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University of Minnesota
Financial Development and the Effect on Income Growth in OECD Countries:
An Empirical and Theoretical Investigation
Madison Whalen
ECON 4331W
Neha Bairoliya
May 6, 2015
INTRODUCTION
Across the globe, there are vastly different standards of living between different
countries. The huge inequality between different countries and cultures can be seen not only in
analysis of data, but also just by the naked eye. Countries like Singapore, for example, have a
very high level of income per capita and most residents are fairly well off. This is apparent in the
buildings and infrastructure that are present in the country. On the opposite end of the spectrum,
many African and Latin American countries do not have nearly as high of standard of living,
which reveals itself through the low level of infrastructure, high levels of hunger, and poverty.
One of the most important questions that modern economics seeks to answer is why there is such
a large difference across countries and what could be done in order to help some of these
countries that are lagging to achieve a higher standard of living.
Many modern economists use Gross Domestic Product per capita as an indication of the
level of wellbeing in a country. That is because GDP per capita is essentially the country’s total
income divided by population which should give a good indication of the average level of
income in a country. One of the most important questions that needs to be asked is what the key
determinants of GDP per capita are across all countries, because this information is invaluable in
order to help boost some of the lagging countries to levels that would be consistent with some of
the wealthier countries across the world. Many theories that have been examined suggest that
some of the primary contributing factors are human capital levels, physical capital levels,
population growth, and the level of technological advancement. Aside from that, there are many
other factors that contribute to higher levels of income per capita, including the level of access to
financial tools and the level of development in a country’s financial system.
This paper seeks to examine the role that the development of financial systems has in
steady state levels of GDP per capita as well as growth in output per capita. Using one of the
most infamous models of economic growth, the Solow Growth model, it can be shown that
higher levels of investment (savings) should lead to higher levels of income per capita. One key
determinant of the level of savings in a country is the level of financial development, because a
more developed financial system will lead to easier access to capital for business and households
alike. This paper will use data to test the hypothesis that higher levels of financial development
as shown by equity market, retail banking, and insurance market indicators are a key
determinant of higher levels of GDP per capita in the steady state and higher GDP per
capita growth rates.
The findings of this paper, which will be discussed in much more detail later, reveal that
only certain indicators of the chosen six show a statistically significant correlation to output per
capita, both in the steady state and its growth rate. Although not all indicators were shown to
have significant correlation, it deepens the understanding of the determinants of output and
economic growth, allowing for a more detailed conclusion in the future. Overall, based on the
findings of this paper and other research, it is apparent that financial development does have an
effect on output, but it remains for the latter portion of this paper to fully explain the magnitude
and components of this effect.
To fully analyze the effect of financial development, this paper will analyze outside
scholarly literary sources in order to further build an understanding and background on the effect
of financial development, which can then be utilized as the basis for further analysis. After
deepening the background understanding, the paper will develop a framework for analyzing this
question before using data to analyze the effects of financial development across the 34 member
countries of the Organisation for Economic Cooperation and Development. Finally, the paper
will conclude with the results of this analysis and examine areas that possible further research
could be performed.
LITERARATURE REVIEW
De Serres et al. (2007)
Alain de Serres’s 2007 paper in OECD Economic Studies journal titled “Regulation of
financial systems and economic growth in OECD countries: An Empirical Analysis,” is an
investigation across approximately 25 OECD member countries of the level of financial
regulation and the effect that this regulation has on economic growth. He improves on past
studies of similar topics that only consider the size and structure of an economy’s financial
system by further looking into the level of regulation in financial systems specifically in the
banking and securities markets. The purpose of examining the regulation of financial systems is
to examine whether sectors that are more dependent on financing from external sources
experience higher growth in countries that have financial regulations allowing for a more
competitive financial environment. The World Bank assembled a survey of financial system
regulation across many countries that provides the basis for many of the indicators that are used
in the paper.
The findings of de Serres indicate that financial regulations have an effect of statistical
significance on the level of output as well as the entry rate of new firms in industries which
utilize large sources of external funding. The paper also finds that there are countries within the
OECD that still have fairly strong regulations on their financial markets, in comparison to OECD
averages, which could limit competition. In countries where competition amongst financial
services firms is more prevalent, the paper finds that these countries have been less susceptible to
instability and uncertainty in their growth. De Serres postulates that this might be due to the idea
that stronger competition creates a need for careful examination of the industries and supports
healthy practices amongst the firms.
While de Serres’ paper provides valuable insight into one aspect of financial
development, there are other ways that financial development can be measured. Financial
regulation is certainly a component of financial development, but this paper seeks to more fully
encompass a country’s level of financial development using a more quantitative approach,
examining the level of development based on share of GDP and utilization of the financial
system instead of focusing on the level of regulation as a determinant of growth. Regardless of
the differences, this paper does highlight the important idea that financial systems play a large
role in growth of an economy.
Law and Singh (2014)
Siong Hook Law and Nirvikar Singh published a paper titled “Does Too Much Finance
Harm Economic Growth,” in the 2014 Journal of Banking and Finance. In this paper, the authors
examine extensive data from 87 countries over the time period from 1980 to 2010. The goal of
the paper is to examine if there is a threshold in the level of financial development at which it
becomes counterproductive to have more development in finance. One of the most important
events that illustrates the need for this type of research is the 2008 financial crisis in which a
country with a very developed financial system experienced strong negative growth for a period
of time, which would not support the idea that larger financial development is one of the largest
components of economic growth. The paper cites previous research from the Bank for
International Settlement and the International Monetary Fund which found the relationship of the
level of financial development and economic growth to be non-linear and in fact a concave down
shaped curve. This would imply that financial development has an optimal level at which it is
most effective in increasing the level of growth, and both above and below that level economies
would see slower growth.
Using measures of private sector credit, liquid liabilities, and domestic credit, as well as
control variables to account for some variation in GDP, the authors find that in all three sectors
there is a threshold limit at which higher financial development actually becomes a burden to
economic growth and slows the rate at which an economy can grow. The results of the study find
that there is a threshold of approximately 88% of GDP for private sector credit above which
there is a negative impact on economic growth. For liquid liabilities, the threshold is found to be
about 91% of GDP, but there is not a significant impact on growth above the threshold. Finally,
domestic credit is found to have a threshold value at 99% of GDP with negative effects on
growth above that. With these findings, the authors conduct robustness tests to verify their
results, solidifying the idea that financial growth is only good up to a certain point.
While the authors examined the effects of financial development on growth, they were
seeking to determine if a threshold exists and where it is located. This paper will examine
different financial indicators but the results of the study should be similar in terms of the effect
on financial growth. The effects of these findings on this paper could be significant, as many
OECD countries have very high levels of financial development. With these findings in mind, it
remains to be seen if a significant correlation is found, and if the effect of financial development
on GDP is not significantly positive the findings of this paper may provide a potential
explanation.
THEORETICAL ANALYSIS
In this section of the paper, the model which this paper is based off of will be discussed
in depth, constructing the basis for the data analysis and OLS regression that is to be performed.
The original model which is being examined stems from Mankiw, Romer and Weil’s 1992 paper
“A Contribution to the Empirics of Economic Growth.” This paper will then make an extension
to the Solow model, accounting for a country’s level of financial development, as well as using
the model to explain growth before finally using the data to analyze the effects of financial
development.
Extended Solow Model
The 1992 writings of Mankiw, Romer and Weil develop an extension to the Solow Model
that accounts for the level of human capital present in an economy. The model that is examined
in their paper can be described by three equations, namely the economy production function and
the capital accumulation equations seen below.
(1) Y = K α H β (AL)1−α−β
(2) K ′ = sk Y − δK
(3) H ′ = sh Y − δH
In the above equation (1), Y is the output of the representative economy which is equal to total
GDP in a country. K is representative of the level of physical capital and H of the level of human
capital. The term AL describes labor augmented technology, that is A is technology and L is
labor. Alpha and beta are given parameters of the equation constrained by 𝛼 + 𝛼 < 1. Equations
(2) and (3) are the capital accumulation equations describing how the path of physical and
human capital accumulate. Physical capital accumulation can be seen as sk, the percentage of
output dedicated to physical capital, times GDP minus δ, the depreciation rate, times the existing
level of physical capital. Human capital accumulation can be seen in a similar manner, the only
difference is sh represents the percentage of output dedicated to accumulating human capital
instead of physical capital. The growth of A and L in the production function are exogenous and
given as g and n, the level of technological growth and population growth respectively. In
equation form,
A′
=g
A
L′
=n
L
Though these equations provide the framework for an economy, data would not be applicable
unless we get it into the form of output per units of technologically augmented labor. In this case,
we will use the lower case variables to represent variables in the proper units as described in the
equations below.
y=
Y
AL
k=
K
AL
h=
H
AL
Now, using the above definitions that are described in technologically augmented units of labor,
we can solve the system of equations and take derivatives using the above capital accumulation
equations to solve for a steady state value of y, which is the steady state value of GDP per unit of
labor. Working through the math, we find an equation representing y*, the steady state level of
GDP per unit of technologically augmented labor, described by the equation below.
α
β
sk
sh
1−α−β
1−α−β
y =(
)
(
)
n+g+δ
n+g+δ
∗
The above equation describes how GDP per unit of technologically augmented labor in the
steady state is a function of physical capital savings rate, human capital savings rate, the rate of
technological and population growth, as well as the depreciation rate. Some simple testing of
numbers would reveal that increases in either savings rate, whether physical or human capital
should lead to an increase in output, while population growth, technological growth and
depreciation have the opposite effect. Although this equation above describes an essential piece
of information for the paper, we cannot estimate the level of output per technologically
augmented labor, rather we can only estimate per capita GDP, which is the level of output per
unit of labor. Thus, as Mankiw, Romer and Weil did in their paper, we must find ÿ*, which is
described below.
ÿ∗ =
Y
Y
= A∗
= A ∗ y∗
L
AL
α
β
sk
sh
1−α−β
1−α−β
ÿ∗ = A ∗ y ∗ = A (
)
(
)
n+g+δ
n+g+δ
Thus in terms of GDP per unit of labor we can see that it is simply a function of technology,
investment rates in physical and human capital, population growth, technological growth, and
depreciation rates. In order for this equation to be examined with data, there must be a linear
relationship between all of these variables. This can be found by simply taking the logarithm of
the equation which yields the linear relationship below.
log(ÿ∗ ) = log(A) +
α
β
α+β
log(sk ) + 1−α−β log(sh ) − 1−α−β log(n + g + δ)
1−α−β
Now that a linear relation has been established, it can be examined in terms of a regression that is
described by the regression equation below.
log(ÿ∗ ) = a + b1 log(sk ) + b2 log(sh ) + b3 log(n + g + δ)
The b terms will represent the regression coefficients and the “a” term, which was initially log of
technology, now is simply the intercept. Because the regression equation has a negative sign in
front of log(n + g + δ) we would simply expect b3 to be negative. Now that we have examined
the initial model, we can make some extensions that are more suited to develop a model that this
paper can test.
Financial Development Augmented Solow Model
With a basic framework for a model now established, we can add in an extension in order
to examine the effect of the level of financial development on output. Extending on de Serres’
2007 theoretical framework that financial regulation is a key determinant of the level of capital
available to a firm, we can find a relation of the level of the capital accumulation rate and the
level of financial development (de Serres et al. 2007). Logically, it makes sense that a more
developed financial system would provide easier access to not only capital for firms, but also
would provide easier access for individuals to invest money in either stocks, insurance, or just a
savings account. All of these types of investments would have an effect on the portion of output
dedicated to physical capital accumulation because the access to these financial instruments
would be much easier in a more developed financial system. If a country has an undeveloped
financial system there would most certainly be less volume of investment into capital
accumulation. Based on this theory and the findings examined above, this paper will assume that
the level of financial development directly affects the amount of capital accumulation in an
economy.
We will start to develop this augmented model using the idea that the capital
accumulation in an economy is directly influenced by the level of financial development. First
we will denote F, the level of financial development, as the product of six variables, namely bank
deposits per capita (BankDep), financial system deposits per capita (FinDep), stock market
capitalization (MktCap), insurance company assets per capita (InsAst), stock market turnover
ratio (MktTrn), and life insurance premium volume per capita (LifeIns). In an equation form
F = BankDep ∗ FinDep ∗ MktCap ∗ InsAst ∗ MktTrn ∗ LifeIns
The variable F now represents an aggregate level of financial development, for which the
methodology will be examined later. Now it has been said that the level of financial development
should have a direct effect on capital accumulation in an economy, more specifically on the level
of investment in physical capital in an economy. We will assume that the depreciation rate is
held constant in the regression, eliminating the idea that financial development would have any
effect on the depreciation rate. So, we modify the capital accumulation equation from the Solow
model in the above section to now be as follows.
K′ = Fsk Y − δK
In the above equation, F is described by the previous equation. Now using the same Solow model
as before, but with the augmented capital accumulation equation, we can solve for a steady state
following the same steps as the non-augmented model, where we arrive at the steady state value
for output per unit of labor, ÿ.
𝛼
𝛼
1−𝛼−𝛼
1−𝛼−𝛼
𝛼𝛼𝛼
𝛼ℎ
ÿ∗ = 𝛼 (
)
(
)
𝛼+𝛼+𝛼
𝛼+𝛼+𝛼
Now, substituting F from above and taking the logarithm then converting to a regression
equation we get the following two steps.
α
α
log(BankDep) +
log(FinDep)
1−α−β
1−α−β
α
α
α
+
log(MktCap) +
log(InsAst) +
log(MktTrn)
1−α−β
1−α−β
1−α−β
α
α
β
+
log(LifeIns) +
log(sk ) +
log (sh )
1−α−β
1−α−β
1−α−β
α+β
−
log(n + g + δ)
1−α−β
log(ÿ∗ ) = log(A) +
log(ÿ∗ ) = a + b1 log(BankDep) + b2 log(FinDep) + b3 log(MktCap) + b4 log(InsAst)
+ b5 log(MktTrn) + b6 log(LifeIns) + b7 log(sk ) + b8 log(sh ) + b9 log(n + g
+ δ)
Now that we have a valid regression equation with a coefficient for each variable being tested,
we can later use this as an OLS regression equation. We expect to see a positive correlation on
all of the coefficients except for log(n + g + δ) which we would expect to be negative. The model
can be taken one step further in order to examine the dynamic state of the model through the
growth rate in output per capita. Below, we derive this equation for the basic Solow model, of
which this model is an extension.
Per Capita GDP Growth in the Basic Solow Model
Aside from the results above, one of the most powerful ideas of the Solow model is that it
allows us to derive a mathematical equation for the growth rate in per capita GDP over time.
Although this can be done in any of the models above, utilizing the basic Solow model that does
not include human capital should be sufficient for this paper as it will demonstrate the idea, and
further extended models shall exhibit similar characteristics in regards to the growth rate of
output per capita with the addition of other variables. The purpose of this derivation is to show
the effect of savings rate on output per capita, allowing the idea that a higher savings rate, which
is correlated with higher financial development, will lead to a higher growth rate in GDP per
capita. To show this, we will begin by defining the basic Solow model below.
(1) Y = K α (AL)1−α
(2) K ′ = sY − δK
These two equations, the production function and the capital accumulation equation, along with
the constant growth rates of population and technology, n and g respectively, which are defined
in the same manner as those in the extended Solow model will allow us to derive the growth rate
in GDP per capita. Per capita capital and output are defined as `k = K/L and `y = Y/L (note the `
preceding the variables to differentiate them from efficient per capita units). In efficient per
capita units, k=K/AL and y = Y/AL. Below we derive the growth rate in `y, output per capita.
k=
K
⇨ log k = log K − log A − log L
AL
Taking the time derivative of the above equation we find the result below.
k ′ K ′ A′ L′ K ′
K′ k′
= − − = −g−n ⇨ = +g+n
k
K A L K
K
k
Utilizing the capital accumulation equation,
K ′ sY
K ′ = sY − δK ⇨ =
− δ=
K
K
sY
AL
K
AL
− δ=
sy
−δ
k
Now, combining the two results from above, we are able to find an equation for the growth rate
in efficient per capita units of capital below.
k′
sy
k ′ sy
+ g + n = − δ ⇨ = − (n + g + δ) = sk α−1 − (n + g + δ)
k
k
k
k
Now that we know the growth rate in capital per efficient units of capital, we can transform that
into the growth rate in output per capita.
y = k α ⇨ log y = α log k ⇨
y ′ αk ′
=
y
k
Using the definition of `y, per capita output and the equation above, the growth rate of output per
capita is found below.
`y ′ A′ αk ′
αk ′
`y = Ay ⇨ log `y = log A + log y ⇨
= +
=g+
`y
A
k
k
`y ′
= g + α(sk α−1 − (n + g + δ))
`y
Above the growth rate in output per capita is shown as a function of exogenous variables, and it
is clear that the function is increasing in s. What this says is that output per capita growth rate
will increase with an increase in savings rate. Higher financial development is a direct
contributor to savings rate, which means that higher financial development should lead to an
increase in the growth rate of output per capita through a higher savings rate. In addition, in the
financial development augmented model, we see that in the steady state, financial development
(F), is simply multiplied by sk in the numerator of the equation. If F were to be present in this
equation it would also be within the same expression we see s, which would mean that the
growth rate would be increasing in F as well. Although this is not explicitly shown, the math
required follows the same derivation in this section with the addition of F as a coefficient of s.
We are now ready to look at the numbers with this theoretical framework and examine the effect
of financial development on not only GDP per capita, but also GDP per capita growth.
DATA
In order to test whether a statistically significant relationship exists between the level of
financial development and GDP per capita, we must examine the regression equation above and
define the relevant variables. All data for the regression has been obtained from the World Bank
database and represents 34 countries, all member countries of the OECD. Some values were in a
percentage of GDP, which was then multiplied by that year’s GDP per capita to get an aggregate
per capita number. To perform the cross country regression, the values for each relevant variable
were averaged over the years 1991-2011 to get an average level then regressed across the
countries. The regression equation from above is
log(ÿ∗ ) = a + b1 log(BankDep) + b2 log(FinDep) + b3 log(MktCap) + b4 log(InsAst)
+ b5 log(MktTrn) + b6 log(LifeIns) + b7 log(sk ) + b8 log(sh ) + b9 log(n + g
+ δ)
In this equation, ÿ∗ is GDP per capita using constant 2005 US Dollars. BankDep represents bank
deposits per capita, FinDep represents financial system deposits per capita, MktCap the stock
market capitalization percentage, InsAst the insurance company assets per capita, MktTrn the
market turnover rate, and LifeIns the life insurance premium volume per capita. In the model this
paper has developed, sk is represented by gross capital formation per capita. The model also uses
primary school completion rate as a proxy for human capital formation, sh. Finally, (n + g + δ) is
represented by the labor force growth rate plus g + δ which has been estimated across all
countries at .05 or 5%. In some cases, there was not data for a certain country so that was
estimated to be the average level of other countries. More specifically, this was present in three
categories for the United Kingdom, two for New Zealand, and one for Portugal and Australia.
While this does create some correlation, it is so minimal amongst the large data set that it should
not have a significant impact on the outcome. To summarize the data, we can look at the average
level of GDP per capita across countries and look at some of the descriptive statistics of GDP per
capita across the countries.
Mean
Median
Standard Deviation
Largest
Smallest
$28,086.17119
$31,294.11667
2,754.803132
$70,664.80476 - Luxembourg
$6,383.00 - Turkey
It is also visually apparent the distribution of the countries when we look at a scatterplot such as
that below in Figure 1.
GDP per Capita (2005 USD)
Figure 1:Financial Deposits per Capita vs. GDP per
Capita
80000
70000
60000
50000
40000
30000
20000
10000
0
0
50000
100000
150000
200000
250000
Financial System Deposits per Capita
We see that most countries lie within the range of 25% to 100% of GDP value for the financial
system deposits, however there is one large outlier with over 300% of GDP value in financial
system deposits. When the data is examined, this happens to be the same country that has the
highest GDP per capita, Luxembourg. This strange phenomenon might be due to a high level of
foreign investment, which would be an indication of a very open, and possibly well-developed
financial system.
Figure 2: Log Financial Deposits per Capita vs. Log
GDP per Capita
Log GDP per Capita
6
5
4
3
2
1
0
0
1
2
3
4
5
6
Log Financial System Deposits per Capita
Figure 2 displays log GDP per capita and log financial deposits per capita to better analyze the
correlation of the data. From the above figure, there seems to be a slightly positive correlation
between the log of GDP per capita and the log of financial system deposits per capita. The true
test of actual correlation will be an OLS regression below. The findings below will be further
augmented with a regression against GDP per capita growth in order to analyze a more dynamic
situation than the steady state.
Steady State GDP Data Analysis
Using the regression equation from above in simplified form, we are able to run an OLS
regression to test the correlation of the data. With the dependent variable as GDP per capita in
constant 2005 US Dollars, the regression is run with the nine explanatory variables. The results
are summarized in the table below.
Coefficient
Value
Std. Err.
P-Value
Reject H0?
b1
b2
b3
b4
b5
b6
b7
b8
b9
-0.042
0.058
0.077
0.096
0.012
-0.076
0.777
-0.033
-0.193
0.244
0.216
0.035
0.035
0.034
0.035
0.091
0.549
0.168
0.864
0.792
0.036
0.012
0.732
0.040
0.000
0.953
0.263
No
No
Yes
Yes
No
Yes
Yes
No
No
R2 = 0.98121
From the results in the table above, we see that there are only four variables that show a
coefficient that is different from 0 with statistical significance. Market capitalization shows a
statistically significant effect meaning a 1% increase in market capitalization leads to a 0.077%
increase in output per capita. We see that insurance company asset volume per capita has a
coefficient of 0.096, meaning that an increase of 1% in insurance company assets per capita
leads to approximately a 0.096% increase in GDP per capita. For life insurance premium volume
per capita, the -0.076 coefficient means that an increase in life insurance premium volume of 1%
per capita would lead to a .076% decrease in GDP per capita. Our proxy for sk, gross capital
formation per capita, shows a significant correlation of 0.777 meaning a 1% increase in gross
capital formation per capita should lead to a .777% increase in GDP per capita. Conversely, the
other variables tested do not show a statistically significant level of correlation to the level of
output per capita. Even though some of the tested variables did not show significant correlation,
we do see an R2 of over .98 meaning the explanatory variables do account for over 98% of the
variation in the data. One of the most prominent findings is that we do not see a correlation of
human capital levels with output per capita, although one of the reasons for this could be that
primary school completion rates are consistently high across all of the OECD countries. This
finding, along with the lack of correlation of the other variables, means that there may be some
measurement errors and that some of the proxies used may not be completely representative of
the information that they were chosen to represent. As far as the overall applicability of F as a
measure of financial development, it leaves some to be desired in terms of finding the correlation
that this paper seeks to find.
GDP Per Capita Growth Data Analysis
Utilizing the basic Solow model, this paper demonstrated how savings rate has a positive
effect on output per capita growth, at least in theory. Higher financial development should lead
to higher savings rates, and in turn higher output per capita growth rates. To test this hypothesis,
an OLS regression was performed, using the same measure of F from above, but the dependent
variable was GDP per capita growth over the period from 1991 to 2011. In order to make this
regression more powerful, two additional explanatory variables were added, population growth
and initial GDP per capita, in order to account for the difference in the level of GDP per capita
across countries. Using the explanatory variables from the steady state regression and the two
new variables, the following results were found from the OLS regression.
Variable
Population
Growth
Initial GDP per
Capita
Coefficient
Standard Error
P-Value
0.076
0.154
0.627
-2.627
0.436
0.000
Bank Deposits
0.105
0.803
0.897
Financial
Deposits
-0.254
0.712
0.725
Market Cap
0.151
0.139
0.289
Insurance
Assets
0.387
0.133
0.008
Reject H0?
No
Yes
No
No
No
Yes
Market
Turnover
Life Insurance
Premiums
-0.037
0.117
0.754
-0.303
0.125
0.024
Sk
1.938
0.435
0.000
Sh
-0.359
1.825
0.846
n+g+δ
-1.122
1.118
0.327
No
Yes
Yes
No
No
R2 = 0.779019002
Looking at the results above, we see that many of the financial development variables do not
have a statistically significant effect on GDP per capita growth, but it is apparent that initial GDP
per capita does have an effect, that is a 1% increase in initial GDP per capita would have a
2.627% percent decrease in output per capita growth rate. Following along to the financial
development indicators, an increase of 1% in insurance company assets leads to a .387% increase
in output per capita growth, while life insurance premiums increasing 1% leads to a decrease of
.3% in output per capita growth. Finally, as expected from the basic Solow model’s theoretical
prediction, we capital savings rate increasing 1% leads to a 1.938% increase in growth rate. The
R2 value over 0.77 means that the model did show strong explanatory power of the variance in
the data but that is not to say that the chosen indicators were necessarily the best possible. Once
again, as with the steady state regression, we see that F as a measure of financial development
leaves to be desired in terms of its predictive power, at least amongst OECD countries. The data
showed that growth in output per capita is affected by some of the explanatory categories, but
not all, and not as many as would like to be seen. Below, F and its applicability as an indicator of
financial development are discussed in order to hopefully provide the basis for results that find
more explanatory power on steady state output per capita as well as output per capita growth.
Validity of F as a Measure of Financial Development
One of the earlier ideas this paper sought to show was that financial development would
lead to higher savings rates across countries. This is due to the easier pathways for people to
invest their money, putting it back into the system to foster financial growth. One other pathway
for financial development to increase investment, sk, would be through an increase in money
supply as invested money increases the money supply by a multiplied effect. This is mainly
present in retail banking due to the reserve requirement, because if the reserve requirement were
only 10% for example, then 90% of the money invested at a bank could be lent out to other
people. This would increase the total money supply in the economy by 10 times the amount that
was initially invested (known as the money multiplier). Below, we look at the correlation of the
components of F to sk, to see if F is a strong or weak determinant of the investment in physical
capital. Using sk as the dependent variable, an OLS regression was run on the six components of
F, yielding the following results.
Coefficient
Value
Std. Dev.
P-Value
Bank
Deposits
Financial
Deposits
Market
Cap.
Insurance Market
Life
Assets
Turnover Insurance
0.820
-0.308
0.050
-0.021
0.086
0.036
0.490
0.453
0.069
0.069
0.057
0.071
0.106
0.503
0.473
0.761
0.145
0.615
R2 = 0.886896
According to the results above, although we have a high R-squared value indicating high
explanatory power of the data, there are no significant correlations of any of the variables to sk,
gross capital investment. One potential reason for this is the measure of sk, gross capital
investment, measures investment in other vehicles than insurance and retail banking. This
conclusion could mean that there is a better model to use than the Solow model for describing
financial development’s effect on output, but that will require future research to see exactly
where financial development has its effect on output per capita. Overall, the model did yield
valuable results, just not what was expected in the framework of this paper.
CONCLUSION
Continuing on the findings of earlier authors, this paper was searching to find that higher levels
of financial development indicated by retail banking, equity, and insurance market indicators
leads to higher growth rates in GDP per capita and a higher level of steady state GDP across
countries. Although the insurance market and equity market capitalization showed significant
correlation to output per capita, most of the other markets did not. Part of this finding, however,
is quite prominent because insurance assets and physical capital savings rate show statistically
significant positive correlation to both output per capita in the steady state and growth in output
per capita. Conversely, life insurance premiums show statistically significant negative correlation
to both GDP per capita and its growth rate. When factors are found to have correlation to both
steady state levels and growth rates, there is a much stronger conviction that these factors do
indeed play a role in OECD economies. The above findings, or lack thereof in some cases, raise
the question that some countries may have too high of a level of financial development, which is
shown in some cases to actually decrease output per capita based on the findings of Law and
Singh in 2014. This follows with this paper’s finding that initial GDP per capita showed a
negative correlation to GDP per capita growth rates in the second regression. Some countries that
have a very high initial GDP per capita may have a more highly developed financial system, too
highly developed in some cases, leading to this negative correlation with growth. Across all
OECD countries there is a very high level of financial development as even the least developed
of these countries are far more developed than many other countries across the world. One area
of research that could possibly be expanded and strengthened is creating a representative
measure of the level of financial development across all countries, since many smaller countries
do not have data that is as accessible as it is for the OECD countries. Until a stronger and more
accurate measure of financial development is available, it might be difficult to assess the true
effect that financial development has on output and it will be hard to use financial development
as a tool to help some of the countries lagging in output and wealth. More research must be done
specifically on equities and retail banking to measure its true effect on output per capita and
growth in output. There is a certain level of financial development that must be present in order
for countries to grow, but in most OECD nations the level of development is already quite high,
which makes it harder to analyze these effects. Although the findings of this paper did not reveal
that all of the chosen indicators had a statistically significant influence on output per capita and
growth rate in output per capita, certain factors were found to have correlation levels that show
significant influence on both output in the steady state and growth. These conclusions provide
some insight into what has been an economic debate for years and will hopefully provide the
basis for the creation of a more robust indicator of financial development in the future.
Bibliography
Alain de Serres, et al. (2007), "Regulation of financial systems and economic growth in OECD
countries: An empirical analysis", OECD Economic Studies, Vol. 2006/2.
Ross Levine, (2004), “Finance and Growth: Theory and Evidence”, National Bureau of
Economic Research Working Papers Series, Vol. 10766, http://www.nber.org/papers/w10766
Siong Hook Law, Nirvikar Singh (2014), "Does Too Much Finance Harm Economic Growth?",
Journal of Banking and Finance, Vol. 41/C, pg. 36-44,
https://ideas.repec.org/a/eee/jbfina/v41y2014icp36-44.html
N. Gregory Mankiw, David Romer, David Weil (1992), “A Contribution to the Empirics of
Economic Growth”, Quarterly Journal of Economics, Vol. 107/2, pg. 407-437,
http://www.jstor.org/stable/2118477
The World Bank. World Development Indicators (WDI) Online. Retrieved February 28, 2015.
Appendix A
DATA SUMMARY
Bank Deposits per Capita
Mean
Median
Standard Deviation
Largest
Smallest
$27,119.26
$20,633.52
40,159.65
$236,582.90 - Luxembourg
$1,683.565 - Mexico
Financial System Deposits per Capita
Mean
Median
Standard Deviation
Largest
Smallest
$26,857.81
$20,038.93
40,231.47
$236,582.90 - Luxembourg
$1,683.565 - Mexico
Market Capitalization per Capita
Mean
Median
Standard Deviation
Largest
Smallest
$21,860.10
$14,455.46
24,175.39
$107,888.50 - Luxembourg
$623.8929 - Slovak Republic
Insurance Company Assets per Capita
Mean
Median
Standard Deviation
Largest
Smallest
$15,933.40
$9,812.886
23,997.87
$135,492.60 - Luxembourg
$17.78516- Estonia
Market Turnover
Mean
Median
Standard Deviation
Largest
Smallest
72.90748%
64.99071%
43.34221
204.019% - Republic of Korea
1.538095% - Luxembourg
Life Insurance per Capita
Mean
Median
Standard Deviation
Largest
Smallest
$1,059.503
$901.3814
169.1378
$3,457.293- Switzerland
$10.9619 - Turkey
Sk per Capita
Mean
Median
Standard Deviation
Largest
Smallest
$6,436.757
$6,479.94
3422.165
$14,009.23 - Luxembourg
$1,342.515 - Turkey
Mean
Median
Standard Deviation
Largest
Smallest
97.61416%
98.32518%
4.41896
103.3605% - Israel
84.18251% - Luxembourg
Sh
Appendix B – Comments
Although the first draft did provide some interesting results, it is apparent that steady
state income per capita might not be the most differentiating measure, because it is being
examined in the steady state. Without the motion of GDP, the growth, it becomes difficult to
examine the effect of financial development because certain countries will inherently have a
higher GDP than others. The addition of a regression against GDP growth rates in this draft helps
explain more of the significance of the indicator as it shows how financial development truly
affects growth. This regression also took into account population and initial GDP per capita in
order to examine the effect that initial GDP level has on the output per capita. The findings of the
added regression were somewhat similar to the findings of the initial regression, but it allows for
a much stronger conclusion since the factors were shown to influence growth as well as steady
state level of GDP per capita. One of the reasons that the initial regression was not changed in
terms of adding initial GDP per capita and population is because this would create
interdependence in the data which could skew the results and provide us with false conclusions
about the correlation, since initial GDP per capita is part of calculating the level of steady state
GDP per capita. With the changes to the paper and a more robust model that describes how the
indicators in the paper can directly influence economic growth, the paper is able to come to a
much more robust conclusion since the effects of these variables can be seen in not only the
steady state but the more dynamic growth state as well.