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Altin Gjini 2013

Adviser of Altin Gjini proofs that following is the approved copy of the dissertation:

THREE ESSAYS ON ECONOMIC GROWTH OF CENTRAL AND EASTERN EUROPEAN COUNTRIES

THE EFFECT OF TFP ON GROWTH: THE CASE OF 12 CEE COUNTRIES

THE ROLE OF REMITTANCES ON ECONOMIC GROWTH: AN EMPIRICAL INVESTIGATION ON 12 CEE COUNTRIES

CRWDING-OUT EFFECT OF PUBLIC INVESTMENT

ON PRIVATE INVESTMENT: AN EMPIRICAL INVESTIGATION

Adviser: Prof. Dr. Agim KUKELI, PhD

THREE ESSAYS ON ECONOMIC GROWTH OF CENTRAL AND EASTERN EUROPEAN COUNTRIES

THE EFFECT OF TFP ON GROWTH: THE CASE OF 12 CEE COUNTRIES

THE ROLE OF REMITTANCES ON ECONOMIC GROWTH: AN EMPIRICAL INVESTIGATION ON 12 CEE COUNTRIES

CRWDING-OUT EFFECT OF PUBLIC INVESTMENT

ON PRIVATE INVESTMENT: AN EMPIRICAL INVESTIGATION

Prepared by: ALTIN GJINI

Dissertation Submitted in Faculty of Business

“Aleksandër Moisiu” University Completely Fulfilled

Of the Requirements for the Degree of Doctor of Economics

“Aleksandër Moisiu” University, Durrës, Albania September 2013

IV

ACKNOWLEDGMENTS

Working on the Ph.D. has been a wonderful and at the same time a very hard

work experience. In any case, I am indebted to many people for making the time working

on my Ph.D. an unforgettable experience.

First of all, I am deeply grateful to my adviser Professor Agim Kukeli. Working

with you was a real pleasure and helpful to me. You have been a steady influence

throughout my Ph.D. program; you have supported with professional feedback for any

question I asked to you; and you have been always patient and encouraging me in times

that I have faced with research difficulties; you have listen to my ideas and discussions

and have given to me very helpful recommendations to carry on with my dissertation.

Your ability to point out and give possible solution for resolving different research

problems, your high scientific standards, and your hard work are an example of a

professional academic work. Once again, I appreciate from my hart your helpful work on

advising me during my Ph.D. program.

Furthermore, I would like to thank my working people in different conferences. It

has been helpful for me working with other PhD candidates in different conferences by

exchanging respective experiences and develope critical thinking in analyzing economic

performance of an economy.

Finally, I would like to thank my daughter, son, wife, and without doubt my mom

and dad for their infinite support that helped me a lot in this project. Without their

support, this project would not have been possible.

v

Deklaratë mbi origjinalitetin

Altin Gjini

Deklaroj se kjo tezë përfaqëson punën time origjinale dhe nuk kam përdorur burime të

tjera, përveç atyre të shkruajtura nëpërmjët citimeve.

Të gjitha të dhënat, tabelat, figurat, dhe citimet në këtë tekst, të cilat janë riprodhuar prej

ndonjë burimi tjetër, duke përfshirë edhe internetin, janë pranuar në mënyre eksplicide si

të tilla.

Jam i vetëdijshëm se në rast të mospërputhjeve, Këshilli i Profesorëve të UAMD-së është

i ngarkuar të më revokojë gradën “Doktor”, që më është dhënë mbi bazën e kësaj teze, në

përputhje me “Regulloren e programeve të studimit të ciklit të tretë (Doktoraturë) të

UAMD-së, neni 33, miratuar prej senatit Akademik të UAMD-së me Vendim Nr.___,

datë ___________.

Durrës, më __ Shtator 2013

vi

PERMBLEDHJE

Kjo temë disertacioni është një përmbledhje e tre eseve në lidhje me zhvillimin ekonomik të vendeve të Evropës Qëndrore dhe Lindore pas viteve 1991. Kjo përmbledhja përbëhet nga tre pjesë kryesore si më poshtë.

I- OBJEKTIVAT KRYESORE TE TEMES Objektivi i pare është të analizoje rolin e faktorit total të prodhimit (FTP) në rritjen ekonomike të performuar nga ekonomitë e dymbëdhjetë vendeve te EQL për periudhën 1992-2010. Gjithashtu, në këtë pjesë zhvillohet një analizë statistikore (empirike) lidhur me rolin e disa faktorëve makroekonomikë në rritjen e faktorit total të prodhimit. Objektivi i dytë i rëndësishëm i kësaj teme është të investikohet roli i remitancave në rritjen ekonomike të performuar nga vendet e EQL. Kjo duke përdorur një model endogjen. Përveç remitancave në model janë përfshire dhe faktorë të tjerë, si investimet direkte të huaja, kapitali i akumuluar dhe hapjes se ekonomisë ndaj tregut ndërkombëtar. Objekti i tretë i rëndësishëm i kësaj teme është investikimi i rolit të investimeve publike në investimet private. Me fjalë të tjera, do të investikohet nëse investimet publike në vendet në zhvillim janë plotësuese apo konkurruese me investimet private.

II- METODOLOGJIA DHE REZULTATET KRYESORE

Për të dhënë përgjigje në lidhje me pyetjet e shtruara për tu investikuar në këtë temë përdoren si modele ekzogjene ashtu dhe endogjene. Ne provojmë ti japim përgjigje pyetjeve lidhur me objektivin e pare të kësaj pune duke përdorur modelin Solow të rritjes, i cili është një model ekzogjen, për llogaritjen e FTP dhe mëpas bëjmë shpërndarjen e rritjes së outputit si rezultat i rritjes së kapitalit, fuqisë punëtore dhe FTP. Nga rezultatet arrijmë në përfundim se rritja mesatare për këto vende ka qenë 2.43%, në të cilin kontributin kryesor e ka dhënë akumulimi i kapitalit me përafërsisht 1.74%, ndjekur nga kontributi i FTP me përafërsisht 0.84%. Ndërsa kontributi i fuqisë punëtore ka pasur efekt negativ me rreth -0.15%. Përvec analizës në grup në këtë pjesë kemi bërë analizën edhe për ç’do ekonomi më vete. Modeli endogjen i rritjes së FTP zhvilluar në këtë pjesë të kësaj pune kërkimore është bazuar në punën e Khan (2006). Në këtë rast kam përdor modele me efekte fikse dhe variable mbi të dhëna të balancuara në formë paneli. Nga rezultatet u arrit në përfundimin se niveli fillestar i prodhimit për frymë, akumulimi i kapitalit të trupëzuar, hapja e ekonomisë, dhe remitancat kanë efekte pozitive dhe statistikisht të vlefshme në rritjen e produktivitetit ndërsa roli i shpenzimeve të konsumit qeveritar ka efekt pozitiv por statistikisht jo sinjifikant. Roli i M2 në rritjen e ekonomike është negative dhe statistikisht significant. Rezultat interesant është roli i FDI në rritjen e FTP, i cili është negative por jo statistikisht sinjifikant. Gjithashtu rezultati negativ i shpenzimeve qeveritare të konsumit ishte jo si pritshmëria.

vii

Lidhur me rolin e remitancave në rritjen ekonomike të performuar nga ekonomitë e vendeve të EQL, unë përdor një model endogjen të rritjes bazuar në punën e Romer (1986). Përdoret modeli ekonometrik LSDV në situata të ndryshme të supozuara. Në model përvec remitancave përfshihen edhe kapitali fiks, investimet e huaja dhe hapja e ekonomisë. Duke parë rezultatet arrijnë në përfundimin se roli i remitancave, kur analizohen të gjithë vendet në grup, është negativ dhe statistikisht i vlefshëm. Në rastin kur vendet i grupojmë në dy grupe të ndryshme rezultatet tregojnë se roli i remitancave në grupin e vendeve me nivel të lartë të të ardhurave ka qenë pozitiv por jo statistikisht i vlefshëm ndërsa në vendet me nivel të ardhurash mesatare të larta efekti i remitancave në rritjen ekonomike është negativ dhe statistikisht i vlefshëm. Bazuar në këtë rezultatat, ne mund të pretendojmë roli i remitancave ritet pozitivisht ndërsa vendi lëviz drejt nivleit të lartë të të ardhurave. Për të investiguar rolin e investimeve publike në investimet private unë do të përdor një model endogjen neoklasik, në të cilin investimet publike janë marrë si një faktor më vete dhe llogaritja e produktivitetit është bazuar në funksionin e prodhimit. Metodologjia e përdorur është ajo e analizës së regresionit të të dhënave në formë panel. Më specifikisht unë përdor modelin ekonometrik WLS bazuar edhe në faktin se vendet ndryshojnë midis tyre në shumë drejtime. Rezultati i arritur në këtë investigim tregon se investimet publike ndikojnë pozitivisht në investimet private. Pra, ato plotësojnë njëra tjetrën. Gjithashtu, arrijmë në përfundimin se ndërsa një vend i afrohet standardit të vendeve të zhvilluar efekti pozitiv i investimeve publike ndaj atyre private vjen duke u zvogëluar.

III- STRUKTURA E DISERTACIONIT Struktura e disertacionit është si vijon. Në kapitullin e parë jepet një përmbledhje ku përshkruhen objektivat kryesore të disertacionit, metodologjia e përdorur, përfundimet, literatura e përdorur dhe struktura e ndërtimit të punës. Në kapitullin e dytë (essay 1) zhvillohet analiza në lidhje me rolin e FTP në rritjen ekonomike të performuar nga ekonomitë e vendeve të EQL, marrë në konsideratë, për periudhën 1992-2010. Në kapitullin e tretë (essay 2) studiohet roli i remitancave në rritjen ekonomike të vendeve të Evropës Qëndrore dhe Lindore si një grup dhe gjithashtu bazuar në ndarjen e vendeve në dy grupe bazuar në nivelin e të ardhurave. Pjesa tjetër e disertacionit është kapitulli 4 (essay 3) në të cilin zhvillohet investigimi lidhur me efektet qe kanë investimet publike në investimet private të vendeve në zhvillim. Në kapitullin 5 (konkluzionet) jepet një përmbledhje e rezultateve kryesore të arritura në këtë temë disertacioni dhe jepen disa sugjerime në lidhje me tema kërkimore që mund të zhvillohen në të ardhmen. Pjesa përfundimtare e këtij disertacioni përbëhet nga Bibliography dhe Appendix. Fjalë kyçe: GDP, FTP, Remitancat, Investimet Publike, Investimet Private, EQL

Summary

This disertation is comprised from three assays according the economic development of Central and Eastern European countries after the fall of political and economical system developed untill 1991 in these countries. Introduction part of this thesis is comprised by three parts as below. I.1 Main Objectives of Research Three are the main objectives of this study. The first general research objective is to analyze the role of TFP on economic growth in 12 CEE countries over the period 1992 to 2010. In addition, by using Solow growth model (exogenous model) we calculate the TFP and after decompose the output growth to capital, labor and TFP growth. We also, in this part analyze empirically the effects of some macroeconomic factors on growth of TFP. The second general research objective is to analyze the role of remittances on growth performance of CEE countries, by using an endogenous growth model. Besides remittances, we include into the model data on other factors such as foreign direct investment, capital formation and openness of the economy. A third general objective of this research is to investigate the effect of public on private investments in emerging economies of Eastern Europe. In other words, I like to investigate if public investments crowds in or out private investments.

I.2 Methodology and Main Results Achieved To give the solutions about questions raised in this research we use both exogenous and endogenous models. We try to answer the first question by using Solow growth accounting model, which is an exogenous model for estimating TFP, and after I decompose output as contribute of capital, labor force, and TFP. Results show that average growth rate over the period of time 1991-2010 for all countries included in investigation is about 2.43%, in which the main driver is capital accumulation with about 1.74%, followed by contribution of TFP and labor force with about 0.84% and -0.15%, respectively. Besides investigation for all countries as a whole, we have also results on separate country economic performance. For analyzing TFP determinants in economies of interest I develop the TFP endogenous growth model based on the work of Khan (2006). I use fixed and random effects models on the balanced panel data. According, the results I conclude that income per capita, gross fixed capital, economic openness, and remittances have positive and statistically significant effect on the growth of TFP. Whereas, the effect of M2 on TFP growth for the period analyzed has been negative and statistically significant. Interesting result is according the negative role of FDI on growth. Also, the negative effect of government consumption expenditures on TFP growth is found to be not as expected. We use an endogenous growth model (see Romer 1986) for analyzing the role of remittances on growth of our sample countries, the second main objective of this study. We include in the model also other traditional factors of growth such gross capital formation, foreign direct investment, and openness of economy. We conclude that workers’ remittances received by countries under investigation as a whole have a negative effect on GDP per capita. Another interesting conclusion is the effect of remittances on GDP per capita in both groups of countries when we use the dummy variable. The effect of remittances on growth is positive but statistically insignificant in the group of countries with high income level. Whereas, for countries classified in the upper-middle income level group the role of remittances is found to

be negative and statisticaly significant. Interpreting results, we can speculate that the role of remittances on growth increase as countries move toward developed stage. In this study I use neoclassical endogenous model of analyzing the effect of public investments on private investments in which the public investment enters as a separate input and on the productivity measure derived from the production function. The methodology used in this study is that of panel data regression analysis. The use of Weighted Least Squares (WLS) is used due to the fact that countries differ in many respects. We conclude that there is no crowding out effect of public investments on private investments. The marginal effect of public investments on private investments is positive and it diminishes as country moves from developing to developed stage. I.3 Structure of the thesis The structure of this thesis is as follows. Chapter I is an intruction part of the desertation. In which are described the main objectives of the work, methodology used, general results achieved, and the structure of this desertation. Chapter 2 (first essay) studies the role of TFP on economic growth experienced by CEE countries of our sample spanning 1992-2010. In chapter 3 (essay 2), we study the role of remittances on economic growth experienced by CEE countries and also in group classification bases. For this we use an endogenous growth model. In the chapter 4 (essay 3), we investigate if public investments crowd in or out private investments. In chapter 5, the conclusion, there are summarized the main results achieved in this research and suggested some possible thesis that can be developed in the future research. In the last part of the dissertation are situated bibliography and appendix. Key Words: GDP, TFP, Remittances, Private investment, Public investment, CEE

x

TABLE OF CONTENTS

ACKNOWLEDGMENTS ............................................................................................................ iv

LIST OF TABLES ........................................................................................................................ xi

LIST OF FIGURES ..................................................................................................................... xii

CHAPTERS

I. INTRODUCTION ...........................................................................................................1

I.1 Main Economic Growth Theories ................................................................2

I.2 Main Objectives of Dissertation ..................................................................7

I.3 A Brief Literature Review .........................................................................12

I.4 Structure of the Thesis ..............................................................................16

II. THE EFFECT OF TFP ON GROWTH: THE CASE OF 12 CEE COUNTRIES .........19

II.1 Introduction ...............................................................................................19

II.2 Brief Literature Review ............................................................................23

II.3 Theoretical Explanation of Growth Accounting Model ...........................27

II.4 Data, Methodology, and Results of Solow Growth model .......................29

II.5 TFP Growth Model ...................................................................................42

II.6 Conclusions ...............................................................................................53

III. THE ROLE OF REMITTANCES ON ECONOMIC GROWTH: AN EMPIRICAL

INVESTIGATION ON 12 CEE COUNTIRES ............................................................57

xi

III.1 Introduction ...............................................................................................57

III.2 Literature Review ......................................................................................61

III.3 Stylized Facts, Data, and Methodology ...................................................64

III.4 Hypothesis and Empirical Models ............................................................70

III.5 Conclusion ................................................................................................79

IV. CROWDING-OUT EFFECT OF PUBLIC INVESTMANT ON PRIVATE

INVESTMENT: AN EMPIRICAL INVESTIGATION ...............................................82

IV.1 Introduction ...............................................................................................82

IV.2 Literature Review ......................................................................................86

IV.3 Stylized Facts, Data, and Methodology ...................................................91

IV.4 Hypothesis and Econometrical Model ......................................................94

IV.5 Conclusions ...............................................................................................99

V. CONCLUSIONS, POLICY IMPLICATIONS, AND POSSIBLE FUTURE

RESEARCH ...............................................................................................................100

V.1 Conclusions ............................................................................................100

V.2 Policy Implications .................................................................................107

V.2 Possible Future Research .......................................................................109

BIBLIOGRAPHY ......................................................................................................................110

APPENDIXE ..............................................................................................................................121

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LIST OF TABLES

II.1 Variable Description and Source Information ......................................................30

II.2 IPS test results .......................................................................................................32

II.3 Estimation Results ................................................................................................34

II.4 Decomposition of Y in K, L, and Z, in percentage ...............................................35

II.5 Variables Description and Source of Information ................................................46

II.6 Summary Statistics ................................................................................................47

II.7 Summary of econometrical results, TFP Dependent variable ..............................50

III.1 Variables Description and Source Information .....................................................67

III.2 Summary Statistics ................................................................................................69

III.3 Panel Unit Root Test on the Levels of Variables ...................................................73

III.4 Summary of Econometrical Results: Dependent Variable: l_GDP ......................75

IV.1 Summary of Econometrical Results.......................................................................97

A.1 Variables mean by country, period 1991 - 2010 ..................................................121

A.2 Variables’ mean by country, period 1996 - 2010 ...............................................122

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LIST OF FIGURES

II.1 Annual GDP growth rate averaged, period 1992-2010 ........................................20

III.1 Annual GDP growth rate averaged, period 1992-2010 ........................................65

III.2 Amount of remittances received, annually from 1996-2010 ................................66

IV.1 Annual GDP growth rate averaged, period 1991-2009 ........................................92

A.1 Presentation of annual growth of Y, K, L, and Z for Albania ............................123

A.2 Presentation of annual growth of Y, K, L, and Z for Bulgaria ...........................124

A.3 Presentation of annual growth of Y, K, L, and Z for Czech Republic ................125

A.4 Presentation of annual growth of Y, K, L, and Z for Croatia .............................126

A.5 Presentation of annual growth of Y, K, L, and Z for Hungary ...........................127

A.6 Presentation of annual growth of Y, K, L, and Z for Lithuania ..........................128

A.7 Presentation of annual growth of Y, K, L, and Z for Latvia ...............................129

A.8 Presentation of annual growth of Y, K, L, and Z for Macedonia .......................130

A.9 Presentation of annual growth of Y, K, L, and Z for Poland ..............................131

A.10 Presentation of annual growth of Y, K, L, and Z for Romania ...........................132

A.11 Presentation of annual growth of Y, K, L, and Z for Slovakia ...........................133

A.12 Presentation of annual growth of Y, K, L, and Z for Slovenia ............................134

1

CHAPTER I

Introduction

In this thesis, introduction part is comprised by four parts. In the first part we give

a short description of the historical development of the growth theories. In this

part are explained three general theories of growth such as: classical theory, neo-

classical theory, and endogenous growth theory. In the second part, we give the

main objectives of this research. Additionally, we raise question that we will try

answering, and the methodology chosen to be used to answer these questions. In

the third part we will give a brief description of the literature used for achieving

our main objectives. Finally, in the last part we describe the structure of the thesis

and specific objectives to be reached by each chapter.

2

I.1 Main Economic Growth Theories

Theories developed for analyzing the economic growth of a country generally are

classified as the classical growth theory, neoclassical growth theory, and

endogenous growth theory.

Classical Growth Theory

The work of Adam Smith (1776) “An Inquiry into Nature and Causes of the

Wealth of Nations” was considered as the beginning of the classical growth

theory. In his famous study the main concern was on economic growth. Smith in

his work concluded that output growth could be driven by population growth,

investment, land growth, and the overall increase in productivity. In his work

Smith considers population growth, investment, and land as endogenous or

predetermined. Accordingly he considers, population growth as depended by the

sustenance available to accommodate the increasing workforce, investment as

determined by the rate of savings mostly by the capitalists, and land growth as

determined by conquest of new land or better the fertility of the old lands through

improvements of technology. Smith considered technological progress, division

of labor, and international trade as engine of growth by facilitating further

3

specialization. Also, he saw income distribution as an important factor in

determining the economic growth of a nation. Smith did not see growth as

eternally rising; he considered a ceiling (and floor) where population growth and

capital accumulation were zero in the form of “stationary state”.

David Ricardo (1817), based on the work of Smith, has developed further

classical growth theory by including diminishing returns to land. So, Ricardo saw

land as fixed in supply. This means that as growth proceeds, more land needed to

be used, but land cannot be “created”. Ricardo’s work compared to the work of

Smith introduces a quicker limit to growth. Ricardo also considers (at first) this

limitation in growth to be checked by the technological improvements of

machinery. But, later he claimed that improvements of machineries will displace

labor which cannot be allocated elsewhere, and as a result it will cause downward

pressure on the wages by lowering labor income. In order to accommodate labor

free set, created by the improvements of machineries, it is needed the increase of

the rate of capital accumulation to continue. But there is not a clear mechanism

for that to happen, considering the tendency for the profits (savings) to be reduced

as a result of the increase of the landowner’s rent over time and requirements for

wage increase by workers over time. At the conclusion we can say that the

Ricardo’s conclusion is more pessimistic than Smith’s conclusion according

growth.

4

Karl Marks (1867 - 1894) is another contributor in the classical growth theory. In

his work Marx, unlike Smith and Ricardo, did not believe that labor supply is

endogenous or determined by the wage. He saw wage as an agreement between

capitalists and workers, which is under the influence of the amount of

unemployed labor force in the economy. He also considered profits and “raw

instinct” as the determinants of savings and capital accumulation. Marx in

contrast to Smith saw decline in the profits as an inducement of capitalists to

reduce workers’ wages, and not having influence on capital accumulation. Like

other Classical, Marx believed that there was a decline rate of profit over the

long-term. But Marks saw the tendency of decline in the rate of profit in the long-

run as a result of the “rising organic composition of capital”, rather than

competition in increasing wages (considered by Smith) or by diminishing

marginal productivity of land (considered by Ricardo). Marks’s concluded that

tendency of capital to be concentrated in fewer hands, combined with the increase

of the misery of labor will destroy capitalism as a whole.

Neoclassical Growth Model

In their work, Harrod (1939) and Domar (1946), have assumed fixed-coefficient

production technologies that gave their models “knife-edge” equilibria. This gives

an implausible result that any deviation at all from equilibrium would cause the

model to diverge further and further away from equilibrium, concluding that

5

steady-state is unstable. In this regard possible solution are; by allowing

propensity to save to adjust by making it depend on the distribution of income

between labor and capital, as Kaldor (1955-56); and by allowing substitution

between labor and capital in case of multi-sector growth models, as Solow and

Samuelson (1953, 1956), and in the case of one commodity model, as Pilvin

(1953). By making production coefficients variable, Solow and Samuelson (1953)

has resolved the problem (unstable steady-state) caused by the fixed coefficients

of the production technologies.

Solow (1956) in his work “A Contribution to the Theory of Economic Growth”

and Swan (1956) in his work “Economic Growth and Capital Accumulation”,

developed a simple, convenient, and powerful model for finding the steady-state

growth path of a one-commodity world (see Dimand and Spencer, 2008). The

resulting model has been known as “Solow-Swan” or simply “Neoclassical”

growth model. In his work Swan (1956) showed also the importance of technical

progress for long-run growth. The technical progress is also considered by Solow

(1956), but the fundamental contribution is given later in his work “Technical

Change and the Aggregate Production Function”, (see Solow, 1957). At a

fundamental level both growth models developed by Solow (1956) and Swan

(1956) are the same. A main difference is expository: Solow’s diagrams focus on

the capital/labor ratio, whereas Swan’s diagrams focus on the output/capital ratio

and rates of growth. In the Neoclassical growth model developed by Solow

6

(1956) and Swan (1956), gross national product depends on technology, labor and

physical capital. Solow model is an exogenous growth model. It is an optimistic

growth model in context of increasing standard of living over time. It concludes

that sustained increase in standard of living can occur, but sustained technological

developments are necessary for this.

Endogenous growth model

Endogenous growth models in difference from exogenous growth model holds

that growth is primary the result of internal factors included in the model and not

by external forces. It holds that long run growth rate of an economy depends on

the policy measures applied. The initial development of the endogenous growth

model was attributed to the work of Paul Romer (1986) in his work “Increasing

Returns and Long-Run Growth”, Lucas (1988) in his work “On the Mechanics of

Economic Development", and Rebelo (1991) in his work “Long Run Policy

Analysis and Long Run Growth”, whom have omitted technological change and

included in their models investments in human capital which have spillover effect

on economy and reduces the diminishing return to capital accumulation. Thus, the

endogenous growth models answer questions not responded by the neo-classical

growth models such as: How does TFP growth responds to the quantity of the

public investment on education? How total factor productivity (TFP) is affected

by the research and development investments? Does the intervention of the

7

government in promoting growth make sense?, which cannot be explained by the

neo-classical growth model. The simplest endogenous growth model is AK

model, which assume constant exogenous saving rate and fixed level of

technology. It also uses the assumption that production function does not exhibit

diminishing returns to capital, which lead to the endogenous growth model. The

further contribution in developing endogenous growth theory was among others

the work of Romer (1987, 1990), Aghion and Howitt (1992), Grossman and

Helpman (1991), in which agents optimally determined the consumption and

saving, optimizing the resource allocation to research and development leading to

technological progress.

I.2 Main Objectives of Dissertation

Three are the main objectives of this study. The first general research objective is

to analyze the role of TFP on economic growth in 12 Central and Eastern

European countries over the period 1992 to 2010. In addition, by using Solow

growth model (exogenous model) we calculate the TFP and after decompose the

output growth to capital, labor and TFP growth. We also, in this part analyze

empirically the effects of some macroeconomic factors on growth of TFP. The

8

second general research objective is to analyze the role of remittances on growth

performance of CEE countries, by using an endogenous growth model. Besides

remittances, we include into the model data on other factors such as foreign direct

investment, capital formation and openness of the economy. A third general

objective of this research is to investigate the effect of public on private

investments in emerging economies of Eastern Europe.

According three general research objectives, following are the main questions that

we will try to give a solution:

• What is the role of factors of production (capital accumulation and

employment) and TFP on economic growth experienced by CEE

countries? Does the economic performance of these countries secure a

sustainable growth in long run perspective?

• What are the effects of some macroeconomic factors, such as initial output

per capita, government consumption, fixed capital, economy openness,

financial sector development, remittances, and foreign investment on

TFP growth in these countries?

• What is the role of remittances on economic growth of CEE countries of

interest after the collapse of political and economical system?

• What is the role of public investments on private investments in new

emerging economies of Eastern Europe? Do they complement or crowd-

out private investments?

9

To give the solutions about questions raised in this research we use both

exogenous and endogenous models. We try to answer the first question by using

Solow growth accounting model, which is an exogenous model. This model is one

of the most used in estimating the productivity. I choose to use this model because

it is a simple model which yet it makes very important prediction according to the

sources of economic growth, such as: What causes the increase of standard of

living over time? What happens to the growth rate of aggregate income when the

saving rate or population rate increases? This is an optimistic model about the

prospects of the standards of living in the long-run. Based on the Solow growth

model if aggregate real output is growing over time, than it comes as a result of

the increase of a factor or factors of production (K or/and L) or/and the increase

of the total factor productivity (TFP). We use econometrical model to estimate the

share of capital that goes to output (α), relaxing the usually assumption of

identical technology across all regions and countries (see Senhadji, 2000) by

grouping countries in two groups based on their income level (see World Bank,

2012). So, we assume that countries belonged to the same group are identically

exposed to technology. For this we use pooled OLS method with

heteroscedasticity corrected standard errors on the balanced panel data. We carry

out the panel unit root test (IPS test) on the independent and dependent variables

in order to avoid the spurious estimated coefficient from the model.

10

Literature about the TFP determinants faces model uncertainty because of the lack

of the theoretical guidance in selecting the proper empirical specification. Even

those few alternative theories that exist give many different models to be

considered by researchers (see Brock and Durlauf, 2001). In this research I

develop the TFP endogenous growth model based on the work of Khan (2006).

We use fixed and random effects models on the balanced panel data. We develop

the work by assuming two different situations. In the first one, we assume that

country specific effects are constant but not equally and in the second scenario we

assume that countries specific effects are not constant but rather are disturbances

and the time effect are not present. After testing the results taken from both

models we choose as true and unbiased estimated coefficients taken from random

effect model (GLS).

We use an endogenous growth model (see Romer 1986) for analyzing the role of

remittances on growth in CEE countries of interest, the second main objective of

this study. This will be developed based on the balanced panel data spanning from

1996 to 2010. The workers’ remittances are generally accepted by policy makers

to be very important factor in accelerating the economic growth in developing

countries. We analyze the role of remittances by using structure break of countries

analyzed in two groups based on their income level (see World Bank 2012). We

include in the model also other traditional factors of growth such gross capital

formation, foreign direct investment, and openness of economy. We carry out the

11

panel unit root test on the levels of the dependent and independent variables used

in the model to test whether the variables are stationary. For this reason, we use

the Im, Pesaran, and Shin (IPS) approach. Based on the results we reject the null

hypothesis at 1% in favor of a stationary relationship between the variables of

interest. In other words, there is a long run relationship between output and the

independent variables used in the present study (REM, FDI, GCF, and TOT).

Three are the situation under which are developed the models. In the first

scenario, we assume that intercept and the slope coefficients are constant over

units and times, and use the fixed-effect model. In the second scenario, we assume

that the interception coefficient differs between groups of countries. In the third

scenario we assume that the interception coefficient and the slopes vary across

groups of countries. In the second and third scenario we introduce also qualitative

variables, we use LSDV models.

There is a vast discussion about the effects of public capital investment on private

investment. The question is, does public investment crowd in or out private

investment? Four common approaches are used for modeling the investment in

the existing literature, such as accelerator model, expected profit model, the

neoclassical model and Tobin’s Q. In this study we use neoclassical endogenous

model of analyzing the effect of public investments on private investments in

which the public investment enters as a separate input and on the productivity

measure derived from the production function. A pool of selected countries,

12

unbalanced panel data analysis, in Eastern European Continent is examined over

the period of time 1991-2009. The methodology used in this study is that of panel

data regression analysis. More specifically it treats the panel data as stacked cross

section units, where units are respective countries. The use of Weighted Least

Squares (WLS) is used due to the fact that countries differ in many respects and

one would find it as plausible to think that space intercept is specific for each of

the countries included in the sample.

I.3 A Brief Literature Review

Growth accounting model was developed by Solow (1956 and 1957), and it is the

starting point for almost all analyses of economic growth models. Even other

models that differ from the Solow model are best understood by comparing with

Solow growth accounting model. Usually the TFP is measured by the Solow

residuals. According to Solow growth model, productivity develops exogenously

as determined by technology. Some of the studies on the field conclude in favor

of factors of production as the main driver of the GDP growth. In this line are the

conclusion in the works of Yang (1995), Kim and Lau (1996), Koop et.al (1999),

Sun (2007), and Rojid and Seetanah (2011). Other works find total factor

13

productivity as an important factor on economic growth and the cross-country per

capita income differences between countries. Solow (1957) has concluded that

long-run growth in income per capita in an economy with an aggregate

neoclassical production function must be driven by growth in TFP. A prediction

of the Solow growth model is that a country’s standard of living can continue to

increase over the long run only if there are sustained increases in total factor

productivity. In this line, Klenow and Rodriguez-Clare (1997), Hall and Jones

(1999), Easterly and Levine (2001), and Aiyar and Dalgaard (2005), conclude that

differences in TFP between countries plays an important role in the gap of income

per capita that exists between rich and poor countries. Jorgenson and Griliches

(1969) suggest that measurement and correct model specification play an

important role to avoid errors in estimation of residuals. The measurement of the

capital input is a very important detail when using growth accounting model. In a

lot of works on the field the share of capital in output is assumed to be 0.3 or 0.4

as generally accepted. Senhadji (2000) in the work “Sources of Economic

Growth: An extensive growth accounting exercise”, by relaxing the general

assumption of identical technology across regions, concludes that contribution of

TFP to output growth depends crucially on the share of physical capital in real

output. So, if we decrease the value of (α) we will overestimate the TFP

contribution on growth and vice versa.

14

Literature on the TFP determinants faces model uncertainty because of the lack of

the theoretical guidance in selecting the proper empirical specification. Even

those few alternative theories that exist give many different models to be

considered by researchers (Brock and Durlauf, 2001). Among others, Griliches

(1994), Basudeb and Bari (2000), and Senhadji (2000) Fernandez et al., (2001),

Sala-i-Martin et al., (2004), and Khan (2006), in their studies have analyzed

determinants of TFP. Conclusions of empirical work about the effects of

remittances on economic development for recipient countries vary. In the line

with the positive effect of remittances on growth are; Guliano & Ruiz-Arranz

(2009) in their work “Remittances, Financial Development and Growth,” found

that remittances promoted growth in financial markets that were relatively

underdeveloped, which made the effect of remittances on growth conditional on

the degree of financial development; Mundaca (2009) concluded that remittances

have a significant positive effect on long-term growth; Fayissa and Nsiah (2010),

and Bugamelli and Paterno (2009) conclude that remittances have a positive

impact on economic growth; Ledesma and Piracha (2004) in their work

“International Migration and the Role of Remittances in Eastern Europe”

analyzing data on 11 CEE countries for the period 1990−1999, conclude that

remittances have a positive influence on economic growth. Other empirical works

have found no direct impact of remittances on growth but only a small spillover

effect. In this line are the work of Rao and Hassan (2012), Koc and Onan (2004),

Russell (1992), and Taylor (1992). Furthermore, another line of empirical work

15

concluded that remittances have, in fact, a negative effect on output growth. In

this context, Chami, Fullenkamp, and Jahjah (2005) in their work “Are immigrant

remittance flows a source of capital development” analyzed data for 113 countries

over the period 1970−1998 , conclude that remittances have a negative impact on

growth; likewise, Barajas et al. (2009) concluded that the growth effects of

remittances are insignificant and at times even negative.

The literature suggests different results on the effect of the public investment on

private investment. The conclusion that public investment has positive effect on

private investment has been supported by studies such as Ramirez (1994),

Odedokun (1997), and Ramirez (2000). Other works such as Blejer and Khan

(1984) for a panel of developing countries conclude that public infrastructure

crowd in private infrastructure and public non infrastructure investment crowds

out private investment. Some other literature such as Way and Wong (1982), and

Nazmi and Ramires (1997), suggest that public investment crowd out private

investment. Erden and Holcombe (2005) for nineteen developing countries and

twelve developing countries suggests that public investment crowd in private

investment for developing countries and crowd out private investment for

developed countries.

16

I.4 Structure of the thesis

The structure of this thesis is as follows. Chapter 2 (first essay) studies the role of

TFP on economic growth experienced by CEE countries of our sample under the

neo-classical growth theory. We use Slow growth model to estimate TFP and than

decompose the growth rate of output to factors of production (capital and

employment) and to productivity. The share of output that goes to capital is

calculated econometrically instead of assuming 0.3 or 0.4 as generally used in the

field. We use Cob-Douglas production function in per capita for estimating the

value of (α). In econometrical model used for estimating portion of capital to

output we use a mixture of quantitative and qualitative variables. So, we divide

countries in our study in two groups: group 1 (countries1 with middle-upper

income level), and group 2 (countries2

1 Albania, Bulgaria, Latvia, Lithuania, Macedonia, and Romania 2 Czech Republic, Croatia, Hungary, Poland, Slovakia, and Slovenia

with high income level) based on

classification made by World Bank (2012), to construct qualitative variable

(dummy) to catch the differences between the groups. At this chapter, we also

develop an endogenous TFP growth model for investigating the effect of

macroeconomic factors on TFP growth, such as such initial output per capita,

government consumption, fixed capital, and openness of economy, financial

sector development, remittances, and foreign direct investment. We use the

17

structure brake to investigate the effects of remittances, foreign investments and

economic openness in different groups of countries.

In chapter 3 (essay 2), we study the role of remittances on economic growth

experienced by CEE countries and also in group classification. For this we

develop the growth model under the endogenous growth theory. The empirical

worked is based on a balanced panel data for 12 CEE countries over the period

1996-2010. We analyze the role of remittances in three different situations: the

first model assumes that there are no groups’ differences in the interception and

slops coefficient, in the second model we assume that there is coefficient

differences between groups of counties and in the third situation we assume that

intercepts and slops coefficients differ across groups of countries.

In the chapter 4 (essay 3), we develop an investigation if public investments

crowd in or out private investments. It is given the methodology used, the

Weighted Least Squares (WLS) due to the fact that countries differ in many

respects and one would find it as plausible to think that space intercept is specific

for each of the countries included in the sample.

In chapter 5, the conclusion, there are summarized the main results achieved in

this research and suggested some possible thesis that can be developed in the

future research.

18

Finally, I would like to remember the reader that three essays of this thesis are

written in a self-contained way, and the reader can read them separately. But this

fact, makes possible that the reader to find for sure some repetitive paragraphs

throughout this thesis.

19

CHAPTER II

The Effect of TFP on Growth: The Case of 12 CEE Countries3

II.1 Introduction

At the end of 1980-s and beginning of 1990’s the most important episode in

Central and Eastern European countries was the collapse of their political and

economical systems. The economic system which was developed until that time

was commanded economy. At the beginning of 1990-s besides political change,

CEE countries also started to reform their economic system toward the free

market economy. The most important objective for the governments of these

countries was to secure sustainable growth in order to increase the standard of

living for their people up to the level of Western countries. In their path for

achieving this objective countries have had different results. Based on the

classification of World Bank (2012) countries such as the Czech Republic,

Hungary, Poland, Slovakia, Slovenia, and Croatia are listed in the group of

countries with high income level, while countries such as Albania, Bulgaria,

Latvia, Lithuania, Macedonia and Romania are in the group of countries with

3 Presented in 9th EBES Conference – Rome, 11 – 13 January, Rome, Italy Published in EBES 2013 Anthology Book

20

upper-middle income. In this paper we use this classification to assume that

countries of each group have different approach to technology, in the case where

we estimate the share of capital to output.

During the last two decades the economic growth experienced by CEE countries

was very impressive. The average growth rate of all countries under investigation

for the period of time 1992-2010 is about 2.63 percent. The highest annual

average growth rate for the period under consideration (1992-2010) is

experienced by Albanian economy with about 5.55 percent, followed by Poland,

Slovakia, Slovenian, and Czech Republic respectively with about 4.46, 3.54, 2.94,

and 2.71 percent (Figure II.1). The other countries have an average growth rate of

GDP below the average as a group (2.63 percent). The lowest average growth rate

was experienced by the Macedonian economy with about 1.22 percent.

Figure II. 1: Annual GDP growth rate averaged, period 1992-20104

4 World Development Indicators, author’s calculation.

21

According to the growth performance of economies of CEE countries analyzed in

this study, a question raised is: do this performance guaranties sustainable growth

in long run bases? Krugman (1994) has warned that East Asia could not keep the

same high level of growth in long time since its economic growth was mainly

caused by injecting heavily factor inputs into economy rather than increasing the

efficiency in using their inputs. Based on the neoclassical growth model the

sustained growth in the long run could be possible if it is accompanied by growth

rate of productivity. In other words economic growth mainly caused by capital

accumulation and not followed by efficiency improvement will be not a guaranty

that it will go at the same direction in the future.

In this chapter we are going to analyze the decomposition of output growth in

factors of production and productivity experienced by CEE countries5

5 Albania, Bulgaria, Czech Republic, Croatia, Hungary, Latvia, Lithuania, Macedonia, Poland, Romania, Slovakia, and Slovenia

of interest.

This study will be developed under the growth accounting framework. Instead of

using the traditional assumption of the share of capital 0.3 or 0.4 and labor share

on output respectively 0.7 or 0.6, we use econometrical model for estimating it. In

this study we analyze economic facts (data) annually over a period of time from

1992-2010. The data are obtained from World Development Indicators (2012).

The choice of country and period of time under investigation is conditioned by the

availability of data. The contribution of capital, labor and TFP besides the entire

22

period (1992-2010) is computed also over four sub periods such as 1992-1995,

1996-2000, 2001-2005, and 2006-2010.

This chapter’s importance stays in the fact that it gives a clear panorama about the

role of factors of production and TFP in economic growth experienced in CEE

countries over the period 1992-2010, this by using econometrical model to

calculate the share of capital to output and also by using a structure break between

countries based on their income level. The second importance of this study stays

in its contribution in the empirical work developed for analyzing the effect of

factors of production and productivity on economic growth in CEE countries.

Another importance of this study stays in the fact that besides the measurement of

TFP it also analyzes some of the factors that influence its growth.

Chapter is organized through six sections. In the next section, literature review,

we present some of the results achieved by other economists in estimating the role

of the TFP in accelerating economic growth under the growth accounting

framework. The third section, we give a short theoretical development of Solow

growth accounting model. In the fourth section, we explain description and

sources of data, and methodology used for this paper. We also, in this part analyze

the results of the growth accounting model. In the fifth section we develop a TFP

growth model, to analyze determinants of the productivity growth in CEE

23

countries. In the last section, conclusion, we make a summary of the results

achieved in this study.

II.2 Brief Literature Review

Growth accounting model was introduced in 1950-s by Robert Solow, who have

also developed Solow growth model (see Solow 1956 and 1957), and it is the

starting point for almost all analyses of economic growth models. Even other

models that differ from the Solow model are best understood by comparing with

Solow growth accounting model. The factors of production used in the standard

growth accounting model are capital input, labor input, and productivity. The

growth accounting model is also called residual approach, in which the value of

residuals is obtained after contribution of physical and labor inputs are

determined. Usually the TFP is measured by the Solow residuals. According to

Solow growth model, productivity develops exogenously as determined by

technology. The conclusions achieved in different studies, that use neo-classical

growth theory, are different according the main determinant of the growth of

output and income per capita.

24

Some of them conclude in favor of factors of production as the main driver of the

GDP growth. Kim and Lau (1996) in their work “The Sources of Asian Pacific

Economic Growth”, after analyzing the sources of economic growth in ten Asian

Pacific countries6 (APCs) in conjunction with four industrialized Western

countries7

6 China, Hong Kong, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, and Thailand 7 France, West Germany, United Kingdom, and United States

(IWCs) in postwar period, find capital accumulation as the main the

main driver of economic growth for APCs, with the possible exception of Hong

Kong and South Korea in its early developing phase, and Singapore. Yang (1995)

in his article “The Tyranny of Numbers” has done a growth accounting exercise

for analyzing economic growth experienced in countries such as Hong Kong,

Singapore, South Korea and Taiwan over the period mid 1960s to the early 1990s.

At the end he concludes that the high growth rate of GDP experienced in four

Asian countries was mainly the results of high growth rate in the capital and

labor. Sun (2007), analyzing productivity growth of the Singapore’s

manufacturing in the work “The conundrum of economic miracle: Manufacturing

growth without TFP growth”, concludes that the average contribution of TFP is -

0.8% and capital accumulation was the main contributor to the economic miracles

of Singapore’s manufactures industries over 1970 – 1997.

25

Other works find total factor productivity as an important factor on economic

growth and the cross-country per capita income differences between countries.

Solow (1956) concludes that residual accounts for the greater part of the output

growth in United States. R. Solow in his article in (1957) has concluded that long-

run growth in income per capita in an economy with an aggregate neoclassical

production function must be driven by growth in TFP. A prediction of the Solow

growth model is that country’s standard of living can continue to increase over the

long run only if there are sustained increases in total factor productivity. In this

line, Klenow and Rodriguez-Clare (1997), analyzing the economic performance

of 98 countries over the period of time 1960 - 1985 in the work “The Neoclassical

Revival in Growth Economics: Has It Gone Too Far?”, conclude that 90% of

countries differences in output per labor was attributed to the countries

differences in productivity growth. Whereas, in the case of three out of four Asian

tigers (Hong Kong, South Korea, and Taiwan) results find TFP growth as the

main driver of the growth of output per worker. Hall and Jones (1999), analyzing

data on 127 countries in their work “Why Do Some Countries Produce So Much

More Output Per Worker Than Others?”, find that countries large differences in

output per worker are explained partially by the differences in physical capital and

human capital. Paralleling the growth accounting literature, levels accounting find

a large residuals that vary considerably across countries. Easterly and Levine

(2001), in their work “It's Not Factor Accumulation: Stylized Facts and Growth

Models”, conclude that total factor productivity rather than capital accumulation

26

accounts for most of the income and the growth differences between countries.

Aiyar and Dalgaard (2005), after analyzing 22 OECD countries from 1980 – 1990

in their work “Total factor productivity revisited: A dual approach to development

accounting”, find among others that differences in TFP growth counts for much of

the differences in income per worker between countries.

Jorgenson and Griliches (1969), in their work “The explanation of productivity

change”, suggest that measurement and correct model specification play an

important role to avoid errors in estimation of residuals. The measurement of the

capital input is a very important detail when using growth accounting model. A

lot of works on the field the share of capital in output is assumed to be 0.3 or 0.4

as generally accepted. Senhadji (2000), in his work “Sources of Economic

Growth: An extensive growth accounting exercise” has developed a growth

accounting exercise for 88 countries from 1960 to 1994 to examine the

differences in TFP growth between countries by relaxing the general assumption

of identical technology across regions. In the end he concludes that contribution

of TFP to output growth depends crucially on the share of physical capital in real

output. So, decreasing the value of (α) will result in overestimating the TFP

contribution on growth and vice versa happens when we overestimate the share of

output attributed to capital accumulation.

27

II.3 Theoretical Explanation of Growth Accounting Model

The growth accounting was introduced by Robert Solow in 1950s and further

developed in the work of Solow (1956, and 1957). The growth model developed

by Robert Solow (1957) is a simple model which yet it makes very important

prediction according to the sources of economic growth: What causes the increase

of standard of living over time? What happens to the growth rate of aggregate

income when the saving rate or population rate increases? This is an optimistic

model about the prospects of the standards of living in the long-run. Based on the

Solow growth model if aggregate real output is growing over time, than it comes

as a result of the increase of a factor or factors of production (K or/and L) or/and

the increase of the total factor productivity (TFP). TFP is computed as the residual

and its value is obtained after the contribution of capital and labor input (K and L)

is determined. In the Solow model the output is equal to the TFP times the

production function of capital and labor, according the equation:

(1) ),( LKZFY =

Where: Y is current output, Z is total factor productivity, F is the production

function, K is capital input and L is labor input. The model framework uses Cobb-

28

Douglas production function for estimating the share of capital and labor into

output. For the production function to be the Cobb-Douglas, it takes the form:

(2) aa LKLKF −= 1),(

Where, 0< α<1, is the fraction of national income that goes to the capital input

and 1 – α is the fraction that goes to labor input. In the model the assumption is

that economy experience constant return to scale. Substituting in equation 1 with

Cobb-Douglass production functions we have:

(3) at

attt LKZY −= 1

From the variables used in the Solow growth model output (GDP), capital input

and labor input are observable and we solve (3) for unknown Z since it is in the

role of the residuals in growth accounting model.

(4) at

at

tt LK

YZ −= 1

The Z, which measures the total factor productivity, is a residual, because it is the

portion not explained by the amount of production function inputs (capital and

29

labor) used in an economy. Increases of total factor productivity could be as a

result of new inventions, new management techniques, changes in fiscal policies,

reduction in the price of energy and oil, etc. In other words the increase of the

TFP comes as a result of the increasing the efficiency of the use of inputs,

producing more output with the same factor inputs.

II.4 Data, Methodology, and Results of Growth Model

In this study we try to decompose the growth rate of output of CEE countries in

the growth of TFP, K, and L. We use econometrical model the Cob-Douglas

production function in per capita on the balanced panel data for estimating the

portion of capital to output. Also, in this analyze is carried a structure break by

dividing countries in two group based on their income level (see World Bank

Classification, 2012). The data for the variables of interest are collected from the

World Bank Development Indicators (WDI 2012) over the period 1991-2010. The

definition and data source for each variable used in the model are provided in

Table II.1.

30

Table II. 1: Variable Description and Source Information Variable Description Y

K

L

Real Gross Domestic Product, measured in constant 2000 US$.

Taken from 2012 World Development Indicators Data Set.

Capital stock series, constructed as perpetual inventory

method ttt IKK +−= −1)1( δ , where tI is Gross Capital Formation

(in current 2000 US$) and δ is the depreciation rate ( 10 << δ )

which is taken 06.0=δ .

The initial capital stock is calculated by

formula )]/()1[(11 δ++= −− ggIK tt , ( g ) is the average growth rate

of the real output for ten first years 1992 – 2001.

Data on Gross Capital Formation are taken from 2012 World

Development Indicators Data Set.

Total Labor Input. Calculated [Employment to population ratio,

15+, total (%) x (population on average 15-64 (% of total) x

Population total)]. Data taken from 2012 World Development

Indicators Data Set.

One of the weakness parts of measuring TFP in the Solow growth model is the

proper estimation of the capital input. In this study the capital stock is measured

based on the perpetually inventory method (PIM), in which 11 )1( ++ +−= ttt IKK δ .

Where, ( tK ) is the capital stock on previous year, (δ ) is depreciation rate, and

31

( 1+tI ) is investment in current year (gross capital formation). We use the capital

depreciation rate equally to 0.06, as suggested by Hall and Jones (1999). The

initial capital stock used in the first year ( tK in 1992) is not observed, and we

calculate it following the BEA8

δ++

=g

gIK tt1

method (see Reinsdorf & Cover 2005, and Sliker

2007), in which . Where ( g ) is the average annual growth rate for

the first ten years (1992-2001) following Bernanke and Garkaynak (2002), under

the assumption that capital and output grow at the same rate at long-run. In our

case the value of ( tI ) is the amount of investment in gross capital formation in

year 1991.

Before we use the equation (4) for calculating the value of TFP we need the value

of (α) and (1-α). In most of the earlier works the share of capital (α) in output is

taken to be 0.3 or 0.4 and share of labor input respectively by 0.7 and 0.6, by

assuming that technology is the same across countries. In this study we estimate

the parameter (α), the share of capital to output, by using econometrical model.

We estimate the share of capital in output by using Cob-Douglas production

function in per capita form, which assumes the traditional constant return to scale

of the economy. The equation form is as follows:

8 Bureau of Economic Analyzes

32

(5) itititiiitit LKLY εαλ ++= )()(log

Or

(6) εαα ++= ky 10

To avoid the spurious estimated coefficient from the regression model we carry

out the panel unit root test on the independent and dependent variables used in the

equation (6). For this reason we use the Im, Pesaran, and Shin test, which

developed a panel unit root test for joint null hypothesis that all groups have unit

root. Based on the results (table II.2) we reject the null hypothesis at 1 percent in

favor of co-integration relation between variables of interest. In other words, there

is a long-run relationship between output per labor and capital stock per labor

force.

Table II.2: IPS test results Variable IPS Statistics Y/L = y -2.7534

K/L= k -4.54612

Notes: Variables are in natural logarithmic forms. The IPS test Ho: all groups have a unit root. The Critical value for 10 percent, 5 percent, and 1 percent is -1.86, -1.96, and -2.16 respectively. Based on the results we can reject the unit root hypothesis at 1 percent.

33

The share of capital to output is measured by the estimated coefficient α. We use

Model I to estimate the slope of capital for entire sample countries by assuming

that countries included in our sample are all equally exposed to technology and

Model II to estimate the slope of capital for each group of countries by assuming

that countries of different groups are not equally opened to technology, as follow:

Model I: ε++= kbby loglog 10

Model II: ε+++= kdumbkbby logloglog 210

We use Pooled OLS method with heteroscedasticity corrected standard errors on

the balanced panel data to estimate the slope coefficients for model I and model

II. We introduce dummy variable in the second equation (Model II) to estimate

the share of output to capital in each group of countries. Where, dummy =1, for

countries with upper-middle income level (group I)9, and dummy = 0, otherwise

for countries with high income level (group II)10

9 Albania, Bulgaria, Latvia, Lithuania, Macedonia and Romania 10 Czech Republic, Hungary, Poland, Slovakia, Slovenia, and Croatia

. In other words, the introduction

of the structure break in model II is for not consider the CEE countries of our

sample as a homogenous group. Results of the estimated coefficients from both

models are given in the table II.3 below.

34

Table II.3: Estimation Results Country Value of (α) Value of (1-α) Group I 0.44***

(0.00112088)

0.56

Group II 0.50

(0.0101471)***

0.50

All 12 Countries 0.61***

(0.0154862)

0.39

Notes: We use OLS to estimate the share of capital to output (α) for countries of interest. *** denotes significance level at p<0.01. Standard errors are in parenthesis. The last column, Value of (1-α), is calculated from authors.

From the results in table II.3, we see that the share of capital to output (α) differs

significantly when we calculate them by separating the countries in two groups

(0.44 and 0.5, respectively) compared with the result on the entire sample

countries by about 0.61. This results are in the line with the work of Senhajdi

(2000), which relaxed the usually assumption of identical technology across all

regions and countries. So, if we use in our analyze the estimated value of the

share of capital contributed to output equally to 0.61 than based on the conclusion

of the work of Senhadji (2000) our results on the effect of TFP growth to output

will be underestimated. In our analyze we take the share of capital to output based

on the group’s results (Model II), and not consider the share of capital to output

the same for all 12 CEE countries analyzed. So, we have assumed that countries

that belong to the same group are identically exposed to technology.

35

Now that we have calculated the share of capital (α) and the share of labor (1-α)

in output, we calculate the value of TFP for countries under investigation by using

equation (4). Variable means by country used in the sample are given in table A.1

in Appendices.

Now by using equation 7 we decompose the growth rate of GDP into growth rate

of TFP, and weighted average of the growth rates of capital input and labor force

(see Senhadji 2000).

(7) )log)(log1(

)log(logloglogloglog

1

111

−

−−−

−−+−+Ζ−=−

tt

tttttt

LLKKZYY

αα

In other words, the growth rate of output is equal with the productivity growth

rate plus α times the growth rate of capital plus (1-α) times growth rate of Labor

force. The results for each individual country, groups, and for all countries

together, and are shown in Table II. 4.

Table II. 4: Decomposition of Y in K, L, and Z, in percentage Country Years Y K L Z Country Y K L Z

ALB

1992-1995 5.54 7.37 -0.52 -1.31

LVA

-10.66 -1.51 -2.50 -6.66 1996-2000 5.31 7.15 -0.04 -1.79 5.51 -0.62 -1.55 7.68 2001-2005 5.25 5.81 0.54 -1.10 7.87 1.48 1.03 5.36 2006-2010 4.94 4.00 0.57 0.37 -0.68 1.39 -1.22 -0.85

36

1992-2010 5.25 6.02 0.17 -0.94 1.10 0.27 -0.98 1.81

BGR

1992-1995 -1.10 -0.67 -0.44 0.01

MKD

-4.36 -0.44 0.20 -4.12 1996-2000 0.23 -0.85 -0.89 1.98 2.93 0.36 0.06 2.50 2001-2005 5.33 1.20 0.00 4.14 1.56 0.50 -0.54 1.61 2006-2010 2.66 2.65 0.38 -0.37 3.31 1.22 1.84 0.25 1992-2010 1.93 0.65 -0.23 1.51 1.13 0.45 0.40 0.28

CZE

1992-1995 1.88 1.78 0.16 -0.06

POL

4.51 2.07 -0.51 2.94 1996-2000 1.82 2.12 -0.50 0.21 5.27 4.32 -0.45 1.40 2001-2005 4.01 1.80 0.09 2.11 3.04 2.34 -0.23 0.92 2006-2010 2.66 1.92 0.20 0.54 4.61 3.11 1.34 0.16 1992-2010 2.63 1.91 -0.02 0.74 4.35 3.01 0.07 1.27

HRV

1992-1995 -2.15 1.41 0.01 -3.57

ROM

0.76 -0.17 0.40 0.54 1996-2000 3.34 2.79 -1.55 2.10 -1.28 -0.56 0.42 -1.14 2001-2005 4.36 3.46 0.39 0.52 5.55 0.37 -1.99 7.17 2006-2010 0.90 3.17 -0.06 -2.21 2.90 1.90 0.12 0.88 1992-2010 1.81 2.78 -0.32 -0.64 2.05 0.41 -0.30 1.93

HUN

1992-1995 0.17 1.23 -0.97 -0.08

SVK

0.24 -0.13 -1.01 1.38 1996-2000 2.90 2.62 0.32 -0.04 3.34 1.27 -0.23 2.30 2001-2005 4.08 2.27 0.08 1.73 4.79 1.17 0.57 3.05 2006-2010 -0.19 1.23 -0.41 -1.00 4.53 1.55 0.41 2.56 1992-2010 1.82 1.87 -0.21 0.16 3.38 1.02 -0.02 2.37

LTU

1992-1995 -12.16 -1.68 -1.28 -9.20

SVN

1.49 2.27 1.23 -2.01 1996-2000 4.35 -1.16 -0.73 6.24 4.25 3.63 -0.15 0.76 2001-2005 7.50 -0.02 0.32 7.20 3.56 3.09 0.45 0.02 2006-2010 1.04 0.57 -1.15 1.62 1.78 2.66 0.02 -0.91 1992-2010 0.83 -0.51 -0.68 2.03 2.83 2.95 0.34 -0.46

G I

1992-1995 -3.66 0.48 -0.69 -3.46

G II

1.02 1.44 -0.18 -0.23 1996-2000 2.84 0.72 -0.46 2.58 3.49 2.79 -0.43 1.12 2001-2005 5.51 1.56 -0.11 4.06 3.97 2.36 0.23 1.39 2006-2010 2.36 1.96 0.09 0.32 2.38 2.27 0.25 -0.14 1992-2010 2.05 1.22 -0.27 1.10 2.81 2.26 -0.03 0.57

12 CEE

1992-1995 -1.32 0.96 -0.44 -1.84 1996-2000 3.16 1.76 -0.44 1.85 2001-2005 4.74 1.96 0.06 2.73 2006-2010 2.37 2.11 0.17 0.09 1992-2010 2.43 1.74 -0.15 0.84 Notes: Author’s calculation

37

Analyzing the results (table II. 4) above we see that the annually averaged

economic growth over the period 1992-2010 for CEE countries part of our study

is 2.43 percent, where the main contribution was given by capital with 1.74

percent, followed by contribution of TFP by about 0.84 percent, and a slightly

negative contribution of labor force by about -0.15 percent. The higher average

growth rate of GDP was experienced by Albanian economy with about 5.25

percent, while Lithuanian economy experiences the lowest growth rate by about

0.83 percent. Besides Albania other country with GDP growth rate higher than

group average (2.43%) is Czech Republic, Poland, Slovakia, and Slovenia. The

average growth experienced by other countries of our study is below the average

growth of cuntries as a whole. According results over the sub-periods we see that

the average output level is negative only in the first period of time (1992-2005),

by about -1.32 percent. During the three other sub-periods of time, the average

output growth has been respectively by about 3.16 percent, 4.74 percent and 2.37

percent. From the results we see that the highest average economic growth was

experienced during the period 2001-2005, by approximately 4.74 percent, in

which the main contributor is TFP by about 2.73 percent, followed by capital and

labor input with about 1.96% and 0.06%, respectively.

Based on the results of this work capital input has played the main role in the

economic growth performed by our sample countries over the entire period 1992-

2010. The average contribution of capital input on growth for the sample is 1.74

38

percent. Moving from the first to the last sub-period the contribution of capital to

growth has increased with about 0.96, 1.76, 1.96 and 2.11 percent respectively. In

country levels the highest level of contribution of capital to growth was

experienced in Albania by about 6.02 percent, followed by Poland, Slovenia, and

Croatia by about 3.01, 2.95, and 2.78 percent, respectively. From the results in

table II.4 we see that the lowest (even negative) level of contribution of capital in

growth was experienced in Lithuania by about -0.51 percent. A very interesting

result is that besides Albania all other countries that belong to group with upper-

middle income level (group I) the contribution of capital to growth has been

below the average contribution of sample countries (1.74%). Mean while for all

countries that now belong to the group with high income level, excepting

Slovakia (1.02 percent), the contribution of capital to growth is higher than the

average contribution for our sample countries as a whole (1.74 percent).

The average contribution of labor force on growth over the entire period (1992-

2010) is negative, with about -0.15 percent. The contribution of labor input over

the first and the second sub-periods (1992-1995, 1996-2000) is the same, with

about -0.44 percent, followed by the positive effect during the third and forth sub-

periods (2001-2005, 2006-2010) with about 0.06 and 0.17 percent, respectively.

One reason about the negative contribution of labor force to growth during two

first sub-periods (92-95; 96-00) could be because at that times a lot of labor force

from the CEE countries emigrated toward Western countries to better their life

39

and the life of their family back home. At country level, the highest labor force

contribution to growth is experienced by Macedonian economy, averaged by

about 0.4 percent, while the lower level of contribution is experienced in Latvia

by about -0.98 percent. The positive contribution of labor to growth in country

level besides Macedonia is experienced also in Slovenia, Albania, and Poland.

From the results we see that the average contribution of labor input on economic

growth over the entire period is negative in both groups of countries (I & II) with

about -0.27 and -0.03 percent, respectively.

The average productivity contribution for the sample countries as a whole over

the entire period of time is approximately 0.84 percent. Form the results in the

table II.4 we see that the contribution of TFP on growth is negative during the

first sub-period of time (1992-1995), followed by increasing positive effect during

the second and third period, and a reduced effect in about 0.09 percent

experienced during the last period (2006-2010). The highest level of TFP

contribution has been experienced during the sub-period 2001-2005 by about 2.73

percent, while the lowest contribution was during the first period by about -1.84

percent. The negative effect of productivity on growth during the first period

(1992-1995) was expected because these years are the first ones after the collapse

of political and economical system in CEE countries, and as a result their

economies came out very weak and inefficient. Results on country level show the

lowest and even negative average contribution of TFP on growth for the entire

40

period was experienced by the economy of Albania with about -0.94 percent,

followed by economy of Croatia and Slovenia with about -0.64 and -0.46 percent,

respectively. While in other countries analyzed the annual average contribution of

productivity on growth has been positive over the entire period under

investigation. The highest level of TFP contribution is experienced in Slovakia by

approximately 2.37 percent. In countries such as Slovakia, Lithuania, Romania,

Latvia, and Bulgaria the TFP is the main driver of economic growth over the

entire period of time. From the results in table II.4, we see that over two sub-

periods of time (1996-2000 and 2001-2005) the average growth of TFP is the

main contributor in economic growth for CEE countries as a whole. The positive

and increasing growth on of TFP over the second and third sub-periods is in line

with the result of Burda and Severgnini (2009), whom in their work “TFP growth

in old and new Europe”, estimating TFP growth in 30 European economies

spanning from 1994-2005, find that the new market economies of Central and

Eastern European exhibit increasing TFP growth over two sub-periods (1994-

1999 and 2000-2005).

Based on the results over the separated groups of countries (group I and II), we

see that the average growth rate for countries with high income level is higher

than those part of the other group (2.81>2.05). Characteristic is that for both

groups the average growth rates of output during the last period (2006-2010) are

nearly the same (2.36 and 2.38 percent) and it is decreased compared with

41

respective previous sub-period (2001-2005). In both groups of countries the main

driver of growth, over the entire period of time, is capital input. For the first group

the contribution of capital is about 1.22 percent and in the second group the

contribution is 2.26 percent. From the factors taken in consideration labor force

has been the least contributor on growth and even negative. For the first group as

a whole for the entire period of time the average contribution of labor is -0.27

percent and for the second group it is about -0.03 percent. The average

contribution of TFP on growth has been more in the group of countries with

upper-middle income level than those with high income level and the effects are

respectively 1.1 percent and 0.57 percent. In both groups the role of TFP on

growth decrease during the last sub period (2006 – 2010) compared with the

respective previous ones, and it is even negative for the countries of group II as a

whole (-0.14 percent). For the countries belonged to group I, TFP is found to be

the main driver of economic growth during the second and the third sub periods of

time (1996-2000, and 2001-2005) with approximately 2.58 and 4.06 percent,

respectively.

In figures A.1 – A.12 in Appendices we show graphically the pattern of output

growth with the combination of factor inputs growth and TFP growth for all

sample countries analyzed.

42

II.5 TFP Growth Model

In this chapter besides the calculation of the TFP we analyze some determinants

of the TFP growth in our sample countries. Literature on the TFP determinants

faces model uncertainty because of the lack of the theoretical guidance in

selecting the proper empirical specification. Even those few alternative theories

that exist give many different models to be considered by researchers (see Brock

and Durlauf, 2001). Among others; Griliches (1994) in his work “Productivity,

R&D, and the data constraint”; Senhadji (2000) in his work “Sources of economic

growth: An extensive growth accounting”; Akinlo (2005), analyzing the data for

34 SSA11

The variables chosen to be included in the model are condition by the availability

of data. Accordingly, initial output per capita (IYPC) is an independent variable

that we use to analyze its effect on the TFP growth. We expect the role of initial

countries from 1980 to 2002, in the work “Impact of macroeconomic

factors on TFP in Sub-Saharan African countries”; Khan (2006) in the work

“Macro determinants of total factor productivity in Pakistan”; and Islam (2008) in

his work “Determinants of Productivity across Countries: An Exploratory

Analysis”, have analyzed determinants of TFP growth.

11 Sub-Saharan African Countries

43

output per capita to have positive effect on TFP growth, more output per capita

means more capital in disposition for investing in new technologies. Ray (2012)

in the work “Determinants of TFP Growth in Selected Manufacturing Industries

in India” concluded that output growth has positive effect on TFP in

manufacturing Industry.

Government final consumption expenditure (GCE) as a share of GDP is the

second independent variable used in this model. We use it to catch the role of

government consumption expenditures on growth of productivity. Generally there

are two opinions of the role of GCE on growth. Advocates of the positive effect of

GCE on growth argue that increasing the government expenditures will give more

valuable public service, such as better roads, education, etc. The opponents of

positive effect of GCE on growth argue that increasing the government

consumption means transferring the additional financial resources from

production sector toward the government which are less effective. We expect the

effect of government expenditures to be positive on TFP, this also is in line with

results in the work of Akinlo (2005), Komain and Tantatape (2007) and Kumar

and Chandan (2008).

Gross fixed capital (GFC) is the third variable taken in consideration. The use of

traditional and old technology can low the total factor productivity. Investment in

the new technology is generally accepted to have positive effect on growth of

44

TFP, since the value of TFP calculated by using Solow growth accounting model

includes the contribute of investments in technology. So, we expect the effect of

the gross capital formation to be positive on TFP growth.

Another factor taken in consideration in this study is openness of the economy

(OP) toward international trade and investment. CEE countries of our sample in

their way to open their economies internationally have implemented several

bilateral and multilateral free trade agreements. It is generally accepted that

openness of the economy brings to the TFP growth through increasing

competitions, advanced technologies, and financial capitals. We expect the role of

economic openness on TFP to be positive, this also is in line with the conclusion

achieved in the work of Edwards (1998), Frankel & Romer (1999), Miller &

Upadhyay (2000), and Alcala & Ciccone (2004), whom find openness of the

economy to international trade as a driver of TFP growth.

The development of financial sector is the fifth independent variable taken in

consideration in this study. The financial sector can affect the TFP growth by

increasing the capital accumulation in a country. A developed financial sector will

induce people for increasing savings or investments on new technologies. We use

the money and quasi money (M2) measured as a percentage of GDP for analyzing

the effect of financial sector development on TFP growth. We expect the role of

financial system on TFP growth to be positive. A positive effect of financial

45

development on TFP has been in line with the conclusion of the work of Jeong

and Townsend (2004), Akinlo (2005), and Rao and Hassan (2011), which

concluded in their works that development of financial sector plays positive effect

on growth.

Remittances, as a share to GDP, is another variable taken in consideration.

Remittances inflows are the second main channel through which passes the

foreign capital in developing countries. Our sample countries came out from their

centralized and closed economy with a limitation in the financial capital stock,

and the use of remittances inflow was found as a useful alternative to fulfill their

needs for financial capital. We expect remittances to have a positive effect on TFP

growth, this also is in line with the works of Mundaca (2009), Bugameli and

Paterno (2009), Ledesma and Piracha (2004), and Fayissa and Nsiah (2010),

whom conclude that remittances have a significant positive effect on long-term

growth.

One way for developing countries to increase employment and level of

technology is by making their countries more attractive for foreign direct

investment (FDI). Besides that, through FDI in developing countries can be

implemented new management skills, new methods of production, increase the

competition. The role of FDI is generally accepted to be positive on growth of

productivity. A positive effect of FDI on TFP has been in line with the conclusion

46

of the works of Akinlo (2005), Alfaro et al (2009), Woo (2009), and Rao and

Hassan (2011).

We develop empirical investigation based on a balanced panel data in 11 CEE

countries. Data about FDI inflow are taken from UNCTAD stat (2012) and for all

other variables are taken from World Bank Indicators (2012), spanning 1996-

2010. CEE countries12

Variable

included in the model and period of time investigated are

conditioned by availability of data. The definition and descriptive statistics for

each variable used in the model are provided respectively in table II.5 and table

II.6. Also, in table A.2 in appendix are given the variables’ mean by country.

Table II.5: Variable’s Description used and Source of Information Description and Sources

TFP

IYPC

GCE

GFC

Total Factor Productivity, Residuals from growth accounting

exercise. Author’s calculation

Initial Output per Capita, measured in constant 2000 US$. Taken

from 2012 World Development Indicators Data Set.

Gross Government Consumptions expenditures, in % of GDP.

Taken from 2012 World Development Indicators Data Set.

Gross Capital Formation, (in % of GDP). Taken from 2012 World

Development Indicators Data Set.

12 Albania, Bulgaria, Czech Republic, Croatia, Hungary, Latvia, Lithuania, Macedonia, Poland, Romania, and Slovenia

47

OP

M2

REM

FDI

Openess of Economy, measure as share of IM + EX to GDP. The

data on share of EX and IM to GDP taken from 2012 World

Development Indicators Data Set.

Money and Quasi Money, as a % of GDP. Taken from 2012 World

Development Indicators Data Set.

Workers' remittances and compensation of employees, received (%

of GDP). Data taken from 2012 World Development Indicators

Data Set.

Foreign direct investment, (% of GDP). Data taken from 2012

UNCTAD stat.

Table II.6: Summary Statistics, using the observations 1:01 - 11:15 Variable Mean Median Minimum Maximum Std. Dev. l_TFP 4.48912 4.49603 4.01343 4.93281 0.185431

l_IYPC 8.17096 8.30074 6.79745 9.53504 0.646861

l_GCE 2.85431 2.94776 1.73876 3.21068 0.307106

l_GFC 3.10857 3.12579 2.42889 3.51636 0.196807

l_OP 4.56117 4.61291 3.80966 5.11553 0.285430

l_M2 3.78322 3.83427 2.42324 4.42251 0.389215

l_REM 0.325554 0.486723 -3.60093 2.91679 1.32242

l_FDI 3.23245 3.34054 0.793698 4.61860 0.709376

Notes: Data are in logarithmic form, annually from 1996 - 2010 for 11 Central and Eastern European countries.

48

The model used in this study to point out the decomposition of TFP growth for

CEE countries under investigation is as follows:

(8) ),,2,,,,( FDIREMMOPGFCGCEIYPCfTFP =

The econometrical model takes equation form as below:

(9) εβββ

βββββ++++

+++++=FDIREMM

OPGFCGCEIYPCTFPit

loglog2loglogloglogloglog

765

43210

Where: logTFP, is natural logarithm of Total Factor Productivity

logIYPC, is natural logarithm of Initial Output Per Capita

logGCE, is natural logarithm of Government Consumption Expenditures

logGFC, is natural logarithm of Gross Fixed Capital Formation

logOP, is natural logarithm of the openness of economy

logM2, is natural logarithm of Money and Quasi Money

logREM, is natural logarithm of Worker’s Remittances and

Compensation of Employees, received

logFDI, is natural logarithm of Foreign Direct Investment, inflow

For estimating parameters corresponding to variables of interest included in the

model, we employ panel data estimation as follows in the equation (10) below:

49

(10) itittiit XTFP ψαγλ +++= )(

Where; itTFP is the productivity in country i at year t; itX is a vector of

independent variables (initial income per capita, government consumption

expenditure, gross fixed capital formation, openness of the economy, financial

development, worker’s remittances, and foreign direct investment) for country

i=1,2,….11 and at time t=1,2,….15; α is a scalar vector of parameters of

821 ,......, βββ ; itψ is stochastic error term with [ ] 0=itEψ and [ ] 2var iit σψ = ;

iλ and tγ are respectively country and time specific effects.

We develop our empirical investigation considering two situations according the

behavior of ti γλ + . In the first scenario we assume that country specific effects

are constant but not equally (i.e. ti γλ = and 0=tγ which gives a one-way fixed

effect) and we use fixed effect model with heteroscedasticity corrected standard

errors (model I).

In the second scenario we assume that countries specific effects are not constant,

but rather are disturbances, and the time effects are not present (i.e. iti υγλ +=

and 0=tγ ) and we use random-effect model (GLS), on the balanced panel data.

The econometrical results are given in the table II.7 below.

50

Table II.7: Summary of econometrical results, Dependent variable: TFP Variable Model 1 Model 2 Constant 3.061***

(0.173)

3.049***

(0.221)

IYPC 0.191***

(0.013)

0.19***

(0.023)

GCE -0.069*

(0.041)

0.066

(0.046)

GFC 0.126***

(0.029)

0.129**

(0.056)

OP 0.095***

(0.019)

0.093**

(0.044)

M2 -0.193 ***

(0.027)

-0.189***

(0.035)

REM 0.124***

(0.005)

0.123***

(0.009)

FDI -0.023**

(0.009)

-0.023

(0.018)

Number of observation 165 165

Number of countries 11 11

R-squared 0.63

51

Durbin-Watson 1.64

Breusch-Pagan test (Chi-square) 3.432*

Hausman test (Chi-square) 1.885

Notes: Variables are in the natural logarithmic form. Model 1 presents estimates for fixed effect model with panel specific heteroscedasticity corrected standard errors. Model 2 present estimates for random effect model. ***, **, and * denotes significance level respectively at p-value of less than 1%, 5%, and 10%. Standard errors are in parenthesis.

Estimated coefficients from both models do not differ a lot. We see that signs of

the coefficients estimated are the same from both models, and changes are in the

significant level. Accordingly, the estimated coefficients of government

consumption expenditures, and foreign direct investment are statistically

significant using the fixed effect model (I) and statistically insignificant when we

use the random effect model (II).

In order to choose which model gives the true unbiased results we analyze results

of Breusch-Pagan test and the Hausman test for the random effects model. So, the

results of the Breusch-Pagan test show that we reject the null hypothesis ( 02 =uσ )

at statistically significance level p=0.064, which means that countries random

differences have variance. The result of the Hausman test shows that we cannot

reject the null hypothesis (at p=0.96), which means that the estimated coefficients

are consistent. The conclusion from these tests is that, estimated coefficients with

GLS model are unbiased.

52

The interpretation of the estimated coefficients is going to be based on the results

taken from GLS model. The equation with coefficients estimated from the model

2 (GLS) takes the form as it follows:

(11) ε+−+

+−++−+=FDIREM

MOPGFCGCEIYPCTFP02.012.0

219.009.013.007.019.005.3

We continue analyzing the determinants of the TFP growth in CEE countries of

our sample based on the results taken from the model 2 (GLS model). From the

results in table II.7 we conclude that initial output per capita, gross fixed capital,

openness of economy, and remittances have positive and statistically significant

effect on TFP. Accordingly, increasing by 10% IYPC, GFC, OP, and REM will

increase the TFP by about 1.9%, 1.3%, 0.9%, and 1.2%, respectively. Unexpected

results are according the role of M2, GCE, and FDI on growth of TFP. Results

about the role of M2, GCE, and FDI on growth of TFP are not as expected. From

the results we see that M2 has negative and statistically significant (at p < 10%)

effect on TFP growth, whereas the government expenditures and foreign direct

investment has negative and statistically insignificant effect. An increase of 10%

in the level of M2 will decrease the TFP by about 1.9%.

53

II.6 Conclusions

The main objective of this Chapter is decomposition of the output growth in

capital, labor, and productivity. Fromm results according the our sample countries

as a whole over the entire period of time (1992-2010) show that the average

growth is 2.73% and the main driver is capital with about 1.74%, followed by

contribution of total factor productivity and labor force with about 0.84% and -

0.15%, respectively.

Results based on the sub periods show that during the period 1992-1995 the

annual average growth is -1.32% and the main contributor is capital with about

0.96% followed by negative contribution of productivity and labor force. Results

on this period are expected since the economies of CEE countries came out from

their regimes with a totally inefficient economy. During the first years after the

collapse of totalitarian regimes, during the end of 1980s and beginning of 1990s,

the high unemployment rate, high level of emigration toward Western countries,

high inflation rate, limitation of financial capital, and other unstable

macroeconomic factor, were the main characteristics of this economies.

54

During the second period, 1996-2000, the annual average growth rate is 3.16%.

Characteristic for this period of time is that the main driver is TFP with about

1.85%, followed by positive contribution of capital (1.76%) and a negative

contribution of labor forces in the same level as in the first period (-0.44%).

During the third period, 2001-2005, the average growth rate is 4.75%, which is

higher than the second period. The main driver in this period has been TFP

growth with about 2.73%, followed by positive contribution of capital and labor

respectively with about 1.96% and 0.06%. Economic growth experienced during

this period come as the increase in all parameters, factors of production and TFP.

During the fourth period, 2006-2010, results show that growth of economy is

positive (2.37%) but smaller than the previous period. Capital is the main driver,

followed by the contribution of TFP and labor force. During this period we see a

sharp decrease in TFP growth rate compared with the previous period. From the

results during the last period and the financial crises that is facing euro zone, we

can expect that positive real economic growth for these countries will be a hard

objective to be achieved.

Based on the groups’ estimated results, for countries with upper-middle income

level (group I), show that average growth rate is 2.05% over the entire period

1992-2010. The main driver is capital followed by the positive contribution of

55

TFP and negative contribution of labor forces. Results over sub periods show that

output growth has been negative during the first period of time (1992-1995) with

about -3.66%, followed with positive average growth rate for period 1996-2000

with about 2.84% and for period 2001-2005 with about 5.51%. Whereas, the

positive growth rate of output experienced during the last period (2006-2010) is

smaller than growth rate experienced during previous one (2.36 < 5.51%). The

average contribution of capital has been growing from the first to the last period.

The contribution of labor force is negative over the first three periods and slightly

positive during the last period. Contribution of TFP is negative for the first period,

followed by positive contribution during the other three following periods. But

contribution of TFP is smaller during the last period compared with the previous

one. For countries of group I the main driver of growth for first and the last period

has been capital, whereas for the second and the third period the main driver is

TFP.

Estimated results for countries with high income level as a whole (group II) show

that annual average growth rate for the entire period 1992-2010 is 2.81%. The

main contribution is capital accumulation with about 2.26%, followed by positive

contribution of TFP with about 0.57% and the negative contribution of labor force

with about -0.03%. Results over sub periods show that the average growth has

been positive over all periods analyzed. In two last periods the rate of economic

growth has been with smaller compared with respective previous ones. From the

56

results we see that capital is the main driver over all four periods analyzed. The

contribution of TFP on growth has been negative during the first and the last

period and positive during the second and third period.

Interesting results are according the TFP contribution in the last sub period 2006-

2010 in both groups of countries. Regarding to this we see that average rate TFP

of contribution on output growth for both group is lower compared with previous

sub period, and even for the group of countries with high income level this is

negative. This is explained by the beginning of the financial crisis which has

spread on EU zone.

According to the results on the TFP growth model we have concluded that initial

income per capita, gross fixed capital, openness of economy, and remittances

have positive and statistically significant effect on growth of TFP. Whereas the

role of government expenditures has been positive effect but not statistically

significant. Estimated results show that effect of M2 on TFP growth is negative

and statistically significant (at p < 1%). Interesting result is according the negative

role of FDI on growth, which can be a sign for the countries of our sample to

reconsider their financial policies they use for attracting the foreign investments.

57

CHAPTER III

The Role of Remittances on Economic Growth: An Empirical Investigation on 12 CEE Countries13

III.1 Introduction

Economic growth is the main objective of developing countries as their way to

bring their standard of living in line with developed countries. Sources of

economic growth have been a main topic that economists have addressed for more

than half a century. Different studies have examined factors such as capital, labor,

technology, and human capital, initial level of gross domestic product (GDP),

foreign direct investment (FDI), remittances, official aid, economic freedom, and

political stability. In this line are works such as: Solow (1956; 1957) in his works

“A Contribution to the Theory of Economic Growth” and “Technical Change and

the Aggregate Production Function”, in which he concludes that an increase in

capital and labor along with technological change will positively affects economic

growth; Denison (1980), in his work “The contribution of Capital to Economic

growth”, concludes that physical capital is one of several important sources of

13 International Business & Economics Research Journal, 2013, Vol. 12, No. 2, pp. 103-204 ISSN: 1535-0754 (Print) ISSN: 2157-9393 (Online) http://journals.cluteonline.com/index.php/IBER

58

economic growth; Romer (1986), in his work “Increasing Returns and Long-Run

Growth”, concludes that technological change has a positive effect on growth;

Barro (1991), after analyzing 98 countries in his work “Economic Growth in a

Cross Section of Countries”, concludes that economic growth was positively

related to human capital and political stability and negatively related to the initial

level of real GDP per capita; De Long and Summers (1991) in their work

“Equipment Investment and Economic Growth”, and De long, Summers, and

Abel (1992), in their work “Equipment Investment and Economic Growth: How

Strong is the Nexus”, find a strong association between investment in fixed

capital (machinery and equipment) and economic growth; Yao and Wei (2007), in

their work “Economic Growth in the Presence of FDI: The Perspective of Newly

Industrializing Economies”, conclude that factors such as labor input, use of

capital, technological change, and human capital, foreign aid, foreign direct

investment, and investing in research and development (R&D) have a positive

effect on economic growth; Wu and Davis (1999), analyzing data on 100

countries for the period 1975 to 1992 in their work “The Two Freedoms,

Economic Growth and Development: An Empirical Study”, found that economic

freedom is a fundamental factor in fostering economic growth; and Scully (2002)

in his work “Economic Freedom, Government Policy and Trade-off between

Equity and Economic Growth”, concludes that economic freedom promoted

economic growth.

59

On their way to fostering prosperity, developing countries are characterized by

shortage of external capital flow, which is an important factor that promotes

investment and accelerates economic growth. In addition to official development

assistance, other official aids and foreign direct investment, remittances are

considered an important way to increase the amount of foreign capital inflow. The

World Bank (2011) reports that the total amount of remittances received by

developing countries in 2010 was about US$325 billion, which is more than

double the amount received in 2004 (US$159 billion).

Remittances offer additional advantages compared to other sources of financing.

First, remittances are the second largest amount of foreign capital inflow after

FDI, surpassing official development assistance and aid received, as well as other

sources of external capital (World Bank, 2011). Second, remittances received by

developing countries are stable. They have continued to increase each year, with

the exception of a decrease in 2009 when the amount of remittances was US$307

billion compared with US$325 billion in 2008 (World Bank, 2011). Third,

remittances are underestimated because the amount flowing reported does not

consider transfers through informal channels (such as family and friends).

After the collapse of political and economic systems in CEE countries in 1991,

the main objective of policy makers was to secure sustainable economic growth to

increase the standard of living to the level of Western countries. Negative growth

60

rates, high unemployment rates, high inflation rates, and pessimistic perspectives

for a better life were characteristics of Eastern European countries during this

time. With this prevailing pessimistic situation, about four million people

emigrated from CEE countries to Western countries during the period 1989 to

1994 (Fassman & Munz, 1994). The majority of people saw the immigration to

Western European countries as a way to ameliorate their life and the lives of their

relatives who remained in the home country. Since then, emigrants’ remittances

have poured toward home countries and have been considered a very important

financial channel for increasing the amount of foreign capital.

The performance of CEE countries in achieving sustainable economic growth has

been remarkable during the period under investigation. Still, not all countries have

achieved the same level of income per capita. Based on World Bank

classifications (2012) countries such as the Czech Republic, Hungary, Poland,

Slovakia, Slovenia, and Croatia now belong to the group of countries with high

income, whereas countries such as Albania, Bosnia Herzegovina, Bulgaria,

Latvia, Lithuania, Macedonia, Montenegro, Romania, and Serbia are in the group

of countries with upper-middle income. In the present paper, we have not

accounted for the data on countries such as Bosnia Herzegovina, Montenegro, and

Serbia because they have missing data for the period examined.

61

The main objective of the present chapter is to highlight the impact of remittances

on the economic growth of twelve CEE countries using balanced panel data

spanning 1996 to 2010. Data are taken from the World Bank Indicators (2012).

The empirical analysis uses fixed-effect models (regression with a mixture of

quantitative and qualitative repressors).

This chapter is organized in five sections. In the second section, the literature

review, we present several conclusions achieved in empirical studies regarding

the role of remittances on growth in developing countries. The third section

consists of stylized facts about the economic performance of the sample countries;

data, definition, and descriptive statistics on each variable; and the methodology

used for regression models. In the fourth section, we present our hypotheses and

develop empirical models for testing them. The last section states our conclusions,

which summarizes the study’s results.

III.2 Literature Review

Workers’ remittances inflow toward developing countries is generally accepted

by policy makers as a very important economic factor which accelerates

economic growth. Remittances are very large in size, stable, and provide direct

62

benefits to households. However, conclusions of empirical work about the effects

of remittances on economic development for recipient countries vary.

Some of works on the field developed argue that remittances have positive effects

on economic growth in developing countries, while others maintain that

remittances have an insignificant impact on growth. Among others; Guliano &

Ruiz-Arranz (2009) in their work “Remittances, Financial Development and

Growth”, analyzing data for about 100 developing countries, conclude that

remittances promote growth in financial markets that were relatively

underdeveloped. In other words, they conclude that positive effect of remittances

on growth is conditional on the degree of financial development; Mundaca (2009)

in her paper titled “Remittances, Financial Market Development, and Economic

Growth: The Case of Latin America and the Caribbean”, concludes that

remittances have a significant positive effect on long-term growth; Bugamelli and

Paterno (2009) in their work “Output Growth Volatility and Remittances”, after

analyzing data on 60 emerging and developing economies for the period

1980−2003, conclude that remittances are negatively correlated to the output

growth volatility; Ledesma and Piracha (2004) in their work “International

Migration and the Role of Remittances in Eastern Europe”, analyzing data on 11

CEE countries for the period 1990−1999 , conclude that remittances play a

positive and statistically significant effect on productivity level and the

investment level; Fayissa and Nsiah (2010) in their work “The impact of

63

remittances on economic growth and development in Africa”, analyzing the

economies of 37 African countries from 1980 to 2004, conclude that remittances

boost economic growth in less developed countries by providing an alternative

way to finance investments and helping in overcoming liquidity constraints.

Other empirical works find no direct impact of remittances on growth but only a

small spillover effect. In this line are research such as: Rao and Hassan (2012) in

their work “Are the Direct and Indirect Growth Effects of Remittances

Significant?”, analyzing 40 high remittance recipient countries, conclude that

remittances have positive effect on growth but they are small and indirect.

Some authors have argued that remittance transfers between migrants and their

relatives in their home countries are used mainly for consumption. At the same

time, others have maintained that these remittances are used for investing in

goods. In this line are authors such as: Koc and Onan (2004), in their work

“International Immigrants’ Remittances and Welfare Status of Left-Behind

Families in Turkey” and Russell (1992) in his work “Migrant Remittances and

Development”, conclude that recipients spend remittances for immediate

consumption; and Taylor (1992) in his work “Remittances and Inequality

Reconsidered: Direct, Indirect and Inter-temporal Effects”, concludes that

remittances are spent to acquire mainly for homes and land.

64

Furthermore, another line of empirical work concluded that remittances have, in

fact, a negative effect on output growth. In this context; Chami, Fullenkamp, and

Jahjah (2005) in their work “Are immigrant remittance flows a source of capital

development” analyzing data for 113 countries over the period 1970−1998 ,

conclude that remittances have a negative impact on growth; Barajas et al. (2009)

in their work “Do Workers’ Remittances Promote Economic Growth?, conclude

that the growth effects of remittances are insignificant and at times even negative.

III.3 Stylized Facts, Data, and Methodology

The main objective of this chapter is to analyze the effect of remittances on

economic growth experienced in 12 CEE countries for the period 1996−2010.

Sample countries analyzed in this part cover a significant portion of CEE

countries. During the last two decades remarkable results according economic

growth are achieved by these economies (Figure III.1). Based on World Bank

(2012) classifications, countries such as the Czech Republic, Hungary, Poland, the

Slovakia, Slovenia, and Croatia are listed in the group of countries with high

income, while countries such as Albania, Bulgaria, Latvia, Lithuania, Macedonia,

and Romania are in the group of countries with upper-middle income. In the

65

present paper we use this classification to compare the impact of remittances on

economic growth in the above-mentioned groupings of countries.

Figure III.1: Annual GDP growth rate averaged, for period 1992–201014

The total amount of remittances received by CEE countries annually for the

period of time 1996–2010 generally has increased from year to year. As shown in

Figure III.2, the total amount of remittances received was reduced in 1997, 1999,

2009, and 2010 compared to the respective previous year. For the period 2000–

2008, however, the amount increased year after year. The total amount of

remittances received in 2008 was 12 times more than the total amount received

during 1996.

14 World Development Indicators; authors’ calculation

66

Figure III.2: Amount of remittances received, annually from1996–201015

Besides remittances, other variables considered are gross fixed capital formation

(gfc), foreign direct investment (fdi), and the openness of the economy (op). We

have not taken as an explanatory variable net official development assistance and

aid received because of missing data for the period under consideration. To study

how these factors have influenced growth we start with a simple double log-linear

Cobb-Douglass production function as follows:

(1) ititititit opfdigcfremy εβββββ +++++= lnlnlnlnln 43210

15 World Development Indicators; Authors’ calculation

67

Where,

ln y, is the natural log of real gross domestic product per capita,

ln rem, is the natural log of remittances per capital,

ln gcf, is the natural log of gross capital formation per capita,

ln fdi, is the natural log of foreign direct investment as percentage of GDP,

ln op, is the natural log of openness of economy.

The methodology used in the present paper is that of balanced panel data

regression analysis. We treat the panel data as stacked, cross-sectional units,

where units are respective countries. We use the fixed-effect model with

heteroskedasticity corrected standard errors. The data on the variables used in this

empirical work are taken from the WB Development Indicators (WDI, 2012) for

the period 1996–2010. The definition and descriptive statistics for each variable

used in the model are provided, respectively, in Table III.1 and Table III.2

Table III. 1. Variable Description and Source Information

Variable Description GDP

REM

Gross Domestic Product Per Capita, measured in constant 2000

US$. Taken from 2012 World Development Indicators dataset.

Workers’ Remittances and compensation of employees

received (per capita), measured in current US dollars. Taken from

2012 World Development Indicators dataset.

68

We develop the work on estimating parameters based on three assumptions. The

first assumption is the situation where intercepts and slope coefficients are

constant over units and time. For this case, we use the fixed-effect Model I.

FDI

GCF

OP

Foreign Direct Investment, net inflow (% of GDP). Taken from

2012 World Development Indicators dataset.

Gross Capital Formation, measured in constant 2000 US$. We

evaluated the Gross Capital Formation input using the perpetual

inventory method ttt IKK +−= −1)1( δ , where tI is Gross Capital

Formation (in current 2000 US$) and δ is the depreciation rate

( 10 << δ ) which is taken 06.0=δ . The initial capital stock is

calculated by formula )]/()1[(11 δ++= −− ggIK tt , Where, ( g ) is

the average growth rate of the real output for the first ten years,

1996–2005. Data for this variable are taken from 2012 World

Development Indicators dataset. Used in the model per capita.

Trade Openness, measured as the value of total trade (imports

plus exports) as a percentage of nominal GDP. Data on exports,

imports, and nominal GDP taken from 2012 World Development

Indicators dataset.

69

The second assumption is that the slope coefficients are constant and that the

intercept varies over the groups of countries. For this case, we use the

classification of the countries by World Bank (2012) as 1- high income countries

and 2- upper-middle income countries. We use a dummy variable for interception

by developing fixed-effect Model II.

The third assumption is the situation where the intercepts and slope coefficients

vary over the groups of countries. In this case, we elaborate the fixed-effect

Model III.

Table III.2: Summary Statistics Variable Mean Median Minimum Maximum Std. Dev. C.V. GDPPC 4,627.44 4,540.78 895.56 13,836.20 2,783.74 0.60

REM_PC 137.90 120.45 0.71 479.17 120.57 0.87

FDI 5.10 3.98 –29.23 52.05 6.67 1.31

GCF_PC 6,780.70 4,768.09 106.67 31,508.50 6,252.79 0.92

TOT 104.49 103.77 45.13 174.82 30.08 0.29

Notes: Data are reported annually from 1996–2010 for 12 Central and Eastern European countries. For our analysis, all data are transformed in natural logarithmic forms.

70

III.4 Hypotheses and Empirical Models

The main goal of this chapter is to highlight the effect of remittances (REM) on

economic growth (GDP). We will also analyze the effects of other traditional

sources of economic growth, such as gross capital formation (GCF), foreign direct

investment (FDI), and openness of the economy (TOT). For this purpose, the five

following hypotheses are stated, tested, and commented as below.

H1: Because we are investigating the direct effect of remittances on growth,

we expect the role of remittances to be negative. Generally accepted by policy

makers is that remittances inflow have positive effect to accelerate economic

growth. Remittances are considered as an alternative way for to overcome

liquidity constraints, to finance investments, to increase consumption, increase

productivity, finance education, etc. In this line conclude in their works Guliano

& Ruiz-Arranz (2009), Mundaca (2009), Bugamelli and Paterno (2009), Ledesma

and Piracha (2004), Fayissa and Nsiah (2010)

H2: We expect the effect of FDI on economic growth to be positive, because

CEE countries in their way to increase their standard of living in line with

Western European countries ones, have faced limitations on capital input. This

situation increases the role of external capital flow in general and consequently

71

the role of FDI as part of it. Also, literature generally support the idea that FDI

implement new technology, increase competitiveness, introducing new

management practices, increases the map of the export destination, etc. The

expectation of this hypothesis (positive effect of FDI on growth) is in line with the

results of other works such as Bende-Nabende and Ford (1998), Soysa and Oneal

(1999), and Sohinger (2005), which conclude that FDI plays a positive role in

economic growth.

H3: We expect a positive effect of gross capital formation per capita (GCF) on

output growth. This can be understood simply, because the increase of fixed

capital inputs increases the output produced. This is in line with the conclusions

of Solow (1958), De Long and Summers (1991), De long, Summers, and Abel

(1992), and Yao and Wei (2007), all of whom concluded that fixed capital is the

key factor that promotes growth.

H4: We expect a positive impact on economic growth caused by the openness

of the economy. Openness of economy toward global market make possible that

our products to be more competitive, increases the exports, make possible to

secure the cheaper factors of production such as row materials, capital and labor,

etc. This expectation is consistent with the results of Buthe and Milner (2008)

who, after analyzing 122 developing countries concluded that joining trade

agreements allowed developing countries to increase economic growth.

72

H5: In the present study, a structural break of units is employed according to

the level of income achieved by the countries as classified by the World Bank.

We expect the positive effect of remittances on growth to be more as country

moves toward high income level groups. The use of remittances flow in countries

with high income level mainly goes for profit driven investments, where for other

countries belonged to group with upper-middle income level we expect that

partially remittances flow goes for consumption. So, we expect positive effect of

remittances on growth to be more in group of countries with high income level

than in other group of countries.

We carry out the panel unit root test on the levels of the dependent and

independent variables used in the models to test whether the variables are

stationary. For this reason, we use the Im, Pesaran, and Shin (IPS) approach,

which developed a panel unit root test for joint null hypothesis that all groups

have a unit root. Based on the results (Table III.3), we reject the null hypothesis at

1% in favor of a stationary relationship between the variables of interest. In other

words, there is a long run relationship between output and the independent

variables used in the present study (REM, FDI, GCF, and TOT).

73

Table III.3: Panel Unit Root Test on the Levels of Variables Variable IPS Statistics Y –2.36817

Rem –3.39558

Fdi –3.41418

Gcf –3.18542

Op –3.55281

Notes: Variables are in natural logarithmic forms. The IPS tests Ho: all groups have a unit root. The critical values for 10%, 5%, and 1% are –1.89, –2.00, and –2.22, respectively. Based on the results, we can reject the unit root hypothesis at 1%.

We use ANOVA and ANCOVA models, which contain both quantitative and

qualitative variables (dummy). Table III.4 includes the results from using the

fixed-effect model on the panel data of 12 CEE countries for the period 1996–

2010. The fixed-effect model is developed by considering three assumptions.

In the first case, we use the fixed-effect model under the assumption that

intercepts and the slope coefficients are constant over units and time. The model

developed for this case is as follows:

(2) ititititit opgcffdiremy εβββββ +++++= lnlnlnlnln 43210

The estimated results in Table III.4 under the Model I column show that

remittances (rem) and foreign direct investment (fdi) have a negative and

74

statistically significant effect on the GDP per capita (at p<0.1). Accordingly, we

find that an increase in REM or FDI by 10% results in decreasing the output,

respectively, by about -0.9% or -0.8%. Negative effect of remittances on growth

is in line with the conclusion achieved in the work of Chami, Fullenkamp, and

Jahjah (2005) after they analyzed data on 113 countries. While, negative effect of

FDI on growth is in line with the conclusions achieved by Yong (1993), Easterly

(1993), Feldstein (2000), and Kim, Lin, and Suen (2012). Estimated slope

coefficients for gross capital formation and openness of economy to international

trade show positive and statistically significant effects on GDP per capita,

respectively, at p<0.01 and p<0.1. Thus, if we increase the gross capital formation

or trade openness by 10%, then the GDP per capita will increase respectively by

about 5.2% and 1.8%. The value 77.02 =R shows that 77% of the variation in the

output per capita is explained by the variation in the predictor variables used in

Model 1.

With the results from Model 1, we have estimated the equation as follows:

(3) ε+++−−= opgcffdiremy 18.052.0085.0091.062.3

75

Table III.4: Summary of Econometrical Results: Dependent Variable: l_GDP Variable Model I Model II Model III Constant 3.62087***

(0.374234) 4.88253*** (0.236155)

5.15300*** (0.311002)

REM –0.0909061***

(0.0187800)

–0.0442234***

(0.0144091)

0.0108753

(0.0154819)

FDI –0.0845064***

(0.0239685)

–0.0740782***

(0.0229581)

–0.0727616**

(0.0359548)

GCF 0.516702***

(0.0259071)

0.353579***

(0.0318821)

0.308653***

(0.0186509)

TOT 0.177996*

(0.0920500)

0.208939***

(0.0595450)

0.185710***

(0.0634375)

Dummy - –0.511499***

(0.0524401)

–0.283493***

(0.0666381)

d_REM - - –0.0564021**

(0.0248691)

d_FDI - - –0.141513***

(0.0523527)

d_GCF - - 0.00494579

(0.0106717)

d_TOT - - 0.00259624***

(0.00127661)

Number of observation 180 180 180

Number of countries 12 12 12

R-squared 0.772832 0.873997 0.889882

Notes: ***, **, and * denote significance at p<0.01, p<0.05, and p< 0.1 levels respectively. The results on standard errors are in parenthesis; Model 1 presents the results of the fixed-effects model corrected for standard errors for all 12 CEE countries. Model 2 presents results by using Least Squares Dummy Variables for differential interception coefficient. Model 3 presents results of the LSDV for differential interception and slope coefficients between the two groups.

76

In the second scenario, we assume that the interception coefficient differs between

the two groups of countries. Thus, besides the quantitative variables, we employ

qualitative variables (dummy) to highlight the differences in the interception

coefficient. For this case, the model is developed as follows:

(4) ititititit totgcffdiremdumy εβββββ ++++++= lnlnlnlnln 43210

Where, dum = 1 for countries with upper-middle income level.

= 0 otherwise or for countries that belong to the group with high income

The econometrical results highlighted from Model 2 show that REM and FDI

have negative and statistically significant effects on GDP per capita (at

p<0.01). Thus, an increase by 10% in REM or FDI will decrease the output per

capita respectively by about -0.44% or -0.74%. The estimated parameters for GCF

and TOT show positive and statistically significant effects on growth (at p< 0.01).

Accordingly, an increase by 10% in the amount of capital or the volume of trade

as a percentage of GDP will cause an increase in output by about 3.5% or 2.1%

respectively. The interception coefficient for the group of countries with upper-

middle income level is slightly lower than that of the group of countries with a

high income level. For the group of countries with the high income level, the

interception coefficient is about 4.88, whereas for the other group of countries, the

77

interception coefficient is about 4.37. Recall that the group of countries with high

income level is used as the benchmark category. As in the first model, the value

87.02 ≈R is a good figure, which indicates that 87% of the variation in GDP per

capita is explained by the variation in the predictor variables included in Model II.

Based on the parameters estimated by Model II, we have estimated the equation

as follows:

(5) ε+++−−−= opgcffdiremdumy 21.0354.0074.0044.051.088.4

In the third scenario, we assume that the interception coefficient and the slopes

vary across the groups of countries. Thus, we use other qualitative variables to

more specifically examine the role of each factor taken into consideration in both

groups of countries. The model developed in this case is as follows:

(6) ititititit

itititit

opdgcfdfdidremdopgcffdiremdy

εβββββββββ

++++++++++=

lnlnlnlnlnlnlnlnln

8765

43210

The results of the regression are given in Table III.4 under the last column (Model

III). In this case, we see that the value 89.02 ≈R is a good figure, which indicates

that 89% of the variation in GDP per capita is explained by the variation in the

predictor variables included in Model III. The estimated equation with the

parameters found by using the econometrical Model III is as follows:

78

(7) ε+++−−

++−+−=opdgcfdfdidremdopgcffdiremdy

_003.0_005.0_142.0_056.0186.0309.0073.0011.0_284.0153.5

The estimated coefficient )011.0( 1 ≈β shows that remittances have a positive but

not statistically significant effect on growth in countries that now belong to the

high-income group. The estimated coefficient )045.0056.0011.0( 5 −=−=β

shows that remittances have a negative and statistically significant effect (at

p<0.05) on output in countries with upper-middle income level. Accordingly, an

increase by 10% in the amount of remittances will increase the output by about

0.11% in countries with high income and will decrease the output by about 0.45%

in the group of countries with upper-middle income level.

The estimated coefficients, 2β and 6β , show that, the role of FDI on growth in

each groups is negative and statistical significant respectively at p<0.05 and

p<0.01. Accordingly, an increase of FDI by 10% will decrease output growth by

about - 0.7% in group of countries with high income and - 2.2% in the group of

countries with upper-middle income level.

The positive effects of GCF and TOT on output are slightly more in countries

with upper-middle income level. Estimated coefficients are positive and

statistically significant (at p<0.01) except the case of GCF in countries that belong

79

to the group with upper-middle income, which is not statistically significant.

Consequently, an increase by 10% in the volume of trade as percentage to the

GDP will effect growth respectively by about 1.86% in countries with high

income and by about 1.88% in countries with upper-middle income level.

III.5 Conclusion

In this chapter we investigate from the empirical point of view the role of

remittances on output per capita, experienced by twelve CEE countries over the

period of time 1996–2010. The role of remittances on growth is also analyzed by

using a structure break in the data due to the level of income achieved by

countries, by separating countries of interest in two groups based on the World

Bank classification (World Bank, 2012). The present study is developed in

contrast with the role of other explanatory variables such as foreign direct

investment, gross capital formation, and economic openness.

The results estimated by Model I, show that workers’ remittances received by

countries under investigation have a negative effect on GDP per capita. We have

highlighted that increasing remittances by 10% will decrease the level of GDP per

80

capita by about 0.9%. This result rejects stated H1: remittances have positive

effect on growth of CEE countries as a whole. Also, estimated coefficient of FDI

on growth shows that on average FDI plays negative effect for our sample

countries as a whole. So, results reject H2: FDI plays positive effect on growth.

Estimated results according the role of openness of economy and gross capital

formation are as expected in H3 and H4 - they have positive effect on growth.

Estimated coefficients under the Model II – in which we assumed that

interception coefficients differ between groups, do not change the signs compare

with them estimated from the Model I.

Results estimated by using Model III, in which have assumed that intercept and

slopes coefficients differ among groups. In this case we see that the role of

remittances in the group of countries with high income level is positive but not

statistically significant at p<10%. While its effect on growth for group with

upper-middle income level is negative. So, these results are as expected in H5: as

countries moves toward high income level, remittances effect on growth is more

positive. The other estimated coefficients do not change their signs compared with

them estimated with other models (I and II).

Negative effect of remittances on growth defined in our investigation is in line

with the other researches such as: Chami, Fullenkamp, and Jahjah (2005) in their

81

work “Are immigrant remittance flows a source of capital development”

analyzing data for 113 countries over the period 1970−1998, conclude that

remittances have a negative impact on growth; Barajas et al. (2009) in their work

“Do Workers’ Remittances Promote Economic Growth?, conclude that the growth

effects of remittances are insignificant and at times even negative. One reason the

role of remittances on growth in the second group of countries is negative can be

because these are used mainly for immediate consumption rather than investing.

The negative effect of FDI on growth could be the case when the FDI crowds out

domestic investment in new technology by accelerating the old, disused

technology (Young, 1993). Another reason that FDI causes negative effect on

growth is when preferential policies on foreign capital (tax treatment, etc.) used

by governments to increase the inflow of FDI create distortion between foreign

and domestic capital (Easterly, 1993).

Important policy implication can be drawn from these results. CEE countries must

consider reforms for making the use of remittances received more efficient

towards growth. These reforms are more necessary for the group of countries with

the upper-middle income level. Also, reforms are necessary for increasing the role

of FDI on growth, this maybe by considering fiscal policies used to attract foreign

capital inward.

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CHAPTER IV

Crowding-Out Effect of Public Investment on Private Investment: An Empirical Investigation16

The crucial concern both for developed and developing countries of European

continent, has been ensuring sustained positive economic growth. The

neoclassical paradigm suggests that technology is the only factor that would

maintain perpetually positive economic growth; it does not underscore the effect

that spending on new capital, be it physical or financial, has on increasing of

productivity at large and that of workers employed in respective industries, where

these investments take place. It is interesting from both theoretical and empirical

standpoint to research on the behavior of spending on new capital: investments.

IV.1 Introduction

16 Journal of Business and Economics Research (JBER), Volume 10, Number 5, May 2012: ISSN: 1542-4448 (Print) ISSN: 2157-8893 (Online), http://journals.cluteonline.com/index.php/JBER

83

Policymakers, economist, and professional researchers have long been studying

investment in varies facts to better understand its behavior, determinants, and its

influence on economic growth.

In this chapter our main objective is to empirically estimate the effect of public

investment on total private investment for a pool of Eastern European countries. It

has been long recognized by theory and empirical studies that private investment

is the main contributor in achieving economic growth (Solow, Lucas, and others).

Countries that we have in our sample have undertaken ample efforts to refurbish

their infrastructure they inherited from the command and control economic

system. While the public investments have been geared towards upgrading

existing infrastructure, establishing a new one that can serve the general public

(schools, hospitals, bridges, etc), and management of government services, the

private investments have been more on setting up productive facilities for private

goods and services. These investments taken together have a determining effect

on the economic growth of the country directly and indirectly. The direct effect is

related with the contribution of new capital on nation’s production process as

capital enters directly in the country’s production function. The indirect effect

comes through increasing the amount of capital per worker, thus increasing

workers’ productivity.

84

Another indirect effect is closely linked with the possibility of public investment

increasing the efficiency of private investments through creating a better

infrastructure, increasing the amount of capital injected in the economy,

facilitating the flow of goods and services, decreasing the cost of transportation,

and cost of acquiring private capital; therefore increasing the profitability of

private investment and increasing the aggregate amount of private investments.

While this is well understood from the point of view discussed, it is not debated at

all in any standard undergraduate macroeconomics textbook. Furthermore text

book model predicts that the increase in public investment will crowd out the

private investment.

Literature in this issue is inconclusive, it finds empirically that crowding out

effect is present in the economy at times, or the opposite at some other times. This

could be due to empirical methods employed, lack of computability power of data

(especially in earlier studies when computability power was not at all near to what

we have today), modeling and specification issues, or due to structural make up of

national economies. The last one, as speculative as it may sound, can be justified

in the grounds of two specific assumptions of general equilibrium model of

crowding out result. First assumption being that the economy works at production

possibility plane (or production possibility frontier in case of two goods, two

factors of production simplification of an economy). Second has to do with the

assumptions of perfect competition.

85

Factors that influence private investments are different, but public investment is

one for which stand different opinions on how it influences the private investment

– does it complements or compete the private investment? It is clear for everyone

that public investment in infrastructure increases private investment, directly and

indirectly by creating better conditions to do business. The results of the

literatures suggest that public investment could crowd out private investment

more often in developed economies, whereas in developing economies public

investment is found more as complementary of private investment. In this study

we are going to analyze the effects of public investment on private investment in

11 European countries by grouping them as developed and developing17

In the next section we will present an extensive literature review in the issue

discussed. In this section we are going to explain main methods developed to

estimate the role of public on private investment. We also in this part will explain

ones.

The argument used in the study is to let the data speak for themselves. A panel

data analysis is employed for the purpose of estimating the marginal effect of

public investment on private investment. A sample of eleven countries of Eastern

European countries over a period from 1991-2009 is used in the analysis.

17 World Bank: Albania, Bulgaria, Latvia, Lithuania, Macedonia, Romania.

86

the different results achieved by other research developed on the field. In the

third section of this chapter, stylized facts, data and methodology, we will

describe in detail the sample under empirical study including the data, their

measurements, and limitation. In the fourth section we will set up the major

hypothesis under discussion that comprises the development of major objectives

of this research. This section will provide a detailed discussion of econometric

model that will be used to estimate the relationship between private and public

investments and the marginal effect. We will also discuss data analysis, statistical

tests, and some of the major findings that are developed. In the last section,

conclusion, we will make a summary of our findings and recommendation for any

further work to be extending on the current state of this findings.

IV.2 Literature Review

There is a vast discussion about the effects of public capital investment on private

investments. The question is, does public investment crowd in or out private

investment? Four common approaches are used for modeling the investment in

the existing literature, such as accelerator model, expected profit model, the

neoclassical model and Tobin’s Q. One way to analyzing the effect of public

investment on economic growth is based on the neoclassical investment model in

which public capital enters as a separate input and on the productivity measure

87

derived from the production function. The Aschauer (1989.a) and (1989.b)

reviewed the impact of the public capital stock on productivity (evidence from US

annual and state level data) and the empirical results indicate that nonmilitary

public capital stock, particularly on core infrastructure, is more important in

determining productivity than is either flow of nonmilitary or military spending.

Munnell (1992) in her work “Infrastructure investment and economic growth”,

after analyzing the effects of infrastructure investment in economic growth stock

concluded that in addition to provide immediate economic stimulus, public

infrastructure investment has a significant, positive effect on output and growth.

Also, the further work of Aschauer (1990) titled “Why is infrastructure important?

Is there a shortfall in public capital investment?” and Cashin (1995) titled

“Economic growth and convergence across the seven colonies of Australia: 1861-

1991”, conclude in the same result that public capital influences positively

economic growth.

Some other studies such as Holtz-Eakin (1994) in his work “Public-Sector capital

and productivity puzzle” and Evans and Karras (1994) in their work “Are

government activities productive? Evidence from a panel of U.S. states”, find

negligible the role of public investment on productivity. Khan and Reinhart

(1990) in their work “Private investment and economic growth in developing

countries”, Khan and Kumar (1997) in their wok “Public and private investment

and the growth process in developing countries”, and Ang (2010) in his work

88

“Determinants of private investment in Malaysia: what causes the post crisis

slumps?”, conclude that for developing countries the increase of the public

investment will influence positively economic growth, but less than contribution

of private investment. Voss (2002) in his work titled “Public and private

investment in the United States and Canada”, suggests that for both countries

there is no evidence to prove that public investment complements private

investment, in fact innovations to public investment tends to crowd out private

investment. Most of the literature that use the neoclassical approach arrive in

conclusion that public capital investment has influence on the economic

productivity but this factor is not a major one.

Another way that is used in analyzing the effect of public investment in economic

prosperity is that of accelerator model, which uses private investment that

incorporate public investment in order to point out the direct effect of public

investment on private investment and also its indirect effect on economic growth

through its effect on private investment. The conclusion that public investment

has positive effect on private investment has been supported by studies such as

Ramirez (1994) for Mexico, Odedokun (1997) for forty-eight developing

countries and Ramirez (2000) for a panel of Latin American countries. Other

works such as Blejer and Khan (1984) for a panel of developing countries

conclude that public infrastructure crowd in private infrastructure and public non

infrastructure investment crowds out private investment.

89

Some other literature such as Way and Wong (1982) for five developing countries

and Nazmi and Ramires (1997) suggest that public investment crowd out private

investment. Erden and Holcombe (2005) for nineteen developing countries and

twelve developing countries suggests that public investment crowd in private

investment for developing countries and crowd out private investment for

developed countries. The literatures suggest different results on the effect of the

public investment on private investment.

Some of the factors that influence private investment sector are described in the

different literature. The most important factor is public investment and especially

public infrastructure investment. Blejer and Khan (1984), Aschauer (1989a,

1989b), Munnell (1992) and Ramirez (1997), conclude that public infrastructure

investment has positive effects on private investment. Erden and Holcombe

(2005) suggest that public investment crowd in private investment in developing

countries and crowd out in developed countries. Ang (2010) analyzing the

determinants of private investment in Malaysia suggest that public investment

have a complementary effect on private investment. Cavallo and Daude (2011)

suggest that in average for 116 developing countries the public investment crowd

out private investment. The case of public infrastructure investment is clear from

every one because as results of better infrastructure enhances private capital

productivity, raising its rate of return and encouraging more private investment.

90

If we see the public investment from the inventories perspective, in this case both

public and private investment compete for the same recourses, the public capital

acts as a substitute for the private capital and crowd out the private investment.

Credit availability is another important factor that influences the level of private

investment, which is supported by Wai and Wong (1982), Blejer and Khan

(1984), Ramirez (1994), Ghura and Goodwin (2000), Erden and Holcombe

(2005), Ang (2010), Cavallo and Daude (2011). The limited financial resources

for private investment are more present in developing countries. Increasing the

availability of credit has positive effect on private investment. Cost of capital is

another factor that effects public investment as concluded by Blejer and Khan

(1984). The decrease of cost of capital also influences positively the private

investment as suggested by Ghura and Goodwin (2000). Change in real gross

domestic product is another factor that influences the private investment

suggested by Ghura and Goodwin (2000), Ang (2010), Blejer and Khan (1984).

Uncertainty is another factor that influences the level of private investment.

Studies such as Hartman (1972) and Abel (1983) show that increase in uncertainty

brings about increase in the expected value of marginal revenue products of

capital and as a result increase investment. Hartman (1985) analyzing the effect of

uncertainty about future levels of government spending has on private investment

suggests that increase uncertainty tend to increase the private investment. Ang

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(2010) analyzing the determinants of the private investment in Malaysia suggests

that macroeconomic uncertainty exerts a negative effect on investment climate in

the private sector. Kumo (2006) investigating the time varying uncertainty on

aggregate private fixed investment in South Africa conclude that, over all, time

varying macroeconomic uncertainty significantly reduces private fixed

investment.

The main objective of this chapter is to analyze the effect of public investment on

private investment in 11 European Countries in contests of developing countries

and also developed countries. Factors that are taken in consideration for achieving

our objective are real gross public investment, real gross private investment, bank

credit, inflation rate, and cost of capital. Data are obtained from world

Development Indicators (World Bank Dada base) for the period 1991 – 2009.

IV.3 Stylized Facts, Data, and Methodology

Data on the countries chosen to be represented in this study reveal that they

constitute good part of Eastern European economy. After a harsh period of

command and control economy, now they have emerged as fully fledged market

oriented economies. Some of them, like Croatia, Estonia, Poland, Slovak

Republic, and Slovenia, once under dictatorial regimes and centralized planning,

92

have moved up to developed countries. The most striking feature of these

economies is their economic growth last twenty years. The graph below shows

these countries performance in the last two decades (Figure IV.1). The figure

shows clear the so called struggle that countries have gone through keeping up

with many reforms they had to undertake to shift their economies from central

planning system to market price system. Albania, Poland, Slovak Republic,

Slovenia, and Estonia have managed to retain an annual economic growth above

1.5 percent. While other countries in the sample have performed at levels less

than 1.5 percent annual economic growth. The lowest annual growth rate is in

Macedonia.

Figure IV.1: Annual growth rate averaged, period 1991-200918

Annual growth rate

00.5

11.5

22.5

33.5

44.5

Albania

Bulgari

a

Croatia

Estonia

Latvi

a

Lithu

ania

Maced

onia

Poland

Rumania

Slovac R

epub

lic

Slovenia

Annual growth rate

18 World Development Indicators, author’s calculations.

93

The sample is comprised of eleven countries. These are: Albania, Bulgaria,

Croatia, Estonia, Latvia, Lithuania, Macedonia, Poland, Rumania, Slovak

Republic, and Slovenia. Following the classification, based on their GDP per

capita the sample can be divided into two groups. In developing countries, which

are Albania, Bulgaria, Rumania, Latvia, Lithuania, Macedonia; and in developed

countries which are Croatia, Estonia, Poland, Slovak Republic, and Slovenia.

Data are taken from WDI (World Bank) for a period of 19 years (1991-2009).

The following variables and their units of measurements are taken from the above

source: Ip = real gross private investment (constant 2000 US million dollar), Ig =

real gross public investment (constant 2000 US million dollar), r = real interest

rate (%), B = domestic credit provided by banking sector (% of GDP), P =

consumer price index (2005=100) as a measurement of inflation. Real gross

investment for private and public sector are authors’ calculation based on the

available data on real GDP, and percentage of real GDP in investment on private

and public sector. The methodology used in this study is that of panel data

regression analysis. More specifically it treats the panel data as stacked cross

section units, where units are respective countries. The use of Weighted Least

Squares (WLS) is due to the fact that countries differ in many respects and one

would find it as plausible to think that space intercept is specific for each of the

countries included in the sample.

94

IV.4 Hypotheses and Econometric Model

The paper aims at empirical estimation of the relationship between private

investment and public investment, the effect that public investments have on

private investment, the effect of bank credit available to private sector on private

investments, the effect of real interest rate (as the cost of capital) on private

investment, the effect of inflation on private investment, and whether there is a

structural break between developing and developed countries on these estimates.

For this purpose five following hypothesis will be stated, tested, and discussed in

sequential comportment in the section below.

H1: Given the fact that all countries in our sample do not comply with efficiency

(PPF-like efficiency), are still far from perfect competition economies, and have

been heavily involved in public investment to refurbish their public infrastructure,

we would expect to find a positive relationship between private and public

investment.

H2: There is a negative relationship of real interest rate on the private investment.

This can simply be understood, if we can think of real interest rate as the real cost

95

of undertaking an investment activity. One would expect that as the cost of

acquiring capital (e.g. real interest rate) increases, potential investors would

withdraw from their projects. This will result on a decrease on the level of private

investments. Therefore we would expect the marginal effect of real interest rate

on private investment to be negative.

H3: There is a positive marginal effect of bank credit available to private sector

and private investment. The countries included in our study have faced

multifaceted challenges in transforming their economies. Due to problems with

collateral, information system, public expectations and others the banking system

had much to accomplish to be able to provide funds for private investment. On the

other hand bank credit available to investors is crucial in providing for

entrepreneurial spirit of young and foreign investors wanted to set up their new

businesses in these countries.

H4: There is a negative marginal effect of inflation on the private investment.

This is due to two effects that inflation will have on private investment. First

effect will work through the expectations that business people form based on the

inflation. These expectations will enter into the investor’s objective function as an

addition to the future cost of investment. Second effect enters the investor’s

objective function as a policy variable. Investors see inflation as a measurement

96

of policy soundness that governments and central banks undertake. Therefore we

would expect this marginal effect to be negative.

H5: There will be a structural break on the data in relation with the effect of

public investment on private investment. This structural break is due to the

economic differences among countries included in this study. The easiest way to

estimate this structural break is to divide the sample in two major groups:

developing and developed countries. We expect the marginal effect of public

investment on private investment is different for these two different groups.

This study will employ two econometrics models, which will be estimated using

panel data analysis of eleven randomly selected European countries for period

1991-2009. The first model (call it Model I) is:

(1) ePbBbrbIgbbIp +++++= )()()()( 43210

And the second model (call it Model II) is:

(2) eDIgbPbBbrbIgbbIp ++++++= _)()()()( 543210

97

We are interested in estimating all coefficients in both models. For this purpose

this study employs the method of fixed effects. The table VI.1 shows the results of

both models. A comparison of both models shows that they have striking

explanatory power. The adjusted R square is high and it increases when dummy

variable is included (second model) to measure the differentiated effect of public

investment in private investment for developing and developed countries.

Table IV.1: Summary of econometric results (model I and II) Variables Model I Model II

Ig 4.65296

(0.370173)***

10.0329

(0.674864)***

R -112.023

(123.302)

-56.8570

(66.1563)

B 385.004

(62.9673) ***

73.1534

(34.4516) **

P -312.808

(55.9007)***

-106.470

(35.4349) ***

Ig_D - -6.89962

(0.672394)***

Adjusted R2 0.774984 0.778726

The estimated equations for both models are:

98

(I) ePBrIgbIp +−+−+= 81.31238502.11266.40

(II) eDIgPBrIgbIp +−−+−+= _9.647.10615.7386.5603.100

Where D=0 for developing countries and D=1 for developed countries. The

estimated marginal effects shows that signs of the effects are in line with

hypothesis and with what the economic theory suggests (model I). It is shown that

there is no crowding out effect of private investment by public investments.

Furthermore there is a 4.66 billion dollar increase in private investment for every

billion dollar increase in public investments (numbers are in 2000US dollar). This

marginal effect decreases for developed countries from 4.66 to 3.24 billion

dollars. It is interesting to see that crowding in effect diminishes as country moves

up in the ladders of development. Not clear it suggests that developed countries

get closer to “production possibilities plane” due to increase in efficiency and in

competitiveness. We could speculate that it might be due to enhancing effect of

institutions on private and public investments as countries develop. What is not

clear from the above regression results is that estimated coefficients are not the

same and it needs further work. The problems with specification model should be

addressed in a future research.

99

IV.5 Conclusion

This study has estimated from the empirical point of view the so called crowding

out effects of public investment in private investment. Panel data analysis of

eleven selected east European countries has been used to estimate the marginal

effect of public investment, bank credit available to private investors, inflation,

and real interest rate on private investment. The results show that there is no

crowding out effect of public investments on private investments. The marginal

effect of public investments on private investments is positive and it diminishes as

country moves from developing to developed group. Results are as we expected

though striking if compared with some of the studies done on this issue. Further

research should be extended on model specification and refining the theoretical

underpinning of such “faked tale”.

100

CHAPTER V

Conclusions, Policy Implications, and Possible Future Research

V.1 Conclusions

Three are the main objectives of this study. The first general research objective is

to analyze the role of TFP on economic growth in 12 Central and Eastern

European countries over the period 1992 to 2010. In addition, by using Solow

growth model (exogenous model) we calculate the TFP and after decompose the

output growth to capital, labor and TFP growth. We also, in this part analyze

empirically the effects of some macroeconomic factors on growth of TFP. The

second general research objective is to analyze the role of remittances on growth

performance of CEE countries, by using an endogenous growth model. Besides

remittances, we include into the model data on other factors such as foreign direct

investment, capital formation and openness of the economy. A third general

101

objective of this research is to investigate the effect of public on private

investments in emerging economies of Eastern Europe.

The first main objective of this dissertation, decomposition of the output growth

in capital, labor, and productivity, was developed in chapter 2 (essay 1) and the

main conclusions are:

Capital accumulation is the main driver of economic growth experienced by our

sample countries as a whole over the entire period 1992-2010, followed by

positive contribution of productivity and the negative contribution of labor force.

The average contribution of capital is more than double of the average

contribution of productivity. The growth secured by countries as a whole does not

secure that it is going to continue in long-run.

Results based on the sub periods treating countries as a whole show that the

average growth rate is negative (-1.32%) for the first period, 1992-1995, where

the main contributor is capital with about 0.96%, followed by negative

contribution of productivity and labor force. Results on this period are expected

since the economies of CEE countries came out from their regimes with a totally

inefficient economy. During the first years after the collapse of totalitarian

regimes, by the end of 1980s and beginning of 1990s, the high unemployment

rate, high level of emigration toward Western countries, high inflation rate,

102

limitation of financial capital, and other unstable macroeconomic factor, were the

main characteristics of this economies.

During the second period analyzed, 1996-2000, the annual average growth rate is

3.16%, where the main contribution goes to TFP followed by positive

contribution of capital growth and negative contribution of labor force.

During the third period, 2001-2005, the average growth rate is 4.75%, which is

higher than the second period. The main driver in this period has been TFP

growth with about 2.73%, followed by positive contribution of capital and labor.

Economic growth experienced during this period was the highest average growth

rate compared with other periods analyzed.

During the fourth period, 2006-2010, results show that growth of economy is

positive but smaller than the previous period. Capital is the main driver of growth,

followed by the contribution of TFP and labor force. During this period we see a

sharp decrease in TFP growth rate compared with the previous period. From the

results during the last period and the financial crises that is facing euro zone, we

can expect that positive real economic growth for these countries will be a hard

objective to be achieved.

103

Based on the groups’ estimated results, for countries with upper-middle income

level (group I), show that average growth rate is 2.05% over the entire period

1992-2010. The main driver is capital followed by the positive contribution of

TFP and negative contribution of labor forces. Results over sub periods show that

output growth has been negative during the first period of time (1992-1995),

followed by increasing positive growth rate during the 2nd and 3rd and a positive

growth rate during the last period but less compared with previous period. Capital

is the main driver of growth during the first and the last period, while during TFP

is during the 2nd and 3rd.

Estimated results for countries with high income level as a whole (group II) show

that annual average growth rate for the entire period 1992-2010 is 2.81%. The

main contribution is capital accumulation, followed by positive contribution of

TFP and the negative contribution of labor force. Results over sub periods show

that the average growth rate has been positive and comes increasing moving from

the 1st to the 3rd, while in the last period average growth rate is positive but less

than previous one. For countries of group II as whole, capital is the main

contributor in growth. Interesting result in this group is negative contribution of

TFP during the last period.

Interesting results are according the TFP contribution in the last sub period 2006-

2010 in both groups of countries. Regarding to this we see that the contribution of

104

TFP in output growth for both groups is lower compared with previous sub

period, and even negative for the group of countries with high income level. Also,

we see that growth rate during the last sub-period is positive but less than

previous one in both groups.

Based on results of TFP growth model we conclude that initial income per capita,

gross fixed capital, openness of economy, and remittances have positive and

statistically significant effect on growth of TFP, whereas the role of government

expenditures has been positive effect but not statistically significant. Estimated

results show that effect of financial development (M2) on TFP growth is negative

and statistically significant (at p < 1%). Interesting result is according the negative

role of FDI to accelerate growth, which can be a sign for the countries of our

sample to reconsider their financial policies they use for attracting the foreign

investments.

The second main objective, analyzing the role of remittances on economic

growth, was developed in chapter 3 (essay 2) and the main conclusions are as

follow. The results estimated by Model I, show that workers’ remittances received

by countries under investigation have a negative effect on GDP per capita. We

have highlighted that increasing remittances by 10% will decrease the level of

GDP per capita by about 0.9%. This result rejects stated H1: remittances have

positive effect on growth of CEE countries as a whole. Also, estimated coefficient

105

of FDI on growth shows that on average FDI plays negative effect for our sample

countries as a whole. So, results reject H2: FDI plays positive effect on growth.

Estimated results according the role of openness of economy and gross capital

formation are as expected in H3 and H4 - they have positive effect on growth.

Estimated coefficients under the Model II – in which we assumed that

interception coefficients differ between groups, do not change the signs compare

with them estimated from the Model I.

Results estimated by using Model III, in which we have assumed that intercept

and slopes coefficients differ among groups do not change more from their other

models used. In this case we see that the role of remittances in the group of

countries with high income level is positive but not statistically significant at

p<10%. While its effect on growth for group with upper-middle income level is

negative. So, these results are as expected in H5: as countries moves toward high

income level, remittances effect on growth is more positive. Other estimated

coefficients do not change their signs compared with them estimated with other

models (I and II).

Negative effect of remittances on growth defined in our investigation is in line

with the other researches such as: Chami, Fullenkamp, and Jahjah (2005) in their

work “Are immigrant remittance flows a source of capital development”

analyzing data for 113 countries over the period 1970−1998, conclude th at

106

remittances have a negative impact on growth; Barajas et al. (2009) in their work

“Do Workers’ Remittances Promote Economic Growth?, conclude that the growth

effects of remittances are insignificant and at times even negative. One reason the

role of remittances on growth in the second group of countries is negative can be

because these are used mainly for immediate consumption rather than investing.

The negative effect of FDI on growth could be the case when the FDI crowds out

domestic investment in new technology by accelerating the old, disused

technology (Young, 1993). Another reason that FDI causes negative effect on

growth is when preferential policies on foreign capital (tax treatment, etc.) used

by governments to increase the inflow of FDI create distortion between foreign

and domestic capital (Easterly, 1993).

The third main objective, analyzing the role of public investments on private

investments, was developed in chapter 4 (essay 3) and the main conclusions are as

follow. There is no crowding out effect of public investments on private

investments. The marginal effect of public investments on private investments is

positive and it diminishes as country moves from developing to developed stage.

Results are as we expected though striking if compared with some of the studies

done on this issue. Another result received from this model is that domestic credit

provided by banking sector (B) has positive and statistically significant effect on

private investments. Were as, inflation (P) has negative and statistically

significant effect on private investments.

107

V.2 Policy Implication

Based on the conclusions achieved in this dissertation some policy implications

are pointed out. The fact that capital is the main factor that accelerated growth

during the last period of time (2006-2010), will make difficult for our sample

countries to keep growth amount of capital into their economies knowing that

Europe continent is facing one of the worst financial crises ever passed and as a

result the cost of financial capital is increased. So, attention of policy makers must

be focused on two directions: one is to lead investment in the fields that are more

competitive in the region and, the other way is by using taxing system to low the

cost of financial capital. So, based on the results of Albanian economy, policy

maker must focus to divert investments in areas that are more competitive

compared with other countries of the region. So, they must low taxes for

investments in truism, agriculture industry, etc. These policies will increase the

role of TFP on economic growth which is going to be sustained in long-term, (see

Solow 1956, 1957). Also, an important attention must be shown to low the cost of

financial capital, for which one way is intervention of central bank to low the

interest rate of financial capital.

Also, results of the TFP growth model have some policy implication. So, the

positive effect of initial output per capita, gross fixed capital, openness of

108

economy, and remittances on growth make policy makers to continue with

policies that increase investments on fixed capital, increase exports and imports,

and low the cost of transferring remittances from abroad. Also, the negative effect

of FDI on growth is a signal for policy makers to reconsider their fiscal policies

applied to attract foreign investments.

Estimated results from endogenous growth model developed in chapter 3 have

also some policy implications. In this regard, policy makers must continue in their

politics that make the economy more opened toward international market and

increase investment on gross capital formation. Whereas, negative effect of

remittances on growth for countries with upper-middle income level and negative

effect of FDI on both groups of countries, is a indication for policy makers to

reconsider their politics used for attracting foreign capital toward their countries.

The negative effect of FDI on growth could be the case when the FDI crowds out

domestic investment in new technology by accelerating the old, disused

technology (Young, 1993). Another reason that FDI causes negative effect on

growth is when preferential policies on foreign capital (tax treatment, etc.) used

by governments to increase the inflow of FDI create distortion between foreign

and domestic capital (Easterly, 1993).

In line with results achieved from work on the effect of public on private

investments, possible policy implication is according the measures that should be

109

taken to keep in control the inflation rate. Another policy implication is for

keeping stable the real interest rate (r).

V.3 Possible Future Research

Empirical investigation developed in TFP growth model (chapter I), endogenous

growth model (chapter II), and Investment growth model (chapter III), make

investigation for the entire period of time. So, some possible and interesting topics

can be to develop these models for analyzing effects on different sub-period of

time.

Investigating indirect effects of determinants of economic growth experienced

from CEE countries is another interesting topic that can be developed in future

works.

Interesting topic to be developed would be to analyze the effect of FDI on

economic growth experienced by the CEE countries after they came out from

system of centralized economy.

110

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Appendixe

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Table A.1: Variable mean by country, for period 1991 – 2010

Country Y K L Z ALB 3,894,451,691 4,844,777,641 1,047,327 99

BGR 14,696,210,614 44,968,100,107 2,546,819 78

CZE 63,291,554,846 192,138,983,735 4,050,416 72

HRV 22,933,566,651 48,709,379,116 1,449,944 88

HUN 48,214,118,435 116,555,915,066 3,186,371 79

LTU 13,649,042,612 64,369,204,754 1,234,093 94

LVA 9,046,231,824 36,887,090,945 848,175 97

MKD 3,660,173,635 10,891,621,995 491,505 91

POL 173,407,712,643 326,657,814,849 12,832,146 85

ROM 43,834,107,546 156,039,287,362 8,483,171 69

SVK 31,856,201,503 108,484,204,713 1,923,910 69

SVN 20,528,231,388 46,650,908,002 751,812 111

Table A.2: The mean of variables used in the model (period 1996-2010) Countr TFP IYPC GCE GFC OP M2 REM FDI

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y ALB 96 1,334 9.40 24.12 65.27 67.09 14.63 14.98

BGR 82 1,892 17.28 20.95 115.59 52.66 3.68 45.15

CZE 73 6,371 20.73 27.34 119.43 62.83 0.54 43.12

HRV 90 5,372 21.42 22.68 86.10 52.81 2.20 31.04

HUN 80 4,935 22.11 22.26 138.54 51.48 1.05 55.72

LTU 103 4,056 20.72 21.72 112.99 32.41 1.58 25.74

LVA 107 4,076 19.98 24.93 99.85 35.20 1.55 30.79

MKD 93 1,821 19.56 17.63 101.79 32.39 3.11 29.02

POL 88 4,793 17.99 20.86 68.25 43.74 1.35 26.51

ROM 73 2,049 10.72 23.46 69.55 33.04 1.81 24.23

SVN 111 10,811 18.78 25.00 116.31 53.63 0.87 19.87

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Figure A.1: Graphical presentation of annual growth of output, capital, labor, and productivity for Albania

124

Figure A.2: Graphical presentation of annual growth of output, capital, labor, and productivity for Bulgaria

125

Figure A.3: Graphical presentation of annual growth of output, capital, labor, and productivity for Czech Republic

126

Figure A.4: Graphical presentation of annual growth of output, capital, labor, and productivity for Croatia

127

Figure A.5: Graphical presentation of annual growth of output, capital, labor, and productivity for Hungary

128

Figure A.6: Graphical presentation of annual growth of output, capital, labor, and productivity for Lithuania

129

Figure A.7: Graphical presentation of annual growth of output, capital, labor, and productivity for Latvia

130

Figure A.8: Graphical presentation of annual growth of output, capital, labor, and productivity for Macedonia

131

Figure A.9: Graphical presentation of annual growth of output, capital, labor, and productivity for Poland

132

Figure A.10: Graphical presentation of annual growth of output, capital, labor, and productivity for Romania

133

Figure A.11: Graphical presentation of annual growth of output, capital, labor, and productivity for Slovakia

134

Figure A.12: Graphical presentation of annual growth of output, capital, labor, and productivity for Slovenia