055 -0.126 -0.019 -0.027 -0.003 -0.105 0.008 -0.017 0.031 -0.160 0.021
Gini index 0.055 0.021 0.036 0.101 0.064 -0.061 -0.108 0.067 -0.097 0.014 -0.010 -0.105 0.048 -0.122 -0.121 -0.015 -0.099
Inflation -0.049 0.012 0.037 0.011 0.056 -0.049 -0.065 0.143 0.028 -0.019 -0.086 0.008 0.048 0.067 0.032 0.137 0.028
General
governmenet
gross debt0.091 -0.043 -0.016 -0.093 -0.037 -0.123 0.008 -0.012 0.063 0.003 0.041 -0.017 -0.122 0.067 0.129 -0.036 0.135
GDP growth -0.008 -0.025 -0.023 -0.074 -0.038 -0.060 0.012 0.003 0.087 -0.029 0.054 0.031 -0.121 0.032 0.129 -0.023 0.064
Unemployment 0.054 0.108 0.088 0.065 0.107 0.052 0.020 0.016 -0.018 0.069 -0.069 -0.160 -0.015 0.137 -0.036 -0.023 -0.012
Rate of GDP
per capita0.004 -0.015 -0.015 -0.061 -0.020 -0.042 0.014 -0.020 0.062 -0.017 0.028 0.021 -0.099 0.028 0.135 0.064 -0.012
a. Reproduced communalities
b. Residuals are computed between observed and reproduced correlations. There are 62 (40.0%) nonredundant residuals with absolute values greater than 0.05.Reproduced Correlations
Reproduced
Correlation
Residualb
Extraction Method: Principal Component Analysis.

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Grouping variables on the four factors are presented in Table 3 . Rotated Component
Matrix.

Table 3 . Rotated Component Matrix
Variables
Component
1 2 3 4
Political Stability and Absence of Violence 0.468
Control of Corruption 0.897
Government Effectiveness 0.917
Regulatory Quality 0.860
Rule of Law 0.887
Early leavers from education and training 0.867
School enrolment lower secondary 0.875
Quality of technology and science 0.542
Life expectancy 0.738
Infant mortality rate -0.858
Electricity sources 0.556
Water sources 0.731
Gini index 0.623
Inflation -0.665 0.518
General government gross debt 0.408
GDP growth 0.953
Unemployment -0.775
Rate of GDP per capita 0.950
Source: Authors` processing and Extraction Method: PCA.
Rotation Method: Varimax with Kaiser Normalization – converged in 5 iterations.

In outlining the proposed model for the construction of a composite index of
public sector performance, the factorial saturation with absolute values greater than
0.35 were held as follows:
 The first proposed factor (subindex) contains the following variables: Political
Stability and Absence of Violence, Control of Corruption, Government
Effectiveness, Regulatory Quality, Rule of Law, Quality of technology and
science, Life Expectancy, Infant Mortality Rate, Water sources, Inflation.
 The second proposed factor (subindex) contains the following variables: Early
leavers from education and training, School enrolment lower secondary,
Electricity sources, Gini index, General government gross debt.
 Within the third factor, we find the following variables: GDP growth, and Rate
of GDP per capita
 The fourth factor comprises : Inflation and Unemployment
Finally, the proposed model for public sector performance measurement is the
computed Public Sector Performance Index.

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Public Sector Performance Index
= (0.468*Political Stability and Absence of Violence + 0.897* Control of
Corruption + 0.917*Government Effectiveness + 0.860*Regulatory Quality +
0.887*Rule of Law + 0.524*Quality of technology and science + 0.738*Life
expectancy + ( – 0.858)*Infant mortality rate+0.731*Water sources +
(-0.665)*Inflation)
+ (0.867*Early leavers from education and training + 0.875*School enrolment
lower secondary + 0.556*Electricity sources + 0.623* Gini index + 0.480*General
government gross debt)
+ (0.953*GDP growth + 0.95 Rate of GDP per capita)
+ (0.518* Inflation +( -0.775)* Unemployment)
By applying the obtained indicator for the performance analysis of EU
countries and considering a two -way analysis of new and old countries in the EU, as
well as an integrated analysis of all EU countries, we obtain the following results:

Table 4 . Old and New EU countries’ indicator values
Old countries’
indicator values CI value New countries’
indicator values CI value
Austria 505.08 Bulgaria 349.52
Belgium 499.42 Croatia 390.45
Denmark 506.64 Cyprus 467.36
Finland 517.44 Czech Republic 419.58
France 478.07 Estonia 438.55
Germany 502.62 Hungary 413.35
Greece 422.07 Latvia 407.93
Ireland 514.12 Lithuania 411.90
Italy 439.71 Malta 499.68
Luxembourg 489.19 Poland 414.64
Netherlands 517.67 Romania 369.40
Portugal 506.41 Slovakia 400.09
Spain 463.04 Slovenia 432.01
Sweden 499.82
United Kingdom 501.94

Figure 3 reports the old and new EU member states classification groups based
on the indicators considered in our analysis and the obtained country scores.
Clustering the old EU states indicates the existence of 3 groups of countries
outlined on the basis of the considered sub -indicators and the composite indicator
value the countries obtained. The first cluster comprises the countries with the
highest level of performance due to the composite index scores (Denmark, Portugal,
Austria, Belgium, Sweden, Germany, United Kingdom, Finland, Netherlands and

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Ireland). We notice that the second cluster comprises the countries with an average
performance according to th e composite index score (France, Luxembourg and
Spain). Finally, the third cluster comprises the countries with the lowest
performance index score (Greece and Italy).

Figure 3 . Dendogram of Old and New EU countries

Even in the case of new states within the EU, we identify the existence of 3
classification groups. The first cluster is composed of the countries with an average
performance index score (Hungary, Poland, Lithuania, Latvia, Czech Republic,
Croatia, Slovakia, Estonia and Slove nia). The second cluster comprises the countries
with the lowest performance composite index score (Bulgaria and Romania). The
third cluster comprises the countries with the highest performance according to
composite index scores (Cyprus and Malta).
Table 5. Ranks
Old/new country N Mean Rank Sum of Ranks
CI value 0.00 15 20.27 304.00
1.00 13 7.85 102.00
Total 28
Table 6 . Mann -Whitney U
Test Statisticsa
VAR00003
Mann -Whitney U 11.000
Wilcoxon W 102.000
Z -3.985
Asymp. Sig. (2 -tailed) .000

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Exact Sig. [2*(1 -tailed Sig.)] .000b
a. Grouping Variable: VAR00005
b. Not corrected for ties.
Taking an integrated approach to the results of all EU countries, we apply the
Mann -Whitney U test (Table 5, Table 6) and deduce that there are significant
differences (U = 11.00, p = 0.000) between EU countries in terms of the aggregate
index, with the scores of the old countries being higher than the scores of new
countries. Analyzing the clustering of all EU member states, we identify three
clusters, of wh ich there is an almost total difference between old and new EU
countries, except for four states, namely, Greece, Italy, Malta and Cyprus (Figure 4).

Figure 4 . Dendogram of all EU countries
The first cluster is composed of the most performant states, as a result of the
composite index score and consists of all the old EU countries, except Italy and
Greece and additionally contains two new EU countries, Malta and Cyprus. The
second cluster contain s two new EU member countries with the lowest performance
results, Bulgaria and Romania. The last cluster comprises the new EU states and
additionally contains two old states in the EU, Italy and Greece; the results of the
states in this cluster are averag es.
Our results are consistent with others s everal stu dies, which have shown that
the public performance of countries is powerful in relationship to the presence of
independent institutions, independent judiciary, consumer protection agencies, an

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indepen dent central bank, and independent regulation. A close analysis of the World
Bank Indicators reveals that the Western and Northern European countries have the
best administration indicators scores. These countries also represent the old EU
countries, the c ountries with the best composite index scores in our study.
Results on countries performance using the Public Sector Performance Index
could be a consequence of the national policy adopted by each country. The
European Commission identifies three essentia l components needed to achieve a
performant national policy to gain development. The components are as follows: (i)
Policy design; (ii) Forward planning and (iii) Consultation and co -responsibility.
Policy design refers to a solid evidence base and a good interpretation. Policymakers
should seek a wide net when choosing sources by including national and
international official statistics, academia, studies, etc. Forward planning refers to the
fact that governments should engage in longer -term strategic plann ing for up to ten
to twenty years. Consultation and co -responsibility refers to the role that citizens and
other interested parties play in national policy agenda. Public consultation plays a
major role in this context.
New EU countries have to learn fr om the executive probity and the performant
public policies of the older countries in the EU. The functional structure of the
European Union encourages constructive coexistence between countries as well as
the process of learning from the experiences of ot hers.
5. Conclusion
In this study, we empirically assess the EU countries public sector aggregated
performance. I t can be notice tha t the analysis of public sector performance is a
challenging and complex action. It was necessary to consider several basic
dimensions within a state , when undertaking a comprehensive and exhaustive
analysis in this respect. Developing a robust comparability framework between
states is a necessity and already was initiated and sustained both by the academic
community and certain international institutio ns. The need to build this framework
also lies in the need for states to shape a performance -benchmarking barometer for
comparison with other countries in the world. In this paper, we identified several
international institutions that perform comparisons b etween countries using
composite indicators, obtained by aggregating different sectors of public
environment that are considered relevant for a dynamic and judicious analysis of
sustainable public sector performance. The starting point of our research was based
on the approach used by Afonso et al. (2003, 2006, 2013) [2 -4], which compiles
performance and efficiency composite indicators by aggregating seven
sub-indicators, as the most exciting and edifying in the performance analysis of the
public sector of all its economic, social and political dimensions. The analysis of
public sector performance outweighs the efficiency analysis framework achieved
through non -parametric methods of reporting the efficiency production frontier and
involves the development of a more complex analysis. We identified principal

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component analysis as an effective tool to construct composite indicators, using the
orthogonal linear transformation that transposes data into a coordinate system.
The results indicate that the composite indicator achieved by the aggregation of
Administration, Education, Health, Infrastructure, Distribution, Stability and
Economic Performance consists of four main components, of which the
Administration plays the most powerful and responsible role. Using P CA, we
determined a composite index able to determine the p erformance of the public
sector . Our results also reveal a strong distinction of indicator values for the
European Union’s “old” and “new“ countries. This fact was also confirmed by the
application of the Mann -Whitney U test. Hence, the results indicate that EU old
countries have the best competitive potential for prospective and perspective for
further development.
Empirical performed analysis illustrated a clear distinction of indicator
value s for old and new EU countries. Only four states made a discordant note of this
phenomenon, two old EU countries being considered with average performance
results and two new EU states being considered with high -performance results, by
using the composite ind ex. Considering the most representative domains within a
state, with importance and high level of impact on public policy outcomes and
spending efficiency, it can be noted that public administration modernization and
transformation is the primary factor re sponsible for development. Its dimensions
should be considered , in a comprehensive manner, namely, Political Stability and
Absence of Violence, Control of Corruption, Government Effectiveness, Regulatory
Quality and Rule of Law , as a conclusive and robust tool for the decision -making
process.
This paper supports and encourages the development of tools and frameworks
to compare the performance of the public sector toward universal measurement
methods that are applicable to all coun tries. Our research contributes to these
approaches through the composite index and the results obtained by using it. In
long-term planning for sustainable development, but not economic performance, a
proper public administration system is the most importa nt area to be looked upon , as
a stand-alone development objective.
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