The Relationship between Financial Structure and GDP.

The Relationship between Financial Structure
and GDP.
A Worldwide Perspective.
A clear lesson of history is that a 'sine qua non' for
sustained economicrecoveryfollowinga financial crisis
is a thoroughgoing repair of the financial system.
Janet Yellen
But Which System?
APPLICATIONS FOR ECONOMICS, MANAGEMENT AND FINANCE
Applied ResearchProject and Report
In submitting this assignment:
1. We declare that this written assignment is our own work and does not include (i)
material from published sources used without proper acknowledgment or (ii) material
copied from the work of other students.
2. We declare that this assignment has not been submitted for assessment in any other
course at any university.
3. We have a photocopy and electronic version of this assignment in our possession.
Stefano Valeri 1672146 ________________________
Edoardo Filippo Ferraresi 1641836 ________________________
Georgina Young Bloom 1806694 ________________________

1 | T h e R e l a t i o n s h i p b e t w e e n F i n a n c i a l S t r u c t u r e a n d G D P
Abstract
This paper is a dissertion analysis on how different financial structures foster
economic growth and development at the aggregate world level. We identified and analyzed
three different financial systems - Bank-Based, Market-Based and Government-Based -
whose differences were derived from five common factors to the worldwide financial realm:
Solvency, Profitability, Market Efficiency, Foreign Presence and Core Revenue Cost
Structure. The different levels of these five underlying factors characterizing each financial
structure led to our conclusion that only a more market oriented system is strictly related to
the economic development in terms of contribution to the “added value” measured by the
Gross Domestic Product.
Introduction
Our interest to uncover and detect the linkage between the financial system and the
global level of economic and social development steems from the recent Great Recession that
characterized the world economy. The brutal changes brougth by the current financial turmoil
have affected major aspects of our everyday life. They took the stage scene in any random
conversation entering the popular knowledge while determining the day-to-day decision
making process of every person. During the past years the major economies were devasted
with no less power than what would have happened in the case of epidemic deseases or war
conflicts, experiencing the gradual fading of the middle class, the disruption of wealth and the
loss of thousand of jobs.
Financial systems are critical to the way the world functions today. They not only help
in the formation of capital but also have a responsibility to mobilize the savings in the form of
money and invest them a productive manner. According to the Indian economist Manmohan
Singh, “if you don’t have a functioning financial system the world economy won’t be revived.
All the major economies have their responsibility to assist at a pace which is required to clean
up the balance sheet of the banking system and to ensure that credit flows are resumed.i”
Financial systems refer to a necessary set of components and mechanisms such as monetary
policies and insurance that allows economic transactions to occur.
For these reasons we wanted to acquire further knowledge and insights over the
linkage between the financial sector of the economy we live in and the level of social and
economic development. However, we extended our research to a more global perspective
trying to detect any possible difference between the various financial sectors and the way they
may shape the level of Gross Domestic Product of the countries in which they are
implemented.
According to our research findings, in line with the literature we analyzed, there are
three main types of financial systems: Bank-Based financial systems, Market-Based financial
systems and Government-Based financial systems. In Bank-Based financial systems, banks
play a leading role in mobilizing savings, allocating capital, overseeing the investment
decisions of corporate managers and in providing risk management vehicles. In Market-Based
systems, securities markets share center stage with banks in transfering investors’ savings to
firms, exerting corporate control and easing risk managementii. Government-Based financial
systems have a double role in preventing buildup of systemic risk and in creating a framework
in which financial failure is managed in an orderly and cost effective manneriii. Financial
systems and GDP (growth domestic product) go hand in hand as the basic assumption is that
financial systems foster growth and economic development. This research paper investigates

if and how the three main types of financial systems influence GDP during the years prior the
2008 Great Recession. We decided not to consider the data from the post-crisis years because
we believed that the values of the financial ratios would have been distorted by the crisis and
because we also encountered an increase in missing values.
Various data from the World Bank Financial Development and Structure Database and
form the Penn World Tables have been used to perform the questions. Factor analysis, a
method to reduce or summarize many variables into one or more indexes based on the
variables' correlations, will be used to develop five latent variables/factors that characterize
financial systems; solvency, profitability, market efficiency, foreign presence and revenue
cost structure. Following this, a cluster analysis, a method of identifying groups within a
population based on measured characteristics in a sample, will be used to identify the three
types of financial systems that affect GDP. Finally, we developed a linear regression using
“PPP GDP” - a GDP index measured based on the purchase power parity- as the dependent
variable and three dummy variables without intercept (to avoid the dummy variable trap) of
the three clusters “Bank-Based financial systems, Market-Based financial systems and
Government-Based financial systems” as the independent variables.. This model will
demonstrate if there is any relationship between the five latent variables representing bank-
based financial systems, market-basked financial systems and government-based financial
systems and the GDP level of a certain country(s). Thus, there have been three hypothesis that
have been tested using this model:
Hypothesis 1: There is a relationship between Bank-Based financial systems and the GDP
level.
Hypothesis 2: There is a relationship between Market-Based financial systems and the GDP
level
Hypothesis 3: There is a relationship between Government-Based financial systems and the
GDP level.
This paper has been devided into two sections. The first one presents a brief background of
vast past literature on the most relevant arguments and topics treated in this analysis
concerning both the financial systems and its relation to GDP, with the aim of helping the
understainding of the research question and the factors utilized. The second section,
describes the data that has been used in the analysis along with the model developed.
1. Background
Financial systems are of doubtless importance for productivity growth and economic
development. Financial structures consist of firms that provide financial services and advice
to clients and their main role is to act as an intermediary between the capital market and debt
market. The ultimate goal of the economy is to design a financial system that facilitates the
optimal allocation of scarce economic resources to promote economic growth that improves
living standardsiv. Our research identifying three main types of financial systems, bank-based,
market-based and government-based, produced similar results to the three types of financial
systems stated in “Market-Based Banking and the International Financial Crises” by Hardie
and Howarth (H&H)v.
With regard to our dependent variable, Growth Domestic Product (GDP) is one of the
most widely used measures of an economy’s output or production. It is the monetary value of
all the finished goods and services produced within a country’s borders in a specific period.

GDP includes all of private and public consumption, government expenditures, investments
and exports less imports that occur within a specific country or region. GDP plays a key role
in today’s financial market since it is commonly used as an indicator of the economic health
of a country and as a way to assess a country’s standard of living. GDP enables policymakers
and central banks to judge whether the economy is contracting or expanding or on the verge
of a recession or inflation. Because of GDP’s essential and wide role, we chose to use it as the
dependent variable to be regressed on the different types of financial systems. As countries
rise along the scale of wealth and income, their financial structures are usually becoming
increasingly rich in financial institutions, assets and markets.
Solvency, profitability, market efficiency, foreign presence and the core revenue cost
structure all characterize financial systems in different and unique ways. Profitability is the
primary goal of all business ventures and without profitability a business would not survive in
the long run since Profitability is a condition yielding a financial gain. An Efficient Market is
one where the market price is an unbiased estimate of the true value of the investment. It
requires that errors in the market price be unbiased (i.e. the prices can be greater than or less
than the true value as long as the deviations are random)vi. Foreign Presence refers to foreign
direct investment, controlling ownership of an asset in one country by an entity based in
another country. Core revenue cost structure relates to cost structure and revenue streams.
Core cost structure is the expenses that a firm must take into account when manufacturing a
product or service such as transaction costs, sunk costs and marginal costs. Core revenue
streams, on the other hand, is a method that a company, organization or individual uses to
collect money from users of their product or service. Solvency represents a necessary
operating condition of the financial system and refers to a company’s long run financial
viability and ability to cover long-term obligationsvii. If a company is unable to meet its
obligations, it is said to be insolvent and must undergo bankruptcy to either liquidate or
restructure. According to Stiglitz in “The Design of Financial Systems for the Newly
Emerging Democracies of Eastern Europe”, solvency is strictly related to the financial
system. Indeed, the government based financial systems provide insurance to depositors to
restore confidence in banks and to prevent bank runsviii.
Ross Levine in her article, “More on Finance and Growth: More Finance, More
Growth?” explicitly declares that while the theory suggests that financial systems influence
growth by easing information and transaction costs and thereby improving the allocation of
capital, corporate governance, risk management and financial exchanges, the empirical
measures do not directly measure these financial functionsix. On the other hand, Robert King’s
“Finance, Entrepreneurship and Growth” research paper states that better financial systems
improve the probability of successful innovation and thereby accelerate economic growthx.
The purpose of this paper is to solve this controversy and examine how the financial systems
affect economic growth, development and GDP, while analyzing the unique characterizing
factors of a financial system.
2. Data Description and Initial Data Screening
The data considered are available on the World Bank Financial Development and
Structure Database as well as on the Penn World Tables. From the latter, we considered only
the GDP adjusted for the Purchasing Power Parity of each country. Using the World Bank
Financial Development and Structure Database, we extracted all the indicators of financial
development and structure across countries and over time that measured the size, the activity,
and the efficiency of financial intermediaries and markets. We then carried out an extensive

initial data screening in order to address all the relevant issues with these measures
(suitability, inclusion, descriptive statics, correlations), an activity described in the following
paragraphs.
Firstly, we divided the data from the World Bank database into one smaller subgroup,
pre-financial crises (before 2008) with a range of five years. We chose these years because
financial crises’ have had significant impact on the historical development of financial
systems. We then calculated the means of each variable for each country (our cases) for our
sub-period. We did this in order to disentangle the chaos that was brought about by the
financial crisis, especially to our main observations and variables (countries and financial
ratios) since they were most likely the ones that suffered and changed.
The main issues encountered were related to the basic structure of our analysis and the
data available, as we had to develop a model based on both factor analysis and cluster
analysis. The issues arose due to conceptual issues rather than statistical considerations. As a
consequence, the sample size was of primary importance to our research because of a trade-
off between sample size representativeness and number and heterogeneity of variables
considered, i.e. the risk of overfitting the data (with regards to factor analysis) and of
describing the random error in the variables instead of the underlying relationship.
Regardless, during the beginning stages, some variables (ofagdpp, bdgdp, prbond, pubond,
intledebt, intledebt net) were completely eliminated before progressing with the analysis
because of an excessive number of missing values in the range of 70% to 90% and their
superficiality. These variables were more meaningful but also similar indicators. Following
this, we removed all the countries that had too many empty variables (more than 10) and that
were meaningless for our analysis, ending up with approximately 153 cases of countries.
At this point, we believed that 30 were too many for two main reasons. Firstly,
conceptually some of them were really close in their meanings and a problem of
multicollinearity could easily arise. Secondly, a higher case-to-variable would be desirable
with 153 cases.
With this in mind, we decided which variables were to be eliminated based on our
knowledge about their intrinsic meaning and on our research on the topic. For example,
Intldebtnet (loans from non-resident banks (net) to GDP%) is the same as nrbloam (loans
from non-resident banks (amount outstanding to GDP%). We retained the outstanding as
more representative of the influx of capital from non-resident financial institutions. Other
variables were disregarded because they did not address our specific research question, such
as inslife and insnonlife, which provided the amount of life insurance premium volume to
GDP (%). Other indicator variables were encompassed into more general, and superior,
indicators like bdgdp (demand, time and saving deposits in deposit money banks as a share of
GDP) into fdgdp (financial system deposits to GDP %).
We then considered the correlation matrix for the remaining variables and check the
assumptions of linearity, homoscedasticity and possibly normality, and their descriptive
statistics. Because these variables are ratios, they ranged over different areas on the number
line and had different variances and distributions, even though the majority of variables were
slightly positively skewed. We decided to tackle the problem straight away instead of
anticipating its arrival during cluster analysis, our second step in the analysis procedure. We
converted each variable to standard scores (Z-scores) by subtracting the mean and dividing by
the standard deviation for each variable. All standardized variables retained their properties
with a mean of zero and a standard deviation of one. Also, thanks to the Central Limit
theorem we could worry less about violations of variables’ normality because this principle

states that as the sample size increases over 30 cases a variable’s distribution can be well
approximated by a normal curve. Moreover, low correlations were definitely not a problem.
Next, the missing values were replaced with the variables means. Even though we
were fully aware of the problem that this choice might create (i.e. depressing the standard
error and the correlation with the other variables), we believed that this solution was the most
appropriate considering our sample data. Since we already deleted various ratios, we tried to
retain as many core values as possible. Fortunately, we were able to confirm that this decision
did not have any significant impact on the variable correlations due to the subsequent tests for
multivariate adequacy. .
The diagnostic tests that we performed in order to check for multivariate adequacy
demonstrated interesting values in all parts one must consider when approaching a factor
analysis. During the first stage of design and data screening, both Barlett’s Test of Sphericity
and Kaiser-Meyer Olkin (KMO) Measure of Sampling Adequecy (MSA) produced
satisfactory results showing that there is enough correlation in the data set to form factors,
meaning the indicator variables could be reduced to latent variables.
The idea behind Barlett’s Test of Sphericity is to check that the correlation matrix is
statistically significant and different from an identity matrix, whereas KMO MSA is useful in
determining if there is satisfactory correlation in the data. This measure shows the proportion
of variance in the dataset that is common (initial estimate) with respect to the partial
correlation among the variables thus providing a logical basis for the creation of the factors if
the value is over and above .7.
We then examined the determinant of the correlation matrix to look for the absence of
multicollinearity. Before analyzing the results, we knew this would be an issue because of the
analysis rational of the research. All of our indicator ratios were taken from financial
institutions that were well interlinked and therefore, we expected that some variables would
follow similar patterns across the countries analyzed.
Indeed, as seen above, the determinant was different then zero, showing no perfect
multicollinearity existed. Rather, the low correlation matrix determinant revealed that a high
correlation between the variables existed. To obtain a factor analysis solution, the correlation
matrix must be invertible and this could only be achieved if the correlation matrix was not
singular with a determinant of zero originating from multicollinear variables. As a result, both
diagnostic tests for multivariate adequacy suggested that the correlation matrix was factorable
having P-value barlett’s equal to zero (probability of obtaining the observed sample results (or
a more extreme result) when the null hypothesis is actually true), KMO MVA higher than 0.7
and a determinant close but different from zero.
The Measure of Sampling Adequacy (MSA) is a measure of prediction of each
variable by the others and is obtained from the anti-image matrix. It quantifies the degree of
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .755
Bartlett's Test of Sphericity
Approx. Chi-Square 1459.336
df 231
Sig. .000 Table 1
Correlation Matrixa
a. Determinant = 5,03E-8 Table 2

inter-correlation among the variables in the dataset. MSA can be used to see whether any
variable must be removed due to its conceptual non-correspondence with other variables. We
produced the anti-image correlation matrix and only considered variables with a value higher
than 0.50, meaning that at least 50% of the variance was explained by other indicator ratios.
For example, bank concentration (assets of three largest banks as a share of assets of all
commercial banks) presented a value close to 0.5 (0.404 from one of the previous trials) but
lower which we believe was unacceptable and as a consequence that indicator was removed.
Once the factors were initially extracted, their Communalities (the proportion of
common variance explained by the extracted factor model) with the variable kept in were all
well above the threshold level of .40 except for one which, although acceptable, was dropped
from the analysis to make the factor interpretation easier and clearer and about which we
write in later paragraphs.
Concerning cluster analysis, fewer problems were encountered especially by means of
fewer statistical data requirements. Considering our final goal, we believed that a sample
composed of 153 countries was sufficiently large to uncover the theoretical typologies in the
data, i.e. three clusters well diversified representing market, bank and governmental
conditions. Having already discarded irrelevant variables in the pre-stages of factor analysis,
we focused on identifying outliers but due to their importance in our research project, we
prefer to explain our decisions in the multivariate analysis part.
Finally, we began our multivariate analysis with the following indicator variables:
3. Multivariate Analysis
Our final purpose is to test the hypothesis that different financial structures do, or do
not, differently affect a country’s development.
Our analysis is divided into three intercorrelated steps: first, we begin with a factor
analysis in order to reduce a number of highly correlated variables into few significant factors
representing the main characteristics of a financial structure; second, we employ a cluster
analysis in order to group the countries considered into different financial structures that show
relevant similarities according to the latent variables; finanlly, with a regression model
(although insignificant per se) we see which model actually differs and has a statistically
impact via a linear equation formed only by dummy variables and no intercept (to avoid the
so called dummy variable trap) as follow:

𝑌 = 𝛽1(𝑋) + 𝛽2(𝑊) + 𝛽3(𝑍)
in which:
X represents a MARKET-BASED FINANCIAL STRUCTURE
W represents a GOVERNMENT-BASED FINANCIAL STRUCTURE
Z represents a BANK-BASED FINANCIAL STRUCTURE
Y represents the Total Real Gross Domestic Product, current price milions
3.1 Factor Analysis
After the initial data screening, we argued in favor of a principal component analysis (PCA)
method of factor extraction given our research question and the type of data we considered.
PCA considers the total variance and derives factors that contain small proportions of unique
variance or error variance. For pure data reduction purposes, this method is the most widely
used in the industry and it is also in line with our factor analysis objective of creating factors
that take into account as much of the variance of the variables as possible by not partitioning
the variance into common and unique variance before extraction of factors. In addition,
provided that our indicators are ratios of financial measures taken from official reports, we
can fairly assume that there is no error in the measurements. In fact, with PCA, we were able
to extract from a set of possibly correlated variables, a set of uncorrelated (before oblique
rotation) principal components i.e. factors. We began our analysis by forming factors with the
remaining indicator variables and produced the following problematic communalities outputs.
Communality can be defined as the variance each variable shares with other variables in the
data set.
Because we were already considering several different indicators for market efficiency
and profitability, we decide to remove these two indicators from our project.
Following this, we re-ran the factoring procedure and evaluated if all criterions were met. As
suspected, the MSA from the ANTI-IMAGE MATRIX spotted a very low value (0.399) for
bcbd (bank credit to bank deposit); this measure was highly correlated with zscore and the
latter’s exclusion was the driver for the former’s elimination.
To determine the number of factors, we opted for a percentage of variance criterions to
choose the numbers of factors to extract and assessed 80% as sufficient. We were confident
that with 15 variables, a scree plot criterion (one that plots the eigenvalues, or variance
explained by each factor, against every factor) is difficult to interpret and to decide which
factor’s eigenvalues are different from the flat portion of the line, whereas on the other hand,
a latent root criterion (one that retains only factors whose eigenvalues are higher than one)
risks of being too restrictive and of extracting too few factors.
Communalities
Initial Extraction
BANK Z-SCORE 1.000 .586
NO. OF LISTED COMPANIES PER 10K POPULATION 1.000 .424 Table 3

Table 4
By chance, latent root and percentage of variance suggested both five factors.
Even though for subsequent analyses it would be more fitting to have independent
factors, especially for cluster analysis, we used an oblique rotation to provide more readily
interpretations of factors solutions. Oblique rotation methods are best suited to the goal of
obtaining several theoretical meaningful factors or constructs because realistically few
constructs in the real world are uncorrelated. We expected the factors found to be correlated.
The solution that was initially extracted was the one that maximized the amount of variance
explained by the first factor, and subsequent factors are based only on the residual
unexplained variance. Thus, by creating mathematically equivalent but more interpretable
solutions in a multivariate space via a simple rotation of axis, rotated solutions assisted our
research to give factors more appropriate labels. As these labels were fundamentals for us to
identify and name target clusters, we prefer an oblique rotation that does not retain the
orthogonality of the initial extracted factors but that (1) provides us with a more meaningful
factor structure (low loadings on almost all but few variables for each factor), that (2) is more
representative of true concepts as we expect these theoretically meaningful factors to be
correlated because they will explain related ideas and that (3) summarizes the data better.
The following solution was acceptable except for stock market capitalization to GDP
variable loadings, which were high on two factors. We also tried different rotations, such as
orthogonal varimax and equimax, but both loadings remained high.
Table 5
We finally obtained our final 5-factor solution (with total variance explained now equal to
87.080%) and name the factors as follows based on the variables that loads high on each.
Total Variance Explained
Component
Initial Eigenvalues Extraction Sums of Squared Loadings
Total
% of
Variance
Cumulative
%
Total
% of
Variance
Cumulative
%
1 4.896 37.663 37.663 4.896 37.663 37.663
2 2.168 16.678 54.341 2.168 16.678 54.341
3 1.661 12.775 67.117 1.661 12.775 67.117
4 1.186 9.126 76.243 1.186 9.126 76.243
5 1.142 8.783 85.026 1.142 8.783 85.026
6 .550 4.227 89.253
7 .460 3.538 92.791
8 .306 2.353 95.144
9 .247 1.899 97.042
10 .160 1.234 98.277
11 .114 .873 99.150
12 .081 .627 99.777
13 .029 .223 100.000
Pattern Matrixa Component
1 2 3 4 5
Zscore(fdgdp) .987
Zscore(llgdp) .957
Zscore(dbagdp) .678
STOCK MARKET
CAPITALIZATION
to GDP (%)
.562 .522
Zscore(roe) .924
Zscore(roa) .876
Zscore(stvaltraded) .941
Zscore(stturnover) .910
Zscore(overhead) .824
Zscore(costinc) -.555 .765
Zscore(netintmargin) -.238 .410 .659
Zscore(offdep) .905
Zscore(nrbloan) .211 .801

Core Revenue-Cost structure
Cronbach's Alpha N of Items
,762 3
Table 6
We formed these factors scores instead of computing summated scales primarily
because of the factor loadings distribution. As the concepts on which the factors are created
are interrelated, we could see from the table that not only the variables with high loading have
an influence on the factors. Although generally this is something that should be avoided, i.e
meaning contamination, it is normal for example that a variable such as net interest margin,
which loads primarily on core revenue-cost structure, loads highly and positively on
profitability which is increasingly related to the management of financial institutions. By
considering the relative contribution of each observed variable to each factor and computing
the regression factor scores, we were able to maximize factor scores validity obtaining the
highest possible correlation of the latter with the intended factors and to account for more
variance in the data than with other methods. Doing this, we were also allowed to retain just 2
variables for each factor.
To confirm we were aiming at the correct targets, we obtain cronbach’s alpha
measures for each factor. This is a correlation-based measure of internal consistency among
the variables used to create the new factor. We believe that all suggest good validity for the
factor scores and, given the high within variables correlations, high reliability.
Tables 7
Market Inefficiency
,881 2
Solidity Solvency
,932 3
Foreign Presence
,652 2
Profitability
,838 2

3.2 Cluster Analysis
With our latent variables identified, we proceeded to the second step of our analysis
and tried to find the clusters specified in our research of the literature. With cluster analysis,
we tried to discover sub-groups in a set of cases based on multiple characteristics (factors),
i.e. polythetic analysis. We will try to maximize the distances (dissimilarities) between
groups, while at the same time minimize them within each cluster. As our research question is
complicated we think that both data reduction and taxonomy/typology description are the
reasons why we deploy it. On the one hand, we face a huge number of cases that are useless
unless classified into manageable groups; on the other hand, given our previous literature
research we would like to find empirically that a 3-group financial structure is indeed present
in our data. While our sample is large enough to assure us that we could find all the relevant
clusters, we do also take into account that with such a large sample data it is difficult to
decide what is a good representation of our hypothetical clusters with a simple hierarchical
method of agglomeration. Thus, we decide to deploy a new method (two step) that is able to
deal with large sample sizes by first clustering cases into pre-clusters and then groups those
pre-clusters hierarchically obtaining meaningful results ia a tree.
Also, this choice allows us to choose a dissimilarity measure (log-likelihood) for
clustering purposes that takes into account outliers and reduce their impact on the final
solution (Assume outliers or noises follow a uniform distribution. Calculate both the log-
likelihood resulting from assigning a record to a noise cluster and that resulting from
assigning it to the closest non-noise cluster. The record is then assigned to the cluster which
leads to the larger log-likelihood). This is fundamental to us as we explain hereby; in fact we
decide not to discard outliers but to keep them in our analysis.
Cluster analysis is extremely sensitive to outliers (cases different from the others) and
irrelevant variables. We have already dealt with the latters extensively during our FA,
whereas the formers may still have a negative impact on our analysis. Thus, we necessary
check for their presence and deal with them consistently with our research question and
literature findings. Because we look for outliers in a multivariate sense (different from other
cases across several variables), we calculate the Mahalanobis’Distance (D^2) which estimates
how far each case is from the center of all the variables’distribution, i.e. Centroid, and test
whether each case is part of the multivariate distribution. If not (PrD^2<0.05), we label it as
an outlier. Specifically, they are: Bahamas, Cyprus, Ghana, Hong Kong SAR, Luxembourg,
Pakistan, Paraguay, Samoa, Saudi Arabia, Switzerland, Syrian Arab Republic. Even though
some of them are not fundamental for the analysis, we strongly believe that removing such
countries as HK or Saudi Arabia or even Switzerland would bias our groups because those are
probably one of the main countries financially speaking of their respective areas of influence
(Orient, Far east and Europe respectively). As a consequence, we decide to keep them in the
analysis at first and check their impact by comparing the results without them later.
Note that this is in line with our purpose of clustering the cases according to (factors)
characteristics to obtain only the main financial structures present around the world, which
would be impossible if we chose not to include some of the most influential countries.
Nevertheless we check which of the two solutions (with and without outliers) is the most
appropriate to see whether the expected structures are actually present in our sample and
conclude that, indeed, (1) the cluster memberships are the same (being based on similar
values for each factor) and (2) they produce the same results regardless of their inclusion with
respect to our final step, a dummy regression model. Thus, by keeping multivariate outliers in

the sample we think we are able to make our research better organized and our clusters more
meaningful and representative, given similar results.
We have already dealt with the problem of different units of measure across different
variables: we standardized all the variables (mean=0 and std dev=1), obtaining factor scores
with equal properties, in order to obtain results that do not depend more on characteristics
with larger measurement scales which would bias the results.
Thanks to the creation of factors prior to the cluster analysis we can be fairly sure that
no multicollinearity problem would arise acting as an implicit weighting of variables and
distorting the results. True, our factors are correlated due to the oblique rotation applied in the
previous step but their correlations are no higher than 0.2 meaning no multicollinearity
problems.
The followings are the results from a two-step clustering procedure that gives a 3
group solution, that we then checked with a k-means analysis that showed a similar 3 group
solution as well. The k-means is also the best method to be used when seeking a cluster
solution with no outliers. It is a procedure that starts from a set of values that act as initial
seeds for the cluster centroid (mean scores on all factors used in CA) and reiteratively
reassigns cases to clusters according to the evolution of the centroid value (as it changes every
time a new case is assigned to a cluster) until no further improvement is reachable.
(Tables 8)
Tables 8
Number of Cases in each Cluster
Cluster 1 58.000
2 22.000
3 62.000
Valid 142.000
Missing .000
Final Cluster Centers
Cluster
1 2 3
Solidity_Solvency -.58968 1.02703 -.00127
Profitability .21766 -.61056 -.02586
Mkt_Inefficiency .39713 -1.52832 .30602
Foreign_Presence -.10627 -.18933 -.11546
Core Revenue_Cost_Structure .87806 -.67023 -.61822

When we compare their composition the two solutions seem fairly similar, although
some minor differences arise.
For example, cluster number 2 from k-means and 1 from two-step can be clearly
named Market-Based financial systems due to their low (high) level of market inefficiency
(efficiency), their negative general profitability due to competitive pressure and their negative
core revenue-structure because of their well known dependence on exotic operations in the
financial markets.
Reasoning similarly, cluster 3 from two-step and cluster 1 form k-means can be named
Bank-Based financial systems; both show similar structure across factors with positive
profitability, positive revenue-cost structure and insolvency. Considering the low level of
market efficiency (high inefficiency), this result makes sense: exploiting the inefficiency and
leveraging highly their structures, the financial institutions in these systems are able to earn
positive returns.
Finally, the Government-Based financial system is as expected: negative results across
all indicators meaning that these systems are driven not with a profit oriented management
style but rather with one directed to the community sacrificing solidity and profitability.
In the end, the sample size are similar between the two with- and without-outliers methods.
We then create 2 clustering variables, one for each method.
It is interesting to notice which factors actually shaped the clusters more, namely Market
inefficiency, Core revenue cost structure and Solidity (from two step analysis). This is in line
with our expectations of different structures: every cluster (type of structure) differs under one
of these aspects and positively lead us to our final step of checking for actual differences
among them.
3.3 Dummy Regression
In our final part, we test whether the aforementioned groups have any impact on a
country’s development. We find similar results both with and without outliers: only the
market-based system actually has an impact on the GDP of each country, whereas the other
two systems do not show statistically significant positive effects.
To do this, we transform the clustering variable into 3 dummy variables and regress
GDP onto these 3 requiring spss not to include an intercept so that we both avoid the dummy
trap (for n dummies, only n-1 dummies are needed because the nth will be the intercept) and
show each dummy’s slope more easily. Dummies are variables that take the value of 1 when
the country is part of the dummy’s group and 0 otherwise. As a last note, it is to say that we
transformed our dependent variable using a log transformation in order to avoid any bias in
our estimations due to its non normality of errors. Tables 9
Consequently, in this analysis it is not important to look at the R^2 because we are not
interested in the actual predictive power of our dummies, but only on their slopes and
significance. In fact, if a dummy slope’s p-value is lower than 0.05 we can be fairly certain (at
a level of 95%) that also in the true population that system will influence the development.
From both analysis (two clustering variables), the results are very similar. Only the
Market-Based system has an impact on the level of GDP of a country.
Coefficientsa,b
K-MEANS MODEL
Unstandardized Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 MARKET_BASED 1793300.958 263062.736 .498 6.817 .000
GOVERNMENT_BASED 166762.391 156702.104 .078 1.064 .289
BANK_BASED 139424.726 162015.542 .063 .861 .391
Coefficientsa,b
TWO-STEP MODEL
Unstandardized Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 MARKET_BASED 1602957.644 247549.203 .465 6.475 .000
BANK_BASED 136873.341 156563.863 .063 .874 .383
GOVERNMENT_BASED 185966.694 145996.555 .091 1.274 .205

Conclusion
Starting from our sample of characterizing financial ratios of the all countires of the
world, thanks to the Factor Analysis, we were able to extract five factors that shape the
financial structure: solvency, profitability, market efficiency, foreign presence and revenue
cost structure.
These factors are common to all financial systems and by executing a Cluster Analysis
we were able to group countries with similar factor’s values in three different financial
structures: Bank-Based financial systems, Market-Based financial systems and Government-
Based financial structure.
After creating clustering dummy variables we regressed the three financial structures
on the dependet variable GDP finding that only the Market-Based financial systems is
statistically significant in explaining the level of growth and the economic development.
Unsurprisingly and definetely in line with the recent development of the world
economy during the last decade, we conclude that a more market efficient economy
contributes greately in terms of “added value” to the global wealth and the social economic
development. Unfortunately the contribution is symmetrical, which implies it could be either
in positive or negative terms, as the striking example of the 2008 Great Recession. Started
overseas in what it is considered to the perfect Market-Based economy, the recession spread
rapifly in the Old Continent before affecting and destroying the world economic as a whole.
Furthermore, our conclusion that the GDP is linked to a Market-Based financial
system perfectly reflect the consolidated Optimal Capital Allocation Principle that ultimately
links with the growth of wealth, and that applies in economies that follow the Efficient
Market Theory.
Other References
Data Sources: Penn World Tables, Version 7.1
World Bank Financial Development and Structure Database
Text Books: Carlson, Newbold, Thorne: Statistics for Business and Economics
Hair, Multivariate data analysis
Norusis; SPSS Statistics 17.0, Statistical Procedures Companion
Web links references: i http://www.brainyquote.com/quotes/keywords/financial_system.html
ii http://www.nber.org/papers/w9138.pdf
iii http://fic.wharton.upenn.edu/fic/papers/01/0115.pdf
ivhttps://books.google.it/books?id=V3RpAgAAQBAJ&pg=PA60&lpg=PA60&dq=types+of+national+financial+
systems&source=bl&ots=TOL3Hfy8DG&sig=9fPS1Z6WAcSY2x4K0vl0onfhOyw&hl=it&sa=X&ei=SRqLVM
jXDousPfrcgRg&ved=0CF8Q6AEwBw#v=onepage&q=types%20of%20national%20financial%20systems&f=f
alse
vhttps://books.google.it/books?id=V3RpAgAAQBAJ&pg=PA60&lpg=PA60&dq=types+of+national+financial+
systems&source=bl&ots=TOL3Hfy8DG&sig=9fPS1Z6WAcSY2x4K0vl0onfhOyw&hl=it&sa=X&ei=SRqLVM
jXDousPfrcgRg&ved=0CF8Q6AEwBw#v=onepage&q=types%20of%20national%20financial%20systems&f=f
alse
vi http://pages.stern.nyu.edu/~adamodar/New_Home_Page/invemgmt/effdefn.htm
vii http://my.liuc.it/MatSup/2007/F84382/Lecture%209-Capital%20Structure%20&%20Solvency.pdf
viii https://www0.gsb.columbia.edu/faculty/jstiglitz/download/papers/1992_Design_of_Financial_Systems.pdf
ix http://research.stlouisfed.org/publications/review/03/07/Levine.pdf
x http://www.sciencedirect.com/science/article/pii/030439329390028E

The Relationship between Financial Structure and GDP.

Recommended

Recommended

More Related Content

What's hot

What's hot (19)

Viewers also liked

Viewers also liked (9)

Similar to The Relationship between Financial Structure and GDP.

Similar to The Relationship between Financial Structure and GDP. (20)

The Relationship between Financial Structure and GDP.