Finance and Economic Development

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THREE ESSAYS ON FINANCIAL DEVELOPMENT AND

ECONOMIC GROWTH

DISSERTATION

Presented in Partial Fulfillment of the Requirements for

the Degree Doctor of Philosophy in the

Graduate School of The Ohio State University

By

Pilhyun Kim, M.A.

* * * * *

The Ohio State University

2006

Dissertation Committee:

Dr. Paul Evans, Adviser

Dr. Masao Ogaki

Dr. Pok-sang Lam

Approved by

Adviser

Graduate Program inEconomics


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ABSTRACT

The primary part of my dissertation investigates the potential effects offinancial

sector development on economic growth. In order to reveal the nature of these effects,

I focus on the potential channels of influence from the financial to the real sector.

I investigate the link between thefi

nancial sector and economic growth focusing on

the role of the financial sector in funding innovative activities. To this aim, I construct

a model where the economy is driven by innovative activities that require both human

capital and external funding. My analysis shows that when certain conditions are

satisfied, there exists a unique equilibrium where the growth rate of the economy

is jointly determined by the levels of human capital and financial development. An

implication of this is that financial liberalization policies that do not adequately

address the fundamentals of the economy can cause bank failures and possibly a

financial crisis. Furthermore, the model suggests that, depending on the parameter

values of the economy, there may be two forms of poverty traps, one with a small

number of bankers and the other with a large number of bankers.

Also, I examine empirically whether financial development has any effect on the

rate of technological innovation using patent applications as a proxy for innovative

output. For a sample of twenty eight countries from 1970 to 2000, my analysis shows

that financial development is indeed significant in raising the growth rate of innovative

output.

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In addition, I investigate whether financial development enhances investment ef-

ficiency. The efficiency channel hypothesis states that financial development may

increase the efficiency of investment by directing the funds to the most productive

uses. I examine if there is any evidence of financial development positively affect-

ing the efficiency of aggregate investment using developing countries as a sample.

Compared to the volume channel, the efficiency channel has received relatively little

attention until recently. I address the issue of the efficiency channel using two alterna-

tive measures of aggregate investment efficiency. I find that, for developing countries,

financial development significantly and positively affects productivity of investment.

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To my parents and my family

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ACKNOWLEDGMENTS

I am indebted to Dr. Paul Evans, my advisor, for his valuable and expert guidance,

insightful comments and encouragement during the course of this study. Without his

patient guidance my dissertation would have been impossible.

Special gratitude is extended to Dr. Masao Ogaki and Dr. Pok-sang Lam for their

comments and valuable help to improve my study.

Financial support from the PEGS Research Grant is greatly acknowledged as well

as the Graduate Teaching Assistantship the Department of Economics have offered

me throughout my residence at the Ohio State University.

In addition, my special thanks go to my wife Jihee and my family who have been

always supporting and praying for me.

Most of all, I would like to express my deepest appreciation to my father and

mother for their belief in my abilities and undying support throughout my life.

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VITA

January 9, 1969 . . . . . . . . . . . . . . . . . . . . . . . . . . . . Born - Gwanju, Korea

1991 ........................................B.A. Economics, University of Wiscon-sin, Madison

1998 . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . .. . . . . . .M.A. Economics, The Ohio State Uni-

versity2001 . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . .. . . . . . .Ph.D Candidate, The Ohio State Uni-

versity

1998 - 2004 .. . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . .Graduate Teaching Associate, TheOhio State University

2004 - 2005 . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . .Lecturer, The Western WashingtonUniversity

FIELDS OF STUDY

Major Field: Economics

Studies in:

Money MacroeconomicsApplied EconometricsEconomic Growth

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TABLE OF CONTENTS

Page

Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii

Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv

Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

Vita . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi

List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x

List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi

Chapters:

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

2. Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.1 Models of the finance-led growth theory . . . . . . . . . . . . . . . 6

2.2 Empirics of the finance-led growth theory . . . . . . . . . . . . . . 9

3. A Finance-led Growth Hypothesis: Revisited . . . . . . . . . . . . . . . . 12

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.2 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163.2.1 Environment . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.2.2 A formal model . . . . . . . . . . . . . . . . . . . . . . . . . 18

3.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

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4. How Does Financial Development Promote Growth? . . . . . . . . . . . 35

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

4.2 Related Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

4.2.1 Theories of the finance-led growth hypothesis . . . . . . . . 374.2.2 Empirical studies . . . . . . . . . . . . . . . . . . . . . . . . 39

4.3 Theoretical background . . . . . . . . . . . . . . . . . . . . . . . . 41

4.3.1 Final goods sector . . . . . . . . . . . . . . . . . . . . . . . 42

4.3.2 Intermediate goods sector . . . . . . . . . . . . . . . . . . . 42

4.3.3 The research sector . . . . . . . . . . . . . . . . . . . . . . . 44

4.3.4 The growth of the economy . . . . . . . . . . . . . . . . . . 45

4.4 Empirical analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

4.4.1 Patents as a proxy for technological innovation . . . . . . . 47

4.4.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

4.4.3 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . 544.4.4 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

4.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

5. Investment Efficiency and Financial Development . . . . . . . . . . . . . 71

5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

5.2 Investment, output growth, and financial development . . . . . . . 74

5.2.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

5.2.2 Estimation strategy . . . . . . . . . . . . . . . . . . . . . . 79

5.2.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

5.3 Investment efficiency . . . . . . . . . . . . . . . . . . . . . . . . . . 845.3.1 Measures of investment efficiency . . . . . . . . . . . . . . . 84

5.3.2 Investment efficiency estimates . . . . . . . . . . . . . . . . 87

5.3.3 Determinants of investment efficiency . . . . . . . . . . . . . 90

5.3.4 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

5.3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

5.3.6 The effects of political stability and legal environment . . . 94

5.3.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

5.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

6. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

Appendices:

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A. Data Sources for Chapter 5 . . . . . . . . . . . . . . . . . . . . . . . . . 113

B. Countries Used in Chapter 5 . . . . . . . . . . . . . . . . . . . . . . . . . 115

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

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LIST OF TABLES

Table Page

4.1 Sample Countries Used for Patent Regression . . . . . . . . . . . . . 67

4.2 Fixed Effects Estimation I . . . . . . . . . . . . . . . . . . . . . . . . 68

4.3 Fixed Effects Estimation II . . . . . . . . . . . . . . . . . . . . . . . . 69

4.4 Fixed Effects Estimation with Selected Independent Variables . . . . 70

5.1 Investment Regression I . . . . . . . . . . . . . . . . . . . . . . . . . 100

5.2 Investment Regression II . . . . . . . . . . . . . . . . . . . . . . . . . 101

5.3 Investment Regression III . . . . . . . . . . . . . . . . . . . . . . . . 102

5.4 Investment Efficiency Estimates using GDP . . . . . . . . . . . . . . 103

5.5 Investment Efficiency Estimates Using IVA . . . . . . . . . . . . . . . 105

5.6 Investment Efficiency Regression I . . . . . . . . . . . . . . . . . . . . 107

5.7 Investment Efficiency Regression II . . . . . . . . . . . . . . . . . . . 108

5.8 Investment Efficiency Regression III . . . . . . . . . . . . . . . . . . . 109

5.9 Investment Efficiency Regression IV . . . . . . . . . . . . . . . . . . . 110

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LIST OF FIGURES

Figure Page

3.1 Screening Costs of Competing Bankers . . . . . . . . . . . . . . . . . 32

3.2 Equilibrium When + < 1 . . . . . . . . . . . . . . . . . . . . . . . 33

3.3 Effects of Deterioration in Education . . . . . . . . . . . . . . . . . . 34

4.1 Long Run GDP Growth vs. Long Run Patent Growth . . . . . . . . . 65

4.2 Movements of Financial Development Indicators for Selected Countries 66

5.1 Output Growth and Investment in Cambodia . . . . . . . . . . . . . 98

5.2 The Share of Industry Value Added in GDP in Developing Countries 99

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CHAPTER 1

Introduction

The primary part of my dissertation investigates the potential effects offinancial

sector development on economic growth. In order to reveal the nature of these effects,

I focus on the potential channels of influence from the financial to the real sector.

The nature of the interaction between the real and the financial sectors has been

hotly debated among researchers. Those who favor the finance-led growth hypothesis

argue that the existence of a vibrant financial sector has growth-enhancing effects. In

this literature, an economy can grow faster due to an efficient allocation of resources

by the financial sector, mainly banks. A number of channels of influence have been

proposed in the literature, which include increased savings, increased investment, in-

creased efficiency thereof, increased human capital accumulation, and positive effects

of the financial sector on innovation processes. Investigations of the validity of these

channels as true agents of long-run growth, so far, have yielded mixed empirical re-

sults. In chapter 2, I review the vast literatue on this subject to examine how the

literature has evolved over time.

In chapter 3, I investigate the link between the financial sector and economic

growth focusing on the role of the financial sector in funding innovative activities. My

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motivation is based on the research by Easterly and Levine (2001) in that it is the

residual that accounts for most of the income and growth differences across nations.

Broadly speaking, innovative activities require both human and financial capital.

They act as complements in the production function of ideas. However, the interaction

between the two has been largely ignored in the literature. In order to improve our

understanding of how the financial sector interacts with the real sector, the nature

of interactions between two major components of innovative activities needs to be

examined more closely. In this chapter, I pursue this goal by constructing a model

where the economy is driven by innovative activities that require both human capital

and external funding from the financial sector. Similar to King and Levine (1993), it is

assumed that the role of the financial sector in this model is to screen the innovators for

their probabilities of success. In formalizing the model, I depart from the conventional

literature in four important ways. Firstly, I define innovation as a success not when

it is realized but when it is commercially successful. This distinction is motivated by

observations that not all innovations are implemented in the production processes.

Secondly, I assume that the magnitude of technological change an innovator comes

up with is a function of that innovators human capital. Thirdly, it is assumed in

this model that external finance is needed not for R&D activities but for utilization

of innovations. Finally, it is assumed that the financial sector pays no setup costs.

My analysis shows that when certain conditions are satisfied, there exists a unique

equilibrium where the growth rate of the economy is jointly determined by the levels

of human capital and financial development. An interesting implication of this is that

financial liberalization policies that do not adequately address the fundamentals of the

economy can bring about bank failures and possibly a financial crisis. Furthermore,

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in addition to showing that poverty traps can be explained without introducing setup

costs, the model suggests that, depending on the parameter values of the economy,

there may be two forms of poverty traps, one with a small number of bankers and

the other with a large number of bankers.

In chapter 4, I examine empirically whether financial development has any effect

on the rate of technological innovation. A flood of empirical studies began to appear

in the 1990s to test the validity of the finance-led growth hypothesis. A typical

test strategy involves regressing some indicator of financial development on some

aggregate growth measures such as investment growth, GDP growth or total factorproductivity growth. By and large, the current empirical literature lacks one crucial

element in that it does not consider the channels of influence suggested by theoretical

models and fails to show how financial development affects economic growth. In

order to examine the validity of the finance-led growth hypothesis, I depart from the

conventional literature. Instead of estimating a relationship between aggregate growth

measures and financial development indicators, I test the validity of the finance-led

growth hypothesis by focusing on the innovation channel of influence, using patent

applications as a proxy for innovative output. Under the framework of ideas-driven

growth, the hypothesis I test is that financial development enhances innovation, which

is the main engine of economic growth. If the finance-led growth hypothesis is right,

as the financial sector develops over time in a certain country, the growth rate of

innovation should be higher, which would, then, lead to faster economic growth due

to a rising level of productivity. Using panel data on twenty eight countries from 1970

to 2000, my analysis shows that financial development is indeed significant in raising

the growth rate of innovative output.

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According to the finance-led growth hypothesis, financial development affects in-

vestment in two ways. Firstly, a better developed financial sector may raise the

investment rate by pooling and risk sharing. This is the so-called volume channel.

Secondly, the efficiency channel hypothesis states that financial development may in-

crease the efficiency of investment by directing the funds to the most productive uses.

In chapter 5, I examine if there is any evidence of financial development positively af-

fecting the efficiency of aggregate investment using developing countries as a sample.

Compared to the volume channel, the efficiency channel has received relatively little

attention until recently. In this chapter, I address the issue of the efficiency channel

using two alternative measures of aggregate investment efficiency. I find that, for

developing countries, financial development significantly and positively affects pro-

ductivity of investment. Further, I depart from the existing studies by focusing on

the banking sector to measure the degree of financial development. In chapter 6, I

conclude.

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CHAPTER 2

Literature Review

The literature on the finance-led growth hypothesis is vast. Accordingly, a few

survey papers have been written on this subject. See, for example, Levine (1997) and

Tsuru (2000). The focus of this review is, therefore, not to present an exhaustive

review of the literature as it would be rather redundant, but to assess critically how

the literature has evolved over time and to identify the remaining issues that need to

be resolved.

Theoretical models of the finance-led growth hypothesis are, in general, modified

versions of endogenous growth theories with risky investment opportunities. Due

to uncertainty about the outcome of investment, the allocation of resources is sub-

optimal. The financial sector enters this world to reduce welfare loss resulting from

this uncertainty by providing information, risk-pooling, and liquidity. As the financial

sector develops, possibly as a result of feedback from economic growth, the provision

of these services becomes more efficient so that a faster rate of economic growth is

realized.

The overriding research theme that came out of the theoretical models and that has

occupied the attention of the empirical researchers for the past decade is a question of

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whether growth rate of an economy is positively correlated with the level offinancial

development. A typical method employed to test this theory has been to regress

some aggregate growth variables on financial development indicators that are based

on some ratio of monetary aggregates to GDP. Simple as it may be in its approach,

this line of research has produced an impressive amount of evidence for the finance-led

growth theory. In what follows, I examine more closely how the literature has evolved

over time both theoretically and empirically.

2.1 Models of the finance-led growth theory

An initial theoretical interest lay in the effects of financial development on the

efficiency of capital accumulation. Greenwood and Jovanovich (1990) is among the

frontiers of this approach. They assume an economy where growth is driven by capi-

tal accumulation, and the risky nature of investment prevents agents from allocating

resources in an efficient manner, resulting in welfare loss in the absence of the finan-

cial sector. However, as the economy grows, it becomes able to pay the setup costs

to establish the financial sector of which the role is to collect and process information

and to pool risks across investors/savers. Once the financial sector is established, it

allows a higher rate of return to be earned on capital, promoting economic growth.

Bencivenga and Smith (1991) pursue a similar vein and present a model where ran-

dom liqudity shocks raise the fraction of savings invested in liquid but unproductive

assets. The financial sector enters this world exogenously, contrary to Greenwood

and Jovanovich, to provide liquidity to economic agents and makes it possible for

them to invest a larger portion of their savings in productive and illiquid assets. Al-

though the specific types of risk assumed in these models are different, the nature of

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the fundamental role the financial sector plays is essentially the same. By processing

information and reducing risks, the financial sector reduces uncertainty associated

with investment and directs funds to their most productive uses, and consequently,

enhances the rate of economic growth.

As doubts about the effectiveness of capital accumulation in promoting long-term

growth arose, researchers shifted their focus to potential positive effects financial

development may have on productivity growth. The main sources of productivity

growth considered in this literature are purposeful innovative activities and market

specialization that are risky. Uncertain nature of the outcomes of innovative activi-ties (Fuente and Marin, 1996) and market specialization characterized by increasing

number of firms (Galetovic, 1996; Greenwood and Smith, 1997) make monitoring

necessary. Since monitoring is assumed to be costly, there is an incentive for the

financial sector to endogenously emerge to economize on the monitoring costs as in

Diamond (1983). The provision of monitoring services by the financial sector then

leads to increased levels of innovative activities and market specialization, which re-

sult in enhanced economic growth. The main weakness of this type of model is that

there seems to be no empirical evidence of the financial sector conducting active

monitoring (Allen and Gale, 2001). Furthermore, the proposition that the financial

sector actively monitors the outcome of investment may have a weaker ground in

those countries where the banking sector provides a large share of external finance

with debt contracts.. Since debt contracts require the investor/borrower to repay a

fixed amount after a certain period independent of the outcome of investment, the

banking sector does not have an incentive to actively monitor the activities of the

investor/borrower except ascertaining the state of the outcome of such activities at

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development rises, possibly suggesting diminishing marginal returns. This evidence

directly contradicts the existing theories where economic growth is a (monotonically)

increasing function of the level of financial development. Finally, the models where

the financial sector actively monitors do not seem to accurately reflect the experiences

of many countries.

2.2 Empirics of the finance-led growth theory

First systematic investigation of the relationship between finance and growth was

conducted by King and Levine (1993). In their study, it was found that the level of

financial development was positively correlated with growth variables such as GDP

per capita growth, investment rate, and total factor productivity growth. Notwith-

standing the rigorous statistical analysis they conducted to reveal the relationship,

their study was more significant in that it brought the issue of simultaneity up to the

center stage. It was pointed out that the use of lagged values offinancial development

indicators, as King and Levine did in their study, does not resolve the simultaneity

bias if the agents behaved in forward-looking manner, which would make their results

hard to interpret. By contrast, De Gregorio and Guidotti (1995) argued that the

use of lagged levels of financial development indicators is justified in cross-sectional

studies by noting that "the theories suggest that economic growth induces growth in

the financial system but this has no implications regarding the size of the financial

system with respect to GDP." Further, their Barro-type cross-sectional estimation

found that the positive effects of financial development on growth vary over time

periods, regions, and income levels, which suggested that the relationship might be

nonlinear.

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As the issues of simultaneity and nonlinearity persisted, it was suggested that a

time-series study focusing on a small group of countries would prove to be beneficial.

The results from the early attempts in this direction cast doubt on the validity of

the finance-led growth hypothesis. For example, Demetriades and Hussein (1996),

who were among the first researchers to employ time-series approach, found, using

cointegration tests, that the relationship between finance and growth is bidirectional

and that this relationship is country-specific. Similar results were obtained by Arestis

and Demetriades (1997), Luintel and Khan (1999), and Shan et al (2001) using VAR

estimation. However, more recent studies have reestablished finance as an important

source of economic growth. To cite a few, Xu (2000) found evidence for the finance-led

growth hypothesis using multivariate VAR, directly contradicting the findings of the

previous time-series studies. Calderon and Lie (2003) agree with Xu, after conducting

Geweke decomposition test on pooled data of 109 countries, and conclude that finance

generally leads growth albeit some evidence of bidirectional Granger-causality. More

recently, Christopoulos and Tsionas (2004) applied panel unit root and cointegration

tests, threshold cointegration test, and panel VECM to find support for unidirectional

causality from finance to growth.

Another strand of research that has been pursued is the use of panel studies.

Compared to the approaches mentioned above, panel study was generally regarded as

more advantageous (Temple, 1999). Using dynamic GMM to control for simultaneity

and unobserved country-specific effects, Beck et al (2000) found that financial devel-

opment promotes growth by improving productivity. Rioja and Valev (2004) adapt

a similar method to investigate whether financial development affects growth differ-

ently according to the income levels. While they concede that developed countries

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growth is enhanced by finance-stimulated productivity growth as in Beck et al, they

argue that the effects offinancial development on growth of developing countries are

via capital accumulation.

Overall, the current trend in this area can be summarized as the following. First,

the consensus on the bidirectional causality seems to be gaining an increasing support.

This may be a natural course of work since the financial sector is, after all, a part of

an economic system. Second, there is an increasing emphasis on the need to employ a

time-series approach when considering the relationship between financial development

and growth. Third, along with the second trend, researchers are increasingly puttingan effort to incorporate infrastructural environments into the analysis. Fourth, the

importance of incorporating microeconomic mechanism in modelling the behavior of

the financial markets is gaining acceptance among researchers in this area. Given

that there has been an increased emphasis on the microeconomic environment in

macroeconomic topics, this seems rather late. At least, the research in this area

seems to be going in the right direction. Finally, since 1996, there has been more

attention on the channels of influence from financial development to economic growth

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CHAPTER 3

A Finance-led Growth Hypothesis: Revisited

3.1 Introduction

Ever since Schumpeter highlighted a potentially growth-enhancing role of banks as

efficient allocators of funds in 1911, the relationship between financial sector and real

sector has been a subject of heated debates. In his argument, banks help an economy

achieve first-best outcome by providing efficient markets for funds. In contrast to

this argument, there are also a group of economists who view the financial sector as

something that merely mirrors the real sector. Most notably, Robinson (1952) argued

that "...finance does not lead growth. Growth leads finance..." The main argument

of those who oppose finance-led growth theories is that, simply put, financial markets

evolve in response to increased demands for better services from a growing economy.

Therefore, the development offinancial markets only mirrors that of real sectors. This

led Lucas to state that economists badly over-stress the role offinancial sectors. These

two polar positions on the role of financial sectors have led economists to consider

the issue of causality extensively.

The approach I take to investigate the link between the financial sector and eco-

nomic growth focuses on the role of the financial sector in financing innovative activ-

ities. Our motivation is based on the research by Easterly and Levine (2001) in that

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it is the residual that accounts for most of the income and growth differences across

nations. Further, the idea that innovative activities need financial assistance is quite

intuitive. We can easily imagine what might have happened to IT companies if there

had been no venture capital. It clearly illustrates the possibility of positive effects

the financial sector may have on the real sector activities.

Broadly speaking, innovative activities require both human and financial capital.

They act as complements in the production function of ideas. However, the interaction

between the two has been largely ignored in the literature. An exception is the study

by Outreville (1999) where he shows empirically that there is a positive relationshipbetween human capital and financial development, although no formal theory was

offered to explain the finding. In order to improve our understanding of how the

financial sector interacts with the real sector, the nature of interactions between two

major components of innovative activities needs to be examined more closely. In this

chapter, I pursue this goal by constructing a model where the economy is driven by

innovative activities that require both human capital and external funding from the

financial sector. Similar to King and Levine (1993), it is assumed that the role of

the financial sector in this model is to screen the innovators for their probabilities

of success. However, unlike King and Levines version of the financial sector that

plays an active screening role of weeding out bad entrepreneurs, the financial sector I

consider is a more passive one. Specifically, it is assumed that the role of the financial

sector is to assess and convey the probability of success to the innovators so that the

latter can make optimal borrowing decisions. It is passive in a sense that it does not

refuse to lend to those innovators with low probability of success. Rather, it penalizes

them by imposing higher repayment requirements. Therefore, contrary to King and

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Levine, the financial sector does not improve the aggregate probability of success by

screening.

In formalizing the model, I depart from the conventional literature in four impor-

tant ways. Firstly, I define innovation as a success not when it is realized but when

it is commercially successful. This distinction is motivated by observations that not

all innovations are implemented in the production processes. For example, we know

from history that the steam engine was invented in Ancient Greece two-thousand

years ago. However, it obviously did not ignite the industrial revolution at that time

as it did in England millenniums later. A similar example can be found in the UnitedStates at the turn of the 20th century. Around the time when mass production of

automobiles was about to be started, an automobile that could run on electricity was

invented. It drove quietly, emitted less pollution and was technologically superior

to gasoline-driven automobiles. However, we all know how the electric-automobile

industry fared in the end. Although it was technologically superior to other competi-

tors, it did not survive the forces of the market because it lacked commercial appeals.

Gasoline-driven cars were simply much faster and more powerful than electric ones,

and hence were more attractive to automobile buyers. In a sense that the aim of

innovation is to improve the welfare, in terms of higher incomes or more convenience,

of economic agents, a steam engine in Ancient Greece or electric cars in the early 20th

century can hardly be regarded as a success. This is especially true if one regards the

purpose of innovation to be improvements of production technology. To put it rather

bluntly, a technology that is not used commercially is the same as a technology that

is not invented as far as its effects on output production is concerned. Accordingly,

in the model considered here, it is assumed that innovators invent new technologies

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with certainty. What is uncertain is whether the new technology would be successful

commercially. Then, the job of assessing the probability of commercial success is

left to the financial sector. Secondly, I assume that the magnitude of technological

change an innovator comes up with is a function of that innovators human capital.

The idea is simply that the smarter the innovator is, the bigger the improvements

over the existing technology will be. It should be noted in advance that although

the magnitude of technological change is endogenized, the resulting implication in

terms of economic growth remains similar to the existing literature. Thirdly, it is

assumed in this model that external finance is needed not for R&D activities but for

utilization of innovations. Venture capital markets justify this assumption. Finally,

it is assumed that the financial sector pays no setup costs. The assumption offixed

setup costs has been used in the literature to explain poverty traps and bidirectional

causality between finance and growth. However, historical experiences of developed

countries do not support this assumption (Galetovic, 1996). My analysis shows that

one does not need the assumption of setup costs to establish bi-causality or to explain

poverty traps.





economy can bring about bank failures and possibly a financial crisis. Furthermore,

in addition to showing that poverty traps can be explained without introducing setup


15


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The rest of the chapter is organized as follows. In section 2, a formal model

preceded by a sketch is presented. In section 3, the models implications are discussed.

In section 4, I conclude.

3.2 Model

3.2.1 Environment

Imagine a small island populated by groups of workers and bankers who are risk-

neutral. While bankers move freely to and from other islands, workers are assumed

not to be mobile. People on this island live infinitely, but their planning horizon

is on a daily basis. In particular, there are L number of identical workers with a

zero population growth rate. The workers daily routine consists of three events in

the morning, afternoon, and evening. At every morning, he is endowed with one

unit of manna for the day. Notice that the manna depreciates completely in one

day so that there is no saving for tomorrow. The worker can use the manna for two

things. He can use some fraction of it to accumulate human capital which will enable

him to become an innovator in the afternoon. With what remains, he produces the

consumption good using the old technology. After obtaining education, the worker

innovates over the existing technology. At this time, the worker/innovator does not

know the likelihood of commercial success of his invention. Once he comes up with

an idea as to how to improve the old technology, he goes to the banker to finance

his production of consumption goods. Notice that the magnitude of improvement

depends on the fraction of the manna he spent to receive education. That is to say,

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the smarter the worker/innovator is, the bigger the improvement will be. When he

meets with the banker, the probability of commercial success is revealed through

the screening of the banker. Then the worker/innovator borrows from the banker to

finance his production activity. In return he promises to pay a certain fraction of

the consumption goods he expects to produce in the afternoon as a repayment to the

banker. In the afternoon, the state of his innovations commercial success is revealed.

If it is a success, the worker uses the new technology to produce consumption goods

of which some fraction is paid back to the banker as a repayment. If it is a failure,

the worker returns what was borrowed, instead of the agreed amount of repayment,

to the banker. In the evening, the innovator/worker consumes what is left, and goes

to sleep to start a new day tomorrow.

There is an arbitrarily large number of identical bankers on this island. Since

they can move freely across different islands unlike the workers, exactly how many

bankers are on the island at any particular time is not relevant. What is relevant in

this economy is how many bankers decide to finance the innovators. In the morning,

the banker is endowed with the capital good. He is also endowed with a technology

that can transform his endowment to the consumption good at the rate of r, and

this process is assumed to take a day. With the endowment, the banker must figure

out whether he should open a bank or use the endowed technology to transform it

to consumption goods for his own use in the evening. Once he decides to open a

bank, he does so without paying setup costs. Then, he waits for the innovators to

come and ask for loans. When the innovator comes in for a loan to implement his

innovation, the banker screens him to gauge the likelihood of the commercial success

of the innovators idea. Based upon the probability of commercial success, which is

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different across the innovators, the banker decides on the amount of repayment, in

terms of the consumption goods that he must receive for lending a given amount of

capital to the innovator. In the afternoon, he receives the proceeds from investing in

the worker/innovator. In the evening, he consumes what he received in the afternoon.

In the next morning, the whole cycle starts anew. With this description in mind, we

move to formalize the model in the next section.

3.2.2 A formal model

Workers

Initially, the island is endowed with a certain level of technology denoted by A0

at time 0. In each day t, there exists L number of workers who live infinitely. It

is assumed that there is no population growth and that L is some arbitrarily large

number less than infinity. As mentioned above, the worker is daily routine consists

of three events. In the morning, he is endowed with one unit of manna. He can use

the manna for two activities: innovation and/or production with an old technology.

His problem is to figure out how to allocate the manna between these two activities.

If he decides to innovate, he spends a fraction vi of the manna to receive education

that will enable him to become an innovator in the afternoon. The interpretation of

vi is that it measures the amount of sacrifice the worker needs to make to make an

innovation as it could have been used to produce the consumption goods with the

old technology. Notice that the education process is assumed to be instantaneous for

simplicity.

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The worker i improves upon the old technology that is available for everyone based

upon the following:

Ait = itAt1. (3.1)

where it = (vit). The parameter stands for the effectiveness of education process

and is greater than zero. is an idea production parameter between 0 and 1. This

specification of implies that the magnitude of the technological change is an increas-

ing function of human capital acquired through education. In essence, it is similar to

Aghion and Howitts model (1999) where the economy is driven by innovation and

creative destruction. In their model, the magnitude of technological change is a func-

tion of some exogenous parameter (their ) and the number of people working in the

research sector. The difference is that I model to be solely a function of human cap-

ital and leave out the exogenous component. In terms of model implications, nothing

is lost by the explicit modelling of in this fashion. An attractive feature, on the

other hand, of specifying this way is that it allows for the possibility of diminishing

marginal returns. R&D literature shows that as research efforts increase, captured

implicitly by v here, the number of innovations that results grows at a decreasing

rate. Aghion and Howitts specification does not coincide with this empirical finding.

Admittedly, the specification of in my model is based on observations only and

rather ad-hoc. However, I argue that specifying explicitly rather than taking it

as exogenous is a step in the right direction toward understanding the impacts that

innovations have on the economy.

Recall that the fact the worker comes up with a new technology does not mean

that the innovation is successful commercially. As discussed in the previous section,

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whether the innovation is a success is judged by its commercial usefulness. After com-

ing up with a new technology, the worker/innovator is randomly and uniformly put

into a location around the circumference of the island. The circumference of the island

is assumed to be equal to 1. In this model, I posit that the probability of commercial

success, p, is a function of the distance between the worker/innovator and the banker

who finances him, and that this distance is not known to the worker/innovator until

he is screened by the banker. This idea is motivated by observations from the venture

capital market. In the case of venture capital, it is accepted that the likelihood of

success for a new idea, business plan, and/or product is critically dependent upon

how close relationship the innovator has with those who provide funds. In this model,

the nature of the relationship between the borrower and the lender is captured by

the distance between the two. An exact specification of the probability of commercial

success will be given in the next section.

Afterwards, the worker/innovator goes to the banker to borrow capital to finance

his production activity using the new technology, after which the probability of com-

mercial success is revealed through the screening of the banker. Given the now-

revealed probability of the commercial success for his innovation, the worker/innovator

decides the optimal amount of capital, xit, to borrow and promises to repay Iit to the

banker if the innovation is commercially successful. Note that the capital is borrowed

after the innovation occurred as described in the previous section.

In the afternoon, the probability of the innovations commercial success is realized

to everyone. If the innovation is commercially successful, the worker uses the new

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technology, Ait, and the capital, xit, borrowed from the banker to produce consump-

tion goods according to a production function:

yHit = Aitxit = (vit)

At1xit (3.2)

where is between 0 and 1. H stands for the production sector that employs a

new technology. Note that after one day, the new technology becomes available

to everyone. If the innovation is unsuccessful, then the worker/innovator returns the

borrowed capital/consumption good, x, instead ofI, to the banker. At the same time,

he spends 1 vit to produce consumption goods with the old technology according

to the production function:

yLit = At1(1 vit) (3.3)

where L designates the production sector with the old technology. In the evening,

the day is complete with the worker consuming what is left.

Bankers

In this model, the bankers are assumed to be symmetrical and to maximize prof-

its. Similar to the workers, each bankers daily routine consists of three events. In

the morning, the representative banker is endowed with arbitrarily large units of cap-

ital/consumption goods less than infinity.1 It should be pointed out that the exact

amounts of endowments do not need to be specified for the same reason that the

number of the bankers present on the island need not be specified. It is implicitly

assumed in this model that the bankers can potentially finance as many workers as

possible as long as it is profitable. Considering that in the real world, the amount

1 This endowment will be designated as a capital good from now on for the sake of convenience.

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of funds that can be potentially available to the innovator is significantly large espe-

cially when there is a relatively free flow of funds across countries, I believe that this

assumption is reasonable. In addition, making this assumption allows us to abstain

from having to deal with the effects of inter-island transfer of capital/consumption

goods.

In the morning, he has to decide what to do with the endowment. He can use it

to invest in the worker/innovator or have it transformed at a rate of r. To use an

analogy, his decision can be described as having to choose between risk-free bonds

that pay an interest of r and risky bonds with a higher rate of return than r. Notice

that opening up a bank does not cost the banker anything. Once he opens the bank,

he meets and screens the worker/innovator to assess the probability of commercial

success of the innovation which is defined as:

p(zit) = ezit, 0 < zit < dt (3.4)

where zit is the distance between the banker and the worker/innovator i at time t.dt

is the maximum distance the worker/innovator can be located from the banker.

The screening is assumed to be costly. Intuitively, the further away the worker/innovator

is from the banker, the more costly it would be for the banker to assess the probability

of commercial success. Hence, the screening costs would be a function of z. Another

way to think about it is to imagine that, in practice, it would cost more for the banker

to find out the likelihood of success if the borrower is of a somewhat dubious nature,

which z represents. To capture this, I define the screening cost to be:

SC(zit) = ezit , 0 < zit < dt (3.5)

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where is a exogenous policy variable that measures the stringency of the government

policies regarding the screening processes. A higher level of implies that more

strict policies are in place, and hence the banker incurs higher costs for screening.

Figure (3.1) illustrates the relationship between two neighboring banks in terms of

the screening costs. It can be seen that for those innovators whose distances z are

between dt and dt, the banker A has a monopoly power over them since it has a cost

advantage over all other banks and in particular over its adjacent bankers B and B.

It can be seen that since the circumference of the island is equal to 1, and the bankers

will open their banks along the circumference, 12dt

= Nt, where Nt is the number of

bankers open for business at time t.

Once the screening is done, the banker informs the worker/innovator of the prob-

ability of commercial success so that the latter can make a optimal decision on how

much to borrow. Then the banker meets the worker/innovators demand by supplying

him with xit. In return, it is promised to the banker that he would receive I from the

worker/innovator if the innovation is commercially successful, and xit if it is not.

In the afternoon, the probability of success is realized and the banker receives

either I or xit depending on the state of the worker/innovator. In the evening, he

consumes what he has received from the worker/innovator along with the rest of the

endowments.

Equilibrium

In order to solve the model, we start with the worker. Given the setup above, the

problem the worker i faces at time t is to choose vit to maximize his consumption in

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the evening. Formally, in the morning, he maximizes

pit(Aitxit Iit) (1pit)xit + At1(1 vit)

by choosing v. Since zit is not yet known at the time of decision, the problem above

becomes in effect

E{pit(Aitxit Iit) (1pit)xit} + At1(1 vit). (3.6)

Before solving the workers problem, it will be convenient to specify I at this

point. When lending xit, the banker charges Iit to hedge against the potential loss in

case the innovation is a failure so that

(1 + r)xit = pitIit + (1pit)xit, (3.7)

where r is the world interest rate and exogenous.

Solving Eq. (3.7) gives Iit as:

Iit =

1 +

r

pit

xit. (3.8)

One can see from Eq. (3.8) that the banker needs to be compensated for the

risk he takes by financing the worker/innovator. As long as pit is less than one, the

return he gets from investing in the worker/innovator is greater than what he would

get from the endowed technology. If pit is equal to 1, then there is no uncertainty in

this world, and therefore the banker does not need to be compensated for investing

in the worker/innovator, which is as safe as the endowed transformation technology.

Also, note that those innovators with low probabilities of success are imposed higher

repayment requirements. In this world, the bankers do not weed out the bad innova-

tors as in King and Levine (1993). Instead, they impose higher penalties and let the

borrowers make the optimal decision.

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Going back to the workers problem, When z is revealed in the afternoon, the

probability of success is now known to the worker/innovator. Then, the workers

problem after z is revealed is

pit(Aitxit Iit) (1pit)xit. (3.9)

Differentiating Eq. (3.9) with respect to xit gives:

xit =

Aitpit1 + r

. (3.10)

Using Eq. (3.10) and Eq. (3.6), the workers problem before z is revealed can be

described as:

maxv

CEn

(Aitpit)1

1

o(3.11)

where CR

11

1

. Since the innovators are randomly and uniformly located

around the circumference of the island,

E(p1

1

it ) =1

dt

Zdt0

ezit1 dzit. (3.12)

Noting that Ait = (vit)At1, Eq. (3.11) becomes:

maxv

(vit)

1 A1

1

t1

1

dt

(1 e

dt1 )C, (3.13)

where C is as defined above. Differentiating Eq. (3.13) with respect to v gives:

vit = D

1 e

dt1

dt

! 1+1

(3.14)

where D

At1(1 )11R(1)

1+1 . Eq. (3.14) shows that the opti-

mal fraction of manna to be spent on receiving education depends on d. Note that by

symmetry, vit = v

jt for i 6= j.

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Next, we examine the behavior of the banker. Given the setup from the previous

section, the banker will decide to enter and finance the worker/innovator only if the

expected average profit from financing is greater than expected average screening

costs. Hence, the entry condition for the representative banker is

E{pitIit + (1pit)xit} = E(ez). (3.15)

It follows then that the decision rule for the banker is given by:

vit =

1

1

R

A

1

(edt 1

1 edt1

)1

(3.16)

where R = 1 + r.

On this island, the equilibrium values of vit and Nt(=12dt

) are jointly determined

by the decisions of the worker/innovator and the banker, represented by Eq. (3.14)

and Eq. (3.16) respectively. Differentiating Eq. (3.14) with respect to d, we find

that how v responds to changes in d depends on the values of two parameters, and

. Specifi

cally, if + 6 1, the optimal level of education, v, rises as more bankers

enter since v/d 6 0. In other words, the workers would devote a larger fraction of

the manna to obtain education if there is a larger number of bankers in the economy.

If + > 1, the opposite situation occurs. By contrast, Eq. (3.16) shows that

the bankers optimal level of d rises as the workers increase their efforts to receive

education.

In the steady state, vit = v and dt = d

(or Nt = N) by symmetry. These

relations imply that pit = pi and xit = xi because zit = zi. Then, since the average

output per worker thats produced using a new technology is AtE(pixi ), the total

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output produced by using new technologies is given by:

yHt = L(v)At1E(pix

i ). (3.17)

Total output produced using old technologies is given by:

yLt = LAt1(1 v). (3.18)

Then the total aggregate output in equilibrium at time t is given by

yt = At1L

(v)E(pixi ) + (1 v

)

. (3.19)

Finally, the growth rate of the economy at the steady state is:

log

yt+1

yt

= log

At

At1

= log(v) (3.20)

Hence, the growth of the economy is driven by the level of efforts the workers exert

to receive education (v), which is jointly determined by the bankers and the workers,

the idea production parameter, , and effectiveness of the educational system, .

3.3 Discussion

As noted above, the behavior of the economy in equilibrium is dependent upon

the summed values of two parameters, and . Evidence from economic growth

literature tells us that the value of is typically estimated to be roughly between

0.3 and 0.4. As for the value of , there is no practical way to measure as its

estimation essentially requires, among other things, separating out and measuring

the technological changes that results from human capital accumulation. However,

it is reasonable to argue that since there is no reason to believe that the value of ,a

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parameter that governs the degree of diminishing marginal returns to human capital,

would be much different from that of , the sum of and is likely to be less than

1. Accordingly, the model has an implication that for a given value of v, there exists

a unique value of d that is optimal, and vice versa for reasonable parameter values

(Figure 3.2).

The model presented in this chapter has some interesting policy implications.

Firstly, the tougher the government toward the financial sector, the better it is for

the economy. A rise in the stringency of policy rules, , shifts graph B in Figure

(3.2) to the left, inducing a higher level of human capital accumulation and a fasterrate of economic growth. Another implication is that the economy of this island

will not benefit from those financial policies that aim to raise the number of banks

without paying attention to the fundamentals of the economy. When d is artificially

lowered, say, by the government, the value of v that is optimal to the workers becomes

greater than that of v optimal to the bankers. Hence, over time, the bankers will

begin to suffer losses, and, as a consequence, some bankers have to exit in order

for the economy to restore its equilibrium. This is in line with experiences of Latin

American and Asian countries. Beginning around the mid-1980s, these countries have

implemented financial policies that aimed to encourage competition by inducing more

entry, but often failed to address the issue of whether and how the fundamentals of

their economies would adapt to such policy changes. One of the results of these

policies was, as we know, rather catastrophic. In some instances, the failure of the

banking sector was so severe as to cause an economy-wide financial crisis. This

model provides an explanation for how financial liberalization policies that ignore the

fundamentals of the economy can lead to bank failures, and ultimately financial crisis.

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Secondly, the model is able to explain the existence of poverty trap without in-

troducing setup costs. Furthermore, it suggests that two kinds of poverty traps may

exist. Firstly, deterioration in the effectiveness of the education system induces the

workers to receive less education, pushing graph W to the left, while shifting graph

B in the same direction. On net, the deterioration of the education system leads to a

lower equilibrium level of d. On the other hand, its effect on v is uncertain. However,

if the workers are more susceptible to deterioration of the education system than the

bankers are, the economy would be stuck in the poverty trap despite the presence

of a large number of banks (Figure 3.3). Therefore, it is possible that a large finan-

cial sector coexists with slow growth of the economy. Secondly, in contrast to the

preceding case, there can be a poverty trap with only a small number of bankers in

the economy as well. This happens when the financial regulations become lax. For

instance, if the regulations regarding the screening processes become relaxed, perhaps

as part of ill-designed liberalization policies, it causes a drop in the equilibrium values

of the number of bankers present in the economy and human capital accumulated,

leading to a slower growth.

Another possibility that has been ignored so far is the case of + > 1. If this were

the case, then the bankers and the workers on this island live in a strange world. Since

it is not possible to analytically examine this case because of the number of parameters

involved, only a qualitative discussion is provided. First of all, depending on the

parameter values, indeterminacy could arise. Secondly, if the parameter values are

such that the bankers are more inelastic than the workers, a rise in actually reduces

the human capital accumulation and, thus, hinders economic growth. However, it

should be reminded again that this scenario is an unlikely depiction of the real world

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given the commonly accepted idea of what the values of these two parameters , and

, should be.

3.4 Conclusion

The exact nature of the relationship between finance and growth has been the

subject of heated debates for many years. Consequently, many models have been

proposed to shed light on this issue. One strand of this literature investigates the role

of the financial sector in promoting innovative activities. This chapter is part that

literature.

The approach I take to investigate the link between the financial sector and eco-

nomic growth focuses on the role of the financial sector in financing innovative activi-

ties. It is motivated by increasing empirical evidence that technological advancement

is a key to economic growth. To study this link, I start with the proposition that

human and financial capital act as complements in the production function of ideas.

Similar to King and Levine (1993), it is assumed that the role of the financial sector

in this model is to screen the innovators for their probabilities of success. However,

unlike King and Levines version of the financial sector that plays an active screening

role of weeding out bad entrepreneurs, the financial sector I consider plays a passive

role of assessing and conveying the probability of success to the innovators so that

the latter can make optimal borrowing decisions.

In formalizing the model, I depart from the conventional literature in four impor-

tant ways. Firstly, I define innovation as a success not when it is realized but when

it is commercially successful. This distinction is motivated by observations that not

all innovations are implemented in the production processes. Secondly, I assume that

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the magnitude of technological change an innovator comes up with is a function of

that innovators human capital. Thirdly, it is assumed in this model that external

finance is needed not for R&D activities but for utilization of innovations. Finally, it

is assumed that the financial sector pays no setup costs.





economy can bring about bank failures and possibly afi

nancial crisis. Furthermore,in addition to showing that poverty traps can be explained without introducing setup




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32

Screening costs for Screening costs for

Banker B Banker B

A B B A

Screening costs forBanker A

-2dt -dt 0 dt 2dt

Note) The vertical axis measures the costs of screening. The horizontal axis measures the

distance between adjoining bankers.

Figure 3.1: Screening costs of competing bankers

Banker B Banker BBanker A


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33

Note) The figure above describes the equilibrium of the economy when 1 + < . (W)

represents the decision rule of the worker/innovator given by Eq. (3.14). (B) represents

that of the banker given by Eq. (3.16).

Figure 3.2: Equilibrium when 1 + <

v

d

(W)

(B)

v*

d*

(B)

d

v'

Rise in


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34

Note) The figure above describes how the economy responds to deterioration of the

education system when 1 + < . (W) represents the decision rule of the

worker/innovator given by Eq. (3.14). (B) represents that of the banker given by Eq.

(3.16).

Figure 3.3: Effects of deterioration in education

v

d

(W)

(B)

v*

d*

(B)

d

v'

(W)


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CHAPTER 4

How Does Financial Development Promote Growth?

4.1 Introduction

What makes an economy grow? Much research has been done to identify the

determinants of economic growth. Among suggested factors, researchers found that

generally investment is one of the most important determinants of economic growth.

This is not surprising. As a child needs a constant supply of nourishment to develop

properly, so does an economy to realize sustained development and growth. Only

that for the economy, nourishment would be in the form of investment in factors of

production such as physical and human capitals and technologies. I start from this

basic proposition that investment in factors of production is one of the fundamental

forces that drive economic growth.

Investment in factors of production generally necessitates the use of funds. The

problem is that the amount of funds is often limited compared to the number of

investment opportunities available and therefore should be allocated to more produc-

tive investment opportunities. Then, who is responsible for allocating these funds

among various investment opportunities? Any undergraduate student who took a

money-and-banking class will tell you with certainty that the answer to the question

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is the financial sector.2 Indeed, that is what conventional textbooks teach us: one

of the basic roles of the financial sector is to allocate funds efficiently. However, the

existence of the financial sector by itself does not guarantee that the allocation will

be efficient. Financial sectors exist in various forms and differ in their level of so-

phistication across countries. Then, in the presence of asymmetric information, those

financial sectors that are relatively more developed will be more able to efficiently

screen out bad investment opportunities. Then, to the extent that investment is an

important determinant of economic growth, it follows that the degree of development

of the financial sector should matter for economic growth. However, albeit much re-

search, there is much controversy among professional economists regarding the role of

the financial sector in promoting economic growth. And, those studies that do show a

positive relationship between GDP per capita growth and a financial development in-

dicator have been criticized for not accounting for a possible endogenous relationship

in their estimations. Furthermore, it was argued that the existing empirical studies

in general do not show how the financial sector does affect growth.

The goal of this paper is to tackle these two main issues. The strategy I take is to

focus on a potential channel of influence from the financial sector to the real sector.

By doing so, I am able to ameliorate the problem of endogeneity as well as to shed

some light on the largely ignored question of how the financial sector affects economic

growth.

2 In this paper, terms such as financial intermediation, financial sector, and banking sector will

be used interchangeably when there is no confusion.

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4.2 Related Literature

4.2.1 Theories of the finance-led growth hypothesis

The origin of the finance-led growth hypothesis can be traced back to Bagehot

(1873). Early studies by Goldsmith (1969), McKinnon (1973), and Shaw (1973)

constitute a first systematic investigation into the link between the financial sector

and the real sector. Although they found a positive relationship between these two

sectors, the finance-led growth hypothesis did not receive much attention at the time

due to the lack of a formal theoretical foundation to back the empirical findings.

Under the exogenous growth regime, the only way thefi

nancial sector could aff

ect thegrowth rate of an economy was via technological innovation, which was not modeled

adequately by then-existing growth theories. With the arrival of endogenous growth

theories, the finance-led growth hypothesis received renewed attention. In a world

governed by endogenous growth theories, the growth rate of an economy can be

enhanced not only by an increase in productivity growth but also by either an increase

in the effi

ciency of capital accumulation or an increase in the savings rate.

Diamond and Dybvig (1983) and Bencievenga and Smith (1991) observe that

the primary role the banking sector plays is the provision of liquidity and argue

that, by providing liquidity, the banking sector enables more investments in illiq-

uid/productive assets and thereby enhances the efficiency of capital accumulation

and economic growth. Roubini and Sala-i-Martin (1995) study an alternative way

the banking sector can enhance the efficiency of capital accumulation where a re-

duction of agency costs due to financial development allows a larger share of savings

to be channeled into investments. However, whether or not financial development

positively affects savings rate is not a clear-cut issue. For example, Devereux and

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Smith (1994) show that a reduction in idiosyncratic risk and the rate of return risk

may either reduce or increase savings rates depending on the degree of risk aversion

of economic agents. Furthermore, Japelli and Pagano (1994) show that reducing liq-

uidity constraints reduces savings since the younger generation in their model borrow

much more in the absence of liquidity constraints. Saint-Paul (1992) builds a model

where the financial sector allows more specialization in productive and risky tech-

nology by reducing idiosyncratic risks. More to the spirit of ideas-driven economic

growth, but not quite, Galetovic (1996) studies the interaction between the financial

sector and the research sector. In his model, the financial sector plays the indirect

role of economizing the costs of monitoring research firms for investors. Contrary to

these models where the financial sector promotes the process of learning by doing,

King and Levine (1993) consider the financial intermediaries as actively involved in

the production of ideas itself by screening and monitoring innovative projects.

It is rather odd to note that the theory of the finance-led growth was revived

by the birth of endogenous growth theories, and yet the researchers have paid only

scant attention, with an exception of King and Levine (1993), on the effects offinan-

cial development on innovative activities in examining the validity of the finance-led

growth hypothesis. In the context of both exogenous and endogenous growth theo-

ries, the ultimate engine of economic growth is a rise in the productivity level as an

outcome of either learning by doing or intentional effort to come up with a better

technology. Also, Easterly and Levine (2001) show that the residual rather than

factor accumulation accounts for most of the income and growth differences across

countries. Further, the importance of purposive technological innovation in enhanc-

ing productivity is well illustrated by the amount of effort a large number of countries

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put into establishing a robust R&D sector. Based on these observations and the fact

that investment in R&D requires funds, at least partly, from the financial sector, I

argue that if the finance-led growth hypothesis is correct, then we should be able to

detect evidence of positive effects offinancial development on innovation. Therefore,

my aim in this paper is to examine this particular channel of influence. In doing so,

I hope to shed further light on the validity of the finance-led growth hypothesis.

4.2.2 Empirical studies

A flood of empirical studies began to appear in the 1990s to test the validity of

the finance-led growth hypothesis. Unfortunately, theoretical models of finance-led

growth do not provide empirical researchers with structural guidelines on which they

can base their estimation. As a result, one is forced to use reduced-form estimation

and test the general conclusion of these models. Since the implication of all the

theoretical models in this area is basically that a better-developed financial sector

enhances economic growth, regardless of the channel of influence, a typical test strat-

egy involves regressing some indicator of financial development on some aggregate

growth measures such as investment growth, GDP growth or total factor productiv-

ity growth. A first attempt in this direction was made by employing cross-sectional

estimation (King and Levine, 1993, and De Gregorio and Guidotti, 1995). Using a

cross-sectional framework, they find a positive relationship between financial develop-

ment and economic growth. However, a potentially endogenous relationship between

financial development and economic growth made interpretation of these results diffi-

cult. Demetriades and Hussein (1996) and Odedokun (1996) take a Granger causality

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approach to avoid these problems and present mixed results. They find that the ef-

fects offinancial development on growth are country-specific. Based on their findings,

they argue that a robust test of the finance-led growth hypothesis should incorporate

the time dimension of the data under consideration. Benhabib and Spiegel (2000)

employ a panel GMM method to reach a similar conclusion.

By and large, the current empirical literature lacks one crucial element in that

they do not consider the channels of influence suggested by theoretical models and

fail to show how financial development affects economic growth. Furthermore, time-

series approach, while potentially resolving endogeneity issue, does not tell us exactly

what the relationship between these two is. In addition, their results are as hard

to interpret as cross-sectional estimation because Granger causality does not really

provide an answer for the causal relationship between financial development and eco-

nomic growth. Researchers who conduct causality tests in this area argue that, for

some countries, economic growth causes financial development, when what they re-

ally need to say is that economic growth Granger-causes financial development. This

does not really address the question of what causes what, especially when one con-

siders that, statistically, Christmas card sales Granger-cause Christmas. In sum, the

current empirical literature suffers from two problems. First, as long as one regresses

GDP growth on a measure of financial development, the issue of endogeneity is not

satisfactorily resolved. Second, the channel of influence has not been specified so far,

thus, limiting our understanding of how the financial sector affects growth.

In order to examine the validity of the finance-led growth hypothesis, I depart

from the conventional literature. Instead of estimating a relationship between ag-

gregate growth measures and financial development indicators as is commonly done

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in the current literature, I test the validity of the finance-led growth hypothesis by

focusing on the innovation channel of influence, using patent applications as a proxy

for innovative output. Under the framework of ideas-driven growth, the hypothesis

I test is that financial development enhances innovation, which is the main engine

of economic growth. If the finance-led growth hypothesis is right, as the financial

sector develops over time in a certain country, the growth rate of innovation should

be higher, which would, then, lead to faster economic growth due to a rising level

of productivity. Using panel data on twenty eight countries from 1970 to 2000, my

analysis shows that financial development is indeed significant in raising the growth

rate of innovative output.

In section 2, I provide a theoretical motivation for the empirical analysis by ex-

tending a standard Romer-type growth model to include agency costs and draw a

testable implication. In section 3, I discuss the estimation strategy employed and the

issues that need to be addressed. In section 4, I conclude.

4.3 Theoretical background

One of the common assumptions made in finance-led growth theories is that the

financial sector (mainly the banking sector) actively monitors borrowers of funds.

Allen and Gale (2001) show that evidence is to the contrary. They show that in most

cases, the banking sector does not serve as an active monitor. The rationale for this is

that often times the banking sector makes a debt contract with the borrowers in which

profits of a lending bank are not dependent upon the borrowers degree of success.

Rather, they simply depend on whether the borrower succeeds or not.3 Therefore,

3 Of course, this is not the case for equity contracts. However, in most cases, equity markets arerelatively small in terms of intermediating funds and are ignored in this paper.

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the welfare of the lending bank will depend more on how well it screens out the bad

borrowers and less on its effectiveness as a monitor. Based on this observation, I

extend a standard Romer-type endogenous growth model and include the financial

sector as a provider of funds for researchers with agency costs to illustrate how the

degree offinancial development affects the rate of technological innovation. Note that

agency costs in this model represent the costs of screening for the financial sector.

The goal of the model is, thus, to show that high agency costs discourage innovation

and growth of the economy.

4.3.1 Final goods sector

A perfectly competitive final goods sector produces a single homogenous consump-

tion good by combining labor and intermediate goods. The production function for

the final goods sector is given by

Y =

AZ0

xj dj, (4.1)

where A is the number of intermediate goods used and x is the amount of inter-

mediate good j used and is between 0 and 1. Given this production function, and

normalizing the price offinal goods to one, a firm in the final goods sector maximizes

its profit;R

xj dj RA0

pjxjdj, where pj is the price of an intermediate good j. Profit

maximization gives the price of an intermediate good j as

pj = x1j . (4.2)

4.3.2 Intermediate goods sector

The intermediate goods sector consists of monopolistic firms that buy designs from

the research sector to be used in production of intermediate goods. These firms are

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monopolistic since the designs they buy are protected by patents that exclude others

from using the same designs. Therefore, each monopolist produces only one type of

intermediate good. With the design in hand, the monopolist produces intermediate

goods using a one-to-one production function. In other words, the monopolist requires

one unit of capital to produce one unit of intermediate good.

Formally, the monopolist maximizes the profit function given by:

pj(xj)xj rxj, (4.3)

where r is the interest rate for borrowing capital. The firms supply ofxj derived from

the profit maximization, together with the demand schedule in Eq.(4.2), determines

the price of xj to be equal to r/, which implies that xj = x and, consequently, that

Y = Ax.

Using Eq.(4.2) and pj = r/, we get x = 2Y/Ar. Then, the profit for each

monopolist can be specified as;

I = (p

r)x

= (1 )Y

A. (4.4)

Further, since the total amount of the intermediate goods used in the final goods

sectorRA0

xidi = Ax, should be equal to the total amount of capital spent in the

intermediate goods sector, (1aK)K, x is equal to (1 aK)K/A. Note that (1 aK)

is the portion of capital stock used in the intermediate goods sector, and K is the

total stock of capital in the economy. Finally, the production function turns out to

be

Y = A1[(1 aK)K]. (4.5)

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4.3.3 The research sector

Recall that my primary goal in this section is to provide a theoretical sketch of

how financial development affects innovation. I aim to show here that the share of

capital spent in research sector is increasing in the degree offinancial development as

proxied by lower agency costs.

In this model, each researcher faces a similar problem as the monopolist in the

intermediate goods sector. In other words, each researcher borrows capital from the

financial sector to finance her innovation. What is different from the monopolists

case is that the researchers cost of borrowing capital is not r but r + c where c

is the exogenous agency costs. The researcher pays an additional cost of c because

the financial sector has to screen the researchers when they borrow funds. With

this environment, the researcher tries to maximize her profit based on her production

function. I make a standard assumption that when the researcher innovates, she takes

the actions of other researchers and the knowledge stock as given so that she faces

the arrival rate of defined as:

= A1[aKK]1, (4.6)

where stands for the arrival rate of new technology per unit of capital spent on

innovation at the individual researchers level.

When a new technology is developed, assuming that the design lasts forever, the

researcher receives a price, pA which is equal to the monopolists profit discounted by

r, (1 )x/. Since x = 2Y/Ar,

pA =(1 )Y

rA. (4.7)

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Given the price pA and the arrival rate , the marginal product of capital spent in

the research sector equals simply pA. Equating this to the marginal cost of capital,

r + c, and noting that r = 2Y

(1aK)K, I get the share of capital used in the research

sector as:

1

(1 aK)

K

A

= a1K

"2

A

K

11

1 aK

1+ c

#. (4.8)

Although the Eq. (4.8) cannot be solved explicitly for aK, it can be seen that

there exists a

Finance and Economic Development

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