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Revista Română de Statistică

SuplimentRomanian Statistical Review

Supplement

6/2016

Institutul Naţional de StatisticăNational Institute of Statistics

www.revistadestatistică.ro/supliment

INSTITUTUL NAŢIONAL DE STATISTICĂRevista Română de StatisticăB-dul Libertăţii, nr. 16, sector 5,BucureştiTelefon/fax: 0213171110e-mail: [email protected]ă.ro/suplimentISSN 2359 – 8972

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EMILIAN DOBRESCU - academician, Academia Română

AUREL IANCU - academician, Academia Română

MARIUS IOSIFESCU - academician, Academia Română

LUCIAN ALBU - academician, Academia Română

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

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

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

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 ştiinţifi c 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

CoordonatoriGheorghe VAIDA-MUNTEANVitty-Cristian CHIRAN

Pre-pressLaurenţiu MUNTEANU

Tiraj: 15 exemplare

Revista Română de Statistică - Supliment nr. 6 / 2016

SUMAR / CONTENTS 6/2016REVISTA ROMÂNĂ DE STATISTICĂ SUPLIMENT

ANALIZA POLICENTRICITĂŢII FUNCŢIONALE A JUDEŢELOR DIN ROMÂNIA 3POLYCENTRICITY FUNCTIONAL ANALYSIS OF THE ROMANIAN COUNTIES 20Cercetător principal III Antonio TACHECercetător principal Monica TACHE Institutul Naţional de Cercetare-Dezvoltare în Construcţii, Urbanism şi Dezvoltare Teritorială Durabilă „URBAN-INCERC”Conf. univ. dr. Sorin Daniel MANOLEUniversitatea “Constantin Brâncoveanu” Piteşti

COMPARATIVE STUDY OF EUROPEAN AND NATIONAL PROGRAMMES REGARDING INNOVATIVE CAPACITY OF SMALL AND MEDIUM ENTERPRISES 37Prof. Constantin ANGHELACHE, PhDBucharest University of Economic Studies, „ARTIFEX” University of BucharestProf. Vergil VOINEAGU, PhDBucharest University of Economic StudiesProf. Alexandru MANOLE PhD.„ARTIFEX” University of BucharestDiana Valentina SOARE PhD Bucharest University of Economic Studies

STUDY ON THE RELATIONSHIP BETWEEN FINANCIAL PERFORMANCE AND LEVERAGE: EMPIRICAL EVIDENCE ON BUCHAREST STOCK EXCHANGE 45Lector univ. drd. Floriniţa DUCAUniversitatea ARTIFEX, Bucureşti

THE EUROPEAN INITIATIVE FOR SMALL AND MEDIUM ENTERPRISES 49Assoc. prof. Mădălina Gabriela ANGHEL„ARTIFEX” University of BucharestProf. Constantin ANGHELACHE, PhDBucharest University of Economic Studies, „ARTIFEX” University of BucharestDaniel DUMITRESCU PhD studentBucharest University of Economic StudiesAlexandru URSACHE PhD studentBucharest University of Economic Studies

59IT&C PLATFORM USED IN PROJECTS FINANCED FROM EUROPEAN UNION FUNDS Prof. Constantin ANGHELACHE, PhDBucharest University of Economic Studies, „ARTIFEX” University of BucharestDiana Valentina SOARE PhD Bucharest University of Economic StudiesDaniel DUMITRESCU PhD StudentBucharest University of Economic Studies

www.revistadestatistica.ro/supliment

Romanian Statistical Review - Supplement nr. 6 / 20162

MODEL FOR ANALYZING THE LIQUIDITY RISK 68Assoc. Prof. Mădălina-Gabriela ANGHEL PhD„ARTIFEX” University of BucharestDaniel DUMITRESCU PhD StudentBucharest University of Economic Studies

KEY MEASURES IN ENSURING SUSTAINABLE DEVELOPMENT IN EUROPEAN HIGHER EDUCATION: RECOMMENDATIONS FOR ROMANIA 71PhD Candidate, Andreea MiricăBucharest University of Economics Studies

Revista Română de Statistică - Supliment nr. 6 / 2016 3

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, valorifi carea 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 identifi carea unor domenii semnifi cative 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 multi-dimensionale şi multi-scalare a policentricităţii, există o ambiguitate în modul în care este defi nită 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: efi cienţă (competitivitate), echitate (coeziune) şi durabilitate. Prin urmare, este imposibil să se stabilească un grad optim al policentrismului între centralizare


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: efi cienţă, echitate şi durabilitate. Sistemul urban policentric poate fi defi nit 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 infl uenţată 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 fi ecare ţară au fost fi xate zonele urbane funcţionale. La nivel european, zonele urbane funcţionale nu au o defi niţie comună. În principiu, zonele urbane funcţionale constau într-un municipiu nucleu la care se adaugă zonele adiacente de navetă. Lipsiţi de o defi niţie cuprinzătoare, pentru a stabili zonele urbane funcţionale trebuie să identifi căm 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 fl uxuri şi cooperarea între

zonele urbane la diferite scări. Policentricitatea este considerată la ora actuală un instrument de planifi care 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


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 specifi ce 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 codifi cările menţionate: Domeniul Populaţie – A: - Indicele de dinamică al populaţiei 20012011I – 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 20082010I – 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

20082011I – 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:


- Numărul de universităţi în anul 2011 – E1; - Numărul studenţilor în anul 2011 – E2; - Indicele de dinamică al numărului de studenţi 20082011I – E3.

Pentru fi ecare 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 fi ecărui domeniu, mai mulţi specialişti în dezvoltare locală au stabilit coefi cienţi de importanţă (ponderi) pentru toţi indicatorii. Pentru fi ecare domeniu, s-a calculat indicele corespunzător unui judeţ ca medie a punctajelor acordate indicatorilor ponderată cu coefi cienţii de importanţă. În mod analog, s-au acordat coefi cienţi de importanţă (ponderi) fi ecărui domeniu de interes şi s-a calculat indicele de policentricitate la nivel NUTS 3 ca medie a indicilor corespunzători acestor domenii ponderată cu coefi cienţii de importanţă. Astfel, s-au folosit următoarele formule: - Indicele domeniului Populaţie: 335,025,0115,0 AAAA ; - Indicele domeniului Economic: 31,027,012,0 BBBB ;; - Indicele domeniului Turism: 435,031,0235,012,0 CCCCC ; - Indicele domeniului Transporturi: 51,0415,0315,023,013,0 DDDDDD - Indicele domeniului Educaţie: 31,0255,0135,0 EEEE ; - Indicele de policentricitate: EDCBAIP 15,02,01,035,02,0

De asemenea, pentru a analiza cât de mult diferă valorile indicilor de la un judeţ la altul, s-a calculat coefi cientul Gini al inegalităţii. Astfel, dacă dispunem de valorile observate aşezate în ordine crescătoare nxxx ,,, 21 cu media x , coefi cientul Gini al inegalităţii (G ) se calculează cu formula următoare (Buchan, 2002):

n

ii xxi

xnG

12 )(2

Coefi cientul Gini ia valori între zero, pentru egalitate perfectă ( nxxx 21 ) i nn 1 , pentru inegalitate perfectă ( ,0121 nxxx

0nx ), tinzând la unu pentru n mare (Halffman şi Leydesdorff, 2010).


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 1Numele

judeţuluiCodul

judeţului A1 A2 A3 B1 B2 B3 C1 C2 C3 C4 D1 D2 D3 D4 D5 E1 E2 E3Vaslui VS 7 5 1 0 1 2 1 2 8 2 0 0 6 6 8 0 0 0Vâlcea VL 5 4 3 1 3 2 8 7 3 6 0 0 3 8 7 0 1 8Teleorman TR 1 4 2 0 2 3 1 1 3 1 0 1 5 5 3 0 1 5Timiş TM 9 8 9 5 9 8 7 5 3 7 7 0 8 5 6 7 7 5Tulcea TL 4 1 1 0 1 7 4 3 2 4 1 3 1 1 1 0 0 0Suceava SV 9 8 5 0 4 5 7 5 5 6 2 0 7 6 6 2 2 7Satu Mare SM 4 4 3 0 3 4 3 3 4 4 1 0 6 4 7 2 1 8Sălaj SJ 5 1 1 1 1 5 2 2 9 1 0 0 6 6 9 0 1 8Sibiu SB 6 4 5 4 5 5 6 5 5 7 4 0 2 2 5 4 5 5Prahova PH 5 9 7 2 7 1 8 6 3 7 0 0 4 5 9 2 2 7Olt OT 2 5 3 3 3 7 1 1 7 1 0 1 5 3 8 0 1 6Neamţ NT 6 6 3 1 3 2 5 4 2 5 0 0 3 6 5 2 1 6Mureş MS 7 6 5 2 5 3 7 5 5 7 5 0 5 4 5 4 3 7Maramureş MM 7 6 4 0 4 6 5 4 4 5 1 0 3 3 4 2 2 7Mehedinţi MH 2 2 1 0 1 4 2 3 4 3 0 0 2 8 7 0 1 2Iaşi IS 8 9 7 1 7 7 3 4 4 6 4 0 6 4 8 7 8 7Ialomiţa IL 5 2 1 0 1 6 3 4 2 2 0 0 7 6 3 0 1 0Ilfov IF 10 3 6 7 6 3 2 3 1 4 0 0 9 9 10 2 1 6Harghita HR 6 3 2 0 2 3 6 4 5 5 0 0 3 5 5 0 1 4Hunedoara HD 1 5 4 1 4 3 4 4 3 4 0 0 5 3 9 3 1 3Giurgiu GR 6 2 2 0 2 10 1 2 5 1 0 2 1 7 5 0 0 0Galaţi GL 5 7 5 3 5 6 2 3 2 3 0 3 7 6 6 3 4 6Gorj GJ 6 4 4 0 4 9 3 3 7 3 0 0 5 6 8 2 1 4Dolj DJ 5 8 6 1 6 5 3 3 3 3 2 1 3 4 5 3 6 3Dâmboviţa DB 6 6 5 2 5 8 3 4 3 3 0 0 2 7 9 2 2 6Covasna CV 7 1 1 0 1 2 4 5 6 4 0 0 3 6 2 0 1 3Constanţa CT 8 8 8 3 8 8 10 10 2 9 3 10 9 6 6 5 6 5Caraş-Severin CS 2 3 3 0 3 8 6 5 2 5 0 0 5 5 2 2 1 3Călăraşi CL 4 3 2 1 2 9 1 1 1 1 0 3 4 8 3 0 1 6Cluj CJ 7 8 8 2 8 6 7 5 1 7 7 0 4 7 8 7 8 7Buzău BZ 5 5 4 2 4 5 3 3 2 2 0 0 5 2 8 0 1 9Braşov BV 6 7 7 3 7 8 9 8 5 8 0 0 7 7 4 3 6 2Botoşani BT 7 5 2 0 2 4 1 2 8 2 0 0 3 7 8 0 1 5Brăila BR 3 4 3 1 3 2 3 4 2 3 0 3 4 3 3 0 1 3Bistriţa-Năsăud BN 8 3 2 0 2 2 3 3 1 3 0 0 7 3 4 0 1 8Bihor BH 6 7 6 1 6 4 8 7 3 6 2 0 7 5 7 4 4 5Bacău BC 5 8 6 0 5 5 3 4 2 4 5 0 4 5 6 3 2 6Arad AR 6 5 5 2 5 6 5 4 5 6 1 0 7 3 4 3 5 7Argeş AG 6 7 7 4 7 4 6 4 1 5 0 0 4 7 10 3 3 4Alba AB 4 4 4 2 4 7 3 3 10 4 0 0 4 6 9 3 1 6Vrancea VN 9 4 2 0 2 6 2 2 4 2 0 0 4 8 6 0 1 10Bucureşti B 8 10 10 10 10 4 9 9 5 10 10 0 10 10 7 10 10 2Sursa: 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


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 2Numele judeţului Codul

judeţului A B C D E Indicele de policentricitate

Vaslui VS 3,90 0,90 2,40 2,60 0,00 1,86Vâlcea VL 3,80 2,50 6,45 2,35 1,35 2,95Teleorman TR 2,85 1,70 1,20 2,10 1,05 1,86Timiş TM 8,50 8,10 5,90 4,65 6,80 7,07Tulcea TL 1,45 1,40 3,45 1,60 0,00 1,45Suceava SV 7,10 3,30 5,75 3,15 2,50 4,15Satu Mare SM 3,65 2,50 3,45 2,50 2,05 2,76Sălaj SJ 1,60 1,40 2,35 2,70 1,35 1,79Sibiu SB 4,65 4,80 5,90 2,30 4,65 4,36Prahova PH 7,70 5,40 6,45 2,25 2,50 4,90Olt OT 3,85 3,40 1,60 2,30 1,15 2,75Neamţ NT 4,95 2,50 4,35 1,85 1,85 2,95Mureş MS 5,80 4,20 6,10 3,35 3,75 4,47Maramureş MM 5,45 3,40 4,55 1,60 2,50 3,43Mehedinţi MH 1,65 1,10 2,90 2,20 0,75 1,56Iaşi IS 8,15 5,80 4,50 3,50 7,55 5,94Ialomiţa IL 2,10 1,30 2,90 2,25 0,55 1,70Ilfov IF 5,10 5,90 2,95 3,70 1,85 4,40Harghita HR 3,10 1,70 4,85 1,70 0,95 2,18Hunedoara HD 4,05 3,30 3,90 2,10 1,90 3,06Giurgiu GR 2,60 2,40 1,75 2,30 0,00 1,99Galaţi GL 6,00 4,70 2,70 3,45 3,85 4,38Gorj GJ 4,30 3,70 3,40 2,45 1,65 3,23Dolj DJ 6,85 4,90 3,00 2,45 4,65 4,57Dâmboviţa DB 5,65 4,70 3,35 2,25 2,40 3,92Covasna CV 1,90 0,90 4,55 1,55 0,85 1,59Constanţa CT 8,00 7,00 8,85 6,75 5,55 7,12Caraş-Severin CS 2,85 2,90 4,90 1,70 1,55 2,65Călăraşi CL 2,80 2,50 1,00 3,00 1,15 2,31Cluj CJ 7,85 6,60 5,70 4,55 7,55 6,49Buzău BZ 4,65 3,70 2,55 1,85 1,45 3,07Braşov BV 6,85 6,30 7,90 2,50 4,55 5,55Botoşani BT 4,25 1,80 2,40 2,30 1,05 2,34Brăila BR 3,50 2,50 3,25 2,25 0,85 2,48Bistriţa-Năsăud BN 3,40 1,60 2,80 1,90 1,35 2,10Bihor BH 6,50 4,80 6,45 3,10 4,10 4,86Bacău BC 6,85 4,00 3,60 3,45 2,75 4,23Arad AR 5,15 4,50 5,00 2,20 4,50 4,22Argeş AG 6,85 6,10 4,45 2,65 3,10 4,95Alba AB 4,00 3,90 4,05 2,40 2,20 3,38Vrancea VN 4,05 2,00 2,20 2,40 1,55 2,44Bucureşti B 9,70 9,40 8,95 6,70 9,20 8,84Sursa: 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).


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 difi cultăţi în dezvoltarea socio-economică viitoare, ceea ce va reprezenta un handicap pentru România în atingerea obiectivului de coeziune teritorială. Aşa cum am precizat anterior, coefi cientul Gini ia valori între zero, pentru egalitate perfectă şi 0,9762421421 nn pentru inegalitate perfectă. Coefi cientul 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:


0

1

2

3

4

5

6

7

8

1 2 3 4 5 6 7 8 9

Series: IND_GENSample 1 42Observations 42

Mean 3.626190Median 3.150000Maximum 8.840000Minimum 1.450000Std. Dev. 1.723145Skewness 0.984420Kurtosis 3.628691

Jarque-Bera 7.475272Probability 0.023810

Dintre elementele furnizate de output, numai câteva prezintă interes pentru studiul nostru. Astfel, indicele de policentricitate mediu (Mean) este 3,63, iar coefi cientul 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



Din analiza cartogramei de mai sus se constată că municipiul Bucureşti şi majoritatea judeţelor în care se afl ă 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 semnifi cativ 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 fi ind Covasna, Mehedinţi, Sălaj şi Tulcea, judeţe cu o populaţie scăzută faţă de media naţională. Coefi cientul 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:

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Series: ASample 1 42Observations 42



Astfel, indicele populaţiei mediu (Mean) este 4,86, mult mai mare decât indicele de policentricitate mediu (3,63). Valoarea coefi cientului 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:


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 fi rmelor 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 fi rme din top 100 companii din România pe teritoriul judeţului. În continuare, se afl ă 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 fi rme 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 sud-estul ţării, judeţe din Moldova, dar şi judeţe din Transilvania, precum: Bistriţa-Năsăud, Sălaj, Covasna. De asemenea, se poate constata că vor avea difi cultăţi judeţele industrializate forţat în perioada comunistă care depind din punct de vedere economic de marile obiective industriale, fi ind vorba despre Vâlcea, Galaţi, Hunedoara, Ialomiţa şi chiar Mehedinţi. Coefi cientul 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:


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Series: BSample 1 42Observations 42



Indicele economiei mediu (Mean) este 3,70, apropiat ca valoare de indicele de policentricitate mediu (3,63). Valoarea coefi cientului de asimetrie ( Skewness) fi ind 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

Sursa: Realizată de autori pe baza datelor din Tabelul 2, prin utilizarea ArcGIS 10.2 Valorile indicelui turismului relevă foarte clar judeţele cu potenţial


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 valorifi cării acestuia, din care fac parte Mureş, Timiş, Sibiu, Suceava, Cluj şi un alt grup de judeţe care deţin un potenţial turistic important, încă insufi cient valorifi cat, 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 nevalorifi cat, cele mai importante dintre acestea fi ind 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. Coefi cientul Gini al indicelui turismului la nivelul judeţelor are valoarea 0,2544, apropiată de cea a coefi cientului Gini al indicelui de policentricitate. Pentru caracterizarea distribuţiei seriei indicelui turismului dispunem de următoarele date:

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Series: C01Sample 1 42Observations 42



Indicele turismului mediu (Mean) este 4,16, mai mare decât indicele de policentricitate mediu (3,63). Distribuţia este moderat asimetrică spre dreapta, deoarece coefi cientul 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


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 fl ux 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 fl ux 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 fl uxului 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. Coefi cientul Gini al indicelui transporturilor la nivelul judeţelor are valoarea 0,1957, cea mai mică dintre valorile coefi cientului Gini al acestor indici. Distribuţia seriei indicelui transporturilor se caracterizează prin următoarele elemente:


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1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0

Series: D01Sample 1 42Observations 42



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 coefi cientul 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



Din analiza cartogramei indicelui educaţiei rezultă că municipiul Bucureşti se afl ă 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 fi gurează Tulcea, Vaslui şi Giurgiu, unde statistica naţională nu înregistrează nici un student. Coefi cientul 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:

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Series: ESample 1 42Observations 42



Indicele educaţiei mediu (Mean) are valoarea 2,64, cea mai mică dintre valorile indicilor medii. Distribuţia este puternic asimetrică spre dreapta, întrucât coefi cientul de asimetrie ( Skewness) are valoarea 1,26 (mai mare decât 1). Abaterea medie pătratică (Std. Dev.) fi ind 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


au un nivel de dezvoltare scăzut în ceea ce priveşte policentricitatea şi fi ecare 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 difi cultăţi. 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 fi e 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 fi ecare 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).

Bibliografi e 1. Brans, J. P., Mareschal, B. (2005) PROMETHEE methods, Multiple criteria

decision analysis: state of the art surveys, 78, pp.163-186 2. Buchan, I. (2002) Calculating the Gini coeffi cient of inequality, Northwest Institute

for BioHealth Informatics, disponibil la https://www.nibhi.org.uk/Training/Forms/AllItems.aspx?RootFolder=%2F Training%2FStatistics&FolderCTID=&View={4223A4850-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

4. Comisia Europeană (2010) Europa 2020. O strategie europeană pentru o creştere 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)

5. Davoudi, S. (2003) Polycentricity in European Spatial Planning: From an Analytical Tool to a Normative Agenda, European Planning Studies, 11(8), pp. 979-999

6. European Spatial Development Perspective (ESDP) (1999) Towards Balanced and Sustainable Development of the Territory of the European Union, Luxembourg, Offi ce for Offi cial Publications of the European Communities, disponibil la http://ec.europa.eu/regional_policy/sources/docoffi c/offi cial/ reports/pdf/sum_en.pdf. (accesat 2 august 2015)

7. ESPON (2004) ESPON 1.1.1. Potentials for polycentric development in Europe, Luxembourg, ESPON Monitoring Committee, disponibil la http://www.espon.eu/mmp/online/website/content/ projects/259/648/fi le_1174/fr-1.1.1_revised-full.pdf. (accesat 26 iulie 2015)


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 coeffi cients 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, Journal of Applied Mathematics and Decision Sciences, 2006, 1–19, pp. 12-31

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 defi nition and measurement for the Italian NUTS-2 regions, European Planning Studies, 20(6), pp. 1017-1037

15. Wegener, M. (2013) Polycentric Europe: More effi cient, more equitable and more sustainable?, International Seminar on Welfare and competitiveness in the European polycentric urban structure, Florence (Vol. 7)


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 signifi cant 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 fi ndings 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 defi ned (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: effi ciency (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,


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: effi ciency, equity and sustainability. The polycentric urban system can be defi ned 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 infl uenced 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 defi nition. Mainly, functional urban areas consisted in a core municipality plus adjacent commuting areas. Lacking a comprehensive defi nition, 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 fl ows 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 fi rst 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,


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 specifi c 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 20012011I – 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 20082010I – 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 20082011I – 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 20082011I – 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


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 coeffi cients 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 coeffi cients of importance. Similarly, the coeffi cients 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 coeffi cients of importance. Thus, the following formulas were used: - the index of the population domain: 335.025.0115.0 AAAA ; - the index of the economic domain: 31.027.012.0 BBBB - the index of tourism domain: 435.031.0235.012.0 CCCCC - the index of transport domain: 51.0415.0315.023.013.0 DDDDDD - the index of education domain: 31.0255.0135.0 EEEE ; - the polycentricity index: EDCBAIP 15.02.01.035.02.0 . Also, in order to analyze how much the values of indices differ from one county to another, Gini coeffi cient of inequality was calculated. Thus, if we have the observed values arranged in ascending order nxxx ,,, 21 with the average x , Gini coeffi cient of inequality (G ) i s calculated as follows (Buchan, 2002):

n

ii xxi

xnG

12 )(2

The Gini coeffi cient ranges between zero for perfect equality ( nxxx 21 ) and nn 1 for perfect inequality ( ,0121 nxxx 0nx ), 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.


Scores corresponding to the relevant indicators given to counties in Romania

Table 1 The name of the county

Code of the county A1 A2 A3 B1 B2 B3 C1 C2 C3 C4 D1 D2 D3 D4 D5 E1 E2 E3

Vaslui VS 7 5 1 0 1 2 1 2 8 2 0 0 6 6 8 0 0 0Valcea VL 5 4 3 1 3 2 8 7 3 6 0 0 3 8 7 0 1 8Teleorman TR 1 4 2 0 2 3 1 1 3 1 0 1 5 5 3 0 1 5Timis TM 9 8 9 5 9 8 7 5 3 7 7 0 8 5 6 7 7 5Tulcea TL 4 1 1 0 1 7 4 3 2 4 1 3 1 1 1 0 0 0Suceava SV 9 8 5 0 4 5 7 5 5 6 2 0 7 6 6 2 2 7Satu Mare SM 4 4 3 0 3 4 3 3 4 4 1 0 6 4 7 2 1 8Salaj SJ 5 1 1 1 1 5 2 2 9 1 0 0 6 6 9 0 1 8Sibiu SB 6 4 5 4 5 5 6 5 5 7 4 0 2 2 5 4 5 5Prahova PH 5 9 7 2 7 1 8 6 3 7 0 0 4 5 9 2 2 7Olt OT 2 5 3 3 3 7 1 1 7 1 0 1 5 3 8 0 1 6Neamt NT 6 6 3 1 3 2 5 4 2 5 0 0 3 6 5 2 1 6Mures MS 7 6 5 2 5 3 7 5 5 7 5 0 5 4 5 4 3 7Maramures MM 7 6 4 0 4 6 5 4 4 5 1 0 3 3 4 2 2 7Mehedinti MH 2 2 1 0 1 4 2 3 4 3 0 0 2 8 7 0 1 2Iasi IS 8 9 7 1 7 7 3 4 4 6 4 0 6 4 8 7 8 7Ialomita IL 5 2 1 0 1 6 3 4 2 2 0 0 7 6 3 0 1 0Ilfov IF 10 3 6 7 6 3 2 3 1 4 0 0 9 9 10 2 1 6Harghita HR 6 3 2 0 2 3 6 4 5 5 0 0 3 5 5 0 1 4Hunedoara HD 1 5 4 1 4 3 4 4 3 4 0 0 5 3 9 3 1 3Giurgiu GR 6 2 2 0 2 10 1 2 5 1 0 2 1 7 5 0 0 0Galati GL 5 7 5 3 5 6 2 3 2 3 0 3 7 6 6 3 4 6Gorj GJ 6 4 4 0 4 9 3 3 7 3 0 0 5 6 8 2 1 4Dolj DJ 5 8 6 1 6 5 3 3 3 3 2 1 3 4 5 3 6 3Dambovita DB 6 6 5 2 5 8 3 4 3 3 0 0 2 7 9 2 2 6Covasna CV 7 1 1 0 1 2 4 5 6 4 0 0 3 6 2 0 1 3Constanta CT 8 8 8 3 8 8 10 10 2 9 3 10 9 6 6 5 6 5Caras-Severin CS 2 3 3 0 3 8 6 5 2 5 0 0 5 5 2 2 1 3Calarasi CL 4 3 2 1 2 9 1 1 1 1 0 3 4 8 3 0 1 6Cluj CJ 7 8 8 2 8 6 7 5 1 7 7 0 4 7 8 7 8 7Buzau BZ 5 5 4 2 4 5 3 3 2 2 0 0 5 2 8 0 1 9Brasov BV 6 7 7 3 7 8 9 8 5 8 0 0 7 7 4 3 6 2Botosani BT 7 5 2 0 2 4 1 2 8 2 0 0 3 7 8 0 1 5Braila BR 3 4 3 1 3 2 3 4 2 3 0 3 4 3 3 0 1 3Bistrita-Nasaud BN 8 3 2 0 2 2 3 3 1 3 0 0 7 3 4 0 1 8Bihor BH 6 7 6 1 6 4 8 7 3 6 2 0 7 5 7 4 4 5Bacau BC 5 8 6 0 5 5 3 4 2 4 5 0 4 5 6 3 2 6Arad AR 6 5 5 2 5 6 5 4 5 6 1 0 7 3 4 3 5 7Arges AG 6 7 7 4 7 4 6 4 1 5 0 0 4 7 10 3 3 4Alba AB 4 4 4 2 4 7 3 3 10 4 0 0 4 6 9 3 1 6Vrancea VN 9 4 2 0 2 6 2 2 4 2 0 0 4 8 6 0 1 10Bucharest B 8 10 10 10 10 4 9 9 5 10 10 0 10 10 7 10 10 2Source: 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


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 2Name of the county Code of the

county A B C D E Polycentricity index

Vaslui VS 3.90 0.90 2.40 2.60 0.00 1.86Valcea VL 3.80 2.50 6.45 2.35 1.35 2.95Teleorman TR 2.85 1.70 1.20 2.10 1.05 1.86Timis TM 8.50 8.10 5.90 4.65 6.80 7.07Tulcea TL 1.45 1.40 3.45 1.60 0.00 1.45Suceava SV 7.10 3.30 5.75 3.15 2.50 4.15Satu Mare SM 3.65 2.50 3.45 2.50 2.05 2.76Salaj SJ 1.60 1.40 2.35 2.70 1.35 1.79Sibiu SB 4.65 4.80 5.90 2.30 4.65 4.36Prahova PH 7.70 5.40 6.45 2.25 2.50 4.90Olt OT 3.85 3.40 1.60 2.30 1.15 2.75Neamt NT 4.95 2.50 4.35 1.85 1.85 2.95Mures MS 5.80 4.20 6.10 3.35 3.75 4.47Maramures MM 5.45 3.40 4.55 1.60 2.50 3.43Mehedinti MH 1.65 1.10 2.90 2.20 0.75 1.56Iasi IS 8.15 5.80 4.50 3.50 7.55 5.94Ialomita IL 2.10 1.30 2.90 2.25 0.55 1.70Ilfov IF 5.10 5.90 2.95 3.70 1.85 4.40Harghita HR 3.10 1.70 4.85 1.70 0.95 2.18Hunedoara HD 4.05 3.30 3.90 2.10 1.90 3.06Giurgiu GR 2.60 2.40 1.75 2.30 0.00 1.99Galati GL 6.00 4.70 2.70 3.45 3.85 4.38Gorj GJ 4.30 3.70 3.40 2.45 1.65 3.23Dolj DJ 6.85 4.90 3.00 2.45 4.65 4.57Dambovita DB 5.65 4.70 3.35 2.25 2.40 3.92Covasna CV 1.90 0.90 4.55 1.55 0.85 1.59Constanta CT 8.00 7.00 8.85 6.75 5.55 7.12Caras-Severin CS 2.85 2.90 4.90 1.70 1.55 2.65Calarasi CL 2.80 2.50 1.00 3.00 1.15 2.31Cluj CJ 7.85 6.60 5.70 4.55 7.55 6.49Buzau BZ 4.65 3.70 2.55 1.85 1.45 3.07Brasov BV 6.85 6.30 7.90 2.50 4.55 5.55Botosani BT 4.25 1.80 2.40 2.30 1.05 2.34Braila BR 3.50 2.50 3.25 2.25 0.85 2.48Bistrita-Nasaud BN 3.40 1.60 2.80 1.90 1.35 2.10Bihor BH 6.50 4.80 6.45 3.10 4.10 4.86Bacau BC 6.85 4.00 3.60 3.45 2.75 4.23Arad AR 5.15 4.50 5.00 2.20 4.50 4.22Arges AG 6.85 6.10 4.45 2.65 3.10 4.95Alba AB 4.00 3.90 4.05 2.40 2.20 3.38Vrancea VN 4.05 2.00 2.20 2.40 1.55 2.44Bucharest B 9.70 9.40 8.95 6.70 9.20 8.84Source: 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).


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 fi nd 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 fi nd 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 diffi culties 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 coeffi cient ranges between zero for perfect equality and

0.9762421421 nn for perfect inequality. The Gini coeffi cient 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:


0

1

2

3

4

5

6

7

8

1 2 3 4 5 6 7 8 9

Series: IND_GENSample 1 42Observations 42



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



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 signifi cant 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 coeffi cient 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:

0

1

2

3

4

5

6

1 2 3 4 5 6 7 8 9 10

Series: ASample 1 42Observations 42



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).


Economic domain For the economy index we have the following cartogram:

The economy index at the level of counties in Romania Map 3

Source: Performed by the authors based on the data from Table 2 by using ArcGIS 10.2 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 fi nd 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 fi nd 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 diffi culties, depending economically on large industrial facilities, such as the case of Valcea, Galati, Hunedoara, Ialomita and even Mehedinti. Gini coeffi cient of the economy index has the value of 0.3036,


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:

0

1

2

3

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5

6

7

1 2 3 4 5 6 7 8 9

Series: BSample 1 42Observations 42



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



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 signifi cant tourism potential, yet insuffi ciently 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 signifi cant problems in terms of competitiveness. Gini coeffi cient of the tourism index at the level of counties has the value of 0.2544, close to that of the Gini coeffi cient of the polycentricity index. In order to characterize the distribution of the tourism index series, we have the following data:

0

1

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Series: C01Sample 1 42Observations 42



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).


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 fl ow 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 fl ow 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 fl ow 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 coeffi cient of the transport index at the level of counties has the value 0.1957, the least of the values of the Gini coeffi cient of these indices. The distribution of the transport index series is characterized by the following elements:


0

4

8

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20

1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0

Series: D01Sample 1 42Observations 42



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



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 fi nd 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 coeffi cient 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:

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9

0 1 2 3 4 5 6 7 8 9

Series: ESample 1 42Observations 42



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


level concerning polycentricity and each of the domains. Taking this into account, the long-term development strategy in the fi eld of spatial and urban planning in Romania must develop integrated projects for those areas facing diffi culties. 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 1. Brans, J. P., Mareschal, B. (2005) PROMETHEE methods, Multiple criteria

decision analysis: state of the art surveys, 78, pp.163-186 2. Buchan, I. (2002) Calculating the Gini coeffi cient of inequality, Northwest Institute

for BioHealth Informatics, available at https://www.nibhi.org.uk/Training/Forms/AllItems.aspx?RootFolder=%2FTraining%2FStatistics&FolderCTID =&View={4223A4850-B4790-4965-4285DBD4220A4841A5430B}. (accessed on 20 July 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

4. Comisia Europeană (2010) Europa 2020. O strategie europeană pentru o creştere inteligentă, ecologică şi favorabilă incluziunii, Bruxelles, available at http://eur-lex.europa.eu/LexUriServ/ LexUriServ.do?uri=COM:2010:2020:FIN:RO:PDF. (accessed on 25 July 2015)

5. Davoudi, S. (2003) Polycentricity in European Spatial Planning: From an Analytical Tool to a Normative Agenda, European Planning Studies, 11(8), pp. 979-999

6. European Spatial Development Perspective (ESDP) (1999) Towards Balanced and Sustainable Development of the Territory of the European Union, Luxembourg, Offi ce for Offi cial Publications of the European Communities, available at http://ec.europa.eu/regional_policy/sources/docoffi c/offi cial/ reports/pdf/sum_en.pdf. (accessed on 2 August 2015)

7. ESPON (2004) ESPON 1.1.1. Potentials for polycentric development in Europe, Luxembourg, ESPON Monitoring Committee, available at http://www.espon.eu/mmp/online/website/content/ projects/259/648/fi le_1174/fr-1.1.1_revised-full.


pdf. (accessed on 26 July 2015) 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 coeffi cients 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, Journal of Applied Mathematics and Decision Sciences, 2006, 1–19, pp. 12-31

13. Ministerul Dezvoltării, Lucrărilor Publice şi Locuinţelor (2008) Conceptul Strategic de Dezvoltare Teritorială – România 2030, available at http://www.mdrl.ro/_documente/publicatii/2008/ Brosura%20Conc_strat_dezv_teritoriala.pdf. (accessed on 29 July 2015)

14. Veneri, P., Burgalassi, D. (2012) Questioning polycentric development and its effects. Issues of defi nition and measurement for the Italian NUTS-2 regions, European Planning Studies, 20(6), pp. 1017-1037

15. Wegener, M. (2013) Polycentric Europe: More effi cient, more equitable and more sustainable?, International Seminar on Welfare and competitiveness in the European polycentric urban structure, Florence (Vol. 7)


Comparative Study of European and national Programmes Regarding Innovative Capacity of Small and Medium Enterprises Prof. Constantin ANGHELACHE, PhDBucharest University of Economic Studies, „ARTIFEX” University of BucharestProf. Vergil VOINEAGU, PhDBucharest University of Economic StudiesProf. Alexandru MANOLE PhD.„ARTIFEX” University of BucharestDiana Valentina SOARE PhD Bucharest University of Economic Studies

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 fi nance 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, fi nancial 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 fi nance for research, development and internationalisation. Specifi cally 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


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 fi elds, 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 confi dentiality 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 fi rst. 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 benefi ts 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


SMEs in the fi eld 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 fi eld) tend to cover their work and option with papers, avoiding that way being held accountable on the success or insucess of the project fi nance. 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


been denaturated into public acquisition contracts hunted by companies that are looking for profi t 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 fi nanced 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 fi nance. This type of fi nancing for innovation, generically referred


to as venture fi nance 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 fi eld, which may originate from the private fi nance 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 specifi c 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 fi nancing mechanism in one of the safest ways of fi nancing 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 refl ect 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 fi rst 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 refl ecting a reality. As stated before, the penalties which the benefi ciary of such fi nancing should support if it fail, merely completes this picture of innovation in Romania. Thus, according to Applicant’s Guide POC, Innovative SMEs, the benefi ciary is obliged until the end of the sustainability of the project (implementation period + monitoring period), generally fi ve years, to cash in revenues from the new developed product, at least at the amount awarded


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 –

2013 per capita Eur

Contracted ammount

billions Eur

Contracted percent %

Payments made

in 2007 – 2013 billions

Eur

Payment percent

(absorbtion) %

Bulgaria 6,674 927 7,7 115% 5,1 77%Czech Republic 26,303 2502 25,2 96% 18,1 69%Estonia 3,403 2588 3,3 98% 3 87%Hungaria 24,921 2523 28 112% 21,7 87%Letonia 4,530 2278 4,8 105% 3,9 86%Latvia 6,775 2301 6,8 100% 6 88%Poland 67,186 1745 68,2 102% 52,5 78%Romania 19,175 961 20,3 106% 10 52%Slovakia 11,651 2149 13,1 112% 7,6 65%Slovenia 4,101 1989 4,3 104% 3,4 83%Source: http://www.ecsif.eu/Pagini/Master-EUG-Bucharest.aspx

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 profi tability of European projects suggest that these should be attractive for private sector fi nancing, 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.


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 fi nance and innovation all of that beeing widelly found in the private fi nancial 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 fi eld. And in terms of fi nancial projections, the fi nancing template for the Application of POC presents some diffi cult to understand requirements for the innovative realm. Thus, according to the applicant’s guide, the applicant must make fi nancial projections for the next 15 years. Considering that most start-ups fail after 3 years, it is hard to believe that the fi nancial projections can be more than an exercise of imagination. In principle innovation is related to the changing economic environment and to present fi nancial estimates for a period of 15 years, is totally unrealistic and unnecessary.


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 benefi ts 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/fi les/Nota+prezentare_mecanism+OUG+9.doc; 16. http://www.gov.ro; 17. http://www.poc.research.ro/programare-2014-2020.


Study on the relationship between fi nancial performance and leverage: empirical evidence on Bucharest Stock ExchangeLector univ. drd. Floriniţa DUCAUniversitatea ARTIFEX, Bucureşti

Abstract This paper seek to investigate the relationship between fi nancial 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 fi rms is analyzed by return on equity. The dataset is obtained from annual reports for 2010. The results indicate a positive and signifi cant relation between return on equity and debt to equity. Key word: Return on equity, debt-to-equity ratio, size fi rm, 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 defi nes 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 fi rms is affected by practicing good corporate governance policies.

Literature review The relationship between performance fi nancial and debt to equity it is a fi eld of study both in academia and in policy makers in recent years.


In 2012, Akhtar, et al. investigates the relationship between fi nancial leverage and fi nancial performance. The result shows that there is a general perception that a relationship exists between the fi nancial leverage and the performance of the fi rms. The fi nancial performance indicators have positive relationship among leverage and the fi nancial performance. Using a panel data analysis, Raza( 2013) examines the determinants of capital structure of Karachi Stock Exchange listed none-fi nancial fi rms 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 signifi cant relationship between leverage and profi tability. In their study, Obradovich and Gill (2013) show that larger board size negatively impacts the value of American fi rms and CEO duality, audit committee, fi nancial leverage, fi rm size, return on assets and insider holdings positive relationship the value of American fi rms.

III. Methodological framework The panel data set covers a on year, with a sample of one hundred fi rms listed at Bucharest Stock Exchange. The data were taken from the annual reports of these fi rms. All fi nancial 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

profi tability by revealing how much profi t 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 fi nancial ratio show the relative proportion of companies equity and debt used to fi nance an companies assets. Debt-to-equity ratio is used as a standard for judging a company’s fi nancial 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.


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

ROE Size Debt-to-equity Mean 0.2663 17.9487 0.8411 Median 0.0426 18.0965 0.3173Maximum 17.5491 24.1899 28.5904Minimum -0.1152 13.8661 -3.3318 Std. Dev. 1.7554 1.6123 2.9221

In this section the results of the inferential statistical techniques used in the study are presented(Table 2).

Method of least squaresTable 2

Dependent variable = Return On Equity(ROE)Variable Coeff. Std. Error t-Stat. Prob. Size -0.0845 0.0299 -2.8252 0.0057Debt-to-equity 0.5703 0.0165 34.5398 0.0000C 1.3040 0.5413 2.4089 0.0179R-squared 0.9282 Mean dependent var 0.2663Adjusted R-squared 0.9268 F-statistic 627.34Durbin-Watson stat 1.9550 Prob(F-statistic) 0.0000

The table above shows that coeffi cient 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 fi rm have a signifi cant relationship with profi tability of the company. Firm size, on the other hand, has a negative and signifi cant.


The result shows that debt-to-equity ratio has a signifi cant impact on return on equity, the value of p-value = 0.0000 <0.05(Table 2). Company size has a signifi cant negative effect on return on equity, the value of p-value = 0.0057 <0.05. The results presented in Table 2 show that fi rm size has a negative and signifi cant relationship with, on the other hand, the debt ratio has a strong and positive correlation with return on equity. This result is confi rmed by research conducted by Abu-Tapanjeh (2006).

Conclusion This paper examines the relationship between fi nancial performance measured by return on equity and debt to equity fi rm’s for a hundred fi rms listed at Bucharest Stock Exchange. The result shows fi rm size is negatively related with return on equity at 5 % signifi cance level, indicating larger fi rms, 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 fi rm size as the only independent variables affecting profi tability 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 fi nancial and debt to equity; improved ways to measure profi tability as well as investigate it in different time periods.

References

1. Akhtar, S., Javed, B., Maryam, A., Sadia, H. (2012). Relationship between fi nancial leverage and fi nancial 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 fi nancial leverage on the value of American fi rms’, 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 Profi tability Relationship: The Case of Jordan, Journal of Economic & Administrative Sciences, Vol. 22, No. 1, 2006, page numbers 41 – 59, ISSN: 1026-4116.


The European Initiative for Small and Medium Enterprises Assoc. prof. Mădălina Gabriela ANGHEL„ARTIFEX” University of BucharestProf. Constantin ANGHELACHE, PhDBucharest University of Economic Studies, „ARTIFEX” University of BucharestDaniel DUMITRESCU PhD studentBucharest University of Economic StudiesAlexandru URSACHE PhD studentBucharest University of Economic Studies

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 signifi cant infl uence on the fi nancing of SMEs and thereby increasing the economic growth. Key words: sme initiative, fi nancial instruments, european funds, banks, sme

Following the global fi nancial 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 diffi culties 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 fi nancial instrument of the European Commission and the EIB Group (European Investment Bank and the European Investment Fund) that aims to stimulate SMEs fi nancing by partial covering the risk associated with banks credit portofolios. The SME Initiative is supported through the fi nancial resources of Member States from ESIF – European Structural and Investments Fund and co-fi nanced by the European


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 fi nancial institutions (banks, leasing companies, guarantee institutions, venture capital funds) protection and assistance for the potential loss of capital costs. In return for sharing risks, fi nancial 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 fi nal benefi ciaries). EIF fi nancial 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 fi nancial instruments that are being developed with funding from the ESIF, the SME Initiative offers the following benefi ts to member states and administrative authorities: Is not requiring co-fi nancing 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 fi nanced 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 fi nancing 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 fi nance. EIF designs and develops tools for risk and growth capital, guarantees and micro tools aimed specifi cally for this market segment. In this role, EIF supports the EU objectives in support of innovation,


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 fi nancial 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 signifi cant infl uence on the fi nancing 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 fi nancial instrument developed at European level indirectly managed by the European Commission through the EIB Group. In this case, the binding ex ante fi nancial 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 fi nancially viable. The credit fi nancing request from the non-fi nancial sector in the period 2009-2012 was quantifi ed at EU level at 112.000.000.000 €. It is estimated that in the future, the SME fi nancing gap at European level will be signifi cantly reduced, however, such improvements will not be enough to cover the loan fi nancing 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.


Option 1 scheme: uncapped guarantee instrumentFig.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 benefi ciate from the SME Instrument as their banks will further fi nance 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 fi nancial 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 signifi cant 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


have the mezzanine tranche. EIB and other institutional investors will invest in senior tranche. The SME Initiative has several important steps, the fi rst of them, the ex-ante 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 fi nancial 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 certifi cation 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 fi nancing 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 fi nancial intermediaries. The SME Initiative adds differentiated value as follows: Member States may not be required additional national co-fi nancing 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 fi nancial instruments in order to address the failure of the existing private fi nance market. Regarding the fi nancial 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 fi nancing conditions and better


contractual terms. As direct effect there will be available fi nance 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 fi nancial instrument is fi nanced 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 fi nancing 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 effi cient use of structural funds and ensure that fi nancing 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 benefi t from lower interes rates. This fi nancial 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 fi nanced 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


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 benefi ciating 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 benefi t from this approach and more than 60 million EUR will be available through loans. These fi nancial 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 fi nancial instruments will benefi ciate from the expertise of EIB Group and from funds from Horizon 2020, having as result the improoved effi ciency 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 signifi cant 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 fi nancial instrument is to improve the access to fi nance 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


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 fi rst 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 fi nancial 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 fi nance 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 fi nancing, 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 fi nance for SMEs in Romania will be implemented exclusively in the form of standardized fi nancial 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 classifi ed, 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 fi nancing 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 specifi c objective of this priority is to facilitate the access to fi nance for SMEs through


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 fi nancial intermediaries in Spain and Malta:Country Selected Bank Type of FI Guarantee Amount EURSpain Banco Santander Portfolio - Guarantee 500,000,000Spain Banco Popular Espanol Portfolio - Guarantee 500,000,000Spain CaixaBank Portfolio - Guarantee 400,000,000Spain Banco Sabadell Portfolio - Guarantee 312,500,000Spain Bankia Portfolio - Guarantee 310,000,000Spain Bankinter Portfolio - Guarantee 150,000,000

Malta Bank of Valletta Portfolio - Guarantee 45,762,712For 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 fi nancial institutions (banks, leasing companies, guarantee institutions, venture capital funds) protection and assistance for the potential loss of capital costs. In return for sharing risks, fi nancial 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 fi nal benefi ciaries). EIF fi nancial 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 fi nancial 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


References 1. Anghelache, C., Manole, A., Anghel, M.G., Dumitrescu, D., Soare, D.V. (2015).

Locul şi rolul României în Uniunea Europeană, cercetare ştiinţifi că concretizată în comunicare susţinută în cadrul Seminarului Ştiinţifi c 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 fi nancing 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.


IT&C platform used in projects fi nanced from European Union FundsProf. Constantin ANGHELACHE, PhDBucharest University of Economic Studies, „ARTIFEX” University of BucharestDiana Valentina SOARE PhD Bucharest University of Economic StudiesDaniel DUMITRESCU PhD StudentBucharest University of Economic Studies

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 fi nancing, and on the other hand by the EU projects management authorities and commercial banks who should provide the necessary fi nancing for the project. The term of “bankable” refers to a project or proposal with high possibility to be accepted for fi nancing by banks (or other fi nancial institutions), on other words means projects that shows a high probability for success in the term of having suffi cient collateral, future cash-fl ow 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


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

CUSTOMER INFORMATION

FINANCIAL INFORMATION

By accessing the “Customer information” button, will open the Screen 1:


Client information screen

Description of the fi eld’s attributes: << Customer name>> - alfa-numerical, editable, maxim 50 characters; << Customers type>> - drop down list à correlated with the information from the fi elds “Average number of employees”, “Turnover”, “Total assets”.

Customer type is defi ned according to Law 346 / 2004 (republished in 2013

Customer type Average number of employees Turnover Total assets

Medium <250

Small >50

Micro <10

<< 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;


<< Document Management System>> - allows uploading of fi les (.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, fi rst two fi gure from CUI zz;

By accessing the “Customer information” button, will open the Screen 2: Financial Statements

Profit and loss account

Balance sheet

Save

In case of the BS and P/L, if there are not fi lled in all the required fi elds, the system will highlight the remained fi elds to be completed and will position the cursor in the fi rst 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”


Ratios calculated automatically by the system

Solvency ratios [Year] [Year] [Year/month]

Own capital ratio (%)Own capital / Total Balance sheetFixed assets coverage ratio (%)(Own capital + Long term debts) / Fixed assetsOwn capital Profi tability (%)Net Profi t / (own capital - Current Profi t)

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

Effi ciency indicators [Year] [Year] [Year/month]

Operational profi t rate (%)Operational result / Operational income

Return on assets (%)(Current result + fi nancial expenses / Total Balance sheet

Comments


Reimbursement capacity

Last closed fi nancial year – with balance sheet for 12 months ths RON

Operational result before fi nancial expenses

+ depreciation (from P/L) + expenses operational leasing (from P/L)

EBITDA 0Long term loans (from BS) + Short term loans (from BS) + total debt fi nancial leasing + total debt operational leasing - Account in banks /Cash (from BS)

Net liabilities to banks and leasing companies – last full year 0

Net liabilities to banks and leasing companies / EBITDA #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-fl ow 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 fi le. 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.

Interest calculation methods in case of French reimbursement plan Variable interest: The interest is calculated on a daily basis starting from the fi rst 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

k ikRn

kCS knk

1 )1(1 )1(

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 fi rst instalment until the „n”-th one

The due interest is computed by the formula:

1

1 360*_30

3601*_ a

ilRdicreditSold

ailRdicreditSold

lD

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


Fixed Interest: The interest is calculated on a daily basis starting from the fi rst 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

k ikRn

kCS knk

1 )1(1 )1(

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 fi rst instalment until the „n”-th one

The due interest is computed by the formula:

30

1 360*_

i

RdicreditSoldD

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 fi nancing, and for commercial banks too.


The proposed solution, try to offer a solution which could contribute to the increase of the fi nancing of EU projects, which until now showed a relatively low absorption rate due to the lack of co-fi nancing 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 fi le. 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, fi nanciar-

bancară şi informatică, Editura Artifex, Bucureşti 2. Anghelache, C. (2009). Metode şi modele de măsurare a riscurilor şi performanţelor

fi nanciar-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 fi nanciar-

contabile, Editura Artifex Bucureşti, 2008 6. Păunică, M., Ştefan, L. (2015). Intelligent Continous Monitoring the Financial

Performance with Cloud Computing, 2nd International Multidisciplinary Scientifi c 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


Model for analyzing the liquidity risk Assoc. Prof. Mădălina-Gabriela ANGHEL PhD„ARTIFEX” University of BucharestDaniel DUMITRESCU PhD StudentBucharest University of Economic Studies

Abstract The liquidity risk has an essential importance in the risk administration process within the fi nancial systems, beeing one of the most common within banking institutions. Mittigating liquidity risk helps address cash fl ow 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: fi nancial risks, banks, madel analysis, assets, fi nancial 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 short-term liquidity needs when loans are not returned as agreed and as a result, the bank must carry out their short-term fi nancing. 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 fl ows 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:


GAP’s liquidity or liquidity position is calculated as the difference between total assets (including funding commitments) and total liabilities (including fi nancing 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. Specifi cally, the bank has suffi cient 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 coeffi cient 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 coeffi cient of liabilities payment date „i”; bi = weighting coeffi cient 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.


Given the profi tability 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 profi ts; RAB2 = coverage of the breach which takes into account operating costs and profi ts; CPB = general expenses and minimum gross realized profi t. 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 fi nancial 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 fi nancial

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


Key measures in ensuring sustainable development in european higher education: recommendations for RomaniaPhD 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 fi ts the sustainable development concept, (2) to investigate the key measures that have proven to be effi cient in these countries and fi nally (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: fi nancing 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 fi nancial 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 effi cient in ensuring sustainability were both legislative as well as practical; (3) regarding Romania, some practical measures were proposed so that they best fi t 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 fi nally develop their own practices that can help Romania progress towards sustainable development through higher education. Keywords: higher education, sustainable development, cluster analysis JEL Classifi cation: I – Health, Education, and Welfare

Introduction In 1987 the concept of sustainable development was defi ned 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


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 defi ned 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 confi dence 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 fi rst offi cial statement made by university administrators of a commitment to environmental sustainability in higher education.

1 http://www.un-documents.net/a21-36.htm accessed 5.05.20142 http://www.un-documents.net/a21-36.htm accessed 5.05.20143 http://www.un-documents.net/a57r254.htm accessed 1.05.2014


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 1.05.20142 http://archive.www.iau-aiu.net/sd/sd_dkyoto.html accessed 5.05.20143 http://archive.www.iau-aiu.net/sd/sd_dkyoto.html accessed 5.05.2014


The fi rst 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 defi nes three priorities: smart growth: (developing an economy based on knowledge and innovation); sustainable growth (promoting a more resource-effi cient, greener and more competitive economy); inclusive growth (fostering a high-employment economy delivering social and territorial cohesion economy). Also, the strategy proposes fi ve 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 fi gure 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.


Population 15-64 years old with tertiary education attainment as a percentage of the total 15-64 years old population

Figure 1

0,05,0

10,015,020,025,030,035,040,0

Europe

anUnion

…

Belgium

Bulgaria

CzechRe

public

Denm

ark

Germ

any(until…

Estonia

Ireland

Greece

Spain

France

Croatia

Italy

Cyprus

Latvia

Lithuania

Luxembo

urg

Hungary

Malta

Nethe

rland

s

Austria

Poland

Portugal

Romania

Sloven

ia

Slovakia

Finland

Swed

en

UnitedKingdo

m

Iceland

Norway

Switzerland

2010 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 (fi gure 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 (fi gure 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 (fi gures 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); specifi c measures for the advancement of women.

1. http://ec.europa.eu/justice/gender-equality/ accessed 18.08.2014, 10:22


Females with tertiary education attainment (percentage of all females)Figure 2

0,05,0

10,015,020,025,030,035,040,045,0

Europe

anUnion

…

Belgium

Bulgaria

CzechRe

public

Denm

ark

Germ

any(until…

Estonia

Ireland

Greece

Spain

France

Croatia

Italy

Cyprus

Latvia

Lithuania

Luxembo

urg

Hungary

Malta

Nethe

rland

s

Austria

Poland

Portugal

Romania

Sloven

ia

Slovakia

Finland

Swed

en

UnitedKingdo

m

Iceland

Norway

Switzerland

2010 2013

Source: Eurostat

Tertiary education participation – Women among students in ISCED 5-6 - as percentage of the total students at this level

Figure 3

0,010,020,030,040,050,060,070,0

Belgium

Bulgaria

CzechRe

public

Denm

ark

Germ

any(until…

Estonia

Ireland

Greece

Spain

France

Croatia

Italy

Cyprus

Latvia

Lithuania

Hungary

Malta

Nethe

rland

s

Austria

Poland

Portugal

Romania

Sloven

ia

Slovakia

Finland

Swed

en

UnitedKingdo

m

Iceland

Norway

Switzerland

2010 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

0,05,0

10,015,020,025,030,0

Europe

anUnion

…

Belgium

Bulgaria

CzechRe

public

Denm

ark

Germ

any(until…

Estonia

Greece

Spain

France

Croatia

Italy

Cyprus

Latvia

Lithuania

Luxembo

urg

Hungary

Malta

Nethe

rland

s

Austria

Poland

Portugal

Romania

Sloven

ia

Slovakia

Finland

Swed

en

UnitedKingdo

m

Iceland

Norway

Switzerland

2010 2012

Source: Eurostat


People (18 years and over) at risk of poverty or social exclusion- females as percentage of all females,

Figure 5

0,010,020,030,040,050,060,0

Europe

an…

Belgium

Bulgaria

CzechRe

public

Denm

ark

Germ

any(until…

Estonia

Greece

Spain

France

Croatia

Italy

Cyprus

Latvia

Lithuania

Luxembo

urg

Hungary

Malta

Nethe

rland

s

Austria

Poland

Portugal

Romania

Sloven

ia

Slovakia

Finland

Swed

en

UnitedKingdo

m

Iceland

Norway

Switzerland

2010 2012

Source: Eurostat

In 2012 vs. 2010, in most of the European countries the number of tertiary students in environmental protection decreased (fi gure 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

0,800,600,400,200,000,200,400,600,801,00

Source: Eurostat

The fi nancial 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 fi gure 7, the fi nancial 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.


Figure 7: Financial aid to students as percentage of total public expenditure on education, at tertiary level of education (ISCED 5,6)

Figure 7

0,05,0

10,015,020,025,030,035,0

2010 2011

Source: Eurostat

As one can observe from fi gure 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

0,000,501,001,502,002,503,00

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 (fi gure 9) is higher for the

fi rst 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.


the unemployment rate (fi gure 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 (fi gure 11) than among tertiary education graduates (fi gure 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 (fi gure 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 (fi gure 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)

Median equivalized net income by educational level, 2010Figure 9

010.00020.00030.00040.00050.00060.000

Europe

an…

Belgium

Bulgaria

CzechRe

public

Denm

ark

Germ

any…

Estonia

Ireland

Greece

Spain

France

Croatia

Italy

Cyprus

Latvia

Lithuania

Luxembo

urg

Hungary

Malta

Nethe

rland

sAu

stria

Poland

Portugal

Romania

Sloven

iaSlovakia

Finland

Swed

enUnitedKingdo

mIceland

Norway

Switzerland

Upper secondary and post secondary non tertiary education (levels 3 and 4)

First and second stage of tertiary education (levels 5 and 6)

Source: Eurostat


Unemployment rateFigure 10

0,05,0

10,015,020,025,030,035,0

Europe

anUnion

…Be

lgium

Bulgaria

CzechRe

public

Denm

ark

Germ

any(until…

Estonia

Ireland

Greece

Spain

France

Croatia

Italy

Cyprus

Latvia

Lithuania

Luxembo

urg

Hungary

Malta

Nethe

rland

sAu

stria

Poland

Portugal

Romania

Sloven

iaSlovakia

Finland

Swed

enUnitedKingdo

mIceland

Norway

Switzerland

Form

erYu

gosla

v…Tu

rkey

2010 2013

Source: EurostatUnemployment rate upper secondary education

Figure 11

0,05,0

10,015,020,025,030,035,0

Europe

anUnion

…

Belgium

Bulgaria

CzechRe

public

Denm

ark

Germ

any(until…

Estonia

Ireland

Greece

Spain

France

Croatia

Italy

Cyprus

Latvia

Lithuania

Luxembo

urg

Hungary

Malta

Nethe

rland

s

Austria

Poland

Portugal

Romania

Sloven

ia

Slovakia

Finland

Swed

en

UnitedKingdo

m

Iceland

Norway

Switzerland

Form

erYu

gosla

v…

Turkey

2010 2013

Source: EurostatUnemployment rate tertiary education

Figure 12

0,0

5,0

10,0

15,0

20,0

25,0

Europe

anUnion

(28…

Belgium

Bulgaria

CzechRe

public

Denm

ark

Germ

any(until19

90…

Estonia

Ireland

Greece

Spain

France

Croatia

Italy

Cyprus

Latvia

Lithuania

Luxembo

urg

Hungary

Malta

Nethe

rland

sAu

stria

Poland

Portugal

Romania

Sloven

iaSlovakia

Finland

Swed

enUnitedKingdo

mIceland

Norway

Switzerland

Form

erYu

gosla

v…Tu

rkey

2010 2013

Source: Eurostat


People at risk of poverty and social exclusionFigure 13

0,0

10,0

20,0

30,0

40,0

50,0

60,0

Europe

anUnion

(28…

Belgium

Bulgaria

CzechRe

public

Denm

ark

Germ

any(until19

90…

Estonia

Greece

Spain

France

Croatia

Italy

Cyprus

Latvia

Lithuania

Luxembo

urg

Hungary

Malta

Nethe

rland

s

Austria

Poland

Portugal

Romania

Sloven

ia

Slovakia

Finland

Swed

en

UnitedKingdo

m

Iceland

Norway

Switzerland

2010 2012

Source: Eurostat

People at risk of poverty and social exclusion tertiary educationFigure 14

0,0

5,0

10,0

15,0

20,0

25,0

30,0

Europe

anUnion

(28…

Belgium

Bulgaria

CzechRe

public

Denm

ark

Germ

any(until19

90…

Estonia

Greece

Spain

France

Croatia

Italy

Cyprus

Latvia

Lithuania

Luxembo

urg

Hungary

Malta

Nethe

rland

s

Austria

Poland

Portugal

Romania

Sloven

ia

Slovakia

Finland

Swed

en

UnitedKingdo

m

Iceland

Norway

Switzerland

2010 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


5-6 - as % of the total students at this level are two indicators that aim to measure the social inclusion of women. The fi rst 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 qualifi ed labor force in the fi eld 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;


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 fi rst 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 fi rst 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 fi nancial 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 fi eld;

The highest income for the higher education graduates is registered among the countries in the fi rst 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 specifi c 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 fi rst 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 fi rst cluster.


The research carried out has identifi ed the main European countries where the higher education best fi ts 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 fi nancial 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) identifi ed four factors for the case of

the Netherlands: staff policy (autonomy to hire and dismiss academic staff, autonomy to set wages), output fl exibility (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


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.

DendrogramFigure 15

Source: designed by the author


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_tertiaryFemales with tertiary education attainment

(percent of all females)28.792857143 31.183333333 16.920000000

env_tertiary Tertiary students studying environmental protection 4009.642857143 4780.833333333 7653.600000000

fi n_aid

Financial aid to students as % of total public

expenditure on education, at tertiary level of

education (ISCED 5, 6)

23.457142857 12.066666667 11.390000000

tertiary_gdp

Total public expenditure on education as % of GDP, at tertiary level of education

(ISCED 5, 6)

1.685000000 1.081666667 .924000000

fem_stud

Tertiary education participation Women

among students in ISCED 5-6 - as % of the total students at this level

55.364285714 56.750000000 56.270000000

income_tertiary Median equivalised net income - tertiary education 27101.357142857 12386.333333333 12099.500000000

income_tertiary_vs_secondary

Median equivalised net income - tertiary education

- 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

poverty_tertiaryPeople at risk of poverty

or social exclusion-tertiary education

9.414285714 15.883333333 9.100000000

poverty_tertiary_females

People at risk of poverty or social exclusion-tertiary

education females10.007142857 17.033333333 9.680000000

Source: designed by the author


Conclusions and recommendations The research carried out has identifi ed 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 signifi cant 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 identifi cation 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 defi ned by this model.


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ISSN 1018-046X

Reproducerea conţinutului articolelor fără acordul Institutului Naţional de Statistică este interzisă, iar utilizarea conţinutului acestei publicaţii, cu titlul explicativ sau justifi cativ, în diferite lucrări este autorizată numai cu precizarea clară a sursei.

Se precizează că punctele de vedere, datele şi informaţiile cuprinse în articolele publicate aparţin autorilor şi nu angajează răspunderea Institutului Naţional de Statistică