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Revista Română de Statistică - Supliment nr. 7 / 2015

SUMAR / CONTENTS 7/2015REVISTA ROMÂNĂ DE STATISTICĂ SUPLIMENT

SELECŢIA ENTITĂŢILOR – O ABORDARE BUSINESS INTELLIGENCE 3 SELECTION OF THE ENTITIES - A BUSINESS INTELLIGENCE APPROACH 13 Prof. univ. dr. Constantin Anghelache Academia de Studii Economice, Bucureşti Conf. univ. dr. Alexandru Manole Universitatea “Artifex” din Bucureşti Lector univ. dr. Mădălina Gabriela Anghel Universitatea “Artifex” din Bucureşti

O SUCCINTĂ ANALIZĂ DE SEZONALITATE A PIEŢEI AUTOTURISMELOR ÎN ROMÂNIA 23

A BRIEF ANALYSIS OF SEASONALITY IN THE ROMANIAN CAR MARKET 33

Prof. univ. dr. habil. Gheorghe Săvoiu Cerc. st. dr. ing. Victor Iorga Simăn Conf. univ. dr. Constantin Manea Universitatea din Piteşti

REGIONAL COMPARATIVE RESIDENTIAL PRICE LEVEL IN ROMANIA 42 PhD Mihai Gheorghe National Institue of Statistics

IMPACT OF PAK-INDIA RELATIONSHIP ON RICE TRADE ON ECONOMY OF PAKISTAN BY USING COMPUTABLE GENERAL EQUILIBRIUM MODEL (CGE) 55

Assistant Professor Faiz Muhammad Shaikh SZABAC-Dokri-Larkana-Sindh Pakistan PhD student Mushtaque Ali Jariko Aalborg University Copenhagen Assistant Professor Dr.Muhammad Saleh Memon University of Sindh-Jamshoro Assistant Professor Abdul Sattar Shah IBA-University of Sindh-Jamshoro

www.revistadestatistica.ro/supliment

Romanian Statistical Review - Supplement nr. 7 / 20152

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The Romanian Statistical Review, issued by the National Institute of Statistics, is in our country specialising in the fi eld of statistical theory and practice. The articles published are addressed to the scientists, researchers and statistical data and information users interested in broadening and deepening their horizon of knowledge by acquiring specialised notions and coming into contact with new papers and reference studies they can later apply in their own fi eld. Through the presentation of papers that are scientifi c in nature and that promote statistical culture, which is necessary in a functional market economy, the Review aims to be a favourable space for exchange of ideas and a challenge at the same time. Any study or opinion that can contribute to the development of the degree understanding statistics as a science is welcome.

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Revista Română de Statistică - Supliment nr. 7 / 2015 3

Selecţia entităţilor – o abordare business intelligence

Prof. univ. dr. Constantin ANGHELACHE Academia de Studii Economice, Bucureşti

Conf. univ. dr. Alexandru MANOLE Universitatea “Artifex” din Bucureşti

Lector univ. dr. Mădălina Gabriela ANGHEL Universitatea “Artifex” din Bucureşti

Abstract Acest articol prezintă un model de selecţie a entităţilor în cadrul portofoliului. Am avut în vedere oportunităţile de investiţii ale pieţei de capital. In primul rând, ne concentrăm pe aspectele teoretice care indică relaţiile care pot fi utilizate. În acelaşi timp, ne referim la confi guraţiile principale care pot fi întâlnite prin studiul evoluţiei ratelor. De exemplu, am acordat atenţie evoluţiei indicatorilor, confi guraţiilor de tip head-shoulders şi zigzag etc. Având în vedere extinderea pieţei de capital, propunem o abordare multidimensională care să asiste companiile în politica lor investiţională. Arhitectura propusă presupune surse de date, un depozit de date şi aplicaţii client. Keywords: construcţia modelului, medii mobile, baze de date, grafi c box, confi guraţie

Introducere Studiile elaborate de Anghel (2013a, 2013b), Anghelache şi Anghel (2014) descriu în detaliu aspectele teoretice şi practice semnifi cative în managementul portofoliului. Canon şi Holder au propus (1979) un model propriu de scoring. In plus, modelul lui Altman’s (1968) este acceptat ca instrument puternic în analiza riscului de faliment. Işfănescu, Robu, Hristea (2010) and Robu, Anghel, Şerban (2014) studiază caracteristicile modelelor de tip aditiv şi scoring în analiza fi nanciară. Vintilă (2010) tratează managementul fi nanciar al fi rmei. Aplicaţiile de tip business intelligence sunt recunoscute ca instrument important de analiză a datelor. Caracteristicile depozitelor de date au fost studiate de Kimball (2015).

Unele aspecte privind emisiunea şi evaluarea acţiunilor

Evaluarea acţiunilor pe baza analizei tehnice presupune punerea în evidenţă a mişcării cursului unei acţiuni ca rezultat al cererii şi ofertei şi oferă informaţii probabile cu privire la evoluţia viitoare de curs.


În cadrul analizei tehnice, cursul acţiunii este cel mai important element, iar toţi factorii care infl uenţează piaţa se refl ectă în nivelul cursului. Mişcarea cursului pe o perioadă sufi cient de îndelungată formează un trend. Analiza tehnică se bazează pe studiul istoric al cursului acţiunilor, în cadrul căruia trebuie evidenţiaţi trendul, ciclul şi factorii aleatorii.

Elemente defi nitorii ale analizei tehniceFig. 1

Principalele confi guraţii care pot fi întâlnite prin studierea evoluţiei cursului sunt: - Confi guraţia „cap-umeri” care se formează pe parcursul unei perioade

de 2,3 luni. O astfel de confi guraţie a evoluţiei de curs indică o scădere cursului, dacă se atinge cea mai de jos linie. Dacă cursul de piaţă depăşeşte această linie, se manifestă semnalul de vânzare, anticipându-se o scădere mai puternică a cursului viitor.

- Confi guraţia „cap-umeri” inversată - Confi guraţia în zigzag. - Construcţia unui grafi c este defi nită prin axa orizontală, pe care sunt reprezentate timpul şi volumul, şi prin axa verticală, pe care sunt înregistrate cursurile. Se folosesc următoarele tipuri de reprezentări grafi ce: live-chart, box-chart, candlestick-chart, point & fi gure-chart. De asemenea, se utilizează metoda mediilor mobile, una dintre cele mai vechi tehnici folosite în statistică, şi este utilizată şi în analiza grafi că a cursului valorilor mobiliare, cu condiţia ca perioada aleasă pentru analiză să fi e sufi cient de îndelungată (30-200 zile) pentru a desprinde tendinţa.


Rata autonomiei fi nanciare, care exprimă ponderea resurselor proprii în totalul resurselor fi nanciare atrase pe termen lung ale societăţii, trebuie să înregistreze o valoare de minim 50%.

(1)

Rata de fi nanţare a stocurilor care măsoară capacitatea de fi nanţare a stocurilor prin intermediul fondului de rulment trebuie să înregistreze o valoare supraunitară pentru ca societatea să fi e considerată viabilă.

(2)

Rata de autofi nanţare a activelor refl ectă măsura în care capitalurile proprii acoperă activele societăţii.

(3)

Rata generală a îndatorării redă îndatorarea totală a societăţii prezentată sub forma împrumuturilor de pe termen scurt, mediu şi lung în raport cu capitalul propriu şi trebuie să înregistreze o valoare subunitară, o valoare supraunitară semnifi când un grad de îndatorare ridicat, iar o valoare de peste 2,33 situaţia este îngrijorătoare, cu un grad foarte ridicat de îndatorare.

(4)

Coefi cientul datoriilor fi nanciare exprimă îndatorarea pe termen mediu şi lung, nivelul maxim normal acceptat fi ind 0.5 – 1.0.

(5)

Rata datoriilor totale reprezintă ponderea surselor de fi nanţate atrase în totalul pasivelor societăţii, în activitatea practică, se recomandă ca valoarea ratei datoriilor să fi e situată sub pragul de 50% pentru ca societatea să fi e apreciată ca fi ind sigură.

(6)

În general, un nivel bun al solvabilităţii patrimoniale depăşeşte valoarea de 0.5. Un nivel cuprins în intervalul 0.3–0.5 refl ectă o situaţie satisfăcătoare.


De regulă, o rată a solvabilităţii patrimoniale sub 0,3 este apreciată ca riscantă de către fi nanţatori.

(7)

Solvabilitatea globală refl ectă posibilitatea acoperirii datoriilor totale cu active. Valoarea indicatorului trebuie să fi e supraunitară, cât mai aproape de 2.

(8)

Lichiditatea patrimonială redă raportul în care drepturile creditorilor pe termen scurt sunt acoperite de valoarea activelor care pot fi transformate în lichidităţi până la scadenţa datoriilor. Valoarea înregistrată de acest indicator trebuie să fi e mai mare decât 1, cu un nivel normal cuprins între 1.7 şi 2. De asemenea, trebuie evitată evoluţia descendentă a acestui indicator întrucât este expresia unei activităţi în declin, creditorii şi furnizorii devin circumspecţi în acordarea de noi credite comerciale şi bancare [5,7,8].

(9)

Lichiditatea curentă refl ectă capacitatea societăţii de a-şi onora obligaţiile pe termen scurt din creanţe şi disponibilităţi şi, în calculul său, exclude stocurile din activele circulante, acestea reprezentând elementul cel mai incert din punct de vedere al valorii şi lichidităţii sale[5,7,8].

(10)

Valoarea înregistrată de ROE trebuie să fi e mai mare de 5% şi se stabileşte prin aplicarea următoarei formule de calcul [5,7,8]:

(11)

Rata de creştere a profi tului obţinut este calculată prin următoarea formulă:

(13) unde: PT = profi t înregistrat în anul T; PT-1 = profi t înregistrat în anul T – 1;


Model de selecţie a entităţilor

Pornind de la faptul că, un pas esenţial în construcţia unui portofoliu constă în alegerea acţiunilor, în cele ce urmează este prezentat un model de selecţie a entităţilor bazat pe analiza situaţiei fi nanciar-patrimonială şi care constă în studiul a trei categorii de indicatori ponderaţi în funcţie de importanţa acestora.

Ponderile de importanţă utilizateTabel nr. 1

Categorie de indicatori PondereI. Indicatori ai gradului de îndatorare 25%II. Indicatori ai evaluării riscului de faliment prin metoda scorurilor 35%III. Indicatori ai echilibrului economico-fi nanciar 40%Total 100%

Pe baza unor criterii ştiinţifi ce aplicate riguros, indicatorilor analizaţi li se atribuie califi cative de importanţă cărora le corespund anumite punctaje prezentate în tabelul următor.

Califi cativele acordate indicatorilor analizaţi Tabel nr. 2

Califi cativ acordat Excelent Bine SlabSimbol califi cativ E B SPunctaj aferent 5 3 0

Punctajul obţinut de fi ecare entitate în parte se calculează prin luarea în considerare a ponderilor prezentate anterior, aplicând următoarea relaţie de calcul:

Punctaj calculat =

0,25 ∙ Punctaj

Categorie I+

0,35 ∙ Punctaj

Categorie II+

0,40 ∙ Punctaj

Categorie III

Punctajul aferent fi ecărei categorii de indicatori se calculează prin aplicarea califi cativelor acordate în concordanţă cu modalitatea de interpretare a valorilor indicatorilor analizaţi regăsită în literatura de specialitate.


Categoria I: Indicatori ai gradului de îndatorare Tabel nr. 3

Indicator Interval Califi cativ Punctaj acordat

Rata datoriilor totale< 30% E 5

[30%–50%) B 3> 50% S 0

Rata generală a îndatorării

< 1 E 5[1 – 2.33) B 3≥ 2.33 S 0

Rata datoriilor fi nanciare[0.5 – 1] E 5(1 – 2.5) B 3≥ 2.5 S 0

Punctaj Categorie I Sursa: calcule proprii

Rata datoriilor totale reprezintă ponderea surselor de fi nanţate atrase în totalul pasivelor societăţii, în activitatea practică, se recomandă ca valoarea ratei datoriilor să fi e situată sub pragul de 50% pentru ca societatea să fi e apreciată ca fi ind sigură.

Rata generală a îndatorării redă îndatorarea totală a societăţii prezentată sub forma împrumuturilor de pe termen scurt, mediu şi lung în raport cu capitalul propriu şi trebuie să înregistreze o valoare subunitară, o valoare supraunitară semnifi când un grad de îndatorare ridicat, iar o valoare de peste 2,33 situaţia este îngrijorătoare, cu un grad foarte ridicat de îndatorare.

Coefi cientul datoriilor fi nanciare exprimă îndatorarea pe termen mediu şi lung, nivelul maxim normal acceptat fi ind 0.5 – 1.0.


Categoria a II-a. Indicatori ai evaluării riscului de faliment prin metoda scorurilor

Tabel nr. 4


Altman > 2.6 E 5[1.1 – 2.6] B 3

< 1.1 S 0Canon & Holder > 9 E 5

[4 – 9] B 3< 4 S 0

Punctaj Categorie II Sursa: calcule proprii

Modelul Canon & Holder se bazează pe următoarea funcţie[5,6]:

Z = 0,24 * x1 + 0,22 * x2 + 0,16 * x3 − 0,87 * x4 − 0,1* x5, unde valorile lui x1 - x5 se determină prin relaţiile:

Modelul Altman se bazează pe următoarea funcţie[1]:

Z = 6,56*x1 +3,26*x2 +6,72*x3 +1,05*x4, unde:


Categoria a III-a. Indicatori ai echilibrului economico-fi nanciar Tabel nr. 5


Rata de autofi nanţare a activelor[0.75–1] E 5

[0.50– 0.75) B 3< 0.5 S 0

Rata de fi nanţare a stocurilor> 2 E 5

[1 – 2] B 3< 1 S 0

Rata autonomiei fi nanciare[0.75–1] E 5

[0.50– 0.75) B 3< 0.5 S 0

Punctaj Categorie III Sursa: calcule proprii

Rata de autofi nanţare a activelor refl ectă măsura în care capitalurile proprii acoperă activele societăţii.

Rata de fi nanţare a stocurilor care măsoară capacitatea de fi nanţare a stocurilor prin intermediul fondului de rulment trebuie să înregistreze o valoare supraunitară pentru ca societatea să fi e considerată viabilă.

Rata autonomiei fi nanciare, care exprimă ponderea resurselor proprii în totalul resurselor fi nanciare atrase pe termen lung ale societăţii, trebuie să înregistreze o valoare de minim 50%.


Model de arhitectură software (BI)

Modelele de analiză şi indicatorii propuşi, care se bazează pe măsuri specifi ce şi relevante, poate fi exploatat prin aplicaţii software de tip business intelligence, organizate în jurul unui depozit de date, şi conectate la surse de date corespunzătoare. Modelul de arhitectură pe care îl propunem poate fi conceptualizat conform cu fi gura de mai jos:

Modelul aplicaţiei business intelligenceFigure 2

Depozit de date

SGBDR

Surse de date tabelare

Alte surse de date

Aplica ii OLAP

Statistic / econometric Software de

analiz

Modelul include următoarele componente: - Surse de date: datele care descriu valorile indicatorilor pot fi extrase

din diferite surse de date, incluzând baze de date relaţionale, fi şiere de calcul tabelar etc.;

- Depozit de date. O structură de date centrală, construită conform principiilor evidenţiate de Kimball;

- Aplicaţii client: aplicaţii de tip OLAP, software orientat pe analize.

Concluzii

Am propus un set de instrumente de analiză capabile să ajute companiile de investiţii în abordarea contextului şi oportunităţilor specifi ce pieţei de capital. Indicatorii pe care i-am propus oferă sufi ciente informaţii pentru a sprijini decizia de investiţii, iar modelul de arhitectură software, pe care intenţionăm să-l dezvoltăm în continuare, se bazează pe principiile de analiză fi nanciară expuse în prima parte a articolului. Considerăm că baza solidă a indicatorilor şi modelelor de analiză economico-fi nanciară, coroborate cu capabilităţile aplicaţiilor software care ar


putea fi utilizate ca suport pentru analiză, oferă un fundament corespunzător pentru a-i ajuta pe investitorii potenţiali să evalueze informaţiile relevante pentru decizia de investiţii şi să-i sprijine în formularea de alternative viabile din care să poată fi aleasă cea mai profi tabilă.

Bibliografi e - Altman, E.I. (1968), Financial ratios, Discriminant Analysis and the prediction of

Corporate Bankrupcy” - Anghel, M.G. (2013), Modele de gestiune şi analiză a portofoliilor, Editura Economică,

Bucureşti - Anghel, M.G. (2013), Modele de construcţie a portofoliilor de instrumente fi nanciare,

Revista Română de Statistică, Supliment - Anghelache, C.; Anghel, M.G. (2014), Modelare economică. Concepte, teorie şi studii

de caz, Editura Economică, Bucureşti - Ciobănaşu, M. (2011), Analiza economico- fi nanciară, Editura Universitară,

Bucureşti, - Conan, Holder (1979), Variables explicatives de performances et controle de gestion

dans les P.M.I. - Işfănescu, A.; Robu, V.; Hristea, A.M. (2010), Analiză economico-fi nanciară, Editura

ASE, Bucuresti - Robu, V.; Anghel, I., Şerban, E.C. (2014), Analiza economică – fi nanciară a fi rmei,

Editura Economică, Bucureşti - Vintilă, G. (2010), Gestiunea fi nanciară a întreprinderii, Editura Didactică şi

Pedagogică, Bucureşti - Information on http://www.kimballgroup.com/wp-content/uploads/2013/08/2013.09-

Kimball-Dimensional-Modeling-Techniques11.pdf


SELECTION OF THE ENTITIES - A BUSINESS INTELLIGENCE APPROACH

Prof. Constantin Anghelache PhD Academy of Economic Studies Bucharest Assoc. prof. Alexandru Manole PhD Artifex University of Bucharest Lecturer Mădălina Gabriela Anghel PhD Artifex University of Bucharest

Abstract

This paper presents a model for the analysis of selection of entities to form a portfolio. We have taken into account the investment opportunities of the capital market. First of all, we focus on the theoretical aspects indicating the relations which might be used. At the same time, we refer to the main confi gurations which may be met through the study of rate evolutions. For example, we give attention to the evolution of indicators, the head-shoulders and zigzag confi guration, and so on. Having in mind the extension of the capital market we propose a multidimensional approach which might be able to provide data to assist the companies in their investments policies. The proposed architecture involves data sources, a data warehouse and client systems. Keywords: construction of the model, mobile means, data bases, box chart, confi guration

Introduction

The studies of Anghel (2013a, 2013b), Anghelache and Anghel (2014), describe in detail the signifi cant theoretical and practical aspects regarding the management of portfolio. Conan and Holder proposed (1979) a particular scoring model. Furthermore, Altman’s (1968) model is accepted as a strong instrument in analyzing the risk of bankruptcy. Işfănescu, Robu, Hristea (2010) and Robu, Anghel, Şerban (2014) study the characteristics of additive and scoring models in fi nancial analysis. Vintilă (2010) treats on fi nancial management of the company. The business intelligence software is recognized as a powerful tool for data analysis. The characteristics of data warehouse were studied and described by Kimball (2015).


Certain aspects concerning the shares issuing and valuation

The valuation of the shares based on the technical analysis implies the underlining of the movement recorded by the share rate as a result of the demand and the offer and gives probable information as regards the future evolution of the rate. In the frame of the technical analysis, the share rate is the most signifi cant element while all the factors which are infl uencing the market are refl ected by the rate level. The movement of the rate over a period large enough is forming a trend. The technical analysis is based on the historical study of the shares rate, pointing out the trend, the cycle and the random factors.

Defi ning elements of the technical analysisFig. 1

The main confi gurations which may be met through the study of the rate evolution are the following: - The confi guration „head- shoulders” which gets formed during a period

of 2-3 months. Such a confi guration of the rate evolution indicates a decrease of the rate in case that the lowest line is reached. If the market rate exceeds this line, the sale signal is given as a stronger decrease of the rate is forecasted in the future.

- The confi guration „head- shoulders” reversed - The confi guration in zigzag


The construction of a graphic is defi ned by the horizontal axis, on which the time and volume are represented, and by the vertical axis, on which there are the rates being recorded. The following types of graphic representations are currently used: live-chart, box-chart, candlestick-chart, point & fi gure-chart.Also, the mobile means method, one of the most ancient techniques used in statistics is applied, as well as the graphical analysis of the equities rates, provided that the period being chosen for the analysis is long enough (30 – 200 days) in order to fi x the tendency.The rate of fi nancial autonomy, which express the weight of the own resources in the total of the fi nancial resources drawn on long term of the company must record a value of minimum 50%.

(Eq. 1)

The rate of stocks fi nancing, which measures that capacity of the stock fi nancing through the bearing fund must record a supra-unitary in order to consider the company as viable.

(Eq. 2)

The rate of assets self-fi nancing is refl ecting the extent to which the own capitals are covering the company’s assets. (Eq. 3)

The general rate of indebting is showing the total indebting of the company in form of short, medium and long term loans as against the own capital and must record a sub-unitary value as a supra-unitary value signifi es a high level of indebting while a value over 2.33 shows a concerning situation, with a very high indebting degree.

(Eq. 4)

The coeffi cient of the fi nancial debts is expressing the indebting on medium and long term, the maximum acceptable level counting for 0.5 – 1.0.

(Eq. 5)


The rate of total debts is representing the weight of the drawn fi nancial sources within the total passives of the company: in practice, it is recommended that the value of the debt rate is placed below the threshold of 50% in order to appreciate a company as a secure one. (Eq. 6)

Generally speaking, a good level of the patrimonial solvency is exceeding the value of 0.5. A level included by the interval 0.3–0.5 is refl ecting a satisfactory situation. As a rule, a rate of the patrimonial solvency below 0.3 is considered as risky by the fi nancers[5,6]. (Eq. 7)

The global solvency is refl ecting the possibility to cover the total debts by assets. The indicator value must by supra-unitary, as close as possible to the value 2. (Eq. 8)

The patrimonial liquidity is giving the ratio in which the claims of the short term creditors are covered by the value of the assets which may be turned into liquidities up to the maturity of the debts. The value recorded by this indicator must be bigger than 1, with a level between 1.7 and 2. Meantime, the down warding evolution if this indicator must be avoided as it is the expression of a declining activity, so that the creditors and suppliers become circumspect as to granting new commercial and banking credits[5,7,8].

(Eq. 9)

The current liquidity is refl ecting the company’s capability to honor its liabilities on short term out of claims and availabilities and, when calculated, it excludes the stock of circulating assets as they are representing the most uncertain element from the point of view of its value and liquidity[5,7,8]:

(Eq. 10)

The value recorded by ROE must be higher 5% and is established by applying the following calculation relation[5,7,8]:


(Eq. 11)

The rate of growing of the achieved profi t is calculated through the following formula:

(Eq. 12) where: PT = profi t recorded in the year T; PT-1 = profi t recorded in the year T – 1;

Model for the selection of the entities

Given the fact that, an esental step for the portfolio construction consists in choosing of the shares are transacted at the Stock Exchange, below is presented a model for the selection of entities based on patrimonial-fi nancial situation analysis and which consists in the study of the three categories of indicators assign according to their importance.

Importance weights usedTable 1

Category of indicators weightI. Indicators of the indebting degree 25%II. Indicators of the valuation of the bankruptcy risk through the scores method 35%III. Indicators of the economic-fi nancial equilibrium 40%Total 100%

On the basis of scientifi c criteria rigorously applied, the indicators analyzed are assigned ratings of importance to which they correspond to certain scores presented in table below:

The granted mark and the scores corresponding to analyzed indicators Table 2

Granted mark Excellent Good WeakSymbol of the mark E G WConnected score 5 3 0

The score obtained by each entity in part shall be calculated by taking into account the weightings above, using the following relationship:

Calculated score = 0,25 ∙ Score

Category I + 0,35 ∙ Score Category II + 0,40 ∙ Score

Category III


The score received by each category of indicators is calculated by applying the grades awarded in accordance with the methods of interpretation analyzed indicators values found in the literature.

Category I: Indicators of the indebting degreeTable 3

Indicator Interval Qualifi cation (mark) Granted score

Rate of total debts< 30% E 5

[30%–50%) G 3> 50% W 0

General rate of indebting< 1 E 5

[1 – 2.33) G 3≥ 2.33 W 0

Rate of fi nancial debts[0.5 – 1] E 5(1 – 2.5) G 3≥ 2.5 W 0

Score Category I Source: self-calculations

The rate of total debts is representing the weight of the drawn fi nancial sources within the total passives of the company: in practice, it is recommended that the value of the debt rate is placed below the threshold of 50% in order to appreciate a company as a secure one.

The general rate of indebting is showing the total indebting of the company in form of short, medium and long term loans as against the own capital and must record a sub-unitary value as a supra-unitary value signifi es a high level of indebting while a value over 2.33 shows a concerning situation, with a very high indebting degree.

The coeffi cient of the fi nancial debts is expressing the indebting on medium and long term, the maximum acceptable level counting for 0.5 – 1.0.


Category II. Indicators of the valuation of the bankruptcy risk through the scores method

Table 4Indicator Interval Qualifi cation (mark) Granted score

Altman > 2.6 E 5[1.1 – 2.6] B 3

< 1.1 S 0Canon & Holder > 9 E 5

[4 – 9] B 3< 4 S 0

Score Category II Source: self-calculations

The model Canon & Holder is based on the following function[5,6]:

Z = 0,24 * x1 + 0,22 * x2 + 0,16 * x3 − 0,87 * x4 − 0,1* x5, where the values of x1 - x5 are established through the relations:

The model Altman is based on the following function[1]: Z = 6,56*x1 +3,26*x2 +6,72*x3 +1,05*x4, where:


Category III. Indicators of the economic-fi nancial equilibriumTable 5

Indicator Interval Qualifi cation (mark) Granted score

Rate of assets self-fi nancing[0.75–1] E 5

[0.50– 0.75) G 3< 0.5 W 0

Rate of stocks fi nancing > 2 E 5

[1 – 2] G 3< 1 W 0

Rate of fi nancial autonomy[0.75–1] E 5

[0.50– 0.75) G 3< 0.5 W 0

Score Category III Source: self-calculations

The rate of assets self-fi nancing is refl ecting the extent to which the own capitals are covering the company’s assets.

The rate of stocks fi nancing, which measures that capacity of the stock fi nancing through the bearing fund must record a supra-unitary in order to consider the company as viable.

The rate of fi nancial autonomy, which express the weight of the own resources in the total of the fi nancial resources drawn on long term of the company must record a value of minimum 50%.

Software architecture model (BI)

The analysis models and indicators proposed, which are based on specifi c and relevant measures, can be exploited through business intelligence software, centered around a data warehouse, and connected to proper data sources. The architecture model we propose can be conceptualized as the following fi gure shows:


Business intelligence modeFig. 2

Data warehouse

RDBMS

Spreadsheet data sources

Other data sources

OLAP

Applications

Specialized Statistic /

econometric Analysis software

The model includes the following components: - Data sources: data describing the values of indicators can be drawn

from various sources, including relational databases, spreadsheet/data fi les etc.;

- Data warehouse. a central data structure, constructed according to principles outlined by Kimball;

- Data analysis applications: OLAP embedded applications, specialized analysis software.

Conclusions

We have proposed a set of analysis tools able to help the investment companies in approaching the capital market context and opportunities. The indicators we proposed offer suffi cient information to support the investments decision, and the software architecture model, which we intent ourselves to further develop, is based on the principles of the fi nancial analysis exposed in the fi rst parts of the article. We consider that the sound basis of the fi nancial analysis indicators and models, corroborated with the capabilities of the software applications that can be chosen as support for the analysis, is a proper foundation to aid the potential investors to appraise the information relevant to investment decisions and to help them formulate viable alternatives from which the most profi table one can be chosen.

References - Altman, E.I. (1968), Financial ratios, Discriminant Analysis and the prediction of

Corporate Bankrupcy” - Anghel, M.G. (2013), Modele de gestiune şi analiză a portofoliilor, Editura Economică,

Bucureşti


- Anghel, M.G. (2013), Modele de construcţie a portofoliilor de instrumente fi nanciare, Revista Română de Statistică, Supliment

- Anghelache, C.; Anghel, M.G. (2014), Modelare economică. Concepte, teorie şi studii de caz, Editura Economică, Bucureşti

- Ciobănaşu, M. (2011), Analiza economico- fi nanciară, Editura Universitară, Bucureşti,

- Conan, Holder (1979), Variables explicatives de performances et controle de gestion dans les P.M.I.

- Işfănescu, A.; Robu, V.; Hristea, A.M. (2010), Analiză economico-fi nanciară, Editura ASE, Bucuresti

- Robu, V.; Anghel, I., Şerban, E.C. (2014), Analiza economică – fi nanciară a fi rmei, Editura Economică, Bucureşti

- Vintilă, G. (2010), Gestiunea fi nanciară a întreprinderii, Editura Didactică şi Pedagogică, Bucureşti

- Information on http://www.kimballgroup.com/wp-content/uploads/2013/08/2013.09-Kimball-Dimensional-Modeling-Techniques11.pdf


O succintă analiză de sezonalitate a pieţei autoturismelor în România

Prof. univ. dr. habil. Gheorghe SĂVOIU (e-mail: [email protected])

Cerc. st. dr. ing. Victor Iorga SIMĂNConf. univ. dr. Constantin MANEA

Universitatea din Piteşti

Rezumat Acest articol prezintă o analiză succintă de sezonalitate axată pe statistici lunare ale unor variabile specifi ce vânzărilor totale de autoturisme autohtone și importate axate pe seriile de date ale ultimilor trei ani ale pieței naționale structurate distinct pe trei tipuri de subpopulații semnifi cative (livrări autoturisme pe benzină, livrări autoturisme pe motorină și livrări autoturisme pe mărcile mai importante). Analiza statistică a sezonalității este axată pe evaluarea mai rapidă a structurii medii generând informații mai interesante referitoare la datele prelucrate, specifi că gândirii complexe stistice şi studiului agregat iar ulterior structurat oferind indicatori originali (ecartul absolut și relativ al coefi cienților structurali de sezonalitate) pe care autorii îi consideră o soluţie interesantă de investigaţie benefi ciind de un grad mai ridicat de simplitate și accesibilitate generală. De asemenea, articolul valorifi că și pachetul de programe Eviews pentru a face vizibilă sezonalitatea în grafi ce speciale econometrice, devenind o a doua dovadă de originalitate comparabilă cu în realizarea grafi celor statistice clasice, ca și în modelarea modernă econometrică imortalizate într-o dinamică medie (metoda mediilor mobile). Cuvinte cheie: structură, sezonalitate, coefi cient de sezonalitate, coefi cent structural de sezonalitate, ajustarea sezonalității, metoda mediilor mobile. Coduri JEL: C19, C46, M31.

Introducere

Studierea fenomenelor şi proceselor social-economice sub aspectul evoluţiei lor în timp și cu precădere pe termen scurt sau infraanual reprezintă o necesitate pentru agenţii economici, o condiţie importantă pentru fundamentarea deciziilor economice, cunoașterea piețelor și anticiparea unor evoluții utile. Aceată lucrare pune un accent deosebit pe analiza statistică a sezonalității fenomenelor specifi ce pieței de autoturisme, din România prelucrând datele


seriilor cronologice disponibile pe http://www.apia.ro/buletin-statistic/ pe un interval minimal de trei ani (2012-2014) identifi când specifi citatea vânzărilor (livrărilor) de autoturisme per total și structurate distinct pe trei tipuri de subpopulații semnifi cative (livrări autoturisme pe benzină, livrări autoturisme pe motorină și livrări autoturisme pe mărcile mai importante). Articolul s-a concentrat asupra datelor referitoare la vânzările cantitative ale pieței de autoturisme în România şi a aplicat de o manieră originală instrumente statistice şi econometrice asupra unor seturi şi structuri de date și informaţii accesibile decidenților din acest sector de activitate. Conţinutul articolului este structurat logic, de la abordarea totală a livrărilor de autoturisme autohtone și importate, la instrumente şi calcule statistice ale coefi cienților de sezonalitate urmate de interpretarea succintă a acestora, la confruntarea şi compararea unor date, aşa cum se face cu toate statisticile piețelor, apelând la o confruntare permanentă cu consecinţele interpretării datelor, în concordanţă cu înţelegerea economică a pieţelor sintetizate în funcţionarea lor prin statistici ofi ciale. Un alt scop al articolului în sine este dat de aplicarea originală a metodei analizei mediilor mobile ca rezultat al cerinţei de a lua decizii în timp şi spaţiu fundamentate pe cunoaşterea anticipativă având ca suport modelarea și ajustarea sezonală a evoluțiilor seriilor de date statistice cronologice prin valuri desezonalitate specifi ce (incluzând aici indicatori sau modele speciale statistice de analiză dar și modele econometrice și realizarea de grafi ce speciale ajustate sezonal cu ajutorul pachetului de programe Eviews), oferind o a altă dovadă de originalitate a investigației statistice și econometrice a autorilor. Pachetele de programe specializate realizează rapid calculele, iar E-Views constituie un bun exemplu de înţelegere a modalităţii de ajustare și de realizare fi nală a unei vizibilități a seriilor de date aparent incerte ca tendințe infraanuale.

Analiza sezonalității vânzărilor specifi ce pieței autoturismelor din România

Această prezentare succintă a unei piețe de automobile nu are intenția de a epuiza diversitatea pieței naționale în sine, dar răspunde concret cerinței de identifi care a unor tendințe de sezonalitate specifi că, pe o perioadă minimală necesară pentru construcția unor coefi cienți de sezonalitate de trei ani (2012-2014). Piata auto in România este relevată per total livrări de autoturisme autohtone (tabel 1) și autoturisme importate (tabel 2) ulterior structurată, asa cum rezultă din tabelele următoare (tabel 5, 6 și 7) care o prezintă detaliat pe capitole distincte de livrări (livrări de autoturisme pe benzină, livrări de autoturisme pe motorină și livrări de autoturisme pe mărcile mai importante) lunar, rexpectiv


infraanual sau sezonal, atat cantitativ, cât și ca ponderi prin coefi cienti structurali de sezonalitate a căror sumă fi nală este egală evident cu 1 (considerând mai simplă și mai expresivă această formă directă de analiză comparativ cu aceea derivată prin calculul indicilor de sezonalitate, valorifi cată și ea în articol ulterior).

Piața livrărilor de autoturisme autohtone în RomâniaTabel nr. 1

Anul

Luna

2012 2013 2014

NumărCoefi cienti structurali

de sezonalitateNumăr

Coefi cienti structurali



de sezonalitateianuarie 887 0,047451 729 0,034867 1034 0,041567februarie 1137 0,060825 1114 0,053281 1481 0,059538martie 1413 0,075590 1490 0,071265 1785 0,071759aprilie 1824 0,097577 1389 0,066434 2067 0,083095mai 2619 0,140106 1230 0,058828 2749 0,110513iunie 1647 0,088108 1688 0,080735 3326 0,133709iulie 1239 0,066281 2790 0,133442 2885 0,115980august 1110 0,059381 1795 0,085852 1265 0,050854septembrie 1876 0,100358 2447 0,117037 2209 0,088804octombrie 1968 0,105280 2380 0,113832 2069 0,083176noiembrie 1688 0,090301 1828 0,087431 2423 0,097407decembrie 1285 0,068742 2028 0,096996 1582 0,063598TOTAL 18693 1,000000 20908 1,000000 24875 1,000000 Sursa: http://www.apia.ro/buletin-statistic/

Între 2012-2014, valul de sezonalitate al vânzărilor de autoturisme autohtone în România este plasat în intervalul mai - iulie, iar al vânzărilor celor importate în mai - iunie și noiembrie.


Piața livrărilor de autoturisme importate în RomâniaTabel nr. 2

Anul

Luna

2012 2013 2014


de sezonalitate Număr




de sezonalitateianuarie 2587 0,048370 2605 0,054505 2821 0,048694februarie 3405 0,063664 3099 0,064840 3627 0,062606martie 4402 0,082305 3831 0,080157 4572 0,078917aprilie 4826 0,090233 3879 0,081161 4701 0,081144mai 5878 0,109902 3879 0,081161 5330 0,092002iunie 5323 0,099525 4031 0,084341 5797 0,100062iulie 4364 0,081594 4315 0,090283 5682 0,098077august 4160 0,077780 3629 0,075930 4694 0,081023septembrie 4883 0,091298 4384 0,091727 5362 0,092554octombrie 4896 0,091541 4696 0,098255 5374 0,092760noiembrie 4700 0,087877 4805 0,100536 5272 0,091000decembrie 4060 0,075911 4641 0,097104 4702 0,081161TOTAL 53484 1,000000 47794 1,000000 57 934 1,000000 Sursa: http://www.apia.ro/buletin-statistic/ Coefi cienții structurali de sezonalitate în valoare medie confruntați pentru cele două categorii de autoturisme sunt prezentați în tabelul 4, unde sunt identifi cate valorile maxime și minime în cadrul celor două piețe specifi ce subliniind diferențe semnifi cative care devin astfel trăsături marcante ale acestora.

Confruntarea coefi cienților structurali de sezonalitate în valoare medie ai piețelor de autoturisme autohtone și de import

Tabel nr. 3

Anul

Luna

Piața autoturismelor autohtone

Piața autoturismelor de import

Media coefi cientilor structurali

de sezonalitate (CSS)

Media coefi cientilor structurali

de sezonalitate (CSS)Observații*

ianuarie 0,0414 minim 0,0505 minimfebruarie 0,0580 0,0637martie 0,0730 0,0805aprilie 0,0825 0,0842mai 0,1032 0,0944 VSPA și VSPIiunie 0,1010 0,0946 maxim VSPA și VSPIiulie 0,1053 maxim 0,0899 VSPAaugust 0,0655 0,0782septembrie 0,1022 0,0919 VSPA și VSPIoctombrie 0,1009 0,0942 VSPA și VSPInoiembrie 0,0905 0,0932 VSPIdecembrie 0,0765 0,0847TOTAL 1,0000 1,0000*Notă:VSPA reprezintă abrevierea valului sezonier al pieței autohtone și VSPI abrevierea


valului sezonier al pieței de importuri de autoturisme în România. În tabelul 5 sunt descrise în mod original valorile ecartului maxim absolut (EMA)

EMA = (CSSmax - CSSmin) (1) și ecartului maxim relativ (EMR) EMR = [(CSSmax : CSSmin)] : CSSmin (2)

Ecarturile maxime absolute și relative determinate din coefi cienți structurali de sezonalitate în valoare medie ai piețelor de autoturisme

autohtone și de import Tabel nr. 4

Indicator Piața autoturismelor autohtone

Piața autoturismelor de import Observații

Ecartul maxim absolut (EMA) 0,0639 0,0441 Minim identic(ianuarie)

Ecartul maxim relativ (EMR) 1,5435 0,8733 Maxim decalat

Se pot enunța două caracteristici rezultate din analiza de sezonalitate completată prin confruntare statistică: a) piața autorismelor autohtone este semnifi cativ mai polarizată (EMR) valorile structurale de sezonalitate maxime fi ind cu 154,35% mai mari decât minimele, comparativ cu numai 87,33% la autoturismele importate; b) identitatea valorilor structurale de sezonalitate minime poziționale la început de an în ianuarie nu are corespondent similar în valul sezonier, ușor decalat în cadrul cele două piețe (autohtone și importate).


Valurile sezoniere ușor decalate ale coefi cienților structurali medii ai vânzărilor de autoturisme autohtone și importate, în piața României

între 2012-2014 Figura nr.1

Sursa: Datele din tabel 3 și 4 Soft utilizat: EViews

Piața parțială a livrărilor de autoturisme alimentate cu benzină în România

Tabel nr. 5Anul

Luna

2012 2013 2014








Din analiza datelor prezentate în tabel 5 și 6 se poate constata mai greu natura şi intensitatea sezonalităţii, iar o primă reprezentare grafi că facilitează mult prea puțin analiza sezonalităţii în Eviews (View→ Graph→ Seasonal split line):

Coefi cienții structurali lunari în cazul autoturismelor alimentate

cu benzină Figura nr. 2

Coefi cienții structurali lunari în cazul autoturismelor alimentate

cu motorină Figura nr. 3

Sursa: datele din tabel 5Soft utilizat : EViews

Sursa: datele din tabel 6Soft utilizat : EViews

Piața partială a livrărilor de autoturisme alimentate cu motorină în România

Tabel nr. 6Anul

Luna

2012 2013 2014








O desezonalizare a seriilor de date care clarifi că tendințele se realizează prin valorifi carea unui grafi c special care permite vizualizarea completă a fenomenului în valorile sale medii lunare ca în fi gurile 4 și 5 (View→ Graph→ Seasonal stacked line) realizate tot cu Eviews:

Mediile coefi cienților structurali de sezonalitate a vânzărilor de autoturisme alimentate cu benzină

Figura nr. 4

Mediile coefi cienților structurali de sezonalitate a vânzărilor de autoturisme alimentate cu motorină

Figura nr. 5

Soft utilizat : EViews

Diferențele devin clare piețele autorismelor alimentate cu benzină și motorină oferind caracteristici semnifi cativ diferite în domeniul vânzărilor: a) puncte de plecare anuale pe paliere diferite în ianuarie, și valuri de sezonalitate medii în mai - iunie la benzină și septembrie -decembrie la motorină. Distributia


pe tipuri de autoturisme relevata cantitativ, prin număr sau bucăți vândute lunar, dar și structural în același timp este prezentată în ultimul tabel al acestei analize (tabel nr. 7)

Piața parțială a livrărilor primelor 10 mărci de autoturisme în România în ultimii doi ani

Tabel nr. 7Anul

Model

2013 2014

Număr Coefi cienti structuralide sezonalitate Număr Coefi cienti structurali

de sezonalitateDACIA LOGAN 7442 0,447 7783 0,459DACIA SANDERO 2969 0,178 3092 0,183VOLK. GOLF 1261 0,076 898 0,053SKODA OCT 791 0,048 836 0,050SUZUKI SXA 734 0,044 0 0OPEL ASTRA 720 0,043 0 0VOLK POLO 688 0,041 1299 0,076OPEL CORSA 685 0,041 0 0SKODA RAPID 676 0,041 1625 0,096DACIA DUSTER 673 0,041 1406 0,083TOTAL 16639 1,000 16939 1,000din care:TOTAL DACIA 11084 0,666 12281 0,725 Sursa: http://www.apia.ro/buletin-statistic/ Pe parcursul ultimilor doi ani, primele trei mărci Dacia (LOGAN, SANDERO, DUSTER) prezintă o pondere dominantă și afl ată în creștere semnifi cativă de la 66,6% în 2013, la 72,5% în 2014, în cadrul topului primelor 10 mărci vândute în piața românească de autoturisme.

O remarcă fi nală

Programul Eviews permite analize de sezonalitate prompte și efi ciente ale piețelor și subpiețelor specifi ce în cele mai variate domenii. Pentru serii de date mai detaliate în timp calculează direct și indicii de sezonalitate în formulele clasice statistice. Acest articol propune însă câțiva indicatori originali și subliniază câteva valențe aplicative ale pachetului de programe Eviews pentru decidenții economici din piața automobilelor din România.


Bibliografi e

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3. Chamberlain, G. 1982. Multivariate regression models for panel data. Journal of Econometrics 18(1), pp. 5-46.

4. Georgescu- Roegen, N. 1998. Opere complete. Vol. 3: Cartea 1. Metoda statistică: elemente de statistică matematică, Editura Expert, Bucureşti.

5. Harrell Jr, F. E., et al., 1985. Regression models for prognostic prediction: advantages, problems, and suggested solutions. Cancer treatment reports 69(10): pp. 1071-1080,

6. Myers, R. H., 1990. Classical and modern regression with applications. Vol. 2. Belmont, CA: Duxbury Press,

7. Nagelkerke, N. J., 1991. A note on a general defi nition of the coeffi cient of determination. Biometrika, 78(3), pp. 691-692,

8. Partha Pratim, R., Kunal, R., 2007. On some aspects of variable selection for partial least squares regression models. QSAR & Combinatorial Science 27 (3), pp. 302-313.

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aplicată în domeniul fi nanciar, Editura Universitară, Bucureşti. 11. Săvoiu G., 2009. Statistica - Mod de gândire şi metode, Editura Universitară

Bucureşti. 12. Săvoiu, G. 2012. The method, the theory and the model in the way of thinking

of modern sciences, In “Limits of knowledge society”. Vol II, Epistemology and Philosophy of Science & Economy, Editors: Sîmbotin, G. and Gherasim, O., Iaşi: Instititutul European, 2012, pp.103-122.

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14. Voineagu V., Ţiţan E, Şerban R., Ghiţă S., Todose D., Boboc C., Pele D., 2007. Teoria şi practica econometrică, Bucureşti, Ed. Meteor Press. București.


A BRIEF ANALYSIS OF SEASONALITY IN THE ROMANIAN CAR MARKET

Professor habil. Gheorghe Săvoiu, PhD ([email protected]) Eng. researcher Victor Iorga Simăn, PhD Senior lecturer Constantin Manea, PhD University in Pitesti

Abstract

This article presents a brief seasonality analysis based on monthly statistics of some variables specifi c to the total sales of home-produced and imported cars focusing on the data series of the domestic market over the last three years, distinctly structured on three signifi cant subpopulations (supply and delivery of car running on petrol, supply and delivery of diesel cars, and delivery of the leading car makes/brands). The statistical analysis of seasonality is focused on the faster assessment of the average structure, generating more interesting information regarding the data processed, which is specifi c to the complex type of thinking of statistics, and the aggregate and subsequently structured study, giving original indicators (absolute and relative gap of structural coeffi cients of seasonality), which the authors consider an interesting investigation solution providing a higher degree of overall simplicity and accessibility. The article also used the E-Views software package to reveal seasonality through special econometric graphs, which becomes a second proof of originality, comparable to the classical statistical charting, as in modern econometric modeling, immortalized in a mean dynamics (the moving average method). Key words: structure, seasonality, seasonality coeffi cient, structural coeffi cient seasonality, seasonality adjustment, the moving average methodâ JEL codes: C19, C46, M31

Introduction Studying the socio-economic phenomena and processes in terms of their changes over time, and especially in the short term, or infra-annually, is a necessity for economic operators, an important condition for economic decision making, for market knowledge and anticipation of useful developments. The paper places particular emphasis on the statistical analysis of the seasonality of the phenomena specifi c to the car market in Romania, by processing the time series data available on http://www.apia.ro/buletin-statistic/ over a


minimal interval of three years (2012-2014), identifying the specifi city of sales (deliveries) of cars overall, and also distinctly structured in three types of signifi cant subpopulations (supply and delivery of car running on petrol, supply and delivery of diesel cars, and delivery of the leading car makes/brands). The article focused on quantitative data relating to sales of the passenger car market in Romania, and originally applied statistical and econometric tools to data sets and structures and information available to decision-makers in the sector. The content of the paper is organized logically, from the overall approach of supply of domestic and imported cars, to the tools and statistical calculations of the seasonality coeffi cients, followed by a brief interpretation of them, to confrontation and comparison of data, as is done in all market statistics, appealing to a permanent comparison with the consequences of data interpretation, in line with the economic understanding of the markets, in their functioning, synthesized by offi cial statistics. Another purpose of the article itself is lent by the original application of the method of analysis of moving averages, as a result of the requirement to make time and space decisions based on predictive knowledge, taking as support the seasonal modeling and adjustment of statistical data series chronological developments, through deseasonalisation specifi c waves (including special indicators or special statistical models of analysis, no less than econometric models, and development of special graphs seasonally adjusted, using the software package E-Views), giving a further proof of the originality of the author’s statistical and econometric investigation. Specialized software packages quickly realize calculations, and E-Views is a good example of understanding how to adjust and fi nally achieve a good visibility for data series apparently uncertain as infra-annual trends.

Analysis of the seasonality of the passenger car market in Romania This brief overview of a national automotive market does not intend to exhaust the diversity of the domestic market itself, but it rather concretely responds to the requirement of identifying specifi c seasonality trends over a minimum period necessary for the construction of seasonality coeffi cients of three years (2012- 2014). The auto market in Romania is revealed by an overall domestic passenger car delivery (Table 1) and imported cars (Table 2), subsequently structured, as follows from the following tables (Table 5, 6 and 7), which present it in a detailed manner by distinct chapters of sales (sale of gasoline cars, sale of diesel cars and sale of major car makes/brands) monthly, or


seasonally, respectively infra-annually, both in volume and as a share by seasonality structural coeffi cients, whose fi nal amount is obviously equal to 1 (considering the method a simpler and more expressive direct form of analysis, compared to that derived by calculating seasonality indices, which is also used subsequently).

Romanian domestic car market supply / salesTable no. 1

YEAR

Month

2012 2013 2014

NumberStructural

seasonality coeffi cients

NumberStructural


NumberStructural


January 887 0.047451 729 0.034867 1034 0.041567February 1137 0.060825 1114 0.053281 1481 0.059538March 1413 0.075590 1490 0.071265 1785 0.071759April 1824 0.097577 1389 0.066434 2067 0.083095May 2619 0.140106 1230 0.058828 2749 0.110513June 1647 0.088108 1688 0.080735 3326 0.133709July 1239 0.066281 2790 0.133442 2885 0.115980August 1110 0.059381 1795 0.085852 1265 0.050854September 1876 0.100358 2447 0.117037 2209 0.088804October 1968 0.105280 2380 0.113832 2069 0.083176November 1688 0.090301 1828 0.087431 2423 0.097407December 1285 0.068742 2028 0.096996 1582 0.063598TOTAL 18693 1.000000 20908 1.000000 24875 1.000000 Source: http://www.apia.ro/buletin-statistic/

Between 2012 and 2014, the seasonality wave of domestic car sales in Romania was placed within the May to July range, while that of the sales of the most imported cars, in May-June and November.

Romanian imported car market supply / salesTable no. 2YEAR

Month

2012 2013 2014

NumberStructural seasonality coeffi cients



January 2587 0.048370 2605 0.054505 2821 0.048694February 3405 0.063664 3099 0.064840 3627 0.062606March 4402 0.082305 3831 0.080157 4572 0.078917April 4826 0.090233 3879 0.081161 4701 0.081144May 5878 0.109902 3879 0.081161 5330 0.092002June 5323 0.099525 4031 0.084341 5797 0.100062July 4364 0.081594 4315 0.090283 5682 0.098077August 4160 0.077780 3629 0.075930 4694 0.081023September 4883 0.091298 4384 0.091727 5362 0.092554October 4896 0.091541 4696 0.098255 5374 0.092760November 4700 0.087877 4805 0.100536 5272 0.091000December 4060 0.075911 4641 0.097104 4702 0.081161TOTAL 53484 1.000000 47794 1.000000 57 934 1.000000 Source: http://www.apia.ro/buletin-statistic/


The seasonality structural coeffi cients, in their average value, compared for the two categories of passenger cars, are shown in Table 4, where the maximum and minimum values are identifi ed in the two specifi c markets, highlighting some signifi cant differences, which thus become their outstanding features.

Comparison of the average structural coeffi cients of seasonality of the markets of domestic and imported cars

Table no. 3

Month

Market of domestic cars Market of import cars

Average of structural seasonalitycoeffi cients (SSC)

Average of structural seasonality

coeffi cients (SSC)Observations*

January 0.0414 minimum 0.0505 minimumFebruary 0.0580 0.0637March 0.0730 0.0805April 0.0825 0.0842May 0.1032 0.0944 VSPA and VSPIJune 0.1010 0.0946 maximum VSPA and VSPIJuly 0.1053 maximum 0.0899 VSPAAugust 0.0655 0.0782September 0.1022 0.0919 VSPA and VSPIOctober 0.1009 0.0942 VSPA and VSPINovember 0.0905 0.0932 VSPIDecember 0.0765 0.0847TOTAL 1.0000 1.0000

*Note: VSPA stands for the Romanian abbreviation of the seasonal wave of domestic car market, and VSPI is the abbreviation of the seasonal wave of imported car market in Romania.

In table 4 there is an original description of the absolute maximal and minimal gaps (AMG) AMG = (SSCmax - SSCmin) (1) and of the relative maximal gap (RMG) RMG = [(SSCmax : SSCmin)] : SSCmin (2)

Absolute and relative maximum gaps determined by the average seasonality structural coeffi cients of the domestic and imported car

marketsTable no. 4

Indicator Domestic car market

Imported car market Observations

Absolute maximal gap (AMG) 0.0639 0.0441 Identical minimum

(January)Relative maximal gap (RMG) 1.5435 0.8733 Offset maximum


Two characteristic features may state, resulting from the seasonality analysis completed by statistical confrontation: a) the domestic car market is signifi cantly more polarized (RMG), with maximal seasonality structural values 154.35% higher than the minimal values, as compared to only 87.33% in the imported car market; b) the identity of the minimum seasonality structural values in early year positions, in January, does not have a similar counterpart in the seasonal wave, which is slightly shifted/offset within the two markets (domestic and imported cars).

The slightly offset waves of average structural coeffi cients of domestic and imported cars in the Romanian market from 2012 to 2014

Figure no.1

Source: Data in Table 3 and 4 Software used: E-Views


Partial market of petrol-fuelled cars in RomaniaTable no. 5

Year

Month

2012 2013 2014

NumberStructural


NumberStructural


NumberStructural


January 1371 0.039003 1343 0.042600 1480 0.041336February 1502 0.042729 1722 0.054622 1885 0.052648March 2426 0.069017 2158 0.068451 2575 0.071919April 3794 0.107934 2167 0.068737 2983 0.083315May 5304 0.150892 2334 0.074034 4173 0.116551June 3501 0.099599 2817 0.089355 4251 0.118730July 2770 0.078803 4056 0.128656 4257 0.118897August 2745 0.078092 2605 0.082630 2687 0.075047September 3273 0.093113 3357 0.106484 3005 0.083929October 3363 0.095673 3472 0.110131 3270 0.091332November 2804 0.079770 2735 0.086753 3130 0.087420December 2298 0.065375 2760 0.087547 2108 0.058876TOTAL 35151 1.000000 31526 1.000000 35804 1.000000Source: http://www.apia.ro/buletin-statistic/

Based on the analysis of the data presented in Tables 5 and 6, it is rather diffi cult to ascertain the nature and intensity of seasonality, and a fi rst graphical representation very little facilitates the analysis of seasonality in Eviews (View→Graph→Seasonal split line):

Monthly structural coeffi cients for petrol-fuelled

carsFigure no. 2

Monthly structural coeffi cients

for diesel-fuelled cars Figure no. 3

Source: Data in Table 5Software used: E-Views

Source: Data in Table 6Software used: E-Views


Partial market of sales of diesel-fuelled cars in RomaniaTable no. 6

Year

Month

2012 2013 2014

NumberStructural


NumberStructural


NumberStructural


January 2103 0.056799 1979 0.053571 2364 0.050543February 3040 0.082107 2467 0.066780 3208 0.068588March 3389 0.091533 3145 0.085133 3757 0.080326April 2856 0.077137 3090 0.083645 3762 0.080433May 3193 0.086239 2765 0.074847 3883 0.083020June 3469 0.093693 2889 0.078204 4853 0.103759July 2833 0.076516 3029 0.081993 4297 0.091871August 2525 0.068197 2798 0.075740 3264 0.069785September 3485 0.094126 3459 0.093633 4546 0.097195October 3501 0.094558 3581 0.096936 4147 0.088664November 3584 0.096799 3868 0.104705 4541 0.097088December 3047 0.082296 3872 0.104813 4150 0.088728TOTAL 37025 1.000000 36942 1.000000 46772 1.000000Source: http://www.apia.ro/buletin-statistic/

A de-seasonalisation of the data series clarifying the trends is achieved by using a special chart that allows a full view of the phenomenon in its monthly average values, as shown in Figures 4 and 5 (View→Graph→Seasonal stacked line), which are also made with E-Views:

Averages of the structural seasonality coeffi cients of petrol-fuelled cars

Figure no. 4


Averages of structural coeffi cients of seasonality in sales of diesel cars Figure no. 5

Software used: E-Views

The differences now become clear, as the markets of petrol and diesel cars exhibit signifi cantly different characteristic features as far as sales are concerned: a) annual starting points at different levels in January, and then average waves of seasonality in May-June for petrol cars, and September-December for diesel cars. The distribution by types of cars, revealed quantitatively, by numbers, or units sold each month, and also structurally at the same time, is shown in the last table of the present analysis (in Table no. 7).

Partial market of sales for the top ten brands/makes of cars in Romania over the past two years

Table no. 7Year

Make and model

2013 2014

NumberStructural


NumberStructural


DACIA LOGAN 7442 0.447 7783 0.459DACIA SANDERO 2969 0.178 3092 0.183VOLK. GOLF 1261 0.076 898 0.053SKODA OCT 791 0.048 836 0.050SUZUKI SXA 734 0.044 0 0OPEL ASTRA 720 0.043 0 0VOLK POLO 688 0.041 1299 0.076OPEL CORSA 685 0.041 0 0SKODA RAPID 676 0.041 1625 0.096DACIA DUSTER 673 0.041 1406 0.083TOTAL 16639 1.000 16939 1.000of which:TOTAL DACIA 11084 0.666 12281 0.725 Source: http://www.apia.ro/buletin-statistic/


Over the past two years, the fi rst three Dacia makes (LOGAN, SANDERO, DUSTER) have a dominant share, signifi cantly growing from 66.6% in 2013 to 72.5% in 2014, as part of the top 10 car brands sold in the Romanian car market.

A fi nal remark

The E-Views program/software allows prompt and effective seasonality analyses for markets and specifi c sub-markets in a wide variety of fi elds. For more detailed time data series, it also directly calculates the seasonality indices in and classical statistical formulas. Indeed, the present paper proposes a few original indicators and highlights several applicative facets of the E-Views software package usable by the economic decision-makers in the Romanian automotive market.

References - Andrei, T, Stancu, S, Iacob, A.I, Tuşa E., 2008. Introducere în econometrie utilizând

Eviews, Ed.Economică, Bucureşti, - Anselin, L., 2002. Under the hood issues in the specifi cation and interpretation of

spatial regression models. Agricultural economics 27 (3), pp. 247-267. - Chamberlain, G. 1982. Multivariate regression models for panel data. Journal of

Econometrics 18(1), pp. 5-46. - Georgescu- Roegen, N. 1998. Opere complete. Vol. 3: Cartea 1. Metoda statistică:

elemente de statistică matematică, Editura Expert, Bucureşti. - Harrell Jr, F. E., et al., 1985. Regression models for prognostic prediction:

advantages, problems, and suggested solutions. Cancer treatment reports 69(10): pp. 1071-1080,

- Myers, R. H., 1990. Classical and modern regression with applications. Vol. 2. Belmont, CA: Duxbury Press,

- Nagelkerke, N. J., 1991. A note on a general defi nition of the coeffi cient of determination. Biometrika, 78(3), pp. 691-692,

- Partha Pratim, R., Kunal, R., 2007. On some aspects of variable selection for partial least squares regression models. QSAR & Combinatorial Science 27 (3), pp. 302-313.

- Săvoiu G., 2011. Econometrie, Ed. Universitară, Bucureşti, - Săvoiu, G., 2013. Modelarea Economico-Financiară: Gândirea econometrică

aplicată în domeniul fi nanciar, Editura Universitară, Bucureşti. - Săvoiu G., 2009. Statistica - Mod de gândire şi metode, Editura Universitară Bucureşti. - Săvoiu, G. 2012. The method, the theory and the model in the way of thinking

of modern sciences, In “Limits of knowledge society”. Vol II, Epistemology and Philosophy of Science & Economy, Editors: Sîmbotin, G. and Gherasim, O., Iaşi: Instititutul European, 2012, pp.103-122.

- Săvoiu, G., Popa, S. 2012. Econometric Eclectic Models of Foreign Direct Investments in Romania, after 1990, Economics and Finance Review Vol. 1(12) pp. 30 – 41.

- Voineagu V., Ţiţan E, Şerban R., Ghiţă S., Todose D., Boboc C., Pele D., 2007. Teoria şi practica econometrică, Bucureşti, Ed. Meteor Press. București.


Regional comparative residential price level in Romania

PhD Mihai Gheorghe National Institue of Statistics

Abstract

The measurement of residential property price constitute distinct challenges for compilers. Residential property is by nature very heterogeneous and is traded infrequently. In normal times this already presents challenges in the observation of transaction prices for comparable houses. Compiling residential prices during times of crises, e.g. when the volume of transactions decreases sharply from the normal level, is even more challenging. Monitoring the property prices is important as boom and busts in real estate markets affecting the real economy through a variety of channels and can be an important source of macroeconomic imbalances Research has been conducted intensively, particularly after the recent crises, on methods of compiling housing price indicators appropriately. The location, history and facilities of each house are different from each other in varying degrees, so there are no two houses that are identical in terms of quality. Even if the location and facilities are the same, the age of the building may differ, in which case the degree of deterioration varies accordingly and the houses are not identical. In other words, houses have particularity with few equivalents. The appropriate methodology applied for construction of property price statistics and its coverage is infl uenced by the type of uses. This paper focuses on the potential uses of residential property prices from the individual household’s and macroeconomics perspectives and on the hedonic method used to compile the regional property prices level on a comparable base. Key words: residential property, hedonic method, characteristic of housing, comparative price level

Introduction

Oscillations in real estate prices have substantial impacts on economic activities. Chihiro Shimizu (2009) mentioned that the increase of real estate prices during the end of 1980s, in Japan, followed by a decline in the early


1990s has led to a decade-long stagnation of the Japanese economy. The most recent fi nancial and economic crisis has accentuated that developments in real estate markets (commercial and a residential segment) can have a major impact on macroeconomic developments and fi nancial stability. In such circumstances, the development of appropriate and reliable indicators for real estate prices is extremely important not only for policy makers but also for market participants who are monitoring the evolution of prices. In November 2010, IMF and the Financial Stability Board sent a clear message for developing statistics on indicators of commercial and residential property prices, by including real estate price statistics as a principal global indicator (PGI) as recommendation 19 in their report to the G20 entitled “The Financial Crisis and Information Gaps”. Property markets have a number of distinctive characteristics compared with other types of asset. The supply of property is thoroughly local; delivery of the new stock can take quite a long time owing to the length of the planning and construction phases; rents can be very sticky because of the use of long-term rental contracts; market prices lack transparency and most transactions occur through bilateral negotiations; the liquidity of the market is constrained because of the existence of high transaction costs; borrowers rely heavily on external fi nance; real estate is widely used as collateral; and short sales are usually not possible. These features cause property prices to behave differently. In particular, in the short run, property prices are more likely to deviate from their long-term fundamentals. And fl uctuations in property prices can arise not only owing to cyclical movements in economic fundamentals, interest rates and the risk premium, but also as a result of the intrinsic characteristics of the property market itself. Research has been conducted intensively on methods of compiling housing price indicators appropriately. The location, history and facilities of each house are different from each other in varying degrees, so there are no two houses that are identical in terms of quality. Even if the location and facilities are the same, the age of the building may differ, in which case the degree of deterioration varies accordingly and the houses are not identical. In other words, houses have particularity with few equivalents. Therefore one of the main approach to construct a housing price index or to calculate the valuation of houses that take into account issues resulting from the aforementioned particularity with few equivalents is the hedonic method. This paper present the main uses of the residential property prices, data sources used to calculate the real estate prices in Romania and the measurement of hose prices in the Romanian’s regions.


Uses of residential property prices

The individuals and fi rms use residential property prices either for practical decision making or to elaborate and conduct of economy policy. Therefore the appropriate methodology applied for construction of property price statistics and its coverage is infl uenced by the type of uses. This paper is going to highlight only the uses related to private household’s perspective and to the macroeconomic perspective. From private household’s perspective the buying or selling of a dwelling is the largest fi nancial transaction they will enter into. That’s why the change in house prices is likely to infl uence substantially the private household’s decisions regarding the budget plans and saving. There are two main reasons why a private household is interesting to purchase a house, either as a mean of obtaining shelter services or as a capital investment, which create a rental income in the case the house is being let. The private household’s decision regarding the time of purchasing is infl uenced by the current level and the developments of prices together with the expectations of the future trends of price developments and mortgage interest rate. In fact, consumer spending is often affected by changes in house prices as a result of wealth effects and its effect on consumer confi dence. The decision of purchasing or constructing of new houses, either by the private household’s or organisations, is going to have a big impact on the economy development. The recent economic and fi nancial crisis highlighted the necessity at the European level and not only to adopt a legislative package regarding a new surveillance procedure, called Macroeconomic Imbalance Procedure (MIP). The aim of MIP is to detect the emergence of imbalance early-on and in case of existing serious imbalances asking the Member States to implement a plan of policies which correct them. The fi rst step of the MIP is an alert mechanism which consists of a monitoring of a scoreboard with early warning indicators put in place by the Commission. One of the scoreboard indicators is the average of 12 months growth rate of the defl ated house price index, with an indicative level of 6%. Monitoring the property prices is important as boom and busts in real estate markets affecting the real economy through a variety of channels and can be an important source of macroeconomic imbalances. In order to understand the link between housing in a market and the macroeconomics is essential to know the defi nition of one of the main economic indicator which measures the development of an economy, namely the Gross Domestic Product. By expenditure side, the GDP is calculated as a sum of


total consumption (including the household and government spending’s), investments and net exports. van Sprundel (2013) mentioned that assuming the defi nition of GDP a housing market bust ultimately leads to a reduction in total consumption and of other components, which in turn could lead to reduction in jobs, meaning the raise of unemployment rate. Also, van Spundel thesis mention the globalization aspects of the housing market developments by providing the example of the 2007 bust of the housing market in US which has been the main reason of the economic system collapsing, not only in the US but also in other developed countries. All these confi rm the interdependency of economic systems and of the housing market with other markets in various countries. The property price statistics is used also on a comparable basis of data on house price levels across regions or countries to generate inter-area or international comparison of living standards. However this statistics are not easy to develop because the inter-area or international comparison comparisons require comparable types of housing across the regional or countries being compared, or the characteristics of housing considered for a hedonic method should be similar in order to construct a comparable quality price level.

Types of data sources

Price data underlying the residential property price indicators stem from various sources: • Residential property prices collected for registration or taxation

purposes usually provide a comprehensive data source. However, data collected for taxation purposes may be prone to underreporting of the prices actually agreed between the buyer and the seller;

• Data recorded by notaries also provide a comprehensive source for housing price statistics

• House price data collected in the process of fi nancing purchases by mortgage loans may also represent only some segments of the market

• Real estate agencies’ price data may not be fully representative of all housing transactions depending on the market segments covered by the agencies’ business activities. A broader market coverage may be reached by simultaneously referring to transaction and offer prices as well as assessments by market experts. Such price data are typically used by real estate consultancies, as one of several sources of price information


For this paper a dataset of 34,380 cases have been used for the analysis. This dataset contains unique (non-duplicate) transacted dwellings in 2013, with a price higher than EUR 5,000, a surface which correspond to the minimal requirements mentioned by the Romanian housing law and parcel surface smaller than 1,500 m². In addition, the dataset does not contain missing values. The sources is represented by public notaries and is assumed to be reliable because they have a mission to provide objective information and because they represent a guarantee of safety and legality in Romania. The data set is not an exhaustiveness one and therefore they are invariably subject to sample selection bias. Real estate prices is a subject of quality change. In other words, to measure a proper price for house, it will be necessary to somehow control for any variations in the amounts of the price determining characteristics of the properties. The most important characteristics are included in the data: the area of the structure (in squared feet or in meters squared); the area of the land that the structure sits on (in squared feet or meters squared); the location of the property; the age of the structure; the type of structure -detached dwelling unit, semi-detached dwelling unit, apartment or condominium building; the materials used in the construction of the house (primarily wood, brick, concrete or traditional materials; i.e., a shack or shanty), and other price determining characteristics such as the number of bedrooms, the existence of a garage, the quality of the neighborhoods, the fi nancing sources for paying the house. Summary statistics of the price and surface variables (see table 1) indicate that the mean of the transaction price is Lei 168,881 (median: Lei 135,162) with a standard deviation of Lei 135,162. This data correspond to a positive skewed distribution. A similar conclusion could be addressed when we look to the summary statistics of the surfaces. However the variation of the surfaces in the sample is much better than of prices by analyzing the ratio of standard deviation to the mean.

Summary statistics of the continuous variablesTable1

N Mean Median Std. Deviation Minimum MaximumValid Missing

pret 34380 0 168881 135162 142290 22000 5221680suprafata 34380 0 57 51 28 14 422

An analysis of summary statistics has been done for the other variables existing into the data set. Large majority of the dwellings transacted are apartments (85.7%), 44.7% of the transactions are dwellings with 2 bedrooms


dwelling and 24.7% with 3 bedrooms dwelling. 31.6% of the transactions are funded through houses loan. As regard the location of the property, the data indicate that 83.1% of dwellings are transacted in municipality, by county the main transactions are registered in Bucharest 28.7% and by region, Bucharest-Ilfov with 37.2% is fi rst in the top followed at the large distance by the North Est region with 11.1%. The dwellings with the building year between 1977 and 1990 are transacted in proportion of 35.6% and are followed by those with the building year after 2001 (27.2%).

Model developments and results

The model applied for calculating the regional house prices is the multivariate hedonic regression. In order to reduce the skewness of price and surface variables they have been transformed by taking the natural logarithm. To allow for nonlinearity the other variables has been divided into category dummies. All in all, the ln of transaction prices is the dependent variable while independent variables are ln of the surface and dummies for building year intervals, house loans, type of dwelling, the materials used in the construction of the house and other price determining characteristics (quality of neighborhoods, comfort, the source for heating, type and quality of fi nishes and the state of the dwelling maintenance). The results of any regression will be biased in the instance that case outliers (extreme values) occur in any of independent variable involved in the regression. It introduces heteroscedasticity in the results. So, I checked each of the house characteristics for extreme values. The price ratio statistics provided in SPSS was used for checking the price/square meter and square meter/number of rooms to check the plausibility of data set. Data were cleaned by following procedure of Tukey’s method (boxplot) and the modifi ed z score method (Iglewicz and Hoaglin, 1993). Another well-known unwanted factor affecting the results of any regression negatively is the occurrence of highly correlated independent variables. In this case, the regression coeffi cients found are biased and unstable. This phenomenon is referred to as multi-collinearity. The independent variable ln of surface (lnsu) has been plotted against the dependent variable ln of transaction price (lnp) in a scatterplot to analyze if there is a linear association.


Scatterplot of ln of price versus ln of surfaceFigure 1

Bivariate correlations (see table 2) between “Lnp” and “Lnsu” has been analyzed to express the association between the variables in a number. The positive association between “Lnp” and “Lnsu” is already known from scatter plots (see above). Results indicate that the bivariate correlation between ““Lnp” and “Lnsu” and is 0.677

Bivariate Correlations between lnp and lnsTable2

lnp lnsu

lnpPearson Correlation 1 .677**

Sig. (2-tailed) .000

lnsuPearson Correlation .677** 1

Sig. (2-tailed) .000

**. Correlation is signifi cant at the 0.01 level (2-tailed).

The collinearity problems has been fi xed by rerunning the regression using z scores of the dependent variables.


Checking fi t of the model: The ANOVA table tests the acceptability of the model from a statistical perspective.

ANOVAa

Table 3Model Sum of Squares df Mean Square F Sig.

1Regression 10449.312 44 237.484 1993.789 .000b

Residual 4089.714 34335 .119Total 14539.025 34379

a. Dependent Variable: lnpb. Predictors: (Constant), jud40, confort_s, anc_2000, jud37, jud39, jud21, jud30, jud20, jud18, jud36, fi nisaj_fara, jud10, jud19, jud14, jud8, jud11, jud5, jud1, jud7, jud38, credit, jud26, jud29, jud32, jud27, jud16, jud33, jud9, jud13, jud4, jud22, ampl_3, Zscore(lnsu), jud17, jud3, decom, jud35, MATERIAL1, jud12, confort_2, ampl_1, anc_2001, apartament, jud23

The regression sums are higher than residual sums of squares, which indicates that more than half of the variation in price of dwellings is explained by the model. While the ANOVA table is a useful test of the model’s ability to explain any variation in the dependent variable, it does not directly address the strength of that relationship. The model summary table reports the strength of the relationship betIen the model and the dependent variable.

Model Summaryb

Table 4

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .848a .719 .718 .34513a. Predictors: (Constant), jud40, confort_s, anc_2000, jud37, jud39, jud21, jud30, jud20, jud18, jud36, fi nisaj_fara, jud10, jud19, jud14, jud8, jud11, jud5, jud1, jud7, jud38, credit, jud26, jud29, jud32, jud27, jud16, jud33, jud9, jud13, jud4, jud22, ampl_3, Zscore(lnsu), jud17, jud3, decom, jud35, MATERIAL1, jud12, confort_2, ampl_1, anc_2001, apartament, jud23b. Dependent Variable: lnp

R, the multiple correlation coeffi cients, is the linear correlation between the observed and model-predicted values of the dependent variable. Its large value indicates a strong relationship. R Square, the coeffi cient of determination, is the squared value of the multiple correlation coeffi cients. It shows that more than half the variation in price is explained by the model.


The table of the coeffi cients show that surface contribute more to the model followed by the location of dwelling (jud40-Bucharest), loans (credit), etc.

Coeffi cientsa

Table 5

Model

Unstandardized Coeffi cients

Standardized Coeffi cients

t Sig.Correlations Collinearity

Statistics

B Std. Error Beta Zero-

order Partial Part Tolerance VIF

1

(Constant) 11.332 .008 1336.523 .000Zscore(lnsu) .403 .002 .620 187.769 .000 .677 .712 .537 .752 1.330credit .218 .004 .156 52.742 .000 .292 .274 .151 .937 1.067anc_2000 .046 .007 .020 6.652 .000 -.003 .036 .019 .936 1.068anc_2001 .141 .005 .097 27.074 .000 .314 .145 .077 .645 1.551apartament -.065 .006 -.035 -10.175 .000 -.233 -.055 -.029 .694 1.441ampl_1 .044 .005 .032 9.616 .000 .122 .052 .028 .763 1.311ampl_3 .042 .004 .030 9.529 .000 .092 .051 .027 .802 1.247decom .036 .004 .025 8.023 .000 .226 .043 .023 .818 1.222confort_s .100 .008 .039 12.882 .000 .205 .069 .037 .888 1.126confort_2 -.068 .005 -.040 -12.695 .000 -.260 -.068 -.036 .823 1.215MATERIAL1 .012 .004 .009 2.743 .006 .034 .015 .008 .800 1.249fi nisaj_fara -.173 .014 -.037 -12.537 .000 -.065 -.068 -.036 .925 1.081jud1 .049 .018 .008 2.712 .007 -.049 .015 .008 .919 1.088jud3 .166 .011 .047 14.635 .000 -.076 .079 .042 .796 1.256jud4 .165 .012 .043 13.475 .000 -.071 .073 .039 .813 1.231jud5 .224 .018 .037 12.215 .000 -.008 .066 .035 .915 1.093jud7 .114 .018 .019 6.464 .000 -.069 .035 .019 .905 1.105jud8 .378 .019 .059 19.681 .000 -.005 .106 .056 .927 1.079jud9 .135 .015 .028 9.089 .000 -.083 .049 .026 .874 1.144jud10 .080 .021 .011 3.739 .000 -.044 .020 .011 .939 1.065jud11 -.085 .020 -.013 -4.350 .000 -.070 -.023 -.012 .912 1.097jud12 .325 .010 .105 31.507 .000 -.013 .168 .090 .735 1.361jud13 .471 .013 .116 36.963 .000 .030 .196 .106 .825 1.212jud14 -.076 .022 -.010 -3.501 .000 -.071 -.019 -.010 .928 1.078jud16 .322 .013 .076 24.478 .000 -.029 .131 .070 .840 1.191jud17 .202 .012 .053 16.541 .000 -.093 .089 .047 .808 1.238jud18 .139 .023 .017 5.933 .000 -.051 .032 .017 .944 1.059jud19 -.074 .020 -.011 -3.609 .000 -.073 -.019 -.010 .932 1.073jud20 -.153 .023 -.020 -6.630 .000 -.082 -.036 -.019 .946 1.057jud21 .096 .028 .010 3.479 .001 -.030 .019 .010 .961 1.040jud22 .419 .012 .109 34.365 .000 -.006 .182 .098 .814 1.228jud23 .475 .009 .204 51.093 .000 .144 .266 .146 .514 1.946jud26 .137 .015 .028 9.162 .000 -.048 .049 .026 .880 1.137jud27 .149 .014 .034 10.851 .000 -.073 .058 .031 .852 1.173jud29 .303 .014 .067 21.812 .000 -.011 .117 .062 .859 1.165jud30 .063 .024 .008 2.608 .009 -.030 .014 .007 .942 1.062jud32 .258 .014 .056 18.110 .000 .002 .097 .052 .846 1.182jud33 .101 .016 .020 6.491 .000 -.061 .035 .019 .874 1.144jud35 .473 .011 .140 42.820 .000 .046 .225 .123 .770 1.300jud36 .313 .022 .041 13.979 .000 -.017 .075 .040 .940 1.064jud37 .108 .047 .007 2.317 .021 -.027 .013 .007 .984 1.016jud38 .160 .016 .031 10.133 .000 -.041 .055 .029 .888 1.126jud39 .279 .036 .022 7.693 .000 -.003 .041 .022 .978 1.023jud40 .710 .007 .494 107.718 .000 .342 .503 .308 .390 2.563

a. Dependent Variable: lnp


To test the economic effect of non-dummy independent variable on lnp a one standard deviation in an independent variable multiplied by its coeffi cient results in the marginal effect. The marginal effect of lnsu on lnp is 17,9%. The economic signifi cance of all dummy variables in the regression can be derived in relation to their reference category and a house that scores 0 on all variables, except the one that is of interest. For instance: a house located in Bucharest has, in relation to the intercept, an additional monetary value of Lei 86,286 (with a standard error of Lei 1992). The histogram presented below show that normality assumption is not violated.

So, on average, the estimated regression coeffi cients are a good approximation of their true value. All in all, the model has an R² of 71.8%. This model has been used to predict “lnp” in the data set. The median percentage difference between actual and fi tted values of transaction prices in the data set is 20.0%. Regarding the data set, 67.7% of the 34,380 cases have a percentage difference lower than 30%, 50.0% of the cases have a percentage difference lower than 20%, 26.2% of the cases have a percentage difference lower than


10% and 13.4% has a percentage difference lower than 5%. There are outliers in the dataset that impose a bias on the results (see fi gure below).

The analysis of comparative price level, calculated based on the quality adjusted prices, indicate (see Romanian map bellow) that 22 of the counties-light- light grey color: Caraş-Severin, Hunedoara, Alba, Mehedinţi, Gorj, Sălaj, Maramureş, Bistriţa-Năsăud, Olt, Teleorman, Giurgiu, Călăraşi, Ialomiţa, Buzău, Brăila, Covasna, Harghita, Neamţ, Suceava, Botoşani, Vaslui, Galaţi - has a price level lower than 75% (National level=100), 6 counties – orange color: Satu Mare, Mureş, Bacău, Vâlcea, Argeş, Dâmboviţa - have a percentage of price level comparative with the national level between 75% and 85%, 5 counties (Bihor, Arad, Dolj, Prahova, Tulcea) between 85% - 95%, 3 –red-color: Cluj, Braşov şi Vrancea - have a price level between 95-100% of the national level and only 6 have prices higher than of the national level (green color - Timiş, Sibiu, Bucureşti, Ilfov, Iaşi, Constanţa).


Conclusion

There are diffi culties to measure properly the regional residential house prices in Romania because of market imperfections and of lack of qualitative data on transaction prices. The urge of measuring the residential property market has been more presented after the 2007 bust of the housing market in the US, which demonstrate clearly that markets are increasingly interlinked (globalization). Even that in Romania there is not a clear conclusion regarding the link between the real estate prices and GDP growth rate, infl ation, urban population growth and the rental yield, after 2009 the information regarding the development of real estate prices is far from being ignored. The hedonic method has been used to decompose regional market value into its characteristics. The dataset of 34,380 cases have been used for the analysis. This dataset contains unique (non-duplicate) transacted dwellings in 2013, with a price higher than EUR 5,000, a surface which correspond to the minimal requirements mentioned by the Romanian housing law and parcel surface smaller than 1,500 m². In addition, the dataset does not contain missing values. All in all, on average, the estimated regression coeffi cients are a good approximation of their true value. All in all, the model has an R² of 71.8%. This


model has been used to predict “lnp” in the data set. The median percentage difference between actual and fi tted values of transaction prices in the data set is 20.0%. Regarding the data set, 67.7% of the 34,380 cases have a percentage difference lower than 30%, 50.0% of the cases have a percentage difference lower than 20%, 26.2% of the cases have a percentage difference lower than 10% and 13.4% has a percentage difference lower than 5%. There are outliers in the dataset that impose a bias on the results (see fi gure below). The analysis of comparative price level calculated for quality adjusted prices indicate (see Romanian map bellow) that 22 of the counties-light blue color- has a price level lower than 75% (National level=100), 6 counties have a percentage of price level comparative with the national level between 75% and 85%, 5 counties between 85%-95%, 3 between 95-100% and only 6 have prices higher than of the national level. The analysis of comparative price level, calculated based on the quality adjusted prices, indicate (that 22 of the counties has a price level lower than 75% (National level=100), 6 counties have a percentage of price level comparative with the national level between 75% and 85%, 5 counties between 85%-95%, 3 have a price level between 95-100% of the national level and only 6 have prices higher than of the national level.

Acknowledgements This paper has been fi nancially supported within the project entitled „SOCERT. Knowledge society, dynamism through research”, contract number POSDRU/159/1.5/S/132406. This project is co-fi nanced by European Social Fund through Sectoral Operational Programme for Human Resources Development 2007-2013. Investing in people!”

References - Eurostat (2013), ‘Handbook on Residential Property Prices Indices’, Luxemborug - W.P.A van Sprundel (2013), ‘Valuation of owner-occupied’ housing in the

Netherlands’, Tilburg University, October 2013 - ANEVAR (2011), ‘Standardele Internaţionale de Evaluare’, Bucureşti, 2011 - Kanutin, A. and Eigleperger,M., ‘The measurement of euro area propert prices

pitfalls and progress’, IFC Bulletin No 39


Impact Of Pak-India Relationship On Rice Trade On Economy Of Pakistan By Using

Computable General Equilibrium Model (CGE)Assistant Professor Faiz Muhammad Shaikh

email:[email protected] Pakistan

PhD student Mushtaque Ali JarikoAalborg University Copenhagen

Assistant Professor Dr.Muhammad Saleh MemonUniversity of Sindh-Jamshoro

Assistant Professor Abdul Sattar ShahIBA-University of Sindh-Jamshoro

Abstract This research investigates the Impact of PAK-INDIA Rice trade on Economy of Pakistan. Data were collected from GTAP-7 database. Data were collected from 60 rice exporters by using simple random technique and data were analyzed by using GEM-software. Different simulation run on GTAP-7 database and various tariff rates applied. It was revealed that if India were removing the sensitive list item, in this scenario both countries would have positive impact on GDP, Export, Import. The results indicates that there is positive impact of Rice export to India. It was further revealed that if Pakistan is given MFN status to India, Pakistan’s import decreased and Export increased and overall positive impact on Economy. The fi rst scenario is when normal trading relation with India will be restored; it means that both countries will give the MFN (Most Favored Nations) status to each other. In the second scenario, the SAFTA will be operative and there will be free trade between India and Pakistan and both countries will remove all tariffs and custom duties from each others’ imports. The Global trade analysis GTAP model is used to analyze the possible impact of SAFTA on Pakistan in a multi country, multi sector applied General equilibrium frame work. Results based on this research reveal that on SAFTA, grounds, here will be net export benefi ts in Pakistan’s economy. Key Words: PAK-INDIA, TRADE, CGE


Introduction

Trade liberalization was the key element of this new policy package and it entailed reliance on tariffs, replacement of quantitative restrictions including import licensing by a revised system of tariffs as well as the relaxation of other controls on trade. In order to encourage both domestic and foreign investment, the Government offered a series of incentives, while attempting to create an environment conducive to investment. In recent years, however, the focus of Pakistan’s trade policy has seemingly shifted towards regionalism, which Pakistan considers a springboard for broader trade liberalization. The rationale for regional cooperation is based on a number of factors, not all of which are necessarily economic in nature. Until the late 1970s, Pakistan’s economic development centered on an inward-oriented development strategy based on import substitution industrialization performed mainly by state owned fi rms. Both tariff and non-tariff barriers were widely used to protect domestic economic activities. Trade restrictive policies were accompanied by other regulatory policies such as control on foreign exchange, fi nance and foreign direct investment. These restrictive economic policies had severe adverse implications on overall economic growth, in particular growth of exports. Pakistan introduced extensive economic reforms in 1971-72 becoming the fi rst country in the South Asian region to do so. The economy was freed from the inward-oriented strategy, and adopted an outward-oriented export-led development strategy, which was followed by many East Asian countries at that time. This research begins with a review of Pakistan’s economic reforms and their coverage. The methodology, will offer a brief description of CGE Modeling including the GTAP. Then we will discuss experimental designs are discussed. Through the model we form unilateral and regional trade liberalization, as a founding member of the WTO, Pakistan as a member fi rmly committed to the multilateral trading system and has already establish a large number of reforms in keeping with the GATT/WTO principles. However, this study will review the outcome of multilateral trade Liberalization. The GTAP model simulation will be analyzed.

Literature Review

Regional trade agreements (RTAs) have emerged as an alternative to achieve trade liberalization as multilateral efforts have faced political and economic obstacles.2,3 The diffi culties of reaching agreements on sensitive issues like agriculture and services have been evident in the Doha Round. The previous rounds were also marked by complex and slow negotiation processes.


For one, as the number of participants increases, it has been more diffi cult to address each country’s demands for special considerations. RTAs convey advantages as well as limitations. By reducing the number of participants in the negotiation they can help expand the discussion to include more dimensions of economic integration. Compared with unilateral liberalization, political support for RTAs also seems to be greater given the perception of reciprocity from other member countries. However, since the early work of Viner (1950), these benefi ts have been weighted against distortions that RTAs can create. By de facto discriminating against nonmembers, RTAs distort resource allocation, favoring regional producers to the potential detriment of local consumers. Recent research also emphasizes the global consequences of multiple and overlapping RTAs in terms of the transaction costs they impose (Feridhanusetyawan, 2005). Although RTAs have varied components, these agreements include some or all of the following eight elements (Bhagwati and Panagariya, 1996 provide an overview): (i) a tariff liberalization program—TLP (transformation of nontariff barriers, e.g. quotas, to their tariff equivalent and the sequential reduction of tariffs; special considerations to least developed countries4 are not uncommon); (ii) sensitive lists (goods or services to be exempt from the tariff reduction program);5 (iii) rules of origin—ROO (prevention of the application of the preferential tariffs to non regional goods or services as defi ned by the agreement);6 (iv) institutional arrangements (establishment of a council or administrative committee responsible for the administration and implementation of the agreement); (v) trade facilitation policies (collection of instruments to reduce transaction costs of importing and The literature about trade agreements is rich in acronyms that denote either their geographical extension or their degree of trade barrier reductions. RTAs refer to agreements involving regional partners. Free Trade Agreements (FTAs) refers to agreements that includes the full elimination of tariffs (and trade barriers) while Preferential Trade Agreements (PTAs) s refer to agreements involving partial tariff elimination. For example, SAPTA is South Asia’s PTA and SAFTA is South Asia’s FTA.Exporting, including homogenization of customs practices and technical assistance specially to the least-developed members); (vi) dispute settlement mechanism (procedures to report and deal with violations to the agreement); (vii) safeguards measures (suspension of preferential treatment on grounds that imports are causing or threatening to cause serious injury to the domestic industrial base); and (viii) parallel reduction in foreign investment barriers and/or trade in services.


South Asia (Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan and Sri Lanka) has been involved in setting up its own RTA. The South Asian Association for Regional Cooperation(SAARC) was formed in 1985 with the objective of exploiting “accelerated economic growth, social progress and cultural development in the region” for the welfare of the peoples of South Asia (SAARC Secretariat, 2006). In 1995, its corresponding RTA (SAPTA) came into force. South Asian Free Trade Agreement (SAFTA) has been ratifi ed and entered into force in mid-2006. In comparison to other African countries, over the past two decades attention of researchers, government, and donors has been focused in Kenya’s horticultural and fl oriculture sectors due to their capacity to grow rapidly and yet sustainably to meet international standards (Jaffee, 2004). The production highly oriented to export markets can be track back at the farm level. While over 90% of smallholder farmers in all but the arid regions of Kenya produce horticultural products, less than 8% cultivate other kind of crops (Tschirley, et al, 2004). SAFTA is expected to increase regional trade (trade creation) but may do so at the expense of trade fl ows from more effi cient non regional suppliers (trade diversion). Baysan and others (2006) argue that it is unlikely that the most effi cient suppliers of the member countries are within the region. Based on that and on the restrictiveness of SAFTA’s sensitive lists and rules of origin, it concludes the economic merits of SAFTA are “quite weak.” Using the static general equilibrium methodology, Bandara and Yu (2003) fi nd that the full elimination of trade barriers between South Asian countries would increase the welfare level of India. To study the effects of RTAs on trade fl ows, typically the gravity equation approach is used. In its simplest version, it postulates a relationship between the “mass” (GDP) of two countries and their trade fl ows. In practical terms, the approach offers a “conditional general equilibrium” relation (Anderson and van Wincoop, 2004) in which bilateral trade is modeled as independent of trade fl ows with third party countries. Gravity equations have also been used to measure unobserved trade barriers, to discriminate between theoretical trade models, and to analyze the effects of trade policies (either in an ex-post or ex-ante fashion).11 The latter has been subject to critiques and refi nements (e.g., Carrère, 2006) among the most important being that for the gravity equation analysis to be appropriate one needs to assume (or “condition on”) that the policy changes being

Anderson and van Wincoop (2004); and Feenstra, Markusen, and Rose (2001). aconsidered do not modify the basic relation between countries’ masses and their trade fl ows.12 Given the relative small size of South Asian countries in the world markets such an assumption appears not to be problematic for


the scenarios considered here. In summary, the general equilibrium approach offers the possibility of answering a richer set of questions but demands data not readily accessible for some of the countries we are interested in.13 Although the evaluation of the benefi ts and limitations of each methodology is beyond the scope of this paper it can be argued that they are complementary rather than substitutes. This paper uses a gravity equation approach and builds on Srinivasan (1994). In particular, it allows the response to trade barriers to differ by source of the goods; treats independently imports and exports of each country pair; and includes all seven members of SAFTA in the analysis. As Bandara and Yu (2003) and Gilbert, Scollay, and Bora (2001) show, welfare and trade volume do not necessarily follow a monotonic relationship and interpreting gravity equation results as describing desirability or welfare can be misleading.15 Nevertheless, by providing three different criteria—trade fl ows, trade balance and customs revenue—the paper provides information on the relative merits of alternative arrangements.

Methodology

It is widely acknowledged that computable general Equilibrium (CGE) modeling has become the tool of choice for analysis of a wide range of trade policy issues such as tariffs and non-tariff barriers (NTBs) in both developed and developing countries in a variety of settings. In particular, CGE modeling is useful for analyzing the welfare effects of trade policy that needs to address second-best issues, where there are signifi cant interactions between policy measures for one sector and distortions elsewhere in the economy. Such models have two distinctive features: they incorporate a number of distinct sectors, and the behavioral equations of the model deal with the response of industries and consumers to changes in relative prices (Adams et al., 1998). This development is explained by the capability of CGE models to provide an elaborate and realistic representation of the economy, including the linkages between all agents, sectors and other economies (Brockmeier, 1996) CGE analysis also provides a valuable tool for putting things in an economy-wide perspective (Hertel, 199).

THE GTAP MODEL In this study, the widely used Global Trade Analysis Project (GTAP), a multi-country, multisector CGE model (Hertel, 1997) has been employed to empirically assess the impact of trade liberalization reforms on Pak-India trade. Multi-country, economy-wide CGE models are designed to work out the relative prices of various inputs and outputs mixes of the economies of


interest as well as indicating the global changes in world trade patterns. Thus, the strength of a global CGE model lies in its ability to help us understand the linkages between sectors, countries and factors on a global scale. The general equilibrium structure recognizes that all parts of the world economy hinge together in a network of direct and indirect linkages. This means that any change in any part of the system will, in principle, have repercussions throughout the entire world. As McDougall (1995, p. 88) clearly points out “its characteristics are that it is economy-wide, it is multi-sectoral, and it gives a central role to the price mechanism. These characteristics differentiate it from partial equilibrium modeling (not economy-wide), macroeconomic modeling (not multi-sectoral), and input-output modeling (agents don’t respond to price signals).”The GTAP model was designed for comparative–static analysis of trade policy issues in an economy-wide framework. Since the changes in trade policies and production levels in any of the regions and sectors will have impacts on other regions and sectors, even though my main focus of this study is on results for Pakistan, it is possible to incorporate the policy changes of other countries within a global CGE modeling framework.

Data Set Data will be collected from secondary sources GTAP-7 data base

LIMITATIONS OF THE CGE MODEL Despite the importance of CGE modeling in policy analysis, a series of questions have been raised about the empirical validity of these models. The core of the critique is focused on unsound parameter selection criteria, because the choice of elasticity values critically affects the results of policy simulations generated by these models. In the calibration method, some parameters are determined on the basis of a survey of empirical literature, some chosen arbitrarily, and the remainders are set at values, which force the model to replicate the data of a chosen benchmark year (Shoven and Whalley, 1992). Most often the estimated elasticities for commodity and/or industry classifi cations are based on econometric studies, which are not totally consistent with the countries represented in the model or they may even be “guesstimates” when no published fi gures are available. Instrument • GTAP-Model • Variables PAK-INDIA TRADE (Independent variable) • SAFTA (Dependent Variable) • Dependent Variables • Textiles (Dependent Variable)


• Pharmaceuticals (Dependent Variable) • Automotive parts and engineering(Dependent Variable) • Agriculture(Dependent Variable) • Financial an insurance services(Dependent Variable) • GTAP-Model ((Hertel, 1997) GTAP-7 Data Base • Data will be analyzed by using GEMS Software

Sectors: Codes RICE PDR

Pak-India Trade Model

Aggregated Regions GTAP Region 1. Pakistan (PK) Pakistan 2. India (IND) India 3. Rest of South Asia Sri Lanka Bangladesh Bhutan Maldives Nepal 4. Rest of the World (ROW) all other Countries


Pak-India Trade Project

Comparative Real GDP-Growth Rate (%)Table1

Region/Country 2009 2010 2011 2012 2013 2014 (P)World GDP -0.6 5.2 4.0 3.2 3.3 4.0Euro Area -4.4 2.0 1.4 -0.6 -0.3 1.1United States -3.1 2.4 1.8 2.2 1.9 3.0Japan -5.5 4.7 -0.6 2.0 1.6 1.4Germany -5.1 4.0 3.1 0.9 0.6 1.5Canada -2.8 3.2 2.6 1.8 1.5 2.4Developing Countries 6.9 9.9 8.1 6.6 7.1 7.3China 9.2 10.4 9.3 7.8 8.0 8.2Hong Kong SAR -2.5 6.8 4.9 1.4 3.0 4.4Korea 0.3 6.3 3.6 2.0 2.8 3.9Singapore -0.8 14.8 5.2 1.3 2.0 5.1Vietnam 5.3 6.8 5.9 5.0 5.2 5.2

ASEANIndonesia 4.6 6.2 6.5 6.2 6.3 6.4Malaysia -1.5 7.2 5.1 5.6 5.1 5.2Thailand -2.3 7.8 0.1 6.4 5.9 4.2Philippines 1.1 7.6 3.9 6.6 6.0 5.5

South AsiaIndia 5.0 11.2 7.7 4.0 5.7 6.2Bangladesh 5.9 6.4 6.5 6.1 6.0 6.4Sri Lanka 3.5 8.0 8.2 6.4 6.3 6.7Pakistan 0.4 2.6 3.7 4.4 3.6 4.4

Source: Economic Survey of Pakistan-2012-13


Growth rate PercentageTable 2

Sectors/Sub-Sectors 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-2013(P)Agriculture 3.4 1.8 3.5 0.2 2.0 3.5 3.3Crops 4.4 -1.0 5.2 -4.2 1.0 2.9 3.2Important Crops 6.5 -4.1 8.4 -3.7 1.5 7.4 2.3Other Crops 2.1 6.0 0.5 -7.2 2.3 -7.7 6.7Cotton Ginning -0.8 -7.0 1.3 7.3 -8.5 13.8 -2.9-Livestock 2.8 3.6 2.2 3.8 3.4 3.9 3.7-Forestry 2.7 8.9 2.6 -0.1 4.8 1.7 0.1-Fishing 0.4 8.5 2.6 1.4 -15.2 3.8 0.7Industrial Sector 7.7 8.5 -5.2 3.4 4.7 2.7 3.5Mining & Quarrying 7.3 3.2 -2.5 2.8 -4.4 4.6 7.6Manufacturing 9.0 6.1 -4.2 1.4 2.5 2.1 3.5-Large Scale 9.6 6.1 -6 0.4 1.7 1.2 2.8-Small Scale 8.3 8.3 8.6 8.5 8.5 8.4 8.2-Slaughtering 3.2 3.3 3.8 3.2 3.7 3.6 3.5Electricity Generation &Distribution & Gas Distt

-12.8 37.2 -12.1 16.7 66.4 2.7 -3.2

Construction 12.9 15.4 -9.9 8.3 -8.6 3.2 5.2Commodity Producing Sector (A+B)

5.5 5.1 -0.9 1.8 3.3 3.1 3.4

Services Sector 5.6 4.9 1.3 3.2 3.9 5.3 3.7Wholesale & Retail Trade 5.8 5.7 -3.0 1.8 2.1 1.7 2.5Transport, Storage and Communication

6.9 5.5 5.0 3.0 2.4 8.9 3.4

Finance & Insurance 9.1 6.3 -9.6 -3.3 -4.2 1.0 6.6Housing Services (Ownership of Dwellings

4.0 4.0 4.0 4.0 4.0 4.0 4.0

General Government Services 2.7 0.2 5.6 8.0 14.1 11.1 5.6Other Private Services 4.6 5.4 6.5 5.8 6.6 6.3 4.0GDP (fc) 5.5 5.0 0.4 2.6 3.7 4.4 3.6

Source: Economic Survey of Pakistan, 2012


Demographic indicators of SAFTA CountriesTable 3

S. No. Item Unit Year/

Period Bangladesh India Pakistan Nepal Sri Lanka Maldives

1 2 3 4 14 15 16 17 18 191. Area 000’Sq.Km 2010 144 3287 796 147 66 0.32. Population Millions 2010 148.70 1224.60 173.60 30.0 20.9 0.3

Millions 2020b 167.10 1385.20 205.20 35.1 22.3 0.4

3. Population Urbanized % 2004b 25.1 28.7 34.9 15.8 15.1 29.6

% 2015b 29.9 32.0 39.6 20.9 15.7 34.8

4. Population under age 15 % 2010 31 31 35 36 25 34

5. Population age 65 and above % 2010 5 5 4 4 8 3.8

6.Population

Annual Growth Rate

% 2000-10 1.4 1.5 1.8 2.1 1.1 1.8

7. Crude Birth Rate Per 1000 Population 2010 20 22 27 24 18 --

8. Total Fertility Rate

Births per woman 2010 2.2 2.6 3.4 2.7 2.3

9. Crude Death RatePer 1000

LiveBirths

2010 6 8 7 6 7 --

10. Infant Mortality Rate

Per 1000 Live

Births2010 38 48 70 41 14 33

11. Mortality Rate Under 5 years age

Per 1000 Live

Births2010 48 63 87 50 17 42

12. No. Of Deaths under 5 years 000’ 1992 103 -- 82 -- --

13. Life Expectancy at BirthMale Years 2010 68 64 64 68 72 67

Female Years 2010 69 67 66 69 78 67Persons Years 2010 69 65 65 68 75 77

Source: GTAP-7 Database


GTAP Substitution Elasticity’sTable 4

GTAP Commodities Value- added (σ VA)

Domestic/ Imports (σ D)

Sourcing of Imports (σ M)

Paddy rice0.24 2.20 4.40Wheat 0.24 2.20 4.40Cereal grains nec 0.24 2.20 4.40Vegetables, fruit, nuts 0.24 2.20 4.40Oil seeds 0.24 2.20 4.40Sugar canes, sugar beet 0.24 2.20 4.40Plant-based fi bers 0.24 2.20 4.40Crops nec 0.24 2.20 4.40Cattle, sheep and goats, horses 0.24 2.80 5.60Animal products nec 0.24 2.80 5.60Raw milk 0.24 2.80 5.60Wool, silk-worm cocoons 0.24 2.20 4.40Forestry 0.20 2.80 5.60Fishing 0.20 2.80 5.60Coal 0.20 2.80 5.60Oil 0.20 2.80 5.60Gas 0.20 2.80 5.60Minerals nec 0.20 2.80 5.60Cattle, sheep and goat, horse meat 1.12 2.20 4.40Meat Products nec 1.12 2.20 4.40Vegetable oils and fats 1.12 2.20 4.40Dairy products 1.12 2.20 4.40Processed rice 1.12 2.20 4.40Sugar 1.12 2.20 4.40Food products nec 1.12 2.20 4.40Beverages and tobacco products 1.12 3.10 6.20Textiles 1.26 2.20 4.40Wearing apparel 1.26 4.40 8.80Leather products 1.26 4.40 8.80Wood products 1.26 2.80 5.60Paper products, publishing 1.26 1.80 3.60Petroleum, coal products 1.26 1.90 3.80Chemicals, rubber, plastic pro 1.26 1.90 3.80Mineral products nec 1.26 2.80 5.60Ferrous Metals 1.26 2.80 5.60Metals nec 1.26 2.80 5.60Metal products 1.26 2.80 5.60Motor vehicles and parts 1.26 5.20 10.40Transport equipment nec 1.26 5.20 10.40Electronic equipment 1.26 2.80 5.60Machinery and equipment nec 1.26 2.80 5.60Manufacture nec 1.26 2.80 5.60Electricity 1.26 2.80 5.60Gas manufacture, distribution 1.26 2.80 5.60Water 1.26 2.80 5.60Construction 1.40 1.90 3.80Trade, transport 1.68 1.90 3.80Financial, business, recreational services (private) 1.26 1.90 3.80

Public admin and defense, education, health 1.26 1.90 3.80

Source: The GTAP Database, Version 7


Commodity Aggregation: 10 Sectors of the ModelTable 5

Aggregated Commodity GTAP Commodity

(1) Agriculture, Forestry and Fishing (AGRI)

Paddy rice (pdr) Wheat (wht)Cereal grains nec (gro) Vegetables, fruit, nuts (v_f) Oil seeds (osd)Sugar cane, suger beet (c_b) Plant based fi bers (pfb)Crops (nec)Bovine cattle, sheep and goats, horses (ctl) Animal products nec (oap)Raw milk (rmk)Wool silk-worm cocoons (wol) Forestry (for)Fishing

(2) Mining and Quarrying (MINQ)Coal (col) Oil (oil)

Gas (gas)Minerals nec (omn)

(3) Processed Food (PROF)

Bovine cattle, sheep and goat, horse meat prods (cmt) Meat products nec (omt)

Vegetables oils and fats (vol)Dairy products (mil)Processed rice (pc)Sugar (sgr)Food products nec (ofd)Beverages and tobacco products (b_t)

(4) Textiles (TEXT) Textiles (tex)

(5) Wearing apparel (WEAP) Wearing apparel (wap) leather products (lea)

(6) Petroleum, Coal Products (PECP) Petroleum, coal products (p_c)

(7) Machinery and Equipment (MAEQ) Electronic equipment (ele)Machinery and equipment nec (ome)

(8) Transport Equipment (TREQ) Motor vehicles and parts (mvh) Transport equipment nec (otn)

(9) Other Heavy Manufactures (OTHM)

Wood products (lum)Paper products, publishing (ppp) Chemical, rubber, plastic products (crp) Mineral products nec (nmm)Ferrous metals (i_s) Metals nec (nfmMetal productsManufactures nec (omf)

(10) Services (SERC)

Electricity (ely)Gas, manufacture, distribution (gdt)Water (wtr) Construction (cns)Trade, transport (t_t)Financial, business, recreational services (osp) Public admin and defence, education, health (osg) Dwelling (dwe)

Source: GTAP-Database-7


Experimental Designs for Pakistan’s Trade on SAFTATable 6

Experiments Level of Tariff Reduction or EliminationUnilateral LiberalizationE-1 Uniform External Tariffs 15% on Global Basis.

Regional LiberalizationE-2 South Asian Free Trade Agreement 5% between Pakistan and SAFTA Countries.

Unilateral cum Regional LiberalizationE-3 SAFTA plus 15% uniform external tariffs

100% between Pakistan and SAARC countries plus 15% on Global basis

Sensitivity Analysis

Unilateral LiberalizationE-4 Uniform External Tariff 15% on Global basis -Central scenarioE-4.1 50% increase of ESUBM 15% on Global basisE-4.2 100% increase of ESUBM 15% on Global basis

Regional Liberalization

E-5 SAFTA 100% between Pakistan and SAFTA countries -Central scenario

E-5.1 50% increase of ESUBM 100% between Pakistan and SAFTA countriesE-5.2 100% increase of ESUBM 100% between Pakistan and SAFTA countries

E-6 Unilateral cum Regional Liberalization

100% between Pakistan and SAARC countries plus 15% on Global basis -Central scenario

E-6.1 50% increase of ESUBM 100% between Pakistan and SAARC countries plus 15% on Global basis

E-6.2 100% increase of ESUBM 100% between Pakistan and SAARC countries plus 15% on Global basis

\


Experiment-1-15 Percent Uniform Import Tariffs Estimated Welfare and Trade Effects

Table 9(Percentage changes,

In millions)Countries EV US$ % of GDP TOT V-Export V-Import Exp-Price Import-Price DTBAL-Price

Price

IND 3213.97 3.40 0.41 0.4 1.23 2.1 3.68 109.74 m

PAK 4442.63 4.35 5.98 2.19 0.61 -8.97 5.44 285.66m

XSA -1592.56 -1.74 -0.57 -3.92 31.54 24.83 -2.12 -1322.73m

XWA -375.79 -0.02 0.00 -0.04 0.00 -0.06 -0.05 149.69m

DescriptionIND=INDIAPAK=PAKISTANXSA = REST OF SOUTH ASIAXWA= REST OF WORLS

All experiments were conducted with the standard general equilibrium closure of the GTAP model. According to the results Base line tariff for India is 18% SAFTA tariff is 5% and given MFN Tariff is 15% and rest of world is 15%..The fi rst experiment considered the Pakistan’s reduction of import tariffs to 15 percent under the unilateral trade liberalization. The impact of this scenario on regional welfare and the resulting percentage changes in sectorial output and trade are reported in Table 9 and 10 respectively. Accordingly, if Pakistan (PAK) reduces its import tariffs to 15 percent unilaterally on a global basis to maintain a uniform external tariff rate, Pakistan’s EV US& 4442.63 and GDP 4.35, and India’s EV US$ 321 million (3.40 percent of the GDP). Under this scenario, Pakistan’s volume of imports rises by 1.23 percent while its volume of exports falls slightly by 0.4 percent refl ecting the fact that the pressure to increase imports is stronger than the increase in demand for Pakistan’s exports by unilateral liberalization. However, as a result of the composite export price increase by 2.1 percent, Pakistan’s experiences a small improvement in the terms-of-trade of 1.5 percent and the real GDP by 0.9 percent. The welfare gains or losses for other regions are quite varied under this simulation. However, since Pakistanis impact on unilateral reduction of import tariffs to 15 percent will not affect other region’s real GDP or terms-of-trade signifi cantly.


Experiment-1: 15 Percent Uniform Import TariffsEstimated Percentage Changes in Regional Output and Trade

Table 10Sector IND PAK XSA XWA

(a) Industry Output (In Millions)PDR -.02 0.77 0.07 -0.03TEX 1.45.03 2.60 0.01 0.11

(b) Export (In Millions)PDR 1.44 1.00 0.07 -0.03TEX -0.16 6.79 0.01 0.11

Tariff Rates 5% SAFTA 15% XWA5% XSA15 MFN

Experiment-2 South Asian Free Trade Agreement - SAFTA- Estimated Welfare and Trade Effect

Table 11

Countries EV US$ % of GDP TOT Vol-

ExportVolume-Import

Export Price

Import-Price

DTBAL US$

IND 5434.97 4.34 0.80 5.40 4.00 9.38 8.68 -1100.90 m

PAK 5643.63 6.35 0.99 7.11 7.77 5.97 7.44 -786.77m

RAS -1592.56 -1.74 -0.57 -3.92 31.54 24.83 -2.12 -1322.73m

XSA -375.79 -0.02 0.00 -0.04 0.00 -0.06 -0.05 149.69m

Tariff RatesSAFTA=5%MFN=10%XWA=10%SAFTA=10

The trade reform scenario (Experiment-2) was conducted under the regional trade liberalization policy option to examine the impact of South Asian Free Trade Agreement- SAFTA in different contexts from the perspective of Pakistan. As a member of the SAFTA, Pakistan. committed to continue major trade liberalization measures, to establish and promote free trad arrangements for strengthening inter-regional economic co-operation and the development of national economies. In this experiment, it was assumed that Pakistan and each of the SAARC member countries in the model (India and the Rest of South Asia comprising Bangladesh, Bhutan, Maldives, Nepal and Sri lanka) remove their tariffs against each other, while maintaining heir tariffs against the rest of the South Asia.


Experiment-2: 10 Percent Uniform Import TariffsEstimated Percentage Changes in Regional Output and Trade

Table 12Sector IND PAK XSA XWA

Industry OutputPDR 8.55 1.79 0.08 -0.08

ExportsPDR 0.45 2.00 0.05 -0.07

Tariff RatesSAFTA=5%MFN=10%XWA=10%SAFTA=10

The trade reform scenario (Experiment-2) was conducted under the regional trade liberalization policy option to examine the impact of South Asian Free Trade Agreement- SAFTA in different contexts from the perspective of Pakistan. As a member of the SAFTA, Pakistan. committed to continue major trade liberalization measures, to establish and promote free trad arrangements for strengthening inter-regional economic co-operation and the development of national economies. In this experiment, it was assumed that Pakistan and each of the SAARC member countries in the model (India and the Rest of South Asia comprising Bangladesh, Bhutan, Maldives, Nepal and Sri lanka) remove their tariffs against each other, while maintaining heir tariffs against the rest of the South Asia. According to results in SAFTA 5% tariff the Pakistan industry output .079 compare to India -0.4 that Pakistan’s will benefi t on SAFTA trade with India The Second experiment considered that Pakistan’s reduction of import tariffs to 10 percent under the unilateral trade liberalization. The impact of this scenario on regional welfare and the resulting percentage changes in sectoral output and trade are reported in Table 12, 13. and 14 respectively. Accordingly, if Pakistan reduces its import tariffs to 10 percent unilaterally on a global basis to maintain a uniform external tariff rate, Pakistan’s experiences a welfare gain around US$201 million (1.53 percent of the GDP). Under this scenario, Pakistan’s volume of imports rises by 3.3 percent while its volume of exports falls slightly by 0.3 percent refl ecting the fact that the pressure to increase imports is stronger than the increase in demand for Pakistan’s exports by unilateral liberalization. However, as a result of the composite export price increase by 1.1 percent, Pakistan’s experiences a small improvement in the terms-of-trade of 1.5 percent and the real GDP by 0.8 percent. The welfare gains or losses for other regions are quite varied under this simulation. However, the impact of Pakistan’s unilateral reduction


of import tariffs to 10 percent will not affect other region’s real GDP or terms-of-trade signifi cantly.

Accordingly, the results suggest that a reduction of import tariffs to 10 percent will increase Pakistan’s welfare and terms-of-trade as well. Although one might expect that the reduction of import tariffs would increase the domestic output and therefore increase export sales, this policy reform would adversely affect Pakistan’s domestic output in most of the sectors because of foreign competition. A similar impact can be seen in export sales too.

Sensitivity Analysis (Experiments 4, 5 & 6) Estimated Welfare and Trade Effects

15 % Uniform Import Tariff SAFTA SAFTA cum 5% Uniform Tariff

Central scenario

50% increase

in ESUBM

100% increase

in ESUBM

Central scenario

50% increase

in ESUBM

100% increase

in ESUBM

Central scenario

50% increase

in ESUBM

100% increase

in ESUBM

E-4 E4-1 E4-2 E-5 E5-1 E5-2 E-6 E6-1 E6-2EV (US$ Mil) 201.84 226.30 237.60 221.55 33.38 390.01 311.11 600.00 722.22EV % of GDP 5.33 5.41 4.77 5.70 6.33 4.10 5.16 5.11 5.22QGDP 1.60 1.33 1.55 1.44 1.55 1.12 4.54 3.20 4.70TOT 1.50 1.55 1.60 4.70 6.22 8.66 6.11 8.00 8.00DT BAl -130.00 -180.00 -155.11 -120.00 -22.22 -233.00 -422.97 -220.00 -256.22Vol. of Exports -0.611 0.77 0.44 0.77 1.44 2.66 -0.95 0.78 0.88

Vol. of Imports 4.00 5.20 6.44 7.00 7.33 16.44 9.55 13.09 14.00

Export Price 1.07 0.90 0.93 4.90 8.11 10.11 6.11 8.11 10.81Import Price 2.6 0.09 0.55 0.30 0.66 0.78 0.85 0.55 0.76

Non-Economic Benefi ts

Besides the welfare and terms of trade gains suggested by the simulations, regional trade liberalization under SAFTA may have many non-economic benefi ts to Pakistan particularly social and political benefi ts; those are diffi cult to account for in a quantitative way. For example, SAFTA can help its members to speak with one voice in global negotiations and develop a common understanding on several global trade-related issues. It could also reduce the political disputes among members and make the region a more attractive location for foreign direct investments. Pakistan


is crucial for obtaining signifi cant benefi ts from FDI, liberalization of trade and FDI policies needs to be complemented by appropriate policy measures with respect to education, R&D, and human capital accumulation if trade negotiation with India will restore.

Sensitivity Analysis (Experiments4,5&6)ContinuedEstimated percentage Change in Pakistan’s Output &Trade

Table17(b) Aggregate Exports (millions)

Sectors E-4 E-4-1 E-4-2 E-5 E-5-1 E 5-2 E-6 E-6-1 E-6-2 Total

AGRI 2.75 3.28 -15.59 35.09 55.21 70.08 26.12 49.19 49.19 63.14 m

PHAR -6.46 -10.10 -11.61 -15.92 -19.12 -17.44 -19.13 -30.91 -30.91 -33.23mAUTO -16.22 -22.71 -28.88 9.51 25.32 62.20 -6.52 2.35 2.35 -29.81 mTEXT 3.82 2.85 4.80 3.09 27.28 29.13 8.8 16.41 16.41 18.50mOFI ISR 21.51 32.22 43.32 -12.45 -23.75 -40.30 4.31 -3.46 -3.46 -15.88mOTPL 24.63 43.42 66.39 -0.14 -1.42 -2.11 23.41 40.20 40.20 -60.65m

(Aggregate Imports millions)

AGRI -1.16 -1.54 -1.83 -1.44 -2.14 -1.10 -1.32 -1.64 -13.64 -3.51mPHAR -1.61 -2.57 -3.34 2.15 5.42 9.91 -0.62 2.87 2.87 6.31mAUTO 25.87 26.21 27.25 17.88 25.73 33.92 41.31 47.54 47.54 53.21mTEXT -11.89 -22.23 -11.20 -2.18 -6.33 -14.21 12.61 9.24 9.24 3.43mOFI ISR 20.11 29.77 39.45 2.27 12.18 -28.54 6.32 0.12 0.12 44.20mOTPL 5.21 6.32 7.14 0.91 0.89 0.86 6.67 11 11 65.18m

Table17 presents the percentage changes in sectoral output, and trade by region under the SAFTA liberalization. The percentage changes in industry output in Pakistan’s , as shown in panel

(a) of Table 17, the performance of the Textile and agriculture sector is remarkable, reporting about 7.9 and 8.5 percent increase, due mainly to the advantages by the cheaper labor and quality of yarn in case of textile garments. The industry output of Auto (3 percent), Pharma (-4 percent), decreased and Insurance (2 percent) decreased as well as Logistics (1) decreased. If Pak-India trade will restore we will win the race in Textile, Agriculture, and auto parts. The removal of import tariffs under the SAFTA will adversely affect India’s domestic output of Agriculture(8 percent), and Textile 11 percent.


As can be seen from panel (b) of Table 19, impact on Import of Pakistan there is a substantial increase in import in Pharmacetical , and tansport and logistics import basket. The overall import bill decreased by 11 percent.

Conclusions

The simulation results presented and analyzed here demonstrate the importance of experimental designs, and the usefulness of the global CGE modeling framework for examining the impacts of the different types of trade policy reforms for Pakistan. The results suggest that Pakistan would experience the highest welfare gain i f under the combined policy reform of the SAFTA cum 15 percent uniform external tariffs while the SAFTA on its own gives the second highest welfare gains. SAFTA allows the participating countries to achieve larger economies of scale in production, attain specialization, increase competitiveness and diversify their export basket, thus assisting domestic economic reform. Therefore, harmonizing economic policies among neighboring countries must receive higher priority in the policy making process. Although, simulation results are highly sensitive to the underlying data and assumptions regarding the reference scenarios, the results clearly provide an assessment of the implications of SAFTA. According to the simulation results suggests that there have a positive impact on PAK-INDIA trade on GDP, EXPORT, and IMPORT under various scenarios, of tariff rates should applied like, MFN. 15 %, and 10%. Pakistan’s has welfare gain of tariff rate 15 % and 10 % respectively but on 8% tariff results shows that there will be negative impact on the selected sectors.

References

1. Centre for Monitoring Indian Economy. (2004). Annual report on Corporate Sector.

2. Government of Pakistan (Various Issues). Census of Manufacturing Industries, Islamabad: Federal Bureau of Statistics.

3. Government of Pakistan (Various Issues). Economic Survey, and Islamabad: Economic Advisor’s Wing, Ministry of Finance.

4. Government of Pakistan (Various Issues). Monthly Statistical Bulletin, and Islamabad: Federal Bureau of Statistics

5. Government of Pakistan (Various Issues). Pakistan Custom and Tariffs year Book Islamabad: Central Board of Revenue (CB).

7. Government of India, Annual Report 2003-04, Department of Commerce and trade.

8. Pakistan gulf economist December. (2002). report By M.E JALBANI, Director, EPB.


9. Government of India, (Various Issues). Economic Survey, New Delhi: Economic Division, Ministry of Finance.

10. Government of India, directorate General of foreign Trade, Ministry of Commerce.

11. International Financial Statistics, CD-ROM. (2004). International Monetary Fund, Washington DC.

12. Ju, Jiandong and Kala Krishna. (1998). Firm Behavior and Market Access in a Free Trade Area with Rules of Origin. NBER working Paper, No. 6857.

13. Panagariya, A. (1994). East Asia and the New Regionalism. World Economy, 17:6, 817-39.

14. Panagariya, A. (1995). Rethinking the New Regionalism’, Paper Presented at the UNDP World Bank Trade Expansion Conference, January, World Bank, Washington DC.

15. Panagariya, A. (2000). Preferential Trade Liberalization: The Traditional Theory and New Developments. Journal of Economic Literature, 38, June 287-331.

16. Purcell, Garry. (2004a). Analyzing the Economic Welfare Consequences of A Fare Trade Agreement: Partial Equilibrium Methods for Industry Level Studies, Manuscript Presented at World bank Dhaka Offi ce.

17. Purcell, Garry. (2004b). An India-Bangladesh Free Trade Agreement? Some Potential Economic Costs and benefi ts, presented during the workshop held at World Bank, Islamabad Offi ce.

18. Summers, L. (1991). Regionalism and the World Trading Systems, Federal Reserve Bank of Kansas City, Policy Implementation of Trade and currency zones


Director: Cristina SACALĂEchipa logistică:

Oana NICOLAU, Costin GOGONEA, Adrian VIŞOIU

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