Revista Română de Statistică Supliment

Transcription

Institutul Naţional de Statistică
National Institute of Statistics
INSTITUTUL NAŢIONAL DE STATISTICĂ
Revista Română de Statistică
B-dul Libertăţii, nr. 16, sector 5,
Bucureşti
Telefon/fax: 0213171110
e-mail: [email protected]
www.revistadestatistică.ro/supliment
ISSN 2359 – 8972
Revista Română
de Statistică
Supliment
Romanian Statistical Review
Supplement
6/2016
www.revistadestatistică.ro/supliment
COLEGIUL ŞTIINŢIFIC
Revista Română de Statistică,
indexată în bazele de date internaţionale
EMILIAN DOBRESCU - academician, Academia Română
AUREL IANCU - academician, Academia Română
MARIUS IOSIFESCU - academician, Academia Română
LUCIAN ALBU - academician, Academia Română
Index Copernicus International
GHEORGHE ZAMAN – Prof. univ. dr., membru corespondent al Academiei Române
TUDOREL ANDREI - Prof. univ. dr., Academia de Studii Economice
DAN GHERGUŢ - Lect. univ. dr. , Universitatea Titu Maiorescu, Bucureşti
KONRAD PASENDORFER – PhD, Director General al Statistics Austria
MARIANA MIHAILOVA KOTZEVA - EUROSTAT
CONSTANTIN MITRUŢ – Prof. univ. dr., Preşedinte al Societăţii Române de Statistică
Directory of Open Access
Journals
CONSTANTIN ANGHELACHE – Prof. univ. dr., Vicepreşedinte al Societăţii Române de Statistică
NICOLAE ISTUDOR – Prof. univ. dr., Rector al Academiei de Studii Economice, Bucureşti
VERGIL VOINEAGU – Prof. univ. dr., Academia de Studii Economice, Bucureşti
TIBERIU POSTELNICU – Prof. univ. dr., Institutul “Gheorghe Mihoc-Caius Iacob”
BOGDAN OANCEA – Prof. univ. dr., Universitatea Bucureşti
EBSCO Information Services
GHEORGHE SĂVOIU - Conf. univ. dr., Universitatea Piteşti
IRINA-VIRGINIA DRAGULANESCU - Prof. univ. dr., University Messina, Italia
DANIELA ELENA ŞTEFĂNESCU - Conf. univ. dr., Institutul Naţional de Statistică
ELISABETA JABA – Prof. univ. dr., Universitatea “Alexandru Ioan Cuza” University
Research Papers in Economics
EUGENIA HARJA - Prof. univ. dr., Universitatea Vasile Alecsandri, Bacău
ŞTEFAN-ALEXANDRU IONESCU - Lect. univ. dr. Universitatea Româno-Americană
CLAUDIU HERŢELIU - Prof. univ. dr., Academia de Studii Economice
ION GHIZDEANU - Dr., cercetător ştiinţific gradul I, Comisia Naţională de Prognoză
ILIE DUMITRESCU - Institutul Naţional de Statistică
SILVIA PISICĂ - Dr., Institutul Naţional de Statistică
ADRIANA CIUCHEA - Institutul Naţional de Statistică
Coordonatori
Gheorghe VAIDA-MUNTEAN
Vitty-Cristian CHIRAN
Pre-press
Laurenţiu MUNTEANU
Tiraj: 15 exemplare
REVISTA ROMÂNĂ DE STATISTICĂ SUPLIMENT
SUMAR / CONTENTS 6/2016
ANALIZA POLICENTRICITĂŢII FUNCŢIONALE A JUDEŢELOR DIN ROMÂNIA3
POLYCENTRICITY FUNCTIONAL ANALYSIS OF THE ROMANIAN COUNTIES 20
Cercetător principal III Antonio TACHE
Cercetător principal Monica TACHE
Institutul Naţional de Cercetare-Dezvoltare în Construcţii, Urbanism şi Dezvoltare Teritorială
Durabilă „URBAN-INCERC”
Conf. univ. dr. Sorin Daniel MANOLE
Universitatea “Constantin Brâncoveanu” Piteşti
COMPARATIVE STUDY OF EUROPEAN AND NATIONAL PROGRAMMES
REGARDING INNOVATIVE CAPACITY OF SMALL AND MEDIUM ENTERPRISES 37
Prof. Constantin ANGHELACHE, PhD
Bucharest University of Economic Studies, „ARTIFEX” University of Bucharest
Prof. Vergil VOINEAGU, PhD
Bucharest University of Economic Studies
Prof. Alexandru MANOLE PhD.
„ARTIFEX” University of Bucharest
Diana Valentina SOARE PhD
STUDY ON THE RELATIONSHIP BETWEEN FINANCIAL PERFORMANCE AND
LEVERAGE: EMPIRICAL EVIDENCE ON BUCHAREST STOCK EXCHANGE 45
Lector univ. drd. Floriniţa DUCA
Universitatea ARTIFEX, Bucureşti
THE EUROPEAN INITIATIVE FOR SMALL AND MEDIUM ENTERPRISES
Assoc. prof. Mădălina Gabriela ANGHEL
Daniel DUMITRESCU PhD student
Alexandru URSACHE PhD student
IT&C PLATFORM USED IN PROJECTS FINANCED FROM EUROPEAN
UNION FUNDS
Daniel DUMITRESCU PhD Student
49
59
www.revistadestatistica.ro/supliment
Revista Română de Statistică - Supliment nr. 6 / 2016
MODEL FOR ANALYZING THE LIQUIDITY RISK
Assoc. Prof. Mădălina-Gabriela ANGHEL PhD
68
KEY MEASURES IN ENSURING SUSTAINABLE DEVELOPMENT IN EUROPEAN
HIGHER EDUCATION: RECOMMENDATIONS FOR ROMANIA
71
PhD Candidate, Andreea Mirică
Bucharest University of Economics Studies
2
Romanian Statistical Review - Supplement nr. 6 / 2016
Analiza policentricităţii funcţionale a judeţelor
din România
Cercetător principal III Antonio TACHE
Cercetător principal Monica TACHE
Institutul Naţional de Cercetare-Dezvoltare în Construcţii, Urbanism
şi Dezvoltare Teritorială Durabilă „URBAN-INCERC”
Conf. univ. dr. Sorin Daniel MANOLE
Universitatea “Constantin Brâncoveanu” Piteşti
Rezumat
Dezvoltarea policentrică la nivel naţional implică dezvoltarea
echilibrată a reţelei de localităţi şi realizarea unei relaţii armonioase între
localitate şi teritoriu pe baza principiilor privind dezvoltarea durabilă,
echilibrarea internă, deschiderea spre exterior, valorificarea potenţialului
existent, complementaritatea funcţională şi sporirea autonomiei locale.
Din acest motiv, evaluarea policentricităţii la nivel de judeţ prezintă o
importanţă deosebită. Metodologia de estimare a gradului de policentricitate
la nivel de NUTS 3 constă în identificarea unor domenii semnificative pentru
caracterizarea policentricităţii şi a unor indicatori relevanţi în cadrul acestor
domenii şi apoi, după o transformare a valorilor indicatorilor în punctaje, în
calcularea unor indici compoziţi corespunzători domeniilor şi policentricităţii.
Din analiza valorilor acestor indici decurg concluzii interesante necesare
pentru formularea unor politici de dezvoltare locală, regională şi naţională.
Cuvinte cheie: policentricitate; indice; judeţ; domeniu; indicator;
România.
JEL: R11, R12, R15, R23, R42, R58
Introducere
Promovarea sistemului policentric urban echilibrat reprezintă unul
dintre cele mai frecvent citate obiective politice ale politicilor teritoriale ale
Uniunii Europene (ESDP, 1999). Cu toate acestea, datorită naturii multidimensionale şi multi-scalare a policentricităţii, există o ambiguitate în modul
în care este definită această noţiune (Veneri şi Burgalassi, 2012; Kloosterman
şi Musterd, 2001; Davoudi, 2003). În plus, nu există nici o metodă de măsurare
a policentrismului la diferite scări spaţiale unanim acceptată şi nici o metodă
de evaluare a impactului policentrismului asupra obiectivelor politicii:
eficienţă (competitivitate), echitate (coeziune) şi durabilitate. Prin urmare, este
imposibil să se stabilească un grad optim al policentrismului între centralizare
3
şi descentralizare sau, altfel spus, între extremele monocentricitate (toate
activităţile sunt concentrate într-un centru) şi dispersare (toate activităţile sunt
egal distribuite în spaţiu). Wegener (2013) argumentează că ambele extreme,
monocentricitatea şi dispersarea, au performanţe slabe cu privire la obiective
de politică: eficienţă, echitate şi durabilitate. Sistemul urban policentric poate
fi definit ca o entitate socio-spaţială integrată funcţional, care este formată din
mai multe noduri urbane, ce pot fi diferite ca mărime, dar care joacă toate
un rol important în sistem şi sunt legate prin relaţii intensive reciproce şi
multidirecţionale. Dezvoltarea sistemului urban policentric este influenţată de
strategiile de guvernare care recunosc, iau în considerare şi susţin consolidarea
intereselor, complementarităţilor, sinergiilor şi posibilităţilor de colaborare
reciproce. Programul ESPON 1.1.1 detaliază aspecte legate de conceptul de
policentricitate şi prezintă metodele operaţionale de măsurare a policentrismului
sistemului urban din Europa. De asemenea, este analizat sistemul policentric
urban european (format din statele membre ale Uniunii Europene, la care se
adaugă Norvegia şi Elveţia), pe baza modelului actual de policentrism, la
trei niveluri spaţiale: nivel regional şi local, nivel naţional şi nivel european,
incluzând şi sistemele transnaţionale urbane. Ca unităţi de analiză, în fiecare
ţară au fost fixate zonele urbane funcţionale. La nivel european, zonele urbane
funcţionale nu au o definiţie comună. În principiu, zonele urbane funcţionale
constau într-un municipiu nucleu la care se adaugă zonele adiacente de navetă.
Lipsiţi de o definiţie cuprinzătoare, pentru a stabili zonele urbane funcţionale
trebuie să identificăm nucleul lor (situarea centrului) şi segmentul din populaţia
totală care locuieşte în zonele învecinate din care este construită zona urbană
funcţională. În această lucrare, se studiază policentricitatea la nivelul NUTS 3
(judeţe), iar metodologia folosită are la bază metodologia aplicată în ESPON
1.1.1 pentru analiza policentricităţii zonelor urbane funcţionale.
Conform ESPON 1.1.1, două aspecte structurale sunt de importanţă
deosebită pentru policentricitate:
- morfologic, referitor la distribuţia zonelor urbane într-un anumit
teritoriu;
- relaţional, cu privire la pe reţelele de fluxuri şi cooperarea între
zonele urbane la diferite scări.
Policentricitatea este considerată la ora actuală un instrument de
planificare spaţială util pentru a spori competitivitatea oraşelor, coeziunea
socială şi durabilitatea mediului (Davoudi, 2003). Există două abordări
esenţiale în conceptualizarea zonelor policentrice. Prima abordare este pur
morfologică, iar potrivit acesteia, zonele policentrice pot fi privite ca un model
de organizare spaţială care este o cale de mijloc între oraşele tradiţionale
compacte şi expansiunea urbană, menţinând avantajele legate de oraşe
4
compacte, cu respectarea tendinţelor spontane ale dispersiei (Camagni et al.,
2002). Cealaltă abordare este atât funcţională, cât şi morfologică, iar conform
acesteia, zonele policentrice reprezintă alternativa pentru zonele monocentrice
(Meijers şi Sandberg, 2008), constând într-o integrare progresivă a centrelor
urbane într-o singură zonă metropolitană.
Metodologie de evaluare a sistemului policentric la nivel de judeţe
(NUTS 3) din România
Indicatorii prezenţi în baza de date spaţiale la nivel de judeţ au fost
aleşi în conformitate cu indicatorii funcţiunilor zonelor urbane din studiul
ESPON 1.1.1 şi caracteristicile naţionale specifice teritoriului românesc.
Pentru caracterizarea policentricităţii au fost considerate mai multe domenii
(care corespund funcţiunilor zonelor urbane din studiul ESPON 1.1.1) şi
s-au calculat indicii corespunzători acestora, precum şi un indice general
de policentricitate, folosind o metodologie originală. Astfel, am avut în
vedere domeniile şi indicatorii următori, pentru care s-au folosit codificările
menţionate:
Domeniul Populaţie – A:
- Indicele de dinamică al populaţiei I 2011 2001 – A1;
- Populaţia în anul 2011 – A2;
- Produsul intern brut în anul 2010, în milioane lei – A3;
Domeniul Economic – B:
- Localizarea primelor 100 de companii din topul realizat după cifra
de afaceri – B1;
- Produsul intern brut pe cap de locuitor în preţuri curente în anul
2010, în euro – B2;
- Indicele de dinamică al Produsului Intern Brut I 2010 2008 – B3;
Domeniul Turism – C:
- Numărul de unităţi turistice din anul 2011 – C1;
- Numărul de înnoptări în unităţi turistice din anul 2011 – C2;
- Indicele de dinamică al numărului de înnoptări în unităţi turistice
I 2011 2008 – C3;
- Numărul de turişti din anul 2011 – C4;
Domeniul Transporturi – D:
- Numărul de pasageri tranzitaţi prin aeroporturi în anul 2012 – D1;
- Cantitatea de mărfuri tranzitate prin porturi în anul 2012 – D2;
- Densitatea căilor ferate în anul 2012 – D3;
- Densitatea drumurilor naţionale în anul 2012 – D4;
- Densitatea drumurilor publice în anul 2012 – D5;
Domeniul Educaţie – E:
5
- Numărul de universităţi în anul 2011 – E1;
- Numărul studenţilor în anul 2011 – E2;
- Indicele de dinamică al numărului de studenţi I 2011 2008 – E3.
Pentru fiecare indicator s-a realizat o grupare a valorilor înregistrate
la nivelul judeţelor pe 10 intervale egale, obţinându-se în acest mod 10 grupe,
cărora, în ordinea crescătoare a valorilor, li s-au atribuit punctaje de la 1 la 10.
Atunci când un indicator a înregistrat valoarea 0 la un judeţ, punctajul atribuit
acelui judeţ la acest indicator a fost tot 0. Prin urmare, toate valorile indicatorilor
selectaţi au fost transformate în punctaje ale grupelor din care fac parte (1,2,…
,10, eventual 0), iar acest lucru a fost realizat cu ajutorul suportului statistic
al programului ArcGIS 10.2. În cadrul fiecărui domeniu, mai mulţi specialişti
în dezvoltare locală au stabilit coeficienţi de importanţă (ponderi) pentru toţi
indicatorii. Pentru fiecare domeniu, s-a calculat indicele corespunzător unui
judeţ ca medie a punctajelor acordate indicatorilor ponderată cu coeficienţii
de importanţă. În mod analog, s-au acordat coeficienţi de importanţă (ponderi)
fiecărui domeniu de interes şi s-a calculat indicele de policentricitate la nivel
NUTS 3 ca medie a indicilor corespunzători acestor domenii ponderată cu
coeficienţii de importanţă.
Astfel, s-au folosit următoarele formule:
- Indicele domeniului Populaţie: A 0,15 A1 0,5 A2 0,35 A3;
- Indicele domeniului Economic: B 0,2 B1 0,7 B 2 0,1 B3 ;;
- Indicele domeniului Turism: C 0,2 C1 0,35 C 2 0,1 C 3 0,35 C 4 ;
- Indicele domeniului Transporturi: D 0,3 D1 0,3 D 2 0,15 D3 0,15 D 4 0,1 D5
- Indicele domeniului Educaţie: E 0,35 E1 0,55 E 2 0,1 E 3;
- Indicele de policentricitate: IP 0,2 A 0,35 B 0,1 C 0,2 D 0,15 E
De asemenea, pentru a analiza cât de mult diferă valorile indicilor de
la un judeţ la altul, s-a calculat coeficientul Gini al inegalităţii. Astfel, dacă
x , x ,, xn cu
dispunem de valorile observate aşezate în ordine crescătoare 1 2
media x , coeficientul Gini al inegalităţii ( G ) se calculează cu formula
următoare (Buchan, 2002):
G
2 n
 i( xi  x )
n 2 x i 1
Coeficientul Gini ia valori între zero, pentru egalitate perfectă
xn ) úi n 1 n , pentru inegalitate perfectă ( x1  x2    xn 1  0,
),
tinzând
la unu pentru n mare (Halffman şi Leydesdorff, 2010).
xn  0
( x1
6
x2
Rezultate şi analize
Punctaje şi indici
Prin transformarea valorilor indicatorilor în punctaje cu ajutorul
suportului statistic al programului ArcGIS 10.2 am obţinut informaţiile din
Tabelul 1.
Punctajele corespunzătoare indicatorilor relevanţi acordate judeţelor
din România
Tabelul 1
Numele
judeţului
Vaslui
Vâlcea
Teleorman
Timiş
Tulcea
Suceava
Satu Mare
Sălaj
Sibiu
Prahova
Olt
Neamţ
Mureş
Maramureş
Mehedinţi
Iaşi
Ialomiţa
Ilfov
Harghita
Hunedoara
Giurgiu
Galaţi
Gorj
Dolj
Dâmboviţa
Covasna
Constanţa
Caraş-Severin
Călăraşi
Cluj
Buzău
Braşov
Botoşani
Brăila
BistriţaNăsăud
Bihor
Bacău
Arad
Argeş
Alba
Vrancea
Bucureşti
Codul
judeţului A1 A2 A3
VS
7 5 1
VL
5 4 3
TR
1 4 2
TM
9 8 9
TL
4 1 1
SV
9 8 5
SM
4 4 3
SJ
5 1 1
SB
6 4 5
PH
5 9 7
OT
2 5 3
NT
6 6 3
MS
7 6 5
MM
7 6 4
MH
2 2 1
IS
8 9 7
IL
5 2 1
IF
10 3 6
HR
6 3 2
HD
1 5 4
GR
6 2 2
GL
5 7 5
GJ
6 4 4
DJ
5 8 6
DB
6 6 5
CV
7 1 1
CT
8 8 8
CS
2 3 3
CL
4 3 2
CJ
7 8 8
BZ
5 5 4
BV
6 7 7
BT
7 5 2
BR
3 4 3
BN
8 3 2
BH
6 7 6
BC
5 8 6
AR
6 5 5
AG
6 7 7
AB
4 4 4
VN
9 4 2
B
8 10 10
B1
0
1
0
5
0
0
0
1
4
2
3
1
2
0
0
1
0
7
0
1
0
3
0
1
2
0
3
0
1
2
2
3
0
1
0
1
0
2
4
2
0
10
B2
1
3
2
9
1
4
3
1
5
7
3
3
5
4
1
7
1
6
2
4
2
5
4
6
5
1
8
3
2
8
4
7
2
3
2
6
5
5
7
4
2
10
B3
2
2
3
8
7
5
4
5
5
1
7
2
3
6
4
7
6
3
3
3
10
6
9
5
8
2
8
8
9
6
5
8
4
2
2
4
5
6
4
7
6
4
C1
1
8
1
7
4
7
3
2
6
8
1
5
7
5
2
3
3
2
6
4
1
2
3
3
3
4
10
6
1
7
3
9
1
3
3
8
3
5
6
3
2
9
C2
2
7
1
5
3
5
3
2
5
6
1
4
5
4
3
4
4
3
4
4
2
3
3
3
4
5
10
5
1
5
3
8
2
4
3
7
4
4
4
3
2
9
C3
8
3
3
3
2
5
4
9
5
3
7
2
5
4
4
4
2
1
5
3
5
2
7
3
3
6
2
2
1
1
2
5
8
2
1
3
2
5
1
10
4
5
C4
2
6
1
7
4
6
4
1
7
7
1
5
7
5
3
6
2
4
5
4
1
3
3
3
3
4
9
5
1
7
2
8
2
3
3
6
4
6
5
4
2
10
D1
0
0
0
7
1
2
1
0
4
0
0
0
5
1
0
4
0
0
0
0
0
0
0
2
0
0
3
0
0
7
0
0
0
0
0
2
5
1
0
0
0
10
D2
0
0
1
0
3
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
2
3
0
1
0
0
10
0
3
0
0
0
0
3
0
0
0
0
0
0
0
0
D3
6
3
5
8
1
7
6
6
2
4
5
3
5
3
2
6
7
9
3
5
1
7
5
3
2
3
9
5
4
4
5
7
3
4
7
7
4
7
4
4
4
10
D4
6
8
5
5
1
6
4
6
2
5
3
6
4
3
8
4
6
9
5
3
7
6
6
4
7
6
6
5
8
7
2
7
7
3
3
5
5
3
7
6
8
10
D5
8
7
3
6
1
6
7
9
5
9
8
5
5
4
7
8
3
10
5
9
5
6
8
5
9
2
6
2
3
8
8
4
8
3
4
7
6
4
10
9
6
7
E1
0
0
0
7
0
2
2
0
4
2
0
2
4
2
0
7
0
2
0
3
0
3
2
3
2
0
5
2
0
7
0
3
0
0
0
4
3
3
3
3
0
10
E2
0
1
1
7
0
2
1
1
5
2
1
1
3
2
1
8
1
1
1
1
0
4
1
6
2
1
6
1
1
8
1
6
1
1
1
4
2
5
3
1
1
10
E3
0
8
5
5
0
7
8
8
5
7
6
6
7
7
2
7
0
6
4
3
0
6
4
3
6
3
5
3
6
7
9
2
5
3
8
5
6
7
4
6
10
2
Sursa: Datele din tabel au fost determinate de autori pe baza informaţiilor Institutului Naţional
de Statistică prin calcule proprii şi prin utilizarea suportului statistic al programului ArcGIS 10.2
7
Folosind formulele prezentate anterior, s-au calculat valorile indicilor
(Tabelul 2).
Valorile indicilor corespunzători domeniilor şi ale
indicelui de policentricitate pentru judeţele din România
Tabelul 2
Numele judeţului
Vaslui
Vâlcea
Teleorman
Timiş
Tulcea
Suceava
Satu Mare
Sălaj
Sibiu
Prahova
Olt
Neamţ
Mureş
Maramureş
Mehedinţi
Iaşi
Ialomiţa
Ilfov
Harghita
Hunedoara
Giurgiu
Galaţi
Gorj
Dolj
Dâmboviţa
Covasna
Constanţa
Caraş-Severin
Călăraşi
Cluj
Buzău
Braşov
Botoşani
Brăila
Bistriţa-Năsăud
Bihor
Bacău
Arad
Argeş
Alba
Vrancea
Bucureşti
Codul
judeţului
VS
VL
TR
TM
TL
SV
SM
SJ
SB
PH
OT
NT
MS
MM
MH
IS
IL
IF
HR
HD
GR
GL
GJ
DJ
DB
CV
CT
CS
CL
CJ
BZ
BV
BT
BR
BN
BH
BC
AR
AG
AB
VN
B
A
3,90
3,80
2,85
8,50
1,45
7,10
3,65
1,60
4,65
7,70
3,85
4,95
5,80
5,45
1,65
8,15
2,10
5,10
3,10
4,05
2,60
6,00
4,30
6,85
5,65
1,90
8,00
2,85
2,80
7,85
4,65
6,85
4,25
3,50
3,40
6,50
6,85
5,15
6,85
4,00
4,05
9,70
B
0,90
2,50
1,70
8,10
1,40
3,30
2,50
1,40
4,80
5,40
3,40
2,50
4,20
3,40
1,10
5,80
1,30
5,90
1,70
3,30
2,40
4,70
3,70
4,90
4,70
0,90
7,00
2,90
2,50
6,60
3,70
6,30
1,80
2,50
1,60
4,80
4,00
4,50
6,10
3,90
2,00
9,40
C
2,40
6,45
1,20
5,90
3,45
5,75
3,45
2,35
5,90
6,45
1,60
4,35
6,10
4,55
2,90
4,50
2,90
2,95
4,85
3,90
1,75
2,70
3,40
3,00
3,35
4,55
8,85
4,90
1,00
5,70
2,55
7,90
2,40
3,25
2,80
6,45
3,60
5,00
4,45
4,05
2,20
8,95
D
2,60
2,35
2,10
4,65
1,60
3,15
2,50
2,70
2,30
2,25
2,30
1,85
3,35
1,60
2,20
3,50
2,25
3,70
1,70
2,10
2,30
3,45
2,45
2,45
2,25
1,55
6,75
1,70
3,00
4,55
1,85
2,50
2,30
2,25
1,90
3,10
3,45
2,20
2,65
2,40
2,40
6,70
E
0,00
1,35
1,05
6,80
0,00
2,50
2,05
1,35
4,65
2,50
1,15
1,85
3,75
2,50
0,75
7,55
0,55
1,85
0,95
1,90
0,00
3,85
1,65
4,65
2,40
0,85
5,55
1,55
1,15
7,55
1,45
4,55
1,05
0,85
1,35
4,10
2,75
4,50
3,10
2,20
1,55
9,20
Indicele de
policentricitate
1,86
2,95
1,86
7,07
1,45
4,15
2,76
1,79
4,36
4,90
2,75
2,95
4,47
3,43
1,56
5,94
1,70
4,40
2,18
3,06
1,99
4,38
3,23
4,57
3,92
1,59
7,12
2,65
2,31
6,49
3,07
5,55
2,34
2,48
2,10
4,86
4,23
4,22
4,95
3,38
2,44
8,84
Sursa: Datele din tabel au fost determinate de autori pe baza informaţiilor din Tabelul 1 prin
calculele proprii
Pe baza metodologiei proprii descrise anterior şi cu ajutorul
programului ArcGIS 10.2 am obţinut cartograma indicelui de policentricitate
(Harta 1).
8
Indicele de policentricitate al judeţelor din România
Harta 1
Sursa: Realizată de autori pe baza datelor din Tabelul 2, prin utilizarea ArcGIS 10.2
Analizând rezultatele obţinute (Harta 1 şi Tabelul 2), constatăm că
există puţine unităţi teritoriale NUTS 3 care au indicele de policentricitate mai
ridicat (implicit şi indicii corespunzători mai multor domenii), anume:
Bucureşti (8,84), Constanţa (7,12) şi Timiş (7,07). În acest clasament, urmează
trei judeţe, distanţate şi între ele şi faţă de cele trei sub aspectul valorilor
indicelui, în ordinea Cluj, Iaşi şi Braşov. În continuare, găsim un grup de
judeţe cu indici de policentricitate cuprinşi între 4,5 şi 5: Argeş, Prahova,
Bihor şi Dolj. În acelaşi timp, observăm că sunt mai multe judeţe cu valori
scăzute ale indicilor corespunzători domeniilor şi cu indicele de policentricitate
foarte mic, mai mic decât 2: Giurgiu, Vaslui, Teleorman, Sălaj şi Ialomiţa.
Ultimele în acest clasament al policentricităţii sunt Covasna (1,59), Mehedinţi
(1,56) şi Tulcea (1,45). Toate aceste judeţe cu indicele de policentricitate mic
vor avea dificultăţi în dezvoltarea socio-economică viitoare, ceea ce va
reprezenta un handicap pentru România în atingerea obiectivului de coeziune
teritorială. Aşa cum am precizat anterior, coeficientul Gini ia valori între zero,
pentru egalitate perfectă şi n 1 n 42 1 42 0,9762 pentru inegalitate
perfectă. Coeficientul Gini
al indicelui de
policentricitate al judeţelor are valoarea 0,2562, ceea ce înseamnă că acest
indice nu diferă prea mult de la un judeţ la altul. Referitor la distribuţia seriei
indicelui de policentricitate avem următoarele informaţii, furnizate de softul
EViews 9.0:
9
8
Series: IND_GEN
Sample 1 42
Observations 42
7
6
5
4
3
2
1
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
3.626190
3.150000
8.840000
1.450000
1.723145
0.984420
3.628691
Jarque-Bera
Probability
7.475272
0.023810
0
1
2
3
4
5
6
7
8
9
Dintre elementele furnizate de output, numai câteva prezintă interes
pentru studiul nostru. Astfel, indicele de policentricitate mediu (Mean) este 3,63,
iar coeficientul de asimetrie (Skewness) are valoarea 0,98 (între 0,5 şi 1), ceea ce
arată că distribuţia este moderat asimetrică spre dreapta (mai multe valori sunt
concentrate la stânga faţă de medie, cu valori extreme la dreapta). Totodată,
valoarea probabilităţii asociate statisticii Jarque-Bera este 0,0238, mai mică
decât 0,05, ceea ce înseamnă că respingem ipoteza nulă a distribuţiei normale.
Domeniul populaţie
Pentru indicele populaţiei am realizat următoarea cartogramă cu
ajutorul programul ArcGIS 10.2.
Indicele populaţiei la nivelul judeţelor din România
Harta 2
10
Din analiza cartogramei de mai sus se constată că municipiul Bucureşti
şi majoritatea judeţelor în care se află marile oraşe: Timiş, Iaşi, Constanţa, Cluj
şi Prahova (datorită gradului ridicat de urbanizare a judeţului) au un indice
al populaţiei ridicat, în concordanţă cu valorile exprimate la nivel european
pentru Zonele Metropolitane Europene de Creştere. Judeţele cu un indice al
populaţiei relativ ridicat sunt: Suceava, Dolj, Braşov, Bacău şi Argeş. Un indice
al populaţiei semnificativ îl au judeţele: Bihor, Galaţi (în special, datorită
volumului populaţiei), Mureş (mai ales datorită PIB-ului). La polul opus, cu un
indice al populaţiei scăzut, se situează judeţele: Caraş-Severin, Teleorman (în
special din cauza PIB-ului), Călăraşi, Giurgiu, Ialomiţa, ultimele fiind Covasna,
Mehedinţi, Sălaj şi Tulcea, judeţe cu o populaţie scăzută faţă de media naţională.
Coeficientul Gini al indicelui populaţiei are valoarea 0,2442, ceea ce arată că în
distribuţia populaţiei nu sunt diferenţe prea mari de la un judeţ la altul.
Descriptive Statistics ne oferă următoarele informaţii despre distribuţia
seriei indicelui populaţiei:
6
Series: A
Sample 1 42
Observations 42
5
4
3
2
1
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
4.855952
4.475000
9.700000
1.450000
2.107021
0.320146
2.248988
Jarque-Bera
Probability
1.704488
0.426457
0
1
2
3
4
5
6
7
8
9
10
Astfel, indicele populaţiei mediu (Mean) este 4,86, mult mai mare
decât indicele de policentricitate mediu (3,63). Valoarea coeficientului de
asimetrie ( Skewness) este 0,32 (între 0 şi 0,5), ceea ce înseamnă că distribuţia
este aproximativ simetrică. Prin urmare, multe valori ale indicelui populaţiei
sunt concentrate în jurul mediei. Remarcăm că variaţia medie a valorilor
indicelui faţă de indicele populaţiei mediu, exprimată prin abaterea medie
pătratică (Std. Dev.), este de destul de mare (2,11).
Domeniul economie
Pentru indicele economiei avem cartograma următoare:
11
Indicele economiei la nivelul judeţelor din România
Harta 3
Din analiza hărţii de mai sus se evidenţiază decalajele economice existente
între judeţele ţării. În ierarhia domeniului economic se detaşează municipiul
Bucureşti cu un indice foarte ridicat, atât datorită PIB-ului pe cap de locuitor, cât
şi a localizării majorităţii firmelor din top 100 companii din România. Judeţul
Timiş urmează municipiului Bucureşti în acest clasament, având ca atuuri PIB-ul
pe locuitor, evoluţia ascendentă a PIB-ului, dar şi existenţa a numeroase firme din
top 100 companii din România pe teritoriul judeţului. În continuare, se află judeţele
Constanţa, Cluj şi Braşov, cu o valoare a PIB-ului pe cap de locuitor superioară
mediei pe ţară şi o evoluţie ascendentă a PIB-ului în ultimii ani. Pe alte trepte mai
jos găsim judeţele Argeş, Ilfov, Iaşi, Prahova şi Dolj, cu un potenţial industrial
ridicat şi cu prezenţa unor firme din top 100, dar cu o evoluţie sinuoasă a PIB-ului
în ultimii ani (excepţie făcând judeţul Iaşi). Clasamentul continuă cu judeţe care
sunt în ascensiune din punct de vedere al nivelului de competitivitate, cum este
cazul judeţelor Sibiu şi Bihor şi cu judeţe industrializate în stagnare sau chiar în
declin, precum Galaţi şi Dâmboviţa. La capătul opus, regăsim judeţele din sudestul ţării, judeţe din Moldova, dar şi judeţe din Transilvania, precum: BistriţaNăsăud, Sălaj, Covasna. De asemenea, se poate constata că vor avea dificultăţi
judeţele industrializate forţat în perioada comunistă care depind din punct de
vedere economic de marile obiective industriale, fiind vorba despre Vâlcea, Galaţi,
Hunedoara, Ialomiţa şi chiar Mehedinţi. Coeficientul Gini al indicelui economiei
are valoarea 0,3036, ceea ce arată că nici în distribuţia dezvoltării economice
nu sunt diferenţe foarte mari de la un judeţ la altul. Referitor la distribuţia seriei
indicelui economiei avem următoarele rezultate:
12
7
Series: B
Sample 1 42
Observations 42
6
5
4
3
2
1
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
3.702381
3.400000
9.400000
0.900000
2.038709
0.712496
3.036213
Jarque-Bera
Probability
3.555845
0.168989
0
1
2
3
4
5
6
7
8
9
Indicele economiei mediu (Mean) este 3,70, apropiat ca valoare de
indicele de policentricitate mediu (3,63). Valoarea coeficientului de asimetrie
( Skewness) fiind 0,71 (între 0,5 şi 1), distribuţia este moderat asimetrică spre
dreapta (mai multe valori sunt concentrate la stânga faţă de medie, cu valori
extreme la dreapta). Totodată, valorile indicelui economiei variază în medie
destul de mult faţă de indicele economiei mediu, deoarece abaterea medie
pătratică (Std. Dev.) este 2,04.
Domeniul turism
Pentru indicele turismului am realizat cartograma de mai jos.
Indicele turismului la nivelul judeţelor din România
Harta 4
Valorile indicelui turismului relevă foarte clar judeţele cu potenţial
13
turistic ridicat şi judeţele cu resurse scăzute pentru dezvoltarea sectorului
turistic. Din studiul hărţii de mai sus, se constată că, la ora actuală, cel mai
mare potenţial turistic au municipiul Bucureşti şi judeţul Constanţa, urmate de
Braşov, care devansează judeţele Prahova, Bihor şi Vâlcea. Totodată, remarcăm
un grup de judeţe care au un potenţial turistic ridicat şi un trend ascendent al
valorificării acestuia, din care fac parte Mureş, Timiş, Sibiu, Suceava, Cluj şi
un alt grup de judeţe care deţin un potenţial turistic important, încă insuficient
valorificat, alcătuit din Arad, Caraş-Severin, Harghita, Maramureş, Covasna,
Iaşi, Argeş, Neamţ şi Alba. Pe o treaptă mai jos sunt situate judeţele cu
potenţial turistic ridicat, dar nevalorificat, cele mai importante dintre acestea
fiind Tulcea, Gorj, Hunedoara şi Bacău. Judeţele cu potenţial turistic scăzut
sunt cele din sud-estul României, care au şi probleme importante în ceea ce
priveşte competitivitatea.
Coeficientul Gini al indicelui turismului la nivelul judeţelor are
valoarea 0,2544, apropiată de cea a coeficientului Gini al indicelui de
policentricitate. Pentru caracterizarea distribuţiei seriei indicelui turismului
dispunem de următoarele date:
7
Series: C01
Sample 1 42
Observations 42
6
5
4
3
2
1
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
4.159524
3.750000
8.950000
1.000000
1.926017
0.678083
3.010368
Jarque-Bera
Probability
3.218761
0.200011
0
1
2
3
4
5
6
7
8
9
Indicele turismului mediu (Mean) este 4,16, mai mare decât indicele
de policentricitate mediu (3,63). Distribuţia este moderat asimetrică spre
dreapta, deoarece coeficientul de asimetrie ( Skewness) are valoarea 0,68 (între
0,5 şi 1). De aceea, seria are mai multe valori apropiate de medie, dar mai mici
decât media şi valori extreme mari. În acelaşi timp, întrucât abaterea medie
pătratică (Std. Dev.) este 1,93, valorile seriei sunt destul de mult dispersate în
raport cu indicele turismului mediu.
Domeniul transporturi
Softul ArcGIS 10.2 generat cartograma indicelui transporturilor la
nivelul judeţelor din România.
Indicele transporturilor la nivelul judeţelor din România
14
Harta 5
Aşa cum se observă din analiza hărţii indicelui transporturilor, o
poziţionare foarte bună în clasamentul acestui indice o au judeţul Constanţa,
municipiul Bucureşti, judeţul Timiş şi judeţul Cluj, ca urmare a densităţilor
relativ mari ale drumurilor naţionale şi căilor ferate, dar şi a prezenţei
aeroporturilor internaţionale cu un flux de pasageri de peste 1 milion – în
cazul municipiului Bucureşti, judeţului Timiş şi judeţului Cluj şi a prezenţei
portului cu un tranzit european de mărfuri – în cazul judeţului Constanţa. Pe
următoarele locuri ale ierarhiei găsim judeţe cu o densitate ridicată a drumurilor
şi căilor ferate şi cu aeroporturi internaţionale cu un flux mediu de pasageri la
nivel naţional pe teritoriul lor, anume Ilfov, Iaşi, Galaţi (care are ca atu portul
Galaţi) şi Bacău. Alte judeţe cu un indice al domeniului transporturi ridicat
sunt: Mureş, Suceava, Bihor (care au, de asemenea, aeroporturi internaţionale),
precum şi Călăraşi (datorită fluxului de mărfuri din portul Călăraşi), Sălaj (cu
densităţi mari de drumuri publice şi căi ferate), Argeş. Judeţele cu un indice al
transporturilor scăzut sunt: Caraş-Severin, Harghita, Covasna şi chiar Tulcea
şi Maramureş, unde există aeroporturi internaţionale. Coeficientul Gini al
indicelui transporturilor la nivelul judeţelor are valoarea 0,1957, cea mai mică
dintre valorile coeficientului Gini al acestor indici. Distribuţia seriei indicelui
transporturilor se caracterizează prin următoarele elemente:
15
20
Series: D01
Sample 1 42
Observations 42
16
12
8
4
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
2.735714
2.375000
6.750000
1.550000
1.150095
2.138862
7.755656
Jarque-Bera
Probability
71.60156
0.000000
0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
Indicele transporturilor mediu (Mean) este 2,74, mult mai mic decât
indicele de policentricitate mediu (3,63). De altfel, acest indice ia valori
între 1,55 şi 6,75, iar mărimea acestui interval este mai mică decât mărimea
intervalelor celorlalţi indici. Deoarece coeficientul de asimetrie (Skewness)
are valoarea 2,14 (mai mare decât 1), distribuţia este puternic asimetrică spre
dreapta, adică foarte multe valori sunt concentrate la stânga faţă de medie, cu
valori extreme la dreapta. Totodată, valoarea probabilităţii asociate statisticii
Jarque-Bera este mai mică decât 0,05, ceea ce înseamnă că respingem ipoteza
nulă a distribuţiei normale.
Domeniul educaţie
Pentru indicele educaţiei am realizat cartograma care urmează.
Indicele educaţiei la nivelul judeţelor din România
Harta 6
16
Din analiza cartogramei indicelui educaţiei rezultă că municipiul
Bucureşti se află în fruntea ierarhiei acestui indice, ca urmare a numărului
mare de universităţi şi a numărului mare de studenţi şi că judeţele care îl
urmează în acest top sunt Iaşi şi Cluj, din aceleaşi motive. În continuare,
găsim judeţele ale căror reşedinţe sunt mari centre universitare, adică Timiş,
Constanţa, Sibiu, Dolj şi Braşov. Un indice al educaţiei relativ ridicat au
judeţele Arad, Bihor, Galaţi, Mureş, Argeş, Bacău, Prahova, Suceava şi
Maramureş. Judeţele cu un indice al educaţiei mic sunt Harghita, Brăila (chiar
dacă Brăila este un municipiu cu rezonanţe istorice), Covasna, Mehedinţi şi
Ialomiţa. În partea de jos a clasamentului figurează Tulcea, Vaslui şi Giurgiu,
unde statistica naţională nu înregistrează nici un student. Coeficientul Gini al
indicelui educaţiei are valoarea 0,4317, care arată că diferenţierea între judeţe
în acest domeniu este mai accentuată. Referitor la distribuţia seriei indicelui
educaţiei dispunem de următoarele informaţii:
9
Series: E
Sample 1 42
Observations 42
8
7
6
5
4
3
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
2.640476
1.875000
9.200000
0.000000
2.192510
1.255338
3.994975
Jarque-Bera
Probability
12.76358
0.001692
2
1
0
0
1
2
3
4
5
6
7
8
9
Indicele educaţiei mediu (Mean) are valoarea 2,64, cea mai mică
dintre valorile indicilor medii. Distribuţia este puternic asimetrică spre dreapta,
întrucât coeficientul de asimetrie ( Skewness) are valoarea 1,26 (mai mare decât
1). Abaterea medie pătratică (Std. Dev.) fiind 2,19, valorile indicelui educaţiei
variază în medie destul de mult faţă de indicele educaţiei mediu. Deoarece
p-value (Probability) pentru testul Jarque-Bera este mai mică decât 0,05,
respingem ipoteza nulă a distribuţiei normale.
Concluzii
Policentricitatea sistemelor de localităţi este considerată ca
factor favorizant al dezvoltării teritoriale durabile, precum şi al reducerii
dezechilibrelor teritoriale. Unităţile teritoriale NUTS 3 pot fi asimilate într-o
oarecare măsură zonelor urbane funcţionale. Din aceste motive, studiul
policentricităţii judeţelor capătă o importanţă deosebită. La toţi indicii
calculaţi predomină valorile mici, ceea ce înseamnă că cele mai multe judeţe
17
au un nivel de dezvoltare scăzut în ceea ce priveşte policentricitatea şi fiecare
dintre domenii. Din aceste considerente, Strategia de Dezvoltare pe termen
lung în domeniul Amenajării Teritoriului şi Urbanismului din România trebuie
să dezvolte proiecte integrate pentru acele zone care au dificultăţi. Totodată,
autorităţile centrale şi cele locale trebuie să conlucreze pentru crearea
condiţiilor unor investiţii directe şi implicit a unui aport mare de capital, astfel
încât să fie atinse obiectivele Strategiei Uniunii Europene pentru perioada
2014-2020 privind politica de coeziune teritorială. Rezultatele obţinute în
privinţa gradului de policentricitate la nivelul unităţilor teritoriale NUTS 3 din
România nu sunt exhaustive, ci mai degrabă reprezintă un exerciţiu util pentru
a emite nişte concluzii privind situaţia actuală şi posibila evoluţie a judeţelor şi
pentru a evidenţia tipologia acestora prin prisma domeniilor studiate. Evaluări
mai precise ale indicilor domeniilor şi implicit ale indicelui de policentricitate
s-ar putea obţine prin transformarea rezultatelor înregistrate pentru indicatori
în utilităţi cu ajutorul funcţiilor lineare (Manole et al., 2011). De asemenea,
departajarea judeţelor s-ar putea realiza prin determinarea intensităţii
preferinţei pentru fiecare judeţ cu ajutorul metodelor PROMETHEE (Brans şi
Mareschal, 2005) sau prin stabilirea unor relaţii de surclasare între judeţe cu
ajutorul metodelor ELECTRE (Milani et al., 2006).
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AllItems.aspx?RootFolder=%2F Training%2FStatistics&FolderCTID=&View={4
223A4850-B4790-4965-4285DBD4220A4841A5430B}. (accesat 20 iulie 2015)
3. Camagni, R., Gibelli, M. C., Rigamonti, P. (2002) Urban mobility and urban
form: the social and environmental costs of different patterns of urban expansion,
Ecological economics, 40(2), pp. 199-216
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inteligentă, ecologică şi favorabilă incluziunii, Bruxelles, disponibil la http://eur-lex.europa.
eu/LexUriServ/ LexUriServ.do?uri=COM:2010:2020:FIN:RO:PDF. (accesat 25 iulie 2015)
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Tool to a Normative Agenda, European Planning Studies, 11(8), pp. 979-999
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(accesat 2 august 2015)
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Luxembourg, ESPON Monitoring Committee, disponibil la http://www.espon.eu/
mmp/online/website/content/
projects/259/648/file_1174/fr-1.1.1_revised-full.
pdf. (accesat 26 iulie 2015)
18
8. Kloosterman, R. C., Musterd, S. (2001) The Polycentric Urban Region: Towards a
Research Agenda, Urban Studies, 38(4), pp. 623-633
9. Halffman, W., Leydesdorff, L. (2010) Is inequality among universities increasing?
Gini coefficients and the elusive rise of elite universities, Minerva, 48(1), pp. 55-72
10. Manole, S. D., Petrişor, A. I., Tache, A., Pârvu, E. (2011) GIS Assessment of
Development Gaps Among Romanian Administrative Units, Theoretical and
Empirical Researches in Urban Management, 6(4), pp. 5-19
11. Meijers, E., Sandberg, K. (2008) Reducing regional disparities by means of
polycentric development: panacea or placebo?, Scienze Regionali, 2008(Suppl.
2), pp. 71-96
12. Milani, A. S., Shanian, A., El-Lahham, C. (2006) Using Different ELECTRE
Methods in Strategic Planning in the Presence of Human Behavioral Resistance,
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13. Ministerul Dezvoltării, Lucrărilor Publice şi Locuinţelor (2008) Conceptul
Strategic de Dezvoltare Teritorială – România 2030, disponibil la http://www.
mdrl.ro/_documente/publicatii/2008/ Brosura%20Conc_strat_dezv_teritoriala.
pdf. (accesat 29 iulie 2015)
14. Veneri, P., Burgalassi, D. (2012) Questioning polycentric development and its
effects. Issues of definition and measurement for the Italian NUTS-2 regions,
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more sustainable?, International Seminar on Welfare and competitiveness in the
European polycentric urban structure, Florence (Vol. 7)
19
POLYCENTRICITY FUNCTIONAL ANALYSIS
OF THE ROMANIAN COUNTIES
Main researcher 3 Antonio TACHE
Main researcher Monica Tache
”URBAN-INCERC” National Institute for Research and Development in
Constructions, Town Planning and Sustainable Territorial Development
PhD. Associate Professor Sorin Daniel MANOLE
”Constantin Brâncoveanu” University of Piteşti
Abstract
The development of polycentricity at the national level involves
the balanced development of network of settlements and the achievement
of a harmonious relationship between settlement and territory based on
principles of sustainable development, internal balance, the opening
towards the exterior, and the exploitation of the exiting potential, functional
complementarity and the growth of local autonomy. For this reason, the
assessment of polycentricity at the county level is extremely important.
The methodology in assessing the degree of polycentricity at NUTS 3 level
consists in identifying certain domains significant for the characterization of
polycentricity and some relevant indicators within such domains and then,
after transformation indicators’ values into scores, it consists in calculating
some composite indicators corresponding to the domains and polycentricity.
The analysis of these findings leads to some interesting conclusions, necessary
for the formulation of some local, regional and national development policies.
Keywords: polycentricity; index; county; domain; indicator;
Romania.
JEL: R11, R12, R15, R23, R42, R58
Introduction
The promotion of the balanced polycentric urban system is one of the
most frequently cited politic objectives of the spatial policy of the European Union
(ESDP, 1999). However, due to the multi-dimensional and multi-scalar nature of
polycentricity, there is an ambiguity in how that concept is defined (Veneri and
Burgalassi, 2012; Kloosterman and Musterd, 2001; Davoudi, 2003). Moreover,
there is not any universally accepted method of measuring polycentrism at
different spatial scales or any method for assessing the impact of polycentrism
on the policy objectives: efficiency (competitiveness), equity (cohesion) and
durability. Consequently it is impossible to decide upon an optimal degree of
polycentrism between centralization and decentralization, or, in other words,
20
between the extremes monocentricity (all activities are concentrated in one center)
and dispersion (all activities are equally distributed over space). Wegener (2013)
argues that both extremes monocentricity and dispersion, perform poorly with
respect to the policy goals: efficiency, equity and sustainability. The polycentric
urban system can be defined as a functionally integrated socio-spatial entity,
which consists in more urban nodes which can be different in size but which
play an important role in the system; they are bound by intensive reciprocal and
multidirectional relationships, with a development influenced by government
strategies which admit, consider and support the further strengthening of interests,
complementarities, synergies and opportunities of mutual cooperation. ESPON
1.1.1 program details aspects related to the concept of polycentricity and shows
the operational methods of measuring the polycentrism of the urban system in
Europe. It is also analyzed the European urban polycentric system (consisting
of the Member States of the European Union plus Norway and Switzerland),
based on the current model of polycentrism, at three spatial levels: regional and
local level, national level and European level, including the trans-national urban
levels. As analysis units in each countries, there were established the functional
urban areas (FUAs). At the European level, functional urban areas do not have a
common definition. Mainly, functional urban areas consisted in a core municipality
plus adjacent commuting areas. Lacking a comprehensive definition, to establish
functional urban areas we need to identify their core (location of the center) and
the share of the total population that lives in the neighboring which make up the
FUA. This paper aims at studying polycentricity at NUTS 3 level (counties), and
the methodology used is based on the methodology used in ESPON 1.1.1 for the
analysis of polycentricity of functional urban areas.
According to ESPON 1.1.1, two structural aspects are of particular
importance for polycentricity:
- morphological, concerning the distribution of urban areas in a given
territory;
- relational – concerning the networks of flows and the cooperation
between urban areas at different scales.
Polycentricity is currently considered a useful spatial planning
tool to enhance the competitiveness of cities, social cohesion and
environmental sustainability (Davoudi, 2003). There are two key approaches
in the conceptualization of polycentric areas. The first approach is purely
morphological, and according to this one, polycentric areas can be seen as a
model of spatial organization which is a middle way between the traditional
compact cities and urban expansion, while maintaining the advantages
associated with compact cities, observing dispersion spontaneous trends
(Camagni et al., 2002). The other approach is both functional and morphological,
21
and according to it, polycentric areas represent the alternative for monocentric
areas (Meijers and Sandberg, 2008), consisting in a progressive integration of
urban centers into a single metropolitan area.
Methodology of assessing the polycentric system at the level of counties
(NUTS 3) in Romania
The indicators present in the spatial database at the county level were
chosen according in compliance with the indicators of functions of urban
areas from the ESPON 1.1.1 study and the national characteristics specific
to the Romanian territory. In order to characterize polycentricity there were
considered more domains (which correspond to the functions of urban areas
in the ESPON 1.1.1 study) and their corresponding indices were calculated, as
well as a general polycentricity index using the original methodology. Thus,
we considered the following domains and indicators, for which the mentioned
encodings were used:
Population domain – A:
- Dynamic index of population I 2011 2001 – A1;
- Population in 2011 – A2;
- Gross domestic product (in million lei) in 2010 – A3;
Economic domain – B:
- The location of top 100 companies (in terms of turnover) – B1;
- Gross domestic product per capita at current prices (in euro) in 2010 – B2;
- Dynamic index of gross domestic product I 2010 2008 – B3;
Tourism domain – C:
- Number of tourist units in 2011 – C1;
- Number of overnight stays in tourist units in 2011 – C2;
- Dynamic index of number of overnight stays in tourist units I 2011 2008 – C3;
- Number of tourists in 2011 – C4;
Transport domain – D:
- Number of passengers transited through the airports in 2012 – D1;
- The volume of goods in transit through the ports in 2012 – D1;
- The railway density in 2012 – D3;
- The density of national roads in 2012 – D4;
- The density of public roads in 2012 – D5;
Education domain – E:
- Number of universities in 2011 – E1;
- Number of students in 2011 – E2;
- Dynamic index of number of students I 2011 2008 – E3.
For every indicator there has been achieved a grouping of values
registered at the level of counties on 10 equal intervals, thus obtaining 10
22
groups, which, in the ascending order of values, were awarded scores from 1
to 10. When an indicator registered a value of 0 at a county, the score given
to that county at this indicator was also 0. Consequently, all the values of
selected indicators were transformed into scores of groups to which they
belong (1,2,…,10, even 0), and this was achieved with the statistical assistance
of the program ArcGIS 10.2. Within every domain, more specialists in local
development established coefficients of importance (weights) for all indicators.
For each domain, the index corresponding to a county was calculated as the
average of scores given indicators weighted by coefficients of importance.
Similarly, the coefficients of importance (weights) were provided to every
domain of interest and the polycentricity index was calculated at NUTS 3 level
as average of indices corresponding to these domains weighted by coefficients
of importance.
Thus, the following formulas were used:
- the index of the population domain: A 0.15 A1 0.5 A2 0.35 A3 ;
- the index of the economic domain: B  0.2  B1  0.7  B 2  0.1  B3
- the index of tourism domain: C 0.2 C1 0.35 C 2 0.1 C3 0.35 C 4
- the index of transport domain: D 0.3 D1 0.3 D2 0.15 D3 0.15 D4 0.1 D5
- the index of education domain: E 0.35 E1 0.55 E 2 0.1 E 3 ;
- the polycentricity index: IP 0.2 A 0.35 B 0.1 C 0.2 D 0.15 E .
Also, in order to analyze how much the values of indices differ from
one county to another, Gini coefficient of inequality was calculated. Thus, if
we have the observed values arranged in ascending order x1 , x2 ,, xn with
the average x , Gini coefficient of inequality ( G )
i s
calculated as follows (Buchan, 2002):
G
2 n
 i( xi  x )
n 2 x i 1
The Gini coefficient ranges between zero for perfect equality
n  1 n for perfect inequality
( x1  x2    xn )
and
( x1  x2    xn 1  0, xn  0 ), approaching one for large n (Halffman
and Leydesdorff, 2010).
Results and analyses
Scores and indices
By transforming the values of indicators into scores with the statistic
assistance of ArcGIS 10.2 program, we obtained the following information as
included in Table 1.
23
Scores corresponding to the relevant indicators given to counties in
Romania
Table 1
The name of
Code of
the county the county
Vaslui
VS
Valcea
VL
Teleorman
TR
Timis
TM
Tulcea
TL
Suceava
SV
Satu Mare
SM
Salaj
SJ
Sibiu
SB
Prahova
PH
Olt
OT
Neamt
NT
Mures
MS
Maramures
MM
Mehedinti
MH
Iasi
IS
Ialomita
IL
Ilfov
IF
Harghita
HR
Hunedoara
HD
Giurgiu
GR
Galati
GL
Gorj
GJ
Dolj
DJ
Dambovita
DB
Covasna
CV
Constanta
CT
Caras-Severin
CS
Calarasi
CL
Cluj
CJ
Buzau
BZ
Brasov
BV
Botosani
BT
Braila
BR
Bistrita-Nasaud
BN
Bihor
BH
Bacau
BC
Arad
AR
Arges
AG
Alba
AB
Vrancea
VN
Bucharest
B
A1 A2 A3 B1 B2 B3 C1 C2 C3 C4 D1 D2 D3 D4 D5 E1 E2 E3
7
5
1
9
4
9
4
5
6
5
2
6
7
7
2
8
5
10
6
1
6
5
6
5
6
7
8
2
4
7
5
6
7
3
8
6
5
6
6
4
9
8
5
4
4
8
1
8
4
1
4
9
5
6
6
6
2
9
2
3
3
5
2
7
4
8
6
1
8
3
3
8
5
7
5
4
3
7
8
5
7
4
4
10
1
3
2
9
1
5
3
1
5
7
3
3
5
4
1
7
1
6
2
4
2
5
4
6
5
1
8
3
2
8
4
7
2
3
2
6
6
5
7
4
2
10
0
1
0
5
0
0
0
1
4
2
3
1
2
0
0
1
0
7
0
1
0
3
0
1
2
0
3
0
1
2
2
3
0
1
0
1
0
2
4
2
0
10
1
3
2
9
1
4
3
1
5
7
3
3
5
4
1
7
1
6
2
4
2
5
4
6
5
1
8
3
2
8
4
7
2
3
2
6
5
5
7
4
2
10
2
2
3
8
7
5
4
5
5
1
7
2
3
6
4
7
6
3
3
3
10
6
9
5
8
2
8
8
9
6
5
8
4
2
2
4
5
6
4
7
6
4
1
8
1
7
4
7
3
2
6
8
1
5
7
5
2
3
3
2
6
4
1
2
3
3
3
4
10
6
1
7
3
9
1
3
3
8
3
5
6
3
2
9
2
7
1
5
3
5
3
2
5
6
1
4
5
4
3
4
4
3
4
4
2
3
3
3
4
5
10
5
1
5
3
8
2
4
3
7
4
4
4
3
2
9
8
3
3
3
2
5
4
9
5
3
7
2
5
4
4
4
2
1
5
3
5
2
7
3
3
6
2
2
1
1
2
5
8
2
1
3
2
5
1
10
4
5
2
6
1
7
4
6
4
1
7
7
1
5
7
5
3
6
2
4
5
4
1
3
3
3
3
4
9
5
1
7
2
8
2
3
3
6
4
6
5
4
2
10
0
0
0
7
1
2
1
0
4
0
0
0
5
1
0
4
0
0
0
0
0
0
0
2
0
0
3
0
0
7
0
0
0
0
0
2
5
1
0
0
0
10
0
0
1
0
3
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
2
3
0
1
0
0
10
0
3
0
0
0
0
3
0
0
0
0
0
0
0
0
6
3
5
8
1
7
6
6
2
4
5
3
5
3
2
6
7
9
3
5
1
7
5
3
2
3
9
5
4
4
5
7
3
4
7
7
4
7
4
4
4
10
6
8
5
5
1
6
4
6
2
5
3
6
4
3
8
4
6
9
5
3
7
6
6
4
7
6
6
5
8
7
2
7
7
3
3
5
5
3
7
6
8
10
8
7
3
6
1
6
7
9
5
9
8
5
5
4
7
8
3
10
5
9
5
6
8
5
9
2
6
2
3
8
8
4
8
3
4
7
6
4
10
9
6
7
0
0
0
7
0
2
2
0
4
2
0
2
4
2
0
7
0
2
0
3
0
3
2
3
2
0
5
2
0
7
0
3
0
0
0
4
3
3
3
3
0
10
0
1
1
7
0
2
1
1
5
2
1
1
3
2
1
8
1
1
1
1
0
4
1
6
2
1
6
1
1
8
1
6
1
1
1
4
2
5
3
1
1
10
0
8
5
5
0
7
8
8
5
7
6
6
7
7
2
7
0
6
4
3
0
6
4
3
6
3
5
3
6
7
9
2
5
3
8
5
6
7
4
6
10
2
Source: The data in the table were determined by the authors based on the information from
the National Institute of Statistics by their own calculations and by using the statistic support
of the program ArcGIS 10.2
24
Using the above-mentioned formulas, the values of the indices were
calculated (Table 2).
The values of the indices corresponding to domains and of
the polycentricity index for the counties in Romania
Table 2
of the
Name of the county Code
county
Vaslui
VS
Valcea
VL
Teleorman
TR
Timis
TM
Tulcea
TL
Suceava
SV
Satu Mare
SM
Salaj
SJ
Sibiu
SB
Prahova
PH
Olt
OT
Neamt
NT
Mures
MS
Maramures
MM
Mehedinti
MH
Iasi
IS
Ialomita
IL
Ilfov
IF
Harghita
HR
Hunedoara
HD
Giurgiu
GR
Galati
GL
Gorj
GJ
Dolj
DJ
Dambovita
DB
Covasna
CV
Constanta
CT
Caras-Severin
CS
Calarasi
CL
Cluj
CJ
Buzau
BZ
Brasov
BV
Botosani
BT
Braila
BR
Bistrita-Nasaud
BN
Bihor
BH
Bacau
BC
Arad
AR
Arges
AG
Alba
AB
Vrancea
VN
Bucharest
B
A
B
C
D
E
3.90
3.80
2.85
8.50
1.45
7.10
3.65
1.60
4.65
7.70
3.85
4.95
5.80
5.45
1.65
8.15
2.10
5.10
3.10
4.05
2.60
6.00
4.30
6.85
5.65
1.90
8.00
2.85
2.80
7.85
4.65
6.85
4.25
3.50
3.40
6.50
6.85
5.15
6.85
4.00
4.05
9.70
0.90
2.50
1.70
8.10
1.40
3.30
2.50
1.40
4.80
5.40
3.40
2.50
4.20
3.40
1.10
5.80
1.30
5.90
1.70
3.30
2.40
4.70
3.70
4.90
4.70
0.90
7.00
2.90
2.50
6.60
3.70
6.30
1.80
2.50
1.60
4.80
4.00
4.50
6.10
3.90
2.00
9.40
2.40
6.45
1.20
5.90
3.45
5.75
3.45
2.35
5.90
6.45
1.60
4.35
6.10
4.55
2.90
4.50
2.90
2.95
4.85
3.90
1.75
2.70
3.40
3.00
3.35
4.55
8.85
4.90
1.00
5.70
2.55
7.90
2.40
3.25
2.80
6.45
3.60
5.00
4.45
4.05
2.20
8.95
2.60
2.35
2.10
4.65
1.60
3.15
2.50
2.70
2.30
2.25
2.30
1.85
3.35
1.60
2.20
3.50
2.25
3.70
1.70
2.10
2.30
3.45
2.45
2.45
2.25
1.55
6.75
1.70
3.00
4.55
1.85
2.50
2.30
2.25
1.90
3.10
3.45
2.20
2.65
2.40
2.40
6.70
0.00
1.35
1.05
6.80
0.00
2.50
2.05
1.35
4.65
2.50
1.15
1.85
3.75
2.50
0.75
7.55
0.55
1.85
0.95
1.90
0.00
3.85
1.65
4.65
2.40
0.85
5.55
1.55
1.15
7.55
1.45
4.55
1.05
0.85
1.35
4.10
2.75
4.50
3.10
2.20
1.55
9.20
Polycentricity
index
1.86
2.95
1.86
7.07
1.45
4.15
2.76
1.79
4.36
4.90
2.75
2.95
4.47
3.43
1.56
5.94
1.70
4.40
2.18
3.06
1.99
4.38
3.23
4.57
3.92
1.59
7.12
2.65
2.31
6.49
3.07
5.55
2.34
2.48
2.10
4.86
4.23
4.22
4.95
3.38
2.44
8.84
Source: The data in the table were determined by the authors based on the information from
Table 1 by their own calculations
Based on our own methodology above mentioned and with the
assistance of the program ArcGIS 10.2 we obtained the cartogram of the
polycentricity index (Map 1).
25
The polycentricity index of counties in Romania
Map 1
Source: Performed by the authors based on the data from Table 2 by using ArcGIS 10.2
Analyzing the results obtained (Map 1 and Table 2), we find that there
are little territorial units NUTS 3 which have a higher polycentricity index
(including the indices corresponding to more domains): Bucharest (8.84),
Constanta (7.12) and Timis ( 7.07). In this ranking, follows three counties,
spaced between each other and from the other three in terms of index values in
the following order Cluj, Iasi and Brasov. Further, we find a group of counties
with polycentricity indices ranging between 4.5 and 5: Arges, Prahova, Dolj,
Bihor. At the same time, we note that there are several counties with low
values of indices corresponding to domains and with a very low polycentricity
index, less than 2: Giurgiu, Vaslui, Teleorman, Ialomita, Salaj. Last in the
ranking of polycentricity are Covasna (1.59), Mehedinti (1.56) and Tulcea
(1.45). All these counties with small polycentricity index will have difficulties
in the future socio-economic development, which will be a disadvantage
for Romania in achieving the objective of territorial cohesion. As we stated
earlier, the Gini coefficient ranges between zero for perfect equality and
n 1 n 42 1 42 0.9762 for perfect inequality. The Gini coefficient
of the polycentricity index of counties has the value of 0.2562, meaning that
this index does not differ too much from one county to another. Concerning
the distribution of the polycentricity index series, we have the following
information, provided by the soft EViews 9.0:
26
8
Series: IND_GEN
Sample 1 42
Observations 42
7
6
5
4
3
2
1
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
3.626190
3.150000
8.840000
1.450000
1.723145
0.984420
3.628691
Jarque-Bera
Probability
7.475272
0.023810
0
1
2
3
4
5
6
7
8
9
Among the elements provided by the output, only a few are of interest
for our study. Thus, the mean polycentricity index is of 3.63, and the skewness
has the value of 0.98 (between 0.5 and 1), which means that the distribution
is moderately skewed to the right (more values are concentrated on left of
the mean, with extreme values to the right). Therewith, the probability value
associated with the Jarque-Bera statistic is 0.0238, less than 0.05 which means
that we reject the null hypothesis of normal distribution.
Population domain
For the population index we performed the following cartogram with
the assistance of the program ArcGIS 10.2
The population index at the level of counties in Romania
Map 2
27
By analyzing the above cartogram we note that Bucharest and most
counties in which big cities are located: Timis, Iasi, Constanta, Cluj and
even Prahova (due to the high degree of urbanization of the county) have an
increased index of population, in line with the values expressed at European
level for Metropolitan European Growth Area. The counties with a relatively
high population index are: Suceava County, Brasov, Bacau and Arges. Also,
a significant population index belongs to the counties of Bihor, Galati (in
particular, due to the volume of population), Mures (especially due to GDP).
In contrast, with an low index of population, are the counties of Caras-Severin,
Teleorman (especially because of GDP), Calarasi, Giurgiu, Ialomita, the last
being Covasna County, Mehedinti, Salaj and Tulcea counties with populations
lower than the national average. Gini coefficient of the population index has the
value of 0.2442, which shows that in the distribution of population there are not
too big differences from one county to another. Descriptive Statistics shows
us the following information on the distribution of the population index series:
6
Series: A
Sample 1 42
Observations 42
5
4
3
2
1
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
4.855952
4.475000
9.700000
1.450000
2.107021
0.320146
2.248988
Jarque-Bera
Probability
1.704488
0.426457
0
1
2
3
4
5
6
7
8
9
10
Thus, the mean population index is 4.86, much higher than the mean
polycentricity index (3.63). The value of skewness is 0.32 (between 0 and 0.5)
which means that the distribution is approximately symmetric. Hence many
population index values are concentrated around the average index. We also
note that the average variation of the index value against the mean population
index, expressed as standard deviation (Std. Dev.), is high enough (2.11).
28
Economic domain
For the economy index we have the following cartogram:
The economy index at the level of counties in Romania
Map 3
By analyzing the above map there are highlighted the economic
disparities between counties. In the economic domain hierarchy the Municipality
of Bucharest emerges with a very high index, both because of the GDP per capita
and because of the localization of most companies in the top 100 companies in
Romania. Timis County follows Bucharest in the ranking, with such advantages
as the GDP per capita, the ascending evolution of the GDP and the existence of
numerous companies in the top 100 companies in Romania in the county. Further,
it is Constanta, Cluj and Brasov, with such advantages as the GDP per capita above
the average for the country and an ascending evolution of the GDP in recent years.
On the other steps below we find Arges, Ilfov, Iasi, Prahova, Dolj Counties, with
a high industrial potential and the presence of companies in the top 100, but with
a sinuous evolution of GDP in recent years (except in Iasi County). The ranking
continues with counties that are rising in terms of the competitiveness level, such
as the counties of Bihor and Sibiu and with industrialized counties in stagnation or
even declining as Dambovita and Galati. At the opposite end, we find southeastern
counties, counties of Moldova and Transylvania, such as Bistrita-Nasaud, Salaj,
Covasna Counties. Also, it appears that the industrialized counties in a forced
manner under the communism will have difficulties, depending economically on
large industrial facilities, such as the case of Valcea, Galati, Hunedoara, Ialomita
and even Mehedinti. Gini coefficient of the economy index has the value of 0.3036,
29
which shows that nor in the distribution of economic development are there very
big differences from one county to the other. Concerning the distribution of the
economy index series we have the following results:
7
Series: B
Sample 1 42
Observations 42
6
5
4
3
2
1
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
3.702381
3.400000
9.400000
0.900000
2.038709
0.712496
3.036213
Jarque-Bera
Probability
3.555845
0.168989
0
1
2
3
4
5
6
7
8
9
The mean economy index is 3.70, close in value to the mean polycentricity
index (3.63). The value of skewness being 0.71 (between 0.5 and 1), the
distribution is moderately skewed to the right (more values are concentrated
on left of the mean, with extreme values to the right). At the same time, the
values of the economy index vary in average enough consistently from the mean
economy index, as the standard deviation (Std. Dev.) is of 2.04.
Tourism domain
For the tourism index, we performed the following cartogram.
The tourism index at the level of counties in Romania
Map 4
30
Tourism index values clearly shows counties with high tourism
potential and counties with low resources for tourism development. From
the study of the map above, it appears that, at present, the greatest tourism
potential belongs to Bucharest and Constanta county, followed by Brasov,
which is ahead of Prahova, Bihor and Valcea. At the same time, we notice a
group of counties that have a high tourism potential and an increasing trend of
promoting it, which includes Mures, Timis, Sibiu, Suceava, Cluj and another
group of counties that have a significant tourism potential, yet insufficiently
exploited, consisting of Arad, Caras-Severin, Harghita, Maramures, Covasna,
Iasi, Arges, Neamt and Alba. On one level below are the counties with high
tourism potential, but unexploited, the most important being Tulcea, Gorj,
Hunedoara and Bacau counties. The counties with low tourism potential are
those in southeast Romania, which have significant problems in terms of
competitiveness. Gini coefficient of the tourism index at the level of counties
has the value of 0.2544, close to that of the Gini coefficient of the polycentricity
index. In order to characterize the distribution of the tourism index series, we
have the following data:
7
Series: C01
Sample 1 42
Observations 42
6
5
4
3
2
1
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
4.159524
3.750000
8.950000
1.000000
1.926017
0.678083
3.010368
Jarque-Bera
Probability
3.218761
0.200011
0
1
2
3
4
5
6
7
8
9
The mean tourism index is 4.16, higher than the mean polycentricity
index (3.63). The distribution is moderately skewed to the right, because
skewness has the value 0.68 (between 0.5 and 1). Therefore, the series has
several values close to average but lower than the average and large extreme
values. At the same time, because standard deviation (Std. Dev.) is 1.93, the
series values are spread enough against the mean tourism index.
Transport domain
The soft ArcGIS 10.2 generated the cartogram of the transport index
at the level of counties in Romania (Map 5).
31
Transport index at the level of counties in Romania
Map 5
As seen from the analysis of the transport index map, a very good
position in the ranking of this index have the county of Constanta, Municipality
of Bucharest, Timis County, Cluj county, due to the relatively high densities of
national roads and railways, and the presence of international airports with a
flow of passengers of over 1 million - for Bucharest, Cluj and Timis counties
and the presence of the port with European goods transit - for Constanta
County. The following places of the hierarchy are held by counties with a
high density of roads and railways and international airports with an average
flow of passengers at national level in their territory, namely Ilfov, Iasi, Galati
(which has the advantage of the port of Galati) and Bacau. Other counties with
a high transport domain index are: Mures, Suceava, Bihor (which also have
international airports) and Calarasi (due to the flow of goods from the port
of Calarasi) Salaj (with high density of public roads and railroads), Arges.
Counties with low transport index are: Caras-Severin, Harghita, Covasna
and even Tulcea and Maramures, where there are international airports Gini
coefficient of the transport index at the level of counties has the value 0.1957,
the least of the values of the Gini coefficient of these indices. The distribution
of the transport index series is characterized by the following elements:
32
20
Series: D01
Sample 1 42
Observations 42
16
12
8
4
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
2.735714
2.375000
6.750000
1.550000
1.150095
2.138862
7.755656
Jarque-Bera
Probability
71.60156
0.000000
0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
6.0
6.5
7.0
The mean transport index is 2.74, much less than the mean
polycentricity index (3.63). Moreover, this index ranges from 1.55 to 6.75,
and the size of this interval is less than the intervals size of other indices.
Because skewness has the value 2.14 (greater than 1), the distribution is highly
skewed to the right i.e. a lot of values are concentrated on left of the mean,
with extreme values to the right. Therewith, the probability value associated
with the Jarque-Bera statistic is less than 0.05 which means that we reject the
null hypothesis of normal distribution.
Education domain
For the education domain we performed the following cartogram.
The education index at the level of counties in Romania
Map 6
33
Analyzing the cartogram of the education index it results that Bucharest
tops the hierarchy of the index, due to the large number of universities and
number of students and that the counties below are Iasi and Cluj for the same
reasons. Next, we find the counties whose homes are large university centers,
namely Timis, Constanta, Sibiu, Dolj and Brasov. A relatively high education
index have the counties of Arad, Bihor, Galati, Mures, Arges, Bacau, Prahova,
Suceava and Maramures. The counties with the lowest education index are
Harghita, Braila (even if Braila is a city with historical resonance), Covasna
Mehedinti and Ialomita. At the bottom of the ranking is included Tulcea,
Vaslui and Giurgiu, where the national statistics registers no student. Gini
coefficient of the education index has the value 0.4317, which shows that
the differentiation between counties in this domain is bigger. Concerning the
distribution of the education index series we have the following information:
9
Series: E
Sample 1 42
Observations 42
8
7
6
5
4
3
Mean
Median
Maximum
Minimum
Std. Dev.
Skewness
Kurtosis
2.640476
1.875000
9.200000
0.000000
2.192510
1.255338
3.994975
Jarque-Bera
Probability
12.76358
0.001692
2
1
0
0
1
2
3
4
5
6
7
8
9
The mean education index has the value 2.64, the least of the mean
indices values. The distribution is highly skewed to the right because the
skewness has the value 1.26 (greater than 1). Standard deviation (Std. Dev.)
being 2.19, the values of the education index vary in average much enough in
comparison with the mean education index. Since the p-values (Probability)
for the Jarque-Bera test is less than 0.05 we reject the null hypothesis of
normal distribution.
Conclusions
The polycentricity of locations systems is considered to be a factor
supportive of territorial sustainability as well as of decreasing territorial
disequilibrium. The territorial units NUTS 3 can be assimilated to a certain
extent to functional urban areas. For such reasons, the study of counties
polycentricity acquires a great importance. For all indices calculated prevail
low values which means that most of the counties have a low development
34
level concerning polycentricity and each of the domains. Taking this into
account, the long-term development strategy in the field of spatial and urban
planning in Romania must develop integrated projects for those areas facing
difficulties. All the same, central and local authorities must work together in
order to create conditions for direct investments and implicitly a higher capital
contribution, so as to achieve the objectives of the European Union Strategy,
for the period 2014-2020 on the policy of territorial cohesion. The results
obtained related to the degree of polycentricity at the level of territorial units
NUTS 3 in Romania are not exhaustive but they rather represent a useful
exercise to reach some conclusions about the current situation and a possible
evolution of counties and to highlight their typology through the areas
studied. More accurate evaluations of the domains indices and thus of the
polycentricity index might get by converting the results for indicators into
utilities using linear functions (Manole et al., 2011). Also, the differentiation
of counties could be achieved by determining intensity of preference for each
county with the help of PROMETHEE methods (Brans and Mareschal, 2005)
or by establishing some out-rating relations between counties with the help of
ELECTRE methods (Milani et al., 2006).
References
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decision analysis: state of the art surveys, 78, pp.163-186
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=&View={4223A4850-B4790-4965-4285DBD4220A4841A5430B}. (accessed
on 20 July 2015)
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form: the social and environmental costs of different patterns of urban expansion,
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(accessed on 25 July 2015)
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ec.europa.eu/regional_policy/sources/docoffic/official/ reports/pdf/sum_en.pdf.
(accessed on 2 August 2015)
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pdf. (accessed on 26 July 2015)
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Research Agenda, Urban Studies, 38(4), pp. 623-633
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36
Comparative Study of European and national
Programmes Regarding Innovative Capacity of
Small and Medium Enterprises
Prof. Vergil VOINEAGU, PhD
Prof. Alexandru MANOLE PhD.
Abstract
The European market is one of the largest at global level, but still
laks competitivity in an comparative analysis with other global economies
like US and some Asian ones. Innovation is now the key word that drags after
it the growing competitivity of the companies and therefore of its economical
environment. Result of research and development activities innovation can
bring exponential economical growth on a more global and digitalysed
market. But growth innovation is also associated with risk failure, therefore
the risk finance realm needs to specialised itself in all its forms of intevention
as it is the case for public grants for RDI.
Key words: research development and investments, financial
analysis, operational programmes, european grants, innovative capacity
The Partnership Agreement signed between the European Commission
and each Member State provides the existing of certain complementarities
between the European Programme for Research and Development - Horizon
2020 and the specialized national programs such as the Competitiveness
Operational Programme in Romania.
From these two programmes we will select the sub-measures that
are addressing the big challange that young innovative SMEs are facing
for accesing finance for research, development and internationalisation.
Specifically we will annalyse the Horizon 2020 program, SME Instrument
and the Operational Programme for Competitiveness, New Innovative SMEs.
Regarding the aspects related to innovation: Both programess
aim at assessing innovation. From this point of view the program Horizon
2020 - SME Instrument the Management Authority regularly launches call
37
of proposals on topics related to smart industries. From the perspective of
the European Research Executive Agency, the evaluation is mainly assesed
through the lens of the experts as the evaluation is carried out by a pannel
of external experts that are specialists in the selected fields, and has strong
knowledge about the leading industrial actors and technical solutions existing
on the market in that area. From the point of view of Romanian POC –
New Innovative SMEs, innovation is mainly demonstrated by submitting
documents that prooves it like patents, doctoral thesis, results of a research
contract.
Protection of intellectual property issues: both programmes are
intended to protect innovation and the investment made through European
funds. From this point of view, the SME Instrument allows any strategy for
intellectual property protection, beggining with patent licensing at national,
European, global level, holding the core technology as an industrial secret,
the use of confidentiality agreements with partners and subcontractors, or one
can choose to publicly disseminate the research of the results as long as it has
the means to exploit it in first. In the POC, protecting intellectual property is
demonstrated mainly through registration of the pattent at national or other
Member State registery and by publishing the doctoral thesis.
Commercialisation: Both programmes are looking for the results of
the research – development – innoovation process to be funded and become
a product, service or process that wil be succesfully launched on the market
(European and global one). So the market potential of the evaluated projects
outcomes are assesed in the Horizon 2020 – SME Instrument program
according to market studies, the presentation and description of benefits that
customers receive, Letters of Intent from distributors, end-users or clients.
Moreover, at European level any business model is accepted as long as it can
demonstrate viable selling chanels and can proove the collection of revenue.
Thus we can consider channels as direct salles to the consumer, selling
through distributors, licensing the technological sollution to other companies,
Internet sales etc. The European Experts will evaluate the commercialisation
potential taking into consideration the complexity of today’s markets and the
variety of possibilities of commercialisation existing thanks to the internet
and automatisation. In case of POC, the commercialization is demonstrated
by presenting a binding Contract of Sale signed between the producer and
the buyers and that have the value at least at the level of the european non
refunding grant.
Without switching to other elements of the evaluation we will draw
a few conclusions about the uptake of innovation at European and Romanian
levels, conclusions which could explain at least in part the results of Romanian
38
SMEs in the field compared to other EU countries. For Romania it is noted that
the assessment is done mainly formal through presentation by the applicant
of written documents stating the criteria to be evaluated. Thus, to prove the
innovation patents of doctoral thesis are required, to evaluate the strategy
for protecting the intellectual property it is demanded to provide a pattent,
to assess the commercial potential it is requested to present a binding sales
agreement signed by a customer that is oblidged to buy the resulted innovative
product at the end of the research and development process.
Horizon 2020 SME Instrument proposals submitted per countries 3
February 2016
Source: European Agency for SME ec.europa.eu/easme/
From the above it can be easily seen the formalism of the evaluation
system of European projects in Romania. In other words, one can observe that
old bureaucratics habits of covering personal work by papers continue to exists
also in the innovation realm. But why? One of the reasons is that in Romania,
compared with the European Union where the evaluation is performed only
by external experts, the assessment of the projects is carried most of the times
by civil servants employed by the Managing Authorities that is related to the
Ministry that coordinates the respective operational program. In other words,
by non-involving in the evaluation process professionals or specialists from
the private sector, we face a regretfull situation where civil servants (and
not precisly experts in the field) tend to cover their work and option with
papers, avoiding that way being held accountable on the success or insucess
of the project finance. Even if beggining with 2013, there have been several
attempts to externalyse the evaluation procedure focusing on contracting
individual experts or specialists, in the last years, even those initiatives have
39
been denaturated into public acquisition contracts hunted by companies that
are looking for profit and less on providing the needed expertise through
individual specialists.
The lack of accountability of the evaluator makes the whole process
of innovation evaluation of European projects in Romania to be a purely
formal activity. The whole Competitiveness Program appears to be a form
without content, an system designed to automatically translate in Romania
the European concepts. This mechanism of automatically copying European
policies have a direct impact on enterprise competitiveness, which partly
explains the statistical results of Romanian SMEs in the European program
Horizon 2020 SME Instrument. As it can be seen in the statistics of the
European Agency for SMEs only one innovative project from Romania was
financed through the H2020 SME Instrument, namely CargoList.eu, which
shows the differences of perception on innovation.
Horizon 2020 SME Instrument awarde projects per countries 3
February 2016
Source: European Agency for SME ec.europa.eu/easme/
Another issue arising from this brief comparative analysis regards
the distorted perception that evaluation in Romania has on risk. Thus, when
analyzing even the strategic documents underlying the program Horizon 2020
and the EU and National Strategies on Competitiveness, we notice that most
projects in research - development - innovation, although they have great
potential for growth, implies a great risk of success. This inverse correlation
between innovation and success is well understood in Europe and especially
in America. Thus, in America, especially in the Silicon Valley a vibrant
ecosystem of innovation has developed that includes a strong component of
private risk finance. This type of financing for innovation, generically referred
40
to as venture finance is generally made through private equity investment
through accelerators, networks of business angels, seed capital funds or
venture capital funds, investors understanding very well the risks associated
with innovative projects, but willing to accept them due to the enormous gains
that arrise from successful innovative projects compared to losses associated
with failed projects.
What we want to stress is that research - development - innovation
projects are high risk projects and requires an assessment carried out by
professional experts in the field, which may originate from the private finance
sector (banks, investment funds, etc.). The fact that in Romania the evaluation
of innovative projects is mainly carried out by civil servants often exceeded
by the daily news in the specific innovative industry, unfortunately justify
the excessive formalization and totally ineffective evaluation process of
innovation.
Misunderstanding the risks associated with innovation made the
Guidelines for Applicants for the POC Innovative SMEs requires the applicant
to prove the commerciability of the products through mandatory documents as
a Contract of Sale that binds the buyer to purchase in the future the expected
results of the research – development – innovation project funded through
European grants. This situation itself is anachronistic because it manages to
turn a risk financing mechanism in one of the safest ways of financing that are
to be found only in factoring for example. If an SME should already have a
purchase agreement with a buyer, it would be more effective to turn to a bank
for a credit or factoring product. Also, if a buyer is obliged to purchase a future
product it could very well turn to a bank for buyer credit for example, making,
why not, and exclusivity deal for marketing that product for a further period
of x years. Unfortunately, requesting such mandatory documents is again a
matter of form that is unlikely to reflect the real intent of the signing parties of
the contract. Anyway the penaly for not buying the product at the established
value of the grant is to reimburse the difference of the European Grant, acting
like credit with free interest for the implementation period (not a first in the
absorbtion of EU funds in Romania). So, more than likely, in this case there
will be presented properly written contracts, that are in a great majority not
reflecting a reality.
As stated before, the penalties which the beneficiary of such financing
should support if it fail, merely completes this picture of innovation in
Romania. Thus, according to Applicant’s Guide POC, Innovative SMEs,
the beneficiary is obliged until the end of the sustainability of the project
(implementation period + monitoring period), generally five years, to cash
in revenues from the new developed product, at least at the amount awarded
41
as grant. The penalty if this criterion is not met by the applicant is the
reimbursement of the difference. Basically, in the example from the above
paragraph we can observe a great precautionary of the Management Authority
to minimize its own risk associated with failure of absorbing funds allocated
to the Opperational Programe. As is known, each MA is assessed and audited
to measure the effectiveness of using European public money. But in this
case, the solution found for the assesment of innovative projects cancels the
innovation aspects associated with such projects.
Implementation of Multianual Financial Framework 2007 – 2013
Member State
Budget
2007
– 2013
billions
Eur
Budget
2007 – Contracted
Contracted
2013 per ammount
percent %
capita billions Eur
Eur
Bulgaria
6,674
927
7,7
115%
Czech Republic 26,303
2502
25,2
96%
Estonia
3,403
2588
3,3
98%
Hungaria
24,921
2523
28
112%
Letonia
4,530
2278
4,8
105%
Latvia
6,775
2301
6,8
100%
Poland
67,186
1745
68,2
102%
Romania
19,175
961
20,3
106%
Slovakia
11,651
2149
13,1
112%
Slovenia
4,101
1989
4,3
104%
Source: http://www.ecsif.eu/Pagini/Master-EUG-Bucharest.aspx
Payments
Payment
made
percent
in 2007
(absorbtion)
– 2013
%
billions
Eur
5,1
77%
18,1
69%
3
87%
21,7
87%
3,9
86%
6
88%
52,5
78%
10
52%
7,6
65%
3,4
83%
Regarding the results of the European funds absorbtion per countries
place Romania on the second lowest place in Europe after Bulgaria. But
concerns of MA to improove the rate of European funds absorbtion should
take into account effective measures and not only formal ones like is the case
of the bankability of European projects. Thus, the bankability of European
projects which translates to the profitability of European projects suggest that
these should be attractive for private sector financing, so it is important that the
expertise assessment associated with the bancability of European projects to
be drawn from the private and integrated into the public domain of European
funds. As this was done only episodically in Romania, the Managing Authority
followed the beaten and safer path, which unfortunately is not right for an
innovative business model.
42
Implementation of Multianual Financial Framework 2007 – 2013
in Romania
Source: http://www.ecsif.eu/Pagini/Master-EUG-Bucharest.aspx
Conclusions:
As recommendations for improving the Competitiveness Operational
Program we recomand the integration of an evaluation model originated in the
private innovation funding. Still, this evaluation mechanism request attracting
specialised competencies in finance and innovation all of that beeing widelly
found in the private financial realm. Also, by attracting external experts in
the evaluation mechanism we can also guarantee the principle of autonomy
by not beeing exposed to any form of pressure from superiors. Also as
external applicants do not know the external experts identity can improve the
assessment ensuring its objectivity. An external expert knows the market and
can better assess the potential of a business through the continuous assimilation
of knowledge and information in the field.
And in terms of financial projections, the financing template for the
Application of POC presents some difficult to understand requirements for
the innovative realm. Thus, according to the applicant’s guide, the applicant
must make financial projections for the next 15 years. Considering that most
start-ups fail after 3 years, it is hard to believe that the financial projections
can be more than an exercise of imagination. In principle innovation is related
to the changing economic environment and to present financial estimates for a
period of 15 years, is totally unrealistic and unnecessary.
43
References:
1. Anghelache, C. (2015). România 2014. Starea economică în continuă creştere,
Editura Economică, Bucureşti
2. Anghelache, C. (2014).România 2014. Starea economică pe calea redresării,
Editura Economică, Bucureşti
3. Anghelache C., Anghel M.G. (2015). Model of Analysis of the Dynamics of the
DFI (DFI) Sold Correlated with the Evolution of the GDP at European Level,
Romanian Statistical Review Supplement, No. 10, pp. 79-85, Romanian Statistical
Review este indexată în bazele de date internaţionale Index Copernicus, DOAJ,
EBSCO, RePEc, ISSN 2359 – 8972
4. Anghelache C., Popovici M., Manole A. (2015). Model of structural analysis of
the DFI sold by sectors and activities, Romanian Statistical Review Supplement,
No. 10, pp. 106-112, Romanian Statistical Review este indexată în bazele de date
internaţionale Index Copernicus, DOAJ, EBSCO, RePEc, ISSN 2359 – 8972
5. Lommelen, T., Hertog, F. den, Beck, L., Sluismans, R. (2009) - Designing plans
for organizational development, lessons from three large-scale SME-initiatives,
United Nations University - Maastricht Economic and Social Research Institute
on Innovation and Technology (MERIT) in its series MERIT Working Papers with
number 027
6. Păunică, M. (2014). Economic benefits of the infrastructure projects implemented
in the Reservation of the Danube Delta Biosphere, Theoretical and Applied
Economics, vol. 18, no. 11 (600), pp. 95-104
7. Stepniak-Kucharska, A. (2016) - The impact of the global downturn on the
economic situation of the SME sector in Poland, Ekonomia i Prawo. Economics
and Law, Volume (Year): 15 (2016), Issue (Month): 2 (June), pp. 235-248
8. Constantin Anghelache, Gabriela Victoria Anghelache, Madalina Anghel, Georgeta
Bardasu, Cristina Sacală (2014) - The International Trade Evolution, Romanian
Statistical Review Supplement, Volume 62, 1/2014, pp. 84-87
9. http://eur-lex.europa.eu;
10. https://ec.europa.eu/easme/;
11. https://ec.europa.eu/competition/state_aid/Studies_reports/sme_handbook_
ro.pdf;
12. https://ec.europa.eu/programmes/horizon2020/;
13. http://ec.europa.eu/eurostat/web/structural-business-statistics;
14. http://www.fngcimm.ro;
15. http://fonduri.mcsi.ro/?q=system/files/Nota+prezentare_mecanism+OUG+9.doc;
16. http://www.gov.ro;
17. http://www.poc.research.ro/programare-2014-2020.
44
Study on the relationship between financial
performance and leverage: empirical evidence on
Bucharest Stock Exchange
Lector univ. drd. Floriniţa DUCA
Universitatea ARTIFEX, Bucureşti
Abstract
This paper seek to investigate the relationship between financial
performance of the companies and the companies debt to equity. The empirical
study was conducted on a sample of one hundred companies listed on the
Bucharest Stock Exchange. Financial performance of firms is analyzed by
return on equity. The dataset is obtained from annual reports for 2010. The
results indicate a positive and significant relation between return on equity
and debt to equity.
Key word: Return on equity, debt-to-equity ratio, size firm, corporate
governance
I. Introduction
Corporate governance, i.e. the system by which companies are
directed and controlled, has become a key topic for legislation, practice and
academia in all modern industrial states(Hopt, 2011). Furthermore, corporate
governance covers all the rules of and constraints on corporate decision-making.
Corporate governance is meant to respond to agency problems created by the
separation of ownership and control. Therefore, it defines the relationship
between shareholders and managers. Good corporate governance requires that
managers have the proper incentives to work on behalf of shareholders and that
shareholders are properly informed about the decisions of the managers. Thus, it
allows for a balance between managers’ and shareholders’ desires (Wells, 2010).
Corporate governance in Romania is at initial stages, so proper
application and practice of corporate governance is not present at this moment
in Romania. The objective of the study is to investigate the relationship
between return on equity and the debt-to-equity ratio in companies listed at
Bucharest Stock Exchange. Performance of the firms is affected by practicing
good corporate governance policies.
Literature review
The relationship between performance financial and debt to equity it
is a field of study both in academia and in policy makers in recent years.
45
In 2012, Akhtar, et al. investigates the relationship between financial
leverage and financial performance. The result shows that there is a general
perception that a relationship exists between the financial leverage and the
performance of the firms. The financial performance indicators have positive
relationship among leverage and the financial performance.
Using a panel data analysis, Raza( 2013) examines the determinants
of capital structure of Karachi Stock Exchange listed none-financial firms for
the period 2004 through 2009. The regression statistical technique was used
for the research. The results indicated a negative relation between performance
leverage. Also, there was no significant relationship between leverage and
profitability.
In their study, Obradovich and Gill (2013) show that larger board
size negatively impacts the value of American firms and CEO duality, audit
committee, financial leverage, firm size, return on assets and insider holdings
positive relationship the value of American firms.
III. Methodological framework
The panel data set covers a on year, with a sample of one hundred
firms listed at Bucharest Stock Exchange. The data were taken from the annual
reports of these firms. All financial data is nominated in terms of romanian
coin. The model used was multiple regressions (more than one independent
variables). I used to study Ordinary Least Squares (OLS) method for analysis
of hypotheses stated in a multiple form. That is, a pooled OLS equation will be
estimated in the form of: Return on Equity = β0 + β1 Debt-to-equity + β2Size
+ μ (1). , Where;

μit = Error term.
Description of variables:


Return on Equity (ROE): Return on equity measures a corporation’s
profitability by revealing how much profit a company generates
with the money shareholders have invested. Return on equity(ROE)
is expressed as a percentage and calculated as: Return on Equity =
Net Income/Shareholder’s Equity.

 The debt-to-equity ratio is a financial ratio show the relative proportion
of companies equity and debt used to finance an companies assets.
Debt-to-equity ratio is used as a standard for judging a company’s
financial standing. It is also a measure of a company’s ability to
refund its debts. A debt-to-equity ratio is calculated by taking the
total debts and dividing it by the shareholders’ equity.

 Firm size is measured by the log natural of total assets.
46
IV. Results and discussion
The current section deals with the results of the study which include
the descriptive statistics, econometric results for the model.
The descriptive statistics are calculated and analysis mean and standard
deviation of all the variables have been presented in Table 1. The result relevant
to the descriptive statistics for the return on Equity is 0.2663. The value is
more than one, it indicates that the market value is higher than the total asset
value and that the company might be overvalued. Debt to equity and size have
positive mean value which to 0.8411 for debt to equity to 17.9487 for size.
Debt to equity have the highest standard deviation of 2.9221. This indicates
that the observations in the data set are widely dispersed from the mean. This
table above also shows that size has value of standard deviation of 1.6123.
Descriptive Statistics
Tabel 1
Mean
Median
Maximum
Minimum
Std. Dev.
ROE
0.2663
0.0426
17.5491
-0.1152
1.7554
Size
17.9487
18.0965
24.1899
13.8661
1.6123
Debt-to-equity
0.8411
0.3173
28.5904
-3.3318
2.9221
In this section the results of the inferential statistical techniques used
in the study are presented(Table 2).
Method of least squares
Dependent variable = Return On Equity(ROE)
Variable
Coeff.
Std. Error
t-Stat.
Size
-0.0845
0.0299
-2.8252
Debt-to-equity
0.5703
0.0165
34.5398
C
1.3040
0.5413
2.4089
R-squared
0.9282
Mean dependent var
Adjusted R-squared
0.9268
F-statistic
Durbin-Watson stat
1.9550
Prob(F-statistic)
Table 2
Prob.
0.0057
0.0000
0.0179
0.2663
627.34
0.0000
The table above shows that coefficient of multiple determinations
R-Square which explains the extent to which the independent variables affect
the dependent variable. In this case, 0.9282 or 92.82% of the variations in the
dependent variable were explained by the independent variables. Value for
F-statistic is 627.3477. Diagnosis suggests that the independent variables, level of
debt-to-equity ratio and size firm have a significant relationship with profitability
of the company. Firm size, on the other hand, has a negative and significant.
47
The result shows that debt-to-equity ratio has a significant impact
on return on equity, the value of p-value = 0.0000 <0.05(Table 2). Company
size has a significant negative effect on return on equity, the value of p-value
= 0.0057 <0.05. The results presented in Table 2 show that firm size has a
negative and significant relationship with, on the other hand, the debt ratio has
a strong and positive correlation with return on equity. This result is confirmed
by research conducted by Abu-Tapanjeh (2006).
Conclusion
This paper examines the relationship between financial performance
measured by return on equity and debt to equity firm’s for a hundred firms
listed at Bucharest Stock Exchange. The result shows firm size is negatively
related with return on equity at 5 % significance level, indicating larger firms,
lower results than their smaller counterparts, and the debt ratio has a strong
and positive correlation with return on equity.
As for limitations, this study choice of debt ratio and firm size as the
only independent variables affecting profitability was dictated by the available
data sources. The database employed is unique and reliable consisting of the
public annual balance sheets and audit reports. The indicators return on equity
are consistent with those used in previous studies, using return on equity. Given
the limitations mentioned above, there are several lines of research which could
be undertaken as a follow up on this paper: adding more variables to study
the relationships between performance financial and debt to equity; improved
ways to measure profitability as well as investigate it in different time periods.
References
1. Akhtar, S., Javed, B., Maryam, A., Sadia, H. (2012). Relationship between financial
leverage and financial performance: Evidence from fuel and energy sector of
Pakistan, European Journal of Business and Management 4(11): 7 – 17.
2. Obradovich, J., Gill, A. (2013). The impact of Corporate Governance and financial
leverage on the value of American firms’, Faculty Publications and Presentations.
Paper 25.
3. Hopt, K. J. (2011). Comparative Corporate Governance: The State of the Art and
International Regulation, American Journal of Comparative Law, Vol. 59, issue 1,
2011, page numbers 1-73, ISSN 0002-919x.
4. Wells, H. (2010). The birth of corporate governance. Seattle University Law Review,
33(4), 2010, page numbers 1247- 1292-73, http://ssrn.com/abstract=1581478.
5. Abu-Tapanjeh, A. M. (2006). An Empirical Study of Firm Structure and Profitability
Relationship: The Case of Jordan, Journal of Economic & Administrative Sciences,
Vol. 22, No. 1, 2006, page numbers 41 – 59, ISSN: 1026-4116.
48
The European Initiative for Small and Medium
Enterprises
Assoc. prof. Mădălina Gabriela ANGHEL
Daniel DUMITRESCU PhD student
Alexandru URSACHE PhD student
Abstract
The SME initiative aims to increase especially the volume of loans
to SMEs in the EU, by concentrating resources of EFSI, COSME, Horizon
2020 and the full use of EIB and EIF funds. The SME Initiative is offering
capital help for European banks in order to make them more robust and
to encourage them to lend more to SMEs. The goal is to have a significant
influence on the financing of SMEs and thereby increasing the economic
growth.
Key words: sme initiative, financial instruments, european funds,
banks, sme
Following the global financial and economic crisis, European banks
were forced to repair their budgets damaged by the failure of SME loans, to
sell their portfolios of receivables in order to comply with new regulatory
requirements of Basel III and CRD IV. These measures had an direct effect
on reducing the volume of credit, market fragmentation and the emergence
of difficulties in providing liquidity to the real economy. The main collateral
victims are undeniable the SMEs that have been mainly affected in the
European Union. In May 2013 the European Central Bank President Draghi
said that the biggest obstacle to returning to economic growth ist o deblock the
channels for SME lending.
The SME Initiative is a joint financial instrument of the European
Commission and the EIB Group (European Investment Bank and the
European Investment Fund) that aims to stimulate SMEs financing by partial
covering the risk associated with banks credit portofolios. The SME Initiative
is supported through the financial resources of Member States from ESIF –
European Structural and Investments Fund and co-financed by the European
49
Union through the COSME and Horizon 2020 programmes and other resources
of the EIB Group.
The initiative includes the implementation of two products: an
uncapped guarantee portfolio and a securitization window. Through the SME
Initiative, the EIF provides to selected financial institutions (banks, leasing
companies, guarantee institutions, venture capital funds) protection and
assistance for the potential loss of capital costs. In return for sharing risks,
financial intermediaries have to make certain loans to SMEs, lease and / or
guarantees on more favorable terms (eg lower interest rates or lower colaterals
requirements for final beneficiaries). EIF financial intermediaries are selected
through a call for expressions of interest. EU Member States have the posibility
to choose to join the SME Initiative by the end end of 2016 by expressing their
interest to the European Commission.
Compared to other financial instruments that are being developed
with funding from the ESIF, the SME Initiative offers the following benefits
to member states and administrative authorities:

 Is not requiring co-financing from national or regional resources;

 Doesn’t need to conduct further ex ante evaluations, that is done by
the European Commission and the EIB in 2013 at EU level;

 The European Commission and the EIB have already adopted
a „model grant agreement”, which is a ready-made model for a
funding agreement to be negotiated between the Member States and
the EIF;


The treatment of state aid was has already been approved by the
European Commission;

 It allows a combination of different resources, including grants and
sources from national promotional banks;

 Thanks to the contribution of the various stakeholders, the ESIF
funds provided by the Member States will have a higher leverage
compared to other ESIF financed EU instruments;

 Will receive strong support from the EU and the mobilize capital
from the EIB Group.
The European Investment Bank (EIB) is the credit institution of
the European Union owned by its Member States. Its principal business is
to provide long-term financing for investments considered strong in order to
contribute EU policy objectives. The European Investment Fund (EIF) is part
of the European Investment Bank Group. Its main task is to assist SMEs in
Europe to help them access finance. EIF designs and develops tools for risk and
growth capital, guarantees and micro tools aimed specifically for this market
segment. In this role, EIF supports the EU objectives in support of innovation,
50
research and development, entrepreneurship, growth and employment, EIF
net commitments to private equity funds totaling more than € 8.8 billion at
the end of 2014. With investments in over 500 funds, the EIF is a leading
financial player in Europe, thanks to the scale and scope of its investments,
especially in the high technology and early development of high competitive
industries. The loan guarantee portfolio amounted to more than 5.6 billion in
over 350 operations at the end of 2014, positioning EIF as leader in supporting
European SMEs.
The SME initiative aims to increase especially the volume of loans to
SMEs in the EU, by concentrating resources of EFSI, COSME, Horizon 2020
and the full use of EIB and EIF funds. The SME Initiative is offering capital
help for European banks in order to make them more robust and to encourage
them to lend more to SMEs. The goal is to have a significant influence on the
financing of SMEs and thereby increasing the economic growth.
The SME Initiative has as legal basis Art. 39 of Reg. (EU) N°
1303/2013 Common Provisions Regulation (hereafter CPR) Title IV Financial instruments, that states the possibility of Member States to make
voluntary contributions of resources from the EFSI (ERDF and EAFRD) to a
financial instrument developed at European level indirectly managed by the
European Commission through the EIB Group. In this case, the binding ex
ante financial instrument evaluation has already been carried out at EU level
by the EIB and the European Commission.
The budget for the SME Instrument corresponds to 7% of ERDF funds
for each Member States and is capped to 8.5 billion EUR. The maximum
allowed contributions from the COSME program is (EUR 175 million) and
from Horizon 2020 (175 million euros). In addition, other national development
banks and / or private investors may supplemented the budget through own
resources.
The ex-ante evaluation of the 28 Member States results emphasize
that 4.1% of all EU SMEs (about 860 000 SMEs), couldnt obtain loans even
if they were considered financially viable. The credit financing request from
the non-financial sector in the period 2009-2012 was quantified at EU level at
112.000.000.000 €. It is estimated that in the future, the SME financing gap
at European level will be significantly reduced, however, such improvements
will not be enough to cover the loan financing gap, especially since there are
differences between Member States
The SME Initiative includes two options that are not mutually
exclusive:
Option 1: uncapped guarantee instrument;
Option 2: Securitization common tool for both new and existing loans.
51
Option 1 scheme: uncapped guarantee instrument
Fig.1
Source: http://www.ecsif.eu/Pagini/Master-EUFI-Bucharest.aspx
Option 1 - uncapped guarantee instrument is the most advanced
ad with the highest market demand. This tool combines the resources of the
ERDF and EAFRD as from COSME, Horizon 2020 in a single household.
Both the EIB and the EIF participate in this risk-sharing mechanism. The
instrument can cover up to 80% of the losses of the banks’ loan portfolios,
but the bank still retains an exposure up to 20%. It is important to note that
the allocated budget of this tool is adapted for each participating Member
State and proportionate to their contribution to the instrument. The lasts will
beneficiate from the SME Instrument as their banks will further finance SMEs
with higher risk (as in innovative areas or startups), providing loans with lower
interest and reduced colateral requirements. Gradually, the banks capital will
be improved so that to allow the granting of new loans to other SMEs.
Option 2 is a common instrument for the securitization of loans,
for existing and new ones and can gather national resources from ERDF and
EAFRD and European resources from COSME and Horizon 2020, the EIB,
the EIF and where appropriate, funds from national promotional banks. The
increase in lending to SMEs will be posible offering capital release through
securitization of capital so that the banks can extend new loans to SMEs.
This financial instrument has two stages:
a) the securitization of a portfolio of existing loans or new and
b) the building of a new portfolio by the bank.
ERDF and EAFRD resources should bear 50% of the most risky
tranche (the junior tranche). The bank should have a significant interest in the
transaction maintaining high standards for the loans in the portfolio and to
ensure community of interest with the sponsor (in this sense, it is recommended
that the bank should bear 50% of the junior tranche). EU funds with EIF will
52
have the mezzanine tranche. EIB and other institutional investors will invest
in senior tranche.
The SME Initiative has several important steps, the first of them, the exante evaluation was carried out by the EIB Group and the European Commission
in the 28 Member States of the European Union. Each participating Member State
shall send the European Commission a single operational program dedicated to
the financial contribution of the ERDF and EAFRD for the thematic objective
describbed in Article 9 (3) above, that will support the ability of SMEs to
develop on national, regional and international markets as in innovation realm.
Resources for the implementation of the SME Initiative should be proposed
in an unique, specially established national program. If there will be different
budgets at regional level (multiple regions), there should be a clear presentation
of each regions budget. The different involved regions should agree on a „single
authority”: the managing authority, the certification authority (if any) that audit
one. Today there is allready a grant agreement template available (EC Decision
2014/660/EU from 11th of September 2014) for the use of the interested parts.
The signing of the agreement financing shall be made within 6 months from
the approval of the dedicated national operational program by the European
Commission. If the SME initiative is established at the regional level, this
should be clearly indicated in the program. Member States may submit a change
request of the national program in order to reallocate budgets to other programs
and priorities in accordance with the requirements for thematic concentration.
After signing the grant agreement, the EIB will grant a request for payment of
the participating Member States. Within 3 months of the request for payment
EIB will approve the transaction with the selected financial intermediaries.
The SME Initiative adds differentiated value as follows:
Member States may not be required additional national co-financing
for the ERDF and the EAFRD. The result shall be to increase the leverage
effect on the ERDF – EAFRD contribution through a combination of resources
involved (Article 39 (5) of CPR). A larger number of SMEs will be supported
and will receive better terms on contracted loans because of the risk sharing
with the EU and the EIB is available. The SME Initiative will complete
existing financial instruments in order to address the failure of the existing
private finance market.
Regarding the financial institutions the SME Instrument will improve
their capital so banks will be able to further provide new loans to SMEs.
Through securitization, new resources will be at banks disposal, allowing to
extend the loans volumme with no direct impact on risk exposure.
The SME Initiative will contribute to more liquidity for SME
investments by providing loans in improved financing conditions and better
53
contractual terms. As direct effect there will be available finance for projects,
which otherwise would have been denied by the banks.
Member States that joined this initiative commisioned EIF for the
implementation and management of the SME Initiative in close cooperation
with the EIB. The SME Initiative is currently available in Spain and Malta,
while Bulgaria and Romania have recently joined the initiative. In the future
the SME Initiative might be extended to other EU Member States.
The SME Initiative in Spain:
The SME initiative was launched in Spain on 26th of January 2015.
This financial instrument is financed by Spain, the European Commission and
the European Investment Bank (and the European Investment Fund). The EIF
is empowered to manage the instrument. The contribution of the Kingdom of
Spain, partly from European Structural Investment Fund (ESIF) and supported
substantially by the 16 Spanish regions, amounts to EUR 800 million and it
is expected to generate with the support of other participants, at least EUR
3,200 million for the financing of SMEs in Spain in the coming years. Spanish
regions that have contributed to the SME Initiative, are: Andalusia, Aragon,
Balearic Islands, País Vasco, Canarias, Cantabria, Castilla-La Mancha,
Catalonia, Castilla y León, Extremadura, Galicia, La Rioja, Comunidad de
Madrid, Murcia, Comunidad Valenciana, Ciudad Autónoma de Ceuta.
The contracts were signed in Madrid by EIB Vice-President Román
Escolano, Director of the FEI Pier Luigi Gilibert and State Secretary Marta
Fernández Currás. The SME Initiative launch in Spain was a sign of the strong
commitment of the EIB Group to SMEs in Europe to assist in the economic
recovery and the creation of new jobs. This pioneering initiative is to ensure
clear progress towards a more efficient use of structural funds and ensure
that financing will be provided to a greater number of small and medium
enterprises in more favorable conditions.
The contribution of € 800 million in Spain and the affected regions will
be extended on a commercial loan by a risk-sharing mechanism. This will lead
to more SMEs from Spain to benefit from lower interes rates. This financial
instrument will act as a catalyst for private investment and job creation.
The SME initiative in Malta:
The EIB Group (EIB and EIF) signed the agreement with the
Government of Malta and the European Commission for the implementation of
the SME Initiative on July 15, 2015. The initiative is financed by the Republic
of Malta, the European Commission and the EIB (European investment Bank
and the European investment Fund), the EIF beeing appointed as a manager of
54
the system on behalf of different taxpayers. The Republic of Malta contribution
is 15 million euros from ESIF and it is expected that with the resources of
other participants, to generate more than EUR 60 million in new funding for
Malta SMEs in the coming years.
The agreement was signed in Valletta by the EIF Chief Executive Pier
Luigi Gilibert and Parliamentary Secretary for the EU presidency and Dr. Ian
Borg Deputy Prime Minister of the Malta Republic.
Maltese government allocation of 15 million euros in the SME
Initiative will support the investment and the development of local SMEs.
This will lead to more SMEs for beneficiating from European funds tby
accesing loans on more favorable terms, such as low interest rates and
improved requirements for colaterals It is expected that more than 800 SMEs
in Malta will benefit from this approach and more than 60 million EUR will
be available through loans. These financial investments provide opportunities
for SMEs. This initiative will play a crucial role in the growth of the Maltese
economy as an important source of new jobs.
The SME Initiative in Bulgaria:
With the decision of joining the SME Instrument, the European
Commission will support the dedicated Bulgarian operational program. The
program will consider the thematic objective of investing for economical growth
and improoved employment in Bulgaria. The budget allocated to the operational
program was reallocated from the operational program for competitiveness and
innovation and has a value of € 102 million and has been selected to be used in the
form of bank guarantees. This form of support in the form of financial instruments
will beneficiate from the expertise of EIB Group and from funds from Horizon
2020, having as result the improoved efficiency of the operational program.
The operational program structured that way will improve access
of small and medium enterprises in Bulgaria and facilitate reaching the EU
targets for reducing disparities between the levels of development of the
various regions of the EU, having a significant contribution in promoting
economic growth, employment and competitiveness. This will be done
through the allocation of a part of the ERDF budget to the SME Initiative in
the form of uncapped guarantee portofolio that will facilitate the access of
bulgarian SMEs to loans and improove economic prosperity, environmental
sustainability and social development.
The main objective of this financial instrument is to improve the
access to finance of SMEs, leading at the end to the increasing investment
activities of SMEs and increasing the productivity. Accordingly, it is expected
that this program also contributes for smart European strategy, sustainable and
55
inclusive, including the thematic objective of improving the competitiveness
of SMEs.
The SME Initiative in Romania:
In July 8 2015, the Romanian Government approved a memorandum
on Romania’s participation in the initiative and on March 29 2016 the European
commission aprooved the submitted Romanian Operational Programme SME
Initiative with a total value of 100 million euros. Since Romania chose the first
option (uncapped portofolio guarantee), it is estimated that the total ammount
of loans that will be offered by banks to SMEs will reach a value of 400 –
600 million EUR as a result of the leveraging effect of this type of financial
instruments, that usually multiply by 4 to 6 times the available budget.
European Commissioner for Regional Policy Corina Crețu said:
„Today Romania joined the group of Member States that want to improve the
business environment for SMEs, by pledging €100 million of EU Regional
Fund to help finance the small and medium enterprises. In a country where
SMEs represent over 99% of the total number of enterprises and face serious
needs of external financing, this programme supports them in order to access
loan products in better conditions. This Initiative will also enable SMEs to
be more innovative and competitive and to grow on regional, national and
international markets.”
In Romania, the SME Initiative will be funded by reallocations from
the Regional Operational Programme. With a total budget of 100 million
euros, the entire 1st Priority of the Regional Operational Program nammed
Improving access to finance for SMEs in Romania will be implemented
exclusively in the form of standardized financial instruments managed by the
European Commission through the EIB Group.
The SME Initiative in Romania will work for all eight regions with no
differences between the Bucharest-Ilfov region, which is a developed region
and the other 7 regions of the country classified, as „less developed”. Loans will
be ensured at national level, depending on the needs of SMEs with no initial
regional targets. In the uncapped guarantee instrument, payments will be made
when loans for SMEs will not be reimbursed in the order of receipt of applications
for payment, with no further strings attached to the place of investments or the
regions. Moreover, regardless of the development of the region, the degree of
European money multiplication will be calculated at national level.
The SME Initiative financing instruments in Romania will be part of
the priority investment 1.1 Supporting SMEs capacity to grow on regional,
national and international markets and involve in innovation. The specific
objective of this priority is to facilitate the access to finance for SMEs through
56
the uncapped guarantee instrument. The results that Romania is searching
to reach with support of the European Union by the uncapped guarantee
implementation tool ist o increase productivity, innovation and the ability of
SMEs to grow in regional, national and international markets.
In April 2016, the European Investment Fund has made the following
selection of financial intermediaries in Spain and Malta:
Country
Spain
Spain
Spain
Spain
Spain
Spain
Selected Bank
Banco Santander
Banco Popular Espanol
CaixaBank
Banco Sabadell
Bankia
Bankinter
Type of FI
Portfolio - Guarantee
Guarantee Amount EUR
500,000,000
500,000,000
400,000,000
312,500,000
310,000,000
150,000,000
Malta
Bank of Valletta
45,762,712
For more information, see http://www.ecsif.eu/Pagini/Master-EUFI-Bucharest.aspx
Conclusions
The SME Initiative includes the implementation of two products:
an uncapped guarantee portfolio and a securitization window. Through the
SME Initiative, the EIF provides to selected financial institutions (banks,
leasing companies, guarantee institutions, venture capital funds) protection
and assistance for the potential loss of capital costs. In return for sharing
risks, financial intermediaries have to make certain loans to SMEs, lease
and / or guarantees on more favorable terms (eg lower interest rates or lower
colaterals requirements for final beneficiaries). EIF financial intermediaries
are selected through a call for expressions of interest. EU Member States have
the posibility to choose to join the SME Initiative by the end end of 2016
by expressing their interest to the European Commission. For Bulgaria and
Romania, the European Investment Fund will organise calls for proposals in
the coming months for selecting the financial intermediaries.
Abbreviations used:
COSME: EU programme for the Competitiveness of Enterprises and Small and
Medium-sized Enterprises
CRR/CRD IV: Capital requirements regulation and directive
EAFRD: European Agricultural Fund for Rural Development
EFSI: European Fund for Strategic Investments
EIB: European Investment Bank
EIF: European Investment Fund
ERDF: European Regional Development Fund
ESIF: European Structural & Investment Funds
SME: Small and Medium Enterprise
57
References
1. Anghelache, C., Manole, A., Anghel, M.G., Dumitrescu, D., Soare, D.V. (2015).
Locul şi rolul României în Uniunea Europeană, cercetare ştiinţifică concretizată în
comunicare susţinută în cadrul Seminarului Ştiinţific Naţional „Octav Onicescu”
organizat de Societatea Română de Statistică în data de 16 iulie 2015
2. Anghel, M.G. (2015). Analysis on the Indicators related to the structuring of the
Monetary Mass in Romania after the adhesion to the European Union, Romanian
Statistical Review Supplement, Vol. 63, Issue 6/2015, pp. 26-33, Romanian
Statistical Review este indexată în bazele de date internaţionale Index Copernicus,
DOAJ, EBSCO, RePEc, ISSN 2359-8972 CNCSIS, categoria B+
3. Bucea-Manea-Tonis, R., Bucea-Manea-Tonis, R. (2014). Actual cash financing
situation of SMEs in Romania and further recommendations, Published in Journal
of Economic Development, Environment and People, Volume (Year): 3 (2014),
Issue (Month): 1 (March), pp. 25-37
4. Ciocoiu C.-E. (2015). The Impact Of The European Regional Development Fund
On Smes – Evidence From Romania, The Journal of the Faculty of Economics –
Economic, Volume (Year): 1 (2015), Issue (Month): 1 (July), pp. 525-532
5. Soare, D.V. (2015). Indicators calculated for Competitiveness Operational
Programme, Revista International Journal of Academic Research in Accounting,
Finance and Management Sciences, Pakistan, Volume 5, Issue 4 (October, 2015);
6. Soare D.V. (2015). Financial Engineering Instruments Financed from
European Structural and Investment Funds and Financial Products issued by
Financial Institutions supporting European Project Implementation / 18.06.
2015, International Conference on education, social science and humanities,
Instambul,
http://www.ocerint.org//socioint15_epublication/papers/531.pdf;
ISI Procedee;Maxwell, A.L. et. al. (2011). Business angel early stage decision
making, Journal of Business Venturing, Volume (Year): 26 (2011), Issue (Month):
2 (March), Pages: 212-225;
7. Soare D.V., Prodan L., Dumitrescu D. (2015). Business and Autochthonous
Investments, 05.15.2015, Economic and Social Evolutions of Romania in European
Context, Universitatea Artifex, Bucuresti, http://www.revistadestatistica.ro/
supliment/index.php/;
8. Eif.org;
9. POR.ro
10. Ecsif.ro.
58
IT&C platform used in projects financed from
European Union Funds
Abstract
This article describe a possible on-line platform which on one hand
is supposed to be used be the Small and Medium Enterprise’s in order to
determine if their projects are suitable for financing, and on the other hand by
the EU projects management authorities and commercial banks who should
provide the necessary financing for the project.
The term of “bankable” refers to a project or proposal with
high possibility to be accepted for financing by banks (or other financial
institutions), on other words means projects that shows a high probability for
success in the term of having sufficient collateral, future cash-flow that could
cover the bank’s exposure.
The users of the platform will be both the banks and the Small and
Medium Enterprise’s. The module could be implemented in the banks’ web
site and accessed by the Small and Medium Enterprise’s. We propose to use
Oracle database for the “ECSIF” platform. In this chapter we will present the
results of the research achieved until now, as well as a detailed description of
the proposed interface, and the functionalities of the platform.
Key words: platform, bankable projects, operations, Small and
Medium Enterprise, client, performance indicators.
1. Terms of using the platform
- The access will be granted based on individual “User Name” and
“Password”, which will be unique at Operating System (OS) level
for every user. Each user could have only one ‚User Name’.
- After 5 unsuccessful attempts to connect, the OS will block
automatically the user’s access to the platform.
- The initial password will be generated by the system, and will be
communicated directly to the user, being avoided the transmission
by electronic channels or by intermediary persons. After receiving
59
the password, the user is requested to change it immediately, and
the new password should have a minimum required complexity
(minimal length, numbers and special characters to be used).
2. Description of the operations:
- Automated operations – are those operations which do not requires
a dedicated “Approval” step to be performed by the User;
- Manual operations – are those which requires a dedicated
“Approval” step to be performed by the User;
- Rejecting the operation – represents the procedure of denying the
execution of the operation based on explicit reasons stated in legal
provisions – General terms and condition, Know your customer
regulations etc. In case of rejection no accounting records are
generated;
- Cancelling – represents the reversal of an operation performed
ONLY in the same day as it was initiated. Cancelling will not
generate accounting records.
3. Functionalities of the Bankable Clients module
The user interface
CUSTOMERINFORMATION
FINANCIALINFORMATION
By accessing the “Customer information” button, will open the Screen
1:
60
Client information screen
Description of the field’s attributes:
<< Customer name>> - alfa-numerical, editable, maxim 50 characters;
<< Customers type>> - drop down list à correlated with the
information from the fields “Average number of employees”, “Turnover”,
“Total assets”.
Customer type is defined according to Law 346 / 2004
(republished in 2013
Customer type
Average number of
employees
Medium
<250
Small
>50
Micro
<10
Turnover
Total assets
<< Average number of employees>> numerical, editable, maxim 5 characters;
<< Turnover>> - numerical, editable, maxim 30 characters;
<< CUI>> - numerical, editable, maxim 30 characters;
<< CAEN>> - drop down list, maxim 6 characters;
<< Phone>> - numerical, editable, maxim 9 characters;
<< E-mail>> - alfa-numerical, editable, maxim 30 characters;
61
<< Document Management System>> - allows uploading of files (.doc, .pdf,
.jpg) and is grouped in 3 sections: Balance sheets, Closing balance June;
Business plan;
<< Customer code>> - automatically generated by the system, numerical, 6
character long structured as: xx00, first two figure from CUI zz;
By accessing the “Customer information” button, will open the Screen
2:
Financial Statements
Profitandlossaccount
Balancesheet
Save
In case of the BS and P/L, if there are not filled in all the required
fields, the system will highlight the remained fields to be completed and will
position the cursor in the first of them.
Following the input of the data in the “Financial information”
screen, by pressing the “Save” button the system will calculate the indicators
described below and will generate a conclusion message about the proposed
project displayed on the screen and sent also by e-mail to the customer:
- “Bankable project – Please check customer status”
- “Non-Bankable project – Please check customer status”
62
Ratios calculated automatically by the system
Solvency ratios
[Year]
[Year]
[Year/month]
Own capital ratio (%)
Own capital / Total Balance sheet
Fixed assets coverage ratio (%)
(Own capital + Long term debts) / Fixed
assets
Own capital Profitability (%)
Net Profit / (own capital - Current Profit)
Comments
Performance indicators
[Year] [Year] [Year/month]
Debt payment to supplier (number of months)
(Average debt to suppliers * number of months) / ( average
expenses with materials/goods/services)
Cashing Receivables (number of months)
(Average receivables from customer * number of months) /
(average operating income)
Inventory turnover (number of months)
(Average stock of goods * number of month) / ( average
expenses with materials/goods/services)
Comments
Efficiency indicators
[Year]
[Year]
[Year/month]
Operational profit rate (%)
Operational result / Operational income
Return on assets (%)
(Current result + financial expenses / Total
Balance sheet
Comments
63
Reimbursement capacity
Last closed financial year – with balance sheet for 12 months
ths RON
Operational result before financial expenses
+ depreciation (from P/L)
+ expenses operational leasing (from P/L)
EBITDA
0
Long term loans (from BS)
+ Short term loans (from BS)
+ total debt financial leasing
+ total debt operational leasing
- Account in banks /Cash (from BS)
Net liabilities to banks and leasing companies – last full year
Net liabilities to banks and leasing companies / EBITDA
0
#DIV/0!
+ Net Cash Flow (year analysed)
- yearly liabilities for long term loans principal (existing)
- yearly liabilities for leasing payments (existing)
- yearly reimbursement for new loan
Positive Cash-flow coverage
0
5. The application used by the bank
If the result of the evaluation shows that is “Bankable project”, then
the Bank representatives will contact the customer in order to collect the
additional data for the loan approval file.
At the date of recording the loan in the system, it will be generated
a reimbursement schedule where the annuities (credit instalment + interest)
will be equal, considering the interest rate at the date of generation. The loans
based in the French plan are considering the calculation method for the interest
calculation based on 30/360, where each month is considered having 30 days,
regardless the real number of days in a certain month. When the reference
interest rate is changed, the interest will be recalculated considering the balance
of the remaining interest but keeping the remaining principal unchanged. In
case of manual generation of the reimbursement schedule, also the principal
could be changed; in this case the whole reimbursement schedule is changed.
64
In case of loan conversion, the current conditions will be kept until the
next payment date, after that until last but one payment date the new conditions
are to be considered and at the last payment date the effective number of days
will be considered, the calculation being made as Actual number of days / 360.

Interest calculation methods in case of French reimbursement plan
Variable interest:
The interest is calculated on a daily basis starting from the first day of
usage until the last day of reimbursement, and is based on the drawn value of
the loan on that day. The interest is due in the same day as the loan principal as
stated in the reimbursement schedule (Annex 1). The reimbursement schedule
is adjusted each time am additional tranche of the loan is drawn, as well as in
case of changes in the interest rate.
The reimbursement schedule based on the French plan is comprised on equal
annuities (loan principal + interest), and it is calculated using the Net Present
Value (NPV) of total expenses:
n
n
Rk


S 
Ck 
( n  k 1 )
k 1
k 1 (1i )
where:
- S = total value of the granted loan
- Ck = loan instalment related to reimbursement number k
- Rk = value of annuity (loan instalment + interest)
- i = interest rate
- n = sequence of paid instalment
- the value of k starts at the first instalment until the „n”-th one
The due interest is computed by the formula:
Dl
30 Sold _ credit * Rd
a 1
i
l 1 ¦ Sold _ credit i * Rd l
¦
360
360
i a
i 1
where:
- D = interest due
- Sold_credit = loan balance in the day of payment
- Rd = interest rate
- l = the month which the instalment is paid for
- a = the day of paying the instalment
65
Fixed Interest:
The interest is calculated on a daily basis starting from the first day of
usage until the last day of reimbursement, and is based on the drawn value of
the loan on that day. The interest is due in the same day as the loan principal as
stated in the reimbursement schedule (Annex 1). The reimbursement schedule
is adjusted each time am additional tranche of the loan is drawn, as well as in
case of changes in the interest rate.
The reimbursement schedule based on the French plan is comprised
on equal annuities (loan principal + interest), and it is calculated using the Net
Present Value (NPV) of total expenses:
n
n
Rk


S 
Ck 
( n  k 1)
k 1
k 1 (1 i )
where:
- S = total value of the granted loan
- Ck = loan instalment related to reimbursement number k
- Rk = value of annuity (loan instalment + interest)
- i = interest rate
- n = sequence of paid instalment
- the value of k starts at the first instalment until the „n”-th one
The due interest is computed by the formula:
D
30 Sold _ credit *Rd
i
¦
360
i 1
where:
- D = Interest due
- Sold_credit = Loan balance in the day of payment
- Rd = Interest rate
Conclusions
Based on the research and the case study presented above, we proposed
an on-line software application which can be a valuable starting point for the
“Bankable projects”.
This approach can be useful beside the EU projects management
authorities, also for the SME’s looking for financing, and for commercial
banks too.
66
The proposed solution, try to offer a solution which could contribute
to the increase of the financing of EU projects, which until now showed a
relatively low absorption rate due to the lack of co-financing of the companies.
If the result of the evaluation shows that is “Bankable project”, then
the Bank representatives will contact the customer in order to collect the
additional data for the loan approval file.
At the date of recording the loan in the system, it will be generated
a reimbursement schedule where the annuities (credit instalment + interest)
will be equal, considering the interest rate at the date of generation. The
loans based in the French plan are considering the calculation method for the
interest calculation based on 30/360, where each month is considered having
30 days, regardless the real number of days in a certain month. When the
reference interest rate is changed, the interest will be recalculated considering
the balance of the remaining interest but keeping the remaining principal
unchanged. In case of manual generation of the reimbursement schedule, also
the principal could be changed; in this case the whole reimbursement schedule
is changed.
In case of loan conversion, the current conditions will be kept until the
next payment date, after that until last but one payment date the new conditions
are to be considered and at the last payment date the effective number of days
will be considered, the calculation being made as Actual number of days / 360.
References
1. Anghelache, C., Anghel, M.G., Manole, A. (2015). Modelare economică, financiarbancară şi informatică, Editura Artifex, Bucureşti
2. Anghelache, C. (2009). Metode şi modele de măsurare a riscurilor şi performanţelor
financiar-bancare, Editura Artifex, Bucureşti
3. Anghelache, C. (2006). Elemente privind modelarea proceselor economice, Editura
Artifex, Bucureşti
4. Manole, A. (2016). Baze de date. Elemente teoretice şi studii de caz, Editura
Artifex Bucureşti
5. Manole, A. (2008). Sistemul informatic pentru modelarea deciziei financiarcontabile, Editura Artifex Bucureşti, 2008
6. Păunică, M., Ştefan, L. (2015). Intelligent Continous Monitoring the Financial
Performance with Cloud Computing, 2nd International Multidisciplinary Scientific
Conference on Social Sciences and Arts SGEM2015, Vol. 2., pp. 245 – 252, edited
by SGEM
7. Soare, D.V. (2015), Indicators calculated for Competitiveness Operational
Programme, Revista International Journal of Academic Research in Accounting,
Finance and Management Sciences, Pakistan, Volume 5, Issue 4 (October, 2015)
8. Stępniak, C. (2015). Interactive maps as a tool of investment processes support,
Collegium of Economic Analysis Annals, Volume (Year): (2015), Issue (Month):
38, Pages: 247-258
67
Model for analyzing the liquidity risk
Assoc. Prof. Mădălina-Gabriela ANGHEL PhD
Abstract
The liquidity risk has an essential importance in the risk administration
process within the financial systems, beeing one of the most common within
banking institutions. Mittigating liquidity risk helps address cash flow blockage
that is one of the most spread problem that occure in the credit institutions.
Dealing with the liquidity risk involve managing bank liabilities, asstes, and
cross management techniques.
Key words: financial risks, banks, madel analysis, assets, financial
indicators
Some considerations on risk liquidity
In practice there is a series of phenomena related to the extensions of
time limits on assets and the reducing ones on liabilities. A bank faces shortterm liquidity needs when loans are not returned as agreed and as a result, the
bank must carry out their short-term financing. The same thing happens when
customers withdraw large sums from bank deposits.
Planning the liquidity is a particularly important function of the
management of assets and liabilities, as it aims at matching the entering /
leaving cash flows from the credit institution, so that at any moment it can
be able to honor the requests from deposits holders on the total or partial
liquidation of those or related to payments ordered by the holders of bank
accounts.
The liquidity management of a credit institution can be achieved in
three ways:

 
by managing bank liabilities - this solution allows the credit
institution to maintain the same level of total balance sheet without
opperating changes in the structure and volume of the held assets;

 
by asset management – meaning the use of part of the assets as
an alternative to attracting new resources to cover withdrawals of
funds;

 by cross managing the balance sheet assets and liabilities.
Bank liquidity indicators:
68
GAP’s liquidity or liquidity position is calculated as the difference
between total assets (including funding commitments) and total liabilities
(including financing commitments given by the credit institution) on each
maturity band.
If GAP is positive, the situation is favorable to the credit institution
represented as actual liquidity of assets is greater than necessary liquidity
represented by liabilities. Specifically, the bank has sufficient liquid resources
to cover obligations that mature on that band.
Liquidity ratio is expressed in percent and indicates the indebtedness
(dependency) of the credit institution to the money market. Values greater
than 100% indicates a downward trend in the indebtedness of the credit
institution’s money market and an increase of their own liquidity.
RLC = rate of liquidity;
ACR = new loans.
DCR = outstanding loans;
The average maturity transformation is the difference between the
weighted average maturity of assets and liabilities weighted average maturity.
The weighting is done through the group of assets / liabilities coefficient for
each period. It is expressed in days, months, years and best suggests liquidity
risk by transforming maturities that must be operated,
Pi = passive payment date „i”;
Ai = active payment date „i”;
TS = average maturity transformation;
ai = weighting coefficient of liabilities payment date „i”;
bi = weighting coefficient of assets with payment date „i”.
Gap coverage ratio is expressed as the ratio between net interest
income of the bank interest received - interest paid) and distinguish active
- passive. The indicator is expressed as a percentage and shows a maximum
interest rate that the bank can pay to procure necessary resources in the case
it would make an additional investment compared to the resources already
available.
69
Given the profitability of this new investment, the bank must decide
whether it is advantageous to attract new resources to market interest rate.
Di = interest earned;
Dp = interest paid;
A - P = resource gap (the gap);
RAB1 = coverage of the breach without running costs and profits;
RAB2 = coverage of the breach which takes into account operating
costs and profits;
CPB = general expenses and minimum gross realized profit.
If recording a surplus of liquidity in any of the maturity bands, except
for the last strip, it will add to the effective liquidity level for the next maturity
band.
Conclusions
Risk liquidity is becomming a major preocupation for bank
management as in the last years we face a massive delevraging of the traditional
banking system as more and more financial institutions alternative arrise and
attract the capital liquidity from the market. Digital transactions, fasten the
speed of money circulation, and therefore the risk liquidity might appear now
quicker than in any other times. Dealing with risk liquidity becomes therefore
an important part of risk administration.
References
1. Anghelache, C. (2010). Methods and models for measuring risk and financial
performance banking - Edition II, Artifex Publishing House Bucharest, Bucharest,
2. Dardac, N. The management of banking systems, postgraduate CD-ROM, ASE,
Virtual Library;
3. Sfetcu, M. (2011). Bank Financial Group Risk Reporting Methodology, Romanian
Statistical Review, Supplement no. 3/2011
4. Sbarcea, I.R. (2015). The Basel III Approach On Liquidity Risk, Revista Economica,
Volume (Year): 67 (2015), Issue (Month): Supplement (September), pp. 161-172
5. Sadka, R. (2014). Asset Class Liquidity Risk, Bankers, Markets & Investors,
Volume (Year): (2014), Issue (Month): 128 (January-February), pp. 20-30
70
Key measures in ensuring sustainable
development in european higher education:
recommendations for Romania
PhD Candidate, Andreea Mirică ([email protected])
Bucharest University of Economics Studies
Abstract
The aims of this paper are (1) to identify the European countries where
the higher education area best fits the sustainable development concept, (2) to
investigate the key measures that have proven to be efficient in these countries
and finally (3) to formulate some policy recommendations that can lead to a
sustainable development in higher education in Romania. Thus, this paper
analyses the European higher education area in the context of sustainable
development using a cluster analysis, taking into account variables which
are consistent with the sustainable development concept and that cover a
wide range of topics, such as: financing higher education, higher education
attainment, gender inequality, social inclusion, higher education outcomes,
environmental studies. It has been found that: (1) countries with the lowest
unemployment and poverty rates are the most committed in supporting tertiary
education: the highest tertiary educational attainment and financial aid to
students as a percentage to the total public expenditure were observed in
these countries; (2) the most relevant measures that have proven to be efficient
in ensuring sustainability were both legislative as well as practical; (3)
regarding Romania, some practical measures were proposed so that they best
fit the country’s sustainability needs. The results of this study may represent
a valuable tool for policy makers in Romania, as they can learn, adapt best
practices with regard to what has been accomplished in other European
countries, and finally develop their own practices that can help Romania
progress towards sustainable development through higher education.
Keywords: higher education, sustainable development, cluster
analysis
JEL Classification: I – Health, Education, and Welfare
Introduction
In 1987 the concept of sustainable development was defined in the
United Nations Report “Our common future” as the “development that meets
the needs of the present without compromising the ability of future generations
to meet their own needs. It contains within it two key concepts: the concept
71
of ‘needs’, in particular the essential needs of the world’s poor, to which
overriding priority should be given; and the idea of limitations imposed by
the state of technology and social organization on the environment’s ability
to meet present and future needs” (United Nations 1987, p.37). As the report
further emphasizes, the goals of economic and social development should be
defined within the sustainability framework. At the same time, one of the areas
of the Agenda 21 is reorienting education towards sustainable development.
Chapter 36 of the Agenda 21 clearly states that “education is critical for
promoting sustainable development and improving the capacity of the people
to address environment and development issues”1. As humanity is facing a
range of global, social, economic, cultural and ecological changes which on the
long term affect the survival of the human species, the Agenda 21 emphasizes
that “it is critical to achieve environmental and ethical awareness, values and
attitudes, skills and behavior consistent with sustainable development and for
effective public participation in decision-making”2.
Education and particularly higher education is mentioned in the
World Summit Outcome as “a mean of poverty eradicating especially among
women” (United Nations 2005, p.10). As the 2005-2014 is the Decade of
Education for Sustainable Development3, an International Implementation
Scheme was developed in 2006. The document outlines the characteristics
of a high quality education for sustainable development (UNESCO 2006,
p.5): “Interdisciplinary and holistic: learning for sustainable development
embedded in the whole curriculum, not as a separate subject; Values-driven:
sharing the values and principles underpinning sustainable development;
Critical thinking and problem solving: leading to confidence in addressing the
dilemmas and challenges of sustainable development; Multi-method: word,
art, drama, debate, experience, different pedagogies for modelling processes;
Participatory decision-making: learners participate in decisions on how they
are to learn; Applicability: learning experiences are integrated in day to day
personal and professional life; Locally relevant: addressing local as well as
global issues, and using the language(s) which learners most commonly use”.
Other international organizations have also committed themselves to
sustainability in education, and particularly in higher education. Conceived
in 1990 at an international conference in Talloires, France, the Talloires
Declaration is the first official statement made by university administrators of
a commitment to environmental sustainability in higher education.
1 http://www.un-documents.net/a21-36.htm accessed 5.05.2014
2 http://www.un-documents.net/a21-36.htm accessed 5.05.2014
3 http://www.un-documents.net/a57r254.htm accessed 1.05.2014
72
The Association of University Leaders for a Sustainable Future
assured the secretariat of the declaration. The Talloire declaration is a
ten points action plan towards sustainable development. The signatories
commit themselves to1: “increase awareness of environmentally sustainable
development, create an institutional culture of sustainability, educate for
environmentally responsible citizenship, foster environmental literacy for all,
practice institutional ecology, involve all stakeholders in interdisciplinary
research and work with national and international organizations to promote a
worldwide university effort toward a sustainable future”.
The International Association of universities adopted the Kyoto
Declaration on Sustainable Development in 1993. The association commits
itself “to urge universities world-wide to seek, establish and disseminate
a clearer understanding of Sustainable Development”2. The association
recommends the universities “to promote sustainable consumption in its
own campus, to encourage interdisciplinary research programs, to promote
interdisciplinary expert networks, to promote the mobility of staff and students
and to establish partnerships with other sectors of the society”3.
The Association for the Advancement of Sustainability in Higher
Education issued a call to action document following the Summit on
Sustainability in the Curriculum held in San Diego in 2010. The paper
highlights that “integrating sustainability into the college and university is very
challenging as unlike other issues related to sustainability curriculum change
cannot be legislated” (Association for the Advancement of Sustainability in
Higher Education 2010, p.3).
In 2009 the presidents of the G8 universities attending the 2009
University Summit agreed that universities “should foster sustainable and
responsible development at a local as much as on a global level through new
approaches within the educational and research system” (G8 University
Summit 2009, p.3).
A renewed commitment to sustainable practices in higher education
was signed on the occasion of the United Nations Conference on Higher
Education held between 20 and 22 June 2012, in Rio de Janeiro. The
signatories engaged themselves “to teach sustainable development concepts,
encourage research on sustainable development issues, develop ecological
campuses, and support sustainability efforts in the local communities, share
results through international frameworks” (United Nations 2012, p.44-45).
1 http://www.ulsf.org/programs_talloires_td.html accessed 1.05.2014
2 http://archive.www.iau-aiu.net/sd/sd_dkyoto.html accessed 5.05.2014
3 http://archive.www.iau-aiu.net/sd/sd_dkyoto.html accessed 5.05.2014
73
The first section of the paper describes the main documents concerning
higher education in the sustainable development framework. The second
section describes the methodology of the paper. In the third section the results
of the research are presented.
Sustainable development in the European higher education
European Higher Education institutions recognized that universities
should be oriented towards sustainable development since 1993 with the
Copernicus University Charta. Signatories of the Charta engaged themselves
to incorporate an environmental perspective in all their work and encourage
interdisciplinary, dissemination of knowledge, technology transfer and
partnerships (Copernicus Alliance 1993, p.2).
The European countries have further committed themselves to
sustainable development with the adoption of the Europe 2020 Strategy.
The strategy defines three priorities: smart growth: (developing an economy
based on knowledge and innovation); sustainable growth (promoting a more
resource-efficient, greener and more competitive economy); inclusive growth
(fostering a high-employment economy delivering social and territorial
cohesion economy). Also, the strategy proposes five targets for 2020: 75 % of
the population aged 20-64 should be employed, 3% of the EU’s GDP should
be invested in R&D, the “20/20/20” climate/energy targets should be met
(including an increase to 30% of emissions reduction, if the conditions are
right), the share of early school leavers should be under 10% and at least
40% of the younger generation should have a tertiary degree, 20 million less
people should be at risk of poverty (European Commission 2010, p.3).
Consistent with the Europe 2020 Strategy, The Rio +20 Treaty on
Higher Education has been developed in 2012. The document underlines
that higher education must transform itself in order to progress to sustainable
development. Yet, the transformation is a complex long term ambition and
must be guided by vision and clarity of purpose; also, transformation requires
fostering respect for and understanding different cultures, innovation and
effective leadership (Copernicus Alliance 2012, p.3).
As one can observe from figure 1, in all the European countries the population
of 15-64 years old with tertiary education attainment as a percentage of the
total 15-64 years old population has increased in 2013 compared to 2010.
Romania is far below the average EU 27 and one with the lowest tertiary
educational attainment.
74
Switzerland
Iceland
Norway
UnitedKingdom
Finland
Sweden
Slovakia
Slovenia
Romania
Poland
Portugal
Austria
Malta
Netherlands
Hungary
Luxembourg
Latvia
2010
Lithuania
Italy
Cyprus
Croatia
Spain
France
Ireland
Greece
Estonia
Denmark
Germany(until…
CzechRepublic
Belgium
Bulgaria
40,0
35,0
30,0
25,0
20,0
15,0
10,0
5,0
0,0
EuropeanUnion…
Population 15-64 years old with tertiary education attainment as a
percentage of the total 15-64 years old population
Figure 1
2013
Source: Eurostat
Considering the social inclusion of women, analyzing the Eurostat
data on tertiary education attainment among women and their poverty risk, the
following could be concluded:

the percentage of females with tertiary education has increased in
all the European countries from 2010 to 2013, yet Romania is one
of the countries with the lowest values for this indicator (figure 2);

the female student population to the total student population
remained approximately constant in most of the European countries,
yet in Romania a slight decrease could be observed between 2010
and 2013 (figure 3);

when analyzing the poverty risk of tertiary educated females as
percentage of all tertiary educated female one can observe that
it is much lower than the poverty risk of females in general; yet
Romania is among the countries with the highest values in 2012 for
both indicators; also, an increase of the poverty risk among tertiary
educated females could be observed between 2010 and 2012 in this
country (figures 4 and 5).
These trends are explained by the European policies concerning gender
equality1: equal treatment legislation; gender mainstreaming (integration
of the gender perspective into all other policies); specific measures for the
advancement of women.
1. http://ec.europa.eu/justice/gender-equality/ accessed 18.08.2014, 10:22
75
30,0
25,0
20,0
15,0
10,0
5,0
0,0
76
2010
Switzerland
Norway
Iceland
UnitedKingdom
Sweden
Finland
Slovakia
Slovenia
Romania
Portugal
Poland
Austria
Netherlands
Malta
Switzerland
Norway
Iceland
UnitedKingdom
Sweden
Finland
Slovakia
Slovenia
Romania
Portugal
Poland
Austria
Netherlands
Malta
Hungary
Lithuania
Latvia
Cyprus
Italy
Croatia
France
Spain
Greece
Ireland
Estonia
2010
Hungary
2010
Luxembourg
Lithuania
Latvia
Cyprus
Italy
Croatia
France
Spain
Greece
Estonia
Germany(until…
Denmark
CzechRepublic
Bulgaria
Belgium
70,0
60,0
50,0
40,0
30,0
20,0
10,0
0,0
Germany(until…
Denmark
CzechRepublic
Bulgaria
Belgium
Switzerland
Norway
Iceland
UnitedKingdom
Sweden
Finland
Slovakia
Slovenia
Romania
Portugal
Poland
Austria
Netherlands
Malta
Hungary
Luxembourg
Lithuania
Latvia
Cyprus
Italy
Croatia
France
Spain
Greece
Ireland
Estonia
Germany(until…
Denmark
CzechRepublic
Bulgaria
Belgium
EuropeanUnion…
45,0
40,0
35,0
30,0
25,0
20,0
15,0
10,0
5,0
0,0
EuropeanUnion…
Females with tertiary education attainment (percentage of all females)
Figure 2
2013
Source: Eurostat
Tertiary education participation – Women among students in ISCED 5-6
- as percentage of the total students at this level
Figure 3
2012
Source: Eurostat
People (18 years and over) at risk of poverty or social exclusion- tertiary
education females as percentage of all tertiary education females
Figure 4
2012
Source: Eurostat
2010
Switzerland
Iceland
Norway
UnitedKingdom
Finland
Sweden
Slovakia
Slovenia
Romania
Poland
Portugal
Austria
Malta
Netherlands
Hungary
Luxembourg
Latvia
Lithuania
Italy
Cyprus
Croatia
Spain
France
Greece
Estonia
Denmark
Germany(until…
CzechRepublic
Belgium
Bulgaria
60,0
50,0
40,0
30,0
20,0
10,0
0,0
European…
People (18 years and over) at risk of poverty or social exclusion- females
as percentage of all females,
Figure 5
2012
Source: Eurostat
In 2012 vs. 2010, in most of the European countries the number of
tertiary students in environmental protection decreased (figure 6). In Romania,
a slight decrease has been registered comparing to other European countries.
Figure 6: Tertiary students studying environmental protection
percentage change 2012 comparing to 2010
Figure 6
1,00
0,80
0,60
0,40
0,20
0,00
Ͳ0,20
Ͳ0,40
Ͳ0,60
Ͳ0,80
Source: Eurostat
The financial aid to students has always been a sensitive point of
the higher education area. Most recent available data on this issue are rather
scarce and from 2011. As one can observe from figure 7, the financial aid to
students as percentage of total public expenditure on education, at tertiary
level of education (ISCED 5,6) increased only in Denmark, Netherlands,
Ireland, Latvia, Poland and Romania.
77
Figure 7: Financial aid to students as percentage of total public
expenditure on education, at tertiary level of education (ISCED 5,6)
Figure 7
35,0
30,0
25,0
20,0
15,0
10,0
5,0
0,0
2010
2011
Source: Eurostat
As one can observe from figure 8, the total public expenditure on
education at tertiary level as a percentage of the GDP decreased in most of the
European countries. Romania is one of the countries with the lowest values for
this indicator. Most recent data for this indicator are available only for a few
countries.
Total public expenditure on education as percentage of GDP, at tertiary
level of education (ISCED 5-6)
Figure 8
3,00
2,50
2,00
1,50
1,00
0,50
0,00
2010
2011
Source: Eurostat
Analyzing some output indicators of the higher education and
sustainable development from a social perspective, it has resulted that:

the median equivalised net income (figure 9) is higher for the
first and second stage of tertiary education graduates, than for the
upper secondary and non tertiary graduates; Romania is among the
countries with the lowest values for this indicator.
78


the unemployment rate (figure 10) increased in 2013 compared
to 2010 in almost all the European countries with the exception
of Germany, Latvia, Lithuania, Malta and Norway; moreover, the
unemployment rate was higher among upper secondary education
(figure 11) than among tertiary education graduates (figure 12) in
almost all the countries; in Romania the unemployment rate among
upper secondary and non tertiary graduates decreased in 2013
compared to 2010, while the unemployment rate among the higher
education graduates increased;
considering percentage of people at risk of poverty (figure 13), only
small oscillations among the European countries could be observed
in 2012 compared to 2010; however, the percentage of people at risk
of poverty and social exclusion who graduated tertiary education
(figure 14) is much lower than the general rate; Romania is among
the countries with the highest values for these indicators; moreover
the risk of poverty among the tertiary graduates increased for this
country in 2013 compared to 2010.
The increase in the unemployment and poverty rates is the direct
result of the slow economic recovery that the European countries are facing.
Yet, even in harsh economic conditions, economic literature shows that more
educated people (especially higher education graduates) have a competitive
advantage on the labour market (Mincer 1991, p.1 and Nunez and Livanos
2012, p.15)
Switzerland
Iceland
Norway
UnitedKingdom
Finland
Sweden
Slovakia
Slovenia
Romania
Poland
Portugal
Austria
Malta
Netherlands
Hungary
Luxembourg
Latvia
Lithuania
Italy
Cyprus
Croatia
Spain
France
Ireland
Greece
Estonia
Denmark
Germany…
CzechRepublic
Belgium
Bulgaria
60.000
50.000
40.000
30.000
20.000
10.000
0
European…
Median equivalized net income by educational level, 2010
Figure 9
UppersecondaryandpostͲsecondarynonͲtertiaryeducation(levels3and4)
Firstandsecondstageoftertiaryeducation(levels5and6)
Source: Eurostat
79
0,0
80
2010
Turkey
FormerYugoslav…
Switzerland
Norway
Iceland
UnitedKingdom
Sweden
Finland
Slovakia
Slovenia
Romania
Portugal
Poland
Austria
Netherlands
Malta
2010
Turkey
FormerYugoslav…
Switzerland
Norway
Iceland
UnitedKingdom
Sweden
Finland
Slovakia
Slovenia
Romania
Portugal
Poland
Austria
Netherlands
Malta
Hungary
Luxembourg
Lithuania
Latvia
Cyprus
Italy
Croatia
France
Spain
Greece
Ireland
Estonia
Germany(until…
Denmark
CzechRepublic
Bulgaria
2010
Hungary
Luxembourg
Lithuania
Latvia
Cyprus
Italy
Croatia
France
Spain
Greece
Ireland
Estonia
Germany(until1990…
Denmark
CzechRepublic
Bulgaria
Belgium
EuropeanUnion…
35,0
30,0
25,0
20,0
15,0
10,0
5,0
0,0
Belgium
Turkey
FormerYugoslav…
Switzerland
Norway
Iceland
UnitedKingdom
Sweden
Finland
Slovakia
Slovenia
Romania
Portugal
Poland
Austria
Netherlands
Malta
Hungary
Luxembourg
Lithuania
Latvia
Cyprus
Italy
Croatia
France
Spain
Greece
Ireland
Estonia
Germany(until…
Denmark
CzechRepublic
Bulgaria
Belgium
EuropeanUnion…
35,0
30,0
25,0
20,0
15,0
10,0
5,0
0,0
EuropeanUnion(28…
Unemployment rate
Figure 10
2013
Source: Eurostat
Unemployment rate upper secondary education
Figure 11
2013
Source: Eurostat
Unemployment rate tertiary education
25,0
Figure 12
20,0
15,0
10,0
5,0
2013
Source: Eurostat
People at risk of poverty and social exclusion
Figure 13
60,0
50,0
40,0
30,0
20,0
2010
Switzerland
Iceland
Norway
UnitedKingdom
Finland
Sweden
Slovakia
Slovenia
Romania
Poland
Portugal
Austria
Malta
Netherlands
Hungary
Luxembourg
Latvia
Lithuania
Italy
Cyprus
Croatia
Spain
France
Greece
Estonia
Denmark
CzechRepublic
Belgium
Bulgaria
0,0
EuropeanUnion(28…
10,0
2012
Source: Eurostat
People at risk of poverty and social exclusion tertiary education
Figure 14
30,0
25,0
20,0
15,0
10,0
2010
Switzerland
Norway
Iceland
UnitedKingdom
Sweden
Finland
Slovakia
Slovenia
Romania
Portugal
Poland
Austria
Netherlands
Malta
Hungary
Luxembourg
Latvia
Lithuania
Cyprus
Italy
Croatia
France
Spain
Greece
Estonia
Denmark
CzechRepublic
Bulgaria
Belgium
0,0
EuropeanUnion(28…
5,0
2012
Source: Eurostat
Methodology
In order to analyze the European higher education area in the context
of the sustainable development, 14 relevant indicators were chosen:

Population with tertiary education attainment (as percentage of
total population) – this indicator is consistent with the European
Strategy 2020, as one of the goals of the strategy is to increase
tertiary education attainment;

Females with tertiary education attainment (% of all females);
Tertiary education participation - Women among students in ISCED
81
5-6 - as % of the total students at this level are two indicators that
aim to measure the social inclusion of women. The first indicator
measures the percentage of women with tertiary education attainment
in the total female population; it provides a general idea about
the importance of women’s education in the respective country.
The second indicator measures the female student population as
percentage of the total student population.

People at risk of poverty or social exclusion-tertiary educated
females – tertiary education is considered by the United Nations
a mean to reduce poverty and social exclusion especially among
women;

Tertiary students studying environmental protection – assuring
highly qualified labor force in the field of environmental protection
is crucial in order to achieve both the objectives of the sustainable
development and Europe 2020 Strategy;

Financial aid to students as % of total public expenditure on
education, at tertiary level of education (ISCED 5,6) – is a social
inclusion indicator consistent with the concept of sustainable
development social dimension;

Total public expenditure on education as % of GDP, at tertiary level
of education (ISCED 5,6) – expresses each country’s commitment
to sustain tertiary education;

Median equivalised net income - tertiary education; Median
equivalised net income - tertiary education versus secondary
education; Unemployment rate; Unemployment rate-tertiary
education; Unemployment rate-tertiary education versus upper
secondary education; People at risk of poverty or social exclusion;
People at risk of poverty or social exclusion-tertiary education –
are output indicators concerning both sustainable development and
higher education from a social perspective.
All data come from the Eurostat portal. In order to assure the
availability of data for as many European countries as possible, the author has
chosen data from 2010. Next, a cluster analysis has been performed.

The author has chosen Ward Hierarchical Clustering Method as it
does not require the predict of the number of clusters;

As the variables are expressed in different unit measures, the author
has chosen to standardize them using the Z scores method, one of
the most frequently used methods;
82
Research results
According to the dendrogram, three clusters have been formed: Cluster
1 (Denmark, Slovenia, Sweden, Iceland, Norway, Belgium, Finland, France,
United Kingdom, Cyprus, Germany, Netherlands, Austria, Switzerland);
Cluster 2 (Estonia, Spain, Latvia, Lithuania, Bulgaria, Ireland); Cluster 3
(Poland, Romania, Czech Republic, Malta, Croatia, Hungary, Portugal,
Slovakia, Italy, Greece);
There are many differences among the three clusters considering the
variables analyzed. Table 1 presents the means of each variable considering
each cluster:

The first cluster registered the highest value for the population with
tertiary education attainment, while the lowest value was registered in
the third cluster. Countries in the first cluster are the most supportive
of the tertiary education as the highest total public expenditure on
higher education as a percent of GDP is registered for this cluster;
also the highest amount of the percentage of financial aid to students
in the total public expenditure was registered for this cluster;

Considering the female inclusion, the highest value for the Females
with tertiary education attainment (percent of all females) and
Women among students in ISCED 5-6 - as % of the total students
at this level were registered for the second cluster; the third cluster
accounted the highest value for the number of students studying in
environmental field;

The highest income for the higher education graduates is registered
among the countries in the first cluster; yet, the highest difference
between the annual income of tertiary graduates and the annual
income of secondary graduates was observed in the second cluster;

The highest unemployment rate (general and specific for the
tertiary graduates) was observed for the second cluster; also, when
comparing the unemployment rates for tertiary graduates with
the unemployment rates for the secondary graduates, the highest
difference was registered in the second cluster; the lowest values for
these variables were registered for the first cluster;

Considering the last three variables, the highest values for the
People at risk of poverty or social exclusion, People at risk of
poverty or social exclusion-tertiary education, People at risk of
poverty or social exclusion-tertiary education females, the highest
values were observed for the second cluster and the lowest values
for the first cluster.
83
The research carried out has identified the main European countries
where the higher education best fits the sustainable development concept as
follows:

from an outcome perspective: Denmark, Slovenia, Sweden, Iceland,
Norway, Belgium, Finland, France, United Kingdom, Cyprus,
Germany, the Netherlands, Austria and Switzerland are countries
with the lowest unemployment poverty rates; they are the most
committed in supporting tertiary education; the highest tertiary
educational attainment and financial aid to students as a percentage
to the total public expenditure were observed in these countries;

from the perspective of ensuring the necessary human resources
in order to achieve sustainable development: the highest amount
of students in environmental sciences was observed countries
like Poland, Romania, Czech Republic, Malta, Croatia, Hungary,
Portugal, Slovakia, Italy, Greece;

from the social inclusion of vulnerable groups perspective: the highest
values for the female social inclusion indicators were registered in
countries like Estonia, Spain, Latvia, Lithuania, Bulgaria, Ireland.
Considering the outcomes of higher education, in terms of low
unemployment rates, low poverty risk and high wages (corresponding to the
social dimension of the higher education), there are some factors in higher
education that contribute to the system effectiveness:

(St. Aubyn et al. 2009, p.70 - 77) identified four factors for the case of
the Netherlands: staff policy (autonomy to hire and dismiss academic
staff, autonomy to set wages), output flexibility (autonomy to set
course content, student-centered learning), evaluation (all study
programmes are evaluated institutionally by an independent agency,
but also by stakeholders, including students – whose evaluations are
public – and labour market actors), funding (based on quality issues
and research grants applications);

(David 2010, p. 14-19,27) highlights the importance of applying
the following principles in higher education (the implementation of
these principles is also to be improved in the United Kingdom, where
the research has been conducted): consistent policy frameworks;
the academic staff should be involved continuously in research;
informal learning is a very important part of the learning process;
building learning networks in order to encourage students to interact
with one another; higher education should encourage students to be
independent and autonomous in their learning; the learning process
84
should be developed on a systemic basis considering what the
student already knows; higher education should develop personal
and academic skill of the students.
Considering gender equality and women empowering, effective
policy measures come from the higher education area in Spain, as described
by (Rice 2012, p.20): publicly funded research projects are now required
to incorporate a gender perspective in all areas; all universities and other
research organizations must have Equity Plans that include incentives for
improvement.
Dendrogram
Figure 15
Source: designed by the author
85
Means by cluster
Table 1
Variable name
Variable label
Mean Cluster 1
(14 countries)
Mean Cluster 2
(6 countries)
Mean Cluster 3
(10 countries)
pop_tertiary
Population with tertiary
education attainment
27.321428571
26.550000000
15.480000000
fem_tertiary
Females with tertiary
education attainment
(percent of all females)
28.792857143
31.183333333
16.920000000
env_tertiary
Tertiary students studying
environmental protection
fin_aid
Financial aid to students
as % of total public
expenditure on education,
at tertiary level of
education (ISCED 5, 6)
tertiary_gdp
fem_stud
income_tertiary
Total public expenditure on
education as % of GDP, at
tertiary level of education
(ISCED 5, 6)
Tertiary education
participation Women
among students in ISCED
5-6 - as % of the total
students at this level
4009.642857143 4780.833333333 7653.600000000
23.457142857
12.066666667
11.390000000
1.685000000
1.081666667
.924000000
55.364285714
56.750000000
56.270000000
Median equivalised net
27101.357142857 12386.333333333 12099.500000000
income - tertiary education
Median equivalised net
income_tertiary_ income - tertiary education
vs_secondary
- percentage dif to
secondary education
26.632299732
47.816702235
46.627149267
unemp_rate
Unemployment rate
6.900000000
16.500000000
10.200000000
unemp_rate_
tertiary
Unemployment rate-tertiary
education
3.950000000
8.583333333
5.650000000
unemp_rate_
tertiary_vs_
secondary
Unemployment rate-tertiary
education vs secondary
education
-2.742857143
-9.283333333
-4.700000000
poverty
People at risk of poverty or
social exclusion
17.635714286
31.500000000
25.000000000
9.414285714
15.883333333
9.100000000
10.007142857
17.033333333
9.680000000
People at risk of poverty
or social exclusion-tertiary
education
People at risk of poverty
poverty_tertiary_
or social exclusion-tertiary
females
education females
poverty_tertiary
Source: designed by the author
86
Conclusions and recommendations
The research carried out has identified three groups of countries
based on the compatibility of their higher education systems to the sustainable
development philosophy. To do so, a cluster analysis has been performed taking
into account variables consistent with the sustainable development concept.
Each cluster performed the best in one of the following areas subsequent
to the sustainable development concept: effectiveness (Denmark, Slovenia,
Sweden, Iceland, Norway, Belgium, Finland, France, United Kingdom,
Cyprus, Germany, the Netherlands, Austria and Switzerland), social inclusion
of vulnerable groups such as females (Estonia, Spain, Latvia, Lithuania,
Bulgaria, Ireland) and ensuring the necessary human resources in order to
achieve sustainable development thought higher education (Poland, Romania,
Czech Republic, Malta, Croatia, Hungary, Portugal, Slovakia, Italy, Greece) .
Regarding the most significant measures that have contributed to the
achievement of sustainable development in higher education in the European
countries, it has been found that consistent policy frameworks, university
autonomy, continuous improvement of teaching methods and gender equality
policies stood behind the achieved learning outcomes.
The results gained from the comparative analysis on European
level showed the need for immediate measures in order to advance towards
sustainable development through higher education in Romania.
Therefore the following measures may help Romania progress
towards sustainability:

a stable legislative framework should be established in order for
universities to conceive their own strategies on medium and long term;

in order to reduce unemployment and poverty risk among those higher
educated, there is an urgent need to improve students’ skills required
by the labour market; thus, the identification of these requirements and
the modernization of teaching methods accordingly, are necessary;

periodical and public assessment of the educational programmes by
the stakeholders including students and labour market actors: each
university should collect data about students satisfaction on each
course and study programme as well as they should track the graduates’
performance on the labour market; also, employers should be asked to
offer detailed feedback on fresh graduates as well as on trainees; the
results of these assessments should be public in one web portal.
The results of this study may represent a starting point for future
research, on identifying the most suitable ways for applying sustainability
in higher education, from the perspective of the requirements defined by this
model.
87
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